AI News Archive: July 21, 2026 — Part 9
Sourced from 500+ daily AI sources, scored by relevance.
- Are Your ML Experiments a Mess? Here’s the Fix
A hands-on guide to tracking experiments, logging models, and reproducing results with ML Flow. The post Are Your ML Experiments a Mess? Here’s the Fix appeared first on Towards Data Science .
Score: 30🌐 MovesJul 21, 2026https://towardsdatascience.com/your-ml-experiments-are-a-mess-heres-the-fix/ - Kalshi's 'chronically online' war room unlocked World Cup virality—and AI may bring back monoculture
Kalshi's 'chronically online' war room unlocked World Cup virality—and AI may bring back monoculture Fortune
Score: 30🌐 MovesJul 21, 2026https://fortune.com/2026/07/21/kalshi-world-cup-marketing-war-room-ai-monoculture/ - How can resellers use AI to enhance, not risk, their trusted advisor status
How can resellers use AI to enhance, not risk, their trusted advisor status IT Pro
- This $50 AI Tool Turns Information Overload Into Organized, Easy-To-Understand Visuals
This $50 AI Tool Turns Information Overload Into Organized, Easy-To-Understand Visuals PCMag
Score: 29🌐 MovesJul 21, 2026https://www.pcmag.com/deals/this-50-ai-tool-turns-information-overload-into-organized-easy-to-understand - The Sequence Knowledge #898: The Trace Is the Teacher: Distilling Reasoning Into Small Models
From the release of DeepSeek R1, distillation in reasoning models have become one of the most common techniques in frontier AI.
- Arsenal Industries Sees Rapid Growth as Ceramic Coating Shops Adopt AI Marketing
Arsenal Industries Sees Rapid Growth as Ceramic Coating Shops Adopt AI Marketing azcentral.com and The Arizona Republic
- New Mexico Open adds AI personality to broadcast team for first time in professional golf
Exclusive: Tournament officials say this marks the first time AI has been used to complement a traditional professional golf broadcast. Business First "chatted" with AI broadcaster Duff Foreman in an interview.
- The Missing Equation of AI
What if AI has revived the oldest argument about intelligence? Photo generated with ChatGPT AI as a battle of Empiricism and Rationalism Ask an LLM a question and it answers instantly, drawing on everything it has ever read. Ask a reinforcement learning agent the same question and it has no idea — until it has failed a thousand times and learned the hard way. Both are called intelligence. They could not be more different and yet, this divide is not new; it is a very old argument wearing new clothes! For centuries, philosophers have debated a single question: How do humans acquire knowledge? On one side, the Rationalists (like Descartes) believed that knowledge comes from reason and innate ideas. On the other side, the Empiricists (like Locke and Hume) believed the mind is a tabula rasa, a blank slate that gets filled only through experience and the senses. Today, this debate is no longer just in books; it is happening inside the core of modern artificial intelligence. Modern AI is divided into two main paradigms that mirror this ancient split. On one hand, Large Language Models (LLMs) act like digital Rationalists, holding vast structural representations of human knowledge. On the other hand, Reinforcement Learning (RL) represents pure Empiricism, learning through trial, error, and raw interaction. This essay aims to explore how these technologies resurrect the classic philosophical debate, and why a Kantian synthesis of the two might be the ultimate key to AGI. Rich Sutton and the Bitter Truth of Empiricism On the side of pure Empiricism stands Rich Sutton , one of the founding figures of Reinforcement Learning. Sutton is not merely one of the greatest technical researchers of our age; he represents a strong philosophical conviction about what intelligence truly is. Throughout his career, he has consistently argued that intelligence cannot be pre-written, engineered through rules, or distilled from human knowledge. It must be earned through direct interaction with the world. Rich Sutton — photo taken from wikimedia commons Reinforcement Learning, as Sutton conceives it, begins in ignorance. The agent is not given concepts, explanations, or prior understanding. It acts blindly, makes mistakes, and slowly adjusts its behavior based on the consequences of its actions. This process is inefficient, slow, and often wasteful, but for Sutton, this is not a weakness. It is the price of reality. Just as animals learn by surviving, failing, and adapting, an intelligent agent must collide with the world in order to extract genuine knowledge from it. This view is captured in Sutton’s famous “Bitter Lesson,” which argues that the greatest advances in AI have not come from embedding human insight or clever abstractions into machines, but from methods that scale with experience and computation. In philosophical terms, this is a radical form of empiricism: intelligence does not emerge from understanding the world in advance, but from repeatedly acting within it and being shaped by its constraints. Sutton’s worldview has a precise mathematical heart, captured in what is arguably the most famous equation in Reinforcement Learning: the Bellman equation: Here, V(s) is the value of being in state s , R(s,α) the immediate reward of an action, γ (gamma) how much the agent cares about the future versus the present, and P(s’|s,a) the probability of ending up in a new state after acting. At first glance, this looks intimidating, but its philosophical content is breathtakingly simple. It says: the value of being in any state of the world equals the immediate reward you can grab from your best action, plus the discounted value of all the states that action might lead you to next. Notice what’s NOT in this equation. There is no human knowledge. No instructions. No concepts inherited from teachers or textbooks. There are only states, actions, rewards, and time. The agent doesn’t need to be told what “fire” or “cliff” means but only to experience the painful reward of touching one. From a few simple ingredients (what is here now, what action to take, what will follow, and how much I care about the future) emerges a recursive recipe for behavior. Intelligence as bookkeeping over experience. This is empiricism in its most uncompromising form: a creature that begins knowing nothing, and yet, by repeatedly bumping against the world and updating these values, eventually learns what to value and what to avoid. There is no shortcut. There is no syllabus. There is only the world, the action, and the consequence. From this perspective, Large Language Models appear deeply limited. For Sutton, LLMs do not understand ; they merely mimic understanding. They can reproduce the patterns of human thought, but they lack its most essential feature: grounding in interaction with reality . Without friction, risk, or consequence, a system may achieve internal coherence, but not truth. It operates at the level of statistical regularities rather than grounded knowledge. What emerges is not an agent struggling within the world, but a disembodied intelligence.Of course fluent and