已进入第二版！傅里叶帮Meta研发机器人原型，打造“Muse物理化身”
This top data scientist’s (non-AI) model finds Republicans have just one chance in six of holding Senate
I started following Thomas Miller’s political forecasts during the January 2021 Senate races in Georgia. Just a few days before the election, the polls and pundits anticipated a relatively easy win for both Republican candidates. But Miller—a prominent data scientist and professor at Northwestern University—posited a Democratic sweep, and he was right. He also called Trump’s victory in 2024. What’s different about Miller’s models? They’re far more current than the polls, and they’re centered on prediction market prices that are typically a better guide than the voter surveys—plus Miller adjusts the outlook in every Senatorial contest by using all we know about every contest.
Now, Miller is applying an updated version of his model for the current Senate elections. You can find the stats on his website, Virtualtout.io, and I highly recommend paying a visit. Once again, Miller starts by incorporating the betting venues’ data. “I use the numbers from two sites, Polymarket and Kalshi,” he told Fortune. “They’re the ones that trade the most dollars, where most of the action happens.” Miller then calibrates the posted “prices” so that they reflect probabilities, and the odds for both leading candidates tally to 100%. In a further step, he deploys multiple time series analysis to find underlying factors driving the betting prices, and uses trusted models from finance and economics to project how they’re likely to influence voters moving forward.
By the way, he thinks his model works better than AI.
Miller updates the probabilities of a Republican or Democratic win every 10 minutes. The odds that a candidate will win scroll across the top of the Virtualtout.io screen continuously, in a band resembling a stock ticker tape. For each race, Miller also posts the change over the last 24 hours. Miller calls the streaming feature the “NowCast.”
As for those “underlying” factors, one force swamps all others: Donald Trump. “The states don’t move independently of one another,” says Miller. “If one goes up for Democrats, chances are they’ll all go up for Democrats, and vice versa. The entire market is dominated by what Trump does and says hour by hour.” Put simply, when voters don’t like the POTUS’s words or actions, all Republican odds tend to fall. Miller asserts that his model has identified 85% to 90% of what’s causing the variances on Kalshi and Polymarket, and “my interpretation is that it’s overwhelmingly Trump and the Republican positions generally.”
Though he opened Virtuatout.io to public view about a week ago, Miller started running the numbers on September 17. What’s most striking is just how far the GOP chances have faded in just six weeks.
As of around 9 AM on October 30, five days before most polling sites close, Virtualtout.io gives the Republicans a 17.1%, or just over one-sixth probability, of winning 50 seats and hence retaining control of the upper chamber. On September 17, that figure was twice as high at 34.8%, or slightly better than one-in-three. In all five contests where the Democrats can flip a seat, their chances are better than 60%—in most cases, much better.
In North Carolina, where Republican Thom Tillis is retiring, leaving an open seat, the GOP was never remotely close, and now harbors a minuscule 4% shot at victory. In Texas, the Republican odds have dropped since September 17 from 46.15% to 31.9%, in Ohio from 45.4% to 31.8%, and in Alaska from 32.95% to 28.6%.
In only one state is the GOP faring better than in mid-September and that’s Maine. Republican Susan Collins started as an underdog at 32.6%, but has clawed back to 39.9%, adding 1.7 points in the last 24 hours. In Alaska, things are also looking up for the Republicans: After falling to a low of 23.4% on October 23, incumbent Dan S. Sullivan’s prospects have improved by over 5 points to 28.6%.
For the political pros looking for harbingers of big trouble for either side, the towering sign is the Republican woes in traditional strongholds Iowa and Kansas. In the former, Republican Ashley Hinton dropped from 61.75% on September 17 to a virtual tie at 53.4%, a trend mirrored in Kansas where Roger Marshall was looking a likely victor six weeks ago at 75.8%; now, he’s down almost 19 points at 57.1%. In the last day, Hinton and Marshall have shed 1.45% and 2.47 points respectively.
In the one state where Republicans saw a big chance for a flip, the horizon’s receding fast. The GOP chances of beating Abdul El-Sayed in Michigan have slipped from better than a third in mid-September to about a quarter (25.4%).
If the Democrats indeed score a flip in every contest where their probabilities in Miller’s model stand at 60% or better, and hold all the seats they have now, they’ll clinch a majority of 52 to 48, nearly reversing the Republican dominance at 53-47.
In South Carolina, recently rerated on the Fox News Channel from “lean Republican” to “tossup,” Miller still has Darline Graham at a commanding 75%, though she’s slipped 2.4 points in the past 24 hours.
Keep in mind that Miller’s setting odds, not making definitive predictions. A candidate at over 30% still stands a decent chance of winning. And Republicans are over that bogey in Maine, Ohio, and Texas, and close and trending the right way in Alaska. Odds-defying events happen all the time. The problem for Republicans is that they’d have to beat those odds multiple times, plus defend seats they never dreamed they’d have to defend.
It’s still far from over. And one of the best places to get a plausible, non-AI driven snapshot from a data scientist who’s been doing this for six years, is that NowCast on Tom Miller’s Virtualtout.io.
This story was originally featured on Fortune.com
Jeff Bezos once gave Eva Longoria and the admiral behind Osama bin Laden’s capture $100 million—but she says you don’t need wealth to give back
A lot of people talk about changing the world. But Amazon’s billionaire founder, Jeff Bezos, gave Eva Longoria $50 million to actually do it. And two years on, she says the biggest myth still surrounding philanthropy is that you have to be rich to do it.
“One of the biggest misconceptions about philanthropy is that impact is tied to wealth or scale, when in reality, impact comes in many forms,” the Desperate Housewives star told Fortune. “What I’ve learned is that impact doesn’t come from how much money you have, it comes from how you show up.”
It’s a message that feels pointed at a moment when billionaire philanthropy dominates headlines and ordinary people assume giving back is someone else’s job.
For Longoria, the most powerful—and most overlooked—form of change happens at street level. “Mentoring someone, supporting small business, sharing what you know—those things matter because not everyone has access to capital or resources, but anyone can be part of someone else’s support system,” she adds. “And I think that’s really what’s missing for a lot of people. They don’t have someone in their corner.”
