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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

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

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

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房间里最安静的人：善于观察如何获得终身成功

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