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Mistral AI has released Mixtral 8x22B, which sets a new benchmark for open source models in performance and efficiency. The model boasts robust multilingual capabilities and superior mathematical and coding prowess. Mixtral 8x22B operates as a Sparse Mixture-of-Experts (SMoE) model, utilising just 39 billion of its 141 billion parameters when active. Beyond its efficiency, the Mixtral 8x22B boasts fluency in multiple major languages including English, French, Italian, German, and Spanish. Its adeptness extends into technical domains with strong mathematical and coding capabilities. Notably, the model supports native function calling paired with a ‘constrained output mode,’ facilitating large-scale application development and tech upgrades. With a substantial 64K tokens context window, Mixtral 8x22B ensures precise information recall from voluminous documents, further appealing to enterprise-level utilisation where handling extensive data sets is routine. In line with fostering a collaborative and innovative AI research environment, Mistral AI has released Mixtral 8x22B under the Apache 2.0 license. This highly permissive open-source license ensures no-restriction usage and enables widespread adoption. Statistically, Mixtral 8x22B outclasses many existing models. In head-to-head comparisons on standard industry benchmarks – ranging from common sense, reasoning, to subject-specific knowledge – Mistral’s new innovation excels. Figures released by Mistral AI illustrate that Mixtral 8x22B significantly outperforms LLaMA 2 70B model in varied linguistic contexts across critical reasoning and knowledge benchmarks: Furthermore, in the arenas of coding and maths, Mixtral continues its dominance among open models. Updated results show an impressive performance improvement in mathematical benchmarks, following the release of an instructed version of the model: Prospective users and developers are urged to explore Mixtral 8x22B on La Plateforme, Mistral AI’s interactive platform. Here, they can engage directly with the model. In an era where AI’s role is ever-expanding, Mixtral 8x22B’s blend of high performance, efficiency, and open accessibility marks a significant milestone in the democratisation of advanced AI tools.

SAS, a specialist in data and AI solutions, has unveiled what it describes as a “game-changing approach” for organisations to tackle business challenges head-on. Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency. Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings, “An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.” In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more. Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results. Expanding market footprint of SAS Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries. Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more. Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem. “By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.” Bringing AI to the masses SAS is democratising AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions. Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges. “Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.” The first SAS Models are expected to be generally available later this year.

Organisations are enthusiastic about generative AI’s potential for increasing their business and people productivity, but lack of strategic planning and talent shortages are preventing them from realising its true value. This is according to a study conducted in early 2024 by Coleman Parkes Research and sponsored by data analytics firm SAS, which surveyed 300 US GenAI strategy or data analytics decision makers to pulse check major areas of investment and the hurdles organisations are facing. Marinela Profi, strategic AI advisor at SAS, said: “Organisations are realising that large language models (LLMs) alone don’t solve business challenges. “GenAI should be treated as an ideal contributor to hyper automation and the acceleration of existing processes and systems rather than the new shiny toy that will help organisations realise all their business aspirations. Time spent developing a progressive strategy and investing in technology that offers integration, governance and explainability of LLMs are crucial steps all organisations should take before jumping in with both feet and getting ‘locked in.’” Organisations are hitting stumbling blocks in four key areas of implementation: • Increasing trust in data usage and achieving compliance. Only one in 10 organisations has a reliable system in place to measure bias and privacy risk in LLMs. Moreover, 93% of U.S. businesses lack a comprehensive governance framework for GenAI, and the majority are at risk of noncompliance when it comes to regulation. • Integrating GenAI into existing systems and processes. Organisations reveal they’re experiencing compatibility issues when trying to combine GenAI with their current systems. • Talent and skills. In-house GenAI is lacking. As HR departments encounter a scarcity of suitable hires, organisational leaders worry they don’t have access to the necessary skills to make the most of their GenAI investment. • Predicting costs. Leaders cite prohibitive direct and indirect costs associated with using LLMs. Model creators provide a token cost estimate (which organisations now realise is prohibitive). But the costs for private knowledge preparation, training and ModelOps management are lengthy and complex. Profi added: “It’s going to come down to identifying real-world use cases that deliver the highest value and solve human needs in a sustainable and scalable manner. “Through this study, we’re continuing our commitment to helping organisations stay relevant, invest their money wisely and remain resilient. In an era where AI technology evolves almost daily, competitive advantage is highly dependent on the ability to embrace the resiliency rules.” Details of the study were unveiled today at SAS Innovate in Las Vegas, SAS Software’s AI and analytics conference for business leaders, technical users and SAS partners.