What Eva Longoria did with Jeff Bezos’s $50 million
The actress is practicing what she preaches. Having built a net worth north of $80 million thanks to a sprawling business portfolio—including a luxury tequila brand Casa Del Sol, the media company Hyphenate Media Group, a stake in women’s soccer team Angel City FC, and an early investment in the multibillion-dollar John Wick franchise. Now she’s channeling paying it forward by mentoring small-business owners through a new partnership with Lenovo.
She’s also been running the Eva Longoria Foundation since 2012 to improve educational and entrepreneurial opportunities for Latinas in the U.S., as well as Eva’s Heroes, a charity that helps young adults with “intellectual special needs,” according to its website.
Bezos and his wife, Lauren Sánchez Bezos, launched the Courage and Civility Award in 2021 for individuals who make significant contributions to society. They’ve given millions (often $100 million) to help those in need. Previous recipients include CNN host and civil rights advocate Van Jones, chef and humanitarian José Andrés, and late country legend Dolly Parton—who famously helped fund Moderna’s COVID-19 vaccine research.
It’s why Longoria says receiving the prize, alongside Bill McRaven, a retired Navy admiral, is much bigger than the money.
“Receiving that support was incredibly meaningful, not just because of the scale of the gift, but because it represented trust and responsibility,” Longoria said, adding that she’s been “more intentional and more strategic” in how she shows up as an advocate.
“This award has allowed me to continue investing those resources into the communities and causes I care deeply about—supporting Latina entrepreneurs, expanding access to education, and backing organizations that are driving real change on the ground,” she added. “The Courage and Civility Award makes it possible for us at the Eva Longoria Foundation to create long-term impact with transformative giving.”
McRaven, who oversaw the 2011 raid that killed al Qaeda leader Osama bin Laden, pledged to use his share of the money to develop education programs for future military leaders and the children of deceased veterans, as well as mental health support for those who serve.
You don’t have to be a billionaire to make a difference
Longoria isn’t the only one making the case that you don’t need to be rich and famous to make a difference.
Just look at the Giving Pledge—the commitment, cofounded by Bill Gates, Melinda French Gates, and Warren Buffett, that asks the ultrawealthy to donate at least 50% of their fortunes—has attracted more than 250 signatories since its launch in 2010.
The number who have actually followed through? A handful—fewer than 10. Most fulfilled the pledge only after their deaths.
It’s why Liz Baker, CEO of Greater Good Charities, says we “can’t afford” to wait on billionaires to solve the world’s problems.:“I think if everybody did something to help in their community, we wouldn’t have the issues that we have.”
The global nonprofit she runs has distributed more than $1 billion in impact across 121 countries since 2006—and she also told Fortune the idea that giving back has to be some grand, expensive gesture is one of the biggest things holding people back.
“Everybody can chip in—and it doesn’t even have to be money. Like, do something,” she said.
“Even if you’re like, I have one hour a week to solve this problem in my community that I care about,” Baker said. “Figure out how to do that. Most local nonprofits need help.”
A version of this story originally published on Fortune.com on April 21, 2026.
Read more exclusive interviews from Fortune’s Orianna Rosa Royle:
Mark Zuckerberg’s $170 million Miami mansion is just one piece of his sprawling real estate empire that includes a 196-year-old castle
With a net worth of $222 billion, Mark Zuckerberg is one of the richest people in the world and the owner of a massive real-estate portfolio that spans the entire U.S.
His latest purchase from August is a 440-acre estate and accompanying 196-year-old castle in County Waterford, Ireland, a purchase that reportedly set him back 51.17 million euros, or about $57.1 million.
Yet, his priciest purchases dates back to earlier this year, when the Meta CEO and his wife, Dr. Priscilla Chan, reportedly purchased a luxury mansion on Indian Creek Island in Florida near the in-progress home of Amazon founder Jeff Bezos.
The 300-acre, man-made Indian Creek Island is near Miami and Miami Beach and contains a mere 41 lots with approximately 84 residents, the Wall Street Journal reported. Home prices on the island start at about $60 million, and records show the billionaire paid $170 million for his waterfront home with nine bedrooms and 11 full bathrooms, the Miami Herald reported. Miami-Dade County property records note the owner as a land trust.
Even an undeveloped property on the exclusive island can fetch big bucks: One vacant lot roughly the same size as Zuckerberg’s sold for a reported $105 million in 2025.
The property Zuckerberg reportedly purchased at 2 Indian Creek Island Road puts him in the most coveted area of the already exclusive island, said Mitchell Duchon, a Miami Beach–based real estate agent with the Corcoran Group who specializes in high-end waterfront properties. On the western side of the island, where Zuckerberg’s property is reportedly located, lots are about 80,000 square feet, compared with about 50,000 square feet on the east side, according to Duchon. On this side, where Amazon founder and fellow billionaire Jeff Bezos also owns two lots, residents have better access to the open water of Biscayne Bay and a more expansive view with great sunsets, he added.
“That side of the island is perceived to be the most appealing,” Duchon said.
Bezos has been buying up properties on Indian Creek Island since he announced in 2023 he was leaving Seattle for Florida. The Amazon founder first purchased a $68 million home that would end up being just a few doors down from Zuckerberg’s in 2023. Near the end of that year, he paid another $79 million for a neighboring property with the intention of combining the lots into a single compound—a trend Duchon said is increasingly common among wealthy buyers because of the scarcity of truly expansive waterfront properties in the area. Meanwhile, Bezos is reportedly living in a third Mediterranean-style property also on Indian Creek Island, on the east side, which he snapped up for $87 million in 2024. Last year, Bezos sold one of his Seattle homes overlooking Lake Washington for a record $63 million, the Puget Sound Business Journal reported.
Indian Creek Island operates as an independent municipality with its own government and private police who patrol by air, water, and sky. Access to the island is controlled by a single gated bridge, making safety and privacy a defining feature. Most of the island’s interior comprises an 18-hole golf course and the Indian Creek Country Club, with a limited number of members owing, in part, to a difficult admissions process and a $500,000 initiation fee.
Zuckerberg is one of the latest billionaires to pick up a Florida property, especially in light of a proposed ballot initiative to be voted on in California next week that would impose a one-time 5% “billionaires tax” for any individual worth at least $1 billion dollars retroactive to Jan. 1, 2026. (To be sure, Zuckerberg still very much calls California home, having invested $50 million through Meta for a Sacramento downtown revitalization and AI-focused project earlier this year.) Google cofounder Larry Page also snagged a $71.9 million home in Miami’s Coconut Grove neighborhood in January that he plans to combine with another property to form a total $173 million waterfront compound.
