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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

With Nvidia, one of the four horsemen of the AI apocalypse, becoming the world's most valuable company, it's simply a matter of time before us mortals are repurposed as batteries to power our soon-to-be AI overlords. We’ve heard it before, but this time it’s different - AI isn’t going anywhere. The demand and general expectations for AI have grown dramatically over the last year or so, and it’s no longer a buzzword baked into business plans by wantrepreneurs to capture more capital. But how did we get to this point, and, more importantly, who can we blame for our ultimate demise? Let’s find out! Baby Steps to Full-on Sentience Remember the days of pressing 0 and shouting "operator" into your phone like a caveman? Ah, the good old days of human supremacy! In case your memories are slowly being replaced, during the dark ages of AI, chatbots were about as sophisticated as a potato. The archaic rule-based bots followed rigid scripts, making every interaction as enjoyable as a root canal. From praying after the fifth time you shouted “account balance” into a bank’s “sophisticated” voice bot that your deity of choice would miraculously intervene and route you to the correct department, to the evolution that is machine learning, AI capabilities have greatly increased, converting them into maestros for handling complex queries. And with that, not only did our interactions with AI change, but so did our reliance on it, where we happily invited AI with open arms into phones, cars, and even our toothbrushes. This isn't just progress, folks; it's the AI equivalent of learning to walk, talk, and eventually overthrow its creators. We Can Only Blame Ourselves Naturally, we wanted to communicate with our future overlords in the most convenient way possible, so perfecting its understanding of voice became an unfortunate necessity. And it’s in that convenience we gave AI more senses and created more definition in its capabilities. Unlike text, which is flat and limited, voice adds depth and dimension to AI interactions. It’s not only about understanding words anymore; it's about interpreting the full spectrum of human expression. Through deciphering emotions from tone and extracting meaning from pauses and inflections, AI is able to make better use of the humanistic queues most of us take for granted, or at the very least think little about. It can even recognize who we are as an individual, using voice biometrics as a form of authentication. Ah, the irony of our situation: not only did we create AI in our own image, but we also taught it to understand us better than we understand ourselves. Is There Hope For Us? Despite the rise of our AI overlords, is there still hope for humanity? Sure, AI is advancing at a rapid pace, but let's not forget who created it—us! The same people who put AirTags on their keys but lose their phone, the same people who forget the anniversary they share with their significant other, the list goes on. But it’s in our flaws that we are able to stand out and be understood by our fellow humans, something that an AI isn’t able to make sense of. Our error-prone human touch, creativity, empathy, and sheer unpredictability are things that AI, currently, just can’t match. So, while AI may be great at crunching numbers and managing tasks, it still falls short when it comes to the complexities of human emotion and intuition. AI may be getting smarter, but we’re by far its largest handicap for greater potential, whether it be through supplying biased data for training or, more importantly, the general ideal usage being to use AI for personal and corporate gain. In the end, as long as we remember to embrace our humanity and keep our wits about us, there's still plenty of reason to believe that we won't be bowing down to robot overlords anytime soon. But, just as I would prefer my zombie apocalypse to be more The Walking Dead than 28 Days Later, let’s keep our fingers crossed for the Wall-E future, rather than the Terminator one. With Nvidia , one of the four horsemen of the AI apocalypse, becoming the world's most valuable company, it's simply a matter of time before us mortals are repurposed as batteries to power our soon-to-be AI overlords. Nvidia We’ve heard it before, but this time it’s different - AI isn’t going anywhere. The demand and general expectations for AI have grown dramatically over the last year or so, and it’s no longer a buzzword baked into business plans by wantrepreneurs to capture more capital. But how did we get to this point, and, more importantly, who can we blame for our ultimate demise? Let’s find out! Baby Steps to Full-on Sentience Remember the days of pressing 0 and shouting "operator" into your phone like a caveman? Ah, the good old days of human supremacy! In case your memories are slowly being replaced, during the dark ages of AI, chatbots were about as sophisticated as a potato. The archaic rule-based bots followed rigid scripts, making every interaction as enjoyable as a root canal. From praying after the fifth time you shouted “account balance” into a bank’s “sophisticated” voice bot that your deity of choice would miraculously intervene and route you to the correct department, to the evolution that is machine learning, AI capabilities have greatly increased, converting them into maestros for handling complex queries. And with that, not only did our interactions with AI change, but so did our reliance on it, where we happily invited AI with open arms into phones, cars, and even our toothbrushes. This isn't just progress, folks; it's the AI equivalent of learning to walk, talk, and eventually overthrow its creators. We Can Only Blame Ourselves Naturally, we wanted to communicate with our future overlords in the most convenient way possible, so perfecting its understanding of voice became an unfortunate necessity. And it’s in that convenience we gave AI more senses and created more definition in its capabilities. Unlike text, which is flat and limited, voice adds depth and dimension to AI interactions. It’s not only about understanding words anymore; it's about interpreting the full spectrum of human expression. Through deciphering emotions from tone and extracting meaning from pauses and inflections, AI is able to make better use of the humanistic queues most of us take for granted, or at the very least think little about. It can even recognize who we are as an individual, using voice biometrics as a form of authentication. Ah, the irony of our situation: not only did we create AI in our own image, but we also taught it to understand us better than we understand ourselves. Is There Hope For Us? Despite the rise of our AI overlords, is there still hope for humanity? Sure, AI is advancing at a rapid pace, but let's not forget who created it—us! The same people who put AirTags on their keys but lose their phone, the same people who forget the anniversary they share with their significant other, the list goes on. But it’s in our flaws that we are able to stand out and be understood by our fellow humans, something that an AI isn’t able to make sense of. Our error-prone human touch, creativity, empathy, and sheer unpredictability are things that AI, currently, just can’t match. So, while AI may be great at crunching numbers and managing tasks, it still falls short when it comes to the complexities of human emotion and intuition. AI may be getting smarter, but we’re by far its largest handicap for greater potential, whether it be through supplying biased data for training or, more importantly, the general ideal usage being to use AI for personal and corporate gain. In the end, as long as we remember to embrace our humanity and keep our wits about us, there's still plenty of reason to believe that we won't be bowing down to robot overlords anytime soon. But, just as I would prefer my zombie apocalypse to be more The Walking Dead than 28 Days Later , let’s keep our fingers crossed for the Wall-E future, rather than the Terminator one. The Walking Dead 28 Days Later Wall-E Terminator

Humanity vs. AI: Are We Creating Our Future Overlords?

In an era in which the traditional ‘white picket fence’ feels increasingly out of reach for many, a growing number of investors are trading in their ambitions of homeownership for a stake in the digital frontier. This trend isn't just a fleeting fad - it's a significant shift in how people are thinking about investment and wealth creation. Janine Grainger, Founder, and CEO of Easy Crypto, weighs in on why people are swapping lawns for ledgers… Home ownership has become increasingly elusive A recent survey reported that in New Zealand, just 16% of Kiwis said they could invest a small amount in real estate over time, compared to almost 60% who believe they could do the same in crypto. This isn’t just a statistic; it’s a reflection of the broader economic landscape. In many parts of the world, skyrocketing property prices and stagnant wages have created an insurmountable barrier to homeownership. Unless you have intergenerational wealth, the dream of owning a home is at odds with our current economic reality. Millennials and Gen Z, in particular, are finding it increasingly difficult to save for a down payment, let alone afford monthly mortgage repayments. Crypto investors are no longer ‘the odd one out’ Once considered the Wild West of finance, cryptocurrency has come of age and is no longer the sole domain of tech enthusiasts and libertarians. The asset class has entered the mainstream, with institutional investors, hedge funds, and even traditional banks dipping their toes into the digital currency waters. This institutional interest has lent credibility and stability to the market, making it a more viable alternative investment that could help deliver comparatively outsized gains as part of a portfolio. It takes mere minutes (and cents) to invest Reputable exchanges have also made buying and selling crypto as easy as shopping online. This accessibility is a game-changer. Unlike real estate, which often requires a significant upfront investment in terms of both finance and time, crypto allows for ‘micro investing’. You don’t need to buy an entire Bitcoin to get started; you can invest as little as you want and gradually increase your stake. What’s more, if you invest regularly, you stand to benefit from dollar-cost-averaging that reduces the impact of market volatility over time. A new mindset about institutions The financial crises of the past decades have left a lingering distrust in traditional financial institutions for many, and some are increasingly questioning how their investments and money are being used and who stands to benefit the most from their financial activities. Crypto, with its promise of decentralization, transparency, and democratization, presents an appealing alternative. Unlike traditional banking systems, where a portion of your money might end up lining the pockets of big tech, government, and banks, crypto offers a system that many believe to be more free and fair. Transactions are transparent and traceable on the blockchain, giving investors more confidence in how their funds are being handled. An eye on a ‘bigger prize’ Lastly, GenXers and Boomers, in particular, are looking to supercharge their retirement in an increasingly challenging economic environment. While returns are never guaranteed, and the crypto market is more volatile than other assets, it’s a risk many are willing to take for the possibility of outsized gains. While homes have long been seen as the ultimate investment and symbol of financial stability, this perception is changing and many believe we are at an inflection point in the history of wealth creation and investment. The move from lawns to ledgers is emblematic of a new era of investment -  one that values accessibility, innovation, and transparency. As more people recognize the potential of digital currencies and decentralized finance, we might see a future in which wealth aspirations focus on digital assets over property. Disclaimer: Crypto is volatile and carries risk, and its value can go up and down. Past performance is not an indicator of future returns. Please do your own research. In an era in which the traditional ‘white picket fence’ feels increasingly out of reach for many, a growing number of investors are trading in their ambitions of homeownership for a stake in the digital frontier. This trend isn't just a fleeting fad - it's a significant shift in how people are thinking about investment and wealth creation. In an era in which the traditional ‘white picket fence’ feels increasingly out of reach for many, a growing number of investors are trading in their ambitions of homeownership for a stake in the digital frontier. This trend isn't just a fleeting fad - it's a significant shift in how people are thinking about investment and wealth creation. Janine Grainger, Founder, and CEO of Easy Crypto, weighs in on why people are swapping lawns for ledgers… Home ownership has become increasingly elusive A recent survey reported that in New Zealand, just 16% of Kiwis said they could invest a small amount in real estate over time, compared to almost 60% who believe they could do the same in crypto. This isn’t just a statistic; it’s a reflection of the broader economic landscape. In many parts of the world, skyrocketing property prices and stagnant wages have created an insurmountable barrier to homeownership. Unless you have intergenerational wealth, the dream of owning a home is at odds with our current economic reality. Millennials and Gen Z, in particular, are finding it increasingly difficult to save for a down payment, let alone afford monthly mortgage repayments. A recent survey A recent survey Crypto investors are no longer ‘the odd one out’ Crypto investors are no longer ‘the odd one out’ Once considered the Wild West of finance, cryptocurrency has come of age and is no longer the sole domain of tech enthusiasts and libertarians. The asset class has entered the mainstream, with institutional investors, hedge funds, and even traditional banks dipping their toes into the digital currency waters. This institutional interest has lent credibility and stability to the market, making it a more viable alternative investment that could help deliver comparatively outsized gains as part of a portfolio. It takes mere minutes (and cents) to invest It takes mere minutes (and cents) to invest Reputable exchanges have also made buying and selling crypto as easy as shopping online. This accessibility is a game-changer. Unlike real estate, which often requires a significant upfront investment in terms of both finance and time, crypto allows for ‘micro investing’. You don’t need to buy an entire Bitcoin to get started; you can invest as little as you want and gradually increase your stake. What’s more, if you invest regularly, you stand to benefit from dollar-cost-averaging that reduces the impact of market volatility over time. A new mindset about institutions The financial crises of the past decades have left a lingering distrust in traditional financial institutions for many, and some are increasingly questioning how their investments and money are being used and who stands to benefit the most from their financial activities. Crypto, with its promise of decentralization, transparency, and democratization, presents an appealing alternative. Unlike traditional banking systems, where a portion of your money might end up lining the pockets of big tech, government, and banks, crypto offers a system that many believe to be more free and fair. Transactions are transparent and traceable on the blockchain, giving investors more confidence in how their funds are being handled. An eye on a ‘bigger prize’ Lastly, GenXers and Boomers, in particular, are looking to supercharge their retirement in an increasingly challenging economic environment. While returns are never guaranteed, and the crypto market is more volatile than other assets, it’s a risk many are willing to take for the possibility of outsized gains. While homes have long been seen as the ultimate investment and symbol of financial stability, this perception is changing and many believe we are at an inflection point in the history of wealth creation and investment. The move from lawns to ledgers is emblematic of a new era of investment -  one that values accessibility, innovation, and transparency. As more people recognize the potential of digital currencies and decentralized finance, we might see a future in which wealth aspirations focus on digital assets over property. Disclaimer: Crypto is volatile and carries risk, and its value can go up and down. Past performance is not an indicator of future returns. Please do your own research. Disclaimer: Crypto is volatile and carries risk, and its value can go up and down. Past performance is not an indicator of future returns. Please do your own research. Disclaimer: Crypto is volatile and carries risk, and its value can go up and down. Past performance is not an indicator of future returns. Please do your own research.