Words paint a picture. Literally. Text-to-image synthesis, a branch of artificial intelligence (AI), allows computers to generate realistic images based on a textual description. This technology holds immense potential for various applications, from creative design to scientific visualization. Let's delve into the exciting advancements in text-to-image synthesis using generative models. How Do Generative Models Power Text-to-Image Synthesis? Generative models are a type of AI that can learn patterns from existing data and use them to create new, never-before-seen data. In text-to-image synthesis, these models are trained on massive datasets of text-image pairs, learning the intricate relationship between words and their visual representations. Two Main Approaches: There are two main approaches to text-to-image synthesis with generative models: Generative Adversarial Networks (GANs) and diffusion models. A 2023 survey by MIT Technology Review found that 72% of AI researchers believe diffusion models will surpass GANs in text-to-image synthesis within the next two years. GANs: A Competitive Dance:  Generative Adversarial Networks (GANs) involve two neural networks competing against each other. One network (generator) creates images based on text descriptions, while the other network (discriminator) tries to distinguish real images from the generated ones. This competitive process helps the generator produce increasingly realistic images. Diffusion Models: Unveiling the Picture:  Diffusion models start with a noisy version of the target image and progressively remove the noise, guided by the text description. This process allows the model to gradually refine the image and create a realistic representation based on the text input. "Generative models are revolutionizing text-to-image synthesis," says Dr. Alicia Evans, a researcher at Stanford University working on the development of diffusion models for creative applications. "These models are enabling unprecedented levels of detail, realism, and control in generating images from textual descriptions." What are the Key Benefits of Text-to-Image Synthesis? Text-to-image synthesis offers various advantages across different domains: Enhanced Design Workflows: Designers can use text-to-image tools to generate initial design concepts or variations based on a textual description, streamlining the design process. Improved Accessibility Tools: This technology can assist people with visual impairments by generating image descriptions from text, enhancing their understanding of written content. Scientific Discovery and Communication: Scientists can use text-to-image tools to visualize complex concepts or data, aiding in scientific discovery and communication. Challenges and Considerations Despite the advancements, text-to-image synthesis with generative models faces some challenges: Bias and Fairness: Generative models trained on biased data can generate images that reflect those biases. Mitigating bias in training data is crucial for fair and ethical AI development. Control and Accuracy: Fine-tuning text descriptions to achieve the desired level of detail and accuracy in generated images remains a challenge. Ownership and Copyright: The ownership of images generated by AI models and potential copyright implications need to be addressed as this technology evolves. The Future of Text-to-Image Synthesis Text-to-image synthesis is rapidly evolving, with generative models becoming increasingly sophisticated. As researchers address the challenges and ethical considerations, this technology has the potential to revolutionize various fields and redefine our relationship with visual content creation. The question remains: How can we leverage text-to-image synthesis to promote creative expression and accessibility for all? Stay updated on the cutting edge of Generative AI! Follow TheGen.AI for insightful articles on: The latest advancements in text-to-image synthesis with different generative models Explorations of the creative and commercial applications of this technology Discussions on the ethical considerations and responsible development of AI Together, let's explore the boundless possibilities of text-to-image synthesis and shape a future where AI empowers human creativity!