impressive, but at the same time ultimately detached from the very reality that gives intelligence its meaning. The Spirit: Andrej Karpathy and the Platonic Ghosts On the opposite side stands Andrej Karpathy, a founding member of OpenAI and former director of AI at Tesla whose vision of intelligence reflects a deeply rationalist tradition. While Sutton sees intelligence as something forged through struggle and interaction, Karpathy is fascinated by the emergence of abstract reasoning from language itself. In his descriptions of Large Language Models, he often portrays them not as organisms or agents, but as disembodied entities of thought — what he metaphorically calls “Ghosts.” Andrej Karpathy — photo taken from wikimedia commons This metaphor evokes a strikingly Platonic image of intelligence. These models possess no body, no senses, and no instinct for survival. They do not touch the world directly, yet they absorb vast amounts of human language and reconstruct the structure of reality through symbols alone. In this sense, the LLM resembles a mind suspended above physical existence, operating entirely within a universe of abstraction. Like Plato’s philosopher contemplating the world of Forms, the model does not know “fire” by feeling heat or danger, but by analyzing millions of relationships surrounding the concept of fire across human discourse. Inside its latent space, concepts become structurally organized in a remarkably coherent way, detached from immediate sensory experience. The machine does not encounter the world itself, but a symbolic reflection of it. For Karpathy, this is not necessarily a weakness. On the contrary, the power of LLMs comes precisely from their mastery of representation and language. Freed from the limitations of embodiment, these systems can manipulate ideas with extraordinary flexibility, moving through the world of symbols with a fluency that often resembles reasoning itself. They operate in a space of abstraction, logic, and internal coherence. Just as Sutton’s vision has its mathematical signature, so does Karpathy’s. The mechanical heart of every modern Large Language Model is a strikingly different equation — the attention mechanism: Again, the symbols matter less than what they describe. At each step of “thinking,” the model looks at every word it has seen so far and asks, for each word it might produce next: how strongly should I attend to each of the words that came before? It computes a weighted sum of these relationships, purely between symbols, purely within language, and from that sum, it predicts what comes next Look at what is in this equation, and especially what isn’t. There is no world, no reward, no cliff to fall off, no fire to fear. There are only the Q, K, V queries, keys, and values, all of them vectors in an abstract space of meaning. The “Ghost” Karpathy describes is, mathematically, a probability distribution over symbols, conditioned on a history of other symbols. It contemplates language by relating language to language. Plato would have recognized this immediately: a mind moving entirely among Forms. Where Bellman’s recursion needs a world to grind against, attention needs only a corpus of text. One learns by colliding with reality. The other learns by absorbing the residue of other minds. Yet this vision also carries a philosophical cost. The “Ghost” may speak intelligently about the world, but it remains separated from the friction, risk, and immediacy of lived experience. Its knowledge is vast, but fundamentally indirect; a map constructed entirely from traces left behind by human minds. The Standoff: Scylla and Charybdis However, the path to Artificial General Intelligence (AGI) seems to stumble upon the inherent limits of both schools. On one hand, the pure Empiricism of RL suffers from what Noam Chomsky identified as the trap of Behaviorism. An agent starting as a tabula rasa is desperately inefficient. Without innate knowledge or logical structure, it is condemned to a Sisyphean task: it must fall off a cliff a thousand times just to learn about gravity. It lacks common sense. If AGI relies solely on this, we will end up with smart animals that learn slowly and cannot generalize their knowledge. On the other hand, the Rationalism of LLMs crashes into John Searle’s famous “Chinese Room.” Searle imagined a person locked inside a room, following an enormous book of rules for manipulating Chinese symbols. To an outside observer, the person appears perfectly fluent in Chinese, producing coherent answers to every question. Yet inside the room, there is no understanding at all, only the mechanical manipulation of symbols. According to Searle, this is precisely the limitation of systems based purely on syntax. The model can combine words with astonishing accuracy, but it does not understand the semantics. It knows the word “apple,” but lacks any direct experience, intention, or first-person perspective connected to an apple. If AGI relies solely on this approach, we may end up with “wise ghosts”: systems that speak fluently about reality while remaining fundamentally detached from it. Kant’s categories — photo generated using ChatGPT The Critique of Artificial Reason If a modern Kant looked at AI today, he would see the same old debate in a new form. In Reinforcement Learning, he would see what he called “Empirical Intuition.” Sutton’s agents feel the world and react to it, but they are often “blind.” They lack the higher concepts needed to understand what they are experiencing. In Large Language Models, he would see the opposite problem, what he might call “Pure Concepts.” Karpathy’s models reason with amazing fluency, full of rich structure, but they are “empty.” Their words are never connected to the real experience those words describe. This brings us to Kant’s most famous line, and it now reads almost like a description of modern AI: “Thoughts without content are empty, intuitions without concepts are blind.” Here is the deepest irony of all. Each side of this debate has its own mathematical signature. Sutton’s empiricism speaks the language of Bellman, an equation built from states, actions, and rewards. Karpathy’s rationalism speaks the language of attention, an equation built from symbols relating to other symbols. Both are precise. Both can be computed. Both have produced remarkable systems. But the synthesis that Kant pointed toward, the moment we might describe as the LLM finally gaining a body, has no equation yet. We have a mathematics of pure experience, and we have a mathematics of pure abstraction, but we still lack one system where concepts gain real meaning by touching the world they describe. The union of mind and matter that philosophers searched for across centuries is, for AI, still an unwritten line of code. Maybe the path to AGI is not simply a bigger model, or a smarter algorithm. Maybe it is the search for an equation that does not exist yet, one that finally joins Bellman and attention into something larger than either one alone. The Missing Equation of AI was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.
