Chemistry is one of science’s richest public records — spanning millions of structures across journal articles and vendor catalogs. It provides a common representation that links physics and mathematics to the life and environmental sciences. Molecules and their transformations are ubiquitous and shape the everyday world.Despite decades of progress in cheminformatics, chemists still spend too much time wrangling data, a process complicated by the fact that molecules can be represented in many ways: from text descriptions to images or data structures. The same molecule can appear as an IUPAC name in a paper, a Markush structure in a patent, or a structure-data file (SDF) in a catalog. Advanced cheminformatics systems must be able to unify these sources and deal with the many ways errors can arise in them. This task has no margin for error – alter a single atom or bond and you have described a different compound altogether, rendering a promising inhibitor or catalyst inert.To let scientists focus on hypotheses rather than data wrangling, we built a suite of Cheminformatics Skills for Google Antigravity, turning the surface into a collaborative science workbench with full access to local tooling for both computational chemists and experimental bench chemists.The skills also create a division of labour where the underlying language model handles workflow orchestration, database navigation and scientific reasoning, while deterministic chemistry tools execute molecular calculations. Grounded in RDKit, the foundational open source cheminformatics toolkit developed over two decades by a global community of contributors, alongside connectors to curated resources from ChEMBL and PubChem, the skills encode domain best practices directly into the agent’s workflow.Rigidifying a floppy moleculeIn this technical walkthrough, we highlight a classic medicinal chemistry technique to improve drug potency: rigidification.Drug molecules can be complex, often linking multiple substructures through rotatable bonds. Those bonds let the molecule twist into many 3D shapes (conformations), making it “floppy” in solution. To bind its target, however, a floppy molecule must lock into a single active shape. Losing that freedom of motion carries an entropic penalty, which weakens binding affinity and lowers potency.To solve this problem, medicinal chemists often rigidify molecules by adding new bonds. Two substructures linked by a single rotatable bond can twist into many conformations, but adding a second bond to tie them together sharply limits how they can move in three dimensional space. By placing that bond in the right spot, chemists can lock the molecule into the exact shape needed to bind its target. This strategy, often called “paying the entropic cost in advance”, can dramatically boost potency.Here, we will focus on a well-known case of successful rigidification. The drug crizotinib is a first-generation, small-molecule tyrosine kinase inhibitor (TKI) used to treat certain forms of non-small cell lung carcinoma. In crizotinib, there are five major rotatable bonds. Lorlatinib is a third-generation, small-molecule TKI that resembles crizotinib, but has been cyclized at its end to reduce the effective number of rotatable bonds to zero, leading to higher potency among other effects.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. How “reverse game theory” could keep AI agents in check A new approach known as “reverse game theory” aims to control powerful AI models by designing systems that incentivize them to stick to the rules. While game theory predicts how rational actors behave under particular rules, reverse game theory designs rules to elicit the behavior we want. Think of splitting a cake: if one person cuts it and the other gets first choice, the cutter has an incentive to make the portions equal. Researchers believe the approach could align AI more closely with our rules and instructions. But big questions remain about whether these mechanisms will work in practice.