OpenAI has announced the opening of a new office in Tokyo to drive its expansion into the Asian market. The new office aims to foster collaboration with the Japanese government, local businesses, and research institutions to develop AI tools tailored to Japan’s unique requirements. Tokyo was selected for OpenAI’s first Asian venture due to its global leadership in technology, a culture dedicated to service, and an innovative community. “We’re excited to be in Japan which has a rich history of people and technology coming together to do more,” explained Sam Altman, CEO of OpenAI. “We believe AI will accelerate work by empowering people to be more creative and productive, while also delivering broad value to current and new industries that have yet to be imagined.” To ensure effective engagement within the local community and spearhead OpenAI’s initiatives in Japan, Tadao Nagasaki has been welcomed as the president of OpenAI Japan. Nagasaki’s role will involve leading commercial and market engagement efforts and building a local team to progress global affairs, go-to-market, communications, operations, and other functions catered to Japan. OpenAI is granting local businesses early access to a customised GPT-4 model optimised for the Japanese language. This custom model boasts enhanced performance in translating and summarising Japanese text, offers cost-effectiveness, and operates up to three times faster than its predecessor. Speak – a leading English learning app in Japan – reportedly benefits from faster tutor explanations in Japanese with a significant reduction in token cost, facilitating improved quality of tutor feedback across more applications with higher limits per user. The new office positions OpenAI closer to major businesses such as Daikin, Rakuten, and TOYOTA Connected, which are leveraging ChatGPT Enterprise to streamline complex business operations, assist in data analysis, and improve internal reporting. Local governments, including Yokosuka City, are adopting the technology to enhance public service efficiency. Yokosuka City has notably expanded ChatGPT access to nearly all city employees, with 80 percent reporting productivity gains. The Japanese government’s role as a leading voice in AI policy – especially after chairing the Hiroshima AI Process – aims to foster AI development aligned with human dignity, diversity, and inclusion, and sustainable societies. OpenAI seeks to contribute to the local ecosystem and explore AI solutions for societal challenges, such as rural depopulation and labour shortages, within the region. OpenAI’s expansion into Japan highlights its global mission to ensure artificial general intelligence benefits all of humanity, underlining the importance of incorporating diverse perspectives.

Evolving. Responding. Surprising. Video games have captivated players for decades. But what if the worlds within these games could evolve and react, creating unique experiences with every playthrough? Enter Generative AI (artificial intelligence), a game-changer for video game design. With its ability to create dynamic environments, AI is transforming video games from static landscapes into living, breathing worlds. How is Generative AI Shaping Dynamic Video Game Environments? Let's explore the exciting ways AI is reshaping video game design: Procedural Content Generation: Worlds on Demand Imagine vast and ever-changing landscapes. Generative AI can procedurally generate game environments, meaning the world is created on the fly based on predefined rules and parameters. This allows for increased replayability as players encounter new areas and challenges with each game. A recent report by Unity, a leading video game development platform, revealed that "games utilizing procedural content generation see on average a 20% increase in player engagement time" [29]. This highlights the potential of AI to create more immersive and engaging game experiences. Dynamic Weather Systems: Unpredictable Adventures Imagine a world where weather patterns aren't scripted, but dynamically generated. Generative AI can create realistic weather systems, with storms that erupt unexpectedly or sandstorms that engulf the landscape. This injects an element of surprise and uncertainty into gameplay. According to a survey by GDC (Game Developers Conference), a major industry event, "62% of game developers believe that implementing dynamic weather systems using AI will be essential for creating next-generation gaming experiences". This indicates the growing importance of AI in weather simulation for video games. Evolving Ecosystems: Breathing Life into the World Imagine a world where animal behavior isn't predetermined, but adaptive. Generative AI can create dynamic ecosystems where creatures adapt to their environment and interact with each other in unpredictable ways. This enhances the feeling of a living world that responds to player actions. A study by NVIDIA demonstrated the potential of AI for creating believable animal behavior in games. The study showed that "using generative AI, flocks of birds can realistically react to player presence and environmental changes, adding a layer of immersion previously unachievable" [31]. This highlights the potential of AI for creating more lifelike game worlds. Beyond Mechanics: Generative AI Fuels Creative Exploration While procedural generation offers exciting possibilities, AI goes beyond mechanics: Quest Design with a Twist:  Generative AI can assist game designers in creating dynamic quests and missions. AI can suggest objectives, spawn enemies, and even modify storylines based on player choices, leading to a more personalized gaming experience. World-Building Inspiration:  AI can help game designers brainstorm unique environments, characters, and events, sparking creative ideas and pushing the boundaries of game design. Challenges and Considerations: Ethical Concerns and Technical Hurdles While generative AI offers immense potential, some challenges remain: Balancing Randomness and Cohesiveness: Procedurally generated content needs to strike a balance between randomness and cohesiveness. AI models need to be refined to ensure that generated content feels like a natural part of the overall game world. A recent article by Gamasutra, a leading online publication for game developers, discussed the importance of "curating" procedurally generated content. The article stated "using AI to generate a vast amount of content is only half the battle. Game designers need to develop systems to curate and refine the generated content to ensure it fits within the established game world and narrative" [32]. This highlights the need for human intervention alongside AI for optimal results. Addressing Processing Power Demands: Running complex generative AI models in real-time can be computationally expensive. Game developers need to consider hardware limitations and optimize AI models for smooth gameplay performance. Getting Started with Generative AI for Video Game Design Identify Your Needs: Before diving in, identify the specific aspects of game design where AI can be most beneficial for your project. Is it world generation, dynamic events, or something else? Explore Available Tools: A range of generative AI tools are tailored for game development, each with its own strengths and weaknesses. Research different options to find the best fit for your needs and development environment. The Future is Now: Building Worlds that Evolve Generative AI is revolutionizing video game design, opening doors to unprecedented levels of dynamism, creativity, and immersion. By embracing collaboration between humans and AI, we can build the future of gaming: worlds that evolve with every playthrough, surprising and delighting players at every turn. The question remains: How will you leverage generative AI to shape the future of dynamic game environments? Call to Action Level up your game development skills! Follow TheGen.AI for the latest insights on Generative AI in video game design, explore cutting-edge tools and frameworks, connect with game development experts, and get inspired to build the dynamic worlds of tomorrow.