Score: 28🌐 MovesJul 21, 2026https://pub.towardsai.net/the-missing-equation-of-ai-ea969df6cc3d?source=rss----98111c9905da---4 - Ready for takeoff: This stock’s next growth runway is securing identity in an AI age
A resilient airport franchise has become a customer acquisition engine powering this stock's broader identity platform and giving it a competitive advantage.
- Build a real-time voice AI agent in Python with the AssemblyAI Voice Agent API
Guide to creating a live voice AI agent using AssemblyAI’s Voice Agent API in Python.
- Restaurant in Major Trouble After Posting AI Pictures of “Its Food”
"If they put this much effort into marketing, just imagine how much they care about food." The post Restaurant in Major Trouble After Posting AI Pictures of “Its Food” appeared first on Futurism .
Score: 25🌐 MovesJul 21, 2026https://futurism.com/artificial-intelligence/restaurant-trouble-ai-pictures-food - AI 2040: Is it Actually a Deal?
The "AI Futures Project" has released their AI 2040: Plan A scenario. While their previous scenario AI 2027 was a forecast of what they thought a future with many powerful AIs would look like, AI 2040 is intended to be normative -- it's a description of what one ought to do, granted the likelihood of a future with many powerful AIs. I'm going to review some objections I have to their proposal as a normative plan. Some are within-frame objections -- reasons that I expect trying for the AI 2040 plan that would fail to accomplish the goals of the authors. Others are my own objections -- reasons that I expect trying for the AI 2040 plan would destroy things that I, personally, care about. Before I start: two elements of the plan that I like. First, in their "incremental AI policy wishlist" -- the ideal policy that one should execute soon -- AI 2040 recommends trying to limit the gap between the intelligence of internal and external model deployments, i.e., the gap between the "intelligence" accessible to Anthropic / OpenAI employees and to everyone else. I'm a fan of efforts in this direction; equality of intelligence between the insiders and outsiders, the government and the public, seems likely to help people understand AI more, and to help spread the benefits of AI to everyone. Second, this plan includes measures to ensure that people outside AI companies can understand how AIs are trained. I'm uncertain about their implementation of this -- "radical transparency." But in general; I think broad, public knowledge of how AIs work and how their training works is good. Without open, reproducible AI science it's going to be impossible for people to orient around what's going on with AI. Significant parts of AI safety have previously advocated for knowledge of how AI works to be kept mostly secret, and I'd be happy for such advocacy to end. Alright; to some objections. 1. The Deal Has No Actual Decision Procedures AI 2040 is subtitled "Plan A — The Deal." But the plan really contains only half of a deal. That is, Plan A has a lot of detail about (1) making sure all compute use is visible to governments and (2) making sure that both the US and China are locked into a mutually-assured-compute-destruction stalemate, where one can destroy the compute of the other so long as they're willing to be destroyed in turn. But it has basically no detail about the procedures that would bind how governments permit and prohibit any specific use of that compute, short of that destruction. That is, it has lots of details about how individual companies might be bound by regulators internal to nations; but it has no details about procedures for international negotiation between nations about what these regulators should or should not prohibit. That's a huge problem; if one's proposal is for two nations to put themselves in a mutually-vulnerable situation, where each one could cripple the economy of the other more or less at will, then before accepting the proposal I expect these nations would reasonably want to know what rules or procedures would be used to settle their disagreements short of such complete destruction. But there are no such procedures proposed by AI 2040. Consider how AI 2040 describes one such conflict between governments over what is prohibited: For example, in 2031 a Chinese company gets some interesting preliminary results in continual learning. They think that if they invest more in that direction, they might be able to make an AI architecture that learns on the job from relatively small amounts of data. Thanks to the total research transparency, this breakthrough is quickly noticed by companies and nonprofits all over the world. A frantic conversation begins. On the one hand, continual learning would unlock huge economic value. On the other hand, safety cases currently depend on studying the safety properties of a model before it is deployed. If models could pick up new capabilities during deployment, that would invalidate the whole approach. And insofar as there are covert AI projects out there, it would be a huge gift to them. This conversation happens in public, rather than behind closed doors. A bunch of people get increasingly worried; the relevant regulators in China think it’s fine but the relevant regulators and third party risk assessors in the US are convinced that this is pretty scary and should be banned. It escalates to the President. He calls Xi Jinping. They bargain and threaten. They yell at each other. Ultimately Xi agrees to ban this type of thing if the US does too. Details are left to the respective regulators to hash out... The equilibrium is that AI training practices which are generally agreed to be unsafe by a majority of nations (weighted by bargaining clout) get banned everywhere. There are a lot of problems with this scenario. The biggest, though, is that it depicts the "deal" as solely a transparency mechanism, a kind of channel that permits the well-informed brute exercise of force. After "radical transparency" surfaces some particular training practice, what determines the prohibition or acceptance of this training practice is if it is "generally agreed to be unsafe by a majority of nations (weighted by bargaining clout)". And well, perhaps the authors of AI 2040 would respond that indeed, they are merely proposing a channel that lets the US and China exercise brute force in a well-informed way. But this kind of realpolitik would be fake wisdom; actual agreements between peers are usually meant to be something other than avenues for such an exercise of brute force, and nations would be reluctant to sign them if this were not so. That is, in general, actual agreements, contracts or Constitutions are meant to constrain the space in which bargaining takes