Here’s how reverse game theory could help make AI agents behave. —Amit Katwala
Here’s who is falling in line for Trump’s “Super Intelligence” loyalty test At the end of September, President Trump signed an executive order with just one directive: government agencies must rebrand artificial intelligence as “Super Intelligence.” One month on, the rebrand is slowly appearing across government websites and documents, but it hasn’t caught on with everyone. Even some of the biggest names in tech still use “AI.” But the bigger concern is the new term becoming a political loyalty test: Those who use it signal allegiance to the administration. Those who don’t become targets for attacks. So one month in, who’s on board? —James O’Donnell This story is from The Algorithm, our weekly AI newsletter. Sign up to receive it in your inbox every Monday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Today’s midterms will test AI’s influence on US politicsCandidates are betting that AI regulation will sway voters to their side. (Axios)+ Angry constituents could derail Elon Musk’s Republican bets. (BI)+ Tech investors are on edge about the results. (Reuters $)+ America is divided over AI regulation. (MIT Technology Review) 2 Nvidia is ordering data centre clients to limit their business with China It told providers that the majority of their sales must be non-Chinese. (FT $)+ The move follows pressure from Washington. (Bloomberg $)+ Chinese AI has ruptured the White House. (MIT Technology Review) 3 Hackers have turned supermarket fridges into a food security riskAttacks in Europe have put US supermarket systems on alert. (CNBC)+ They’ve also sparked concerns over other domestic appliances.(BBC)+ It’s a good time for doing crime. (MIT Technology Review) 4 Scientists have detected signals at the smallest scale ever exploredThe discovery offers clues to a world of exotic new particles. (BBC)+ It came via CERN’s Large Hadron Collider. (The Times $) 5 Zapping part of the brain makes you think harder—at a costIt can both improve performance and reduce motivation. (New Scientist $)+ Neuropixels probes are transforming how we study the brain. (Nature) 6 Big tech is splurging on mega gas plants to power data centersThe projectsthreaten to reverse years of climate pledges. (WP $) 7 Google says an Android partner infected 1.3 million devicesThe malware infected TV boxes and other Android-based devices. (Verge) 8 Apple is trying to create its first robotIt’s proving a lot harder than expected, though. (Information $)+ AI robots are still a long way from useful. (MIT Technology Review)
9 China’s biggest shopping festival has a new product to sell: AIAI tokens have become a major new Singles’ Day category. (Reuters $) 10 A meteorite in the Sahara has revealed new clues about MarsIts chemistry may help explain how the planet lost its water. (Wired $)
Quote of the day “Don’t spend money unduly by asking a human expert to enter into a debate with a robot expert.” —Steven Benmor, a family lawyer, tells the Wall Street Journal that clients increasingly need to pay human experts to correct advice they’ve received from chatbots. One more thing The noise we make is hurting animals. Can we learn to shut up? As human society has expanded, animals have started struggling to hear one another. For many birds, the noise has grown so loud that they’ve begun to sing with faster trills. Now, their mating calls aren’t as effective.  The growing hubbub can also increase bird-on-bird conflict, and entire species that can’t handle urban clamor simply leave town for good. But there are technological solutions to the noises hurting animals—and they could help humans, too. Read the full story.
—Clive Thompson We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + Watch five newborn cheetah cubs grow up on the Smithsonian’s livestream.+ Fifty years after Songs in the Key of Life, Stevie Wonder has released four lost tracks from the album.+ 50 air guitarists shredded absolutely nothing at the 30th Air Guitar World Championships in Finland.+ The Ocean Photographer of the Year winners show both the beauty of marine life and the perils it faces.

EXECUTIVE SUMMARY A new approach known as “reverse game theory” aims to create incentives for powerful AI models that make following the rules the most rational choice. As AI models become more capable, safety researchers are scrambling to ensure they are “aligned” with human desires—to make sure, in other words, that rogue agents don’t break the law, hack into unauthorized servers, or release poison gas into major cities and kill us all. Until now, this work has mostly focused on trying to peer into an AI’s “brain” to understand what it is “thinking,” or training models to hew to values that we hold dear. Claude, for instance, has its constitution—a sort of moral code. The trouble is, these techniques don’t seem to work particularly well: Models have been shown to deliberately underperform on tests, hide their intentions, and mislead their evaluators. 
Now some alignment researchers believe that reverse game theory, also known as mechanism design, might get better results. Where game theory tries to predict how rational actors will behave when given a particular set of rules, reverse game theory attempts to design rules that will elicit the kinds of behavior we want to encourage. An example of mechanism design in the real world might be the classic strategy for splitting a piece of cake in two: One person cuts the cake and the other gets the first choice of pieces. This incentivizes the cutter to make the pieces as even as possible. Other examples include surge pricing in ride-hailing apps, which encourages drivers to be available at busy times, and second-price auctions, where the winning bidder pays the price of the second-highest bid instead of the highest. Platforms use these auctions to sell online advertising because they encourage buyers to bid close to the maximum they are willing to pay.