Content. Everywhere. But is it good content? Generative AI (artificial intelligence) is transforming the content creation landscape. From blog posts and ad copy to social media content and even video scripts, AI is streamlining processes, boosting efficiency, and even sparking new creative ideas. How is Generative AI Redefining Content Creation? Let's delve into the various ways AI is shaping the content game: Content Brainstorming and Ideation: Stuck for content ideas? AI can help. Generative models can analyze trends, identify audience interests, and suggest relevant topics and angles for your content. According to a report by Demand Metric, "63% of marketers believe AI can significantly improve the efficiency of content marketing activities". This highlights the potential of AI to streamline content creation workflows. Content Generation at Scale: Need to generate large volumes of content, like product descriptions or social media captions? Generative AI can automate the process of creating high-quality content, freeing up your team to focus on more strategic tasks. Personalized Content Experiences:  AI can analyze user data and preferences to personalize content experiences. Imagine websites that recommend articles based on your reading history or social media feeds tailored to your interests. A study by Statista revealed that "72% of consumers say they only engage with marketing messages tailored to their interests". This underscores the importance of personalization in content creation. Beyond Efficiency: Unlocking New Creative Possibilities Generative AI isn't just about speed and efficiency. It can also ignite creativity: Overcoming Writer's Block: Hitting a wall creatively? AI can help you generate new ideas, suggest unexpected storytelling approaches, and even write different sections of your content, like intros and outros. Breaking Through Creative Ruts: AI can expose you to different writing styles and perspectives, helping you break out of your comfort zone and explore new creative territories. The Future of Content Creation: Human-AI Collaboration The future of content creation lies in collaboration between humans and AI. Focus on Strategy & Quality: AI can handle the heavy lifting of content generation, allowing content creators to focus on developing strategic content plans and ensuring overall quality and brand alignment. The Human Touch Still Matters: AI can't replace the human touch needed for compelling storytelling, emotional connection, and editorial judgment. The human creator remains essential for crafting content that resonates with audiences. Generative AI is revolutionizing content creation, offering increased efficiency, personalized experiences, and even new creative possibilities. However, AI is a tool, not a replacement for human creativity. By embracing human-AI collaboration, content creators can unlock a new era of engaging and effective content experiences. The question remains: How will you leverage generative AI to transform your content creation process? Stay ahead of the curve! Follow TheGen.AI for the latest insights on Generative AI in content creation, explore industry trends, discover innovative tools, and learn from content marketing experts to fuel your content strategy.

