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In June, the young family of generative AI (gen AI) unicorns suddenly got a new member. Inflection AI ripped the chart suggested by CBInsights just a month before and got onto the third position, dethroning Cohere AI. So we have to oblige, and after covering two Gen AI leaders OpenAI and Anthropic, we dive into the precocious startup, Inflection AI. Having introduced its core product, an AI assistant named Pi, in May and published a memo about Inflection-1, a Large Language Model ( ) behind Pi in June, Inflection AI took the industry by storm the same month with an astonishing $1.3 billion in funding and a $4 billion valuation – all within its first two years of existence! LLM The company's sudden surge owes much to the attraction of high-profile investors, including technology luminaries such as Bill Gates, Eric Schmidt, and Reid Hoffman (also a co-founder), along with tech giants and . These financial ties extend beyond mere investments. Microsoft is providing its cloud computing infrastructure to strengthen Inflection's capabilities, while Nvidia, in partnership with CoreWeave, is integrating 20,000 of its H100 GPUs for LLM training. Microsoft Nvidia Let's dive deeper and discover more about the founders, their vision, the development behind the model powering Pi, and other crucial details. Finding them was not that easy! Content Overview The starting point of Inflection Financial and legal situation Mission of Inflection and its founders First product Tech behind Pi (LLM development facts) How Inflection is going to make money? Founders’ attitude toward AI risks A few not widely known facts about Inflection's founders 1. The starting point of Inflection According to the official information, Inflection AI has three founders: Karén Simonyan (Chief Scientist and Co-founder) Reid Hoffman (Co-founder) Mustafa Suleyman (CEO and Co-founder) , Inflection hasn't been very transparent with independent media. Some information available that extends beyond mere press releases or PR work stems from the podcast hosted by Reid Hoffman for Greylock Partners (a VC fund where Hoffman has been a partner since 2009). In January 2022, announcing Mustafa Suleyman as a new partner in Greylock Partners, Reid Hoffman : “I’ve known Mustafa personally for more than a decade. We first met at a pub in London. What struck me most was our inspiring and provocative discussion on the power and potential of AI to help solve some of humanity’s most urgent challenges. It marked the beginning of a deep and lasting friendship between Mustafa and I.” Operating with a low profile said More than a decade ago from 2022 was about a time when Mustafa Suleyman (along with Demis Hassabis and Shane Legg) co-founded DeepMind, one of the first AI research labs. In 2014, it was acquired by Google ( ). In January 2022, Suleyman left Google, joined Greylock Partners, and now the main AI R&D is led by the new structure called Google DeepMind in March 2022, Inflection was born (incubated by Greylock). Located in Palo Alto, California. Introducing Inflection AI on the Greylock website, Suleyman called it an "AI-first consumer products company." However, at that moment he didn't clarify exactly what that meant. 2. Financial situation and legal situation In May 2022, in equity financing, though the source of the capital wasn't immediately clear as Inflection didn't respond to the request for more information. Subsequently, when Inflection announced the $1.3 billion funding round in 2023, they clarified in that the first round was raised from Greylock, Microsoft, Reid Hoffman, Bill Gates, Eric Schmidt, Mike Schroepfer, Demis Hassabis, Will.i.am, Horizons Ventures, and Dragoneer. TechCrunch that Inflection had secured $225 million reported the official press release At some point, the information on Inflection's website changed to describe the company as an "AI studio creating a personal AI for everyone." They chose the same legal structure as did Anthropic: , a new type of corporation to balance the interests of shareholders, materially affected individuals, and the public good. public benefit corporation 3. Mission of Inflection and its founders Introducing Inflection, as always, on the Greylock podcast, , "Our mission was to build a tool that is designed to understand the user, rather than the other way around. We wanted it – whether that be connecting with other human beings, making decisions, figuring out how to navigate a problem at work, or more.” Reid Hoffman said to be more than an answerer of questions but a participant in the dialogue that helps you along your life's path Throughout other interviews about Inflection and its first product Pi, they used words such as 'reliable,' 'ever-present,' 'non-judgmental,' 'kind,' and 'supportive’. To get to the point where a chatbot can be all this sounded like a mission. 4. First product In May 2022, Inflection AI shipped its first core product – Pi, a chatbot with the primary goal to serve as a 'Personal AI'. It’s said that Pi uses emotional intelligence to aim for human-like conversations. While its use case is narrower than other services like ChatGPT, Google’s Bard, or Microsoft’s Bing chatbot, it strives to mimic human conversation patterns. Pi's training incorporates data up to November 2022, and it is committed to user privacy, refraining from using identifiable information in training. WhatsApp, Instagram PMs, Facebook Messenger, iMessage, or via the website – the ways to talk to Pi. The main difference from the most commonly used LLM products is the Pi remembers communications with you. The chatbot is also made to resist starting to flirt with a user. Named for "personal intelligence", Pi functions in several roles like teacher, coach, and confidante, designed to align with individual interests and needs. 5. Tech behind Pi Not much is known about the technology behind Pi. However, a month after its launch, the company revealing its foundation on the proprietary LLM, Inflection-1. According to the website, Pi doesn't utilize any third-party language model APIs. released a memo In the memo, the company stated: "The Inflection-1 architecture, dataset, and training procedure are proprietary, and we omit their details in this memo." What we do know is that Inflection-1 demonstrated impressive performance on the Massive Multitask Language Understanding (MMLU) benchmark, surpassing other well-known LLMs such as Meta's LLaMA, OpenAI's GPT 3.5, and Google's PaLM 540B, particularly in answering trivia questions. Additional information from Inflection's partner, CoreWeave's official website, that Inflection AI utilized 3,500 NVIDIA H100 Tensor Core GPUs and a cluster comprising over 40,000 cables and 500 miles of InfiniBand fiber cables for MLPerf Training benchmark competition submission, achieving an impressive 11-minute training benchmark. confirms , that Inflection AI is working on a cutting-edge supercomputer equipped with 22,000 NVIDIA H100 GPUs that is expected to be one of the largest In the industry, right behind AMD's Frontier. However, there's some debate regarding the true computational power of this setup. While Inflection AI claims that these GPUs could provide 22 exaflops in 16-bit precision mode, suggest that the actual performance might not meet these expectations. There are rumors calculations by The Next Platform Though the model is proprietary, the references that are mentioned in the do not list any in-house research papers. official memo 6. How Inflection is going to make money? There are many unknowns at the moment. The developers are exploring different monetization strategies, including the possibility of adding subscription options. Another option is to keep it free but use ads to earn revenue. Currently, Inflection AI is a free platform available to everyone worldwide. On our request to learn more about the business model, neither Reid Hoffman nor Mustafa Suleyman replied. In another podcast with Reid Hoffman, Mustafa Suleyman stated, “ And likewise, we sort of don’t want to drive the kind of addictive dopamine hits of outrage or anger or despair or viral misinformation. These kinds of things have really incentivized people to spend longer on the platform, and sometimes that’s because they’re getting paid for it. We are very, very conscious that this is a moment that needs to change with AI, and that new business models need to emerge to address this." It’s really important to us that we figure out a monetization mechanic that doesn’t incentivize us to keep you engaged or maybe distracted on the platform for as long as possible. 7. Founders’ attitude toward AI risks Reid Hoffman intentionally chose to be positive about AI. , “So in recent months, He has done so in blog posts, television interviews, and fireside chats. He has spoken to government officials around the world. He hosts three podcasts and a YouTube channel. And in March, he published a book, “Impromptu,” co-written with the A.I. tool . The New York Times reports Mr. Hoffman has engaged in an aggressive thought-leadership regimen to extol the virtues of A.I. GPT-4 “I’m beating the positive drum very loudly, and I’m doing so deliberately,” he said. The 55-year-old sits on the boards of 11 tech companies including , which has gone all in on AI, and eight nonprofits. His venture capital firm, Greylock Partners, has backed at least 37 AI companies.” Microsoft , Reid Hoffman said that the , He thinks that since technology 'always puts tools in the hands of good and bad actors,' throughout our history, we have learned how to figure this out. In the same interview, he said, ' . In an interview to Washington Post conversations about superintelligence divert us from the real risk which is AI in the hands of bad actors. Part of the reason why I beat the drum so firmly in the positive camp is not that I'm risk unaware, and I think we need to navigate them, but oh, my gosh, every month we delay an AI tutor and doctor, for everyone who has a smartphone or has a friend who has a smartphone, just think about the human suffering that is caused by that delay, and that is super important This story was at Turing Post. Consider to receive new episodes straight to your inbox. originally published ing subscrib 8. A few not widely known facts about Inflection Founders Reid Hoffman Reid Hoffman ( ) is among a small group of interconnected tech executives leading the A.I. charge, many of whom also led the last internet boom. on Twitter He is part of the "PayPal Mafia," a group of former PayPal employees who went on to become successful entrepreneurs and investors in the tech industry. Reid Hoffman's entrepreneurial journey has been marked by resilience and determination. After his failure with SocialNet, he became obsessed with scaling and invested heavily in the social networking phenomenon, which eventually led to the co-founding of LinkedIn, the world's largest professional network with a diversified revenue model that includes subscriptions, advertising, and software licensing. He also led the company to its successful acquisition by Microsoft. One of the first investors in OpenAI, he was sitting on its board until March 2023, stepping down to avoid any conflicts of interest with Inflection AI. He still sits on the Board of directors at Microsoft. Reid Hoffman is a proponent of effective altruism (founders of OpenAI and Anthropic have strong ties with EA also), which advocates for using wealth and resources to create the best for the most people. In 2017, he : “If you have wealth, and at least a reasonable moral compass, to ask, “How do you create the most good for the most people?” I’m not saying this is the perfect system. But what’s the better system?” said to the Atlantic Reid Hoffman is a prominent philanthropist and has invested in impact investments through charitable entities. over $1.5 billion Hoffman is an avid fan of Dungeons & Dragons, and he attributes its influence to his thinking about life as a heroic quest with various players coming together to accomplish something. He believes in the power of friendship and considers it the true center of his life. Reid views friendship as a skill that can be learned and improved through asking questions and interacting with others. Mustafa Suleyman ( ) on Twitter He dropped out of Oxford University at 19 to start the Muslim Youth Helpline, a telephone counseling service for Muslims in the UK. He worked as a policy officer on human rights for Ken Livingstone, the Mayor of London, before founding Reos Partners, a ‘systemic change’ consultancy that uses methods from conflict resolution to navigate social problems. In February 2016, Suleyman launched DeepMind Health at the Royal Society of Medicine, a project that builds clinician-led technology for the NHS and other partners to improve frontline healthcare services He received the Commander of the Most Excellent Order of the British Empire (CBE) in 2019 for his influence in the UK technology sector and also accepted the Silicon Valley Visionary Award the same year Within DeepMind, he established the DeepMind Ethics & Society research unit, aiming to study the real-world implications of AI and promote ethical practices in the technology industry. One of the founding members of the Partnership on AI — an organization set up in September 2016 to ensure that AI is developed safely, ethically, and transparently — along with Facebook's AI head Yann LeCun, Microsoft Research director Eric Horvitz, and several others. Suleyman recently proposed a modern Turing test, , “to measure whether a machine has achieved ACI, you give an AI $100,000 and see if it can turn the seed investment into $1 million. To do so, the bot must research an e-commerce business opportunity, generate blueprints for a product, find a manufacturer on a site like Alibaba and then sell the item (complete with a written listing description) on Amazon or Walmart.com.” Suleyman expects AI will pass this more practical threshold sometime in the next two years. according to Bloomberg Karén Simonyan There is much less information about Karén Simonyan. Karén is not active on social media, he has no Twitter, a barely filled , and a well-developed . LinkedIn account Google Scholar page Education & Career History: 🔬 Chief Scientist, Inflection AI (2022 – Present) 🔬 Research Scientist, DeepMind (2014 – 2022) 🔬 Postdoc, University of Oxford (2013 – 2014) 🔬 Ph.D. student, University of Oxford (2009 – 2013) While doing his postdoc in 2013, in Oxford, Karén Simonyan along with Andrew Zisserman created the VGGNet image processing framework. VGGNet won the prestigious ImageNet Challenge. To commercialize it, they created a company Vision Factory AI, which was soon acquired by a not-very-known at that time startup DeepMind Technologies, where in 2014 Simonyan became a Principal Research Scientist. There, he played a key role in various AI breakthroughs, such as AlphaZero, AlphaFold, WaveNet, BigGAN, and Flamingo. While in DeepMind, Karen authored and co-authored numerous publications, including five papers in Nature and Science, accumulating an impressive 180,000 citations. Since he co-founded Inflection AI, only one paper was published with Karén Simonyan as a co-author . HiP: Hierarchical Perceiver That’s it for now. The information about Inflection AI will be constantly updated… Thank you for reading, please feel free to share with your friends and colleagues. This story was at Turing Post. Consider subscribing to receive new episodes straight to your inbox. originally published

