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The Olympics are over. But that didn't stop a smart guy from using Generative AI to show us how the Olympics would look in a 1000 years. As if we're gonna be there to see it. Breaking - AI Video Imagines the LA Olympics in 1,000 Years’ Time. With the advent of generative AI, artists are now able to take on such large-scale undertakings. Josh Kahn used Runway, an AI movie generator, to imagine what the West Coast would look like in 3028. You will see right away that the ocean has gotten closer to the city due to rising sea levels. Venice and other coastal areas of Los Angeles are presumably under water at this point. After that, there's a tour of the several stadiums, all of which have ambitious and futuristic designs. The soccer stadium bears a striking resemblance to the Rome Colosseum, while the swimming arena is situated in the center of a lake. While it's interesting to speculate about what the Olympics might look like in a millennium, Kahn tells the MIT Technology Review that the movie is really a showcase for the potential of AI video. Kahn tells the MIT Technology Review, "We were watching the Olympics and the amount of care that goes into the cultural storytelling of the host city." Los Angeles has a narrative and imaginative culture that has influenced people all over the world. Isn't it interesting to present what the Olympics would look like in Los Angeles if they were held there a millennium from now? Similar to AI picture generators, AI video operates by having users write in a prompt, which an algorithm then analyzes to create an artificial image. It's similar to waiting for a Polaroid to appear when you're waiting for photographs to appear, but with AI, the appearance of the pictures is far less predictable than it is with Polaroid photos. Users of generative AI, according to Kahn, should exercise patience and be ready to make mistakes, as reported by the MIT Technology Review. I should also mention how hard it is to make an AI video that is consistent. "At the moment, these technologies are always superior to really nuanced human interaction on large-scale things," claims Kahn. Filmmakers may initially employ AI video for broad shots where the magnifying of AI's errors isn't as noticeable, according to Kahn. Generative AI olympics The Paralympics are still in progress and the Olympic Games concluded in Paris last month, so it seems like a long way off until the Summer Olympics in Los Angeles in 2028. However, Josh Kahn, a sports entertainment director who has created video for both the Chicago Bulls and LeBron James, is looking even further ahead at the potential of viewing the games in his hometown: what might the Los Angeles Olympics of 3028 look like? With the release of OpenAI's Sora earlier this year, AI video production has become more widely used, making this the ideal kind of creative practice. With generators such as Runway or Synthesia, users can quickly produce reasonably high-definition video by inputting commands. Compared to more conventional creation methods like computer-generated imagery (CGI) or animation, it is quick, inexpensive, and has less technical challenges. Even though not every frame is flawless—hands with six fingers or disappearing items are classic examples of distortions—there are, theoretically, a plethora of commercial uses. With the help of technology, businesses, ad agencies, and content producers could swiftly and affordably produce videos. Using the most recent iteration of Runway, Kahn—who has been experimenting with AI video tools for some time—imagined what the Olympics of the future may look like. For each shot, a new cue was entered into the model. In this little over a minute long film, sprawling aerial views of a futuristic Los Angeles are shown. Sea levels have risen dramatically, leaving the metropolis packed tightly against the coast. Beach volleyball courts are housed in a dome in the midst of the bay, while a football stadium is placed atop a tower. The film, which was made specifically for MIT Technology Review, aims to show off what artificial intelligence is already capable of rather than serve as a blueprint for the city. Kahn says, "We were watching the Olympics and the amount of care that goes into the host city's cultural storytelling." Los Angeles has a narrative and imaginative culture that has influenced people all over the world. Isn't it interesting to present what the Olympics would look like in Los Angeles if they were held there a millennium from now? Above all, the video demonstrates how beneficial generative technology could be for artists. It also shows what's preventing it, though. While Kahn did not reveal his shot lists or the number of takes required to get each shot just perfect, he did warn that anyone hoping to produce high-quality material with AI had to be cool with making mistakes. In his futuristic concept, getting the AI model to think creatively about design was especially difficult. For instance, the majority of AI models' training data does not contain many examples of stadiums floating over water. The future of Generative AI video OpenAI's Sora has raised the bar for AI moviemaking. Here are four things to bear in mind as we wrap our heads around what's coming. It's also challenging to maintain a sense of continuity throughout a video, since every shot calls for a different set of cues. Consistency in color, sun angle, and building shapes is a challenging task for a video generating model. Additionally, there are no close-ups of humans in the film, something Kahn claims AI models still frequently have trouble with. He claims that "right now, these technologies are always better on large-scale things as opposed to really nuanced human interaction." Because of this, Kahn believes that the first uses of generative video in cinema would likely involve long takes of people or landscapes. Alex Mashrabov, an expert in AI video who founded Higgsfield AI, a new AI video firm, after leaving his position as director of generative AI at Snap last year, concurs about the shortcomings and failings of AI video as it stands now. Additionally, he makes the point that producing high-quality dialogue-heavy video using AI is challenging because it frequently depends on nuanced body language and facial expressions. Because it takes a lot of time to cue the models repeatedly until the desired outcome is achieved, some content creators might be hesitant to employ generative video. According to Mashrabov, the success rate is usually one in twenty, although it often takes fifty or one hundred tries. But it's sufficient for a lot of things. Mashrabov reports seeing an increase in AI-generated video commercials from large vendors such as Temu. Video generators are in significant demand in nations that produce commodities, such as China, because they can swiftly create confrontational video advertisements for specific products. It might take an AI model a lot of prompts to produce a usable advertisement, but filming it with real actors, cameras, and equipment could cost a hundred times as much. As the technology gradually advances, applications such as these may represent the first large-scale application of generative video, he says. “Although I think this is a very long path, I’m very confident there are low-hanging fruits,” Mashrabov says. “We’re figuring out the genres where generative AI is already good today.” What did the Olympics do different in generative AI? In terms of incorporating artificial intelligence (AI) technology into sports and important international contests, the 2024 Summer Olympics in Paris are expected to be a historic occasion. With the Olympic Games embracing the digital era, the International Olympic Committee (IOC) has set forward an ambitious plan to use artificial intelligence (AI) to improve the entire Olympic experience. This article addresses the possible effects, difficulties, and ethical issues surrounding this technological revolution as it investigates how artificial intelligence (AI) will change the 2024 Summer Olympics in Paris, from athlete performance to fan involvement. A comprehensive framework for incorporating AI into the Olympic Movement, the Olympic AI Agenda was created by the IOC in acknowledgment of the revolutionary potential of AI. By embracing AI's potential, this agenda seeks to inspire and accelerate good change across global sports, driven by the Olympic ideals of excellence, respect, and friendship. The five pillars around which the Olympic AI Agenda is based are: Foster an ethos of integrity Improve access and fairness Scale creation and distribution of moments that matter Honor traditions while embracing innovation Collaborate for shared impact The IOC's approach to implementing AI is based on these principles, which make sure that technology breakthroughs are consistent with the fundamental ideals of the Olympic Movement. AI technology will be used in five major areas during the Paris 2024 Olympics, all of which are intended to maximize the advantages for competitors, fans, and the larger Olympic community. The athletes are the center of the Olympic Movement, and at Paris 2024, artificial intelligence (AI) will be a key component in improving their performance, guaranteeing fair competition, and advancing athlete safety.. Talent identification and development Large-scale data on athlete performance, physical characteristics, and abilities will be analyzed by AI algorithms to find promising athletes in a variety of sports. This technology can assist identify prospective Olympic stars who might otherwise go undiscovered, which will be especially helpful for nations with minimal resources. Machine learning algorithms, for instance, may watch videos of young athletes and compare their physical characteristics and gaits to those of Olympians who have achieved success. This might make it easier for national sports organizations and coaches to spot and develop talent, which could result in a more diversified pool of competitors at upcoming Olympic Games. Advanced training and performance optimization Paris 2024 will see a shift in athlete training regimens thanks to AI-powered devices. Wearable technology, biomechanical sensors, and video recordings all provide data that AI can analyze to deliver individualized insights to improve an athlete's performance. These systems might include: Real-time form and technique feedback during training Predictive models for scheduling peak performance, enabling athletes to prepare for competition at optimal level Tailored diets and recuperation schedules depending on physiological information Gymnasts, for example, may employ artificial intelligence (AI) to evaluate their motions during practice and receive real-time feedback on their landing force, rotation speed, and balance. This could assist the athlete in fine-tuning their technique, which could help them cut vital seconds off their performance time or raise their execution ratings. Enhancing judging and refereeing At Paris 2024, one of the most important uses of AI will be in refereeing and judging. Sports requiring exact measurements or subjective scoring will benefit from the use of computer vision systems in conjunction with machine learning techniques to support human judges. For instance, AI systems could evaluate live video feeds in gymnastics to evaluate routine complexity and execution, giving judges unbiased information to back their score decisions. AI-powered sensors could provide incredibly accurate timing and measurement in track and field events, removing human error in close finishes. This technology aims to reduce bias, increase accuracy, and enhance the fairness of competition across all Olympic sports. Promoting clean competition In Paris in 2024, artificial intelligence will be vital to the fight against doping. Sophisticated machine learning algorithms will examine the biological data of athletes, identify irregularities, and highlight possible cases of doping for additional examination. These systems could: Determine any odd trends in the performance statistics of athletes that might point to the use of performance-enhancing drugs. Improve the efficiency of your analysis of blood and urine samples to maybe find new or designer medications. Identify times or circumstances that pose a high danger of doping for athletes. By using AI in anti-doping efforts, the Paris Olympics aim to preserve competitive integrity and level the playing field. Ensuring athlete safety and well-being AI technology will also be used to enhance athlete safety and well-being at Paris 2024. Machine learning models will analyze data from wearable devices and training logs to predict and prevent injuries[1]. These systems could: Alert coaches to signs of overtraining or fatigue Recommend personalized recovery strategies based on an athlete’s physiological data Monitor mental health indicators to provide early intervention for psychological support Additionally, AI will be employed to create safer sporting environments by detecting and preventing harassment and abuse. For instance, natural language processing algorithms could be used to monitor social media and identify targeted online abuse towards athletes, allowing for swift intervention and support. AI is transforming athlete performance and training for the 2024 Summer Olympics in Paris. Artificial intelligence (AI)-driven tools, such as the Technogym Checkup, offer individualized training plans by evaluating athletes' physical conditions and customizing exercises to meet their requirements. By ensuring that athletes receive the best possible training and recuperation, AI integration improves performance while lowering the chance of injury. Real-time feedback from the digital coaches enables quick modifications and advancements, which makes AI a crucial instrument for reaching optimal athletic performance during competition. Ensuring equal access to the benefits of AI A key focus of the Paris 2024 Olympics will be leveraging AI to promote inclusivity and equal access to sports technology across the global athletic community. Democratizing high-level training AI-powered coaching platforms will be made available to athletes and coaches worldwide, regardless of their location or resources. These platforms could provide: Virtual coaching sessions using computer vision to analyze and correct technique AI-generated training plans tailored to individual athletes’ needs and available resources Access to a global knowledge base of training methodologies and best practices This democratization of high-level training resources aims to level the playing field between athletes from developed and developing nations, furthering the Olympic ideal of global participation and excellence. Language translation and cultural exchange AI-powered real-time translation systems will be deployed throughout the Olympic Village and venues, breaking down language barriers and fostering cultural exchange among athletes and delegations from different countries. These systems could facilitate: Seamless communication between athletes, coaches, and officials from different linguistic backgrounds Enhanced media interviews, allowing athletes to express themselves in their native languages while providing instant translations for global audiences Cultural exchange programs within the Olympic Village promoting understanding and friendship among participants AI education and capacity building The IOC will implement AI education programs for National Olympic Committees (NOCs) and International Federations (IFs) to ensure that all members of the Olympic Movement can benefit from these technological advancements. These initiatives might include: Workshops on implementing AI in sports administration and event management Training programs for coaches on using AI-powered performance analysis tools Guidance on ethical considerations and best practices for AI adoption in sports By providing these educational resources, the Paris 2024 Olympics aim to create a more technologically equitable sporting landscape beyond the Games. Optimizing olympic and paralympic games operations with a focus on sustainability AI technology will play a crucial role in streamlining operations and enhancing sustainability at the Paris 2024 Olympics, from the planning stages through to post-Games legacy management. Sustainable venue design and management AI algorithms will be employed in the design and operation of Olympic venues to maximize energy efficiency and minimize environmental impact. This could involve: Predictive models for optimal venue layout and capacity based on expected attendance and usage patterns Smart energy management systems that adjust lighting, heating, and cooling in real time based on occupancy and weather conditions AI-powered waste management systems for efficient sorting and recycling For example, the Olympic Village could use AI