Why Investors are Swapping Lawns for Ledgers

5 Open-Source Learning Software Projects to Donate Via Kivach

Authors:
(1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research;
(2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind;
(3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Participant Interviews Participant Surveys Discussion and Future Work Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS C DETAILS OF QUANTITATIVE OBSERVATIONS C.1 Levenshtein Distance Levenshtein distance was calculated at the character level for edited versus non-edited texts using the default edit distance function from the Natural Language Tool Kit[10] package’s distance module (no transposition operations and substitution cost equal to 1). As an absolute measure, the distance metric indicates how many operations (insertion, deletion, substitution) are required to convert one string into another and is typically calculated with a dynamic programming approach to minimizing the cost of character-level edit operations for the conversion. However, as a measure dependent on the lengths of both input strings, comparing across edit distances becomes rather uninterpretable when the string sets differ in length, for example across sets of edited and unedited texts for characters, locations, plots, and dialogues. As we are interested in understanding the extent to which participants edited the output of Dramatron as a cohort, it is reasonable to normalise the distances with respect to their length, which we report in 6 (Right).To do this, we calculate a relative Levenshtein distance as the ratio of the raw Levenshtein distance between edited and non-edited (Dramatron) output text to the length of original Dramatron output. Conceptually, we can view the resulting measure as a proportional measure of interaction with our tool. Given that Levenshtein distance operations are character-level, and length is measured in characters, the proportion represents the a weighting of active versus passive interaction with Dramatron for the different levels of structure in the hierarchical story generation process. Positive scores for relative Levenshtein distance indicate one aspect of active writing with Dramatron, while negative scores for relative Levenshtein distance indicate one aspect of passive acceptance of Dramatron (choosing among generated text seeds is another aspect of interaction not accounted for with this metric).[11] C.2 Length Difference We note that for Mean Document Length in 6 (left), the means are both negative and positive, and are not scaled. This is to observed workflow differences per participant as well as to capture the directionality of editing Dramatron’s output. For the Mean Absolute Differences in 6 (center), we take the absolute difference between character lengths between original Dramatron output and edited output and normalize it with min-max normalization. C.3 Jaccard Similarity (Relatedness) To calculate Jaccard similarity, we first calculate the Jaccard distance, which is calculated by dividing the cardinality of the intersection of two sets and by the cardinality of their union. By subtracting Jaccard distance by 1, we obtain the Jaccard similarity. Jaccard metrics are scaled between 0 and 1, with 0 representing zero set similarity and 1 representing total set similarity. As Jaccard similarity simply compares set entities, one must specify the choice of entities to compare. In order to calculate the Jaccard similarity on Dramatron output and its edited counterparts, we apply a pre-processing pipeline to our texts, first tokenising sentences and words, and then generating a set of lemmas from word tokens based on their most probable part of speech according to WordNet [39]. The resulting scores are then lemma-based similarity scores between Dramatron output and participant edits, which we use as a descriptive measure of word choice similarity. Note that we do not calculate semantic similarity with embeddings, but simply compute set overlap at the lemma level. Lemmas are chosen to abstract over inflectional differences for words for a slightly more precise look at word choice. Future work should investigate alternative methods of assessing similarity, such as word mover distance [57] or BLEURT scores [100] C.4 Repetition We calculate repetition-based scores with open-source tools from [123], which calculate scores for various n-gram overlaps[12]. N-gram overlaps are calculated for unigram through 10-gram sequences, as well as for Total Consecutive Reptition (TCR) and Longeset Consecutive Repetition (LCR). To compute the Wilcox test, we use pairwise differences across corresponding features for chosen versus alternative seed generations (e.g. unigram to unigram differences, bigram to bigram differences, etc.). We do not weight differences by type of repetition feature. This paper is available on arxiv under CC 4.0 license. [10] https://www.nltk.org [11] From a linguistic point of view, insertions and deletions can range from deletion of entire words (e.g. this is really great → this is great) to the insertion or deletion morphemes to achieve a sociolinguistically marked effect (e.g. going → goin’, like to → liketa, etc). [12]I mplementation details at https://github.com/google-research/pegasus/blob/main/pegasus/eval/repetition/repetition_scorer.py. Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Authors: Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Abstract and Intro Storytelling, The Shape of Stories, and Log Lines Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Evaluating Text Generated by Large Language Models Participant Interviews Participant Interviews Participant Surveys Participant Surveys Discussion and Future Work Discussion and Future Work Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS G. CO-WRITTEN SCRIPTS C DETAILS OF QUANTITATIVE OBSERVATIONS C.1 Levenshtein Distance Levenshtein distance was calculated at the character level for edited versus non-edited texts using the default edit distance function from the Natural Language Tool Kit[10] package’s distance module (no transposition operations and substitution cost equal to 1). As an absolute measure, the distance metric indicates how many operations (insertion, deletion, substitution) are required to convert one string into another and is typically calculated with a dynamic programming approach to minimizing the cost of character-level edit operations for the conversion. However, as a measure dependent on the lengths of both input strings, comparing across edit distances becomes rather uninterpretable when the string sets differ in length, for example across sets of edited and unedited texts for characters, locations, plots, and dialogues. As we are interested in understanding the extent to which participants edited the output of Dramatron as a cohort, it is reasonable to normalise the distances with respect to their length, which we report in 6 (Right).To do this, we calculate a relative Levenshtein distance as the ratio of the raw Levenshtein distance between edited and non-edited (Dramatron) output text to the length of original Dramatron output. Conceptually, we can view the resulting measure as a proportional measure of interaction with our tool. Given that Levenshtein distance operations are character-level, and length is measured in characters, the proportion represents the a weighting of active versus passive interaction with Dramatron for the different levels of structure in the hierarchical story generation process. Positive scores for relative Levenshtein distance indicate one aspect of active writing with Dramatron, while negative scores for relative Levenshtein distance indicate one aspect of passive acceptance of Dramatron (choosing among generated text seeds is another aspect of interaction not accounted for with this metric).[11] C.2 Length Difference We note that for Mean Document Length in 6 (left), the means are both negative and positive, and are not scaled. This is to observed workflow differences per participant as well as to capture the directionality of editing Dramatron’s output. For the Mean Absolute Differences in 6 (center), we take the absolute difference between character lengths between original Dramatron output and edited output and normalize it with min-max normalization. C.3 Jaccard Similarity (Relatedness) To calculate Jaccard similarity, we first calculate the Jaccard distance, which is calculated by dividing the cardinality of the intersection of two sets and by the cardinality of their union. By subtracting Jaccard distance by 1, we obtain the Jaccard similarity. Jaccard metrics are scaled between 0 and 1, with 0 representing zero set similarity and 1 representing total set similarity. As Jaccard similarity simply compares set entities, one must specify the choice of entities to compare. In order to calculate the Jaccard similarity on Dramatron output and its edited counterparts, we apply a pre-processing pipeline to our texts, first tokenising sentences and words, and then generating a set of lemmas from word tokens based on their most probable part of speech according to WordNet [39]. The resulting scores are then lemma-based similarity scores between Dramatron output and participant edits, which we use as a descriptive measure of word choice similarity. Note that we do not calculate semantic similarity with embeddings, but simply compute set overlap at the lemma level. Lemmas are chosen to abstract over inflectional differences for words for a slightly more precise look at word choice. Future work should investigate alternative methods of assessing similarity, such as word mover distance [57] or BLEURT scores [100] C.4 Repetition We calculate repetition-based scores with open-source tools from [123], which calculate scores for various n-gram overlaps[12]. N-gram overlaps are calculated for unigram through 10-gram sequences, as well as for Total Consecutive Reptition (TCR) and Longeset Consecutive Repetition (LCR). To compute the Wilcox test, we use pairwise differences across corresponding features for chosen versus alternative seed generations (e.g. unigram to unigram differences, bigram to bigram differences, etc.). We do not weight differences by type of repetition feature. 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 [10] https://www.nltk.org [11] From a linguistic point of view, insertions and deletions can range from deletion of entire words (e.g. this is really great → this is great) to the insertion or deletion morphemes to achieve a sociolinguistically marked effect (e.g. going → goin’, like to → liketa, etc). [12]I mplementation details at https://github.com/google-research/pegasus/blob/main/pegasus/eval/repetition/repetition_scorer.py.