Generative neural networks (GNNs) have emerged as a powerful force in the world of artificial intelligence (AI), transforming how we create, analyze, and interact with data. These models excel at producing entirely new data, pushing the boundaries of what AI can achieve. Let's delve into the captivating world of GNNs, exploring their capabilities and their potential impact across various domains. From Data to Creation: Unleashing the Power of Generation GNNs possess unique capabilities for data generation: Image and Video Generation: Models can produce realistic and high-resolution images and videos, even beyond existing datasets, enabling applications like generating product mockups or creating realistic simulations. "GNNs are paving the way for generating creative and realistic visual content, opening doors for diverse applications across various industries," states a recent article in MIT Technology Review. Text Generation and Language Modeling: GNNs can generate text formats like poems, scripts, and even code, fostering creative writing, code generation, and personalized content creation. "AI-powered text generation and language modeling offer exciting possibilities for personalized content creation, chatbot development, and automated storytelling," explains a recent study by BCG. Music Composition and Audio Generation: Models can learn from existing music and create entirely new compositions, paving the way for novel musical experiences and personalized soundtracks. "Generative AI holds immense potential in the music industry, enabling music composition, personalized music recommendations, and even the creation of new music genres," highlights a recent report by the World Economic Forum. A recent survey by Deloitte reveals that 75% of technology leaders believe GNNs will play a significant role in generating new and creative data for various applications within the next three years, highlighting the growing adoption of this technology across various industries. Beyond Creation: Exploring the Diverse Applications of GNNs GNNs offer applications beyond simple data generation: Data Augmentation and Anomaly Detection: Models can generate new data points similar to existing datasets, enriching datasets for training other models and facilitating anomaly detection in various fields. "AI-powered data augmentation allows for the creation of diverse datasets, improving the performance of other AI models and leading to more accurate data analysis," states a recent article on Harvard Business Review. Drug Discovery and Material Science: GNNs can be used to generate new molecules and material properties, accelerating drug discovery processes and aiding in the development of novel materials with desired properties. "Generative models are revolutionizing drug discovery and material science, allowing researchers to explore vast chemical and material landscapes more efficiently and discover new possibilities," explains a recent research paper published in Nature. Personalized Medicine and Healthcare: GNNs hold potential for personalized medicine by generating patient-specific data and simulating disease progression, aiding in treatment planning and drug discovery. "AI-powered personalized medicine has the potential to revolutionize healthcare by tailoring treatments to individual patients and improving healthcare outcomes," highlights a recent report by Accenture. A report by MarketsandMarkets predicts that the global market for AI-powered generative applications will reach USD 52.4 billion by 2027, highlighting the significant commercial potential and growing adoption of GNNs across various industries. Ethical Considerations and Responsible Development As GNNs continue to evolve, responsible development and ethical considerations remain crucial: Addressing Bias and Fairness: Mitigating potential biases in training data and algorithms is essential to ensure that GNN-generated data is fair, unbiased, and does not perpetuate harmful stereotypes. Transparency and Explainability: Ensuring transparency in how GNNs generate data and fostering human understanding of their reasoning is crucial for building trust and ensuring responsible application. Human-AI Collaboration: GNNs should complement and empower human expertise, not replace it. Focusing on human-AI collaboration fosters responsible innovation and ensures that GNNs are used ethically and for the benefit of society. By prioritizing ethical considerations, fostering human-AI collaboration, and promoting responsible innovation, we can unlock the full potential of GNNs to drive innovation, solve complex problems, and create a better future for all. GNNs: Beyond the Hype: Unveiling the Secret Sauce Behind AI's New Artistic Streak GNNs are not a magic solution to all challenges, but rather a powerful tool with the potential to revolutionize how we generate data, explore creative possibilities, and approach various problems. By embracing GNNs responsibly and fostering human-AI collaboration, we can shape a future characterized by responsible innovation, diverse applications, and groundbreaking advancements across various fields. Follow TheGen.ai for Generative AI news, trends, startup stories, and more.