to optimize energy consumption by predicting peak usage times and adjusting power distribution accordingly, potentially reducing overall energy consumption by a significant margin. Transportation and logistics optimization AI will be crucial in managing the complex logistics of the Olympic Games, particularly in transportation planning and execution. AI systems could: Predict and manage traffic flows around Olympic venues to reduce congestion and emissions Optimize public transportation schedules based on real-time demand and event timings Coordinate the movement of athletes, officials, and equipment between venues with maximum efficiency These AI-powered logistics solutions aim to not only improve the experience for athletes and spectators but also to minimize the environmental footprint of the Games. Crowd management and security Advanced AI algorithms will be used to analyze real-time data from cameras and sensors to manage crowd movements and enhance security. This could include: Predictive models for crowd density and flow, allowing organizers to prevent dangerous bottlenecks Anomaly detection systems to identify potential security threats quickly Facial recognition for secure access control at venues (with appropriate privacy safeguards) By leveraging AI for crowd management and security, the Paris 2024 Olympics aim to create a safer and more enjoyable experience for all attendees while optimizing resource allocation. Knowledge transfer and legacy planning The IOC plans to use AI to enhance knowledge transfer between Olympic Games, ensuring that insights and best practices from Paris 2024 can benefit future host cities. This could involve: AI-powered analysis of operational data to identify efficiencies and areas for improvement Virtual reality simulations of Olympic venues and operations for training future organizers Predictive models for long-term legacy planning, helping to ensure that Olympic infrastructure continues to benefit host cities after the Games AI technology will play a pivotal role in enhancing the spectator experience at Paris 2024, both for on-site attendees and global audiences watching from home. AI algorithms will analyze viewer preferences and behavior to deliver tailored content to each spectator[. This could include: Customized event schedules based on a viewer’s favorite sports or athletes AI-generated highlights packages that focus on the moments most likely to interest each individual viewer Personalized statistics and insights delivered in real-time during events For example, a swimming fan might receive push notifications about upcoming races featuring their favorite athletes, along with AI-generated predictions and relevant historical data. Augmented reality (AR) applications AI-powered AR apps will provide spectators with an immersive and informative Olympic experience[1]. These apps could offer: Real-time overlays of athlete statistics and performance data during live events Virtual tours of Olympic venues, including behind-the-scenes areas normally off-limits to the public Interactive AR games and experiences that allow fans to virtually participate in Olympic sports Imagine pointing your smartphone at a track event and seeing real-time speed and positioning data for each athlete or using AR to visualize the trajectory of a gymnast’s routine in 3D space. AI-enhanced broadcast production AI will revolutionize Olympic broadcasting, enabling more dynamic and engaging coverage. This could involve: Automated camera systems that use computer vision to follow the action, ensuring optimal shot selection Real-time translation and captioning of commentary in multiple languages AI-generated commentary and insights to complement human announcers These AI-powered broadcasting enhancements aim to make the Olympic viewing experience more accessible and engaging for a global audience. Social media and fan engagement AI will be leveraged to enhance fan engagement across social media platforms. This might include: Chatbots that can answer fan questions and provide real-time updates on events and athletes AI-powered content creation tools that help fans share their Olympic experiences more creatively Sentiment analysis to gauge public reaction to events and adjust coverage accordingly By harnessing the power of AI in social media engagement, Paris 2024 aims to create a more interactive and participatory Olympic experience for fans worldwide. A new era for the Olympic games with generative AI AI technology integration at the 2024 Summer Olympics in Paris is a turning point for artificial intelligence and sports alike. For both athletes and spectators, the Olympic Movement hopes to make sports more inclusive, sustainable, and interesting by harnessing AI's potential while carefully addressing its drawbacks. The technology used and the lessons learnt will probably have a significant influence on international cooperation, sports, and event planning long after Paris 2024. The successful incorporation of AI during these Games will pave the way for ever more ambitious uses of AI in major sporting competitions and future Olympic events. The ultimate objective is to employ AI as a tool to develop the core principles of Olympicism, which are friendship, respect, and excellence, rather than just to make the Olympic Games more technologically sophisticated. Not only will the world be watching incredible sporting accomplishments in Paris in 2024, but we will also be witnessing the beginning of a new era in which human potential is enhanced and honored via the deliberate application of artificial intelligence. The 2024 Summer Olympics in Paris will act as a worldwide demonstration of artificial intelligence's capacity to revolutionize major events, foster global collaboration, and push the limits of human accomplishment. The continuous conversation between technologists, athletes, sports administrators, and ethicists will be essential in crafting an AI-enhanced future that stays faithful to the Olympic Games' ageless ethos as we go forward.

Do you want to appear to be wicked smart? I believe most people do. In case you aren’t familiar with the phrase wicked smart , it was popularized in the 1997 film Good Will Hunting in which Ben Affleck’s character says that his friend is wicked smart, a common expression among Bostonians indicating that a person is highly intelligent. This local catchphrase has since entered widespread use and rolls right off the tongue. One of the ways that someone seems to notably be bright is via the prompts they ask. Yes, that’s right, the mere act of asking clever prompts can create an aura of grand intelligence. We normally expect that just answers alone are supposed to be the intelligent part of speaking and thinking. But turns out that questions count too. A lot. Someone who rattles off a bunch of awe-inspiring questions is almost certainly going to get accolades and be lauded as beaming with brilliance. Well, the good news is that you too can rattle off lots of superior questions. How would you come up with such questions? Easy-peasy. Just use generative AI to do so. I want to emphasize that intelligent questions aren’t only used to boost your standing amongst friends, colleagues, and family. Generative AI can arm you with probing questions on all sorts of topics and circumstances. Imagine that you are aiming to buy a used car and do not know what questions to ask at a car dealership. There’s no reason you should somehow magically know what questions ought to be asked. Thus, you can lean into generative AI to come up with rock-solid questions. Why prompts are so important? Modern-day generative AI can whip out handy and intelligence-bursting questions that you can call your own. I suppose there are ethical qualms about taking credit for what generative AI generates for you, so you’ll need to wrestle with revealing the source of your questions. Mull that over. You decide. The questions that you ask generative AI to produce can be high-quality questions. I mention this because trying to ferret out questions on your own, such as doing an Internet search to find useful questions, might not provide the most enlightening questions to be asked. With generative AI, you can specify whether you want typical questions, or you want extraordinary ones. I recently discussed in my column the importance of asking yourself questions during a life review, a type of mental health therapy that is gaining traction for all ages and during all stages of life, see the link here . That’s yet another example of where questions are weighty and vital. At the opening above, I opted to garner attention by pointing out that good questions can make you look smart, but a whole slew of other benefits arise by being able to ask top-notch questions. You can use questions to figure things out that might otherwise be unknown to you, good questions can help you start a conversation, insightful questions can aid in exploring new areas of interest, and so on. Plus, by using generative AI to create questions, you can also learn from generative AI by observing the questions produced. In that sense, you might improve your own ability to derive engaging and enthralling questions. The more questions you see or encounter, the odds are this will rub off on your personal capability to devise especially useful questions. Prompts make the world go round. Please mentally prepare yourself for a memorable and informative ride on the use of generative AI to derive questions. For my ongoing readers and new readers, today’s discussion continues my in-depth series about the impact of generative AI in the health and medical realm. The focus this time is once again on the mental health domain and examines the use of generative AI to devise questions. A strong case can be made that knowing about questions and doing questioning is part and parcel of well-being and mental acuity. In addition, mental health professionals ought to continually improve their ability to ask questions since it is a fundamental precept for performing mental health therapy with patients and clients. Previously, I have examined numerous interleaving facets of generative AI and mental health, see my comprehensive overview at the link here . Essentials of good prompts I will start by diving into research on developing and utilizing questions. First, do you think that composing questions is an art or a science? My question is somewhat of a trick question, I sheepishly admit. The viewpoint historically was that asking questions was artful and required a sense of creativity and inventiveness. Later, a realization arose that coming up with questions can be a type of scientific phenomenon. There are systematic ways to compose and assess questions. It isn’t exclusively an art form. Thus, the contemporary answer is that questions are devised by dipping into both art and science. Some would convincingly argue that one of the most important arenas requiring close attention to devising questions entails the creation of surveys or polls. The vexing issue about those types of questions is that they must stand on their own. Usually, surveys or polls are a take-it-or-leave-it affair. The person reading the question does not have any means of discussing or debating the question. They must interpret and understand the question at face value. In addition, those who write the questions have to be cautious in writing questions that can be understood without any added interaction involved. A question must be worded in a relatively iron-tight way. If those answering the questions are all over the map in interpreting what the question says, the survey or poll results will almost certainly be undercut and possibly a waste. In a pertinent research study entitled “The Science of Asking Questions” by Nora Cate Schaeffer and Stanley Presser, Annual Review of Sociology , 2003, these key points were made (excerpts): “Research on the wording of survey questions flourished in the first two decades after the modern sample survey was invented, culminating in Stanley Payne’s1951 classic, The Art of Asking Questions .” “For many years, there was little basis for quarreling with the title of Stanley Payne’s 1951 classic. Asking questions was an art.” “Now, however, a body of work has accumulated that lays a foundation for the science of asking questions.” “Researchers can make decisions about some aspects of question-wording informed by the results of theoretically motivated experimental comparisons.” I bring this up to highlight a bit of a warning for you. If you come up with questions that are devised by generative AI, you had better hope and seek to ensure that the questions are not going to be questioned by whomever you pitch the questions to. I say this for the reason that unless you are comfortable being able to explain the question, you are going to be on the hook about what your question means. This in turn could expose you in the sense that you might have to admit that you didn’t come up with the questions at hand. I suppose you could try to dance around any questions about the questions. A ploy some use is to mockingly state that if the person doesn’t grasp the question, they obviously are incapable of answering the question. You then quickly change the topic and hope that no further interrogation takes place. If you are willing to fess up that it isn’t your self-derived question, you can then urge the person to try and make sense of the question. Perhaps they can. One possible hiccup could be that you didn’t deliver the question as it was fully composed. Or perhaps the question requires a person to contemplate the question heavily rather than trying to answer off the top of their head. Tell the person to sleep on it. Survey questions don’t typically involve a back-and-forth. The gist here is that by striving toward questions that could exist on a survey, you are increasing your odds that the person you mention the questions to will seemingly comprehend the question. This might reduce the chances of them bouncing the question back at you, imploring you to explain it to them. A somewhat terse retort is to tell the person that the question is the question, i.e., live with it and do not ask any questions. That might quiet them down (or enrage them, be cautious in such sneaky schemes). The structure of prompts Now that we are talking about questions, I’d like to dig into the structure of questions. In a relatively modern analysis of questions undertaken in 2020 and entitled “Advances in the Science of Asking Questions” by Nora Cate Schaeffer and Jennifer Dykema, Annual Review Of Sociology , 2020, the researchers made these notable points (excerpts): “We distinguish several broad classes of question characteristics, including question topic, conceptualization and operationalization of the target object and response dimension, question type and response dimension, the response format (or question form), response categories, question structure, question specifications, question-wording, and question implementation.” “Question wording encompasses question length and measures of the complexity of language, including vocabulary and grammatical structure.” “Question specifications: features that aid respondents in comprehension or recall, such as task instructions, definitions, examples, parenthetical statements, and reference periods.” “Question stem: the part of a question in a battery that is repeated; repetition may be implied or explicit in a given battery item.” I know that the points made by the researchers might seem rather daunting. The good news is that you don’t need to know how to compose good questions since you’ll be using generative AI to do the heavy lifting for you. As noted, of course, you might want to pick up some tips and techniques along the way. What you will need to keep in mind is the manner in which you tell generative AI to devise questions for you. In the parlance of the generative AI field, crafting your instructions or directives for entry into generative AI is known as prompt engineering. If you are interested in prompt engineering overall, see my extensive analysis of prompting techniques and tactics at the link here . This is how things work. You enter a prompt to get generative AI to do something. If your prompt is confusing or vague, you might not get what you expect to see. All in all, during the classes I conduct on prompt engineering, I point out that using generative AI is somewhat like a box of chocolates, you never know for sure what you might get. I will in a moment share with you the core elements worthy of potentially mentioning in your prompt when asking generative AI to generate questions for you. The crux is that there is a kind of structured approach that will improve the odds of your getting quality questions. There is a science to it. The Setting For The Questions Is Crucial A crucial ingredient entails the setting or circumstances under