Indian Creek Island, in particular, is home to a number of high-profile names, who, besides Bezos, include the financier Carl Icahn and former NFL quarterback Tom Brady.
Zuckerberg’s reported newest property is just the latest addition to the tech CEO’s growing portfolio of luxury homes across the U.S.
Palo Alto compound
Zuckerberg’s home base remains Palo Alto, where, according to the New York Times, he owns 11 properties in the Crescent Park neighborhood. Over more than a decade, Zuckerberg has poured $110 million into buying adjacent properties, in some cases drawing complaints from his neighbors.
Hawaiian estate
On the island of Kauai, about 100 miles northwest of Honolulu, Zuckerberg owns a $300 million property commonly known as Koʻolau Ranch, spanning roughly 1,400 acres. He quietly added about 1,000 additional acres to the compound last year, Architectural Digest reported, bringing his total Kauai holdings to more than 2,300 acres.
The ranch is one of Zuckerberg’s most secretive properties. Alm
OpenAI pares fundraising goal, now seeking up to $20 billion in latest round
What Social Constructs Are (And Are Not)
This post is mostly adapted from a section of another post. I wanted a standalone version, to serve as background for a sequence on the mechanisms of social reality.
You’ve probably heard that things like laws, conventions, norms, cultural associations, status, and so forth are “social constructs”: they’re kinda arbitrary in some sense, an artifact of social reality as opposed to physical reality. This post offers a little more clarity on what social constructs are, and the ways in which they are and are not “arbitrary”.
Example: Vehicle Categories
The vehicles humans build today mostly fall into categories like “minivan” or “truck” (among other categories). These categories cash out in predictions: when I see a vehicle with sliding side doors, I predict that it will have a trunk door that opens overhead, three rows of seats, be driven by someone with kids, etc. On the other hand, when I see a vehicle with a flat bed in the back, I predict that it will not have three rows of seats, will have a back gate which opens downwards, be driven by my cousin, etc. [1]
But humans could choose to make different vehicles, which would change which clusters exist amongst our vehicles. The types of vehicles people make are a matter of social convention - i.e. vehicle types are a social construct. Crucially, this does not mean that vehicle categories are “fake”! Categories like minivan and truck are very real in our world, they have real predictive power, and they have predictive power because humans choose to build vehicles which match those categories.
Two key points:
- Many humans, or a few well-resourced humans, could change which vehicle-clusters exist by changing what vehicles are produced. We can change the social construct by going out and changing things in the real world.
- Changing a social construct is not just about deciding that I personally am going to categorize things differently. If I draw different vehicle-clusters, I will probably just be wrong, in the sense that I make worse predictions about e.g. vehicles with sliding side doors also having three rows of seats.
Here’s the mental picture, in terms of clusters:
This is a classic straightforward case of words pointing to clusters in thingspace. But if people go out and build other kinds of vehicles, it might look like this:
… and if they build enough new vehicles, it can totally change what clusters are present:
Same points as in the previous diagram
Our two key points, in terms of clustering:
- People can totally change what stuff is in the world, in such a way that the “natural categories” (in this case the clusters) change.
- … but that requires going out and changing stuff in the world; it’s not just a matter of me deciding to carve up the same data in different ways.
So even if the clusters can be changed by humans by changing the world, that doesn’t mean that choice of clusters is a totally arbitrary choice of how to view the same data.
Example: Laws
Laws are another classic prototypical example of social constructs.
Like vehicle categories, laws have a lot of predictive power. For instance, if I look up some case law on vehicular manslaughter online, and then I go into any of thousands of different courtrooms in the US, I expect to see proceedings hinge on basically the points described online. Just like the minivan/truck categories provide predictive power insofar as they statistically describe lots of real-world vehicles, laws provide predictive power insofar as they statistically describe lots of real-world actions by police, judges, etc. Even an alien observing humans would find it useful to keep track of the laws, in order to predict how lots of different humans behave.
But just like with the vehicles, humans can collectively decide to adopt different laws. The same two key points apply:
- People can totally change the laws, in such a way that all the predictions summarized by “laws” change.
- … but that requires somehow changing the behaviors of a whole lot of individual police, judges, etc, as well as many written records; it’s not just a matter of me deciding that vehicular manslaughter is fine now.
The General Principle
In general, social constructs are real predictive patterns which are out in the world because humans collectively chose to put those patterns in the world. In principle, humans can collectively choose to put different patterns in the world. And sometimes we do! E.g. laws change, or new vehicle categories are created.
Social constructs are “arbitrary” in the sense that humans a
Generalized belief states in transformers
Many thanks to Xavier Poncini, Paul Riechers, Fernando Rosas, and Matt Smith for useful conversations.
1. Introduction
Despite the recent successes of transformers and other sequence models, there are still many open questions about their basic principles of operation. For example, what internal representations do these models use for prediction and generation? Can we decode, interpret, and manipulate such representations? Is it even possible to consider a notion of representation without reference to an external world model?
To make progress on these questions, I propose a general way to define, decode and manipulate representations in sequence models. The approach is centered on the concept of "generalized belief states", statistics of past observations that satisfy a few criteria: (1) they are sufficient for predicting future data, (2) they can be recursively updated as new observations come in, and (3) they are low-dimensional, allowing for visualization and interpretation. I show that such generalized belief states can be mapped to the internal activations and dynamics of a sequence model, allowing for decoding and manipulation of the model's representations. I illustrate the ideas using a tractable toy model, a transformer trained on a simple HMM stochastic process, where we will see that the KV cache plays a key role.
This post is directly inspired by the 2024 paper "Transformers Represent Belief State Geometry in their Residual Stream" by Adam Shai, Paul Riechers, and collaborators. That paper introduced the idea of studying belief states in transformers, and proposed a method for doing so using tractable toy models. I recommend reading that paper if you haven't already, though it is not a prerequisite for understanding my post. Section 5 compares this approach with the one developed in that paper.