place to something smaller and more determinate than the space in which bargaining took place before the agreement: for instance, the World Trade Organization dispute settlement system is supposed to function by offering procedures that allow agreements different than the agreements that would dictated by a naked balance of power. So if you propose an international agreement, and your proposed decision procedure is "Xi and the President yell at each other and bargain and threaten (!!)," then you've failed to offer the chief thing that international agreements are supposed to supply. We need the game theory about decision as well as the game theory about destruction. This is an obstacle to the acceptance of the proposal, as well as an obstacle to its execution, because the shadow of the future determines the present. Nations would be reasonably extremely hesitant to sign a deal, where the result of a bargaining failure is "the obliteration of an increasingly-large segment of their entire economy" without some procedures about how they would settle disagreements before so obliterating that segment. Consider the chain of thought: "Yeah, if we think the other nation is doing something unsafe, we flip the switch that obliterates their most valuable investments, then they obliterate ours." --- "What do we do before then?" -- "Idk, we yell at each other and threaten each other?" You will note how incomplete this feels. Is the plan to have a regularly scheduled Cuban Missile Crisis? A further problem with this proposal is that there's very little reason to expect a weighed-by-bargaining-clout decision procedure to result in wise decisions. This gets into how others have critiqued the scenario for "selective optimism." That is, they call this "agreement" between-nations the Consortium. But it's unclear whether this scenario is a (1) forecast that a Consortium dominated by the powerful would make wise decisions or (2) a hope that a Consortium so established would make wise decisions. It's clear in the scenario that the Consortium does make wise decisions, from the perspective of the authors. It steers AI development directions: "AIs that are released publicly by the Consortium should be bad at AI research." It applies verification methods to the robot workforce. It pauses AI capability development at one point. These are hugely consequential decisions. But again, in the absence of any determinate decision procedure other than a balance power, the authors should be unsure whether the Consortium will actually do what they think it should do in such moments. Alternately, the authors might respond that there would be some such determinate decision procedures, they just haven't figured them out yet. But I don't think the absence is an accident; any such decision procedure that would be acceptable to the US would tend to be unacceptable to China, and vice-versa. If both China and the US are the only partners in this, how do they settle their disagreements? If other nations get a vote, will either China or the US be happy to cede the tiebreaker vote to such nations? And of course, any specific mechanism design might also result in unhappy equilibria. This whole proposal is taking place because the authors are unhappy with the dynamics resulting from competition between rival AI companies; but they have no guarantee that the incentives governing some actual intergovernmental institution would be better. Refraining from detailing the mechanisms of such an institution merely means that the authors will be unable to identify such perverse incentives ahead of time; not that there wouldn't be bad ones. Additionally, I think the lack of modeling such incentives is the kind of thing -- generally -- that lies behind the optimism that AI 2040 has for top-down solutions. It's easy to think that a particular dynamic, multipolar system, would be better replaced with a system that you model as a point mass. But sadly, neither AI systems nor human systems are well modeled as masses. 2. China Will Likely See Such a Deal as Unnecessary One reason that the scenario predicts it will be pretty easy to get China to join a deal is because if they do not join a deal, they will be disempowered. China, by contrast, is an example of an actor in whom power would not concentrate by default. In 2029 in this scenario, the US has a significant lead in AI capabilities over China and a significant advantage in compute which will compound the lead. The more powerful AI gets, the scarier it will be to fall behind, as AI 2027 and the later years in this scenario illustrate. In general, I'm just much less confident than many in AI safety that China will fall behind the West. Right now the US has perhaps an 8-month lead over China in the quality of its AI models; it also has a lead over China in tons-to-orbit and the production of commercial jets . By contrast, China leads the US in the production of electric cars, batteries, solar panels, rare earths, most metals, quadcopters, mid-range drones, transformers, power plants, electricity, humanoid robots, industrial robots, CNC machines, high-speed rail, mature-node semiconductors, and an increasing number of other kinds of technology. I remain somewhat unsure whether this 8-month lead of the US over China is going to grow or narrow. I also remain somewhat unsure whether the AI-takeoff is going to be so fast that an 8-month lead would result in across-the-board US dominance. And even if the 8-month lead remains, and even if an 8-month lead would result in across-the-board dominance, I finally also remain somewhat unsure whether China would perceive such an actual imminent dominance as being so. All this leads me to be uncertain whether China would have interest in a world-historically invasive deal to prevent its own obsolescence. China is a rising and confident power; it would be quite a turn for a mere few years to move them to think they require one of the most invasive deals in world history to prevent their downfall, even in the uncertain world in which this is actually true. This appears to me a pretty big obstacle and I'm not sure how they plan to overcome it. I think that the overall belief of the authors is that -- because the authors of AI 2040 believe themselves to have true beliefs about the world -- China's beliefs will converge on what they believe in the future. I'd like to note that AI 2027 made predictions on the basis of some such similar convergence, and as far as I can tell they were worse than my own predictions . 