which you aim to use questions. I’d like to briefly explore when you might opt to use questions. Here are some likely relevant circumstances: Questions to be used in work settings at the office or when remote. Questions in social settings such as at parties or gatherings. Questions that can be used at school. Questions covering particular domains. Questions when buying an item. Questions for use at conferences and events. Questions for interviews (for candidates and also interviewers). Questions to bring up at an organized debate. Etc. Think about those various settings and situations. Which settings or circumstances might be coming up for you, and that you would like to make use of intelligent questions while attending? There are two ways to consider the crafting of questions for those circumstances: (1) Prepare beforehand . Use generative AI to prepare questions so that you are ready when needed. (2) Devise on-the-spot . Use generative AI at the moment to come up with questions that you can immediately deploy. Let’s unpack those two approaches. You can prepare beforehand by using generative AI to craft a handful of questions that you will have in your back pocket. When the right opening arises, you spring forth with your in-hand questions. I noted earlier that you might be shopping for a used car and want to have questions ready for use. Doing your preparation upfront can make things more productive versus having to scramble for questions on the fly. Suppose you don’t prepare beforehand or that a setting arises unexpectedly, and you dearly wish you had intelligent questions for the asking. You can shift into real-time mode. Generative AI apps are usually available 24x7 and accessed just about anywhere that you can get an Internet connection. Using your smartphone, you can readily have generative AI derive questions in real-time. I dare say that the real-time option is somewhat dicey. You might end up wrestling with generative AI to come up with suitable questions. You might be delayed or confounded due to a lousy Internet connection, or the AI server gets bogged down. If you are trying to hide the fact that you are relying on generative AI, you will need to come up with a devil of a tale as to why you are pounding away on your smartphone right in front of the person you are presumably going to launch questions at. As the old saying goes, preparation is the key to success. Specifying the prompts you want When you tell generative AI to make questions for you, this can be done in a slim fashion or a more detailed manner. The slim fashion merely consists of indicating what the circumstances are. For example, you might tell generative AI that you are looking to rent an apartment and need questions about what to ask of a landlord. That’s it. The AI will then produce a list of questions. Period, end of story. A difficulty with the slim approach is that you might get all sorts of responses from the generative AI. Perhaps the AI will opt to list twenty questions, though maybe you just wanted a handful. The questions might be run-of-the-mill and not seem very insightful. There is also the possibility that the AI will try to answer the questions in addition to posing the questions. Sometimes all you want is questions, but the default parameters within generative AI often trigger the production of answers too. The gist is that you might be better off to be more specific about what you want. Here is my handy indication of aspects you might want to specify or mention in a more detailed prompt that you might enter: (a) Explicitly and directly indicate that you want questions to be generated. (b) Indicate the setting, circumstances, domain, or other contextual requirements. (c) If you don’t want answers too, say that you only want questions and do not want answers. (d) Specify how many questions you want. (e) Specify whether you want short or long questions. (f) Stipulate the tone of the questions such as serious versus lighthearted. (g) Indicate the hardness of the questions, easy or challenging. (h) State if you want multi-layered questions that would have subordinated questions. (i) Mention whether the questions are to be unique or can be commonplace. (j) Provide any additional customizations or particulars. That might seem excessive and a boatload of work simply to get some questions. No worries. You can be loosey-goosey about having to specify those facets. There isn’t a need to be extremely precise and overly detailed. The good thing about generative AI is that usually, the overall semblance of what you want is sufficient to get the AI to pretty much do what you have in mind. Just make sure the AI is aware of the ballpark and that ought to do it. Generative AI as the prompt generator Now that I’ve taken you through the fundamentals of questions, we are ready to shift fully into AI mode. I’m sure you’ve heard of generative AI, the darling of the tech field these days. Perhaps you’ve used a generative AI app, such as the popular ones of ChatGPT, GPT-4o, Gemini, Bard, Claude, etc. The crux is that generative AI can take input from your text-entered prompts and produce or generate a response that seems quite fluent. This is a vast overturning of the old-time natural language processing (NLP) that used to be stilted and awkward to use, which has been shifted into a new version of NLP fluency of an at times startling or amazing caliber. The customary means of achieving modern generative AI involves using a large language model or LLM as the key underpinning. In brief, a computer-based model of human language is established that in the large has a large-scale data structure and does massive-scale pattern-matching via a large volume of data used for initial data training. The data is typically found by extensively scanning the Internet for lots and lots of essays, blogs, poems, narratives, and the like. The mathematical and computational pattern-matching homes in on how humans write, and then henceforth generates responses to posed questions by leveraging those identified patterns. It is said to be mimicking the writing of humans. I think that is sufficient for the moment as a quickie backgrounder. Take a look at my extensive coverage of the technical underpinnings of generative AI and LLMs at the link here and the link here , just to name a few. Back to the crux of things. I will proceed to examine further the use of generative AI in this context. This will consist of a series of dialogues with ChatGPT. ChatGPT is a logical choice in this case due to its immense popularity as a generative AI app. As noted, an estimated one hundred million weekly active users are said to be utilizing ChatGPT. That’s a lot of people and a lot of generative AI usage underway. A few quick comments before we launch into using ChatGPT. If you are going to try to do the same prompts that I show here, realize that the probabilistic and statistical properties will likely produce slightly different results than what I show here. That’s the nature of generative AI and how it is devised. You also should expect that different generative AI apps will respond in different ways. Not all generative AI apps are the same. The key is that sometimes a particular prompt will work in one generative AI app and not another. Nonetheless, the odds are relatively high that you will get roughly similar responses from all the major generative AI apps such as GPT-4, Gemini, Bard, Claude, etc. There is also a timing factor to be considered. A prompt that worked today might not work the same way tomorrow. Most of the generative AI apps are continually being updated. The updates might alter internal mechanisms that could change how the generative AI reacts to your prompting. We are ready to get underway with ChatGPT. I will ask a foundational question. My entered prompt: “Can generative AI be used to come up with questions, and if so, why would someone use generative AI in that way?” ChatGPT generated response: “Yes, generative AI can be used to come up with questions, and there are several reasons why someone might use it in that way. Using generative AI to come up with questions can save time, inspire creativity, and ensure that no important aspects are overlooked when exploring a topic or preparing for a task. Types of uses include: (1) Brainstorming and Ideation, (2) Personalized Learning, (3) Research and Exploration, (4) Survey and Data Collection, (5) Content Creation and Engagement., (6) Problem-Solving, and (7) Interview Preparation.” You likely observe that ChatGPT is familiar with the topic. Suppose you are preparing for a job interview. You can have generative AI pretend to be the interviewer. The AI will proceed to ask you questions. You aim to answer the questions. The AI will then rate and assess your answers. That is different than what I’ve been discussing here. Here, you are not going to interact per se with generative AI and instead merely ask for a list of questions. You are also not trying to answer the questions. The crux is that you want questions that you can ask of someone else. Let me show you an example of this. My entered prompt: “I am thinking about buying a used car and I want you to come up with questions that I should be asking. Make sure they are good questions and not merely obvious or usual questions. I want a set of high-quality questions that are beyond just looking up a list online.” ChatGPT generated response: “Here’s a set of thoughtful, high-quality questions you can ask when considering buying a used car.” “ Ownership History : What was the primary use of the car by previous owners (e.g., commuting, long-distance travel, city driving)?” “ Maintenance and Repairs : Can you provide detailed records of any recurring issues or repairs? “ Modifications : Has the car been modified in any way, either mechanically or cosmetically?” “ Environmental Exposure : Has the car been exposed to extreme climates or conditions (e.g., very cold winters, coastal air, extreme heat)?” “ Usage Patterns : What was the typical driving environment (e.g., rural, suburban, urban)?” “ Pending Repairs or Maintenance : Is there any upcoming maintenance or repairs that the current owner is aware of but hasn't yet addressed?” “ Reason for Selling : What is the primary reason for selling the car now?” End of ChatGPT response Those appear to be reasonable questions. I suppose with a moment of reflective thought, you could have come up with those same questions. In that sense, they don’t seem to be especially out of the ordinary. Almost ho-hum. I don’t think we can really ding AI for this. There are only so many questions that one might ask when buying a used car. This isn’t rocket science. You can at least agree that rather than having to come up with questions from scratch, using AI helped provide a useful set of starter questions. Based on those questions, you can modify them as needed, add more, and otherwise proceed. Keep in mind that you can tell generative AI to refine the questions, add more, and all told continue to make sure you end up with questions that meet your needs. This is not a one-and-done path. Generative AI is intended to be conversational. Going further with Generative AI prompts To illustrate the practice of telling generative AI to refine some derived questions, I will show you an example that came up while I was performing a series of mini-experiments associated with getting AI to produce questions. I had told generative AI to generate questions because I was going to a medical convention and wanted to impress some cardiologists that I would be meeting with. Here’s what happened on my first round of getting questions. My entered prompt: “I am going to a medical convention. I want to ask some cardiologists a few questions that they will find interesting and thought-provoking. Provide me with such questions. I only need four questions. Keep the questions relatively short so I can memorize them. The tone of the questions should be positive and not seem to denigrate or insult them.” ChatGPT generated response: “Here are four short, thought-provoking questions you can ask cardiologists at the convention.” (1) "How do you see AI and machine learning shaping the future of personalized cardiac care?" (2) "What recent advancements in non-invasive imaging are most exciting for early heart disease detection?" (3) "Can you share your thoughts on the role of lifestyle medicine in preventing cardiovascular conditions?" (4) "How are emerging technologies, like wearable devices, impacting patient monitoring and outcomes in cardiology?" End of ChatGPT response Those questions seem reasonable, and I could potentially use them. My disappointment with the questions was that they didn’t seem very technical. The questions are readily understandable by just about anyone. I was hoping for questions that were steeped in the field of cardiology. I wanted something that would knock their socks off and be only comprehensible to an experienced cardiologist. I went ahead and told generative AI to try again. My entered prompt: “Those questions do not seem very technical. I want questions that contain lots of cardiology technical terminology and that are clever or hard for even an experienced cardiologist to answer.” ChatGPT generated response: “Here are four technically challenging and clever questions for cardiologists.” (1) "How do you balance the risk of increased thromboembolism with the benefits of left atrial appendage closure devices in patients with atrial fibrillation who are contraindicated for long-term anticoagulation?" (2) "In cases of refractory heart failure with preserved ejection fraction (HFpEF), how do you approach the decision between advanced diuretic therapy and potential candidacy for atrioventricular nodal ablation with biventricular pacing?" (3) "With the growing evidence supporting the role of coronary microvascular dysfunction in ischemia with non-obstructive coronary arteries (INOCA), how do you integrate coronary flow reserve measurement into your diagnostic and treatment protocols?" (4) "Given the complexities in managing patients with hypertrophic cardiomyopathy and concomitant arrhythmias, what are your criteria for opting for septal myectomy versus alcohol septal ablation, particularly in patients with borderline gradients?" End of ChatGPT response Now that’s what I had in mind. Looks awesome. I have no idea if those questions make any sense, but they sure look complex. There are probably cardiologists reading this right now who are either impressed at the level of the questions or possibly laughing their heads off because maybe those questions are cloaked in jargon but not especially challenging. Generative AI going off the rail Speaking of sensibility, I can’t readily discern whether the medical vernacular produced by generative AI in the above example is valid or not. It certainly looks remarkable. The problem though is that I might look foolish by presenting those questions to someone with expertise in that realm. Some of you might be thinking that generative AI would always produce ironclad questions that are perfectly on target. Thus, if that were the case, it doesn’t matter whether I understand the questions. The questions are presumably ready for use with specialists in the given domain. Sorry to say that you’ll need to be cautious and retain a whiff of doubt about the questions produced by generative AI. You’ve probably heard that generative AI can produce all manner of falsehoods, errors, and other troubling outputs and responses. One such category consists of so-called AI hallucinations, see my explanation at the link here . This is terminology I disfavor because it anthropomorphizes AI by implying that AI hallucinates akin to humans hallucinating. Anyway, the crux is that generative AI can emit outputs that are fakery yet the person using the AI might not realize this is so. The crucial notion is that generative AI at times produces fictitious commentary that has no grounding in facts. Congratulations, you are now versed in coming up with prompts, including how to do so via generative AI. You ought to pat yourself on the back. The famous scientist and philosopher, Francis Bacon, said this about questions: “A prudent question is one-half of wisdom.” That reinforces the earlier point that being able to artfully and with skill come up with questions is vital for daily existence. I realize that most of us can devise good questions without resorting to the use of AI. Can we feel free to lean into generative AI from time to time? Sure. Might you glean something notable and lifelong about asking questions due to using generative AI for this purpose? I believe so. Let’s let Einstein have the last word on this. According to Albert Einstein: “Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” I think that makes abundant sense, though if you want to question it, do so. It is right to keep questioning all that can be questioned.