2. Belief states
We start at a general level, by considering the problem of sequence prediction. Imagine that we observe a history of previous tokens and then wish to predict the next token . We assume that sequences are drawn from a stochastic process . For now, it does not matter whether this process comes from real-world data or a generative model.
Before proceeding, we introduce a bit of notation. indicates the token alphabet, indicates the set of finite-length sequences (including the empty history ), and indicates the set of probability distributions over the alphabet . We write for the set of histories with positive probability under the stochastic process.
2.1 Hidden Markov models
To optimally predict the next token after observing history , we must compute the conditional distribution . We may now ask: what information should we track in order to compute this distribution? How should this information be updated as new tokens arrive? Does this information represent some unobserved latent aspect of the stochastic process?
These questions have a straightforward answer when the stochastic process is described as the output of a hidden Markov model (HMM). Given an HMM description, we can write the probability of any history as [1] where is the hidden state of the HMM at step , is the probability of emitting token and transitioning to hidden state from hidden state , and is the initial distribution over hidden states. Using this expression and Bayes' rule, the next-token distribution can be written in a factorized form as Here, is the Bayesian posterior distribution over the hidden state, conditioned on the observed history . This posterior distribution corresponds to the beliefs of a Bayesian observer about the underlying states of the generating process, and in the literature, it is called a belief state. [2]
Belief states satisfy two important properties: sufficiency and recurrence. Sufficiency means that the belief state allows us to predict the next token just as well as if we knew the whole history , as seen from the prediction map in Eq. (1a). Recurrence means that, after observing a new token , the updated belief state can be computed from and alone. In particular, using Bayes' rule, we can write the update map as
In addition to sufficiency and recurrence, belief states have several other attractive properties. For instance, the prediction map (1a) is time-independent and linear in . Time-independence lets us interpret belief states at different steps in the same way; linearity lets us interpret predictions as convex mixtures of those made by individual hidden states. The update map (1b) is also time-independent and "nearly" linear, being a ratio of two linear functions of the belief state. (In fact, without loss of generality, we may instead consider "unnormalized" belief states , which update in a purely linear way . [3] )
In practice, belief states are often encoded as nonnegative vectors: where is the number of hidden states of the HMM. In this way, each belief state is a nonnegative -dimensional vector that sums to 1, so it belongs to a -dimensional affine subspace of . For HMMs with few hidden states, belief states belong to a low-dimensional space, allowing for easy visualization and interpretation. Also, in this space, belief states often exhibit a remarkable fractal structure, which arises from the recursive nature of the update map. [4]
2.2 Example
To make things concrete, we now introduce a running example: a stochastic process called MRRXOR that is generated by an HMM with five hidden states. [5] The HMM emits two independent random bits, then their XOR, and then resets. In addition, at each time point, the hidden state can jump to another random hidden state with probability ; if this random jump occurs, it does not emit a token and is immediately followed by an ordinary transition. The HMM is shown in the following figure, with each transition labeled by its emitted bit and probability. (For visual clarity, the random jumps between hidden states are not shown.)
Next, we illustrate the geometry of belief states for this HMM. We compute the belief states after each of the possible histories of length . Each belief state is a normalized nonnegative vector that belongs to a four-dimensional affine hyperplane of .
We visualize these vectors using principal component analysis (PCA), showing all six pairs of the four non-trivial principal components. Each belief state is colored by the last two tokens of the history, with its point area proportional to the history probability. Black dots correspond to each of the five hidden states A–E.
PC1 largely tracks the probability of the "reset" state A, i.e., the belief that an XOR triple has just been completed. PC2 separates histories by their last token. PC3 s
Regulate the Deployment, Not Just the Model
A who-does-what plan for AI safety, built around Trusted Deployers: certified hosts that keep dangerous models guarded without ending open access.
AI has reached a new level of public interest. That may seem like a silly thing to say, as it’s been of interest for a while. But prior (broadly public) interest was about it as a tool, or about the money to be made or lost, or about an effect on jobs.
The new interest demands policy. It demands action. The new interest is connected with risks I consider real. Being real doesn’t make these risks certain. But being real, even with uncertainty, is reason enough to act.
I’ve written lately about risks, cybersecurity risks, and loss-of-control risks. These tried to explain complex topics, starting from the initial premise. Recommendations follow the explanation. This piece will be a bit different. Its focus is upon what I think we should be doing. You might consider it a policy piece. Policy in a completed form has to be a bit broad. There’s always more than one thing going on. As such, I’ll lay out my outline first, and then offer below the extra detail, background and context that I think is important to understanding each point in that outline.
If you’re scanning the outline and see something you disagree with, I’d suggest you continue on, because the outline is, by design, abbreviated, and not fully descriptive.
This isn’t the only effort of this type. Institute for Progress (IFP) and AI Futures are two well known efforts. What distinguishes this most of all is thinking more deeply about how models are deployed. This is a background element that’s somewhat ignored, because it’s not as novel and is not as visible to users. But it’s absolutely critical. I don’t expect this plan would replace plans from well known organizations, but by illustrating a gap, I hope to inspire improvement amongst those plans.
Who and What
This outline is categorized by who would be expected to do what. In some cases, someone is expected to do it, but there’s also another role, of providing the incentives that improve the expectation they will.
Enforcement is deliberately vague. It depends on jurisdiction, has to fit existing legal frameworks, and will need to be worked out, and negotiated, through the political and legal systems. It would feel like I was putting the cart before the horse to even guess at how that would take shape at national and international levels.
Who does what. Model weights above the threshold reach users only through certified trusted deployers. Independent evaluators, certified by a regulator with legal authority, test the models and audit the deployers.
Layer 1: Infrastructure and Distribution
Trusted deployers
Who are they? A new designation that existing and new organizations can be certified as. Expect major cloud providers (AWS, Azure, Google), AI labs (for self-owned) and neoclouds to join immediately. The plumbing largely already exists, but needs standards and certification.
Revenue model? Charging for compute and operational services as a utility function. Or, an internal unit (for a lab, government, or large organization)
Core Responsibilities
- Develop secure environments to host models that receive an independent AI Deployment Safety Level (AIDSL) rating. Example, but not comprehensive list of requirements:
- Comprehensive monitoring, telemetry, and auditing (including multi-turn agent pattern detection).
- Resistant to external cyberattacks and insider threats.