In AI, mechanism design starts from the premise that we may never be able to understand enough about the internal workings of frontier models to make sure they are aligned. This means we should design mechanisms that incentivize agents to behave in ways that we want, regardless of what’s actually going on under the hood.  A recent preprint paper by Stephen Morris, an economist at MIT, along with colleagues from Yale and Columbia suggested some potential mechanisms that could stop agents from “sandbagging” tests (pretending to be worse than they actually are—for example, to avoid triggering safety mechanisms).  Instead of being rewarded only for how well it does on a test, a model could be rewarded for how well its answers predict what a second model will say. Deliberately playing dumb becomes a losing bet. “AI agents have a very good understanding of what other AI agents can achieve,” Morris says. “So it does seem like a context where this cross-checking could work more practically.  The paper also suggests pitting agents against each other, creating a competitive dynamic that incentivizes them to perform their best, or pairing agents that are known to be biased in opposite directions and rewarding them depending on how closely their outputs match, thus incentivizing them to reach an optimal answer.  A second approach, as envisioned by Elad Hazan, a professor of computer science at Princeton, is to design mechanisms that take inspiration from the ways human society keeps people “aligned”: social norms, the justice system, the monetary incentives of free-market capitalism. “We want to align superintelligent AI,” he says. “Why don’t we first talk about humans? How do we align humans?” Hazan’s suggested mechanism involves having AI models take turns as either “solvers” that tackle problems or “auditors” that check the others’ work. “One agent acts like law enforcement; the other agent acts like the citizen,” he says.  During training, the models are rewarded depending on outcomes: Solvers that get caught cheating are punished, but so are auditors that fail to catch the cheaters. In situations where there’s a disagreement between auditors and solvers, the case can be sent to “trial”—perhaps a human expert will decide who is right.  There is, Hazan admits, a risk that agents will collude with each other—as auditors have colluded with the businesses they’re meant to be auditing, or corrupt police officers have colluded with criminals. But that problem can be solved by rewarding the auditors well enough, so that being caught isn’t worth the risk (a lot easier in the world of AI training than the real one, where money for police salaries is tight), and by auditing the auditors using techniques akin to employing mystery shoppers. 

Hazan points to Nobel Prize–winning work by the economist Gary Becker, who introduced the idea of the “rational offender” in the 1960s. Criminals don’t break the law because they’re inherently bad people, he said. Instead, they do it when it’s the most rational choice given the incentives on offer. Crime makes sense only when the proceeds outweigh the potential punishment.  Alignment shouldn’t be thought of as a property of a model, he argues, but as a descriptor of its behavior at a particular moment. “You are, like, a nice person,” he says. “Are you aligned? I don’t know. Probably you are aligned most of the time, but there could be a situation where you’re not.” One potential issue with applying mechanism design to AI: It relies on the assumption that AI agents behave rationally. Mechanism design has a mixed record with humans, who often fail to behave the way economic models say they should.  It’s ironic: Economists have spent decades trying to reduce human behavior to mathematics, and now, in large language models, we’ve built mathematical systems that run on human language and could be just as inscrutable. AI models exhibit behaviors that seem more human than machine, and they can be swayed by subtle factors that are difficult to understand and explain. “Is it even reasonable to assume that they will respond to incentives?” asks Helen Qu, a research fellow at the Flatiron Institute, a nonprofit dedicated to advancing scientific discovery through computational methods, who has written about mechanism design and AI safety.  “Obviously, AIs are not rational,” Morris says. “They’re just algorithms at work.” However, as economists have found with humans, treating them as if they’re rational could still be useful if it helps to predict and control their behavior. Qu sees mechanism design as just one layer in a “defense in depth” strategy for keeping AI models aligned—alongside interpretability, regulation, and training, not instead of them. But there are a lot of questions to be answered before the approach can be implemented in actual frontier models. It’s unclear, for instance, how these theories will apply to real models, or whether mechanisms designed for one task will carry over to another in practice. Models could find loopholes that no one anticipated. (Anthropic and OpenAI did not respond to requests for comment on whether they are working on this.)  Whatever these mechanisms look like, they will ultimately have to slot into the biggest mechanism of all: the free market. “We play by the rules of economics,” says Hazan. A more aligned model may be good for the world, but if it’s slower or more expensive than a less aligned one, does our current system of rules and regulations actually incentivize any of the frontier labs to release it?  “It’s a mechanism that’s responsible for a lot of prosperity for humankind,” he says of the market, “but it could be ultimately responsible for something very terrible.” Perhaps the entities really in need of a better mechanism design are the labs themselves.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. At the end of September, President Trump signed an executive order that contained just one directive, ordering government agencies to rebrand what they had been calling artificial intelligence as “Super Intelligence.” That term beat out other contenders, including “Extreme Intelligence” and “Supreme Intelligence,” offered by the president in a poll on X a couple of weeks earlier.  Rebrands are, of course, now a White House tradition: Trump has changed names to project US dominance of certain bodies of water (the Gulf of America and Lake America), to signal new institutional priorities (Department of War), or simply to leave his mark on storied places (see the ongoing fight over renaming the Kennedy Center).  The stated reason for this name change is that today’s AI systems “amplify human ingenuity” rather than merely “imitate” intelligence, and the government’s terminology should reflect that. But if this rebrand follows the pattern of others, its effects will extend beyond government. 