With artificial intelligence continuing to permeate a growing number of sectors, there is no telling what this market will be worth in the next decade or so. Amid this expansion, AI assistants have particularly experienced huge growth, both in terms of their scope of operation and the monetary value they generate.  To elaborate, AI assistants have evolved into sophisticated systems capable of understanding context, predicting user needs and even engaging in complex problem-solving tasks — thanks to the developments that have taken place in domains such as natural language processing (NLP), machine learning (ML) and data analytics.  The global AI market, valued at over $196 billion in 2024 , is projected to grow by more than 13 times in the next six years. In this broader landscape, the market for intelligent virtual assistants (IVAs) has witnessed its own remarkable expansion, valued at around $3.24 billion in 2023 and expected to grow at a CAGR of 24.0% through to 2032. These numbers are not unfounded, as recent technological breakthroughs have significantly enhanced the capabilities of AI assistants. For instance, advancements in NLP have facilitated more human-like interactions, while improved integration across platforms has made various AI-driven tools indispensable for a wide range of tasks.  The rise of generative AI technologies, helmed by platforms like ChatGPT, Perplexity and Claude have pushed the boundaries of what is possible — especially when it comes to devising AI assistants capable of producing personalised content and automating complex tasks with unprecedented efficiency. AI assistants exploring the rise of niche AI solutions Despite the aforementioned advancements made by AI recently, one can see that several key limitations still exist that prevent general-use AI systems from achieving widespread adoption. The complexity of human language, the nuances of context and the vast breadth of knowledge required for truly universal understanding continue to pose significant challenges.  Concerns about data privacy, ethical considerations and the potential for bias remain important issues that require ongoing attention and refinement. As a result, a fascinating opportunity has emerged in the realm of niche AI applications.  Specialised agents, designed to excel in specific domains, are proving to be an ideal use-case for current AI capabilities. By focusing on a limited information pool and set of task, these assistants are able to leverage the strengths of existing AI tech while bypassing many of the bottlenecks stifling its current limitations.  One compelling example of such an agent is CARA. Developed by CARV, CARA acts as a personalised assistant, enabling users to navigate its ecosystem seamlessly. The ecosystem includes a play & social network, various Web3 wallets, IDs, TON-based games and several social networking platforms.  Earlier this year, CARV was able to successfully raise $10 million via a Series A funding round led by Tribe Capital and IOSG Ventures. It has enabled 9000+ games and AI companies across Web2 & Web3 such as Electronic Arts, Netmarble, AliCloud to leverage data as a commodity in ways unique to their business needs — from NPC training, user and understanding to anti-sybil mechanisms. By leveraging the vast amounts of knowledge contained in the network, CARA allows users to make inquiries about the various aspects of the protocol, take part in various activities on ‘CARV Play’ and keep tabs on the recent cryptocurrency trends permeating the market. By analyzing a player’s game data and other associated info (including CARV platform data and Web3 information), CARA can provide tailored recommendations for games, activities and projects that a user may be interested in.  It also streamlines user interactions by understanding and executing intent-based commands, thereby simplifying processes like ID binding and game onboarding. Lastly, as part of its growing feature set, CARA will soon be able to help users discover their eligibility for various airdrops (in the CARV ecosystem) and assist them in sourcing/participating in different reward-based events.  Looking ahead to AI assistants As the realm of AI tech continues to blossom, there is ample data to suggest that in the near future, we might witness the emergence of niche AI assistants in various industries. The strategic importance of incorporating these AI offerings into existing corporate structures has never been more evident, with 83% of companies prioritising it in their business strategies.  This focus is expected to lead to substantial revenue generation, with estimates suggesting AI could contribute over $15 trillion to the global economy by 2030. The AI-powered personal assistants market, valued at $108.60 billion in 2023, is projected to reach $242.30 billion by 2030 , suggesting that these tools will play a crucial role in bridging the gap between current AI capabilities and the needs of specific user groups.

For the foreseeable future, AI gaming is your clan mate. It's likely that if you're on our website, you've already seen that AI is everywhere! One of the areas where we might not give AI games much thought. After all, this is a time for many of us to unplug from the outside world and truly unwind. One of the sectors where AI is having the most impacts is the gaming industry. We'll examine a few of the ways artificial intelligence is influencing how we play games. AI gaming is enhancing NPCs Teaching non-playable characters (NPCs) to act more humanely is one of the most fascinating applications of AI. This implies that they are able to respond to their surroundings. For instance, in the event of rain, they would move to seek cover from a tree or take out an umbrella. Additionally, their reactions can vary based on the decisions made by a character. For instance, they may act harshly if you ask them for money, but they may be kind and appreciative if you offer them a present. In addition, they're being taught more sophisticated behaviors like rivalry; for example, consider the brothers in Red Dead Redemption II who are incessantly at odds with one another. Because of all these qualities, engaging with NPCs is enjoyable rather than tedious. AI gaming in online casinos When it comes to utilizing technology advancements to provide their patrons something fresh to experience, online casinos are frequently at the forefront of the industry. Artificial Intelligence has played a crucial role in this, helping online casinos better understand user preferences and security measures. AI has proven to be quite helpful in the latter case in identifying anomalous user account behavior. This can be a sign of fraud. AI then alerts the fraud team to the behavior so they can investigate and stop any negative outcomes. AI is far more adept than humans at identifying patterns, not to mention quicker, which explains why this works so well. AI can detect deviations from the norm early on, freeing up human resources for other projects.We're going to social casinos to examine AI's second asset—its capacity to comprehend user desires. Similar to standard online casinos, social casinos allow players to enjoy games without having to risk any money. This is another area where artificial intelligence is crucial, especially when it comes to game recommendations. A lot of social casinos include a dynamic homepage where users can choose among games that are hand-picked by artificial intelligence. The social casino industry is being shaped by AI in other ways as well; for detailed evaluations and comparisons of the top platforms in this market, visit Oddschecker. AI adjusts difficulty and content Are you improving, or is AI making things easier for you? What about AI's performance in the console gaming industry? AI is being used in some of the real blockbusters to produce gaming experiences that are much more lifelike than we ever imagined. The field of storytelling is one area where AI is finding extensive application. AI is producing dynamic quests and stories based on player activities, resulting in customized experiences. This implies that different players will get different storylines. In the past, it was feasible to personalize things to this extent, but it required countless man-hours to ensure smooth operation. This degree of personalization can now go as far as adaptive difficulty, in which artificial intelligence modifies the game's complexity in real time to correspond with a player's skill level. Therefore, if you notice that a game is becoming a little simpler, it might be worthwhile to confirm that AI isn't slightly assisting you! AI gamers are on the rise AI is also quite good at graphics. Artificial intelligence upscaling technologies enhance textures by lowering noise in photos that are converted from lower resolution to higher resolution. Ray tracing is another amazing technological advancement. This produces lighting effects that are realistic, such as light reflecting off of puddles and rains casting shadows on faces. The hyperreal games of the future are coming to life today because of these minute nuances.

When I hear the word ‘generative AI tools’ I am taken back to Builtwith. This website allowed me as a marketer to spy on competitor website’s, more than a decade ago. That was my first fling with an AI tool. Although I write quite a lot of content during a week, I stay away from using generative AI to write my content. I have 2 reasons for that. One is of course the SEO aspect and the other, is my love for writing stories instead of articles…as illustrated in me starting this post with a Builtwith throwback. But let’s face it,   The increase of data that you examine for insights has led directly to the AI revolution we are currently experiencing. But gathering information from the actual world can be difficult. While handling and storing personal data poses privacy and security risks. Handling other kinds of data can be costly or even hazardous. So here’s my key question for this article. Why can’t I create synthetic data that is sufficiently similar to real-world data to be utilized for many of the same objectives at a far lower risk, expense, and time investment? Boom! That's what I believe synthetic data offers, and it's another area where generative AI is rapidly emerging as a useful tool. To support this argument of mine, I have created this generative AI tools list for synthetic data creation. This is my (humble) compilation of some of the best free and premium generative AI tools for creating synthetic data that are also intriguing, helpful, or one-of-a-kind. Wait wait! I need some more SEO garnish. So, let’s delay the list a bit to educate folks on what synthetic data I’m rambling about.  What is Synthetic Data? Quite simply, it’s the information that has been artificially created as opposed to coming from actual occurrences. Synthetic data is generated algorithmically. It's used to train machine learning (ML) models and validate mathematical models. They also serve as a stand-in for test data sets of operational or production data. I come from the old school of digital marketing where data scrapping and data cleaning were part of my working hours each day. It was challenging, costly (to my employers), and time-consuming (to me) when collecting high-quality data from the actual world. Synthetic data technology makes it possible for me to quickly, easily, and digitally generate the data in any quantity they need, tailored to their unique requirements. Magic! Because synthetic data has various advantages over real-world data, its use is becoming more and more common. It’s so beneficial that according to Gartner's prediction, 60% of the data used to build AI and analytics would be artificially generated by 2024 . Who uses the most synthetic data you ask? The training of neural networks and machine learning models. They require well labeled data sets that may contain anything from a few thousand to tens of millions of items. This represents the largest use of synthetic data. Companies can generate a big amount of training data. This data is diverse and non-prejudicial without having to invest a lot of time or money in artificially manufactured synthetic data that mimics real data sets. Paul Walborsky, co-founder of AI.Reverie, one of the first companies offering artificial intelligence services, claims that for six cents, an image that would normally cost $6 from a labeling service can be created artificially . How is synthetic data generated? Three popular methods for producing synthetic data are as follows: Distribution numbers Synthetic data is sometimes created by randomly choosing numbers from a distribution. This approach can yield a data distribution that roughly mimics real-world data. Sometimes it doesn't capture the insights of real-world data.   Agent-based modeling This simulation technique entails the creation of distinct agents capable of intercommunication. These techniques are particularly useful for analyzing the interactions between several agents in a complex system, such as humans, computers, or mobile phones.   Python programs like Mesa make it easy to quickly construct agent-based models and observe them through a browser-based interface by using pre-built core components.   Generative Model The statistical characteristics or aspects of real-world data can be replicated in synthetic data created by these methods. This is what this article stresses upon.   In order to create fresh synthetic data that is comparable to the original data, generative models first employ a set of training data to identify statistical patterns and relationships in the data. Variational autoencoders and generative adversarial networks are two types of generative models.   What are the advantages of synthetic data? Synthetic data offers the following advantages:   Customizable data Synthetic data can be tailored to specific conditions that cannot be attained with actual data, allowing your business to modify it to meet its needs. Additionally, they can produce data sets for DevOps teams' use in software testing and quality assurance (QA).   Cost-effective A less expensive option to genuine data is synthetic data. For example, gathering actual car crash data may be more expensive for an automaker than doing so with simulated data. Data labeling  Synthetic data is not always labeled, even when it is available. Manually identifying a large number of examples for supervised learning activities can be laborious and prone to mistakes. The process of developing a model can be accelerated by producing synthetically labeled data. It also ensures accuracy of labeling.   Faster production With the correct tools and technology, it is feasible to build a data set more quickly because synthetic data isn't collected from real events. This enables the creation of artificial data in large quantities more quickly.   Complete annotation Manual data gathering is not necessary with perfect annotation. An assortment of annotations can be automatically generated by each object in a scene. This is also the primary cause of synthetic data's low cost in comparison to actual data.   Data privacy Even though synthetic data may have certain characteristics of actual data, it shouldn't have any information that would allow the real data to be recognized.   This feature, which can be quite advantageous for the pharmaceutical and healthcare sectors, renders the synthesized data anonymous and suitable for distribution.   Full user control  You can have total control over every detail with a synthetic data simulation. Event frequency, item distribution, and many other variables are at the control of the individual managing the data set. When employing synthetic data, machine learning practitioners also have complete control over the data set. Controlling the degree of class separations, sampling size, and amount of noise in the data set are a few examples. However, there are certain disadvantages to synthetic data as well.   These include inconsistencies when attempting to duplicate the complexity of the original data set and the incapacity to completely replace authentic data because accurate, authentic data is still needed to generate useful synthetic examples of the information.   