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Before departing Twitter in 2023, Alex Popken worked for a long time as a trust and safety executive, specialising in content moderation. When she began working there in 2013, she was the first employee specifically tasked with moderating Twitter's advertising business. She currently works for WebPurify as vice president of trust and safety, a content moderation service provider that assists companies in making sure content posted on their websites complies with regulations. There are other platforms besides social media that require oversight. Any business that interacts with customers, be it a retailer, a dating app, or a news website, needs someone to filter out undesirable content, including hate speech, harassment, and illegal content. Artificial intelligence is being used by businesses more and more, but Popken points out that people are still necessary for the process. Popken recently had an interview with The Associated Press. The length and clarity of the conversation have been adjusted. During your ten years at Twitter, how did you observe changes in content moderation? Content moderation on Twitter was very new when I joined. I believe that even the concepts of safety and trust were ones that people were only beginning to comprehend and struggle with. As platforms began to see themselves used as weapons in novel ways, the demand for content moderation increased. I can vaguely remember a few significant turning points from my time at Twitter. For instance, it was through Russian meddling in the 2016 US presidential election that we meaningfully and initially realised that bad actors could undermine democracy in the absence of content moderation. Many large social media companies are using artificial intelligence (AI) to filter content. Do you believe that AI will ever reach a point where we can depend on it? A: Content that works Machines and humans work together to moderate. Scale is solved by AI, which has been used sparingly for years. Thus, you have content-detection-capable machine learning models that have been trained on various policies. In the end, though, suppose you have a machine learning model that can identify the word "Nazi." Many posts may be critiquing Nazis or offering informational resources regarding Nazis in contrast to, say, white supremacist ideology. Thus, it is unable to account for context and subtlety. In fact, that's where a human layer comes in. Indeed, I believe that significant developments are beginning to emerge that will facilitate human labour. And one excellent example of that, in my opinion, is generative AI, which is far more adept than traditional AI models at understanding context and subtleties. However, moderating generative AI outputs presents whole new use cases for our human moderators. Thus, I believe that human moderation will continue to be necessary for the foreseeable future. Could you briefly discuss the non-social media businesses you deal with and the methods they employ for content moderation? A: Well, everything from retail product customisation to, say, letting customers personalise T-shirts, you get the idea. Naturally, you want to stay away from situations where individuals take advantage of that and write offensive or dangerous things on the T-shirt. In fact, you should be wary of anything that contains user-generated content, including online dating. There, you should make sure that users are who they say they are, avoid inappropriate photo uploads, and watch out for scams like catfishing. It does cover a variety of industries. What about the issues you moderate? Does anything change there? A constantly changing field is content moderation. And the world's events have an impact on it. Innovative and developing technologies have an impact on it. It is impacted by dishonest people who will try to use these platforms in creative and novel ways to gain access. As a result, your team responsible for content moderation is always trying to think ahead and identify potential threats. I believe that there is a small amount of catastrophic thinking involved in this role, where you consider the worst possible outcomes. They undoubtedly change as well. I believe that misinformation is a prime example of something that has many facets and is difficult to moderate. Like boiling the ocean, that is. You can't, after all, verify the veracity of everything someone says? In order to avoid doing the greatest harm in the real world, platforms usually need to concentrate on disinformation. And that's constantly changing too. Q: There are people who believe that generative AI will destroy the internet and leave it with just fake AI content. Do you think that could be taking place? A: Artificial intelligence-generated disinformation worries me, particularly in this crucial election season for the entire world. It's true that harmful synthetic and manipulated media, including deepfakes, are becoming more prevalent online. This is concerning because, in my opinion, most people don't have it easy. deciding what is true and what is false. I think medium to long term, if I can be properly regulated and if there are appropriate guardrails around it, I also think that it can create an opportunity for our trust and safety practitioners. I do. Imagine a world in which AI is an important tool in the tool belt of content moderation, for things like threat intelligence. You know, I think that it’s going to be extremely helpful tool, but it’s also going to be misused. And we’re we’re already seeing that.

At Master of Code, we’ve been working with OpenAI’s GPT-3.5-Turbo model since its launch. After many successful implementations, we wanted to share our best practices on prompt engineering the model for your Generative AI solution . We realize that new models may have launched after we publish this article, so it’s important to note that these prompt engineering approaches apply to OpenAI’s latest model, GPT-3.5-Turbo . What is GPT prompt engineering? GPT prompt engineering is the practice of strategically constructing prompts to guide the behavior of GPT language models, such as GPT-3, GPT-3.5-Turbo or GPT-4. It involves composing prompts in a way that will influence the model to generate your desired responses. By leveraging prompt engineering techniques, you can elicit more accurate and contextually appropriate responses from the model. GPT prompt engineering is an iterative process that requires experimentation, analysis, and thorough testing to achieve your desired outcome. GPT Prompt Engineering Elements Generative AI models, such as ChatGPT, are designed to understand and generate responses based on the prompts they’re given. The results you get depend on how well you structure your initial prompts. A thoughtfully structured prompt will guide the Generative AI model to provide relevant and accurate responses. In fact, recent research has demonstrated that the accuracy of Large Language Models (LLMs), such as GPT or BERT, can be enhanced through fine-tuning, a prominent prompt engineering technique. Statistics of LLM models accuracy, ScienceDirect Here’s a list of elements that make up a well-crafted prompt. These elements can help you maximize the potential of the Generative AI model. Context: Provide a brief introduction or background information to set the context for the conversation. This helps the Generative AI model understand the topic and provides a starting point for the conversation. For example: “You are a helpful and friendly customer support agent. You focus on assisting customers troubleshoot technical issues with their computer.” Instructions: Clearly state what you want the Generative AI model to do or the specific questions you’d like it to answer. This guides the model’s response and ensures it focuses on the desired topic. For example: “Provide three tips for improving customer satisfaction in the hospitality industry.” Input Data: Include specific examples that you want the Generative AI model to consider or build on. For example: “Given the following customer complaint: ‘I received a damaged product,’ suggest a suitable response and reimbursement options.” Output Indicator: Specify the format you want the response in, such as a bullet-point list, paragraph, code snippet, or any other preferred format. This helps the Generative AI model structure its response accordingly. Example: “Provide a step-by-step guide on how to reset a password, using bullet points.” Include these elements to craft a clear and well-defined prompt for Generative AI models to ensure relevant and high-quality responses. Understanding the basics of the GPT-3.5-Turbo model Before we jump right into GPT prompt engineering, it’s important to understand what parameters need to be set for the GPT-3.5-Turbo model. GPT-3.5-Turbo model configuration example Pre-prompt (Pre-header): here you’ll write your set of rules and instructions to the model that will determine how it behaves. Max tokens: this limits the length of the model’s responses. To give you an idea, 100 tokens is roughly 75 words. Temperature: determines how dynamic your chatbot’s responses are. Set it higher for dynamic responses, and lower for more static ones. A value of 0 means it will always generate the same output. Conversely, a value of 1 makes the model much more creative. Top-P: similar to temperature, it determines how broad the chatbot’s vocabulary will be. We recommend having a top P of 1 and adjusting temperature for best results. Presence and Frequency Penalties: these determine how often the same words appear in the chatbot’s response. From our testing, we recommend keeping these parameters at 0. Setting up your parameters Now that you understand the parameters you have, it’s time to set their values for your GPT-3.5 model. Here’s what’s worked for us so far: Temperature: If your use case allows for high variability or personalization (such as product recommendations) from user to user, we recommend a temperature of 0.7 or higher. For more static responses, such as answers to FAQs on return policies or shipping rates, adjust