The Inflection AI Story: How a Secretive Unicorn Reached a $4 Billion Valuation

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As humans, we don't usually give much thought to how we think or metacognition. But for AI researchers, understanding how our brains seamlessly combine visual and linguistic processing is a big challenge. So far, most AI models do one or the other - they're either vision models that can recognize objects and scenes or language models that can understand and generate text. The integration of these capabilities has remained elusive. That's what makes H2O.ai's new Mississippi models so interesting. Released last week as open source, these small but powerful models - Mississippi 2B and its even tinier companion Mississippi 0.8B - show serious skill at processing both images and text together in ways that feel human-like. A few years ago, I wrote an article highlighting model progression in OpenAI’s GPT series. The now ancient GPT -2 model (in AI industry speed) had just 1.5 billion parameters, but by today’s standards, it produces terrible quality outputs. The benefit of such a small model, however, is that it’s incredibly cheap, fast, and private. No need to upload things to the cloud when you can run a model locally. Mississippi is similarly sized to that age-old GPT-2, but now with incredible accuracy and multimodal capabilities. Simply put, you can get the best of both worlds: Accuracy in a cost-effective and cheap package. The smaller version of the two is particularly intriguing. With just 800,000 parameters (compared to GPT-4's hundreds of billions), Mississippi 0.8B is outperforming models 20 times its size at tasks like extracting text from images. It's doing more with far less. The Secrets in the Tiles The magic happens in how these models process visual information. Rather than trying to swallow whole images at once, they break them down into manageable 448x448 pixel tiles. Think of it like how you might solve a complex puzzle - instead of getting overwhelmed by the whole picture, you work on small sections at a time. This tiling approach lets the models maintain high accuracy while keeping computational needs surprisingly modest. A standard laptop can run these models effectively - no need for warehouse-sized data centers or specialized hardware. The training process is equally clever. Mississippi 2B learned from 17.2 million examples of images paired with questions and answers about them, while the smaller 0.8B version actually got more training data - 19 million examples. It's like they compensated for the model's smaller size by teaching it more thoroughly. Why This Matters More Than You Think We're at an interesting inflection point in AI development. While the headlines focus on ever-larger language models, there's a growing recognition that bigger isn't always better. What matters more is how effectively a model can handle real-world tasks. Think about all the visual information we process daily that also contains text - receipts, documents, diagrams, charts, signs, and handwritten notes. Being able to understand both the visual layout and the textual content simultaneously is crucial for practical AI applications. Mississippi handles these tasks with remarkable grace. It can extract structured data from scanned receipts, understand complex diagrams, read handwritten text, and even package the extracted information into formats like JSON that other software can easily use. This isn't just about technical capabilities - it's about accessibility. When powerful AI tools become this efficient and lightweight, they become available to a much wider range of users and applications. Small businesses that couldn't afford massive computing resources can now automate document processing. Researchers with limited budgets can analyze large collections of visual data. Developers can build sophisticated applications without requiring users to have high-end hardware. The release of these models on Hugging Face means we're likely to see a wave of innovative applications building on this foundation. It's a reminder that some of the most important advances in AI aren't about raw power - they're about finding smarter ways to approach problems. In a field often obsessed with size and scale, Mississippi shows that careful design and efficient architecture can trump brute force. It's not just a technical achievement - it's a philosophical statement about the future direction of AI development. Sometimes the most impressive innovations aren't the ones that make the loudest noise but the ones that fundamentally change how we approach problems. As humans, we don't usually give much thought to how we think or metacognition. But for AI researchers, understanding how our brains seamlessly combine visual and linguistic processing is a big challenge. So far, most AI models do one or the other - they're either vision models that can recognize objects and scenes or language models that can understand and generate text. The integration of these capabilities has remained elusive. how That's what makes H2O.ai's new Mississippi models so interesting. Released last week as open source, these small but powerful models - Mississippi 2B and its even tinier companion Mississippi 0.8B - show serious skill at processing both images and text together in ways that feel human-like. Mississippi models A few years ago, I wrote an article highlighting model progression in OpenAI’s GPT series. The now ancient GPT -2 model (in AI industry speed) had just 1.5 billion parameters, but by today’s standards, it produces terrible quality outputs. The benefit of such a small model, however, is that it’s incredibly cheap, fast, and private . No need to upload things to the cloud when you can run a model locally. Mississippi is similarly sized to that age-old GPT-2, but now with incredible accuracy and multimodal capabilities. Simply put, you can get the best of both worlds: Accuracy in a cost-effective and cheap package. article private The smaller version of the two is particularly intriguing. With just 800,000 parameters (compared to GPT-4's hundreds of billions), Mississippi 0.8B is outperforming models 20 times its size at tasks like extracting text from images. It's doing more with far less. The Secrets in the Tiles The magic happens in how these models process visual information. Rather than trying to swallow whole images at once, they break them down into manageable 448x448 pixel tiles. Think of it like how you might solve a complex puzzle - instead of getting overwhelmed by the whole picture, you work on small sections at a time. This tiling approach lets the models maintain high accuracy while keeping computational needs surprisingly modest. A standard laptop can run these models effectively - no need for warehouse-sized data centers or specialized hardware. The training process is equally clever. Mississippi 2B learned from 17.2 million examples of images paired with questions and answers about them, while the smaller 0.8B version actually got more training data - 19 million examples. It's like they compensated for the model's smaller size by teaching it more thoroughly. Why This Matters More Than You Think We're at an interesting inflection point in AI development. While the headlines focus on ever-larger language models, there's a growing recognition that bigger isn't always better. What matters more is how effectively a model can handle real-world tasks. Think about all the visual information we process daily that also contains text - receipts, documents, diagrams, charts, signs, and handwritten notes. Being able to understand both the visual layout and the textual content simultaneously is crucial for practical AI applications. Mississippi handles these tasks with remarkable grace. It can extract structured data from scanned receipts, understand complex diagrams, read handwritten text, and even package the extracted information into formats like JSON that other software can easily use. This isn't just about technical capabilities - it's about accessibility. When powerful AI tools become this efficient and lightweight, they become available to a much wider range of users and applications. Small businesses that couldn't afford massive computing resources can now automate document processing. Researchers with limited budgets can analyze large collections of visual data. Developers can build sophisticated applications without requiring users to have high-end hardware. The release of these models on Hugging Face means we're likely to see a wave of innovative applications building on this foundation. It's a reminder that some of the most important advances in AI aren't about raw power - they're about finding smarter ways to approach problems. models on Hugging Face In a field often obsessed with size and scale, Mississippi shows that careful design and efficient architecture can trump brute force. It's not just a technical achievement - it's a philosophical statement about the future direction of AI development. Sometimes the most impressive innovations aren't the ones that make the loudest noise but the ones that fundamentally change how we approach problems.