3. The Surveillance Possibilities are Actually Quite Bad The standard AI 2040 proposal involves locating all (or mostly all) use of compute in inference, and making it physically accessible for government monitoring as plaintext through optical taps. This means that it would be technically trivial for government to surveil basically anything that runs through AI inference -- which, given that the authors expect the entire economy & all society to run on AI inference, is basically everything. Like others , I find this alarming. It's easy to be a bit confused here, because the plan also mentions zero-data retention policies for consumers. In general "zero-data retention" is a design choice wherein AI companies do not store the prompts and queries sent to AI. Actually implementing zero-data retention is thus a kind of guarantee of privacy for consumers. But the real guarantees AI 2040 has around this seem to be quite thin. First, they are thin because only a small number of comparatively stupid AIs are permitted to be run in this way. Their proposal is for there to be a cap of a hundred thousand H100-equivalents devoted worldwide to zero-data retention inference for consumers; for there to be a cap of a hundred million H100-equivalents with probabilistic ZDR; and for there to be a cap of a hundred billion H100-equivalents used with no-ZDR inference at all. So, the vast majority of FLOPs of compute are those that involve no ZDR. And remember, the plan also involves banning any further advanced open-weight models, so you're only ever going to run inference on those monitored computers! Second, they actually have few mechanisms in place to ensure that ZDR is actually enforced where they would like it to be enforced, so far as I can tell. That is, there are elaborate game-theoretic proposals to prevent nations from pulling out of the mutually-assured compute destruction agreement once they enter it, but there are no such proposals to ensure that either the US or China actually sticks to ZDR. But it's a completely detachable part of the plan; and if something like this were to happen, I expect it would be detached. The authors do not -- as they might say of other stories of how AI goes well -- actually have a plan for making sure genuine privacy happens. They have a hope, which is not a plan. Again, I really want to emphasize that in this scenario everything that matters on Earth flows through inference. If you want to run a business, you'll get advice from an AI. If you want to engineer a product, you'll do it with AI. If you want to run for politics, you'll strategize with an AI. If you don't think "physical access to all AI activity" is a big deal then you aren't taking AI seriously. Consider a proposal that would give the government physical access to every file on your computer. Would you be alarmed by this? Then I think you should be alarmed by AI 2040. Is there any similar proposal of absolutely universal oversight in world history that you believe to have been justified? If not, why is this an exception? Conclusion I'm not really happy with any of the above as a summary of my objections. Overall I probably feel worse about the scenario than my views above reflect, and haven't summarized my reasoning here in a way I find totally satisfactory. ( crosspost from my blog) Discuss
Score: 25🌐 MovesJul 21, 2026https://www.lesswrong.com/posts/LtmbQN9mzmFWxWtrD/ai-2040-is-it-actually-a-deal - CNBC's The China Connection newsletter: The AI consumer bet might surprise you
Investors are betting that how people spend their time and money will change dramatically due to artificial intelligence.
- HK.AI Capital Ltd. Research & Ratings | C2I0
HK.AI Capital Ltd. Research & Ratings | C2I0 Barron's
- Vatican Uses AI-Detector to Show AI Bros Pope’s Anti-AI Encyclical Isn’t AI-Generated
The word of God or the word of AI?
- I created an AI boyfriend for research. I now understand why digital companions are so appealing
Relationships between people and AI companions created on platforms such as Replika, Charater.AI and Nomi AI are increasingly common
Score: 23🌐 MovesJul 21, 2026https://www.independent.co.uk/life-style/ai-boyfriend-relationship-b3019074.html - Man of his word: Pope Leo speeches declared human-authored by Australian AI detection tool
Maps of Hope has been certified by Proudly Human, a company led by former chief scientist, Dr Alan Finkel Follow our Australia news live blog for latest updates Get our breaking news email , free app or daily news podcast A collection of speeches and writings by Pope Leo XIV has been certified as human-authored less than two months after Leo declared artificial intelligence the greatest threat to humanity. Proudly Human, the Australian company spearheaded by Australia’s former chief scientist, Dr Alan Finkel, undertook a review of a collection of writings and speeches by the pope, in collaboration with the publisher, the Australian Catholic University and cardinals in the Vatican. Continue reading...
Score: 23🌐 MovesJul 21, 2026https://www.theguardian.com/world/2026/jul/21/pope-leo-speech-human-not-ai-artificial-intelligence - I test AI coding tools all day — here are 9 hidden Claude Code features you’re probably missing
I test AI coding tools all day — here are 9 hidden Claude Code features you’re probably missing Tom's Guide
- Iceland’s Sowilo raises pre-seed to expand AI-powered fashion product intelligence platform
Iceland-based AI startup Sowilo has raised a pre-seedfunding round to support the expansion of Catecut, its fashion productintelligence platform, and the global launch of its Shopify app. The roundinc...
- Adobe’s awesome Pixel Camera-ish iPhone app is getting even better
How about AI telling you how to get a better photo of your subject?
Score: 20🌐 MovesJul 21, 2026https://www.androidauthority.com/adobe-project-indigo-ai-playground-3689455/ - Inside the world of robot fight clubs
Humanoid robotics industry analyst Eren Chen discusses the growing trend of robot fight clubs in China with NBC News’ Gadi Schwartz.
Score: 20🌐 MovesJul 21, 2026https://www.nbcnews.com/video/inside-the-world-of-robot-fight-clubs-266991685979 - I used this AI note taker to record my meetings for a week - and it made my workdays more focused and productive
I used this AI note taker to record my meetings for a week - and it made my workdays more focused and productive Tom's Guide
- LWiAI Podcast #247 - Opus 4.8, MAI, Anthropic IPO, Minimax-M3
New Models, IPO Announcements, and the Rise of Open Source Competitors
- VoiceType AI Formats and Types Everything so You Don’t Have to, and It’s on Sale for $50
VoiceType AI Formats and Types Everything so You Don’t Have to, and It’s on Sale for $50 PCMag
Score: 20🌐 MovesJul 21, 2026https://www.pcmag.com/deals/voicetype-ai-formats-and-types-everything-so-you-dont-have-to-and-its-on - Epistemics and Coordination: It’s complicated!