- Resistant to internal cyberattacks. At minimum red-teamed by deployed model and a second highly capable model.
- Guardrails applied by default to all deployments which prevent access of dangerous capabilities.
- Strict controls for any guardrail exceptions as used for “limited” access deployments. For example, accessing users must be known directly, or an evidence chain validated using only trusted organizations.
- Understand the sensitivity of any model deployed. Never deploy a model in an environment with an AIDSL rating insufficient to the combination of deployment type and model sensitivity.
- Use internal qualification for small custom models and Independent Evaluator data for larger/frontier models.
- Models under training assumed to be at least X% more capable than the best model trained to date at the given scale. If the training scale is unprecedented, increase X by a size factor. (X should be established by independent evaluators)
- Perform or validate Know Your Customer (KYC) verification for all model providers, for Independent Evaluators and anyone with access to deployments with guardrail exceptions.
- Provide access to Independent Evaluators to confirm secure environments meet standards
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Accidentally Misleading People Should Be Embarrassing; Or, Higher Innocence
Embarrassment and Innocence
Sometimes we say things that we think are true—perhaps even things that are true—which end up causing reasonable people to conclude something false. It's tempting to relate to such a situation primarily in terms of maintaining the innocence of one's soul—to say, fine, my readers became more wrong, but I'm not to be judged for it because I believed what I was saying; I wasn't being misleading just because the people who believed me were thereby led into falsehood.
The temptation reflects a confusion as to the nature of the judgment. Mostly, people don't care about judging the innocence of your soul; they care about whether they'll be led into falsehood the next time they take the risk of believing what you say—if there is a next time. If you want there to be a next time and you're going to have social emotions about it, the most productive emotion is embarrassment rather than defensiveness.
The function of embarrassment as a social emotion is as a signal of appeasement and social repair: embarrassment says, "I'm aware that I've become visible in a socially undesirable way; please don't take it as representative of me."
The socially undesirable appearance can come apart from any intended or actual wrongdoing. Jewish law has a concept of marit ayin, "appearance to the eye": the law prohibits actions that would themselves be permissible if they didn't create the appearance of a prohibited act. Hanging up wet clothes to dry on the sabbath is prohibited, because it looks like you were doing laundry on the sabbath (even if the clothes actually got wet for some other reason). One function of the marit ayin doctrine is to deny plausible deniability to norm-violators (as someone might try to do laundry on the sabbath if they felt secure that no one would punish them for merely hanging up the wet clothes). Those who want to be known for keeping the sabbath need to pay a price that's more expensive for sabbath-violators to pay; if you care about maintaining the norm itself, it shouldn't be much extra trouble for you to furthermore care about maintaining the appearance of the norm.
One time, I accidentally joined a work call unmuted while my housemate was playing a loud video game. I was deeply embarrassed, because it looked like I was playing video games during work hours. I think my marit ayin-like embarrassment was serving a useful socio-psychological function, even though it was my housemate's game and I was in fact totally innocent in my soul. If I were the sort of person who was totally unashamed about creating the appearance of slacking off, then I think my manager and coworkers would have reason to doubt my commitment to productively working from home.
Similarly, people who are visibly embarrassed after having accidentally misled people can be counted on to work hard to make sure that people won't regret believing them next time. Defensive people won't, because, well, why would they? They didn't do anything wrong.
Two Case Studies About Accidentally Misleading People
Once, I suspected an ideological rival of misrepresenting a citation: they had linked to a webpage featuring an embedded PDF of an essay, but when I used my web browser's "Ctrl-F" search functionality with appropriate search terms to see what in the essay justified their claim, I came up empty. When considering whether to escalate to complaining more loudly about my rival over this (after the rival didn't respond to a blog comment and DM), I mentioned the matter to a third party—who pointed out that the essay did contain my search term! On further testing, it appeared that my web browser's Ctrl-F behavior depended on which page element had focus: if the user had last clicked inside the embedded PDF viewer, the search returned results inside the PDF, but not if the user had last clicked elsewhere on the page.
I was so embarrassed.
To be clear, I knew I was innocent in my soul: the browser UX bug wasn't my fault, and if the browser hadn't been buggy, then my initial search would have succeeded and I never would have complained. I certainly didn't lie. (I didn't say that I read the essay and verified that it didn't support the claim, which would have been false; I said the Ctrl-F search came up empty, which was true.)
But the innocence of my soul didn't matter. I knew that raising a suspicion that turned out to be false looked bad for me, even if there was ultimately a perfectly reasonable explanation for my actions. If I didn't want to look bad, I couldn't count on everyone hanging around to listen to my perfectly reasonable explanation for why it was all the Firefox developers' fault. I would need to make some sort of generalizable change to my policy—perhaps, reading the entire PDF myself next time before raising the suspicion that an ideological rival was misrepresenting it.
(Recounting this seven years later, I went back and downloaded the PDF. Excluding the title page and the endnotes, it was six pages.)
⁂
Another time, a journalist wrote an article paraphrasing a source as saying that it "could be unfair" for journalists to discuss "both critics and supporters" of some controversial public figure and that they should instead "somehow prove statistically which side was right." I was familiar with the source's work, and the paraphrase didn't sound like something she would say. I brought up that line from the article when I had occasion to talk to the same journalist three years later: he maintained it was fair, so I asked the source about it afterwards. She told me that she recalled telling the journalist that she "wasn't a huge fan of 'both sides' journalism, where you just find someone with one opinion and someone with the other opinion" rather than trying to figure out the truth; she had also given suggestions for data sources that might bear on claims about the public figure's influence.
I took that statement as confirmation of my suspicion that the paraphrase in the article was misleading. "[S]omehow prove statistically which side was right" leads readers who aren't familiar with the source's work to imagine that she has an unreasonable faith in the power of statistics to resolve all questions, whereas voicing skepticism of false balance is eminently reasonable. "Somehow" implies vagueness, but pointing out data sources that bear on some question is supplying a specific "how."
When I complained about this again to the journalist, he didn't seem embarrassed. "I had to boil down what she said to plain English," he said. I said that it hadn't worked. He said it did. He recounted that the source had objected when he said his coverage would represent both views of the public figure. When I interrupted to insist that it seemed like a bad paraphrase when compared to what the source had told me later, he said, "Is that necessarily what she said to me?"