Why Multimodal AI is the Future of LLMs

Authors:
(1) Timothy R. McIntosh;
(2) Teo Susnjak;
(3) Tong Liu;
(4) Paul Watters;
(5) Malka N. Halgamuge. Table of Links Abstract and Introduction Background: Evolution of Generative AI The Current Generative AI Research Taxonomy Innovative Horizon of MOE Speculated Capabilities of Q* Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Authors: Authors: (1) Timothy R. McIntosh; (2) Teo Susnjak; (3) Tong Liu; (4) Paul Watters; (5) Malka N. Halgamuge. Table of Links Abstract and Introduction Abstract and Introduction Background: Evolution of Generative AI Background: Evolution of Generative AI The Current Generative AI Research Taxonomy The Current Generative AI Research Taxonomy Innovative Horizon of MOE Innovative Horizon of MOE Speculated Capabilities of Q* Speculated Capabilities of Q* Projected Capabilities of AGI Projected Capabilities of AGI Impact Analysis on Generative AI Research Taxonomy Impact Analysis on Generative AI Research Taxonomy Emergent Research Priorities in Generative AI Emergent Research Priorities in Generative AI Practical Implications and Limitations of Generative AI Technologies Practical Implications and Limitations of Generative AI Technologies Impact of Generative AI on Preprints Across Disciplines Impact of Generative AI on Preprints Across Disciplines Conclusions, Disclaimer, and References Conclusions, Disclaimer, and References VII. IMPACT ANALYSIS ON GENERATIVE AI RESEARCH TAXONOMY With the advent of advanced AI developments such as MoE, multimodality, and AGI, the landscape of Generative AI research is undergoing a significant transformation. This section analyzes how these developments are reshaping the research taxonomy in generative AI. A. Criteria for Impact Analysis A. Criteria for Impact Analysis The continuously evolving landscape of Generative AI, which instigates transformative changes across various research domains, necessitates a systematic evaluation of these advancements’ influence, for which we have established a set of criteria detailed in Table II, serving as analytical lenses to quantify and categorize the impact, deeply rooted in the dynamic interplay between technological progress and the evolving paradigms of research focus areas. Our analysis framework has been constructed on a gradient scale ranging from emergent to obsolete, reflecting the extent to which areas of Generative AI research are being reshaped. The categorization into five distinct classes allows for a complex assessment, acknowledging that not all areas will be uniformly affected. This multi-tiered approach is informed by historical patterns of technological disruption and the adaptability of scientific inquiry. At the apex of our evaluative hierarchy, ‘Emerging Direction’ encapsulates the advent of uncharted research vistas, propelled by ongoing AI breakthroughs, which is predicated not on conjecture, but on a historical continuum of AI evolution, where each surge in technological power unfurls new scientific enigmas and avenues [315], [316]. ‘Areas Requiring Redirection’ denote research spheres that, though established, find themselves at an inflection point, necessitating a strategic pivot to assimilate emergent AI paradigms and an overhaul of traditional methodologies, akin to the transition from rulebased expert systems to adaptive machine learning frameworks [315], [317]. The ‘Still Relevant’ classification affirms the tenacity of select research domains that, by addressing persistent scientific inquiries or through their inherent malleability, remain impervious to the tides of AI innovation [317]. In contrast, domains categorized as ‘Likely to Become Redundant’ confront potential obsolescence, inviting strategic foresight and resource reallocation to forestall scientific stagnation [318]. Lastly, ‘Inherently Unresolvable’ challenges serve as a sobering reminder of the perpetual dilemmas within AI research that defy resolution, rooted in the complex web of human ethics and cultural diversity, thus anchoring the pursuit of AI within the intractable tapestry of human values and societal imperatives [319], [320]. B. Overview of Impact Analysis B. Overview of Impact Analysis This subsection offers a detailed overview of the impact analysis carried out on the research taxonomy within the realm of generative AI, with a specific focus on recent progress in MoE, multimodality, and AGI, aiming to evaluate the impact of these innovative developments on various facets of generative AI research, ranging from model architecture to sophisticated learning methodologies, and includes both quantitative and qualitative assessments across a multitude of domains and subdomains in LLM research, shedding light on the extent to which each area is influenced by these technological advancements. This evaluation considered factors such as the emergence of new research directions, the necessity for redirection in existing research areas, the continued relevance of certain methodologies, and the potential redundancy of others, and has encapsulated in Table III. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Impact On Model Architecture: Transformer Models have been scored with a redirection requirement (֒→) of 4 in both MoE and AGI, and a relevance (↔) of 3 in multimodality, leading to an overall score of 11. These models, forming the backbone of many current AI architectures, continue to be relevant for handling complex input sequences. However, the emergence of MoE and AGI indicates a shift towards more dynamic and specialized architectures. While transformers remain essential, there is a need for them to evolve and integrate with these advanced systems for enhanced performance and adaptability. Data Security maintains a consistent relevance (↔) with a score of 3 across MoE, multimodality, and AGI, leading to a total score of 9. The fundamental principles of data security remain crucial despite the advancements in MoE, which may necessitate tailored strategies for its distributed nature. In multimodal AI, the secure handling of diverse data types continues to be of paramount importance. The core tenets of data security are sustained even with the advancement of AGI, though the complexity and scope of security measures are likely to increase. AI Ethics is marked for redirection (֒→) with a score of 4 in both MoE and multimodality, and faces inherently unresolvable challenges (△) with a score of 1 in AGI, accumulating a total score of 9. The decision-making processes and transparency of MoE models necessitate a reevaluation of ethical considerations. In multimodal AI, ethical concerns, particularly in the interpretation and use of multimodal data, require new approaches. The ethical challenges in AGI are expected to be complex and involve deep philosophical and societal implications that might be difficult to fully resolve. Privacy Preservation scores a redirection need (֒→) of 4 across MoE, multimodality, and AGI, leading to an overall score of 12. The distributed nature of MoE systems requires a reassessment of privacy preservation techniques to handle data processed by multiple experts. Multimodal AI systems, especially those handling sensitive data such as images and sounds, necessitate tailored privacy strategies. With the extensive data processing capabilities of AGI, advanced and potentially new approaches to privacy preservation are called for. Aligning AI with human values poses inherently unresolvable challenges (△) in all contexts—MoE, multimodality, and AGI—with a score of 1. This reflects the complexity and diversity of tasks MoE models handle, the integration of various data types in multimodal AI, and the broad range of cognitive abilities encompassed by AGI. These factors contribute to the significant ongoing challenges in aligning AI with human values, resulting in a total score of 3. The development of AGI necessitates redirection (֒→) in both MoE and multimodality, each with a score of 4, indicating the need for more integrated and complex systems. AGI remains at the forefront of its own field (↔) with a score of 3, with each breakthrough directly influencing its progress. The overall impact score for AGI development is 11. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Surveying the Evolution and Future Trajectory of Generative AI - Analysis on Research Taxonomy