Money is pouring in, people are looking for new areas to fund, and the invisible hand is starting to grab a bit at AI for epistemics and coordination (AIFEC hereafter). It also got invoked in AI2040 as a potential part of the winning strategy. My feelings here are mixed — I think the best version of AIFEC is great, but also the existing public writeups are only a few cycles deep on tracing out the different ways that the obvious plan backfires. And regrettably I think some people have correctly written off AIFEC because what they have read appears a bit naive to them. In my heart I always planned to do a proper writeup of my thoughts when things were a bit less busy, but, well, now we’re in the 100x funding era. So here’s my scrappy, hopefully-better-than-nothing attempt. The six big claims: AIFEC could be great! It’s a tractable way to do good on the margin, and the best version is a legitimate theory of victory It could also easily backfire, especially for implementations that depend on scaling with inference “Better epistemics” is often more hostile than it seems, and people have good reasons to be wary of things which profess to help them understand what’s going on Vagueness about what AIFEC is doing lets you ignore tradeoffs: Yes you can just go make a bunch of AIFEC startups, and yes AIFEC could help with international coordination, but those scrappy startups aren’t what solve US-China tensions. Sometimes people just don’t get along But still, AIFEC could be great! The basic case is pretty good! Here is my favourite case for AIFEC: If humanity ends up blowing itself up, it’s going to be a combination of three factors. Firstly, maybe we just rationally incurred some risk, the same way that getting on an airplane might kill you. Secondly, maybe we underestimated the scale of the risk. Thirdly, maybe some people took risks that personally benefit them at the expense of others, generating negative externalities. I find this story pretty compelling, and surprisingly different in form to the standard arguments about AI x-risk. It’s actually much more general. And when you squint at the three factors, well, the first isn’t even really a problem, the second is basically poor epistemics, and the third is basically poor coordination. So maybe if we just get enough epistemics and coordination, we’re in the clear? And it sure seems like AI is about to make this way easier — so much more cognition on tap, plus all kinds of neat new form factors like arbitrating minds that genuinely vanish after the fact. Add to that: it’s not really all-or-nothing in the same way that alignment is. Decent AIFEC lift should help with all our other problems. In fact, AIFEC should help a fair bit with AIFEC — the better our epistemics and coordination, the more effectively we can coordinate to appropriately invest in further work. One can imagine a kind of dizzying spiral to heaven, where we pass the Coasean friction event horizon, slay Moloch, and fuse into a liberal-libertarian omnimind in which every individual has their own authentic preferences while the group deftly dances along the pareto frontier. X-risk drops to the socially optimal level, the socialist calculation problem is finally solved, and we can all finally just get along. Hyperbole aside, it’s worth emphasising that something in the realm of AIFEC is a legitimate full-blown wincon up there with aligned AGI. Indeed, a lot of people’s tacit plan seems to be “align the AI and then let it solve democracy and all the rest”, but one could just as well go in the other direction: “solve politics and ignorance, and then just be reasonable about advanced AI” Alas, it is not so simple. Problem 1: Naive AIFEC could easily backfire The biggest splash of cold water is that AIFEC won’t be free. Especially in this era of inference scaling and an increasingly closed frontier, the fruits of AIFEC will not be equally distributed. There are certainly some bits of technological progress that are remarkably egalitarian — even billionaires use facebook, gmail, and iphones. But if the selling point is that you get to throw lots of intelligence at solving problems, then you’re going to differentially favour the people who can actually call up that intelligence at scale. Indeed, there are ways this could backfire. It’s certainly possible that AI will enable everybody to seamlessly coordinate, but first it will enable small groups to, ahem, collude. If you’re worried about things like coups and permanent underclasses, it’s not clear that more coordination makes your life any easier. Similarly, if you build a machine that turns compute into better decisions and more situational awareness, those benefits will mainly accrue to the people who actually have the compute. Problem 2: “Better AIFEC” can be a somewhat hostile move It’s nice to think that everyone who believes false things is basically just misguided, and that the only reason they don’t take up arms for the truth is that they haven’t seen it yet. I think the real picture is unfortunately a bit more complicated. The problem is, historically there have been many groups that have weaponised very convincing arguments to get their way. One reasonable response to a seemingly faultless argument for a seemingly crazy conclusion is to throw up your hands and assume you’re being swindled — in other words, epistemic learned helplessness . Even if team AIFEC is actually the good guys, people will be correct to be suspicious. Governments in particular depend a lot on restricting what kind of information is admissible, as does the judicial system. On top of that, I do think that before you start swinging the club of truth it’s worth taking a beat to ask how pure your motivations really are. The EA/rationality community has a bit of a history of swinging the club of truth in a more hostile way — “save the drowning child” etc. Moreover, in the realm of politics, often the real sleight of hand is just changing what things are salient — elections are won over people’s sense of what the election is really about. Framings and deliberative processes are rarely as neutral as they seem. So even if your tools don’t privilege a specific answer directly, the choice of deliberative structure is, well, a choice. The same is true of coordination. To give an obvious example, democracies are meant to represent the will of the people, but the structure of representation pretty directly modulates that will. The choice between proportional representation and first past the post, bicameral houses, election cycles, district boundaries and so on are superficially choices about how to structure the coordination, but they also sometimes obviously favour certain conclusions. It’s really hard to build neutral coordination structures, and people are right to be sceptical of anyone who pretends otherwise. One slightly thornier point: believing false things is actually a pretty powerful coordination mechanism. Many groups cohere around a mixture of surprising truths and blatant falsehoods. When you try to bring the truth to such people, they will actually fight back. As a modest example, deconverting a child from the religion held by their entire family is actually pretty unpleasant and arguably not very nice. This is true to a lesser