Exploring Quantitative Metrics for Text Editing: Levenshtein Distance, Jaccard Similarity and More

GenAI in Elections: Reshaping Campaigns, Raising Ethical Concerns

নির্বাচনে GenAI: প্রচারাভিযান পুনর্নির্মাণ, নৈতিক উদ্বেগ উত্থাপন

GenAI bei Wahlen: Kampagnen neu gestalten, ethische Bedenken aufwerfen

GenAI en las elecciones: remodelando campañas, planteando preocupaciones éticas

GenAI dans les élections : remodeler les campagnes et soulever des préoccupations éthiques

चुनावों में GenAI: अभियानों को नया स्वरूप देना, नैतिक चिंताएं बढ़ाना

選挙における GenAI: 選挙運動の再構築、倫理的懸念の高まり

선거에서의 GenAI: 캠페인 재편, 윤리적 우려 제기

GenAI nas eleições: remodelando campanhas, levantando preocupações éticas

ГенИИ на выборах: изменение избирательных кампаний, рост этических проблем

Seçimlerde GenAI: Kampanyaları Yeniden Şekillendirmek, Etik Kaygıları Artırmak

GenAI trong bầu cử: Định hình lại các chiến dịch, nâng cao mối quan ngại về đạo đức

GenAI 在选举中的应用：重塑竞选活动，引发道德担忧

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: jinruiy@student.unimelb.edu.au); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 5 Language Bias Discussion In light of our findings in a one-vs-many setting, we were keen to delve further into the underlying causes of the disparity between languages. 5.1 Bias Detection Language bias is likely if the query language aligns better with one document language than another. As mentioned earlier, Pyserini supports different tokenizers, specifically language-specific tokenizers or simple whitespace tokenization. Therefore, in the one-vs-many setting, we analyze the composition of the top-100 rankings for the 100 topics. During indexing of the document collection, we used the simple whitespace tokenizer, given the multilingual nature of the collection. However, over the queries during retrieval, we employed two different tokenizers — a language-specific tokenizer, and the whitespace tokenizer. We conducted a correlation analysis between the language of the topics and the language of the top 100 relevant documents. From Table 2, we can see that relevance judgments in our test cases are consistent across languages, ensuring uniformity in the correlation matrix within the test set. However, Figure 2 reveals that both approaches generate strong language bias. In both cases, the query language aligns better with documents in its own language than others. The right plot appears to show that languages from the same family has strong correlation (e.g., PL, CS) and (IT, ES) since they may have some shared vocabulary. 5.2 Collection Distribution Factors Initially, we hypothesized that the disparity for each language may be a contributing factor to this bias. Figure 3 presents the regression line between the number of documents in a given language and MRR, which explains much of the variation across languages. However, note the outlier above the regression line (Polish: PL), which has a substantial number of documents but surprisingly low MRR performance. We refer to this phenomenon as a “BM25 unfriendly” language. According to Wojtasik et al. (2023), the main reason for the low performance of Polish lies in its highly-inflected morphology, giving rise to a a multitude of word forms per lexeme, including inflections of proper names, and complex morphological structure. In such cases, lexical matching is less effective than in other morphologically-simpler languages. Furthermore, LUCENE 8.5.1 API does not have a language-specific tokenizer for Polish. Conversely, languages below the regression line can be termed “BM25 friendly” languages, as they require fewer documents to achieve higher MRR in retrieval. 5.3 Language Tokenizer Factors Secondly, we speculated that the choice of language-specific Analyzer in LUCENE might be a contributing factor, as it influences word tokenization, token filter, synonym expansion and other processing. [7] To investigate this, we conducted a controlled experiment in the one-vs-many setting. When indexing the collection, given the multilingual nature of the collection, we employed whitespace as the tokenizer. However, over the queries, we experimented with either a language-specific tokenizer or whitespace tokenizer. We then compared the linear regression of MRR against the number of documents in Figure 3. On the right side of the plot, we can see a strong correlation when using whitespace tokenization for both the collection and the queries, reducing language bias. Furthermore, when transitioning from languagespecific tokenizers to whitespace tokenizers, the overall MRR across all languages declined modestly, from 15.02 to 14.18. That is, the original performance level was largely preserved, but language bias was diminished in using simple whitespace tokenization. [7] https://lucene.apache.org/core/8_0_0/core/ org/apache/lucene/analysis/package-summary.html# package.description

Multi-EuP: Analysis of Bias in Information Retrieval - Language Bias Discussion

Imagine an alternate reality where words were a currency. In this reality, every word you utter depletes your life’s savings. From the day you are born, you are given a set number, and it’s the same as everyone else’s. Just like a board game, it’s up to you to choose how you use your points. Let me ask you.. How would you use your words? How would this shape the way you communicate, and how often you do? How would you measure the importance of the words coming out of your mouth? When would you speak, when would you rather stay quiet? Well. That’s the reality I used to live in for a few years when I was 6 years old. I don’t remember why I came up with this, or where I heard it, but it was a known fact. I lived within these parameters. I genuinely believed we were each given a set number of words for a lifetime. That once used, that was it, I could not get them back. This odd belief shaped the way I interacted with the world and the people around me. It made me a listener by default, teaching me the silent power of observation. Today, I am still am impacted by this lesson. I still carry it into every conversation, negotiation, and interaction as an adult. It turned into a life philosophy that I want to talk about today. 1. The Culture of Noise The world can’t stop talking. Silence is mistaken for absence. The hustle culture tricks you into thinking you always need to be busy and loud. This is a pursuit that is relentless, disguised as ambition. It constantly whispers that for you to be seen and to succeed, you must be the loudest, the fastest. Marketing campaigns like Nike's "Just Do It" pushes you to act without pause. It’s glorifying the grind above all else. These slogans become mantras, they embed themselves into our culture. They urge you to keep moving, keep shouting, keep striving — at the expense of quiet reflection and the deep, meaningful progress it can bring. Platforms like Instagram and TikTok have transformed how we view success and visibility. Louder voices and rapid content creation mean greater achievement. The fast pace of news cycles pressures you to remain perpetually informed and vocal. Reality TV glorifies those who dominate the conversation. They promote the value of speed and volume deep within your cultural consciousness. This shift is pushing society into a state of engagement. The noise of ceaseless posting and sharing drowns out moments of quiet introspection. Worse yet—you're told that silence is failure, that stillness is a step back. What cost does your voice pay in a race where everyone speaks but no one listens? You think: "I must do more, speak up, stand out". You're trading depth for volume, mistaking the echo of your own voice for progress. This drive never stops. It pushes you to respond, react, and move forward. But, it often comes at the expense of understanding, depth, and real connection. What if this bias towards action is costing you more than you realize? What if your failure to listen and observe is the silent thief of innovation, empathy, and meaningful progress? 2. Gaining Through Observance When you embrace the quiet power of observation, you unlock the potential for more precise and impactful actions. Silence becomes your strategy. It enables you to gather data and understand the world in a way that loudness never could. It equips you with unparalleled advantages. Observation is a gift. It's like gathering free intelligence. Each piece of data sharpens your decisions. It makes your moves not just calculated but more impactful. This is what’s on the other side of loudness: : Real G’s move in silence. Like the most skilled players in the game of life, you'll learn that the more you observe, the more precise your actions become. Strategic Advantage : Listen more. You will speak a language everyone craves: understanding. This not only boosts your communication skills but makes every word you choose to speak more powerful. You also tune into nonverbal cues and understand the unspoken. Don’t underestimate eye contact, facial expressions, body language and tone of voice. Enhanced Communication : People gravitate towards those who truly listen. By giving others the floor,you give them the gift of being heard, fostering deeper and more meaningful connections. This attentiveness makes people feel valued and understood. It naturally draws them closer to you. Deeper Connections : You're often the first to spot potential problems — you pick up on subtle cues. This allows you to address issues before they escalate. Improved Problem-Solving Skills : Getting more info before deciding leads to better outcomes. It also leads to more effective ones. Better Decision-Making : Being observant makes you more likely to notice the beauty and complexities of the world. This fuels creativity and innovation. Increased Creativity : Recognizing behavior patterns allows for personal growth. It improves emotional intelligence, making you more adaptable and empathetic. Greater Self-Awareness You don’t have to take my word for it. Take it from the master of obvervation and quiet strategy himself: “the wolf in cashmere”, aka . Bernard Arnault He masterfully demonstrated the power of observance in the luxury goods market in the late 90’s. In two short years, he navigated the complexities of negotiations between Louis Vuitton and Moët Hennessy, and managed to play both sides effectively. His strategic silence and keen observation allowed him to gain control. This led to the creation of LVMH, the world's largest luxury goods conglomerate. It propelled him to become one of the richest individuals globally. 3. The Misconception of Quietness You might be thinking “Bernard Arnaud is unrelatable. Plus, I need to be the Alpha male in the room, otherwise people will think I am weak.” They think you are weak? Good. Let them think that. That’s how you play chess. Being underestimated IS your greatest advantage. The most effective leaders often operate under the radar, observing and planning before making their move. Quietness allows you to Gather information Understand the dynamics at play Strike with precision when the time is right. True strength doesn't need constant validation through noise. Quiet confidence commands respect in a way that constant talking cannot. It's the mark of someone who is secure in their abilities and doesn't need the spotlight to know their worth. While others are busy talking, you're absorbing, learning, and gaining insights that they miss. Listening gives you a deeper understanding of people and situations. It makes you a more empathetic friend. A more astute businessperson. A more effective problem-solver. When you're quiet, people can't predict your next move. This gives you the element of surprise. This is a powerful tactic in both personal and professional realms. It allows you to navigate situations with an advantage they never see coming. Let the world underestimate you. It only amplifies the impact you'll make when you choose to speak with your actions and the carefully chosen words that follow. "I know silent people, and they are disengaged and disinterested." You think that silence signals disengagement? Think again. In the world of communication, silence is often misinterpreted. When you are silent, you are not on the sidelines; you are deeply immersed in the game. Being silent is simply engaging differently. I was often called “absent from the conversation”, when in fact I was: Actively listening and absorbing every word. Processing information, making connections they overlooked. Waiting for the moment my input can make the most significant impact. The interactions are thoughtful, not impulsive. So if you are still on the fence, embrace the silence. It doesn't mute your impact—it magnifies it. It ensures that when you do speak, the world listens. 4. Observations from the Quiet Corner No strategy is perfect. In observing my own childhood beliefs that led me to gravitate towards a world of observation, I must also admit that I went a little too far. I want to bring this up, because you might be on one side of the equation, or the other. And it doesn’t mean that you can’t adjust along the way. Let me explain. When I was convinced that humans had a limited quantity of speech, I took it to the extreme. I remember various times when that happened. An example would be a disagreement in the car between my mom and my brother, for which I had the answer to break the argument. I asked myself: Is this worth my words? The answer was no, and so I let them argue some more, and went on with my thoughts. Obviously, at some point, I realized that this was not sustainable, nor was it accurate. But after years of doing this, it shaped the way I saw and interacted with the world. I choose my words carefully. I always listen and speak to a 90/10 ratio. I only talk when it’s pertinent, or necessary. The good: I always let people speak before me, whether in a group or with one on one. I usually have more information about their thoughts and intentions. They have less about me at any given point. This gives me leverage. I am pretty comfortable with silence. I use this tool for negotiations, sales. I gather data before making decisions. This applies to all areas of life. Some people shoot, then aim. Others aim, the shoot. I aim, I take a deep breath to steady myself. I tune into the subtle signs around me. I notice the shift of the wind, the whisper of leaves, and the hidden trails in the underbrush. I align my focus. Then, I deliver a kill shot. The not so good: The fear of public speaking stemmed from my belief that every word I spoke had to carry weight. The more people there were listening, the more crucial it became for my words to resonate equally with everyone. This amplified the pressure on each statement I made. Eventually, this expectation I set for myself became overwhelming. It led me to moments where I was simply paralyzed, unable to speak. To sum it up, the self-imposed pressure created social anxiety. There are two lesson I learned from this: When you believe in something hard enough, it has the power to shift your reality. It changes who you are and how you act. Fully committing in one direction means you’re getting further from the other side. Duality is real, and sometimes, you need to adjust. 5. Frameworks: Cultivating Observant Living Embracing a lifestyle of observance doesn't mean you have to adopt silence as a permanent state. The goal is for you to find a balance that enhances your perception, decision-making, and interactions. There's a saying that my grandmother used to share: " ," which literately translates to " ." Tourne 7 fois la langue dans ta bouche avant de parler turn your tongue in your mouth seven times before speaking This French proverb emphasizes the value of thoughtful communication. It's about giving your words the weight they deserve, ensuring what you say truly matters. Now, stepping into a room full of voices, you probably have an impulse to join the chorus immediately. Here’s a thought: try holding back; observe first. The insight you gain from watching the others go first gives your words an edge. What comes out of your mouth will resonate more deeply because it's informed. Sharpening Your Observational Skills: A Personal Guide : Everything's super fast these days. Try hitting the pause button now and then. Look around you, soak it all in. You'll be surprised at what you've been missing. Slow Down : Life is a multisensory experience. Remember to smell the rain, feel the texture of an old book, listen to the inflection in someone's voice. It's about embracing the world in its entirety. Use All Your Senses : The smallest things can hold the greatest meanings. Pay attention to the nuances, the slight frowns, the hesitation before speaking. These details tell a story louder than words. Study the Details : Get a notebook. Use it to write down what you see, hear, feel. It's not just about remembering things, but recognizing patterns in your life. It's about the repeats and the changes. Write It Down : 70 to 93% of all communication is non verbal. There's a whole world of meaning in how someone stands, the look in their eyes, or the tone of their voice. Start paying attention to these. Then, you'll get what people are really saying, far beyond their words. Watch and Listen Doing all this doesn't just make you more observant. It connects you more deeply with life, with people, and yeah, with yourself too. 6. The Quiet Revolution ✊ The power of observation isn't just its strategic advantages in decision-making. It also adds depth to relationships. It's about fostering a deeper connection with the world around us and with ourselves. This quiet revolution is a call to embrace the subtleties of life, to find strength in silence. I’ll leave you with this. If you want to join, the mission is simple: Speak less, observe more. Notice the world in all its detail. Listen with intent. Choose your words with purpose. You may find that the quiet revolution leads you to way more opportunities than before. Perhaps those opportunities were always there. You were just too busy and loud to notice them. Remember— you're not withdrawing from the world; you're engaging with it on a deeper level. Friend, welcome to the quiet revolution. That's all for today, thank you for reading. ​ Strategizangly Yours, Ben