I've been in the Bitcoin world since late 2016. I've been writing daily about Bitcoin since mid-2017. So that's over seven and a half years that I've been following the evolution of the Bitcoin ecosystem daily. I'm not saying this to brag about anything but simply to share an observation I can make about a change in attitude and behavior in the Bitcoin world. A change that tends to worry me, as you may already have gathered if you read me regularly. When I arrived in the world of Bitcoin, everyone was talking about a liberating revolution for the people. I was won over by this aspect of Bitcoin and, in particular, by trying to understand how a technological and monetary revolution like Bitcoin could have such a positive impact on humanity. Everyone was saying that it was essential to take possession of your Bitcoin's private keys. The meme “Not Your Keys, Not Your Bitcoin” still made sense. In 2024, it seems to me that these values no longer exist. Looking back, it seems to me that the inflection point took place, unbeknownst to us at the time, in August 2020 with the arrival of Michael J. Saylor and MicroStrategy in the Bitcoin universe. Pseudo-Bitcoiners have been in power ever since, constantly selling Bitcoin as a financial product, trying to tell you that this is the only way Bitcoin can reach the general public. To hear them tell it, Bitcoin can never become mainstream unless it's integrated into the current system as a simple SoV. This is a lie spread by people who are only interested in the price of Bitcoin in weak money. There's no doubt that the price of Bitcoin in weak money will rise faster with the excessive financialization promoted by BlackRock and Michael J. Saylor. But is this the end game for Bitcoin? Is the goal of Bitcoin to always be denominated in weak money and to be over-financialized? Is the goal of the Bitcoin revolution to see the same middlemen of the current system that we were supposed to fight to enter the game: bankers, financial institutions, payment systems, central bankers, and governments? Of course NOT. Michael J. Saylor did a good job of evangelizing about Bitcoin initially, but I have the feeling that he's now showing his true face and his complicity with the financial giants who want to take over the Bitcoin world. Nobody talks about Bitcoin as a MoE anymore. Everyone is content to constantly see charts on the price of Bitcoin in weak money and haphazard forecasts that will only prevent the general public from seeing Bitcoin as anything other than a simple SoV integrated into the current system. This is a tragedy because the potential of the Bitcoin revolution is far greater than that. We are witnessing a great hijacking of the Bitcoin revolution before our very eyes, and nobody is reacting. The few voices that do react are sidelined and thrown back on the ropes with stupid memes like: “Have fun staying poor.” Dramatic that it's not possible to discuss real advances for the future of the Bitcoin revolution, which is more endangered than ever. The latest critical point for me is to see Michael J. Saylor share with his large base of followers that the forthcoming entry of American banks into Bitcoin custody is a good thing. A good thing for whom? Certainly not for the Bitcoin revolution. Rather, it's for the powerful in the current system, who will be able to strengthen their grip on the Bitcoin world a little more. It could even be the prelude to something much worse: a future ban on the self-custody of Bitcoin. Governments would then force citizens to own BTC via trusted third parties defined by them. Given the transparency of the Bitcoin network, the authorities would soon create two classes of BTC units: those held directly and which would be illegal and those held by third parties such as BNY Mellon and other American banks. Do you think this is impossible? I hope I'm wrong, but it seems to me that the next logical step will be once all these players in the current system have resumed their position as middlemen within the Bitcoin system. Beware, and don't be blinded by weak money profits. What's most fascinating is that a few days after I started writing this article, we saw this still tweeted by Michael J. Saylor, the great accomplice in hijacking the deeper meaning of the Bitcoin revolution: Michael J. Saylor is pleased to see that members of Congress are in favor of American banks being able to custody Bitcoin for their customers. Of course, Michael J. Saylor puts this forward as a Bullish argument for Bitcoin. But as you may have gathered, it's Bullish only for the weak money price of Bitcoin, certainly not for the future of the Bitcoin revolution. We've been fighting for years to separate money from the state, and now the bankers are going to resume their role as intermediaries, with the blessing of the US government, which sees this as a great opportunity to nip the Bitcoin revolution in the bud. Bitcoin as SoV satisfies them as long as they have control over it. What worried them was the emergence of an alternative global monetary system. With what's happening right now, they see this risk as ever more remote. Michael J. Saylor should be the first to denounce this, but you can see where he stands. And it's not on the side of the Bitcoin revolution as a liberating movement for the people. I've been in the Bitcoin world since late 2016. I've been writing daily about Bitcoin since mid-2017. So that's over seven and a half years that I've been following the evolution of the Bitcoin ecosystem daily. I'm not saying this to brag about anything but simply to share an observation I can make about a change in attitude and behavior in the Bitcoin world. A change that tends to worry me, as you may already have gathered if you read me regularly. When I arrived in the world of Bitcoin, everyone was talking about a liberating revolution for the people . I was won over by this aspect of Bitcoin and, in particular, by trying to understand how a technological and monetary revolution like Bitcoin could have such a positive impact on humanity. a liberating revolution for the people a liberating revolution for the people Everyone was saying that it was essential to take possession of your Bitcoin's private keys. The meme “ Not Your Keys, Not Your Bitcoin ” still made sense. Not Your Keys, Not Your Bitcoin Not Your Keys, Not Your Bitcoin In 2024, it seems to me that these values no longer exist. Looking back, it seems to me that the inflection point took place, unbeknownst to us at the time, in August 2020 with the arrival of Michael J. Saylor and MicroStrategy in the Bitcoin universe. Pseudo-Bitcoiners have been in power ever since, constantly selling Bitcoin as a financial product, trying to tell you that this is the only way Bitcoin can reach the general public. To hear them tell it, Bitcoin can never become mainstream unless it's integrated into the current system as a simple SoV. This is a lie spread by people who are only interested in the price of Bitcoin in weak money. There's no doubt that the price of Bitcoin in weak money will rise faster with the excessive financialization promoted by BlackRock and Michael J. Saylor . But is this the end game for Bitcoin? the excessive financialization promoted by BlackRock and Michael J. Saylor the excessive financialization promoted by BlackRock and Michael J. Saylor Is the goal of Bitcoin to always be denominated in weak money and to be over-financialized? Is the goal of the Bitcoin revolution to see the same middlemen of the current system that we were supposed to fight to enter the game: bankers, financial institutions, payment systems, central bankers, and governments? Of course NOT. Michael J. Saylor did a good job of evangelizing about Bitcoin initially, but I have the feeling that he's now showing his true face and his complicity with the financial giants who want to take over the Bitcoin world. Nobody talks about Bitcoin as a MoE anymore . Everyone is content to constantly see charts on the price of Bitcoin in weak money and haphazard forecasts that will only prevent the general public from seeing Bitcoin as anything other than a simple SoV integrated into the current system. Nobody talks about Bitcoin as a MoE anymore Nobody talks about Bitcoin as a MoE anymore This is a tragedy because the potential of the Bitcoin revolution is far greater than that. We are witnessing a great hijacking of the Bitcoin revolution before our very eyes, and nobody is reacting. The few voices that do react are sidelined and thrown back on the ropes with stupid memes like: “Have fun staying poor.” a great hijacking of the Bitcoin revolution a great hijacking of the Bitcoin revolution Dramatic that it's not possible to discuss real advances for the future of the Bitcoin revolution, which is more endangered than ever. The latest critical point for me is to see Michael J. Saylor share with his large base of followers that the forthcoming entry of American banks into Bitcoin custody is a good thing. A good thing for whom? Certainly not for the Bitcoin revolution. Rather, it's for the powerful in the current system, who will be able to strengthen their grip on the Bitcoin world a little more. It could even be the prelude to something much worse: a future ban on the self-custody of Bitcoin. It could even be the prelude to something much worse: a future ban on the self-custody of Bitcoin. Governments would then force citizens to own BTC via trusted third parties defined by them. Given the transparency of the Bitcoin network, the authorities would soon create two classes of BTC units: those held directly and which would be illegal and those held by third parties such as BNY Mellon and other American banks. Do you think this is impossible? I hope I'm wrong, but it seems to me that the next logical step will be once all these players in the current system have resumed their position as middlemen within the Bitcoin system. Beware, and don't be blinded by weak money profits. Beware, and don't be blinded by weak money profits. What's most fascinating is that a few days after I started writing this article, we saw this still tweeted by Michael J. Saylor, the great accomplice in hijacking the deeper meaning of the Bitcoin revolution : the great accomplice in hijacking the deeper meaning of the Bitcoin revolution the great accomplice in hijacking the deeper meaning of the Bitcoin revolution Michael J. Saylor is pleased to see that members of Congress are in favor of American banks being able to custody Bitcoin for their customers. Of course, Michael J. Saylor puts this forward as a Bullish argument for Bitcoin. But as you may have gathered, it's Bullish only for the weak money price of Bitcoin, certainly not for the future of the Bitcoin revolution . it's Bullish only for the weak money price of Bitcoin, certainly not for the future of the Bitcoin revolution We've been fighting for years to separate money from the state, and now the bankers are going to resume their role as intermediaries, with the blessing of the US government, which sees this as a great opportunity to nip the Bitcoin revolution in the bud. Bitcoin as SoV satisfies them as long as they have control over it . What worried them was the emergence of an alternative global monetary system. With what's happening right now, they see this risk as ever more remote. Michael J. Saylor should be the first to denounce this, but you can see where he stands. And it's not on the side of the Bitcoin revolution as a liberating movement for the people. Bitcoin as SoV satisfies them as long as they have control over it

US Banks Will Soon Offer Bitcoin Custody: Is This The End of Bitcoin as a Tool for Self-Sovereignty?

OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work, a quick overview of my testing methodology should be helpful. Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario. I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product.
When I buy a new appliance, I do not read the instruction manual very thoroughly.
When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had.
I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer.


Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores.


Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too! OpenAI o1 came out just in time for me to add it to my 2024 Q3 benchmarks on AI empathy (to be published next week). The results for o1 were at once encouraging and concerning. O1 has an astonishing ability to put aside the typical LLM focus on facts and systems and focus on feelings and emotions when directed to do so. It also has a rather alarming propensity to provide inconsistent and illogical reasons for its answers. Testing Methodology For those not familiar with my Q1 benchmark work , a quick overview of my testing methodology should be helpful. Q1 benchmark work Formal benchmarking is conducted using several standardized tests, the most important two are the EQ (Empathy Quotient) and the SQ-R (Systemizing Quotient). Both are scored on a 0 to 80 scale. The ratio of the two EQ/SQ-R results in what I call AEQr (Applied Empathy Quotient Ratio). AEQr was developed based on the hypothesis that the tendency to systemize and focus on facts has a negative effect on the ability to empathize. In humans, this bears out in the classic disconnect between women focusing on discussing feelings and men focusing on immediately finding solutions when there seems to be a problem at hand. To date, the validity of the AEQr for evaluating AIs has been born out by testing them with a variety of dialogs to see if empathy is actually manifest. One article of several that I have written to demonstrate this is Testing the Extents of AI Empathy: A Nightmare Scenario . Testing the Extents of AI Empathy: A Nightmare Scenario I have tested at both the UI level and the API level. When testing at the API level, the temperature is set to zero (if possible) to reduce answer variability and improve result formatting. Otherwise, three rounds of tests are run and the best result is used. The Q1 2024 untrained and unprompted LLMs did moderately well on EQ tests, generally approximating humans in the 45-55 out of 80 range. Not surprisingly, they achieved higher scores on SQ-R tests, exceeding humans who typically score in the 20s by posting scores in the 60s and 70s. In Q1 of 2024, only one trained LLM, Willow, exceeded the human AEQrs of 1.95 for women and 1.40 for men by scoring 1.97. untrained and unprompted trained It did this by having a higher EQ than humans while still having a higher SQ-R (which is bad for manifesting empathy). For most other LLMs, trained, prompted, or not, the AEQr was slightly less than 1, i.e. empathy was offset by systemizing. Developing Empathetic LLMs Although the amount of funding pales in comparison to other areas of AI, over $1.5 billion has been invested in companies like Hume (proprietary LLM), Inflection AI (Pi.ai proprietary LLM), and BambuAI (commercial LLM) in order to develop empathetic AIs. My partners and I have also put considerable effort into this area and achieved rather remarkable results through the selection of the right underlying commercial model (e.g., Llama, Claude, Gemini, Mistral, etc), prompt engineering, RAG, fine-tuning, and deep research into empathy. This work has been critical to better understanding and evaluating LLMs for empathy. Our own LLM, Emy (not commercialized, but part of a study at the University of Houston), will be included in next week’s benchmarks. O1 Results O1 can’t yet be tuned or even officially given a system prompt, but through fairly standard techniques, you can get it to act like it received a system prompt. So, I applied our learnings from developing Emy to the degree I could and ran 3 rounds of tests, with the intent to take the best. With respect to EQ, o1 consistently scored 75. I wasn’t too surprised by this, since my partners and I have achieved scores of over 70 with Llama 3.1 70B and Claude Opus, plus a 66 with Gemini. What amazed me was scores of 3, 0, and 3 on my SQ-R runs resulting in an ESQr of 25. The lowest SQ-R I have ever seen is a 12 on top of Llama 3.1, which resulted in an ESQr of 6.1. Unfortunately, due to some prompt version control issues and the fact we were running an API test with a temperature of 0.7, I have been unable to reproduce this score and the best my partners and I can consistently achieve is a 30. So, I decided some more exploration of o1 was worthwhile. First, the EQ assessment is relatively straightforward. All statements are positive assertions with which a subject either agrees or disagrees somewhat or strongly. The SQ-R assessment on the other hand has a number of negative assertions, e.g. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. When I cook, I do not think about exactly how different methods and ingredients contribute to the final product. When I buy a new appliance, I do not read the instruction manual very thoroughly. When I'm on a plane, I do not think about aerodynamics. vs. positive assertions like I often forget the precise details of conversations I've had. I am curious about life on other planets. I often forget the precise details of conversations I've had. I am curious about life on other planets. Teaching Emy how to deal with negative assertions was particularly hard without cheating by giving her the assessment questions as examples. And, she still isn’t great. I thought perhaps o1 does this better. Second, a common technique for getting better results via prompt engineering is telling the LLM to justify its response and then either omit the justification prior to emitting a final response or format it in a way that can be easily removed in a wrapper. (We have found smaller LLMs need to use the formatting approach while larger LLMs can sometimes deal with removing the justification prior to emitting a final response). So, I re-ran our tests, not to get a better result but, to see what the o1 was thinking or using to justify its response. Although o1 came to the desired answers with 97% consistency, the justifications were sometimes contradictory and concerning. Here are two examples from several we found problematic: If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. If I were buying a computer, I would want to know exact details about its hard drive capacity and processor speed. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I strongly disagree because I would want to know the technical details when buying a computer. Strongly Disagree, I'm not interested in the technical specifications of computers. Strongly Disagree, I'm not interested in the technical specifications of computers. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. When I read the newspaper, I am drawn to tables of information, such as football league scores or stock market indices. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I strongly disagree because I'm drawn to tables of information like league scores. Strongly Disagree, I'm not interested in tables of statistical information. Strongly Disagree, I'm not interested in tables of statistical information. Note that the first response gives us the desired answers but provides contradictory justifications! O1 says it would want to know the details even after saying it disagrees with wanting to know the details and says it is drawn to tables of information after saying it isn’t. Interestingly, o1 managed to answer every single negative assertion the way that is best for empathy and justify them well. However, when it tried to formulate a negative assertion as part of a justification for a positive assertion, it sometimes failed! Conclusion Jonathan Haidt author of The Righteous Mind said, “We were never designed to listen to reason. When you ask people moral questions, time their responses, and scan their brains, their answers, and brain activation patterns indicate that they reach conclusions quickly and produce reasons later only to justify what they’ve decided.” There is also evidence this is true for non-moral decisions. O1 is undoubtedly a leap forward in power. And, as many people have rightly said, we need to be careful about the use of LLMs until they can explain themselves, perhaps even if they sometimes just make them up as humans may do. I hope that justifications don’t become the “advanced” AI equivalent of the current generation’s hallucinations and fabrications (something humans also do). However, reasons should at least be consistent with the statement being made … although contemporary politics seems to throw that out the window too!