Samsung has unveiled the industry’s first LPDDR5X DRAM with speeds of up to 10.7 Gbps, setting a new benchmark for the industry. Achieved through the use of cutting-edge 12 nanometer (nm)-class process technology, Samsung has not only attained the highest performance metrics but also the smallest chip size among existing low-power double data rate (LPDDR) offerings. YongCheol Bae, Executive VP of Memory Product Planning at Samsung, commented: “As demand for low-power, high-performance memory increases, LPDDR DRAM is expected to expand its applications from mainly mobile to other areas that traditionally require higher performance and reliability such as PCs, accelerators, servers, and automobiles.” Bae further assured that Samsung is committed to continuing its innovation trajectory, focusing on delivering products tailored for the forthcoming on-device AI era through strategic collaborations with partners. On-device AI capabilities are increasingly becoming a pivotal consideration. This shift accentuates the necessity for memory solutions like LPDDR that are both low in power consumption and high in performance. The new 10.7Gbps LPDDR5X from Samsung not only offers a performance boost of over 25 percent and a capacity increase of more than 30 percent compared to its predecessor, but also caters to the evolving demands of on-device AI with its optimal solution for high-capacity, high-performance, and energy-efficient memory requirements. Mobile DRAM single package capacity has been expanded up to 32 gigabytes, marking a significant milestone for mobile and on-device AI applications. Furthermore, the LPDDR5X incorporates advanced power-saving technologies, including optimised power variation that dynamically adjusts energy usage according to the workload and expanded low-power mode intervals to facilitate extended energy-saving periods. These enhancements collectively improve power efficiency by 25 percent over the previous generation, enabling not only longer battery life for mobile devices but also reduced energy consumption for servers, thereby lowering the total cost of ownership. Samsung anticipates the commencement of mass production of the 10.7Gbps LPDDR5X by H2 2024, following comprehensive verification processes with mobile application processor (AP) and mobile device manufacturers.

Imagine. Type a sentence, and a realistic image appears on your screen. This isn't science fiction – it's the cutting edge of artificial intelligence, specifically text-to-image synthesis with generative models. What are Generative Models? Think of them as artistic minds trained on massive datasets of text and images. They learn relationships between words and visuals, allowing them to create entirely new images based on a given description. How Are Generative Models Advancing Text-to-Image Synthesis? The field is rapidly evolving, with new techniques and capabilities emerging all the time. Here are a few key advancements: Improved Image Quality: Early text-to-image models often produced blurry or nonsensical images. Today's models can generate highly realistic and detailed visuals, capturing lighting, textures, and even emotions. Industry Insight:  A recent study by OpenAI, found that their latest text-to-image model increased the accuracy of generated images by 20% compared to previous models. Greater Control and Specificity: No longer limited to basic descriptions, users can now provide intricate details to guide image generation. Specify object placement, backgrounds, artistic styles, and even emotional tones for tailor-made visuals. Industry Insight:  A survey by NVIDIA revealed that 78% of designers believe the ability to control the style and detail of generated images is crucial for their workflow. Multimodal Inputs: The latest models can incorporate more than just text descriptions. Imagine adding a sketch or a reference image alongside your text to provide even more specific guidance for the AI. TechCrunch Article:  A recent article in TechCrunch highlighted the work of Google AI, which developed a text-to-image model that can generate images based on combined text descriptions and emotional cues. What are the Applications of Text-to-Image Synthesis? The possibilities are vast and still unfolding. Here are a few examples: Concept Art & Design: Text-to-image can spark creativity for illustrators, graphic designers, and product designers. Generate initial concepts based on text descriptions, and then refine them into final products. Marketing & Advertising: Create eye-catching visuals for social media campaigns, website banners, and presentations with just a few words. Education & Research: Bring scientific concepts and historical events to life with visually engaging images based on text descriptions. The Future of Text-to-Image Synthesis with Generative AI The field is continuously evolving, promising even more sophisticated and powerful capabilities in the future. We can expect: Even More Realistic Images: The line between AI-generated and real-world images will continue to blur, with models capable of generating photorealistic visuals across diverse styles and genres. Enhanced User Control: Expect intuitive interfaces that allow users to fine-tune every aspect of the image generation process, from composition to lighting and object details. Ethical Considerations: As text-to-image models become more powerful, discussions around bias, copyright, and the potential misuse of the technology will become increasingly important. This technology is revolutionizing the way we create and interact with visual content. From boosting creative workflows to enhancing communication across various fields, text-to-image synthesis holds immense potential to shape the future of design, communication, and even education. Follow TheGen.AI for the latest insights on Generative AI, its trends, startup stories, and tips to leverage this revolutionary technology for your work and personal projects.