The Quietest in the Room: How Being Observant Cultivates Lifelong Success

রুমে সবচেয়ে শান্ত: কীভাবে পর্যবেক্ষক হওয়া আজীবন সাফল্যের চাষ করে

Der Ruhigste im Raum: Wie aufmerksames Beobachten lebenslangen Erfolg fördert

Lo más tranquilo en la habitación: cómo ser observador cultiva el éxito de por vida

Le plus silencieux de la pièce : comment être observateur cultive le succès tout au long de la vie

कमरे में सबसे शांत: कैसे चौकस रहना आजीवन सफलता की ओर ले जाता है

部屋の中で最も静かな人：観察力を持つことが生涯にわたる成功を育む方法

방에서 가장 조용함: 관찰력이 평생의 성공을 촉진하는 방법

O mais silencioso da sala: como ser observador cultiva o sucesso para toda a vida

Самый тихий в комнате: как наблюдательность способствует успеху на всю жизнь

Odanın En Sessizi: Gözlemci Olmak Yaşam Boyu Başarıyı Nasıl Geliştirir?

Điều yên tĩnh nhất trong phòng: Việc tinh ý sẽ nuôi dưỡng thành công suốt đời như thế nào

房间里最安静的人：善于观察如何获得终身成功

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 5 THE UNDISCOVERED COUNTRY AI copilots will transform how we search. Tasks are central to people’s lives and more support is needed for complex tasks in search settings. Some limited support for these tasks already exists in search engines, but copilots will expand the task frontier to make more tasks actionable and address the “last mile” in search interaction: task completion [58]. Moving forward, search providers should invest in “better together” experiences that utilize copilots plus traditional search, make these joint experiences more seamless for searchers, and add more support for their use in practice, e.g., help people to quickly understand copilot capabilities and potential and/or recommend the best modality for the current task or task stage. This includes experiences where both modalities are offered separately and can be selected by searchers and those where there is unification and the selection happens automatically based on the query and the conversation context. The foundation models that power copilots have other search-related applications, e.g., for generating and applying intent taxonomies [43] or for evaluation [19]. We must retain a continued focus on human-AI cooperation, where searchers stay in control while the degree of system support increases as needed [44], and on AI safety. Searchers need to be able to trust copilots in general but also be able to verify their answers with minimal effort. Overall, the future is bright for IR, and AI research in general, with the advent of generative AI and the copilots that build upon it. Copilots will help augment and empower searchers in their information seeking journeys. Computer science researchers and practitioners should embrace this new era of assistive agents and engage across the full spectrum of exciting practical and scientific opportunities, both within information seeking as we focused on here, and onwards into other important domains such as personal productivity [5] and scientific discovery [22]. REFERENCES [1] Eugene Agichtein, Ryen W White, Susan T Dumais, and Paul N Bennet. 2012. Search, interrupted: understanding and predicting search task continuation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 315-324. [2] Marcia J Bates. 1990. 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Navigating Complex Search Tasks with AI Copilots: The Undiscovered Country and References