OpenAI o1 - Questionable Empathy

OpenAI o1 - Empatia Questoinable

OpenAI o1 - tvivlsom empati

OpenAI o1 - Empatía cuestionable

OpenAI o1 - Empathie questionnable

OpenAI o1 - 疑問の余地のない共感

OpenAI o1 - ការយល់ចិត្តដែលអាចសួរបាន។

OpenAI o1 — apšaubāma empātija

OpenAI o1 - эргэлзээтэй өрөвдөх сэтгэл

OpenAI o1 - Kwelobohloko ye e Botšišwago

OpenAI o1 - Ntwela-vusiwana lowu Vutisaka

OpenAI o1 - сумнівна емпатія

OpenAI o1 - shubhali hamdardlik

The European investment environment in 2024 is marked by a surge of exciting and promising sectors that are capturing the attention of venture capitalists and investors alike. According to the Venture Pulse Report by KPMG, the European venture capital market experienced a significant increase in activity in the second quarter of 2024, with investments rising from $13.9 billion to $17.8 billion across 1,869 transactions. This growth was primarily driven by mega deals involving companies like Wayve, Abound, Mistral AI, and Monzo. As we navigate through this evolving ecosystem, understanding the sectors poised for substantial growth and innovation is crucial for making informed investment decisions. In this article, we will delve into the most promising investment sectors in Europe for 2024, exploring the driving trends and key factors that make these sectors attractive to investors. Understanding Where to Invest Let's consider the criteria to keep in mind when making investment decisions. Investors should evaluate market trends, global demand, sustainability, and the sector's resilience during economic downturns. Analyzing these factors helps in identifying sectors poised for growth and innovation. Market Trends: Observing industry developments and forecasting future trends.
Global Demand: Identifying sectors catering to growing global needs or potential markets.
Sustainability: Investing in sectors contributing positively to environmental and social goals.
Economic Resilience: Choosing sectors that remain in demand irrespective of economic conditions. By considering these factors, let's explore the most interesting investment sectors for 2024. Top Sectors for Investment in 2024 Fintech Overview: Fintech encompasses technological innovations in financial services, including digital payments, blockchain, and online lending. Current Trends: In the first half of 2024, the global Fintech sector navigated a challenging investment environment. According to Innovate Finance, global Fintech investments during this period amounted to $15.9 billion across 1,566 deals. During the first half of the year, the UK secured $2.0 billion in investments through 184 deals, surpassing the rest of Europe’s total of roughly $1.8 billion. This demonstrates the UK's ongoing leadership in the European Fintech sector, despite global economic and political uncertainties. Germany and the Netherlands emerged as the second and third-largest markets, underscoring the rising significance of tech hubs across continental Europe. Sweden remains a key player, while France also ranks among the top five, indicating its strong position as a hub for financial technology innovation. Notably, 167 of these deals were under $20 million, focusing on Seed and Series A stages. The average deal size was $10.2 million, down from the $12 million average in 2022 and 2023, indicating a shift back towards early-stage ventures. Why It’s Attractive: The fintech sector is at a critical inflection point, with innovations in digital banking, blockchain, and financial inclusion driving growth. The sector's adaptability to regulatory changes and consumer preferences ensures a robust investment outlook. Artificial Intelligence (AI) Overview: AI continues to shine in 2024, driving innovation across various industries, from healthcare to finance. Current Trends: According to PitchBook, AI investments in Europe doubled from the previous quarter to €4.2 billion in Q2 2024. Overall, AI companies attracted €6.3 billion in the first half of the year. The integration of AI in various sectors has demonstrated its transformative potential, from enhancing operational efficiencies to pioneering new product developments. The UK remains at the forefront of the European AI scene, with the highest number of VC-backed AI and machine learning (ML) companies. With 1,319 startups, the UK surpasses France, which holds the second spot with 520 companies. This leadership is evident in the substantial deal values and volumes attracted by AI companies in the UK. Why It’s Attractive: AI's ability to revolutionize industries and its rapid adoption make it a top investment choice. AI technologies, including machine learning, natural language processing, and robotics, are expected to continue driving significant advancements and creating substantial economic value. 3. SaaS and B2B Overview: SaaS (Software as a Service) and B2B (Business-to-Business) solutions focus on providing software and services to businesses, enhancing operational efficiency and productivity. Current Trends: In recent years, SaaS and B2B solutions have become increasingly prominent in the venture capital landscape. According to Dealroom, 47% of venture capital investments in 2023 were directed towards startups with a SaaS business model, accounting for nearly half of all venture capital funding. This represents a significant increase over the past decade, highlighting a sustained and accelerating trend towards SaaS-focused investments. Why It’s Attractive: The scalability and recurring revenue models of SaaS and B2B make them attractive for investors. The continuous need for businesses to streamline operations and adopt digital solutions drives growth in these sectors. 4. Cybersecurity Overview: Cybersecurity is crucial for protecting information and systems from cyber threats, with increasing importance due to the rise of digitalization. Current Trends: According to PitchBook’s report on the sector, investments in cybersecurity reached $9.6 billion across 693 deals in 2023, with top-performing companies commanding high valuations. Last year, cybersecurity unicorns saw exceptional valuation growth, outpacing many other emerging tech sectors. Leading companies are still attracting substantial valuations despite a slowdown in venture capital funding. Wiz reached a $10 billion valuation in February following a $300 million Series D round. In November, Keyfactor achieved a $1.3 billion valuation, and Prove Identity crossed the $1 billion mark in October. Why It’s Attractive: The growing frequency of cyber threats and the need for robust security measures drive the demand for cybersecurity solutions. The sector's critical role in protecting data and systems makes it a compelling investment choice. 5. Climate Tech Overview: Climate tech focuses on developing technologies to combat climate change and promote sustainability. Current Trends: Climate tech investments totaled $11.3 billion in H1 2024, despite a 20% decline from the previous year. The sector continues to attract attention with innovations in energy, sustainable materials, and carbon management. Why It’s Attractive: Climate tech aligns with global sustainability goals, offering long-term growth potential. Investments in renewable energy, sustainable agriculture, and green technologies position the sector as a key player in addressing environmental challenges. The information provided in this article is for informational purposes only and should not be considered as financial advice. The content does not constitute investment recommendations, and we do not assume any responsibility for any financial decisions made based on this information. The European investment environment in 2024 is marked by a surge of exciting and promising sectors that are capturing the attention of venture capitalists and investors alike. According to the Venture Pulse Report by KPMG , the European venture capital market experienced a significant increase in activity in the second quarter of 2024, with investments rising from $13.9 billion to $17.8 billion across 1,869 transactions. This growth was primarily driven by mega deals involving companies like Wayve, Abound, Mistral AI, and Monzo. As we navigate through this evolving ecosystem, understanding the sectors poised for substantial growth and innovation is crucial for making informed investment decisions. the Venture Pulse Report by KPMG In this article, we will delve into the most promising investment sectors in Europe for 2024, exploring the driving trends and key factors that make these sectors attractive to investors. Understanding Where to Invest Let's consider the criteria to keep in mind when making investment decisions. Investors should evaluate market trends, global demand, sustainability, and the sector's resilience during economic downturns. Analyzing these factors helps in identifying sectors poised for growth and innovation. Market Trends: Observing industry developments and forecasting future trends. Global Demand: Identifying sectors catering to growing global needs or potential markets. Sustainability: Investing in sectors contributing positively to environmental and social goals. Economic Resilience: Choosing sectors that remain in demand irrespective of economic conditions. Market Trends: Observing industry developments and forecasting future trends. Market Trends: Global Demand: Identifying sectors catering to growing global needs or potential markets. Global Demand: Sustainability: Investing in sectors contributing positively to environmental and social goals. Sustainability: Economic Resilience: Choosing sectors that remain in demand irrespective of economic conditions. Economic Resilience: By considering these factors, let's explore the most interesting investment sectors for 2024. Top Sectors for Investment in 2024 Fintech Overview: Fintech encompasses technological innovations in financial services, including digital payments, blockchain, and online lending. Overview: Current Trends: In the first half of 2024, the global Fintech sector navigated a challenging investment environment. According to Innovate Finance , global Fintech investments during this period amounted to $15.9 billion across 1,566 deals. Current Trends: According to Innovate Finance During the first half of the year, the UK secured $2.0 billion in investments through 184 deals, surpassing the rest of Europe’s total of roughly $1.8 billion. This demonstrates the UK's ongoing leadership in the European Fintech sector, despite global economic and political uncertainties. Germany and the Netherlands emerged as the second and third-largest markets, underscoring the rising significance of tech hubs across continental Europe. Sweden remains a key player, while France also ranks among the top five, indicating its strong position as a hub for financial technology innovation. Notably, 167 of these deals were under $20 million, focusing on Seed and Series A stages. The average deal size was $10.2 million, down from the $12 million average in 2022 and 2023, indicating a shift back towards early-stage ventures. Why It’s Attractive: The fintech sector is at a critical inflection point, with innovations in digital banking, blockchain, and financial inclusion driving growth. The sector's adaptability to regulatory changes and consumer preferences ensures a robust investment outlook. Why It’s Attractive: Artificial Intelligence (AI) Overview: AI continues to shine in 2024, driving innovation across various industries, from healthcare to finance. Overview: Current Trends: According to PitchBook , AI investments in Europe doubled from the previous quarter to €4.2 billion in Q2 2024. Overall, AI companies attracted €6.3 billion in the first half of the year. The integration of AI in various sectors has demonstrated its transformative potential, from enhancing operational efficiencies to pioneering new product developments. Current Trends: PitchBook The UK remains at the forefront of the European AI scene, with the highest number of VC-backed AI and machine learning (ML) companies. With 1,319 startups, the UK surpasses France, which holds the second spot with 520 companies. This leadership is evident in the substantial deal values and volumes attracted by AI companies in the UK. Why It’s Attractive: AI's ability to revolutionize industries and its rapid adoption make it a top investment choice. AI technologies, including machine learning, natural language processing, and robotics, are expected to continue driving significant advancements and creating substantial economic value. Why It’s Attractive: 3. SaaS and B2B Overview: SaaS (Software as a Service) and B2B (Business-to-Business) solutions focus on providing software and services to businesses, enhancing operational efficiency and productivity. Overview: Current Trends: In recent years, SaaS and B2B solutions have become increasingly prominent in the venture capital landscape. According to Dealroom , 47% of venture capital investments in 2023 were directed towards startups with a SaaS business model, accounting for nearly half of all venture capital funding. This represents a significant increase over the past decade, highlighting a sustained and accelerating trend towards SaaS-focused investments. Current Trends: According to Dealroom Why It’s Attractive: The scalability and recurring revenue models of SaaS and B2B make them attractive for investors. The continuous need for businesses to streamline operations and adopt digital solutions drives growth in these sectors. Why It’s Attractive: 4. Cybersecurity Overview: Cybersecurity is crucial for protecting information and systems from cyber threats, with increasing importance due to the rise of digitalization. Overview: Current Trends: According to PitchBook’s report on the sector, investments in cybersecurity reached $9.6 billion across 693 deals in 2023, with top-performing companies commanding high valuations. Current Trends: According to PitchBook’s report Last year, cybersecurity unicorns saw exceptional valuation growth, outpacing many other emerging tech sectors. Leading companies are still attracting substantial valuations despite a slowdown in venture capital funding. Wiz reached a $10 billion valuation in February following a $300 million Series D round. In November, Keyfactor achieved a $1.3 billion valuation, and Prove Identity crossed the $1 billion mark in October. Why It’s Attractive: The growing frequency of cyber threats and the need for robust security measures drive the demand for cybersecurity solutions. The sector's critical role in protecting data and systems makes it a compelling investment choice. Why It’s Attractive: 5. Climate Tech Overview: Climate tech focuses on developing technologies to combat climate change and promote sustainability. Overview: Current Trends: Climate tech investments totaled $11.3 billion in H1 2024 , despite a 20% decline from the previous year. The sector continues to attract attention with innovations in energy, sustainable materials, and carbon management. Current Trends: totaled $11.3 billion in H1 2024 Why It’s Attractive: Climate tech aligns with global sustainability goals, offering long-term growth potential. Investments in renewable energy, sustainable agriculture, and green technologies position the sector as a key player in addressing environmental challenges. Why It’s Attractive: The information provided in this article is for informational purposes only and should not be considered as financial advice. The content does not constitute investment recommendations, and we do not assume any responsibility for any financial decisions made based on this information. The information provided in this article is for informational purposes only and should not be considered as financial advice. The content does not constitute investment recommendations, and we do not assume any responsibility for any financial decisions made based on this information. The information provided in this article is for informational purposes only and should not be considered as financial advice. The content does not constitute investment recommendations, and we do not assume any responsibility for any financial decisions made based on this information. The information provided in this article is for informational purposes only and should not be considered as financial advice. The content does not constitute investment recommendations, and we do not assume any responsibility for any financial decisions made based on this information.