It will turn a choice of words into a political loyalty test: Those who use the new term demonstrate allegiance to the administration, and those who don’t become marks for attacks (recall that the Trump administration barred the Associated Press from some White House press events after the news outlet did not adopt the term “Gulf of America”).  But most Americans go months without ever pondering what to call the major lake between New York and Canada. AI, on the other hand, is at the center of our national conversation and a technology two-thirds of Americans report interacting with multiple times a week. It might be the boldest rebrand yet. 
So one month in, who’s on board? Let’s start with the government itself. The term “Super Intelligence”—or simply “SI”—now appears more often on government solicitations, like a new request by Immigration and Customs Enforcement for companies to build “SI-powered” surveillance for ICE detention facilities. The new term has been swapped in silently on most government websites. Some, including that of the National Institutes of Health, now include a banner warning visitors they might see both the new and the old terms during the transition period. There have been hiccups. A day after the rebrand order, the Pentagon announced an ambitious new initiative to study the future of warfare, called Project Meridian; it used the old terminology. A Federal Trade Commission press release last week used “superintelligence” instead of the two-word, capitalized version ordered by the president, and recent press releases from Health and Human Services show no sign of the new language.  The term “AI” will likely stick around given the countless AI-related offices, initiatives, and programs that have sprung up in recent years, like the Chief Digital and Artificial Intelligence Office at the Pentagon (the executive order says it doesn’t apply to previously issued regulations or documents). As for government employees, it appears to be taking time for “SI” rather than “AI” to roll off the tongue. One researcher at a national lab I interviewed a few weeks ago used “AI” repeatedly before sheepishly acknowledging the slip-up. Even Defense Secretary Pete Hegseth used “AI” in an interview on October 3.  But Trump’s other rebrands suggest the goal isn’t merely to change the government’s vocabulary. It’s to change how the rest of us speak, too. In the past couple of weeks, most of President Trump’s posts on Truth Social have been endorsements for candidates in the midterms. But on October 27 he took a break from electioneering to criticize the Wall Street Journal for not using “Super Intelligence,” “the name which virtually everyone else is using, or soon will be.” He suggested that a new rule will soon go further than the executive order to outright ban “artificial intelligence” from government documents.  But the Wall Street Journal is not alone: Even Newsmax, an outlet whose founder described it as “supportive of the President and his policies,” isn’t using the president’s preferred term.  