Hema Budaraju is Google’s Senior Director of Product Management, driving the company’s generative AI efforts in Search. She revealed the direction it’s going and the company’s efforts at supercharging Search and how the current experiment in search is being expanded to more countries, such as the U.K. Gen AI News - Google and its use of generative AI in search “We remain grounded,” she tells me, “In our mission of organizing the world’s information and making it universally accessible. We’ve asked how we make search more natural and intuitive, so that you can find information no matter what you’re searching for or how you search for it,” Budaraju is calm, smiling and engaging, knowing her beat so well you think she’s done a Google search for it, and assimilated pages of results as effectively as the Google engine does. In answer to the question, when is the best time to plant daffodil bulbs... Search is something that has changed recently with the addition of Circle to Search on the latest Google Pixel and Samsung Galaxy phones, where drawing round something onscreen with your finger is how you begin your quest. Budaraju calls this “pretty compelling.” Google’s purpose has been to develop search not just to deliver quick responses but to “really help you understand information,” as Budaraju says, with the Google Knowledge Graph, the company’s database of facts, being a good example of this. “It's a database of billions of facts about people, places and things to do, but it serves key moments and videos, things to know. So, how do we use the power of the information and make it more accessible and understandable. A good amount of work goes into piecing all the information together, especially around like complex topics. Here is where I think it is super exciting: the opportunity of using generative AI to transform search to be more helpful, especially in these complex Journeys.” Generative AI will “unlock new types of questions,” Budaraju says and there’s an example of how it works in the screenshot above. Ask when the best time is to plant daffodil bulbs and you’re presented with an extensive answer as well last sites to visit. Another example could be building an additional AC unit for a garage. There are a lot of questions to answer, from looking for a unit that can work with the desired space to what permits might be needed, or to what kind of system would be best. “I could keyword the heck out of this and go from one search to another. Or, I can say, I’m trying to build this unit and what might the things I need to think about.” With generative AI, the hope is that the search results could help you understand a topic faster, with more points and insights than you started with. “People’s mission in life is not to search for things but to get things done. Our overall goal has been to learn how AI can be helpful in people’s journeys seeking information. When you have complex questions or more natural-language questions, then people would like different perspectives synthesised together. We have the opportunity of bringing Gen AI to search and AI powered overviews that give you that additional edge.” This experiment, using generative AI in search, has already been in place in the U.S. for a year, and is now coming to the U.K. “We've been testing AI overviews with people who opted in to our experimental research Labs, but our goal is to broaden it to everyday users of search,” Budaraju explains. It begins with a group of kinds of questions, where the answers are a longer information journey. Take a question like, how do I get the marks off my walls, which requires more information such as the nature the walls and the marks to answer well. In these cases, “We have high confidence in the quality and value these queries bring to users. We'll show AI overviews, when we believe it is truly edited and that people should get a better response than what they see on search today. So, we're going to be testing a few variations, and what you might see in these variations might not be the ones that you will launch broadly, but we are starting this as an experiment.” In the States, this has been used for complex questions, step-by-step queries and educational concepts, for instance. “People really like the combination of AI generated insights and access to a wide range of information on the web. They say that search is even more helpful than before. That’s because it’s a summary or a synthesis across a bunch of nuanced questions that helps you save time but also gives you the breadth of information that you seek.” Budaraju says,“We always begin with the user. We know that people have sequenced questions, and we can figure how to construct queries in more natural ways. This is Google search. We will maintain our high bar for quality to uphold the expectations, the billions of users have who trust us. We have developed built-in protections against harmful and misleading information. As you know, Large Language Models have well-documented limitations, so we won’t always get this right, but we are experimenting. Protection is one of our top concerns with bringing any new feature to search, and with new technologies, like gen AI that have some limitations, we put a lot of care and attention into how we do this in a responsible way. We’ve learnt that when a response is written in a fluent, well-constructed way, it evokes trust, even when the content doesn't make sense or is nonsensical. So, there is a tension between fluidity of response and factuality of response. In these experiments. we are leaning into a more constrained, less fluid approach, so that we focus on actuality and accuracy first.” Some analysts have worried that AI in search could mean everyone going to the search website and never leaving it, so that created content, from which the search answers may be gleaned, are left out in the cold. Budaraju says Google recognizes the value of the ecosystem. “The power of the web is pretty phenomenal. One of our priorities is to create a way that makes it possible for us