The contemporary discourse on generative Artificial Intelligence (AI) presents a nuanced and dualistic narrative of technological development. I will delve into contrasting perspectives surrounding the current state and future of generative AI, juxtaposing the pervasive sentiments of disillusionment against the sustained advancements in the field. Notably, despite apparent stagnation in some sectors and declining public interest, significant strides in AI technology and applications continue to emerge. This analysis provides a nuanced examination of the cycle of hype, stabilization, and potential revolutionary impact of generative AI, comparing it with historical technological disruptions such as the printing press, electricity, and the internet. Through a balanced review of both setbacks and breakthroughs in the AI landscape, I aim to discern whether generative AI is merely experiencing a temporary lull or if it signifies a broader technological revolution. By critically assessing the evidence and forecasting future trends, the article also explores the implications of this technology on societal norms and industry practices, ultimately questioning the revolutionary potential of generative AI amidst ongoing challenges and evolving expectations. Economic Performance and Market Valuations Recent data suggest that the economic outcomes related to generative AI have not aligned with prior expectations. For instance, despite significant investments in hardware, such as the $50 billion spent on Nvidia equipment, the return, in terms of revenue, has been relatively modest, amounting to only $3 billion (The Wall Street Journal). Additionally, there is a notable stagnation in visits to AI-focused websites (Exponential View), and venture capital firm Sequoia has highlighted the overvaluation of AI startups, suggesting a disconnect between market valuations and underlying economic realities (The Information). Industry Challenges and Strategic Adjustments Several high-profile AI startups have experienced difficulties, signaling potential instability within the sector. For example, InflectionAI has been effectively disbanded, with Microsoft acquiring its assets, including the expertise of former CEO Mustafa Suleyman (TechCrunch). Moreover, StabilityAI faces uncertain prospects following leadership controversies (Forbes). Additionally, the broader enterprise adoption of AI technologies is hampered by concerns over security and ethical implications (Diginomica). Public Perception and Technological Engagement While the initial public enthusiasm for AI technologies like ChatGPT has waned, a subset of users, particularly software developers, continue to find value in these tools (Pew Research Center). This contrasts with the broader perception that AI tools serve more as novelties rather than practical aids in productivity (The Register). Innovative Developments and Future Prospects Despite these challenges, the development of AI technologies continues at a rapid pace. New models like Claude 3 from Anthropic are setting benchmarks in AI capabilities (Anthropic). The field also anticipates the arrival of advanced models such as OpenAI's GPT-5, signaling ongoing innovation and potential market recovery (Business Insider). Historical Perspective on Generative AI Development Tyler Cowen, in his analysis, proposes that the current state of AI reflects a natural progression seen in other revolutionary technologies. Drawing parallels to the adoption of the printing press, electricity, and the internet, Cowen suggests that the lifecycle of transformative technologies typically transitions through phases of hype, stabilization, and eventual widespread utility. This pattern, he argues, is evident in the current trajectory of generative AI technologies (Bloomberg). Cowen articulates that while the initial excitement surrounding generative AI has diminished—a phase characterized by inflated expectations and speculative investments—the underlying advancements in technology continue to progress significantly. This 'silent lull' is not a regression but a recalibration of expectations as the technology matures and integrates more deeply into various sectors. Generative AI, despite facing skepticism regarding its immediate impacts, mirrors the developmental paths of its historical counterparts. Notably, the internet, post-dot-com bubble, and the gradual integration of electricity into industrial processes both experienced periods of underwhelming performance followed by significant, long-term contributions to economic and social structures. Recent developments in AI technology further support Cowen's thesis. OpenAI's enhancement of enterprise services and the advancement of models like Google's GPT-4 competitor illustrate ongoing innovation within the field (ZDNet, Google). Moreover, the rapid progression of open-source AI models, although less visible to the casual user, underscores a robust, underlying growth in AI capabilities that continues to push the boundaries of what these technologies can achieve (Archive). The Path Forward Despite the optimism expressed by some industry leaders, there are notable challenges and setbacks that temper the enthusiastic projections about AI. Issues such as the slower-than-anticipated adoption rates in enterprises and the mixed results of new AI products like Google Gemini Advanced highlight the complex reality facing AI development (Diginomica, The Algorithmic Bridge). The question remains whether generative AI will indeed follow the transformative paths of its predecessors or diverge due to unique challenges and societal dynamics. The industry's ability to navigate these challenges, coupled with ongoing technical advancements, will likely determine the trajectory of AI's impact on society. Concluding Thoughts Generative AI's journey is marked by cycles of hype and recalibration, similar to earlier technologies. While immediate outcomes may seem underwhelming, the continued development and integration of AI into various sectors may very well underpin the next wave of significant technological and societal transformations. As with historical innovations, the true measure of AI's impact will likely be observed in hindsight, shaped by a complex interplay of technological capabilities, market forces, societal acceptance, and regulatory environments. References "A Peter Thiel-backed AI startup seeks $2 billion valuation" - The Wall Street Journal. Available at: https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d
"Growth and investment in AI technology" - Exponential View. Available at: https://substack.com/@exponentialview/note/c-52677620
"AI startups overvalued" - The Information. Available at: https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal
"Inflection AI dismantled" - TechCrunch. Available at: https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/
"Stability AI's uncertain future" - Forbes. Available at: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/
Tyler Cowen on AI's ongoing revolution despite subdued hype - Bloomberg
OpenAI introduces business version of ChatGPT - ZDNet
Challenges in AI adoption within enterprises - Diginomica
Google's GPT-4 and its marketplace challenges - The Algorithmic Bridge The contemporary discourse on generative Artificial Intelligence (AI) presents a nuanced and dualistic narrative of technological development. I will delve into contrasting perspectives surrounding the current state and future of generative AI, juxtaposing the pervasive sentiments of disillusionment against the sustained advancements in the field. Notably, despite apparent stagnation in some sectors and declining public interest, significant strides in AI technology and applications continue to emerge. This analysis provides a nuanced examination of the cycle of hype, stabilization, and potential revolutionary impact of generative AI, comparing it with historical technological disruptions such as the printing press, electricity, and the internet. Through a balanced review of both setbacks and breakthroughs in the AI landscape, I aim to discern whether generative AI is merely experiencing a temporary lull or if it signifies a broader technological revolution. By critically assessing the evidence and forecasting future trends, the article also explores the implications of this technology on societal norms and industry practices, ultimately questioning the revolutionary potential of generative AI amidst ongoing challenges and evolving expectations. Economic Performance and Market Valuations Economic Performance and Market Valuations Recent data suggest that the economic outcomes related to generative AI have not aligned with prior expectations. For instance, despite significant investments in hardware, such as the $50 billion spent on Nvidia equipment, the return, in terms of revenue, has been relatively modest, amounting to only $3 billion ( The Wall Street Journal ). Additionally, there is a notable stagnation in visits to AI-focused websites ( Exponential View ), and venture capital firm Sequoia has highlighted the overvaluation of AI startups, suggesting a disconnect between market valuations and underlying economic realities ( The Information ). The Wall Street Journal Exponential View The Information Industry Challenges and Strategic Adjustments Industry Challenges and Strategic Adjustments Several high-profile AI startups have experienced difficulties, signaling potential instability within the sector. For example, InflectionAI has been effectively disbanded, with Microsoft acquiring its assets, including the expertise of former CEO Mustafa Suleyman ( TechCrunch ). Moreover, StabilityAI faces uncertain prospects following leadership controversies ( Forbes ). Additionally, the broader enterprise adoption of AI technologies is hampered by concerns over security and ethical implications ( Diginomica ). TechCrunch Forbes Diginomica Public Perception and Technological Engagement Public Perception and Technological Engagement While the initial public enthusiasm for AI technologies like ChatGPT has waned, a subset of users, particularly software developers, continue to find value in these tools ( Pew Research Center ). This contrasts with the broader perception that AI tools serve more as novelties rather than practical aids in productivity ( The Register ). Pew Research Center The Register Innovative Developments and Future Prospects Innovative Developments and Future Prospects Despite these challenges, the development of AI technologies continues at a rapid pace. New models like Claude 3 from Anthropic are setting benchmarks in AI capabilities ( Anthropic ). The field also anticipates the arrival of advanced models such as OpenAI's GPT-5, signaling ongoing innovation and potential market recovery ( Business Insider ). Anthropic Business Insider Historical Perspective on Generative AI Development Tyler Cowen, in his analysis, proposes that the current state of AI reflects a natural progression seen in other revolutionary technologies. Drawing parallels to the adoption of the printing press, electricity, and the internet, Cowen suggests that the lifecycle of transformative technologies typically transitions through phases of hype, stabilization, and eventual widespread utility. This pattern, he argues, is evident in the current trajectory of generative AI technologies ( Bloomberg ). Bloomberg Cowen articulates that while the initial excitement surrounding generative AI has diminished—a phase characterized by inflated expectations and speculative investments—the underlying advancements in technology continue to progress significantly. This 'silent lull' is not a regression but a recalibration of expectations as the technology matures and integrates more deeply into various sectors. Generative AI, despite facing skepticism regarding its immediate impacts, mirrors the developmental paths of its historical counterparts. Notably, the internet, post-dot-com bubble, and the gradual integration of electricity into industrial processes both experienced periods of underwhelming performance followed by significant, long-term contributions to economic and social structures. Recent developments in AI technology further support Cowen's thesis. OpenAI's enhancement of enterprise services and the advancement of models like Google's GPT-4 competitor illustrate ongoing innovation within the field ( ZDNet , Google ). Moreover, the rapid progression of open-source AI models, although less visible to the casual user, underscores a robust, underlying growth in AI capabilities that continues to push the boundaries of what these technologies can achieve ( Archive ). ZDNet Google Archive The Path Forward The Path Forward Despite the optimism expressed by some industry leaders, there are notable challenges and setbacks that temper the enthusiastic projections about AI. Issues such as the slower-than-anticipated adoption rates in enterprises and the mixed results of new AI products like Google Gemini Advanced highlight the complex reality facing AI development ( Diginomica , The Algorithmic Bridge ). Diginomica The Algorithmic Bridge The question remains whether generative AI will indeed follow the transformative paths of its predecessors or diverge due to unique challenges and societal dynamics. The industry's ability to navigate these challenges, coupled with ongoing technical advancements, will likely determine the trajectory of AI's impact on society. Concluding Thoughts Concluding Thoughts Generative AI's journey is marked by cycles of hype and recalibration, similar to earlier technologies. While immediate outcomes may seem underwhelming, the continued development and integration of AI into various sectors may very well underpin the next wave of significant technological and societal transformations. As with historical innovations, the true measure of AI's impact will likely be observed in hindsight, shaped by a complex interplay of technological capabilities, market forces, societal acceptance, and regulatory environments. References "A Peter Thiel-backed AI startup seeks $2 billion valuation" - The Wall Street Journal. Available at: https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d "Growth and investment in AI technology" - Exponential View. Available at: https://substack.com/@exponentialview/note/c-52677620 "AI startups overvalued" - The Information. Available at: https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal "Inflection AI dismantled" - TechCrunch. Available at: https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/ "Stability AI's uncertain future" - Forbes. Available at: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ Tyler Cowen on AI's ongoing revolution despite subdued hype - Bloomberg OpenAI introduces business version of ChatGPT - ZDNet Challenges in AI adoption within enterprises - Diginomica Google's GPT-4 and its marketplace challenges - The Algorithmic Bridge "A Peter Thiel-backed AI startup seeks $2 billion valuation" - The Wall Street Journal. Available at: https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d https://www.wsj.com/tech/ai/a-peter-thiel-backed-ai-startup-cognition-labs-seeks-2-billion-valuation-998fa39d "Growth and investment in AI technology" - Exponential View. Available at: https://substack.com/@exponentialview/note/c-52677620 https://substack.com/@exponentialview/note/c-52677620 "AI startups overvalued" - The Information. Available at: https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal https://www.theinformation.com/articles/ai-agenda-openai-is-a-relative-steal "Inflection AI dismantled" - TechCrunch. Available at: https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/ https://techcrunch.com/2023/06/29/inflection-ai-lands-1-3b-investment-to-build-more-personal-ai/ "Stability AI's uncertain future" - Forbes. Available at: https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ https://www.forbes.com/sites/kenrickcai/2024/03/29/how-stability-ais-founder-tanked-his-billion-dollar-startup/ Tyler Cowen on AI's ongoing revolution despite subdued hype - Bloomberg Bloomberg OpenAI introduces business version of ChatGPT - ZDNet ZDNet Challenges in AI adoption within enterprises - Diginomica Diginomica Google's GPT-4 and its marketplace challenges - The Algorithmic Bridge The Algorithmic Bridge

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Генеративный ИИ в 2024 году: путь эволюции от хайпа к трансформации

2024'te Üretken Yapay Zeka: Heyecandan Dönüşüme Evrimi Yönlendirmek

AI sáng tạo vào năm 2024: Điều hướng quá trình phát triển từ cường điệu đến chuyển đổi

2024 年的生成式人工智能：从炒作到转型

In a significant move that is contributing to the evolution of real estate investment, Libertum is thrilled to announce a collaboration with Tulum Land and Properties. This partnership punctuates the end of the traditional approach to real estate investment in the Tulum region of Mexico. The partnership represents an evolution to an era where property tokenization is the norm. It is appropriate that the vibrant real estate scene of Tulum, Mexico, known for its stunning properties and burgeoning market potential, will now also be recognized as a place where real estate tokenization is a reality. Tulum Land and Properties and Libertum are set to introduce a new era of investment opportunities by tokenizing a unique collection of villas with an initial portfolio of 300 million dollars. This innovative venture offers forward-thinking investors a unique chance to be part of Tulum's flourishing real estate sector, positioning it as a prime location for progressive investment. Javvad Azam, CEO at Libertum, said: "Tulum's real estate market is at an inflection point, observed with increasing interest from global investors. Our collaboration with Tulum Land and Properties is not just about introducing groundbreaking investment opportunities; it’s about crafting a legacy in one of the most mesmerizing destinations in the world.” The collaboration is timely, as Tulum continues to capture the attention of international investors and enthusiasts, with property values in the area poised for significant growth. Engaging in this venture allows investors to secure their stake in what is predicted to be a lucrative market, leveraging the expertise and insight of both Libertum and Tulum Land and Properties. Furthermore, this partnership harbors the potential to assist Libertum, paving the way for expansion beyond Tulum into Cancun and further exploration of the captivating lands of Mexico. This strategic move underlines Libertum's commitment to fostering growth and expanding its footprint in the international real estate market. Be part of this extraordinary journey with Libertum and Tulum Land and Properties. For a closer look at the unique area and to explore the opportunities this collaboration will create, you are encouraged to visit www.tulumlandandproperty.com and www.libertum.io This is more than an invitation to invest; it's a call to be part of an industry evolution that is now inevitable. About Libertum Libertum offers a complete infrastructure for real estate companies, property owners, agencies, and other RWA projects to tokenize assets. From digitizing rental income to ownership transfers through DAO, Libertum caters to all your on-chain real estate requirements. List properties, trade tokens, borrow via our lending protocol or reserve on-chain properties for a smooth experience. About Tulum Land and Properties For over four thrilling years, Tulum Land & Property (TLAP) has been at the forefront of Tulum's dynamic real estate market, offering unparalleled consultancy and brokerage services specifically designed for international investors. This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ In a significant move that is contributing to the evolution of real estate investment, Libertum is thrilled to announce a collaboration with Tulum Land and Properties. This partnership punctuates the end of the traditional approach to real estate investment in the Tulum region of Mexico. The partnership represents an evolution to an era where property tokenization is the norm. It is appropriate that the vibrant real estate scene of Tulum, Mexico, known for its stunning properties and burgeoning market potential, will now also be recognized as a place where real estate tokenization is a reality. Tulum Land and Properties and Libertum are set to introduce a new era of investment opportunities by tokenizing a unique collection of villas with an initial portfolio of 300 million dollars. This innovative venture offers forward-thinking investors a unique chance to be part of Tulum's flourishing real estate sector, positioning it as a prime location for progressive investment. Javvad Azam, CEO at Libertum, said: "Tulum's real estate market is at an inflection point, observed with increasing interest from global investors. Our collaboration with Tulum Land and Properties is not just about introducing groundbreaking investment opportunities; it’s about crafting a legacy in one of the most mesmerizing destinations in the world.” "Tulum's real estate market is at an inflection point, observed with increasing interest from global investors. Our collaboration with Tulum Land and Properties is not just about introducing groundbreaking investment opportunities; it’s about crafting a legacy in one of the most mesmerizing destinations in the world.” The collaboration is timely, as Tulum continues to capture the attention of international investors and enthusiasts, with property values in the area poised for significant growth. Engaging in this venture allows investors to secure their stake in what is predicted to be a lucrative market, leveraging the expertise and insight of both Libertum and Tulum Land and Properties. Furthermore, this partnership harbors the potential to assist Libertum, paving the way for expansion beyond Tulum into Cancun and further exploration of the captivating lands of Mexico. This strategic move underlines Libertum's commitment to fostering growth and expanding its footprint in the international real estate market. Be part of this extraordinary journey with Libertum and Tulum Land and Properties. For a closer look at the unique area and to explore the opportunities this collaboration will create, you are encouraged to visit www.tulumlandandproperty.com and www.libertum.io www.libertum.io www.libertum.io This is more than an invitation to invest; it's a call to be part of an industry evolution that is now inevitable. About Libertum Libertum offers a complete infrastructure for real estate companies, property owners, agencies, and other RWA projects to tokenize assets. From digitizing rental income to ownership transfers through DAO, Libertum caters to all your on-chain real estate requirements. List properties, trade tokens, borrow via our lending protocol or reserve on-chain properties for a smooth experience. Libertum About Tulum Land and Properties For over four thrilling years, Tulum Land & Property (TLAP) has been at the forefront of Tulum's dynamic real estate market, offering unparalleled consultancy and brokerage services specifically designed for international investors. Tulum Land & Property (TLAP) This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ This story was distributed under HackerNoon’s Business Blogging Program. Learn more about the program here: https://business.hackernoon.com/ https://business.hackernoon.com/