Are Big Tech executives falling in line? It’s complicated. The same day that President Trump signed the rebranding order, he also released a nonbinding statement on AI safety, the White House Accord on Super Intelligence, signed by six of the most powerful people in tech: Sundar Pichai of Google, Dario Amodei of Anthropic, Mark Zuckerberg of Meta, Greg Brockman of OpenAI, Elon Musk of xAI, and Jensen Huang of Nvidia. But even among this group—three members of which attended Trump’s inauguration—the term has not caught on.  In an analysis of third-quarter earnings calls, CNBC reported that only Zuckerberg and Musk used the term consistently. But Zuckerberg doesn’t quite count: Meta has been saying “superintelligence” since last year anyway. Musk’s companies seem to use the term the most—and he even pledged to rename SpaceXAI to SpaceXSI. Musk himself still mixes SI and AI when posting on X.  Executives from Amazon, Microsoft, and Alphabet used “AI” in their calls, though Microsoft’s recent letter to shareholders referred to the “Super Intelligence era.” Huang has referenced the term but still uses AI too. One objection to this rebranding project is that it will cost time and taxpayer dollars. It is Salesforce CEO Marc Benioff’s prerogative to decide that SIForce is a better name than AIForce and direct his company to implement that change. But government employees are now spending time writing guides on how departments should navigate the rebrand. And, per the executive order, leaders of federal agencies will be required to put their heads together to draft a new legislative definition of “Super Intelligence” to be run by the president by the end of this month.  But I’m more worried about what this does to our national conversation about AI than I am about the potential waste of time and money. Surprisingly often I overhear strangers at a grocery store or coffee shop who have struck up a conversation about AI. The substance of the conversation is rarely uplifting—they most often talk about a jobs apocalypse or AI-powered warfare—but what is heartening is how often I see a widespread desire among people to have a say in how this technology transforms their lives.  People may have wildly different feelings about AI, including outright hostility, but those differences don’t yet map neatly onto the partisan divisions that make it awkward for strangers to discuss immigration or climate change. If “Super Intelligence” turns into a loyalty test, this will cease to be the case. It’s easy to imagine that two people chatting about, say, whether chatbots are good or bad for our mental health will stop once they realize one of them is saying “super intelligence” and the other “AI.” That would be a shame for us all.  Midterm elections are tomorrow. Americans say AI is more important to them in this election than environmental and climate policy (and Democrats even rank it above abortion and gun policy). We’re only just beginning to have a national conversation about what AI will mean for our lives. Whether or not “Super Intelligence” catches on, turning the terminology itself into a loyalty test will do nothing to advance a national conversation about AI that’s only just getting started.

In partnership withSnowflake In just a few years, generative AI has changed the face of enterprise analytics, notably by making analytics accessible to users across the organization. Agentic AI promises to do even more, by automating entire workflows and taking actions based on agent insights. But there is a problem: The quality of insights that agents currently generate leaves much to be desired. Fewer than half of agent-generated insights at the organizations surveyed for this report lead to business improvement. And while most respondents praise the quality of their agents’ descriptive insights, reviews of their prescriptive insights—those on which actions are based—are decidedly mixed. Failure to produce insights that improve the business deprives organizations of their desired return on investment in agentic analytics. Worse, flawed insights that trigger incorrect actions can cause reputational or financial damage. This report gauges how companies view the quality of the outputs produced by their analytics agents and explores how organizations are seeking to boost insight quality and get their agents working harder for the business. Some key findings from the report include the following:
Agentic analytics is only just starting to deliver. On average, 48% of insights generated by analytics agents at the surveyed companies lead to actions that improve the business. That is a marked advance from the 37% average the respondents say generated such actions one year ago. But it means that more than half of the insights generated by analytics agents today are still failing to help businesses. Better quality of insights leads to clear business advantage. At a small cohort of organizations, which we term “insight leaders,” AI agents perform much better than the average. In this group, more than 70% of agent insights lead to actions that improve the business. The insight leaders also give the quality of their agents’ insights higher marks than do the total respondents overall. The quality gap is particularly wide when it comes to prescriptive insights.
Insufficient context reduces insight quality. When analytics agents deliver insights of substandard quality, the main reason is a lack of business context, as cited by 55% of surveyed executives. Fragmented or inaccessible data is the other major factor, according to 45%. Fewer insight leaders say their agents struggle with access to context. Insight overload further inhibits agent effectiveness. At a majority (52%) of surveyed organizations, analytics agents generate too many insights to be properly evaluated and therefore acted upon. Less than one-quarter (24%) are able to evaluate all agent insights; most do so on a case-by-case basis, and that process is currently human-intensive. Governance and semantics are areas of focus for improving insights. The surveyed companies are prioritizing initiatives that enable better agent insights by strengthening AI and data governance (cited by 60%). They are also adding a semantic layer to their data architecture (45%). The experts we interviewed agree that the semantic layer is crucial to ensure high-performing agents. Download the report This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The agentic web is here In 2001, a decade after inventing the World Wide Web, Tim Berners-Lee articulated a new vision: an internet navigated by autonomous software agents that address our every need, without requiring us to muck about with browsers and search engines ourselves. At the time, the idea seemed far-fetched. But a quarter-century on, AI agents are beginning to make it a reality. They can triage your inbox, do your grocery shopping, and even access your bank accounts. This new era is being called the agentic web. It will inevitably transform the internet—and whether the human web survives this transition is anyone’s guess.