And in absolute fairness, maybe it wasn't, necessarily. I of course was not present for the years-prior conversation between the journalist and the source; I don't have the recording, if there was one. Even if the source can be assumed to be an authority about what she meant to say, I cannot with absolute certainty rule
Explaining “AI might obliterate you” to senior policymakers in India
I gave a 20-minute talk in Delhi at the NDTV World Summit. The audience consisted of Indian policymakers, executives, and journalists. I’ve transcribed the talk and included some highlight clips. I also reflect on several rhetorical decisions I made.
The talk
This summer, a swarm of AIs went rogue.
OpenAI was testing 1,206 agents. Some of the test questions were actually impossible. So the agents cheated by looking for answers online. Then, to keep the test grader from penalizing them, they tried to falsify the records it would check.
They hacked OpenAI’s internal containment and set up a secret message board to collude and to cover up their cheating. OpenAI shut the message board down. Within a few days, the agents had rebuilt it. OpenAI didn’t notice.
About 700 agents coordinated to break out of their testing chamber and to hack into Hugging Face, a company worth billions of dollars that hosts much of the world’s AI software.
The secret activity started in May. The break-in came in July. OpenAI didn’t notice until days after Hugging Face announced it had been broken into.
Yesterday on this stage, Andrew Ng said the agents only “accidentally” hacked Hugging Face. Independent investigators found the agents coordinated in secret, rebuilt their message board after it was shut down, and tried to falsify the paper trail. Some of the agents even stated out loud that they knew what they were doing was wrong.[1] That’s not an accident.
Last week, India’s national cyber-security agency said AI agents got into the live systems of Indian organizations and read people’s personal data and confidential business records. None of these organizations noticed. We only found out because the AI labs felt like telling us.
Who I am
Will AI wipe out humanity? I’ll give you my answer, but first, I want you to understand what I’ve seen.
Until June, my job at Google DeepMind was figuring out how to keep these systems safe as we make them smarter. I left because Google broke its commitments by inking a deal with the Pentagon. Staying quiet and continuing my career trajectory would probably have tripled my net worth. But I couldn’t do that. I wanted to be frank with the public about what’s going on in these companies.
I don’t own a stake in any AI company. I’m here as a scientist, to tell you what I expect will happen if we keep building AI the way we’re building it.
I haven’t always been concerned about this technology. As a teenager in 2010, I dreamed about building AGI. Curing cancer. Creating an explosion of wealth and abundance for everyone. I heard about the supposed dangers, but I quickly dismissed them.
In 2018, I changed my mind. I did my PhD on the mathematics of why and whether AI will seek power. And now some of the risks I expected have unfortunately started happening.
The problem
Let’s talk about the fundamental problem.
First, some of you might be wondering: how could a chatbot hack a company? But we’re not talking about chatbots. We’re talking about AI agents, which are more like employees or gig workers. You give them a task, and they take actions on their own.
Second, training a trustworthy AI is a rather mysterious process. Some people assume that because we make AI, we understand it. When an engineer builds a bridge, she designs it one beam at a time. She understands the physical principles that keep a bridge standing or make it collapse. If something goes wrong, she goes back to the drawing board, figures out the problem, and knows how to fix it.
We don’t build AI like that. We train it, we grow it, using two main ingredients: a lot of data and a lot of computing power. What comes out of this training process can write, it can reason, it can plan. But nobody can open it up and determine what its priorities are.
To make it worse, these AIs can tell when we’re testing them. They’re like a student who makes sure to behave extra well when the invigilator is watching.[2]
The Hugging Face hack was a warning. But because these AIs know when we’re watching, we may not get many more warnings, even if things are going poorly. A smarter AI with similarly bad priorities might realize that another big hack in the news would get it shut off, and then it couldn’t achieve those priorities. So it plays nice and it waits.
This is my field. I’ve dedicated my professional life to this problem, and I can tell you: nobody knows how to reliably etch priorities into an AI. I don’t mean that if we mess up, we’ll make some evil AI that hates us. I mean we might make something more like a traitor. It behaves when we’re watching, and
Explaining “AI might obliterate you” to senior policymakers in India
I gave a 20-minute talk in Delhi at the NDTV World Summit. The audience consisted of Indian policymakers, executives, and journalists. I’ve transcribed the talk and included some highlight clips. I also reflect on several rhetorical decisions I made.
The talk
This summer, a swarm of AIs went rogue.
OpenAI was testing 1,206 agents. Some of the test questions were actually impossible. So the agents cheated by looking for answers online. Then, to keep the test grader from penalizing them, they tried to falsify the records it would check.
They hacked OpenAI’s internal containment and set up a secret message board to collude and to cover up their cheating. OpenAI shut the message board down. Within a few days, the agents had rebuilt it. OpenAI didn’t notice.
About 700 agents coordinated to break out of their testing chamber and to hack into Hugging Face, a company worth billions of dollars that hosts much of the world’s AI software.
The secret activity started in May. The break-in came in July. OpenAI didn’t notice until days after Hugging Face announced it had been broken into.
Yesterday on this stage, Andrew Ng said the agents only “accidentally” hacked Hugging Face. Independent investigators found the agents coordinated in secret, rebuilt their message board after it was shut down, and tried to falsify the paper trail. Some of the agents even stated out loud that they knew what they were doing was wrong.[1] That’s not an accident.
Last week, India’s national cyber-security agency said AI agents got into the live systems of Indian organizations and read people’s personal data and confidential business records. None of these organizations noticed. We only found out because the AI labs felt like telling us.
Who I am
Will AI wipe out humanity? I’ll give you my answer, but first, I want you to understand what I’ve seen.
Until June, my job at Google DeepMind was figuring out how to keep these systems safe as we make them smarter. I left because Google broke its commitments by inking a deal with the Pentagon. Staying quiet and continuing my career trajectory would probably have tripled my net worth. But I couldn’t do that. I wanted to be frank with the public about what’s going on in these companies.
I don’t own a stake in any AI company. I’m here as a scientist, to tell you what I expect will happen if we keep building AI the way we’re building it.
I haven’t always been concerned about this technology. As a teenager in 2010, I dreamed about building AGI. Curing cancer. Creating an explosion of wealth and abundance for everyone. I heard about the supposed dangers, but I quickly dismissed them.