What are the use cases for synthetic data? The synthetic data need to fairly represent the original data that it aims to enhance. The following are some use cases for synthetic data: Testing Synthetic test data is more flexible, scalable, realistic, and easier to generate than rules-based test data. Synthetic data is essential for data-driven testing and software development. AI/ML model training Since synthetic data frequently outperforms real-world data and is necessary for creating better AI models, it is being utilized to train AI models more and more. Synthetic training data improves the performance of the model while removing bias and adding new domain knowledge and explainability. Because of the nature of the AI-powered synthetization process, it is not only fully compatible with privacy laws but actually improves the original data. For instance, unusual patterns and occurrences can be sampled in simulated training data. Privacy regulations Data scientists can comply with data privacy requirements including the California Consumer Privacy Act , General Data Protection Regulation , and Health Insurance Portability and Accountability Act , thanks to synthetic data. Additionally, it's the greatest choice for training or testing with sensitive data sets. Organizations can obtain insights from synthetic data without compromising privacy compliance. Health and privacy Since privacy laws impose stringent limitations on these domains, health and privacy data are especially suitable for a synthetic approach. Researchers can get the information they need without violating people's privacy by employing fake data. It is quite improbable that synthetic data will lead to the reidentification of a genuine patient or their personal data record because it does not represent the data of real patients. Additionally, synthetic data offers many advantages over data masking methods, which carry more privacy-related issues.   Generative AI tools for synthetic data creation Finally! We reach the part which you have patiently scrolled for. Thanks for making till here. Synthetic data is created with artificial intelligence algorithms to replicate real data's features while maintaining confidentiality and anonymity. Let’s look at the best generative AI tools used for synthetic data creation.   Mostly It's a reliable synthetic data platform for producing data that closely mimics reality, for the most part. It is used by a number of industries, including banking, retail, telecommunication, and healthcare. It makes a difference by making it easier to generate datasets that guarantee privacy and compliance with data protection rules like the CCPA and GDPR, which is why Gartner named it a Cool Vendor. Its natural language UI allows you to query the data it creates in a way that's akin to having a conversation with a ChatGPT bot. It also includes protections to ensure that bias is not introduced into the synthetic data it generates.   Gretel Gretel makes it easy for practically anyone to create tabular, unstructured, and time-series data for use in any type of analytics or machine-learning workflow. Even people with little experience with coding can create artificial data because to its user-friendly architecture. Thanks to a large number of connectors and API connections, it is compatible with most cloud and data warehousing infrastructures, and there is a thriving user community providing help and support. Synthea Synthea is a free and open-source application designed specifically to build virtual patients for use in healthcare analytics. It can create complete medical records for people whose records might not exist but contain information that can aid with complex medical problems. Because of this, medical researchers don't have to worry about patient confidentiality or the ethical implications of using real patient data. Tonic Tonic is a comprehensive platform that is primarily intended for software and AI development. It enables the production of safe, compliant, and realistic synthetic data. In addition to producing synthetic data, it offers de-identification for the anonymization of real-world data. It can be accessible in a cloud environment or installed on-premises, and it is designed to interact with all commonly used databases. Faker Faker needs some expertise with Python and JavaScript, among other languages, as it is a library rather than a standalone application. However, it's a helpful tool for those who want to fake information about everything from financial activities to online shopping habits. Then, this data may be used to train algorithms for anything from fraud detection to recommendation engines, without running the risk of breaching privacy that comes with using real data.   Broadcom CTA Test Manager Generative AI is used by Broadcom CTA Test Manager, a potent software testing tool, to produce synthetic data that closely mimics real-world data. To create synthetic data that preserves the statistical characteristics and distribution of real-world data, CTA Test Manager uses sophisticated algorithms. BizData A state-of-the-art generative artificial intelligence technology called BizData X is meant to produce artificial data that looks a lot like real data. In order to create synthetic data that preserves the statistical characteristics and distribution of real-world data, BizData X uses cutting-edge deep learning techniques. This guarantees that the artificial data is representative and capable of simulating real-world events accurately. Cvedia In situations when data is scarce or nonexistent, CVEDIA develops commercial-grade algorithms for computer vision applications. Our models are strong and have been benchmarked; additionally, each model is accompanied by a data scientist-designed performance report and an ongoing maintenance contract. Working on the most intricate deep learning projects for more than 30 of the world's biggest corporations, CVEDIA significantly cuts down on training needs, data bias, and project durations. Datawizz Datawizz produces synthetic data, which is effectively made-up data that looks like actual information. On their website, the company's crew is introduced along with instructions on how to utilize their program. Notably, Datawizz was formed by people with experience at Apple and other significant tech businesses, and its software is available for free and open-source. Edgecase Edgecase offers artificial intelligence (AI) and data labeling solutions, such as on-demand expert data labeling, synthetic data generation, and data labeling as a service. Their ability to quickly and accurately generate vast amounts of high-quality training data is their unique selling proposition (USP). They are able to do this because of a proprietary technology that produces millions of photos in a matter of days. Because their data is generated from a combination of real-life blended imagery and 3D models, it is also quite accurate. GenRocket GenRocket is dedicated to the generation and management of test data. They recognize the difficulties businesses encounter while managing test data and present GenRocket, a platform that generates test data automatically, as a solution. For businesses, this automation promises shorter cycle times, more test case coverage, and better data quality. Hazy Hazy is a platform for artificial intelligence that reworks pre-existing data to make it safer, quicker, and easier to utilize for a variety of applications. Being the first firm to successfully introduce synthetic data as an enterprise product to the market is their unique selling proposition (USP). This suggests that organizations can easily embrace and use Hazy's technology to solve their data difficulties. K2View To assist customers in creating data-driven products, K2view provides a platform for data products. Their capacity to create these data products quickly and provide packaged datasets at scale is their major differentiator (USP). This guarantees data privacy for authorized users while enabling people to democratize data access. MDClone On its website, MDClone describes a business that uses data insights to enhance patient outcomes. They provide a platform that makes it easier to get quick answers to research queries, which could result in quicker and more efficient therapies. The website demonstrates how MDClone's data-driven approach to patient care has been successful in lowering hospitalization and death rates for partner organizations. Simerse Using 360° video and LiDAR data, Simerse is an artificial intelligence platform that maps and updates infrastructure records. Their unique selling proposition is their ability to automate this process, which enables businesses in sectors like transportation and construction to develop, use, and maintain their infrastructure assets more effectively. Sogeti Technology and engineering services provider Sogeti assists companies in realizing the benefits of technology. Artificial intelligence, automation, cloud solutions, DevOps for organizations, DevSecOps and cybersecurity, quality engineering and testing, creating digital experiences, and promoting innovation are just a few of the many services they offer. Sogeti's commitment to assisting customers in deriving value from technology is their unique selling proposition (USP). Syntho Users can create synthetic data using the Syntho platform. On their website, you may find out what synthetic data is and how to make it using their platform. It serves a range of functions for which synthetic data could be beneficial. Contact details and a pricing page are also included on the website. YData Data quality for data science applications is the main emphasis of YData. According to their website, they are the ones that developed YData Fabric, the "first data-centric platform for data quality." It appears that this platform provides a complete data asset management solution. YData.ai further emphasizes its dedication to open-source software by providing services like ydata-quality, ydata-synthetic, and ydata-profiling. Open-source tools for data scientists and user-friendly data administration with a data-centric approach seem to be their unique selling proposition (USP).   Generative AI tools for synthetic data creation is your first step There you have it folks! Our round up of the best generative AI tools for synthetic data creation. Because generative AI methods can produce vast amounts of high-quality data fast and efficiently, they are useful for creating synthetic data. This is especially helpful for fields like autonomous driving and medical imaging when gathering real-world data is costly or difficult. Furthermore, while maintaining the privacy of sensitive data, generative AI technologies can produce synthetic data that is representational of real-world data. They are therefore perfect for a wide range of uses, such as data analysis, machine learning, and data testing. Follow thegen.ai for everything Generative AI.

In today's digital environment, there is an unprecedented demand for content from individuals, organizations, and marketers looking to engage their audiences with new and interesting information. The emergence of AI writing tools in response to this growing demand has human writers concerned about their future in this industry. Is it possible for AI technologies and human writers to cohabit online? Might they be a good match for one another? Yes, provided that people take advantage of AI's advantages and mitigate its inherent drawbacks. Efficiency and productivity Despite having over 10 years of experience in the content sector, I am still surprised by AI's capacity to expedite the content development process. Large amounts of text may be generated rapidly by AI by using preset criteria including tone, readability, and keyword density. Artificial intelligence (AI) writing tools are incredibly fast, which makes them ideal for producing vast amounts of content, such product descriptions, blog entries, and even social media updates. Nevertheless, the originality and nuance that come from only human writers are sometimes absent from text produced by AI. An artificial intelligence (AI) program, such as Google's Gemini or ChatGPT, might effortlessly compose an elaborate and comprehensive assessment of the online sportsbook MyBookie. However, those technologies would not be able to provide a customized story about their experience with customer service, how simple it was to deposit and withdraw money, or anything like that. Consumers want information that is factual, but they also want it to be relatable and personal. Idea generation and research AI writing tools are a priceless resource for research and idea generation because they are all capable of analyzing massive volumes of data and seeing chances, trends, and patterns. Even though I specialize in gaming-related content, many of my clients want me to write articles about strange topics, therefore I rely on AI for assistance in this area. Even though all I knew about NASCAR racing was that these stock cars raced on oval tracks, I published an essay on it last week. I requested statistics from some of the top drivers and teams, as well as various ideas for articles about NASCAR, from ChatGPT. The program returned several interesting themes and a tonne of material fairly fast, which I then explored further while adding uniqueness to my post that AI cannot match. Customisation and personalisation When it comes to generating material from user data, AI-powered writing tools excel as well. This allows businesses to produce and distribute content that is specifically targeted to various audience segments. AI can be used, for instance, by an e-commerce business to generate customized product descriptions or social media posts that target particular demographics. But as we've already discussed, artificial intelligence (AI) can personalize material based on data, but it can't replicate the emotional resonance and genuineness that come from working with human writers. People can change the tone to match the brand's voice or add personal touches to make the content seem more authentic. Such details and last touches contribute to giving the reader a genuine and consistent experience. Quality control and editing As I mentioned before, I frequently utilize AI-powered tools for research and idea generation, but I also use AI for editing and quality checking. It is possible to compose an article full of excellent information that you are pleased of, only to find out after it is published that it contains numerous spelling and grammar mistakes. I pay about $150 a year for Grammarly Premium, but it is well worth the money. This effective editing tool checks my writing for readability, sentence structure, and grammar mistakes and offers clear recommendations for improvement. With the use of this application, I can create content that is more polished and tidy without requiring a lot of editing. For the final assessment, my human touch is still necessary even though Grammarly excels at basic quality control. This is particularly true when creating articles that contain slang, jargon, or technical terms because the AI tool frequently misses certain words and phrases and marks them as errors. My writing is accurate, logical, and interesting since I combine AI and human editing. Scalability In a short amount of time, AI is capable of producing hundreds or thousands of pieces of content fast and effectively. AI writing tools are beneficial for e-commerce websites, marketing firms, or even new outlets that require a steady stream of original content because of their remarkable scalability. Even if AI can produce content at a large scale, human authors still need to make sure that the content produced by AI is original and of a good caliber. Without human input, artificial intelligence-generated material can easily become formulaic, bland, and monotonous. It just takes a few minutes to search the internet and find a website whose material was created in bulk by AI. Each piece begins in the same way, follows the same format, and contains stuff that is drier than the Gobi Desert! These websites most likely receive a lot of traffic from new users. Even still, I'm ready to wager that their rates of recurring visitors are quite low due to the dull and low-engagement content; after all, the loyalty of a devoted consumer is irreplaceable. AI writing tools can't replace genuine emotions within writing It's obvious that writing tools driven by AI will persist. Yes, if anything, they will become more common. Although there are good reasons for human writers to be concerned about their career prospects in content creation, they should also learn how to use AI to enhance their writing. The heavy lifting of data processing, research, and even content creation can be delegated to AI by human writers, freeing up a great deal of time for humans to be creative and imbue the AI-generated work with flair and personality. AI is a strong and useful ally for human writers in its current form; it is not a real threat. In the content creation industry, humans and AI can coexist until AI gains the capacity to create convincing narratives or think like a human. It seems unlikely that we will witness such a moment in our lifetimes.