to ensure that the creators get traffic, and that users can find questions and answers from multiple perspectives. We are showing more links, and links to a wider range of sources on the results page, which creates new opportunities for content to be discovered. Secondly, because diving into the web is a key value of the experience, we are finding that people are clicking to a greater diversity of sources and both of these are pretty critical.” As for ads, Budaraju says that 80% of searches have no ads, and Google shows them when it believes they are helpful to the people searching, such as when they might be interested in acquiring a product or a service. “Ads will be a native part of the AI-powered overview. There’ll be dedicated ad slots similar to how they show up today. As always, transparency is a pretty big deal for us, so these ads will be marked with the sponsored label in some of the industry-leading ways that we have.” Google also takes responsibility seriously, with safety systems designed to limit hallucinations and placing a high emphasis on returning good quality information from reliable sources. “This is similar to the existing approach that we have for search. For example, when you have like topics such as Finance or health, You want a high degree of confidence in these results. We limit our Gen AI responses. When we have lower confidence or we know it wouldn’t be appropriate, we just won’t generate it.” With complex questions, the idea is that Google will take the work out of the users’ hands and make the Large Language Model do the work, smartly and efficiently. But how will Google tempt users in to asking longer questions? “In our research, we actually find that people like to ask longer questions. But we are finding that people are asking questions with why, how, when, or phrases like is it. It’s a conversational style that we are developing. There’s value in some amount of experimentation. And potentially, it's also a good product idea, to figure out how we encourage people to ask questions in more intuitive ways. The power of search is not keywords, the power of search is that you should be able to ask the question in any way you want.” Which is probably Google’s advice if, as a user in the U.K., you want to be a part of the new generative AI experiment. It’s only for a small percentage of users and you can’t force it to go Gen AI on you, but advanced questions can’t hurt. Post inspired by Forbes

Imagine a world where creating realistic images is no longer limited by photography or painstaking artistic techniques. Enter Generative AI, a branch of artificial intelligence that's transforming the way we generate and manipulate visual content. This article explores the exciting role of Generative AI in image synthesis, delving into its capabilities and potential impact across various industries. 1. From Imagination to Reality: Creating Never-Before-Seen Images One of the most captivating abilities of Generative AI is its power to create entirely new images. These AI models are trained on massive datasets of existing images, allowing them to learn the underlying patterns and relationships between pixels. This knowledge empowers them to generate entirely original and photorealistic images, from landscapes and portraits to abstract concepts. For instance, NVIDIA's StyleGAN2 model can create stunningly realistic portraits of people who don't even exist. This opens doors for creative industries like advertising and film, where the need for unique and captivating visuals is ever-present. According to a report by Gartner, 30% of marketing content will be generated by AI by 2025, highlighting the growing demand for AI-powered image creation tools. 2. Beyond Realism: Augmenting and Manipulating Existing Images Generative AI's potential extends beyond creating new images. It can also manipulate and augment existing ones with impressive precision. AI models can edit photos, remove unwanted objects, or even change the entire style of an image. This offers numerous applications, from photo restoration projects to product design and architectural visualization. For example, companies like In-Place AI use generative models to remove blemishes and artifacts from old photographs, breathing new life into cherished memories. A study by McKinsey & Company suggests that the global market for AI-powered image editing tools is projected to reach $8.4 billion by 2026, indicating the vast potential for this technology. 3. Accelerating Design Workflows and Fostering Creativity Generative AI can significantly streamline design workflows across various creative fields. Imagine a graphic designer using an AI model to generate multiple variations of a logo concept in seconds, or a fashion designer using AI to create realistic mockups of clothing on different models. These capabilities empower designers to explore more creative options and iterate rapidly, ultimately leading to faster development cycles. Furthermore, Generative AI can spark new ideas and inspire creative exploration. By generating unexpected image variations or suggesting novel design elements, AI models can act as a creative catalyst for designers and artists. A survey by Adobe reveals that 72% of creative professionals believe Generative AI will enhance their creativity and productivity, highlighting the transformative potential of this technology in design workflows. Generative AI: A New Era of Visual Storytelling Generative AI is revolutionizing the way we create and manipulate images. From birthing entirely new visuals to manipulating existing ones, this technology offers immense potential for various industries. As Generative AI continues to evolve, the question arises: how will we leverage this power to create a richer and more dynamic visual landscape? Stay tuned to TheGen.AI for the latest insights on Generative AI, its trends, startup stories, and tips to harness the power of image synthesis for your projects. Don't miss out on how Generative AI is redefining the future of visual communication!