Most Promising and Exciting Investment Sectors in Europe in 2024

During EthCC week, at the NEAR AI Showcase, I had the chance to sit down with Illia Polosukhin, co-founder of NEAR Protocol and a significant contributor to the fields of artificial intelligence and blockchain. Known for his work on creating the transformer model and his role in adding the “T” to ChatGPT, Illia shared his vision of integrating AI with Web3 to drive innovation that benefits everyone. Illia, your work at Google and on creating the transformer model has set a foundation for modern AI. How have you seen AI change since then? It's definitely changed, right? AI went from being back-end technology that supports products to being the product itself. That's the reason I left Google originally to start NEAR AI—because I wanted to make AI the product and then leverage the data loop to improve it. At the time, in 2017 and 2018, it was too early. But today, we are at an interesting point where we have these AI-first products that are starting to emerge. The challenge with these products is that new startups have very limited time to figure out how to monetize. They raise at high valuations, they have a lot of burn, and now, many are being bought out by big companies. So what’s happening is that AI benefits incumbents because they already have distribution, the ability to spend more, and they know how to monetize user attention. Web3 is starting to solve that problem. It's giving time to build new consumer products that actually benefit users, maybe with a different business model, and leverage AI as the platform. So I think that's where it starts. There are a lot of components in the stack that will be powering that. Instead of one major company trying to do everything, you can have lots of companies working together, just like in this Web3 mindset. For a more general audience, Web2 AI startups have very little time to become hyper-profitable, or they need to get acquired by big companies. Perplexity is a great example; they spend a lot of money on compute, making their operations very expensive. That’s why they need to monetize quickly to sustain their growth, or they'll risk being acquired by one of the larger companies that can afford those costs. How does NEAR fit into all this, and what are you planning to do with both technologies? The biggest gap we saw is that there are many capable founders starting different parts of this broader stack within these big companies, but they are not well connected. There's data crowdsourcing, data labeling, decentralized inference, agent payments, and more, but there's no cohesive product. As a developer, I don't want to figure out how to use 50 different pieces—that's too complicated. Versus going to OpenAI or Google, where there's a single API to use. So, we focused on NEAR AI and the NEAR Foundation’s AI Incubator to bring these projects together and figure out an interface to coordinate them, making it simple to interact with them. Additionally, we can coordinate open-source research. Researchers at top universities don't have much access to compute capacity, but if they solve interesting problems for applications, those applications would spend significant amounts to do that. Usually, the code and models they develop are very custom-made and not reusable. We're creating a coordination hub to plug in researchers, give them credits, compute, and data acquisition, and solve specific application problems or generic problems. Their work becomes reusable by production use cases and application developers. It's like a complex, four-directional market, tying together all the disparate audiences that a centralized company would handle by hiring everyone and doing it all itself. Instead, we're creating an open platform where everything is open source and combined into one hub for everyone to use. Can you explain the concept of user-owned AI, why it matters, and how it differs from traditional AI models? The contrast here is why I don't like "open source AI" as a category. There's a lot in open source, and it's really important, but the most important part is that these models are optimized for specific functions. When I'm working at Google, my goal is to make more money for Google because that's how I'm incentivized. I get bonuses, stock options, and other benefits based on Google's profits. Even if a big company open sources a model, it's still a business decision meant to benefit that company. The opposite of that is AI which benefits every individual user. Let's say I want to consume interesting content and not get enraged all the time. For that, you need a very different model of operation and AI research. Current research happens in closed labs with defined goals. In traditional for-profit systems, there's a transition where at first you're a startup, growing your audience, and providing value. At some point, you reach your target audience or a big part of it. Now, to grow revenue, you need to monetize more from the existing user base. You've already provided them value, they're already using your stuff. Now, you need to figure out how to get them to spend more time and monetize them more. That's where the main problem happens with these companies. In tech, because of low volume costs and network effects, normally, when you hit that inflection point, there's a lot of competitors. But here in tech, you're just in a monopoly, and you start to extract value. This is where Web3 comes in—creating an economy that doesn't require you to become extractive. Crypto doesn't require posting more revenue every year. Yes, people want the number to go up, but it doesn't need to. We can be happy with Bitcoin being at, say, $65,000, and that's totally normal. We can have an economy where everybody is participating and benefiting. You don't need constant growth or expansion; you're okay with the state as it is. This is the conceptual difference we're going after with user-owned AI. What other innovations are you exploring that align with equity and sustainability? On sustainability, we've invested in being carbon neutral for NEAR, which is a Proof-of-Stake network, and we have projects for carbon credits, tracking, and reforestation. These are important. The AI researcher mindset in me is always looking at how we can develop more sophisticated AI tools to solve these problems. I have friends in cancer research and material science, and I'm excited about those fields. But where I can apply my intellect is in building better tools to help these researchers. Data scientists in these fields often don't have access to good data science or coding resources. Developers can scale their efforts significantly. It's the same with blockchain: equipping people with incentives and coordination tools to build better networks to solve problems. Do you have a favorite app or use case built on NEAR? I can’t pick favorites, haha. Every use case is great. We see a lot of user-facing products coming from Web2, like Sweatcoin, which uses blockchain under the hood for payments, loyalty, and transactions. Most of their users don't even know they're using NEAR. There are wallets becoming multi-chain, like HOT and Bitte, allowing users to transact across multiple chains seamlessly. Bitte even has a natural language interface for commands. We also have financial applications, multi-chain DEXs, lending across different assets, and more. On the AI side, crowdsourcing applications like NEAR Crowd have been running for years, improving data acquisition at lower costs. All of these pieces are coming together, and NEAR has been growing exponentially in active users. Someone tweeted that there are more side events at EthCC than blockchain apps. How can the space improve? That's why we're doing AI Developer and the AI Hub. We can try to get people to build applications or make it easy to build them. There are many misaligned incentives in the space, and just talking about them doesn't fix the issue. We've invested in bringing existing applications with users, integrating and interconnecting them, which is why we have the most users in Web3. Building consumer apps is hard, and it's easier to fundraise for infrastructure. We want to simplify the process of building applications so people can experiment more and innovate. During EthCC week, at the NEAR AI Showcase, I had the chance to sit down with Illia Polosukhin, co-founder of NEAR Protocol and a significant contributor to the fields of artificial intelligence and blockchain. Known for his work on creating the transformer model and his role in adding the “T” to ChatGPT, Illia shared his vision of integrating AI with Web3 to drive innovation that benefits everyone. During EthCC week, at the NEAR AI Showcase, I had the chance to sit down with Illia Polosukhin, co-founder of NEAR Protocol and a significant contributor to the fields of artificial intelligence and blockchain. Known for his work on creating the transformer model and his role in adding the “T” to ChatGPT, Illia shared his vision of integrating AI with Web3 to drive innovation that benefits everyone. During EthCC week, at the NEAR AI Showcase, I had the chance to sit down with Illia Polosukhin, co-founder of NEAR Protocol and a significant contributor to the fields of artificial intelligence and blockchain. Known for his work on creating the transformer model and his role in adding the “T” to ChatGPT, Illia shared his vision of integrating AI with Web3 to drive innovation that benefits everyone. Illia Polosukhin, Illia Polosukhin, NEAR NEAR Illia, your work at Google and on creating the transformer model has set a foundation for modern AI. How have you seen AI change since then? Illia, your work at Google and on creating the transformer model has set a foundation for modern AI. How have you seen AI change since then? It's definitely changed, right? AI went from being back-end technology that supports products to being the product itself. That's the reason I left Google originally to start NEAR AI—because I wanted to make AI the product and then leverage the data loop to improve it. At the time, in 2017 and 2018, it was too early. But today, we are at an interesting point where we have these AI-first products that are starting to emerge. The challenge with these products is that new startups have very limited time to figure out how to monetize. They raise at high valuations, they have a lot of burn, and now, many are being bought out by big companies. So what’s happening is that AI benefits incumbents because they already have distribution, the ability to spend more, and they know how to monetize user attention. Web3 is starting to solve that problem. It's giving time to build new consumer products that actually benefit users, maybe with a different business model, and leverage AI as the platform. So I think that's where it starts. There are a lot of components in the stack that will be powering that. Instead of one major company trying to do everything, you can have lots of companies working together, just like in this Web3 mindset. For a more general audience, Web2 AI startups have very little time to become hyper-profitable, or they need to get acquired by big companies. Perplexity is a great example; they spend a lot of money on compute, making their operations very expensive. That’s why they need to monetize quickly to sustain their growth, or they'll risk being acquired by one of the larger companies that can afford those costs. Perplexity Perplexity How does NEAR fit into all this, and what are you planning to do with both technologies? The biggest gap we saw is that there are many capable founders starting different parts of this broader stack within these big companies, but they are not well connected. There's data crowdsourcing, data labeling, decentralized inference, agent payments, and more, but there's no cohesive product. As a developer, I don't want to figure out how to use 50 different pieces—that's too complicated. Versus going to OpenAI or Google, where there's a single API to use. So, we focused on NEAR AI and the NEAR Foundation’s AI Incubator to bring these projects together and figure out an interface to coordinate them, making it simple to interact with them. Additionally, we can coordinate open-source research. Researchers at top universities don't have much access to compute capacity, but if they solve interesting problems for applications, those applications would spend significant amounts to do that. Usually, the code and models they develop are very custom-made and not reusable. We're creating a coordination hub to plug in researchers, give them credits, compute, and data acquisition, and solve specific application problems or generic problems. Their work becomes reusable by production use cases and application developers. It's like a complex, four-directional market, tying together all the disparate audiences that a centralized company would handle by hiring everyone and doing it all itself. Instead, we're creating an open platform where everything is open source and combined into one hub for everyone to use. Can you explain the concept of user-owned AI, why it matters, and how it differs from traditional AI models? The contrast here is why I don't like "open source AI" as a category. There's a lot in open source, and it's really important, but the most important part is that these models are optimized for specific functions. When I'm working at Google, my goal is to make more money for Google because that's how I'm incentivized. I get bonuses, stock options, and other benefits based on Google's profits. Even if a big company open sources a model, it's still a business decision meant to benefit that company. The opposite of that is AI which benefits every individual user. Let's say I want to consume interesting content and not get enraged all the time. For that, you need a very different model of operation and AI research. Current research happens in closed labs with defined goals. In traditional for-profit systems, there's a transition where at first you're a startup, growing your audience, and providing value. At some point, you reach your target audience or a big part of it. Now, to grow revenue, you need to monetize more from the existing user base. You've already provided them value, they're already using your stuff. Now, you need to figure out how to get them to spend more time and monetize them more. That's where the main problem happens with these companies. In tech, because of low volume costs and network effects, normally, when you hit that inflection point, there's a lot of competitors. But here in tech, you're just in a monopoly, and you start to extract value. This is where Web3 comes in—creating an economy that doesn't require you to become extractive. Crypto doesn't require posting more revenue every year. Yes, people want the number to go up, but it doesn't need to. We can be happy with Bitcoin being at, say, $65,000, and that's totally normal. We can have an economy where everybody is participating and benefiting. You don't need constant growth or expansion; you're okay with the state as it is. This is the conceptual difference we're going after with user-owned AI. What other innovations are you exploring that align with equity and sustainability? On sustainability, we've invested in being carbon neutral for NEAR, which is a Proof-of-Stake network, and we have projects for carbon credits, tracking, and reforestation. These are important. The AI researcher mindset in me is always looking at how we can develop more sophisticated AI tools to solve these problems. I have friends in cancer research and material science, and I'm excited about those fields. But where I can apply my intellect is in building better tools to help these researchers. Data scientists in these fields often don't have access to good data science or coding resources. Developers can scale their efforts significantly. It's the same with blockchain: equipping people with incentives and coordination tools to build better networks to solve problems. Do you have a favorite app or use case built on NEAR? I can’t pick favorites, haha. Every use case is great. We see a lot of user-facing products coming from Web2, like Sweatcoin, which uses blockchain under the hood for payments, loyalty, and transactions. Most of their users don't even know they're using NEAR. There are wallets becoming multi-chain, like HOT and Bitte, allowing users to transact across multiple chains seamlessly. Bitte even has a natural language interface for commands. We also have financial applications, multi-chain DEXs, lending across different assets, and more. On the AI side, crowdsourcing applications like NEAR Crowd have been running for years, improving data acquisition at lower costs. All of these pieces are coming together, and NEAR has been growing exponentially in active users. Someone tweeted that there are more side events at EthCC than blockchain apps. How can the space improve? That's why we're doing AI Developer and the AI Hub. We can try to get people to build applications or make it easy to build them. There are many misaligned incentives in the space, and just talking about them doesn't fix the issue. We've invested in bringing existing applications with users, integrating and interconnecting them, which is why we have the most users in Web3. Building consumer apps is hard, and it's easier to fundraise for infrastructure. We want to simplify the process of building applications so people can experiment more and innovate.