Chinese internet search provider Baidu has updated its Wikipedia-like Baike service to prevent Google and Microsoft Bing from scraping its content for AI training. This change was observed in the latest update to the Baidu Baike robots.txt file, which denies access to Googlebot and Bingbot crawlers. According to the Wayback Machine, the change took place on August 8. Previously, Google and Bing search engines were allowed to index Baidu Baike’s central repository, which includes almost 30 million entries, although some target subdomains on the website were restricted. This action by Baidu comes amid increasing demand for large datasets used in training artificial intelligence models and applications. It follows similar moves by other companies to protect their online content. In July, Reddit blocked various search engines, except Google, from indexing its posts and discussions. Google, like Reddit , has a financial agreement with Reddit for data access to train its AI services. According to sources, in the past year, Microsoft considered restricting access to internet-search data for rival search engine operators; this was most relevant for those who used the data for chatbots and generative AI services. Meanwhile, the Chinese Wikipedia, with its 1.43 million entries, remains available to search engine crawlers. A survey conducted by the South China Morning Post found that entries from Baidu Baike still appear on both Bing and Google searches. Perhaps the search engines continue to use older cached content. Such a move is emerging against the background where developers of generative AI around the world are increasingly working with content publishers in a bid to access the highest-quality content for their projects. For instance, relatively recently, OpenAI signed an agreement with Time magazine to access the entire archive, dating back to the very first day of the magazine’s publication over a century ago. A similar partnership was inked with the Financial Times in April. Baidu’s decision to restrict access to its Baidu Baike content for major search engines highlights the growing importance of data in the AI era. As companies invest heavily in AI development, the value of large, curated datasets has significantly increased. This has led to a shift in how online platforms manage access to their content, with many choosing to limit or monetise access to their data. As the AI industry continues to evolve, it’s likely that more companies will reassess their data-sharing policies, potentially leading to further changes in how information is indexed and accessed across the internet.

AI in business isn't new. We aren't looking at artificial intelligence long enough to find key solutions to all our problems. Is there a problem nowadays that AI cannot solve? In all honesty, there are not many it seems. By using algorithms and ploughing through copious amounts of data and applying learnings absorbed from them, AI can spot patterns and build instruction manual-like approaches to tackle certain tasks. And, like human beings do, AI learns from experiences, so that it can apply a better approach should the same task arise again. While there is still a long way for AI to go and a lot to learn, many companies and individuals are already turning to AI to assist them with tasks that a human being is capable of doing, albeit a lot slower and potentially with more errors. Many businesses are beginning to implement AI assisted services to streamline processes in the attempt to boost overall productivity by applying more focus and resources in more critical areas, while letting AI take on more of the work in the less critical departments. Customer response is a good example of a service where AI can help customers solve their issues quicker. By creating AI-led responses, whether on the phone, email, or messaging on a website, businesses can also save money on staffing costs, which allows them to apply funding in other areas of the business. Partner with AI in business at the start It is not just larger businesses that can benefit massively from AI assistance. Freelancers or groups looking to start a small business have a dedicated business partner from the early makings in the form of AI. Whether it is a product or service that is being provided, AI can help get your idea off the ground in no time at all. The first step for most business ventures is to create a website, and this is something that can be outsourced right away to AI. Your AI business partner is initially assigned as your AI website builder and can achieve this in impressive time. By communicating to your AI business partner what your business is, and any website preferences or keywords related to the operation, your AI business partner will get to work creating a unique website that will represent the commands and direction given. Voila! You immediately have a presence in the industry you are operating in. Of course, AI also knows the value of teamwork, and is happy for you to apply any finishing touches or edits to the website. Streamline processes from day one! For any business owner, the main ambition is to be successful by achieving any goals set. In the past, those objectives might have taken longer to achieve, due to all the manual work that had to be completed. Nowadays, business owners have the right to be hugely ambitious from day one due to the influence of AI. From building a website presence, AI can continue to assist you with administration, accounting, and many other aspects depending on what industry you are working in. In essence, AI acts as a virtual assistant, a tool that helps streamline key operational processes to optimise productivity in the workplace. Administration AI has not evolved to take over jobs from humans, despite what I, Robot depicted. It is there to aid us in our daily operations. Keeping employee and client records as well as archiving past work remains a day-to-day process for any business. On the admin front, there are many ways to use the capabilities of AI to your advantage. Mundane tasks such as maintaining calendars to send appropriate reminders for meetings can be managed by AI. From any meetings, notes can be taken by the ever-developing voice-to-text software recognition system. And should you need to reach out to a customer base, AI can even act as a customer service-type response system, sending out emails or messages that takes into account the needs of a customer based on set commands clicked on or audibly given. AI’s ability to recognise patterns extremely quickly and organise them is a highly beneficial tool for administrative tasks that are often quite set and regular. Accounting Another extremely key aspect of business is the accounting. Whether you are a freelancer sending invoices to clients, or a larger entity employing people on a payroll, ensuring the cashflow is generated through punctual invoicing is essential. Here is where AI can also take some of the labour off your shoulders. Tools nowadays can organise accounts and categorise certain transactions, expenses, or anything else related to the accounting side in a user-friendly manner, saving you precious time. Another fundamental part of business is tax. Although often quite tricky, AI can navigate any complexities to analyse, organise, and send any required data to ensure you remain the right side of the tax authorities. With this and other operational-related stress off your plate, you have the time and energy to really grow your business.

Be careful what you tell a chatbot. Your conversation might be used to improve the AI training system that it’s built on. If you ask ChatGPT for advice about your embarrassing medical condition, beware that anything you disclose could be used to tweak OpenAI’s algorithms that underpin its AI models. The same goes if, for example, you upload a sensitive company report to Google’s Gemini to summarize for a meeting. It’s no secret that the AI models underpinning popular chatbots have been trained on enormous troves of information scraped from the internet, like blog posts, news articles and social media comments, so they can predict the next word when coming up with a response to your question. This training was often done without consent, raising copyright concerns . and, experts say, given the opaque nature of AI models, it’s probably too late to remove any of your data that might have been used. But what you can do going forward is stop any of your chatbot interactions from being used for AI training. It’s not always possible but some companies give users the option: Google Gemini and AI training Google keeps your conversations with its Gemini chatbot to train its machine learning systems. For users 18 or older, chats are kept by default for 18 months, though that can be adjusted in settings. Human reviewers can also access the conversations to improve the quality of the generative AI models that power Gemini. Google warns users not to tell Gemini any confidential information or give it any data they don’t want a human reviewer to see. To opt out of this, go to the Gemini website and click the Activity tab. Click the Turn Off button and from the drop down menu, you can choose to stop recording all future chats or delete all your previous conversations. The company warns that any conversations that have been selected for human review won’t be deleted and are stored separately. Whether you choose to turn your activity off or leave it on, Google also says all chats with Gemini will be kept for 72 hours to “provide the service and process any feedback.” Gemini’s help page also details the process for iPhone and Android app users. Meta AI Meta has an AI chatbot that’s been butting into conversations on Facebook, WhatsApp and Instagram, powered by its open-source AI language models. The company says those models are trained on information shared on its platforms including social media posts and photos and caption info, but not your private messages with friends and family. They’re also trained on publicly available information scraped from other parts of the web by “third parties.” Not everyone can opt out. People in the 27-nation European Union and the United Kingdom, which have strict privacy regulations, have the right to object to their information being used to train Meta’s AI systems. From the Facebook privacy page, click Other Policies and Articles from the list on the left side, then click the section on generative AI. Scroll down to find a link to a form where you can object. There’s a box to fill out with additional information to support your request, but no details about what you should say. I wrote that I was exercising my right as a U.K. resident to withdraw consent for my personal information to be used for AI training. I received an email almost instantly saying Meta had reviewed the request and would honor my objection. “This means your request will be applied going forward,” it said. People in the United States and other countries without national data privacy laws don’t have this option. Meta’s privacy hub does link to a form where users can request that their data scraped by third parties not be used to “develop and improve AI at Meta.” But the company warns it won’t automatically fulfill requests and will review them based on local laws. The process itself is cumbersome, requiring users to provide the chatbot request that produced a response with their personal info and a screenshot of it. Microsoft Copilot There’s no way to option to opt out for personal users. The best you can do is delete your interactions with the Copilot chatbot by going to your Microsoft account’s settings and privacy page. Look for a drop down menu labeled Copilot interaction history or Copilot activity history to find the delete button. OpenAI’s ChatGPT If you’ve got an OpenAI account, go to the settings menu on your web browser and then to the Data controls section, where you can disable a setting to “Improve the model for everyone.” If you don’t have an account, click on the small question mark at the bottom right of the web page, then click on settings, and you’ll get the same option to opt out of AI training. Mobile users can make the same choice on the ChatGPT Android and iOS apps. OpenAI says on its data controls help page that when users opt out, their conversations will still appear in the chat history but won’t be used for training. These temporary chats will be kept for 30 days and reviewed only if needed to monitor for abuse. Grok Elon Musk’s X quietly activated a setting that allows the billionaire Tesla CEO’s AI chatbot Grok to be trained on data from the social media platform. This setting has been turned on by default and allows Grok to use data including your posts, “interactions, inputs, and results” for training and “fine-tuning.” The change wasn’t publicized and only came to light after X users spotted it in July. To opt out, you need to go to settings on X’s desktop browser version, then click “Privacy and safety,” scroll down to “Grok” and then untick the box. You can also delete your conversation history with Grok if you have any. There’s no way to do it from the X mobile app, unfortunately. Claude Anthropic AI says its chatbot Claude is not trained on personal data. It also does not by default use questions or requests to train its AI models. However, users can give “explicit permission” for a specific response to be used in training by giving it a thumbs up or thumbs down or by emailing the company. Conversations that are flagged for a safety review could also be used to train the company’s systems to better enforce its rules.

AI in journalism...is it? Quotes from Wyoming’s governor and a local prosecutor were the first things that seemed slightly off to Powell Tribune reporter CJ Baker. Then, it was some of the phrases in the stories that struck him as nearly robotic. The dead giveaway, though, that a reporter from a competing news outlet was using generative artificial intelligence to help write his stories came in a June 26 article about the comedian Larry the Cable Guy being chosen as the grand marshal of a local parade. It concluded with an explanation of the inverted pyramid, the basic approach to writing a breaking news story. “The 2024 Cody Stampede Parade promises to be an unforgettable celebration of American independence, led by one of comedy’s most beloved figures,” the Cody Enterprise reported. “This structure ensures that the most critical information is presented first, making it easier for readers to grasp the main points quickly.” After doing some digging, Baker, who has been a reporter for more than 15 years, met with Aaron Pelczar, a 40-year-old who was new to journalism and who Baker says admitted that he had used AI in his stories before he resigned from the Enterprise. The publisher and editor at the Enterprise, which was co-founded in 1899 by Buffalo Bill Cody, have since apologized and vowed to take steps to ensure it never happens again. In an editorial published Monday, Enterprise Editor Chris Bacon said he “failed to catch” the AI copy and false quotes and apologized that “AI was allowed to put words that were never spoken into stories.” Journalists have derailed their careers by making up quotes or facts in stories long before AI came about. But this latest scandal illustrates the potential pitfalls and dangers that AI poses to many industries, including journalism, as chatbots can spit out spurious if somewhat plausible articles with only a few prompts. AI has found a role in journalism, including in the automation of certain tasks. Some newsrooms, including The Associated Press, use AI to free up reporters for more impactful work, but most AP staff are not allowed to use generative AI to create publishable content. The AP has been using technology to assist in articles about financial earnings reports since 2014, and more recently for some sports stories. It is also experimenting with an AI tool to translate some stories from English to Spanish. At the end of each such story is a note that explains technology’s role in its production. Being upfront about how and when AI is used has proven important. Sports Illustrated was criticized last year for publishing AI-generated online product reviews that were presented as having been written by reporters who didn’t actually exist. After the story broke, SI said it was firing the company that produced the articles for its website, but the incident damaged the once-powerful publication’s reputation. In his Powell Tribune story breaking the news about Pelczar’s use of AI in articles, Baker wrote that he had an uncomfortable but cordial meeting with Pelczar and Bacon. During the meeting, Pelczar said, “Obviously I’ve never intentionally tried to misquote anybody” and promised to “correct them and issue apologies and say they are misstatements,” Baker wrote, noting that Pelczar insisted his mistakes shouldn’t reflect on his Cody Enterprise editors. After the meeting, the Enterprise discovered seven stories that included AI-generated quotes from six people, Bacon said Tuesday. He is still reviewing other stories. “They’re very believable quotes,” Bacon said, noting that the people he spoke to during his review of Pelczar’s articles said the quotes sounded like something they’d say, but that they had never actually talked to Pelczar. Baker reported that seven people told him that they had been quoted in Pelczar stories but hadn’t spoken to him. Pelczar did not respond to an AP phone message left at a number listed as his asking to discuss what happened. Bacon said Pelczar declined to discuss the matter with another Wyoming newspaper that reached out. Baker, who regularly reads the Enterprise because it’s a competitor, told the AP that a combination of phrases and quotes in Pelczar’s stories aroused his suspicions. Pelczar’s story about a shooting in Yellowstone National Park included the sentence: “This incident serves as a stark reminder of the unpredictable nature of human behavior, even in the most serene settings.” Baker said the line sounded like the summaries of his stories that a certain chatbot seems to generate, in that it tacks on some kind of a “life lesson” at the end. Another story — about a poaching sentencing — included quotes from a wildlife official and a prosecutor that sounded like they came from a news release, Baker said. However, there wasn’t a news release and the agencies involved didn’t know where the quotes had come from, he said. Two of the questioned stories included fake quotes from Wyoming Gov. Mark Gordon that his staff only learned about when Baker called them. One quote was entirely fabricated and another was partially fabricated, said Michael Pearlman, a spokesperson for the governor. It’s not difficult to create AI-generated stories. Users could put a criminal affidavit through an AI program and ask it to write an article about the case including quotes from local officials, said Alex Mahadevan, director of a digital media literacy project at the Poynter Institute, the preeminent journalism think tank. “These generative AI chatbots are programmed to give you an answer, no matter whether that answer is complete garbage or not,” Mahadevan said. Megan Barton, the Cody Enterprise’s publisher, wrote an editorial calling AI “the new, advanced form of plagiarism and in the field of media and writing, plagiarism is something every media outlet has had to correct at some point or another.” Barton wrote that the newspaper now has a system in place to recognize AI-generated stories and will “have longer conversations about how AI-generated stories are not acceptable.” In his editorial, Bacon invoked the legacy of former professional baseball player Robin Ventura, who famously got the worst of a scuffle with Hall of Fame pitcher Nolan Ryan after charging the mound during a 1993 game in a scene that is reposted on social media to this day. The editor compares his own shame to the ridicule that the then-Chicago White Sox infielder (Bacon misidentified him as a member of the Chicago Cubs) experiences to this day. “I always suspected that the thought of being taken to the woodshed in front of millions in a fight Ventura started hurt more than his face. Now, your editor having been taken to the woodshed in the Wyoming press, I am sure of it.” The Enterprise didn’t have an AI policy, in part because it seemed obvious that journalists shouldn’t use it to write stories, Bacon said. Poynter has a template from which news outlets can build their own AI policy. Bacon, a military veteran and former air ambulance pilot who was named editor in May after a few months working as a reporter, plans to have a policy in place by the end of the week. “This will be a pre-employment topic of discussion,” he said.