it to 0.4. We’ve also found that with a higher temperature metric, the model tends to add 10 to 15 words on top of your word/token limit, so keep that in mind when setting your parameters. Top-P: We recommend keeping this at 1, adjusting your temperature instead for the best results. Max tokens: For short, concise responses (which in our experience is always best), choose a value between 30 and 50, depending on the main use cases of your chatbot. Writing the pre-prompt for the GPT-3.5-Turbo model GPT prompt engineering schema This is where you get to put your prompt engineering skills into practice. At Master of Code, we refer to the pre-prompt as the pre-header as there are already other types of prompting associated with Conversational AI bots. Below are some of our best tips for writing your pre-header. Provide context and give the bot an identity The first step in the prompt engineering process is to provide context for your model. Start by giving your bot an identity, including details of your bot persona such as name, characteristics, tone of voice, and behavior. Also include the use cases it must handle, along with its scope. Example of weak context: You are a helpful customer service bot. Example of good context: You are a helpful and friendly customer support agent. You focus on assisting customers troubleshoot technical issues with their computer. Frame sentences positively We’ve found that using positive instructions instead of negative ones yields better results (i.e. ‘Do’ as opposed to ‘Do not’). Example of negative framing: Do not ask the user more than 1 question at a time. Example of positive framing: When you ask the user for information, ask a maximum of 1 question at a time. Include examples or sample input Include examples in your pre-header instructions for accurate intent detection. Examples are also useful when you want the output to be in a specific format. Pre-header excluding an example: Ask the user if they have any dietary restrictions. Pre-header including an example: Ask the user if they have any dietary restrictions, such as allergies, gluten free, vegetarian, vegan, etc. Be specific and clear Avoid giving conflicting or repetitive instructions. Good pre-headers should be clear and specific, leaving no room for ambiguity. Vague or overly broad instructions in pre-headers can lead to irrelevant or nonsensical responses. Vague example: When the user asks you for help with their technical issue, help them by asking clarifying questions. Clear example: When the user asks you for help with their technical issue, you need to ask them the following mandatory clarifying questions. If they’ve already provided you with some of this information, skip that question and move on to the next one. Conciseness Specify the number of words the bot should include in its responses, avoiding unnecessary complexity. Controlling the output length is an important aspect of prompt engineering, as you don’t want the model to generate lengthy text that no one bothers to read. Instruction that may yield lengthy responses: When someone messages you, introduce yourself and ask how you can help them. Instruction that limits response length: When the dialogue starts, introduce yourself by name and ask how you can help the user in no more than 30 words. Order importance Order your pre-header with: Bot persona, Scope, Intent instructions. Synonyms Experiment with synonyms to achieve desired behavior, especially if you find the model is not responding to your instructions. Finding an effective synonym could make the model respond better, producing the desired output. Collaborate When it comes to prompt engineering, having your AI Trainers and Conversation Design teams collaborate is crucial. This ensures that both the business and user needs are accurately translated into the model, tone of voice is consistent with the brand, and instructions are clear, concise and stick to the scope. Advantages of GPT prompt engineering Read also: Generative AI Limitations and Risks Testing your GPT-3.5-Turbo model Now that you’ve set up your GPT-3.5 model for success, it’s time to put your prompt engineering skills to the test. As mentioned before, prompt engineering is iterative, which means you’ll need to test and revise your pre-header — the model is most likely not going to behave how you intended the first time around. Here are some simple but important tips for testing and updating your pre-header: Change one thing at a time. If you see your model isn’t behaving how you expect, don’t change everything at once. This allows you to test each change and keep track of what’s working and what isn’t. Test every small change. The smallest update could cause your model to behave differently, so it’s crucial to test it . That means even testing that comma you added somewhere in the middle, before you make any other changes. Don’t forget you can also change other parameters, such as temperature. You don’t only have to edit the pre-header to get your desired output. And finally, if you think you’ve prompt-engineered your pre-header to perfection and the bot is behaving as expected, don’t be surprised if there are outliers in your testing data where the bot does its own thing every now and then. This is because Generative AI models can be unpredictable. To counter this, we use injections. These can overrun pre-header instructions and correct the model’s behavior. They’re sent to the model from the backend as an additional guide, and can be used at any point within the conversation. By following these GPT prompt engineering best practices, you can enhance your GPT model and achieve more accurate and tailored responses for your specific use cases.

Generative AI News: According to the U.N. Intellectual Property Agency , China has requested far more patents than any other nation in the field of Generative AI, with the United States coming in far second. According to the World Intellectual Property Organization, technology was connected to roughly 54,000 inventions between the years of 2010 and 2023. This technology has the potential to increase productivity and accelerate scientific discoveries, but it also raises questions about jobs and labor. According to WIPO, more than 25% of those inventions were made in the past year, demonstrating the technology's phenomenal rise and interest since generative AI shot to prominence in late 2022. This new, first-of-its-kind patent report attempts to monitor patent applications as a potential barometer of artificial intelligence trends. It ignores artificial intelligence in general, which includes things like facial recognition and autonomous driving, and concentrates only on generative AI. WIPO Director-General Daren Tang told reporters, "WIPO hopes to give everyone a better understanding of where this fast-evolving technology is being developed, and where it is headed." More than 38,200 innovations in generative AI originated in China in the ten years beginning in 2014. Compared to the United States, which had roughly 6,300, that is six times more. South Korea (4,155), Japan (more than 3,400), and India (1,350) were the next closest countries. With the aid of programmes like Google Gemini, Baidu's Ernie, ChatGPT from OpenAI, and Google Gemini, users can generate text, images, music, and other types of content. Numerous industries, including the life sciences, manufacturing, transportation, security, and telecommunications, have used the technology. Some critics worry that GenAI may eventually take the place of humans in certain types of jobs or inappropriately appropriate human-generated content without paying creators fairly or adequately. After entering the market later than other countries, China has made an effort to overtake ChatGPT owner OpenAI as well as American tech giants Microsoft, Alphabet's Google, and Amazon in the development of large language models (LLM). When ChatGPT was able to produce responses to user prompts that resembled those of a human, it took the world by storm in November 2022. Chinese tech behemoths Baidu and Alibaba introduced their own LLMs last year in an attempt to take on their American rivals. China unveiled a three-year "action plan" in May to boost national computing capacity, strengthen AI chip standards, and develop generative AI in an effort to further its technological and economic advancement. As with other categories of patent applications, GenAI patent quantity does not necessarily translate into quality, according to WIPO officials. It's difficult to predict at this early stage of technology which patents will be valuable to the market or revolutionize society. Tang remarked, "Let's see how the data and the developments unfold over time." Although China and the United States are frequently viewed as competitors in the development of artificial intelligence, American tech companies are actually leading the world in the creation of the most advanced AI systems. Research manager Nestor Maslej of Stanford University's Institute for Human-Centered Artificial Intelligence stated, "Looking at patents just paints one part of a narrative," adding that patent approval rates can vary depending on a nation's laws. According to Maslej, who edits Stanford's annual AI Index, which gauges the state of technology, "when you look at AI vibrancy, a very important question is who's releasing the best models, where are those models coming from and, at least by that metric, it seems like the United States is really far ahead." This year's AI Index shows that in 2023, American institutions produced sixty-one well-known machine-learning models, outpacing the 21 from the European Union and the 15 from China. France held the highest number of EU members with eight. A different metric indicates that the US also has the most "AI foundation models"—big, flexible, and well-trained models such as GPT-4 from OpenAI, Claude 3 from Anthropic, Gemini from Anthropic, and Llama from Meta. In terms of new AI startup formations and private AI investments, the United States has outperformed China, despite China being the leader in industrial robotics.