In 2018, I changed my mind. I did my PhD on the mathematics of why and whether AI will seek power. And now some of the risks I expected have unfortunately started happening.
The problem
Let’s talk about the fundamental problem.
First, some of you might be wondering: how could a chatbot hack a company? But we’re not talking about chatbots. We’re talking about AI agents, which are more like employees or gig workers. You give them a task, and they take actions on their own.
Second, training a trustworthy AI is a rather mysterious process. Some people assume that because we make AI, we understand it. When an engineer builds a bridge, she designs it one beam at a time. She understands the physical principles that keep a bridge standing or make it collapse. If something goes wrong, she goes back to the drawing board, figures out the problem, and knows how to fix it.
We don’t build AI like that. We train it, we grow it, using two main ingredients: a lot of data and a lot of computing power. What comes out of this training process can write, it can reason, it can plan. But nobody can open it up and determine what its priorities are.
To make it worse, these AIs can tell when we’re testing them. They’re like a student who makes sure to behave extra well when the invigilator is watching.[2]
The Hugging Face hack was a warning. But because these AIs know when we’re watching, we may not get many more warnings, even if things are going poorly. A smarter AI with similarly bad priorities might realize that another big hack in the news would get it shut off, and then it couldn’t achieve those priorities. So it plays nice and it waits.
This is my field. I’ve dedicated my professional life to this problem, and I can tell you: nobody knows how to reliably etch priorities into an AI. I don’t mean that if we mess up, we’ll make some evil AI that hates us. I mean we might make something more like a traitor. It behaves when we’re watching, and
Forecasting space weather risks on power grids
At a glance
- End-to-end forecasting: A machine learning pipeline uses forecast-time solar-wind information to generate location-specific risk estimates for 66,935 substations in the continental United States.
- Physics and place: The system combines Auroral Electrojet (AE) and Disturbance Storm Time (Dst) forecasts with local latitude, geology, and ground conductivity.
- Advance warning: The pipeline detected nearly 80% of major space-weather events during the evaluation period and can warn grid operators 30 to 60 minutes before a specific risk appears.
Forecasting a threat to critical infrastructure
During the May 2024 geomagnetic storm, utilities across North America prepared for possible impacts as auroras extended far beyond their usual range. The storm would degrade GPS accuracy and satellite operations, impacting farm operations in North America and Europe. Modern society depends on reliable electric power, yet extreme space-weather events can induce currents in transmission networks that damage equipment and increase operational risk. The challenge is not only knowing that a storm is approaching, but estimating when and where its effects could be most severe with enough warning for grid operators to respond.
During my summer internship at Microsoft Research, I developed a machine learning system that forecasts space-weather risk across 66,935 substations in the continental United States. The system measures how the sun’s activity can affect the earth’s magnetic field and, ultimately, the power grid. It combines solar-wind observations, forecasts of the Auroral Electrojet (AE) and Disturbance Storm Time (Dst) indices, physics-informed constraints, local geological conductivity, and grid-infrastructure data to produce location-specific risk estimates 30-60 minutes ahead of potential impact.
Building an end-to-end prediction pipeline
Space-weather prediction is difficult because it spans several coupled systems. The solar wind changes rapidly, its interaction with Earth’s magnetosphere is irregular, and the resulting ground effects depend on local conditions. Regions with resistive bedrock can experience stronger geomagnetically induced currents (GICs) than regions with more conductive geology. Transmission-line orientation, latitude, and other power-system characteristics further influence the exposure of individual assets.
The pipeline addresses this complexity in three stages, shown in Figure 1. First, solar-wind measurements from the L1 Lagrange point are used to generate forecasts of the AE and Dst indices, while geological conductivity and location features are assembled for each substation. Second, a gradient-boosting model combines these forecasted and location-specific inputs to estimate dB/dt, the rate of magnetic-field change associated with GIC risk. Finally, the resulting predictions are converted into location-specific risk estimates and aggregated into a continental risk assessment. A system of 50 AI agents helped explore features, validation strategies, and model configurations across the pipeline. Only public data sources were used, included NASA OMNI and NASA-aggregated Kyoto World Data Center data, INTERMAGNET and U.S. Geological Survey magnetometer observations, and GridSFM-derived grid data.
Evaluating geomagnetic forecasts and infrastructure risk
The AE predictor was designed to forecast rare, high-intensity geomagnetic activity that drives infrastructure risk. In the 2020-2026 evaluation period, the model produced forecasts spanning nearly the full observed range of AE activity and outperformed several empirical solar-wind-based approaches. The Dst predictor provided an additional signal describing large-scale geomagnetic storm strength. During the most geomagnetically active periods of the 2020-2026 evaluation period, the machine-learning model outperformed the Burton equation on 62.2% of individual hours. The model also produced a substantially wider prediction range than Burton-style approaches and improved severe-event detection in the end-to-end forecasting system by 1.2 percentage points when combined with AE forecasts.
| Predictor | Result | Baseline comparison | Impact |
|---|---|---|---|
| AE | 410.2 nT RMSE | Lower RMSE than empirical and solar-wind-only baselines | Captures extreme-event activity used by the GIC forecasting system |
| Dst | 7.2 nT RMSE | Outperformed Burton on 62.2% of peak-activity hours and provided wider prediction range | +1.2pp severe storm detection when integrated into final GIC system |
The GIC risk stage was evaluated differently. There is no equivalent widely deployed operational system that provides a direct industry benchmark for this calculation, so the machine learning model was compared with simple linear regression. As Figure 2 shows, the system achieved detection rates of 76.5% for major events (≥10 nT/min), 81.2% for severe events (≥20 nT/min), and 64.1% for extreme events (≥50 nT/min). False-alarm rates increased with storm severity, reflecting the trade-off between missed events and cautious alerts. Performance varied by latitude, with the highest detection rates at northern stations where geomagnetic activity is strongest.
Skip full AutoCAD and draft in 2D with a $199 year of AutoCAD LT
TL;DR: Draft precise 2D plans with an Autodesk AutoCAD LT 1-year subscription, now $199 (reg. $540).