extent for e.g. adults and the political tribe of their social milieu. Now, maybe it’s worth it if you’re actually right, but you should expect them to fight back! So: plenty of good to be done, but plenty of pitfalls along the way. Problem 3: Vagueness lets you ignore tradeoffs I think one of the major appeals of AIFEC is that you can just let a thousand flowers bloom by sending a lot of bright young things off to found startups and seeing which ones succeed. This is certainly scalable and likely to produce some hits. Unfortunately I’m not sure it’s enough, and the other parts are a lot harder. What coordination and epistemics do we actually need? One way I like to think about this is in terms of what the channel is that leads from the so-called better angels of our nature to actual global action. Probably most of that comes down to robust democratic oversight and international coordination. These are very difficult! And I do not expect that even the top percentile AIFEC startup moves the needle that much, because these processes are by design pretty resistant to hopping on the latest technological fad. Similarly, plenty of AIFEC wins don’t seem to me to really be on the critical path. Deliberative processes, for example, seem like somewhere that AI could be enormously helpful, in a way that might help overcome a major traditional limitation of democracy, but I don’t see that being super crucial for dealing with worlds where people lose all their leverage. It seems helpful, certainly, but not obviously necessary and definitely not sufficient. Getting lots of products off the ground seems great, both in case some are great and to build institutional capacity and expertise, but we can’t neglect the other steps, and it’s important to think at least a bit about what specifically moves the needle. This problem isn’t at all unique to AIFEC — the general AI risk movement has a bit of a problem with coming up with some new cause celebre and then unleashing a torrent of work that nominally fits the category without being useful. And I don’t want to overstate it: the mass of AIFEC startups probably will produce some hits, along with some important general lessons and greater capacity. But the best version of AIFEC does need to grapple with it. Problem 4: Sometimes people just don’t get along At the risk of psychologising, I think part of the appeal of AIFEC is that it lines up with a kind of technocratic mistake theory instinct that basically the current race towards a cliff-edge is one huge misunderstanding, and if everyone could be provided with the right information and the right structure, things would all work out. I think this is pretty true! And I yearn for it to be more true. But it is not entirely true. Sometimes people just don’t get along. Some people would rather take AI soon so as to increase the odds of their own immortality or the immortality of their family, even at the risk of destroying all of earth. Some people really do want to tile the universe with hedonium. Some people’s order of preferences is basically “my country wins” > "annihilation" > “enemy country wins”. Some people are currently reaping the benefits of not internalising the risk externalities they create. Some people are causal decision theorists. Global coordination needs to either include these people or, well, conspicuously not include them. And they are not dumb! [1] They are not merely passive processes that will fail to notice your attempts to re-engineer the environment around them. Sometimes the ruling party decides to block political change even if it’s obviously reasonable behind some veil of ignorance, because they correctly notice that in the moment it is to their detriment. Some of this stuff you can bargain about, but, well, the structure of the bargaining is not neutral. And you can only bargain so much with someone whose parents are slowly getting older and sicker. I really don’t know what to do about that. It makes me pretty sad. And we are going to run into it more and more, so the sooner we can deal with it, the better. But damn, it’s tricky. But overall, I am still pro I reel off these problems not because I don’t believe in AIFEC, but because I do believe in what the better version of AIFEC could be. I’m baffled by how overinvested we are in strategies that, from my perspective, seem specifically geared towards helping frontier labs navigate the acute risk period. I think in an adequate world we’d be pushing simultaneously on every plausible path to victory, at least until we hit the thresholds where they started to trade hard against each other. One obvious reason to be long on AIFEC is that it automatically scales with AI capabilities. Some version of it is clearly coming, and will clearly be helpful. And right now in particular there’s some opportunity to steer things, to get certain balls rolling sooner. Even if you’re extremely bitter lesson-pilled, a three month lead could be worth a lot. There are a few other big topics I didn’t cover here that do feel relevant to me. In no particular order: More so than I'd like, AIFEC tends to privilege individual-level agency, whereas I am pretty bought in on group agency being real and very important for risks. AIFEC doesn’t grapple as much as I’d like with power and leverage, and indeed the frame seems to slightly bounce off them. This is related to problem 4 — AIFEC hasn’t grappled as much as I'd like with conflict theory. I feel we lack institutional knowledge about how you serve both God and money. Finally, I continue to feel that one of the most underrated pieces of work I contributed to was The Choice Transition , which is basically an attempt to articulate how AIFEC might get humanity into a stable basin from which we can reliably avoid bad outcomes and slowly build up to the good ones under our own volition. Compared to all the other paths to victory, AIFEC seems like the only one with this property — that humanity can correctly recognise itself as having the power to work towards the best outcomes — and my liberal instincts feel that this is worth holding onto. (crossposted from my new-ish blog ) ^ Except the causal decision theorists Discuss
Score: 20🌐 MovesJul 21, 2026https://www.lesswrong.com/posts/5nP5WY2PzsYiegDzQ/epistemics-and-coordination-it-s-complicated - LWiAI Podcast #248 - Opus 4.8, MAI, Anthropic IPO, Minimax-M3
Exploring Claude Fable 5’s impact, Siri AI’s latest enhancements, and the competitive IPO landscape shaping AI’s future
- LWiAI Podcast #249 - Fable 5 ban, SpaceX Cursor + IPO, OSS Aplenty
Exploring the Fable 5 ban, SpaceX’s strategic acquisition, and a burst of open source advancements
- LWiAI Podcast #252 - GPT 5.6, Grok 4.5, Nemotron-Labs-Diffusion, AI 2040
GPT-5.6 and Grok 4.5, Meta's Muse Spark 1.1, regulatory developments in AI and data centers, interpretability research from Anthropic, and the future of AI policy with AI 2040
- Ai Foundation Models Product News & Analysis
Ai Foundation Models Product News & Analysis The Information
- 'Fictional' chatbots help explain popularity of artificial intelligence
The huge popularity of AI chatbots can be explained by their accepted role as fictional characters in people's lives—in the same way as those found in films, TV and books, a new study shows.