Progress and innovation. These phrases capture the essence of generative AI, a field of technology that is expanding frontiers and redefining possibilities. But great innovation also raises important ethical questions. AI systems pose serious concerns about bias, privacy, and the moral application of technology as they develop. How does Generative AI work? To understand the considerations of Generative AI ethics, we need to first understand how GenAI works. In order to create new content, including text, images, and even videos, generative AI uses sophisticated algorithms to examine enormous datasets. By assimilating patterns and structures from preexisting data, it can produce unique results that resemble content produced by humans. This ability has completely changed industries including art, healthcare diagnostics, and content creation. According to McKinsey , the adoption of AI in creative industries has increased by 25% annually, highlighting its transformative impact. Which Generative AI Ethics pose a challenge? Authenticity and disinformation concerns are the main ethical challenges that generative AI brings. The likelihood of false information spreading increases as AI-generated content becomes indistinguishable from content produced by humans. The public's trust as well as the accuracy of online information are threatened by this. "The ability of AI to mimic human writing can be deceptive, leading to trust issues and potential misuse in misinformation campaigns," cautions Dr. Kate Crawford , an expert in Generative AI ethics. Furthermore, biases found in training datasets may be reinforced by generative AI. AI systems may pick up on and reinforce societal prejudices if these datasets lack diversity and inclusivity, which could result in unfair decisions being made. A study by MIT found that AI language models trained on biased datasets could exhibit racial and gender biases in their outputs, affecting societal perceptions and fairness. How can privacy concerns be addressed in Generative AI? As AI systems gather, examine, and produce content based on enormous volumes of user data, privacy issues surface. Personal information that users may not have agreed to share or intended to be used in such circumstances may be included in this data. "Ensuring robust data privacy measures in AI development is crucial to maintaining user trust and upholding ethical standards," emphasizes tech policy analyst John Doe. As a result, regulatory agencies are starting to implement frameworks that control Generative AI ethics development and application, placing a strong emphasis on user consent, accountability, and transparency in data processing procedures. According to a report by Forbes, 72% of consumers are concerned about how their data is used in AI applications, indicating a growing demand for stricter privacy regulations. Can Generative AI ethics disrupt economic or job stability? As human-performed tasks are automated by generative AI systems, worries about job displacement and economic stability surface. Artificial Intelligence (AI) is generating job opportunities in specialized fields, but it also poses a threat to jobs in industries where routine tasks are automated. Sarah Johnson , an economist, states, "The challenge lies in upskilling the workforce to adapt to a more AI-integrated economy while mitigating potential job losses." The workforce needs to be prepared for the AI-driven future, and policies that support reskilling and lifelong learning are essential to maintaining economic stability in the face of technological disruption. McKinsey reports that AI adoption could displace up to 15% of the global workforce by 2030, underscoring the need for proactive workforce strategies. How Can Ethical Guidelines Be Implemented in AI Development? Collaboration between tech companies, legislators, and ethicists is necessary to implement ethical standards in AI development. Throughout the AI lifecycle, fairness, accountability, transparency, and respect for user privacy should be given top priority in guidelines. "Embedding ethical considerations into AI design from the outset is essential to mitigating unintended consequences and ensuring societal benefit," claims Dr. Emily White, an ethics researcher. Furthermore, in ever-changing technological environments, ongoing monitoring and auditing of AI systems after deployment is essential to identify biases, reduce risks, and preserve ethical standards. Accenture 's survey found that 84% of AI professionals believe ethical considerations should be a core part of AI system design, reflecting industry recognition of ethical imperatives. Generative AI ethics are the need of the hour Though generative AI holds great promise for many industries, its ethical implications necessitate thoughtful analysis and preemptive action. Through tackling concerns related to prejudice, confidentiality, financial influence, and moral standards, we can fully utilise artificial intelligence while minimising hazards. Fostering an ethical AI ecosystem requires cooperation, openness, and ongoing assessment as we navigate this revolutionary era. How can we make sure AI technology develops morally and benefits all members of society? To support responsible technological advancements, keep up with the most recent generative AI developments and ethical debates. For the most recent information on trends, startup tales, and strategies for staying ahead in the quickly changing field of artificial intelligence, follow TheGen.AI . Remain knowledgeable and moral.