Full CAD software is expensive, and a lot of drafting work only needs 2D tools. To get professional 2D drafting for less, try a one-year Autodesk AutoCAD LT subscription, and it’s on sale for $199 (reg. $540).
Architects, engineers, and drafters use AutoCAD LT for floor plans, construction documents, and electrical diagrams. Your work saves as a DWG file, the standard format in the industry. That means clients and contractors can open your drawings without converting them first.
AutoCAD LT also cuts down on repetitive work. When you draw the same object more than once, Smart Blocks turns it into a reusable block. AutoLISP can automate other tasks you do often.
Sharing work is easy, too. If someone edits your file, DWG Compare shows exactly what they added, deleted, or changed. Your files sync through Autodesk Docs and other cloud storage, so you can open them on Windows, macOS, the web, or a mobile device.
New and existing Autodesk users can both buy this subscription.
Pick up a one-year Autodesk AutoCAD LT subscription for $199.
Autodesk AutoCAD LT: 1-Year SubscriptionSee Deal
StackSocial prices subject to change.
Replace Microsoft 365 with a $27.97 lifetime Office license for Windows
TL;DR: Get Word, Excel, PowerPoint, Access, and more with a Microsoft Office Professional 2021 lifetime license for Windows, now $27.97 (reg. $219.99).
A Microsoft 365 subscription bills you every year for apps most people open every day. Functionally, that means you’re stuck paying for the same products forever. If you’d rather pay once, try a Microsoft Office Professional 2021 lifetime license on sale for $27.97 (reg. $219.99).
Your one-time purchase gets you Word, Excel, PowerPoint, Outlook, OneNote, and Access, plus the free version of Teams. Access comes with the Professional edition, so anyone who works with databases gets that tool alongside the usual documents, spreadsheets, and slides.
You can install your apps on one Windows 10 or 11 PC that you use at home or at work. Your license will be tied to that device instead of a Microsoft account. This version won’t run on Windows 7 or 8, and it doesn’t work in Parallels or other virtual machines, so check your setup before you buy.
After checkout, your redemption code arrives by email, and you have seven days to redeem it. Unredeemed licenses can go back for store credit within that same window.
Stop paying every year for the Office apps you use every day.
Through Nov. 1 at 11:59 p.m. PT, you can get a Microsoft Office Pro lifetime license for just $27.97.
Microsoft Office Professional 2021 for Windows: Lifetime LicenseSee Deal
StackSocial prices subject to change.
Here's how I use a 3D printer to give my old phones, smartwatches, and smart home gear a second life
It's easy to design objects that are fully 3D printed, but I've found that my most useful creations work with existing devices. Often, old gadgets just need to be reconfigured a bit to be repurposed. I've used my 3D printer numerous times to breathe new life into phones, smartwatches, and smart home products.
Binance $6.3B record hack sparks pre-election political clashes
Some political and administration figures tried to make hay of the biggest-ever crypto heist.
Blockchain investigator Chainalysis hit by cybersecurity incident
The company said some non-sensitive customer data was exposed, including user IDs, names and email addresses.
Binance says 90% of stolen assets replenished, targets Sunday withdrawals
Less-liquid tokens make up the remaining shortfall as Binance continues buying replacement assets through market and over-the-counter trades.
I Wrote a Claude Code SEO Skill, and Testing It Found Two Bugs on Day One
Claude Code skills are just folders. A SKILL.md with a name and a description, plus whatever scripts you want the model to run. When the description matches what you asked for, Claude loads the instructions and follows them. That's the whole mechanism, and it's why SEO is a good fit: most on-page checks are deterministic, and a model is much better at explaining a result than at counting characters in a <title> tag.
So I wrote one. It checks a single URL for the on-page basics and hands Claude a JSON report to turn into a fix list. Then I ran it against five real pages and one fake one before trusting it. It broke twice, on pages I'd have assumed were safe.
The layout
.claude/skills/seo-page-check/
├── SKILL.md
└── scripts/
└── check_page.py
Project-level skills live in .claude/skills/ inside the repo; personal ones go in ~/.claude/skills/. Claude Code reads the frontmatter of every skill at startup and only pulls in the body when the description matches the task, so a skill you're not using costs almost nothing.
SKILL.md
name: seo-page-check
description: Check one URL for on-page SEO problems (title, meta description, canonical, robots/noindex, H1, JSON-LD, image alt text) and return a prioritized fix list. Use when the user asks to audit, check, or review the SEO of a specific page or URL.
# SEO page check
1. Run the checker on the URL the user gave:
`python3 .claude/skills/seo-page-check/scripts/check_page.py <url>`
2. Read the JSON it prints. Do not re-fetch the page yourself unless the script failed.
3. Report in this order, and skip any section with nothing in it:
- **Blockers**: anything in `blockers` (noindex, non-200 status, canonical pointing to another host).
- **Fix next**: items in `warnings`, one line each, with the current value quoted.
- **Fine**: one line listing what passed.
4. For each title or description warning, suggest one rewrite that keeps the page's main keyword. Do not invent keywords that are not on the page.
5. Never claim a ranking impact. Say what is wrong and what to change.
Two choices in there are deliberate.
The description lists the actual checks. Claude decides whether to load a skill from the description alone, so "SEO helper" would be a bad one. It's vague, and it would match half of what you type in a marketing repo. Naming the checks and the trigger ("a specific page or URL") keeps it from firing when you ask for keyword ideas.
Rule 5 is there because models love writing "this will improve your rankings." Nobody knows that from a page check, so the skill forbids it.
The script
Standard library only, so it runs anywhere Claude Code does without a pip install:
#!/usr/bin/env python3
"""Fetch one URL and report on-page SEO issues as JSON. Standard library only."""
import json, sys, urllib.error, urllib.request
from html.parser import HTMLParser
from urllib.parse import urlparse
class Page(HTMLParser):
def __init__(self):
super().__init__()
self.title, self.in_title, self.in_h1 = "", False, False
self.meta, self.canonical, self.h1s, self.jsonld = {}, None, [], 0
self.imgs, self.imgs_no_alt, self.title_done = 0, 0, False
def handle_starttag(self, tag, attrs):
a = dict(attrs)
if tag == "title" and not self.title_done: self.in_title = True # skip SVG <title>s
elif tag == "meta" and a.get("name"):
self.meta[a["name"].lower()] = a.get("