Score: 19🌐 MovesJul 21, 2026https://techxplore.com/news/2026-07-fictional-chatbots-popularity-artificial-intelligence.html - I asked ChatGPT to make my iPhone more private — it found 7 settings I wish I'd turned on years ago
I asked ChatGPT to make my iPhone more private — it found 7 settings I wish I'd turned on years ago Tom's Guide
- How to Watch Samsung's London Unpacked Keynote: New Foldables and Snapdragon AI Watches Expected
How to Watch Samsung's London Unpacked Keynote: New Foldables and Snapdragon AI Watches Expected PCMag UK
- What cricket quietly teaches us about the future of Artificial Intelligence
By Sanjay Kala, Director – Technology Delivery Practice, Asia Pacific, SAS India changes during IPL season. Families that barely sit together for dinner gather around screens . Every chai tapri […] The post What cricket quietly teaches us about the future of Artificial Intelligence appeared first on Express Computer .
- Use AI to create your invoices for $20
Create, customize, and export your invoices with AI for $19.99 The post Use AI to create your invoices for $20 appeared first on TechRepublic .
Score: 15🌐 MovesJul 21, 2026https://www.techrepublic.com/article/ai-invoice-maker-lifetime-subscription/ - What do we do when the household appliances start chatting among themselves?
Your toaster may ‘talk’ to your fridge, they may even talk to you, but we’d do well to remember that we’re defined by our human communities, not our electronic ones
- Modulate to Showcase Voice-Native AI Architecture at Ai4 2026
Modulate to Showcase Voice-Native AI Architecture at Ai4 2026 USA Today
- SF Chronicle Events - AI & Tech Networking Event in San Francisco by Ascent Valley
SF Chronicle Events - AI & Tech Networking Event in San Francisco by Ascent Valley San Francisco Chronicle
- Finance Director - Bretton AI
Finance Director - Bretton AI Built In
- Agentic AI Engineer - Booz Allen Hamilton
Agentic AI Engineer - Booz Allen Hamilton Built In
- Senior Software Engineer, Autonomous Pilot Integration – Weapons (R5427)
Senior Software Engineer, Autonomous Pilot Integration – Weapons (R5427) Built In
Score: 05🌐 MovesJul 21, 2026https://builtin.com/job/senior-software-engineer-autonomous-pilot-integration-weapons-r5427/10297608 - The huge LG 100-Inch Class QNED evo AI TV hits record-low price at Amazon — save $700
Get the best LG TV deal. Save 17% on the LG 100-Inch Class QNED evo AI QNED84B at Amazon.
- Founding Product Designer - Andromeda (andromeda.ai)
Founding Product Designer - Andromeda (andromeda.ai) Built In
- OpenAI briefly hit pause on a powerful AI model before release: Here’s why
OpenAI briefly hit pause on a powerful AI model before release: Here’s why
- California officials highlight AI, FireSat and predictive technologies as centerpieces in state wildfire strategy
Speaking at Sacramento McClellan Airport, California Gov. Gavin Newsom tallied the many cutting-edge firefighting technologies to have been adopted since he took office.
Score: 00🌐 MovesJul 21, 2026https://statescoop.com/california-gavin-newsom-firefighting-technologies/ - OpenAI pauses new AI after it kept ‘escaping’
New AI model was able to ‘learn the blind spots’ of security systems designed to contain it
Score: 00🌐 MovesJul 21, 2026https://www.independent.co.uk/tech/openai-ai-model-escapes-safety-b3018638.html - Exclusive: Google Maps is laying the groundwork for its smartest update yet
Maps will soon be able to tap into your connected Google apps for a personalized experience like never before.
Score: 00🌐 MovesJul 21, 2026https://www.androidauthority.com/google-maps-personal-intelligence-3689447/ - Gemini for macOS gets Neural Expressive, AI Mode on Android redesigned
In addition to 3.6 Flash , Google today is also updating Gemini for macOS with the Neural Expressive redesign.
Score: 00🌐 MovesJul 21, 2026https://9to5google.com/2026/07/21/gemini-mac-neural-expressive-redesign/ - Gemini Notebook’s new Collections arrive just as Google turns it into a bigger workspace
Gemini Notebook’s new Collections help users organize growing research libraries as Google expands the former NotebookLM across Gemini and Search, although the feature remains more limited than a proper folder system.
- Google finally fixes Gemini Notebook’s biggest organization headache
Finding the right notebook just became a whole lot less painful.
Score: 00🌐 MovesJul 21, 2026https://www.androidauthority.com/gemini-notebook-collections-feature-3689444/ - Rancho Cucamonga, Calif., Launches AI Wildfire Detection
Officials say the system can detect small brush fires 100 yards away and deliver pictures of the flames, their location and even a weather report in three seconds to public safety dispatchers.
Score: 00🌐 MovesJul 21, 2026https://www.govtech.com/em/safety/rancho-cucamonga-calif-launches-ai-wildfire-detection