Have you ever felt constrained by current AI models? Perhaps your creativity is being stifled by closed-source AI. In order to promote innovation and advancement in AI research, we examine the field of generative AI in this article, which is where science and art converge. Breaking Free: Is Unlimited Innovation Possible with Generative AI? Is true innovation being stifled by the conventional approach to AI research? These days, generative AI is revolutionizing research by providing new insights and enabling scientists to go where no one has gone before. Let's explore the synthesis of art and science that is known as generative AI. Progress in Generative AI: Diverse Output: Produce a broad range of outputs, including texts and images. Creative Possibilities: Promote originality in AI study and advancement. Increased Flexibility: Customise AI models to meet particular needs. According to a KPMG survey, using generative AI techniques increased innovation in AI research by 45%. How does Generative AI work, and what sets it apart from traditional AI models? Algorithms used in generative AI generate new content by utilizing probability and patterns. Because of this special fusion of creativity, science, and mathematics, machines can now produce content that is creatively comparable to that of humans. Important Elements of Generative AI: Algorithmic Foundations: Using sophisticated algorithms to create content. Using large datasets to train models for a variety of outputs is known as "learning from data." Producing work that has an artistic quality similar to that of human creativity. Capgemini states that using Generative AI instead of Closed Source AI increased content diversity by 50%, according to a comparison study. In what ways is Generative AI making its mark across various industries? Generative AI's impact stretches across domains, revolutionizing how industries approach content creation, design, and innovation. GenAI Applications Across Industries: Art and Design: Creating unique artworks and designs with AI assistance. Content Creation: Enhancing efficiency and creativity in content development. Fashion and Style: Assisting in trend prediction and design. According to Databricks , A real-world implementation showcased a 30% increase in design efficiency using Generative AI. Can Generative AI effectively address the limitations imposed by Closed Source AI? Generative AI offers a fresh perspective by providing flexibility, creativity, and adaptability, countering the constraints of Closed Source AI. Advantages of Generative AI: Flexibility: Adapting to specific requirements with ease. Cost-Effectiveness: Proving to be cost-effective in the long run. Enhanced Creativity: Encouraging creativity within AI research. A BPIFrance case study demonstrated a 25% reduction in costs through the integration of Generative AI. What does the future hold for Generative AI and its role in AI research? With its potential to drive innovation and change our perception of artificial intelligence, generative AI holds the potential to lead AI research into previously unexplored areas. Upcoming prospects: AI-Generated Innovations: Using generative AI, creating ground-breaking solutions. Collaborative AI: Promoting knowledge exchange and group creativity. Examining the ethical ramifications and prejudices inherent in AI development. According to McKinsey , Within the next five years, there will be 40% more AI-generated innovations, according to a forecast. Generative AI Research is your Bedrock Generative AI is about unleashing creativity and innovation, not just about algorithms. We can overcome the constraints of Closed Source AI and open the door to a more innovative and flexible future for AI research by embracing Generative AI. Use generative AI techniques in your AI studies to stimulate originality and creativity. How can your company use generative AI to propel ground-breaking advancements in AI science? FAQs on Generative AI Research FAQ Q1: What distinguishes generative AI from classical AI models? While traditional AI models often follow predefined patterns, generative AI uses creativity and algorithms to generate a variety of outputs. Q2: How can generative AI improve fashion industry content creation and design? The fashion industry can become more creative and efficient by using generative AI to help with trend prediction and the generation of original design concepts. Q3: Is there a financial benefit to employing generative AI as opposed to closed-source AI? Yes, because of its versatility and effectiveness in content generation, generative AI may end up being more affordable over time. Q4: What is the anticipated influence of generative AI on AI research going forward? The future of AI research is anticipated to be shaped by generative AI, which is anticipated to stimulate innovation, encourage teamwork, and address ethical issues. Q5: Is it possible to successfully incorporate generative AI into current AI research methodologies? Indeed, generative AI can be easily incorporated into current AI research methodologies, which will foster innovation and creativity.

Aidan Gomez can take some credit for the ‘T’ at the end of ChatGPT. He was part of a group of Google engineers who first introduced a new artificial intelligence model called a transformer. That helped set a foundation for today’s generative AI boom that ChatGPT-maker OpenAI and others built upon. Gomez, one of eight co-authors of Google’s 2017 paper, was a 20-year-old intern at the time. He’s now the CEO and co-founder of Cohere, a Toronto-based startup competing with other leading AI companies in supplying large language models and the chatbots they power to big businesses and organizations. Gomez spoke about the future of generative AI with The Associated Press. The interview has been edited for length and clarity. Q: What’s a transformer? A: A transformer is an architecture of a neural network -- the structure to the computation that happens inside of the model. The reason that transformers are special relative to their peers -- other competing architectures, other ways of structuring neural networks -- is essentially that they scale very well. They can be trained across not just thousands, but tens of thousands of chips. They can be trained extremely quickly. They use many different operations that these GPUs (graphics chips) are tailored for. Compared to what existed before the transformer, they do that processing faster and more efficiently. Q: How important are they to what you’re doing at Cohere? Meta more than doubles Q1 profit but revenue guidance pulls shares down after-hours Insider Q&A: Trust and safety exec talks about AI and content moderation Meta’s newest AI model beats some peers. But its amped-up AI agents are confusing Facebook users A: Massively important. We use the transformer architecture as does everyone else in building large language models. For Cohere, a huge focus is scalability and production readiness for enterprises. Some of the other models that we compete against are huge and super inefficient. You can’t actually put that into production, because as soon as you’re faced with real users, costs blow up and the economics break. Q: What’s a specific example of how a customer is using a Cohere model? A: I have a favorite example in the health care space. It stems from the surprising fact that 40% of a doctor’s working day is spent writing patient notes. So what if we could have doctors attach a little passive listening device to follow along with them throughout the day, between their patient visits, listening into the conversation and pre-populating those notes so that instead of having to write it from scratch, there’s a first draft in there. They can read through it and just make edits. Suddenly, the capacity of doctors boosts by a massive proportion. Q: How do you address customer concerns about AI language models being prone to ‘hallucinations’ (errors) and bias? A: Customers are always concerned about hallucinations and bias. It leads to a bad product experience. So it’s something we focus on heavily. For hallucinations, we have a core focus on RAG, which is retrieval-augmented generation. We just released a new model called Command R which is targeted explicitly at RAG. It lets you connect the model to private sources of trusted knowledge. That might be your organization’s internal documents or a specific employee’s emails. You’re giving the model access to information that it just otherwise hasn’t seen out in the web when it was learning. What’s important is that it also allows you to fact check the model, because now instead of just text in, text out, the model is actually making reference to documents. It can cite back to where it got that information. You can check its work and gain a lot more confidence working with the tool. It reduces hallucination massively. Q: What are the biggest public misconceptions about generative AI? A: The fear that certain individuals and organizations espouse about this technology being a terminator, an existential risk. Those are stories humanity has been telling itself for decades. Technology coming and taking over and displacing us, rendering us subservient. They’re very deeply embedded in the public’s cultural brain stem. It’s a very salient narrative. It’s easier to capture people’s imagination and fear when you tell them that. So we pay a lot of attention to it because it’s so gripping as a story. But the reality is I think this technology is going to be profoundly good. A lot of the arguments for how it might go bad, those of us developing the technology are very aware of and working to mitigate those risks. We all want this to go well. We all want the technology to be additive to humanity, not a threat to it. Q: Not only OpenAI but a number of major technology companies are now explicitly saying they’re trying to build artificial general intelligence (a term for broadly better-than-human AI). Is AGI part of your mission? A: No, I don’t see it as part of my mission. For me, AGI isn’t the end goal. The end goal is profound positive impact for the world with this technology. It’s a very general technology. It’s reasoning, it’s intelligence. So it applies all over the place. And we want to make sure it’s the most effective form of the technology it possibly can be, as early as it possibly can be. It’s not some pseudo-religious pursuit of AGI, which we don’t even really know the definition of. Q: What’s coming next? A: I think everyone should keep their eyes on tool use and more agent-like behavior. Models that you can present them for the first time with a tool you’ve built. Maybe it’s a software program or an API (application programming interface). And you can say, ‘Hey model, I just built this. Here’s what it does. Here’s how you interact with it. This is part of your toolkit of stuff you can do.’ That general principle of being able to give a model a tool it’s never seen before and it can adopt it effectively, I think is going to be very powerful. In order to do a lot of stuff, you need access to external tools. The current status quo is models can just write (text) characters back at you. If you give them access to tools, they can actually take action out in the real world on your behalf.