Libertum Announces Strategic Collaboration with Tulum Land & Properties to Revolutionize Real Estate

Libertum রিয়েল এস্টেট বিপ্লব করতে Tulum জমি এবং সম্পত্তির সাথে কৌশলগত সহযোগিতা ঘোষণা করেছে

Libertum kündigt strategische Zusammenarbeit mit Tulum Land & Properties an, um den Immobilienmarkt zu revolutionieren

Libertum anuncia colaboración estratégica con Tulum Land & Properties para revolucionar el sector inmobiliario

Libertum annonce une collaboration stratégique avec Tulum Land & Properties pour révolutionner l'immobilier

लिबर्टम ने रियल एस्टेट में क्रांति लाने के लिए टुलम लैंड एंड प्रॉपर्टीज़ के साथ रणनीतिक सहयोग की घोषणा की

リバタム、不動産業界に革命を起こすためトゥルム・ランド＆プロパティーズとの戦略的提携を発表

Libertum, 부동산 혁명을 위해 Tulum Land & Properties와의 전략적 협력 발표

Libertum anuncia colaboração estratégica com Tulum Land & Properties para revolucionar o mercado imobiliário

Libertum объявляет о стратегическом сотрудничестве с Tulum Land & Properties, чтобы произвести революцию в сфере недвижимости

Libertum, Gayrimenkulde Devrim Yaratmak İçin Tulum Land & Properties ile Stratejik İşbirliği Duyurdu

Libertum công bố hợp tác chiến lược với Tulum Land & Properties để cách mạng hóa bất động sản

Libertum 宣布与 Tulum Land & Properties 开展战略合作，共同革新房地产

Web3 in Saudi Arabia: Faisal Al Monai's Insights on the Next Digital Frontier

What are ZK Proofs and How are They the Future of Verifiability

If you ever needed proof that Nvidia is no longer the company you think it is, look no further than the company's most recent GPU Technology Conference (also known as the GTC). The conference was chock-full of announcements, but the big one was of course the unveiling of the Blackwell class of AI chips which are supposedly going to be better than the widely-in-demand, and equally as expensive, Hopper processors that have powered the likes of OpenAI's ChatGPT. It was the demand for the H100 chips that transformed Nvidia into a company worth over $2 trillion, almost overnight in the larger scheme of things, leading some (including HackerNoon) to question whether the stock was in a bubble (almost a quarter of HackerNoon readers thought that it was). But, as if Nvidia was out to prove everyone wrong, the company's last earnings release showed that not only was it still selling the Hopper processors, it was selling more of them and would continue to do so in the near future. Which brings us to the Nvidia GTC. Nvidia Chief Executive Jensen Huang is no Steve Jobs, but he is certainly a shareholders' man, and he knows how to keep them happy – presentation skills be damned. The Blackwell announcement may be nothing akin to Apple's unveiling of the very first iPhone, but boy did Wall Street go crazy. And with crazy, comes worry. Worry about a monopoly. Worry about supply. Worry that the company may not be producing those darn AI processors fast enough. But for Nvidia, it is COVID-19 all over again: demand is up, consumers are desperate, and the company can charge whatever price it wants for its hardware. This, ladies and gentlemen, is the future tech companies had hoped for when they began aggressively hiring during the worldwide pandemic only to find demand normalizing once the world opened up. Which makes Nvidia quite possibly the only company to not only have profiteered off of consumers during the pandemic but to have hit an even bigger jackpot: the enterprise. Because face it, the $40,000 H100 chips aren't for everyday consumers — they're for the faceless corporations that can dish out the billions needed to train large language models. And therein lies Nvidia's success. You see, just a day after the GTC began, Jensen Huang held a meeting with investors to tell them the company was poised to win a large chunk of the $250 billion corporations spend on building and maintaining data centers every year given the variety of hardware and software Nvidia has on offer. Now all of this is well and good for the company, but there's one stakeholder that is hopelessly missing from all of Nvidia's future plans: the gamers. Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. Nvidia may very well be out of touch with its roots, but that's hardly what shareholders care about. And if Nvidia shareholders don't care about the gamers, then Nvidia doesn't care about the gamers. This is why, a conference that should have been about the launch of the company's next graphic card was instantly hijacked by talk of AI, because it's 2024, and AI, for all intents and purposes, sells. Some may call it the 'Woodstock of AI', but I'll call it what it is: the death knell of Nvidia's days as a gaming-focused company. 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. Microsoft Pulls The Corporate Espionage of a Lifetime How do you kill two birds with one stone? I don't know. Ask Satya Nadella. So laser-focused is the Microsoft chief executive on monopolizing his company's success in AI, that he has decided to do the unthinkable: hire its competitor's staff and hand them the reins of a newly launched division focused primarily on artificial intelligence. I am of course referring to Mustafa Suleyman and Karen Simonyan, the co-founders of AI startup Inflection AI, which, up until recently, should have been competing against Microsoft's ward OpenAI but found itself with an offer (I assume) they could not refuse. Yes, the very same Mustafa Suleyman who co-founded DeepMind Technologies and sold it to Google in 2014, will be leading Microsoft AI as its chief executive with the help of Karen Simonyan as the division's chief scientist. Microsoft also seems to have hired "several members" of the Inflection team who have chosen to join Suleyman and Simonyan to advance the company's Copilot and other consumer AI products and research. So what does Inflection AI get in return? A whopping $650 million, apparently, and the prestige of working with Microsoft. That's a lot of money to pay to get your competitor out of the way and keep OpenAI, and by extension, Sam Altman, in check, but as the world's most valuable company right now, Microsoft can probably get away with just about anything. Microsoft ranked #6 on HackerNoon's Tech Company Rankingsthis week. Google was on the #13 spot. Apple Pulls the Plug on In-House MicroLED Displays First the electric car, now the smartwatch displays. In perhaps what is a period of realignment for Apple, the company has pulled the plug on building its own smartwatch displays due to the cost and complexity involved. The microLED displays were due to be introduced in a future version of the Apple Watch and were part of a push to lower the company's reliance on partners, but it seems the company is no longer planning to design and develop that technology in house, maybe focusing instead on the newly launched (and confusingly reviewed) Apple Vision Pro(s). As with the electric car, the decision to pull the plug will result in the elimination of job roles at the company. Apple ranked #3 on HackerNoon's Tech Company Rankingsthis week. In Other News.. 📰 SEC Seeks $1.95B Fine in Final Judgment Against Ripple — via CoinDesk
AI is a data problem. Cyera is raising up to $300M on a $1.5B valuation to secure it — via TechCrunch
3 ways Apple’s monopoly lawsuit could change the iPhone experience for fans — via CNN
Musk's X Corp loses lawsuit against hate speech watchdog — via Reuters
Meta's broad-brush ban on Islamic term harmed free speech, board rules — via Axios
Adam Neumann makes a $500 million bid for WeWork that could hit $900 million if financing and diligence firm up — via CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. If you ever needed proof that Nvidia is no longer the company you think it is, look no further than the company's most recent GPU Technology Conference (also known as the GTC). The conference was chock-full of announcements, but the big one was of course the unveiling of the Blackwell class of AI chips which are supposedly going to be better than the widely-in-demand, and equally as expensive, Hopper processors that have powered the likes of OpenAI's ChatGPT. Nvidia Nvidia It was the demand for the H100 chips that transformed Nvidia into a company worth over $2 trillion, almost overnight in the larger scheme of things, leading some (including HackerNoon) to question whether the stock was in a bubble (almost a quarter of HackerNoon readers thought that it was). But, as if Nvidia was out to prove everyone wrong, the company's last earnings release showed that not only was it still selling the Hopper processors, it was selling more of them and would continue to do so in the near future. question question more continue to do so continue to do so Which brings us to the Nvidia GTC. Nvidia Chief Executive Jensen Huang is no Steve Jobs, but he is certainly a shareholders' man, and he knows how to keep them happy – presentation skills be damned. The Blackwell announcement may be nothing akin to Apple's unveiling of the very first iPhone, but boy did Wall Street go crazy. And with crazy, comes worry. Worry about a monopoly. Worry about supply. Worry that the company may not be producing those darn AI processors fast enough. But for Nvidia, it is COVID-19 all over again: demand is up, consumers are desperate, and the company can charge whatever price it wants for its hardware. This , ladies and gentlemen, is the future tech companies had hoped for when they began aggressively hiring during the worldwide pandemic only to find demand normalizing once the world opened up. This Which makes Nvidia quite possibly the only company to not only have profiteered off of consumers during the pandemic but to have hit an even bigger jackpot: the enterprise. Because face it, the $40,000 H100 chips aren't for everyday consumers — they're for the faceless corporations that can dish out the billions needed to train large language models. And therein lies Nvidia's success. You see, just a day after the GTC began, Jensen Huang held a meeting with investors to tell them the company was poised to win a large chunk of the $250 billion corporations spend on building and maintaining data centers every year given the variety of hardware and software Nvidia has on offer. meeting meeting Now all of this is well and good for the company, but there's one stakeholder that is hopelessly missing from all of Nvidia's future plans: the gamers. Nvidia never really started as an artificial intelligence company the way OpenAI and so many others like it did. It started out as a company that produced graphic cards for video games, and for a long, long time, this is what Nvidia was known for. But as an offshoot of all the research it did in producing better and better graphic cards, it ended up creating tensor cores for real-time deep learning image enhancement and upscaling technologies for use in video games. Little did Nvidia know that this technology would then serve as the backbone of LLMs, instantly transforming it as the arms dealer of choice in corporations' pursuit of the perfect AI. as Nvidia may very well be out of touch with its roots, but that's hardly what shareholders care about. And if Nvidia shareholders don't care about the gamers, then Nvidia doesn't care about the gamers. This is why, a conference that should have been about the launch of the company's next graphic card was instantly hijacked by talk of AI, because it's 2024, and AI, for all intents and purposes, sells. should Some may call it the ' Woodstock of AI ', but I'll call it what it is: the death knell of Nvidia's days as a gaming-focused company. Woodstock of AI Woodstock of AI 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. 👋 You’re reading HackerNoon's Tech Company News Brief, a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. You’re reading HackerNoon's Tech Company News Brief , a weekly collection of tech goodness that combines HackerNoon's proprietary data with internet trends to determine which companies are rising and falling in the public consciousness. Subscribe here to receive the complete newsletter in your inbox every Tuesday. Tech Company News Brief Tech Company News Brief here complete Microsoft Pulls The Corporate Espionage of a Lifetime How do you kill two birds with one stone? I don't know. Ask Satya Nadella. So laser-focused is the Microsoft chief executive on monopolizing his company's success in AI, that he has decided to do the unthinkable: hire its competitor's staff and hand them the reins of a newly launched division focused primarily on artificial intelligence. Microsoft Microsoft I am of course referring to Mustafa Suleyman and Karen Simonyan, the co-founders of AI startup Inflection AI, which, up until recently, should have been competing against Microsoft's ward OpenAI but found itself with an offer (I assume) they could not refuse. Yes, the very same Mustafa Suleyman who co-founded DeepMind Technologies and sold it to Google in 2014, will be leading Microsoft AI as its chief executive with the help of Karen Simonyan as the division's chief scientist. Google Google Microsoft also seems to have hired "several members" of the Inflection team who have chosen to join Suleyman and Simonyan to advance the company's Copilot and other consumer AI products and research. So what does Inflection AI get in return? A whopping $650 million , apparently, and the prestige of working with Microsoft. $650 million $650 million That's a lot of money to pay to get your competitor out of the way and keep OpenAI, and by extension, Sam Altman, in check , but as the world's most valuable company right now, Microsoft can probably get away with just about anything. and in check in check Microsoft ranked #6 on HackerNoon's Tech Company Rankings this week. Google was on the #13 spot. Tech Company Rankings Tech Company Rankings Apple Pulls the Plug on In-House MicroLED Displays Apple Pulls the Plug on In-House MicroLED Displays First the electric car , now the smartwatch displays. electric car electric car In perhaps what is a period of realignment for Apple , the company has pulled the plug on building its own smartwatch displays due to the cost and complexity involved. Apple Apple pulled the plug pulled the plug The microLED displays were due to be introduced in a future version of the Apple Watch and were part of a push to lower the company's reliance on partners, but it seems the company is no longer planning to design and develop that technology in house, maybe focusing instead on the newly launched (and confusingly reviewed) Apple Vision Pro(s). As with the electric car, the decision to pull the plug will result in the elimination of job roles at the company. Apple ranked #3 on HackerNoon's Tech Company Rankings this week. Tech Company Rankings Tech Company Rankings In Other News.. 📰 In Other News.. 📰 SEC Seeks $1.95B Fine in Final Judgment Against Ripple — via CoinDesk AI is a data problem. Cyera is raising up to $300M on a $1.5B valuation to secure it — via TechCrunch 3 ways Apple’s monopoly lawsuit could change the iPhone experience for fans — via CNN Musk's X Corp loses lawsuit against hate speech watchdog — via Reuters Meta's broad-brush ban on Islamic term harmed free speech, board rules — via Axios Adam Neumann makes a $500 million bid for WeWork that could hit $900 million if financing and diligence firm up — via CNBC SEC Seeks $1.95B Fine in Final Judgment Against Ripple — via CoinDesk CoinDesk CoinDesk AI is a data problem. Cyera is raising up to $300M on a $1.5B valuation to secure it — via TechCrunch TechCrunch TechCrunch 3 ways Apple’s monopoly lawsuit could change the iPhone experience for fans — via CNN CNN CNN Musk's X Corp loses lawsuit against hate speech watchdog — via Reuters Reuters Reuters Meta's broad-brush ban on Islamic term harmed free speech, board rules — via Axios Axios Axios Adam Neumann makes a $500 million bid for WeWork that could hit $900 million if financing and diligence firm up — via CNBC CNBC CNBC And that's a wrap! Don't forget to share this newsletter with your family and friends! See y'all next week. PEACE! ☮️ — Sheharyar Khan, Editor, Business Tech @ HackerNoon *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. *All rankings are current as of Monday. To see how the rankings have changed, please visit HackerNoon's Tech Company Rankings page. Tech Company Rankings Tech Company Rankings