Heard the huge news about Google AI Overviews? Ninety-five percent of the time, one or more of the webpages from the top 10 Google organic search results match the sources indicated in Google AI Overviews. Wow! That's huge folks! Why the huge buzz about Google AI Overviews? Mark Traphagen, vice president of product marketing and training at seoClarity, an enterprise SEO platform, posted this fresh information on LinkedIn. The conclusion stems from a study of 36,000 keywords conducted by seoClarity. Google AI Overviews has long been a popular issue to discuss. I was particularly intrigued about it when they were announced as 'Search Generative Experience' back in early 2023. In 2022, programs such as ChatGPT suddenly became popular, raising questions about how this might affect content production. Then, it was initially disclosed at the Google I/O conference in May 2023 that 'Generative AI Overviews' is finding its way into Google Search as "Search Generative Experience," or SGE. A "snapshot" of generative AI may appear in response to your searches in place of conventional results. See the greyed out section beneath the search query. Being an AI content curator myself, I wanted to closely monitor Google AI Overview's impact on content and SEO, and also how you and I should prepare for a world where search results are driven by AI. This seems to be a significant shift. Google has frequently displayed sources outside of the top 10 organic results ever since the Search Generative Experience (SGE) began to take shape and eventually evolved into AI Overviews. As a result, Google began to display certain content and sources in AI Overviews that weren't very reliable or authoritative. Let's take a step back... What's the Google AI Overviews like on Google? Launched by Search Labs on May 10, 2023 as Google Search Generative Experience (SGE), Google AI Overview is an experimental pilot that combines generative AI with Google Search. This includes the capacity to create images, pose queries, and summarize search results. Several large language models (LLMs), such as PaLM 2 and an enhanced version of MUM, which have been taught to perform tasks related to searches, power SGE. How does the Google AI Overviews or Search Generative Experience on Google function? Every time a user searches on Google, they will receive an AI-generated summary of the results as part of the Search Generative Experience. In order to get more details, they will also have the choice to "ask a follow-up" or choose from a few more pre-written questions. The Google Shopping Graph, which has over 35 billion product listings, provides the Google AI Overviews with a breakdown of goods, product descriptions, reviews, ratings, pricing, and photos when you search for a product. Additionally, you can produce text and visual content by using Google Search. The search engine will create and display up to four created images in the results when a user submits a request to generate an image. Content produced by AI can also be exported to your Gmail or Google Docs. An image's description and editing options are displayed when you click on it. Additionally, users can directly create photos in Google photos. This AI snapshot appears in a box above the well-known "10 blue links," but it does not entirely replace regular search results. Along with several recommended follow-up questions, there will be a variety of clickable tiles that go to pertinent other websites. Users will be able to get more information and ask follow-up questions using a "Conversations" function. “You’ll see an AI-powered snapshot of key information to consider, with links to dig deeper… You’ll also find helpful jumping-off points to web content and a range of perspectives that you can dig into.” Additionally, these AI responses might have tiny dropdown arrows that, when users select them, refer them to pertinent online resources. Google has kept revealing more SGE feature updates. Specifically, the AI-generated snippets will include both images and videos. How can you as a user get into Google AI Overviews or SGEs? Users can already access Google AI Overviews in more than 120 countries and territories. By visiting the Search Labs website and using the toggle option under the AI Experiments tab, you can activate SGE. Additionally, you can register using Search Labs on the desktop Chrome browser or the Google app for iOS and Android. Following criticism for providing erroneous and harmful responses, Google drastically decreased the visibility of AI Overviews while making enhancement pledges. In the last 12 months since its initial launch, AI Overviews have experienced a great deal of turbulence. How significant is this development, then? An astounding 93.8% of the time in January, the sources that showed up in SGE differed from the top 10 organic search results on Google. The Authoritas analysis produced that conclusion. According to that report: "In this dataset, at least, 93.8% of the generative links originated from sources other than the highest-ranking organic domains. Just 1.6% of generated URLs display a different URL from the same organic ranking site, and only 4.5% of generative URLs exactly match a page 1 organic URL." It's possible that Google is now using more conventional search ranking factors in its unique Gemini methodology. In Traphagen's words: "Google optimization is now all that remains of AI Overview optimization! Rising in Google search results is currently one of the best ways to be mentioned in an AIO." Updated on August 23, SEOClarity released two more fresh pieces of information. AI Synopses cite: About 80 percent of the time, one or more of the top three ranking results. So, What are Google's safety measures for Google AI Overviews? In order to prevent abuse and maintain user safety, Google built some fundamental safeguards into their AI Overviews. This is achieved by restricting the categories of queries for which generative search will be activated: For example, the Google AI Overviews won't return results for queries pertaining to "vulnerable situations," such as those involving self-harm, or those that are explicit or dangerous (in this case, Google Search will surface the contact information for a local support organization). In addition, in the event of a data blank or information gap, SGE will not generate automated responses. This could result in low accuracy because it won't react if there aren't enough publicly accessible information on a certain subject. It is important to mention that Search warns consumers, saying, "Generative AI is experimental." Information quality may differ "is a general caution regarding the possibility of false information. Regarding pictures, Google AI Overviews will indicate that they were produced by AI by labeling AI-generated photographs with information and an integrated watermark. This lessens the possibility of situations similar to deep fakes, in which people mistakenly think that fake photos are real. What makes SGE or Google AI Overviews significant? The fact that Google Search now has generative AI capabilities shows how consumer access to generative AI is growing. In the future, LLMs will enhance the search experience for a subset of consumers by surfacing and summarizing the content that most closely matches their initial query. Combining search and generative AI into a single, all-inclusive solution, this enhances Google Search's current features and helps set Google apart from OpenAI's well-liked chatbot, ChatGPT. Most importantly, it will eliminate the requirement for users to trawl through content lists and read through specific blogs and articles in order to get insights. Alternatively, customers can use summaries produced by AI to locate the important data that corresponds with their search query. What are the challenges of Google AI Overviews? Although Google's Search Generative Experience presents new hazards, it also has the ability to improve search results for end consumers. The dissemination of false information is the main danger that SGE poses. Since generative AI and LLMs are susceptible to hallucinations, they may fabricate data, figures, and other facts. Because of this, there's a chance that the underlying LLM will mislead you into entering a query into Google Search by creating a popup that offers false information. This means that you should confirm the accuracy of the information presented by the summary by checking it again. ‘Google AI Overviews while browsing’ Additionally, Google has hinted that Google AI Overviews would be able to follow us around the internet while we browse, not simply on search results. The goal behind the very aptly titled SGE while surfing is to assist users in finding and consuming certain sections of long-form literature by giving them overviews. These are AI-generated key points. Currently being tested on Chrome and the Google app, it is categorized as an experiment. SGE when browsing: designed to facilitate your assimilation of lengthy content. Will every Google search query trigger SGE? No. It is anticipated that AI-generated photos won't be present for most searches. Sixty percent of the ninety thousand queries examined in Michael King's SGE study did not result in an AI snapshot. That's correct: not every search will yield results from generative AI. It will be especially avoiding topics like health and finances, which are referred to as "your money or your life" (YMYL) subjects. Consider it as how, at the moment, only specific searches result in a featured snippet. Furthermore, I am are aware that AI frequently has trouble with location- and geographic-based searches. Its output may not be trusted by users, which will subsequently impact how widely it is adopted. In other words, SGE's introduction should be extremely cautious and deliberate; Google will be terrified of AI hallucinations. Which brings us nicely to the million-dollar query... When is Google AI Overviews getting the next update? Google AI Overviews or Search Generative Experience, was launched in May 14, 2024 during their annual developer conference, Google I/O 2024. Their last update includes: More prominent links Google is testing adding links to supporting web pages within AI Overviews, which has increased traffic to publisher sites.  Expanded availability Google is gradually expanding AI Overviews to six more countries, including the United Kingdom, India, Japan, Indonesia, Mexico, and Brazil, with local language support.  Video understanding Google is using advancements in video understanding to take visual search to a new level. For example, users can ask questions with video to get an AI Overview with steps and resources to troubleshoot.  Google also has plans for future updates, including: More detailed AI search results, Multi-step reasoning, and AI-created images. How will Google AI Overviews affect my search visibility and traffic? Will my traffic be harmed by AI responses in search results? In theory, yes, but it's not likely to pose an existential threat. Data regarding the effects of SGE on websites are few because it's still too early to determine. You will undoubtedly continue to be very visible as long as you continue to produce informative, user-first content that complies with Google's E-E-A-T quality requirements. SGE provides citations for its work, and the customary blue links remain underneath the image. Remember that it won't be activated for every query as well. “As we bring generative AI into Search, we’re committed to continue sending valuable traffic to sites across the web.” Naturally, I'm not going to imply that your traffic won't be impacted in any way. SGE was shown to have caused an overall 18–64% decrease in traffic to 23 technology industry websites in one (admittedly tiny) research. One, nevertheless, saw a traffic increase of 219%! Ranking well for simple "what is" and informational searches—that is, for topics or inquiries where users can receive an answer right away and don't feel compelled to study more or delve deeper—might have less significance. How significant are these lost sessions is the crucial topic to consider. Probably not very, if you are merely looking for a very basic immediate response. The benefit is that individuals who do visit your website will be more knowledgeable and motivated. The most crucial searches for your brand's financial performance are those that are driven by commerce, and it doesn't appear that these will be much impacted. 8 ways Google AI Overviews might impact SEO and content creation 1. Goodbye featured snippets — hello AI For a time now, featured snippets have been the pinnacle of any content SEO strategy. These are the text passages that show up at the top of search results for particular searches. It might not be completely out of the question to propose that SGE will take the place of the highlighted excerpt as a more thorough, dynamic, and information-rich substitute. In any case, Google has been progressively cutting back on staff in recent years. 2. Compelling, relevant meta images may become increasingly important SGE output contains links to external websites, thus there's a good chance your website will be featured. But you have to be unique. Adding an alluring meta image that is obviously connected to the page is one approach to improve the look of the cards that link to your website. The clickthrough key? Your meta pictures may be very important in SGE. 3. Photos and videos will still be key for some searches Making sure users have access to this kind of content is essential for success in the AI search space, especially since Google recently revealed that images and videos are included in SGE output for specific searches. This may be applicable to video-led search results for topics like "downward dog yoga pose," "how to do kick ups," and "how to install decking." Elevating your visual content could be crucial for SGE success if you want to draw in visitors who are making queries similar to these. 4. Less importance on the ‘top 10’ Relying solely on ranking positions may cause you to overlook the wider picture as Google gradually shifts away from the classic "10 blue links" and toward SERPs with more and richer features. Although being ranked in the top 10 will always be a proxy for success in search visibility, becoming recognized as a resource in a generative AI snapshot may be just as important for building consumer awareness, authority, and trust. 5. SGE might impact traffic for some informational queries Google has been marketing "zero click" searches for a while now. In other words, trying to answer every query a user has without making them leave the search results page. Should Google provide a comprehensive response to a user's query on the results page, you may have to prepare for a decline in organic traffic. As I mentioned previously, this might be especially true for simple, one-time informational requests, like recipes, when more investigation isn't usually required. Notwithstanding, considering the intricacy of numerous user journeys, it is probable that customers would still wish to examine the mentioned resources. Even so, those more valuable, profit-driven users will still require website visits. In the end, these are the sessions that are most important for conversions. This is just conjecture at this stage. Additionally, keep in mind that not every query will cause generative AI to activate. What will Google's AI models be educated on if it doesn't continue to compensate and support content creators? Google does want to make it clear that they will not be stealing your traffic. “SGE is designed to be a jumping-off point for exploring helpful information on the web, with links to search results included alongside each AI overview." 