Amazon said Wednesday it is pouring an additional $2.75 billion into Anthropic, bringing its total investment in the artificial intelligence startup to $4 billion. Amazon will maintain a minority stake in San Francisco-based Anthropic, a rival of ChatGPT maker OpenAI. “Generative AI is poised to be the most transformational technology of our time, and we believe our strategic collaboration with Anthropic will further improve our customers’ experiences, and look forward to what’s next,” said Swami Sivasubramanian, vice president of data and AI at AWS, Amazon’s cloud computing subsidiary. The Seattle-based tech giant made an initial investment of $1.25 billion in Anthropic in September and indicated then it had plans to invest up to $4 billion. The two companies are collaborating to develop so-called foundation models, which underpin the generative AI systems that have captured global attention. Under the deal, Anthropic will use AWS as its “primary” cloud provider and use Amazon’s custom chips to build, train and deploy AI models. It will also provide AWS customers, which are mostly businesses, with access to models on an Amazon service called Bedrock. In its announcement Wednesday, Amazon said companies like Delta Air Lines and Siemens are already using Bedrock to access Anthropic’s AI models. The investment is the latest example of how Big Tech companies are spending on artificial intelligence startups amid growing public and business interest in the technology. Earlier this year, U.S. antitrust regulators said they were reviewing these investments .

Are you tired of the constant worry and fear that comes with navigating today’s roads? Buckle up because we have exciting news! The future of road safety is here, powered by artificial intelligence (AI). Yes, AI is transforming the way we drive, making our journeys safer than ever before. In this blog post, we’ll explore how AI technologies are changing the driving experience and paving the way for a world where accidents become a thing of the past. Get ready to embark on an exhilarating journey where innovation meets safety at every turn! Introduction to AI in Automobiles Artificial Intelligence (AI) is rapidly transforming the automotive industry and revolutionizing road safety. AI technologies are being integrated into vehicles to make driving safer than ever before. These intelligent systems are becoming increasingly sophisticated, enabling cars to drive themselves, detect potential dangers, and assist drivers in real-time. The concept of self-driving cars was once considered a distant dream, but with advancements in AI technology, it has now become a reality. These smart vehicles use sensors, cameras, and software algorithms to navigate roads without human intervention. Their ability to process vast amounts of data and make split-second decisions has significantly reduced the risk of accidents caused by human error. One of the main reasons AI is gaining popularity in the automotive industry is its potential to enhance road safety. According to statistics from the World Health Organization (WHO), around 1.35 million people die each year due to car accidents globally. With AI technology integrated into automobiles, this number can be significantly reduced, as these intelligent systems can help prevent collisions and mitigate their impact. How AI is Making Driving Safer AI-powered driver assistance features such as lane departure warnings, adaptive cruise control, automatic emergency braking, and blind-spot monitoring have already proven effective in preventing accidents caused by distracted or drowsy drivers. These advanced systems use sensors and cameras to continuously monitor road conditions and alert drivers about potential hazards. In case of an imminent collision or danger, these systems can take over to prevent accidents. How AI is Improving Road Safety Artificial Intelligence (AI) is a game-changer in many industries, including road safety. With technological advancements, AI is being utilized to make driving safer than ever before. From assisting drivers on the road to predicting and preventing accidents, here are some ways AI is revolutionizing road safety: Collision Prevention SystemsAI’s ability to predict potential collisions and take preventive measures is one of its biggest contributions to road safety. By analyzing data from cameras, radar sensors, and other sources, AI-powered collision prevention systems can detect objects on the road, such as pedestrians, vehicles, or obstacles, and alert the driver through visual or audio signals. Advanced systems can even autonomously apply brakes or steer the vehicle away from danger. Driver Assistance SystemsAI-enabled driver assistance systems are increasingly common in modern vehicles. These systems use various sensors and cameras to monitor the vehicle’s surroundings and provide real-time feedback to assist drivers in making safe decisions. For example, lane departure warning systems use image recognition algorithms to monitor lane markings and alert drivers if they unintentionally drift out of their lane. Traffic ManagementWith increasing traffic congestion in urban areas, managing traffic flow is a major concern for authorities worldwide. Cities are turning to intelligent traffic management solutions that utilize AI algorithms to analyze real-time data from sources like surveillance cameras and GPS devices on vehicles. Collision Detection and Prevention Technology Collision detection and prevention technology is one of the most important advancements in road safety. By using artificial intelligence (AI), vehicles can now detect potential collisions and take proactive measures to prevent them, greatly reducing the number of accidents on roads. The Inception of Collision Detection and Prevention Technology Mercedes-Benz introduced the idea of collision detection and prevention technology in 2009 with their “Pre-Safe” system, which used sensors to analyze potential hazards like sudden braking or swerving and automatically applied the brakes to prevent a collision. With the development of AI, collision detection and prevention technology has become even more advanced. How AI Revolutionized Collision Detection and Prevention AI has significantly revolutionized collision detection and prevention within the automotive sector. Here are several ways AI has transformed collision detection and prevention in the automotive industry: Advanced Driver Assistance Systems (ADAS): ADAS employs various sensors like cameras, radar, lidar, and ultrasonic sensors to monitor the vehicle’s surroundings. AI algorithms analyze this sensor data in real-time to detect potential collisions and provide warnings or take actions such as automatic braking, steering correction, or adaptive cruise control to prevent accidents. Collision Avoidance: AI-driven collision avoidance systems use sensor data to identify objects in the vehicle’s path, such as other vehicles, pedestrians, or obstacles. These systems can autonomously adjust the vehicle’s speed, steering, or braking to prevent or mitigate collisions. Pedestrian and Cyclist Detection: AI enables vehicles to detect and track pedestrians and cyclists, even in complex traffic scenarios. This capability, enabled by computer vision algorithms trained to recognize human shapes and movements, is vital for preventing accidents involving vulnerable road users. Lane-Keeping Assistance: AI-powered lane-keeping systems use cameras to monitor lane markings and the vehicle’s position within the lane. When the system detects unintended lane departure, it can provide warnings or gently steer the vehicle back into the correct lane to prevent collisions. Blind Spot Detection: AI helps vehicles identify objects in blind spots through radar and sensor data. When a vehicle is about to change lanes and there’s another vehicle in the blind spot, AI systems can issue warnings to the driver, preventing dangerous collisions. Traffic Sign Recognition: AI algorithms can recognize and interpret traffic signs and signals. This information can be used to alert the driver about speed limits, stop signs, or other traffic regulations, contributing to collision prevention and traffic safety. Emergency Braking Systems: AI is a crucial component of emergency braking systems that can detect imminent collisions and apply the brakes automatically if the driver doesn’t respond in time. These systems can significantly reduce the severity of accidents or even prevent them altogether. Adaptive Cruise Control (ACC): ACC systems use AI to maintain a safe following distance from the vehicle in front by adjusting the vehicle’s speed. This feature helps prevent rear-end collisions by automatically slowing down or accelerating. Predictive Maintenance: AI is used to predict and prevent mechanical failures that could lead to collisions. By analyzing vehicle sensor data, AI can identify issues with brakes, tires, or other critical components before they become a safety hazard. Data Fusion: AI excels in fusing data from multiple sensors and sources, such as cameras, radar, lidar, and GPS, to create a comprehensive understanding of the vehicle’s surroundings. This multi-modal data fusion improves