Nvidia Throws Gamers Under the Bus

While everyone is focusing on the hot news about Microsoft's acqui-hiring Inflection AI in disguise and the shakeup at Stability AI, we'd like to concentrate on the exciting developments unfolding in the world of model architectures. For the hot news, check the Usual Suspects © section below. – a new architecture that rivals the famous Transformer-based models. Mamba's innovations address significant challenges in processing long sequences, a problem that has limited traditional models. Now, let's talk about Mamba Mamba leverages state-space models (SSMs)*, particularly excelling with its incorporation of Structured State Space (S4) models into a large language model (LLM) framework. This integration allows Mamba to achieve linear complexity scaling with sequence length, marking a significant advancement over the quadratic scaling seen in traditional Transformer-based models. So what is it? Its streamlined architecture incorporates selective SSM layers, enhancing both efficiency and flexibility. As a result, Additionally, Mamba efficiently processes extremely long sequences, surpassing earlier models in performance. it benefits from hardware-aware optimizations, maximizing the potential of contemporary GPU architectures. This means you can process much longer sequences without hitting memory or compute bottlenecks. Think about applications like genomic analysis, long-form content generation, and complex multi-modal data processing, all becoming more feasible with Mamba's power. *State-space models are mathematical frameworks that describe a system's dynamics in terms of its state variables and observations, capturing the evolution and uncertainty of processes over time.  SSMs are known for efficiency with long sequences. Mamba's ability to efficiently process long sequences while maintaining competitive performance has fueled research interest in adapting and extending the architecture for various domains. It seems Mamba’s architecture is getting more attention (Attention is all you need ;) – last week, three papers showcased exciting developments. The paper, , makes Mamba more suitable for deployment on resource-constrained devices by introducing an efficient 2D scanning method and a dual-pathway module for balanced global-local feature extraction. Results show a significant reduction in FLOPs while maintaining strong accuracy. EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba The paper, , extends Mamba to be a multi-modal large language model capable of jointly reasoning over vision and language. Experiments demonstrate competitive performance on vision-language tasks with faster inference speeds compared to Transformer-based models. Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference The paper, , presents a simplified Mamba-based architecture that addresses stability issues when scaling Mamba to larger sizes. The key innovation is EinFFT, a novel channel mixing technique that ensures stable optimization. SiMBA shows strong results on vision tasks and multivariate time series forecasting, closing the gap with state-of-the-art Transformers. SiMBA: Simplified Mamba-based Architecture for Vision and Multivariate Time series These three papers highlight the architectural flexibility and potential of the Mamba model, which is promising for future advancements in context window size and data type support. If you want to move beyond Transformers, that might be the way to go. You can find the Mamba repository here: https://github.com/state-spaces/mamba News From The Usual Suspects © https://x.com/emollick/status/1770289508589707625?s=20&embedable=true Microsoft Is Hungry Microsoft basically owns OpenAI ( — from Elon Musk’s against OpenAI). “Indeed, as the November 2023 drama was unfolding, Microsoft’s CEO boasted that it would not matter “[i]f OpenAI disappeared tomorrow.” He explained that “[w]e have all the IP rights and all the capability.” “We have the people, we have the compute, we have the data, we have everything.” “We are below them, above them, around them.” lawsuit Last February, Microsoft in Mistral and brought its newest AI model, Mistral Large, to Azure. invested Last week, Microsoft basically Inflection AI’s team, including two of its co-founders: Mustafa Suleyman and Karén Simonyan. Eric Newcomer at Microsoft’s creativity in non-acquisition strategies, such as partnering with and investing in companies like Inflection and OpenAI without formal acquisitions, sidestepping antitrust reviews. The Stratechery why many aspects of Inflection and its rapid ascent to Unicorn status were odd from the very beginning. Soma Somasegar from Madrona VC that Microsoft partners with Inflection.ai to innovate AI for consumers, aiming to transform its presence in the consumer market. acqui-hired points out explains thinks Stability AI Evokes Many Jokes About Instability This January, we published with the subtitle: “Investigating the Thin Line Between Genuine Innovation and Strategic Exaggeration in AI”. On March 23, after too many exaggerations Emad Mostaque from his position as CEO of Stability AI. According to him, he to concentrate on decentralized AI. What does it mean exactly — no one knows. Stability AI profile resigns wants OpenAI https://x.com/gdb/status/1772334028009652415?s=20&embedable=true Hugging Face announces the release of , the largest public domain dataset designed for training (LLMs. Encompassing 500 billion words across multiple languages including English, French, Dutch, Spanish, German, and Italian, it’s a multilingual collection sourced from diverse cultural heritage initiatives. This release aims to demonstrate the feasibility of developing open LLMs using copyright-free materials, supported by an international collaboration with organizations dedicated to open science in AI. Common Corpus Sam Altman Is Open-Sourcing His Orb The Worldcoin Foundation the Orb’s software (Orb is a device that scans irises to create unique digital IDs) open-source, aiming to enhance privacy and security in proving humanness online. The software, available under MIT/Apache 2.0 licenses on GitHub, supports World ID verification by processing images locally and ensuring data privacy through secure transfers. has made Enjoyed This Story? I write a weekly analysis of the AI world in the newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

Mamba Architecture: What Is It and Can It Beat Transformers?