6. User-first, informative content is as important as ever SGE may increase the likelihood of zero-click searches and decrease organic traffic, but you shouldn't think about giving up on creating SEO content. The argument for thorough, user-first content is actually stronger than ever, if not more so. You must prove your brand competence in order for your card to be cited in the generative AI output. Aim to provide comprehensive, in-depth material, such as buyer's guides and instructional articles, to address every query your clients may have. Brands that produce content that demonstrates "E-E-A-T" — expertise, experience, authority, and trustworthiness — are rewarded by Google's search ranking systems, and this isn't likely to change anytime soon. Make the most of your inside specialists as you continue to produce original, user-focused content for your audience. 7. The rise of the long-tail keyword Compared with conventional searching, SGE offers a more interactive experience. This may have an impact on our search engine usage. More organic, long-tail search inquiries could result from the new Conversations feature in particular. Take Google's example, "what's better for a family with kids under three and a dog, bryce canyon or arches?" It's probably time to check that you're utilizing natural language in your content and investigating the most often asked questions. Long-tail search terms can gain traction. generative artificial intelligence (search) on blog.google.com Will we have to provide more "snackable" and conversational content? The goal of providing answers to your consumers' questions and meeting their wants hasn't changed, but our approach to doing so in our content may. 8. Watch your publish dates According to Google, references included in the AI output will have a publish date . Therefore, remember to update the publish date of any old content you're optimizing to add new information. This will make the content appear more current and relevant to readers, which will encourage SGE clicks. Through several technology advancements over the previous 25 years, we have kept reimagining and extending the capabilities of Google Search. Our fundamental information quality processes have been painstakingly refined to assist you in finding the greatest content available online. And in order to provide you with reliable information at the touch of a button, we have amassed billions of data about people, locations, and objects in our knowledge base. With generative AI, Search is now more capable than you could have ever dreamed. Google will therefore handle the legwork for you when you ask it questions about anything that's on your mind or that needs to be done, such as planning, brainstorming, or research. Quick Google Gemini update for Google AI Overview The new Gemini model, specially designed for Google Search and Google AI Overviews, makes all of this possible. It combines our best-in-class Search systems with Gemini's sophisticated features, such as multi-step reasoning, planning, and multimodality. Get quick answers with AI Overviews There are moments when you need an answer quickly and don't have the time to gather all the facts you require. With AI Overviews, Search will take care of the work for you. Through my experiment in Search Labs, AI Overviews have already been used billions of times by people. They like that you may provide connections to other resources and a brief synopsis of a subject. We've discovered that users of AI Overviews utilize Search more frequently and are happier with the outcomes. People are accessing a wider variety of websites to get assistance with more complicated concerns thanks to AI Overviews. Furthermore, I observe that when links in AI Overviews are provided, more people click on them than when the page displays as a conventional online listing for that search query. We'll keep our attention on driving relevant traffic to publishers and creators as we grow this experience. As usual, advertisements will stay in designated areas on the website and will be clearly labeled as sponsored and organic results. Adjust your AI Overview You'll soon have the ability to modify your AI Overview by adding options to either reduce the language or provide more detailed explanations. This might be especially helpful if you're attempting to pique your child's curiosity about something or if you're new to the subject. Search Labs will soon receive this upgrade for English queries in the United States. Ask your most complex questions With our custom Gemini model’s multi-step reasoning capabilities, AI Overviews will help with increasingly complex questions. Rather than breaking your question into multiple searches, you can ask your most complex questions, with all the nuances and caveats you have in mind, all in one go. For example, maybe you’re looking for a new yoga or pilates studio, and you want one that’s popular with locals, conveniently located for your commute, and also offers a discount for new members. Soon, with just one search, you’ll be able to ask something like “find the best yoga or pilates studios in Boston and show me details on their intro offers, and walking time from Beacon Hill.” These multi-step reasoning capabilities are coming soon to AI Overviews in Search Labs , for English queries in the U.S. Plan ahead Beyond finding the right answer or information for a complex question, Search will also be able to plan with you. With planning capabilities directly in Search, you can get help creating plans for whatever you need, starting with meals and vacations. Search for something like “create a 3 day meal plan for a group that’s easy to prepare,” and you’ll get a starting point with a wide range of recipes from across the web. If you want to change anything, you can easily ask for whatever adjustments you need, like swapping dinner to a vegetarian dish. Just like that, Search will customize your meal plan. You’ll be able to quickly export your meal plan to Docs or Gmail. Meal and trip planning are available now in Search Labs in English in the U.S. Later this year, we’ll add customization capabilities and more categories like parties, date night and workouts. Explore an AI-organized results page When you’re looking for fresh ideas, it can take a lot of work to find inspiration and consider all your options. Soon, when you’re looking for ideas, Search will use generative AI to brainstorm with you and create an AI-organized results page that makes it easy to explore. You’ll see helpful results categorized under unique, AI-generated headlines, featuring a wide range of perspectives and content types. For English searches in the U.S., you’ll start to see this new AI-organized search results page when you look for inspiration — starting soon with dining and recipes, followed by movies, music, books, hotels, shopping and more. Take a video, get help from AI Search is so much more than just words in a text box. Often the questions you have are about the things you see around you, including objects in motion. Thanks to advancements in video understanding, we’re able to take visual search to a whole new level, with the ability to ask questions with video. Maybe you bought a record player at a thrift shop, but it’s not working when you turn it on and the metal piece with the needle is drifting unexpectedly. Searching with video saves you the time and trouble of finding the right words to describe this issue, and you’ll get an AI Overview with steps and resources to troubleshoot. Searching with video will be available soon for Search Labs users in English in the U.S., and we’ll expand to more regions over time. This is just a glimpse of how we’re reimagining Google Search — combining the very best of Search today with the Gemini model’s advanced capabilities. Soon, Google will do the searching, simplifying, researching, planning, brainstorming and so much more. Google AI Overviews in the UK: Key takeaways First of all, don't panic; the traffic impact will probably only be felt for simple, low-value informational searches. Remember that users will still be able to visit the conventional blue links; AI snapshots won't be activated for every query. Additionally, if Google doesn't keep rewarding content producers, the AI snapshots won't have anything to learn from in the near future. Additionally, keep in mind that Google Search is by far the company's most profitable and successful offering. Undoubtedly, modifications will be meticulously measured and computed. Who can forget the commotion over the failed voice search revolution? Make sure to check SGE's prevalence for your most essential search terms and subjects once it starts to roll out in the UK. Keep GA4 and Google Search Console handy to track any changes in traffic and conversion rates.

AI capabilities have exploded over the past two years, with large language models (LLMs) such as ChatGPT, Dall-E, and Midjourney becoming everyday use tools. As you’re reading this article, generative AI programs are responding to emails, writing marketing copies, recording songs, and creating images from simple inputs.  What’s even more remarkable to witness is the rate at which both individuals and companies are embracing the AI ecosystem. A recent survey by McKinsey revealed that the number of companies that have adopted generative AI in at least one business function doubled within a year to 65%, up from 33% at the beginning of 2023.  However, like most technological advancements, this nascent area of innovation is not short of challenges. Training and running AI programs is resource intensive endeavour, and as things stand, big tech seems to have an upper hand which creates the risk of AI centralisation.  The computational limitation in AI development  According to an article by the World Economic Forum, there is an accelerating demand for AI compute; the computational power required to sustain AI development is currently growing at an annual rate of between 26% and 36%.    Another recent study by Epoch AI confirms this trajectory, with projections showing that it will soon cost billions of dollars to train or run AI programs.  “The cost of the largest AI training runs is growing by a factor of two to three per year since 2016, and that puts billion-dollar price tags on the horizon by 2027, maybe sooner,” noted Epoch AI staff researcher, Ben Cottier.  In my opinion, we’re already at this point. Microsoft invested $10 billion in OpenAI last year and, more recently, news emerged that the two entities are planning to build a data center that will host a supercomputer powered by millions of specialised chips. The cost? A whopping $100 billion, which is ten times more than the initial investment.  Well, Microsoft is not the only big tech that’s on a spending spree to boost its AI computing resources. Other companies in the AI arms race, including Google, Alphabet, and Nvidia are all directing a significant amount of funding to AI research and development.  While we can agree that the outcome could match the amount of money being invested, it is hard to ignore the fact that AI development is currently a ‘big tech’ sport. Only these deep-pocketed companies have the ability to fund AI projects to the tune of tens or hundreds of billions.  It begs the question; what can be done to avoid the same pitfalls that Web2 innovations are facing as a result of a handful of companies controlling innovation?  Stanford’s HAI Vice Director and Faculty Director of Research, James Landay, is one of the experts who has previously weighed in on this scenario. According to Landay, the rush for GPU resources and the prioritisation by big tech companies to use their AI computational power in-house will trigger the demand for computing power, ultimately pushing stakeholders to develop cheaper hardware solutions. In China, the government is already stepping up to support AI startups following the chip wars with the US that have limited Chinese companies from seamlessly accessing crucial chips. Local governments within China introduced subsidies earlier this year, pledging to offer computing vouchers for AI startups ranging between $140,000 and $280,000. This effort is aimed at reducing the costs associated with computing power. Decentralizing AI computing costs Looking at the current state of AI computing, one theme is constant — the industry is currently centralised. Big tech companies control the majority of the computing power as well as AI programs. The more things change, the more they remain the same.  On the brighter side, this time, things might actually change for good, thanks to decentralised computing infrastructures such as the Qubic Layer 1 blockchain. This L1 blockchain uses an advanced mining mechanism dubbed the useful Proof-of-Work (PoW); unlike Bitcoin’s typical PoW which uses energy for the sole purpose of securing the network, Qubic’s uPoW utilizes its computational power for productive AI tasks such as training neural networks.  In simpler terms, Qubic is decentralising the sourcing of AI computational power by moving away from the current paradigm where innovators are limited to the hardware they own or have rented from big tech. Instead, this L1 is tapping into its network of miners which could run into the tens of thousands to provide computational power.  Although a bit more technical than leaving big tech to handle the backend side of things, a decentralised approach to sourcing for AI computing power is more economical. But more importantly, it would only be fair if AI innovations would be driven by more stakeholders as opposed to the current state where the industry seems to rely on a few players.  What happens if all of them go down? Make matters worse, these tech companies have proven untrustworthy with life-changing tech advancements.  Today, most people are up in arms against data privacy violations, not to mention other affiliated issues such as societal manipulation. With decentralised AI innovations, it will be easier to check on the developments while reducing the cost of entry.   Decentralization to the rescue AI innovations are just getting started, but the challenge of accessing computational power is still a headwind. To add to it, Big tech currently controls most of the resources which is a big challenge to the rate of innovation, not to mention the fact that these same companies could end up having more power over our data – the digital gold.   However, with the advent of decentralized infrastructures, the entire AI ecosystem stands a better chance of reducing computational costs and eliminating big tech control over one of the most valuable technologies of the 21st century.