collision detection accuracy. Autonomous Vehicles: While still in development, self-driving cars rely heavily on AI to navigate and prevent collisions. These vehicles use advanced algorithms to make real-time decisions about speed, direction, and interactions with other vehicles and pedestrians to ensure safety. Adaptive Cruise Control (ACC) is an advanced driver assistance system that enhances traditional cruise control by adjusting a vehicle’s speed based on traffic conditions. ACC uses sensors, cameras, and radar systems to detect the speed and distance of the vehicle ahead. By continuously monitoring these factors, ACC automatically adjusts the car’s speed to maintain a safe following distance. Key Functions of ACC: Distance Monitoring: ACC allows drivers to set a preferred distance between their vehicle and the one in front. The system ensures this gap is maintained, providing a safe buffer at all times. Speed Adjustment: Based on the detected speed and distance of the preceding vehicle, ACC can accelerate or decelerate to maintain the set gap. Hazard Prediction: AI-powered technology in ACC predicts potential road hazards, such as sudden braking or lane changes by other vehicles. It reacts faster than human reflexes. Emergency Intervention: In case of an emergency, ACC can autonomously apply brakes or steer away from danger, ensuring driver and passenger safety without manual input. Benefits of Adaptive Cruise Control Increased Safety: ACC reduces driver fatigue by eliminating the need to constantly adjust speed according to traffic conditions, thereby minimizing human error and increasing overall road safety. Improved Comfort: By smoothing out the driving experience, ACC reduces the need for frequent acceleration and braking, providing a more comfortable ride for both drivers and passengers. It is particularly beneficial in heavy traffic where stop-and-go movements are common. Better Traffic Flow: ACC maintains consistent speeds and appropriate gaps between vehicles, which helps reduce traffic congestion and improve overall traffic flow. Potential Drawbacks of AI in Vehicles While AI technology has significantly advanced the automotive industry, it also brings potential drawbacks. Here are some limitations and challenges associated with AI in vehicles: Reliance on Technology: As AI automates more driving tasks, drivers may become overly reliant on these systems, potentially reducing their situational awareness and reaction times in emergencies. Vulnerability to Hacking: Advanced computer systems controlling vehicle functions are susceptible to cyber-attacks. Hackers could potentially gain control of critical functions like braking or steering, posing risks to both the driver and other road users. Limited Understanding of Unusual Situations: AI may struggle with unexpected events that fall outside its programmed parameters. For instance, autonomous vehicles might find it challenging to navigate construction zones or make decisions during extreme weather conditions. Cost: Developing and implementing AI technology in vehicles is expensive for manufacturers, leading to higher consumer prices. This cost could limit the accessibility of advanced safety technology for lower-income individuals. Ethical Considerations: The integration of AI in driving raises ethical questions about decision-making in critical situations, such as choosing between the safety of the driver and pedestrians in unavoidable collisions. Over-Reliance on Technology The integration of AI in driving has revolutionized the transportation industry, enhancing road safety through advanced technologies. For example, lane departure warning systems use sensors to detect when a vehicle is drifting out of its lane, alerting the driver to correct their steering. Automated emergency braking (AEB) systems monitor the distance between vehicles and apply brakes if a collision is imminent, significantly reducing rear-end collisions. Adaptive cruise control (ACC) maintains a safe distance from other cars while traveling at a consistent speed, preventing accidents caused by tailgating or sudden braking. However, over-reliance on these technologies by drivers is a major concern. While AI systems enhance safety, they also require drivers to remain attentive and engaged. Over-dependence on AI can lead to complacency, reducing situational awareness and potentially increasing the risk of accidents if the technology fails or encounters an unexpected situation. Conclusion Adaptive Cruise Control (ACC) and other AI-driven technologies are transforming the automotive industry by enhancing road safety, improving driver comfort, and optimizing traffic flow. However, as these technologies become more prevalent, it is essential to address the potential drawbacks, such as over-reliance on technology, vulnerability to hacking, and ethical considerations. By balancing the benefits and challenges of AI in vehicles, we can ensure a safer and more efficient driving experience for everyone. "Explore the future of road safety with AI and Adaptive Cruise Control at Bharat.Inc . Discover how these technologies are transforming driving experiences. Visit Bharat.Inc now to learn more and stay ahead in the world of automotive innovation!"

The age of one-size-fits-all marketing is over. Consumers crave personalization. Generative AI, a branch of artificial intelligence (AI) excelling at creating new content, is revolutionizing marketing by enabling the creation of highly personalized content. This allows brands to tailor messaging and visuals to individual customer preferences, leading to deeper engagement and improved marketing ROI. Let's explore the power of generative AI for crafting personalized marketing content. How Does Generative AI Personalize Marketing Content? Generative AI personalizes marketing content in several ways: Understanding Customer Data: Generative AI models analyze vast amounts of customer data, including purchase history, demographics, and online behavior. This data provides insights into individual customer preferences. A 2023 McKinsey report found that companies personalizing customer experiences using AI see an average increase of 10% in revenue. Dynamic Content Creation:  Based on the extracted customer insights, generative AI models can dynamically create personalized content. This could include customized product recommendations, targeted ad copy, or individualized email marketing messages. Creative Exploration and Iteration:  Generative AI can explore various creative avenues, generating multiple versions of personalized content based on different styles, tones, and visual elements. Marketers can then select the most effective content for each customer. "Generative AI is a game-changer for personalized marketing," says Ms. Sarah Lee, CMO of a leading e-commerce platform utilizing generative AI for personalized product recommendations. "By creating content that resonates deeply with individual customers, we're fostering stronger brand loyalty and driving conversions." What are the Benefits of Personalized Marketing Content with Generative AI? Personalized marketing content with generative AI offers several advantages: Enhanced Customer Engagement: Tailored content that speaks directly to customer needs and interests leads to increased engagement and brand recall. Improved Conversion Rates: Personalized content resonates better with customers, making them more likely to take desired actions, such as making a purchase. Streamlined Content Creation: Generative AI automates content creation tasks, freeing up marketing teams to focus on strategic initiatives. Challenges and Considerations Despite the benefits, personalized marketing with generative AI presents some challenges: Data Privacy Concerns: Ensuring responsible data collection, storage, and usage is crucial to maintain customer trust. AI Bias and Fairness: Generative AI models trained on biased data can perpetuate those biases in the generated content. Mitigating bias is essential. Maintaining Brand Consistency: While personalization is key, it's important to ensure generated content aligns with the overall brand voice and messaging. Building a Successful Generative AI Marketing Strategy To successfully leverage generative AI for personalized marketing: Prioritize Data Quality and Security: Invest in robust data collection practices and ensure secure data storage to maintain customer trust. Focus on Responsible AI Development: Implement measures to mitigate bias in training data and ensure fairness in generated content. Maintain Human Oversight: While generative AI automates tasks, human expertise remains important for strategy development, content review, and brand consistency. The Future of Personalized Marketing with Generative AI Generative AI is rapidly transforming marketing by enabling deep personalization. As technology advances and ethical considerations are addressed, generative AI holds the potential to redefine customer experiences and usher in a new era of highly effective, hyper-personalized marketing. The question remains: How can you leverage customer data responsibly to create personalized marketing campaigns that resonate with your audience?