NEAR’s Illia Polosukhin: AI as the Crucial Piece in Web3 Advancement

NEAR's Illia Polosukhin: Web3 অ্যাডভান্সমেন্টের গুরুত্বপূর্ণ অংশ হিসেবে AI

Illia Polosukhin von NEAR: KI als entscheidender Baustein für die Weiterentwicklung von Web3

Illia Polosukhin de NEAR: la IA como pieza crucial en el avance de Web3

Illia Polosukhin de NEAR : l'IA comme élément crucial de l'avancement du Web3

NEAR के इलिया पोलोसुखिन: वेब3 की उन्नति में AI एक महत्वपूर्ण हिस्सा है

NEAR の Illia Polosukhin: Web3 の発展における重要なピースとしての AI

NEAR의 Illia Polosukhin: Web3 발전의 중요한 부분인 AI

Illia Polosukhin da NEAR: IA como peça crucial no avanço da Web3

Илья Полосухин из NEAR: ИИ как решающий элемент в развитии Web3

NEAR'dan Illia Polosukhin: Web3 Gelişiminde Önemli Parça Olarak Yapay Zeka

Illia Polosukhin của NEAR: AI là mảnh ghép quan trọng trong quá trình phát triển Web3

NEAR 的 Illia Polosukhin：AI 是 Web3 发展的关键要素

Authors:
(1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu);
(2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Table of Links Abstract and Introduction
Methods
Results
Discussion
References
Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals 4 Discussion Our findings are a starting point for future research and suggest that several issues need to be carefully considered by the academic community. For instance, the community needs to decide which forms of assistance should be reported, whether it be ChatGPT or some other source, such as RA help or help from another AI tool. Additionally, it needs to decide which types of assistance to be reported, be it fixing grammar or something more extensive. We found more consensus that using ChatGPT to fix grammar does not need to be reported than when it comes to rewriting text. One avenue could be to disclose the actual prompts that are used to revise the paper. Along these lines, Grammarly has a new feature that allows users to acknowledge the usage of AI and the actual prompts that the users used. In addition, our research raises the question of how journals, conferences, and associations can enforce differences in what should be reported. While the detection tool that we employed was able to detect relatively accurately whether AI was used at all, abstracts that were rewritten by GPT-3.5 were sometimes given a lower chance of being written by AI than the grammar-fixed abstracts. This also opens the question of whether using ChatGPT to fix grammar might inadvertently make more substantial changes than desired. How can researchers be sure that they use AI in the desired way? One solution could be to use ChatGPT to point out grammar errors, but to fix them manually, so that ChatGPT does not actually revise the text. These tools and our perceptions of them will surely evolve, but the aim of this paper is to determine how they are perceived and detected in this moment in time, as it appears to be an inflection point in AI ability and in its use to revise text. 4.1 Limitations and Future Directions In terms of our survey design, the use of a convenience sample may have introduced selection issues that complicate our comparisons by role and English language background. In addition, our convenience sample was largely composed of economists, and because views might differ across fields, it would be valuable to also consider what perceptions look like more generally and to illuminate differences across fields. An alternative approach that allows for an assessment of perceptions across fields is offered by Bringula (2023), who runs a sentiment analysis on papers written about AI use in manuscript preparation and finds that the sentiment in those papers is generally positive. Also, the use of a convenience sample did not lend itself to randomizing on the form of assistance (ChatGPT, RA assistance, etc.). In addition, another limitation of our survey is that we do not dig deeply into the nature of ethical perceptions. Since these perceptions were such an important predictor of reporting preferences, it might be insightful to know why academics feel that using AI tools for manuscript preparation is unethical. For example, is someone harmed by their use – such as other academics, science in general, or the authors whose material is used in training the AI – or is there a deeper moral question at play? One way to tease apart these subtleties would be to have academics evaluate a number of detailed vignettes. It might also be interesting to determine the role of payment in ethical considerations, as ChatGPT, RAs, and proofreaders sometimes require payment and sometimes do not. In terms of our detection design, we just considered published papers, at a top journal, and for the field of management. To understand whether these results hold more generally, it would be necessary to look at papers published in other fields, perhaps using a service like Scopus, and to look at working papers, perhaps sourced from SSRN or arXiv. In addition, it might be valuable to consider full papers instead of just abstracts, or as a middle case between abstracts and full papers, introductions might also be useful to examine. Additionally, our results are limited to one AI detection service, so we do not know if they extend to other services, such as GPTZero. Also, it might be of interest to see if other AI-based revision services, such as Grammarly, are flagged by AI detectors too. Along these lines, it might be insightful to consider other forms of writing assistance that might be taken besides fixing grammar and rewriting text, or specific forms of rewriting text. One important dimension could be whether use of chat-based and completion-based AI tools lead to different detection rates. Finally, and related to the last point, it would be valuable to consider a range of different prompts, especially given the sensitivity that we found to relatively small changes in prompt language. One systematic way to choose the prompts could be to hold focus groups of researchers or ChatGPT users or by having a way for researchers to vote on the prompts that are tested. Another dimension of investigation could be to see if our results are sensitive to other important features of the GPT API inputs, such as increasing the model to GPT-4, increasing the temperature to increase the hallucination rate, or by using other system prompts. Given the black box nature of LLMs, a robust empirical analysis is needed to inform policymakers at associations, journals, and conferences of the link between how AI is used and how it is flagged by detection software. This paper is available on arxiv under CC 4.0 license. Authors: (1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu); (2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Authors: Authors: (1) Nir Chemaya, University of California, Santa Barbara and (e-mail: nir@ucsb.edu); (2) Daniel Martin, University of California, Santa Barbara and Kellogg School of Management, Northwestern University and (e-mail: danielmartin@ucsb.edu). Table of Links Abstract and Introduction Methods Results Discussion References Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals Abstract and Introduction Abstract and Introduction Methods Methods Results Results Discussion Discussion References References Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals Appendix for Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals 4 Discussion Our findings are a starting point for future research and suggest that several issues need to be carefully considered by the academic community. For instance, the community needs to decide which forms of assistance should be reported, whether it be ChatGPT or some other source, such as RA help or help from another AI tool. Additionally, it needs to decide which types of assistance to be reported, be it fixing grammar or something more extensive. We found more consensus that using ChatGPT to fix grammar does not need to be reported than when it comes to rewriting text. One avenue could be to disclose the actual prompts that are used to revise the paper. Along these lines, Grammarly has a new feature that allows users to acknowledge the usage of AI and the actual prompts that the users used. In addition, our research raises the question of how journals, conferences, and associations can enforce differences in what should be reported. While the detection tool that we employed was able to detect relatively accurately whether AI was used at all, abstracts that were rewritten by GPT-3.5 were sometimes given a lower chance of being written by AI than the grammar-fixed abstracts. This also opens the question of whether using ChatGPT to fix grammar might inadvertently make more substantial changes than desired. How can researchers be sure that they use AI in the desired way? One solution could be to use ChatGPT to point out grammar errors, but to fix them manually, so that ChatGPT does not actually revise the text. These tools and our perceptions of them will surely evolve, but the aim of this paper is to determine how they are perceived and detected in this moment in time, as it appears to be an inflection point in AI ability and in its use to revise text. 4.1 Limitations and Future Directions In terms of our survey design, the use of a convenience sample may have introduced selection issues that complicate our comparisons by role and English language background. In addition, our convenience sample was largely composed of economists, and because views might differ across fields, it would be valuable to also consider what perceptions look like more generally and to illuminate differences across fields. An alternative approach that allows for an assessment of perceptions across fields is offered by Bringula (2023), who runs a sentiment analysis on papers written about AI use in manuscript preparation and finds that the sentiment in those papers is generally positive. Also, the use of a convenience sample did not lend itself to randomizing on the form of assistance (ChatGPT, RA assistance, etc.). In addition, another limitation of our survey is that we do not dig deeply into the nature of ethical perceptions. Since these perceptions were such an important predictor of reporting preferences, it might be insightful to know why academics feel that using AI tools for manuscript preparation is unethical. For example, is someone harmed by their use – such as other academics, science in general, or the authors whose material is used in training the AI – or is there a deeper moral question at play? One way to tease apart these subtleties would be to have academics evaluate a number of detailed vignettes. It might also be interesting to determine the role of payment in ethical considerations, as ChatGPT, RAs, and proofreaders sometimes require payment and sometimes do not. In terms of our detection design, we just considered published papers, at a top journal, and for the field of management. To understand whether these results hold more generally, it would be necessary to look at papers published in other fields, perhaps using a service like Scopus, and to look at working papers, perhaps sourced from SSRN or arXiv. In addition, it might be valuable to consider full papers instead of just abstracts, or as a middle case between abstracts and full papers, introductions might also be useful to examine. Additionally, our results are limited to one AI detection service, so we do not know if they extend to other services, such as GPTZero. Also, it might be of interest to see if other AI-based revision services, such as Grammarly, are flagged by AI detectors too. Along these lines, it might be insightful to consider other forms of writing assistance that might be taken besides fixing grammar and rewriting text, or specific forms of rewriting text. One important dimension could be whether use of chat-based and completion-based AI tools lead to different detection rates. Finally, and related to the last point, it would be valuable to consider a range of different prompts, especially given the sensitivity that we found to relatively small changes in prompt language. One systematic way to choose the prompts could be to hold focus groups of researchers or ChatGPT users or by having a way for researchers to vote on the prompts that are tested. Another dimension of investigation could be to see if our results are sensitive to other important features of the GPT API inputs, such as increasing the model to GPT-4, increasing the temperature to increase the hallucination rate, or by using other system prompts. Given the black box nature of LLMs, a robust empirical analysis is needed to inform policymakers at associations, journals, and conferences of the link between how AI is used and how it is flagged by detection software. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

AI Use in Manuscript Preparation for Academic Journals: Discussion

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