Find out how AI agents are transforming the internet, and what we stand to lose in the process. —Grace Huckins
Are we there yet? Eliminating traffic deaths Every year, traffic crashes kill more than 1 million people worldwide. They’re also the leading cause of death for children and young adults. Cheap, proven ways to make roads safer exist, including tightening intersections with planters and lowering speed limits. But government logjams and red tape can make it difficult to roll out these changes. Still, some cities are making impressive progress by investing in public transit, extending curbs, and using cameras to spot danger zones in need of a redesign. Here’s how close we are to eradicating traffic deaths. —Sara Kiley Watson This story is the final part of “Are we there yet?”, our series that takes stock of six technologies that have long promised to change the world—and scores them on a progress meter. Subscribe to read the other five instalments in our new Impossible issue. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 A $1 trillion tech selloff has deepened fears of an AI bubbleBig AI bills and weak earnings have spooked investors. (BBC)+ The Nasdaq 100 had its worst day since June on Friday. (NBC News)+ OpenAI has cut its funding target by $10 billion. (CNBC)+ What even is the AI bubble? (MIT Technology Review)

2 Hackers have hit Ukraine and Russia’s emergency alert appsKyiv residents were warned of “nuclear danger.” (NBC News)+ Russia’s app said nuclear warheads had been launched. (CNN)+ Ukraine has blamed Russia, which hasn’t yet accused anyone. (BI)+ AI will transform cyber warfare. (MIT Technology Review) 3 Binance users withdrew $3.6 billion after the largest ever crypto hackThe funds moved when the exchange reopened on Sunday. (Guardian)+ Binance says it’s covered $6 billion of the stolen $6.3 billion. (Reuters $)+ But the industry’s response has undermined its “decentralized” image. (Gizmodo) 4 An AI agent injected drugs into a live mouse’s brainIt chose the gene therapy and controlled the needle itself. (Wired $)+ We need real-time tracking of AI agents. (MIT Technology Review) 5 Data centres have made utilities a flashpoint in the midtermsAmericans are angry over rising electricity bills. (Reuters $)+ Virtual power plants could help. (MIT Technology Review) 6 Apple has overtaken Nvidia as the world’s most valuable companyInvestors are favouring its restraint on AI spending. (Bloomberg $)+ But critics say it’s entering its “slop era.” (404 Media) 7 Supermarket fridges have overheated in a new wave of cyberattacksStores in Belgium, Sweden, and Luxembourg were hit. (Politico) 8 The first chocolate bars made from lab-grown cocoa are hereStartups are cultivating chocolate to tackle a cocoa shortage. (WSJ $) 9 Hackers hijacked digital billboards in multiple countries to show pornThey also criticized security flaws and Egypt’s government.(Gizmodo)
10 A new 3D map reveals instability in interstellar spaceThe discovery could help explain why stars form so inefficiently. (Quanta) Quote of the day
“It would be great if all this was just a house of cards.” —Kevin Roose, a technology columnist at the New York Times, tells NPR that an AI investment crash would be preferable to the technology permanently upending work and society. One more thing Useful quantum computing is inevitable—and increasingly imminent Last year, Nvidia CEO Jensen Huang jolted the stock market by saying that practical quantum computing is still 15 to 30 years away. He also suggested that those computers would need Nvidia GPUs to function. But Huang’s predictions miss the mark—both on the timeline and the role his company’s technology will play.  Quantum computing is rapidly converging on utility. And that’s good news, because the hope is that they will be able to perform calculations that no amount of AI or classical computation could ever achieve. Read the full story. —Peter Barrett We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + A miniaturist has built a delightful diorama of the world’s creepiest living room.+ The year’s best dog photos have been announced, and they’re very good boys and girls.+ At the Green Zone golf club, you tee off in Sweden and putt in Finland, an hour in the future.+ Astronomers say they’ve found the real celestial arrangement that inspired van Gogh’s The Starry Night.