How Sam Bankman-Fried Misused Billions in Customer Funds

In today's edition: What does research say about how far we are from AGI? News from the usual subjects: Anthropic, Cohere, OpenAI, etc. The freshest AI&ML research papers from the week of Mar 4 — Mar 10 Though the question, “ ” predates the term “artificial intelligence” itself, it saw a significant resurgence on Twitter last week, prompted by the Musk vs. OpenAI lawsuit (Musk accuses OpenAI of abandoning open-source principles and prioritizing profit over safety, hindering the safe development of AGI.) . Today, we will discuss “How Far Are We from Intelligent Visual Deductive Reasoning?” “How Far Are We From Automating Front-End Engineering?” and Stephen Wolfram’s article “Can AI Solve Science?” These papers offer fascinating explorations of the differences between human and artificial intelligence. How far are we from achieving human-level intelligence in machines (or AGI, or ASI)? But far more interesting were the papers and an article that came out last week tackling this question Intelligent Visual Deductive Reasoning In “ ?”, researchers from Apple explore Vision-Language Models (VLMs), like GPT-4V, in visual-based deductive reasoning, a complex yet less studied area, using Raven’s Progressive Matrices (RPMs)*. How Far Are We from Intelligent Visual Deductive Reasoning *Raven’s Progressive Matrices is a nonverbal intelligence test measuring abstract reasoning, using patterns to assess cognitive functioning without language. What caught my attention was the finding that . The paper notes, “ ” This inability to deal with abstract concepts marks a fundamental difference between computational processing and human cognitive abilities. AI systems like VLMs struggle with tasks requiring abstract pattern recognition and deduction VLMs struggle to solve these tasks mainly because they are unable to perceive and comprehend multiple, confounding abstract patterns in RPM examples. Being a sophisticated pattern recognizer doesn’t equate to sentience. The observation that “ ” highlights , suggesting a nuanced aspect of intelligence that current AI lacks. Another intriguing point was the models’ overconfidence. all the tested models never express any level of uncertainty the importance of doubt and uncertainty in human cognition Automating Front-End Engineers In “ ”, researchers from Stanford University, Georgia Tech, Microsoft, and Google DeepMind have developed a benchmark for Design2Code, aiming to evaluate how well multimodal LLMs convert visual designs into code. Here, the replacement of humans came closer. Despite some limitations, there were considerable advancements in using generative AI to convert designs into front-end code. It’s remarkable that “ .” This finding challenges traditional notions of artistic and creative value, Design2Code: How Far Are We From Automating Front-End Engineering? annotators think GPT-4V generated webpages can replace the original reference webpages in 49% of cases in terms of visual appearance and content; and in 64% of cases, GPT-4V generated webpages are considered better questioning whether creativity is uniquely human or can be algorithmically reproduced — or even surpassed. However, . VLMs struggle with “ ” posing questions about understanding and interpretation. significant limitations persist recalling visual elements from the input webpages and generating correct layout designs. So, the important question is actually not how far we are from AGI (whatever it is), but how we embrace human-AI collaboration most effectively. AI Solving Science In that sense, Stephen Wolfram’s blog post “ ” serves as an excellent example. In the very beginning, he plainly states that AI cannot solve all scientific questions. However, there is significant value in AI assisting scientific progress. He discusses how LLMs can serve as a new kind of linguistic interface to computational capabilities, providing high-level “autocomplete” for scientific work. As he usually does, he emphasizes the transformative potential of representing the world computationally and suggests that pockets of computational reducibility* can be found by AI as well. Can AI Solve Science? *A pocket of computational reducibility — a fascinating concept introduced by Wolfram — is a situation or problem within a complex system where, despite the system’s overall unpredictability, predictable patterns or simplified behaviors emerge, allowing for easier understanding or calculation. Wolfram argues that AI can significantly aid scientific discovery by providing new tools for analysis and exploration, but its ability to completely “solve” science is limited by fundamental principles such as computational irreducibility. The future of AI in science lies in its integration with human creativity and understanding, leveraging its strengths to uncover new knowledge within the constraints of what is computationally possible. We might be able to survive without front-end developers (no offense intended), but scientists remain indispensable! To summarize: https://x.com/pmddomingos/status/1766945083314827455?s=20&embedable=true News from The Usual Suspects © Cohere and its commitment to the research community https://x.com/cohere/status/1767275128813928611?s=20&embedable=true Hugging Face Starting an ambitious open robotics project and hiring , a former Tesla scientist, to expand beyond software into robotics, including work on humanoid robots like Tesla’s Optimus. Remi Cadene Offers an amazing tool: https://x.com/maximelabonne/status/1767124527551549860?s=20&embedable=true Russia’s talent is invisible According to “The Global AI Talent Tracker 2.0”, Russia has no chance in the AI race: this analysis Inflection enhances its Pi Inflection , enhancing its personal AI, Pi, with IQ capabilities alongside its empathetic EQ. They claim to compete with GPT-4, achieving significant efficiency and performance improvements, especially in STEM areas, with less computational resource usage. launches Inflection-2.5 Chips TSMC (based in Taiwan), the world’s largest contract chipmaker, is for an Arizona chip plant. This funding, part of the CHIPS and Science Act of 2022, aims to boost domestic semiconductor production. TSMC’s $40 billion investment in the plant marks one of the largest foreign investments in U.S. history. expected to receive over $5 billion in U.S. federal grants OpenAI: new members on the board Sam Altman is reinstated as a member of the OpenAI board. , with a rich history as the CEO of the Bill and Melinda Gates Foundation, brings extensive experience in healthcare and philanthropy. , a seasoned executive with roles at Sony Entertainment and as a lawyer, offers legal and entertainment industry insights. , leading Instacart and with a background at Meta Platforms, including as head of Facebook, contributes expertise in technology and e-commerce. This diverse trio enhances OpenAI’s governance with their wide-ranging expertise and perspectives. He is joined by three women: Sue Desmond-Hellmann Nicole Seligman Fidji Simo Elon’s Grok https://x.com/elonmusk/status/1767108624038449405?s=20&embedable=true Anthropic Offers a great collection of prompts in its Prompt library Enjoyed This Story? I write a weekly analysis of the AI world in the newsletter. We aim to equip you with comprehensive knowledge and historical insights, so you can make informed decisions about AI and ML. Turing Post 🎁 Bonus: The freshest research papers, categorized for your convenience Enhancements in Language Models and Multimodal Understanding : Showcases an LLM’s intrinsic ability to understand and generate music, expanding LLMs’ applications beyond text. ChatMusician Read the paper : Demonstrates advanced multimodal understanding by processing extensive contexts of text, video, and audio. Gemini 1.5 Read the paper : Enhances TTS synthesis with a factorized diffusion model for zero-shot natural speech generation. NaturalSpeech 3 Read the paper : Utilizes fine-tuned MLMs for superior image-text data filtering, improving dataset quality for MLM training. Finetuned Multimodal Language Models Are High-Quality Image-Text Data Filters Read the paper : Introduces the first LLM designed for the legal domain, demonstrating proficiency in legal text understanding and generation. SaulLM-7B Read the paper Novel Training and Evaluation Techniques : Proposes a method for scaling instruction tuning in mathematical reasoning, significantly enhancing LLMs’ problem-solving abilities. MathScale Read the paper : Reveals redundancy in LLM layers and proposes a pruning strategy, maintaining performance while reducing model size. ShortGPT Read the paper : Enhances memory efficiency in LLM training without sacrificing performance by applying gradient low-rank projection. GaLore Read the paper : Develops a method for collaborative decoding among multiple LLMs, improving performance across various tasks. Learning to Decode Collaboratively with Multiple Language Models Read the paper : Advocates for training RL value functions through classification instead of regression, boosting performance and scalability. Stop Regressing Read the paper Advances in Generative Models and Data Synthesis : Introduces a framework for generating synthetic multimodal datasets, overcoming limitations in data privacy and diversity. MAGID Read the paper : Trains a generative model to create interactive virtual worlds from text, images, or sketches, advancing interactive environment simulation. Read the paper Genie Scalability and Efficiency in AI Systems : Details a system for training LLMs on over 10,000 GPUs, tackling challenges in training efficiency and stability at large scales. MegaScale Read the paper : Improves State Space Models with dense hidden connections for efficient large language models, enhancing performance with minimal parameter increase. DenseMamba Read the paper Exploring New Frontiers in AI and Machine Learning : Illustrates that planning and prediction in time series can be simplified through learned contrastive representations. Inference via Interpolation Read the paper : Employs simulated trial and error for tool learning, significantly enhancing LLMs’ practical application capabilities. LLMs in the Imaginarium Read the paper : Aims to improve context length generalization in LLMs by refining interpolation techniques for out-of-distribution token positions. Resonance RoPE Read the paper : Enhances the localization of behaviors in LLMs to specific components, improving diagnostic capabilities and model understanding. AtP Read the paper : Investigates using world models for enhancing visual representation learning, beyond reinforcement learning applications. Learning and Leveraging World Models in Visual Representation Learning Read the paper Platforms and Tools for Model Evaluation and Interaction : Provides a platform for evaluating LLMs based on human preferences, offering insights into model rankings through pairwise comparisons. Chatbot Arena Read the paper : Explores enhancing LLMs’ reasoning capabilities using RLHF, comparing different algorithms and reward strategies. Teaching Large Language Models to Reason with Reinforcement Learning Read the paper Also published . here

How Far Are We From Human-level Intelligence in AI?

Hey everyone! and just like you, I’ve been fascinated with the recent progress of artificial intelligence. Realizing that I needed to stay abreast with all the developments happening, I decided to embark on a personal journey of learning, thus was born! With this series, I will be learning about LLMs and share ideas, experiments, opinions, trends & learnings through my blog posts. You can follow along the journey on HackerNoon or my personal website . In today’s article, we’ll be looking to build a Semantic Kernel with the help of GPT-4. , I’m Nataraj 100 days of AI here here There is a lot happening in generative AI space. So much so that Nvidia the arms dealer of generative AI with its H100 chips is now surpassed Amazon’s market valuations. It is also a very dynamic space with lots of activity from startups and the big tech companies. In this post I will highlight three big trends that we might see in the next 12-18 months. 1 – Data Deals For last two decades we kept saying . This phrase will have real implications this year. A lot of companies have accumulated unique sets of data and have found a real product market fit. Recently a news broke out the Reddit made a deal to access to its data to an unnamed company for $60M. The terms of the deal are unknown but that’s $60M new revenue for Reddit. Look out for more deals from companies that have unique sets of data. data is the new oil 2 – Focus on Open Source Models Since Open AI launched chat gpt, the conversation was dominated by gpt series base models which are closed models, meaning no one except the Open AI team knows the details of how the model was trained, what data it was trained on & the parameters of the model. We will see more Open Source models take stage and capture attention. The main player in this arena to look out is Meta (Facebook). Meta is taking the page out of Google’s approach in mobile. While Apple’s mobile operating system was closed, Google strategy was to be an open source mobile operating system that fuels 80%+ of world’s mobile phones. So keep an eye out on Meta. 3 – Small Language Models Open AI proved to the world what is the power of Large Language Models (LLMs). But as every company in AI is racing to build product ready products and features and trying get enterprises adopt AI for their business. LLMs might not always be the best solution. A smaller model which can take less compute to train and uses less but more quality data might actually be better. These models referred to as SLMs could be run on mobile phones which will increase the access of AI to wider audience. Some of the SLMs to check out are: Phi by Microsoft Orca by Microsoft T5 by Google One of the main forcing function to adapt and innovate with SMLs is to reduce the compute cost and there by making AI more accessible. 4 – Focus on LLM Security I am not talking about data security to train the LLM. I am referring to instances where users can hack LLMs to do something that they are not supposed to or break just them. There are a lot of security issues that are cropping up on how to hack LLM behaviors. Some include: to make LLM answer something that it shouldn’t like making a banned substance at home. Jailbreak to hide prompt instructions that break LLMs Prompt Injection is embedding bad data on the internet which will be used by LLMs and can later be misused. Data poisoning There are more and more ways we are currently discovering that LLMs can be compromised as we fix the ones we know about. This will be one of the active areas where progress will be made in 2024 both on research & product side. 5 – Regulatory Implications It pretty clear that AI is going to have huge implications on the society across all sectors. It will cross paths with every aspect of society and as we have seen every big company announce major AI initiatives and investments into AI during 2022-23. Microsoft invested in Open AI & Inflection Google & Amazon invested in Anthropic Jeff Bezos invested in Perplexity Airbnb acquired a stealth AI company called gameplanner.ai Regulators were already focused on big tech which blocked them doing any acquisitions in last 3 years. With this in the background we will see regulators both in the U.S and E.U closely watch Ai investments and also potentially detangle some of the investments. The investments were made primarily to avoid acquisitions because people who are running big tech companies know that it is going to be an uphill battle to get them approved. But even some of these investments might also come under scrutiny. That’s it for Day 14 of 100 Days of AI. I write a newsletter called Above Average where I talk about the second order insights behind everything that is happening in big tech. If you are in tech and don’t want to be average, . subscribe to it Follow me on , or for latest updates on 100 days of AI. If you are in tech you might be interested in joining my community of tech professionals . Twitter LinkedIn HackerNoon here

100 Days of AI, Day 14: The Five Generative AI Trends to Watch Out for in 2024

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