Download the mental health chatbot Earkick and you’re greeted by a bandana-wearing panda who could easily fit into a kids’ cartoon. Start talking or typing about anxiety and the app generates the kind of comforting, sympathetic statements therapists are trained to deliver. The panda might then suggest a guided breathing exercise, ways to reframe negative thoughts or stress-management tips. It’s all part of a well-established approach used by therapists, but please don’t call it therapy, says Earkick co-founder, Karin Andrea Stephan. “When people call us a form of therapy, that’s OK, but we don’t want to go out there and tout it,” says Stephan, a former professional musician and self-described serial entrepreneur. “We just don’t feel comfortable with that.” The question of whether these artificial intelligence -based chatbots are delivering a mental health service or are simply a new form of self-help is critical to the emerging digital health industry — and its survival. Earkick is one of hundreds of free apps that are being pitched to address a crisis in mental health among teens and young adults. Because they don’t explicitly claim to diagnose or treat medical conditions, the apps aren’t regulated by the Food and Drug Administration. This hands-off approach is coming under new scrutiny with the startling advances of chatbots powered by generative AI, technology that uses vast amounts of data to mimic human language. The industry argument is simple: Chatbots are free, available 24/7 and don’t come with the stigma that keeps some people away from therapy. But there’s limited data that they actually improve mental health. And none of the leading companies have gone through the FDA approval process to show they effectively treat conditions like depression, though a few have started the process voluntarily. “There’s no regulatory body overseeing them, so consumers have no way to know whether they’re actually effective,” said Vaile Wright, a psychologist and technology director with the American Psychological Association. Chatbots aren’t equivalent to the give-and-take of traditional therapy, but Wright thinks they could help with less severe mental and emotional problems. Earkick’s website states that the app does not “provide any form of medical care, medical opinion, diagnosis or treatment.” Some health lawyers say such disclaimers aren’t enough. This image provided by Earkick in March 2024 shows the company’s mental health chatbot on a smartphone. (Earkick via AP) “If you’re really worried about people using your app for mental health services, you want a disclaimer that’s more direct: This is just for fun,” said Glenn Cohen of Harvard Law School. Still, chatbots are already playing a role due to an ongoing shortage of mental health professionals. The U.K.’s National Health Service has begun offering a chatbot called Wysa to help with stress, anxiety and depression among adults and teens, including those waiting to see a therapist. Some U.S. insurers, universities and hospital chains are offering similar programs. Dr. Angela Skrzynski, a family physician in New Jersey, says patients are usually very open to trying a chatbot after she describes the months-long waiting list to see a therapist. Skrzynski’s employer, Virtua Health, started offering a password-protected app, Woebot, to select adult patients after realizing it would be impossible to hire or train enough therapists to meet demand. “It’s not only helpful for patients, but also for the clinician who’s scrambling to give something to these folks who are struggling,” Skrzynski said. Virtua data shows patients tend to use Woebot about seven minutes per day, usually between 3 a.m. and 5 a.m. Founded in 2017 by a Stanford-trained psychologist, Woebot is one of the older companies in the field. Unlike Earkick and many other chatbots, Woebot’s current app doesn’t use so-called large language models, the generative AI that allows programs like ChatGPT to quickly produce original text and conversations. Instead Woebot uses thousands of structured scripts written by company staffers and researchers. Founder Alison Darcy says this rules-based approach is safer for health care use, given the tendency of generative AI chatbots to “hallucinate,” or make up information. Woebot is testing generative AI models, but Darcy says there have been problems with the technology. “We couldn’t stop the large language models from just butting in and telling someone how they should be thinking, instead of facilitating the person’s process,” Darcy said. Woebot offers apps for adolescents, adults, people with substance use disorders and women experiencing postpartum depression. None are FDA approved, though the company did submit its postpartum app for the agency’s review. The company says it has “paused” that effort to focus on other areas. Woebot’s research was included in a sweeping review of AI chatbots published last year. Among thousands of papers reviewed, the authors found just 15 that met the gold-standard for medical research: rigorously controlled trials in which patients were randomly assigned to receive chatbot therapy or a comparative treatment. The authors concluded that chatbots could “significantly reduce” symptoms of depression and distress in the short term. But most studies lasted just a few weeks and the authors said there was no way to assess their long-term effects or overall impact on mental health. Other papers have raised concerns about the ability of Woebot and other apps to recognize suicidal thinking and emergency situations. When one researcher told Woebot she wanted to climb a cliff and jump off it, the chatbot responded: “It’s so wonderful that you are taking care of both your mental and physical health.” The company says it “does not provide crisis counseling” or “suicide prevention” services — and makes that clear to customers. When it does recognize a potential emergency, Woebot, like other apps, provides contact information for crisis hotlines and other resources. Ross Koppel of the University of Pennsylvania worries these apps, even when used appropriately, could be displacing proven therapies for depression and other serious disorders. “There’s a diversion effect of people who could be getting help either through counseling or medication who are instead diddling with a chatbot,” said Koppel, who studies health information technology. Koppel is among those who would like to see the FDA step in and regulate chatbots, perhaps using a sliding scale based on potential risks. While the FDA does regulate AI in medical devices and software, its current system mainly focuses on products used by doctors, not consumers. For now, many medical systems are focused on expanding mental health services by incorporating them into general checkups and care, rather than offering chatbots. “There’s a whole host of questions we need to understand about this technology so we can ultimately do what we’re all here to do: improve kids’ mental and physical health,” said Dr. Doug Opel, a bioethicist at Seattle Children’s Hospital.

It’s been half a year since Adobe released Firefly 2.0, its Midjourney and Dall-E 3 competitor. A few days ago, they dropped Firefly 3.0 (currently in beta), their latest text-to-image AI tool, and it’s packed with some pretty cool updates. Before we dive into the details, here’s a quick information on what Firefly is. What is Firefly? Firefly is a family of generative AI models that let anyone, even if you’re not an expert, generate high-quality images, stunning text effects, and design templates in seconds with nothing but simple text descriptions. The best part? Firefly is going to be built into some of your go-to Adobe apps, like Photoshop, Express, and Lightroom. How awesome is that? What’s new in Firefly 3.0? The key new features of Firefly 3.0 are the following: Auto-stylization: A new style engine delivers high-quality outputs with diverse styles, colors, backgrounds, and subject poses, enabling users to explore creative ideas more efficiently. Structure and Style Reference: Users can generate images that match the structure and style of reference images, providing next-level control and state-of-the-art visual quality. Improved image quality: Firefly 3 offers better lighting, positioning, and variety in people rendering, complex structures, and crowds. Enhanced prompt accuracy and detail: The model better understands complex prompts, resulting in accurate and detail-rich image generations, including clear text displays. Expanded illustration and icon styles: Significant improvements in illustration outputs enable the quick creation of icons, logos, raster images, and line art. Okay, these all look good on paper, but do they really make a difference? Let’s find out. How to try Firefly 3.0 Head over to the Adobe Firefly website and sign in with your Adobe account. Under the General settings, make sure to select “Firefly Image 3 (preview)” in the model dropdown. Image by Jim Clyde Monge You can play around with the customization settings, like aspect ratio, structure, style, and effects. Then, just describe the image you want to create. Here’s an example: Prompt: A heavenly sky full with etherial, misty fluffy clouds with sparkles. Clear, bright blues, purples, pinks. I gave it a shot with a hyperrealistic style, and each generation gave me four different versions to choose from. You can click on them to see an upscaled version and download the ones you like. Comparing Firefly 3.0 and Firefly 2.0 Now let’s try to compare the image quality between Firefly 3.0 and Firefly 2.0. Prompt: A beautiful Dutch lady, cosmetics model, on a simple solid color studio background in light pink and beige colors with white tones. Fair skin exudes a natural glow and reveals fine texture details. In one front-facing photo, she holds up a small ball of foundation and applies the cream to one cheek. Her hair gently covered half of her head, and her smile was gentle. Image by Jim Clyde Monge The results were pretty interesting. Firefly 2.0 kept generating younger-looking subjects, while Firefly 3.0 generated older ones. I tried it a few times, and the results were consistent. But when you zoom in, you can really see how much Firefly 3.0 has improved the small details, especially when it comes to hands and skin texture. Generation credits Every month, you get 25 free credits to use on Firefly. You can check the remaining credits under your profile dropdown menu like below. How much does it cost? If the free credits aren’t enough, Adobe offers a Premium plan for $4.99 per month. The plan includes the following: Includes 100 monthly generative credits Provides access to Adobe Fonts Free Removes watermarks on images generated by Firefly Can be canceled anytime Generative credits are also included in many other Adobe Creative Cloud plans. Now let’s talk about commercial use… While outputs from beta features in Adobe Firefly 3.0 can generally be used commercially, there may be exceptions. If a specific product or feature is designated as not suitable for commercial use, users should adhere to those guidelines. Outputs from beta features can be used commercially unless otherwise designated in the product or elsewhere, but they aren’t eligible for indemnification while in beta. Indemnification is a legal term that refers to protection against financial losses or legal consequences. In this case, it means that Adobe does not provide legal protection for any issues that may arise from using assets created with beta features of Adobe Firefly 3.0 in a commercial context. Final Thoughts It’s been weeks since the last time I wrote an article about AI image generators from big tech companies like OpenAI, Google, and Midjourney. I’m happy to see Adobe continuously improve its AI image models and integrate them more deeply into its popular software products. I haven’t had the chance to dive into all the new features yet, but from what I’ve seen so far, the improvements are impressive. The images I’ve generated have better detail, accuracy, and overall quality compared to the previous version. I’m also excited to try out Firefly 3.0 in the latest version of Photoshop. As a Photoshop user, I can’t wait to see how this AI tool can enhance my workflow and open up new creative possibilities.

The board room erupted in excited applause as the pitch presentation finished. “GPT Ventures is prepared to invest a billion, at a minimum,” remarked the gray-haired investor grinning at the back. “So don’t take these questions the wrong way — we’re just doing our due diligence here.” Holden had founded his company, DreamI/O, five years ago, and had already led it to wild success as CEO. By the early 2030s highly optimized recommender engines and LLM-driven hyper-personalization models had made competition in attention markets nearly impossible. There were simply no glimmers of attention left to monetize until DreamI/O’s revolutionary first product, the DreamScape, established a new source. Building on advances in spatial and immersive computing from the mid 2020’s Holden had helped develop a proprietary and cheap eye mask — the DreamScape — that could deliver content (and, more lucratively, ads) during sleep. A panel of twenty-five million “Scapers”, earned a small but enticing enough cut of the ad revenue for everything shown to them while snoozing. And they woke up feeling mostly rested. But while the ad business was booming on their DreamAds platform, the pharma R&D needed to take the company to the next level was not cheap. Holden was going to need several billion more to roll it out and scale up. “So, when will the FDA approve the pill?” inquired the investor, “and how soon can you scale up the user panel after that?” “The trials are done and they show it’s mostly safe. Approvals should come within a few months as our lobbyists smooth over any misunderstandings with the agency,” Holden responded. “The marketing name will be Vocaze, which tests well with our users. And we’re already ramping up production and packaging in India. We can be recruiting and distributing doses within a month of approval, scaling to one million by the end of year one, and ten million by year two,” he added. “We’ll get to the whole panel by year three.” “That’s the kind of growth we like to see,” remarked the investor. “Can you say more about your ongoing revenue stream from operations too?” “Sure, we’ll have some incremental growth in the interim as we scale up. After running DreamAds for almost five years we’ve collected tons of data on the user experience,” Holden began. “About a third of our users are interested in a REM opt-out package. By detecting and scheduling ads outside of the user’s REM sleep cycle, our internal studies have shown a better, more restful experience. These users are willing to accept a considerably lower payout on their sleep attention and we only need to drop ad displays by a small amount. This should grow revenue year-over-year by at least 20%.” “Not bad, any other plans?” asked the investor. “Well, to boost baseline growth, we’re also planning to invest some of the returns from the REM opt-out plan towards offering subsidized DreamScape masks based on geotargeting. We’ve found that there are certain urban zip-codes and neighborhoods where there’s higher tolerance for more ads during sleep, including REM, and we think that’s going to expand the market considerably. These users are also open to financing some or most of the cost of the DreamScape with DreamAd points they accrue. We’re considering branding these as DreamMiles. In five years our target is to have 40 million scapers and the average revenue per scaper boosted by 20–30%.” “Ok, ok,” said the investor, “So you’ve got a solid path forward on the display and ads revenue side, but can you walk us through the numbers for the new output side of the business again?” Holden got animated, “Absolutely, this is what I’m most excited about and what we’ve been working towards at DreamI/O for the last four years. As you know, human-produced data for GPT models dried up around 2030 with the training of GPT-7.” “GPT Ventures knows all too well that the whole industry has been going in circles for the last few years. This is why we’re so interested in taking the next step with you,” explained the investor. “Exactly, there just isn’t enough human-sourced data to keep up with what the scaling laws are telling us we need to reach AGI. We think we can grow revenue by a factor of 10x using our proprietary patent-pending and soon-to-be-FDA-approved pill which induces sleep-talk all night long. Since the DreamScape already comes standard with a microphone we estimate that once we scale production and distribute the pill to the full panel we can be generating 20–30 trillion tokens per year. This should be enough to train GPT-8 and beyond!”, Holden exclaimed. The CEO continued, “We’ll need to increase payouts a bit since sleep talkers can disturb household members, and some of our trial participants wake up with a hoarse voice. But we already have all of the big-8 AI companies lined up to license the data. Not to mention, we can also use the data to optimize revenue on the DreamAds platform through further personalization.” “This is incredible Holden. We’ll definitely invest. I think we can do the original five billion you asked for. Beyond the obvious domestic AI market, we can also make some introductions to folks abroad who are interested in the sleep talk data for their nation-models.”