EXECUTIVE SUMMARY Perimenopause has entered the chat. Perimenopause—and its better-known relative, menopause—used to be considered taboo. Not anymore, thanks at least in part to TV doctors and social media influencers. Perhaps it’s my age, but these days, both my algorithm and my conversations with friends increasingly swing toward perimenopause. Menopause is defined as the life stage that occurs a year after a person has had their last period. Perimenopause is the sometimes years-long period before that point, which can also feature all the symptoms we’d typically associate with menopause. Today, information about perimenopause is more prevalent and accessible than ever. If you’re a woman in your 40s and you’re not feeling 100%, chances are there’ll be someone online ready to tell you you’re in perimenopause. And that you might want to start spending your money on blood tests, apps, and supplements or demanding hormone replacement therapy. But as regular readers might have guessed by this point, it’s not that simple. Perimenopause tends to start around the age of 46 or 47. It’s during this time that many women start to experience some symptoms like hot flashes, irregular or unusually heavy periods, or anxiety, for example. And it can be heavy going. “Often symptoms are at their worst in the perimenopause,” says Mary Ann Lumsden, former president of the International Menopause Society.
That’s because hormones can fluctuate wildly. Levels of estrogen, progesterone, luteinizing hormone, and follicle-stimulating hormone can roller-coaster before leveling off after menopause. And that’s why, despite what some marketers will claim, there is no test for perimenopause. “You can’t interpret hormone [measures] because they change so much,” says Lumsden. “And that is quite normal.”
That doesn’t mean women should have to put up with symptoms. But exactly how those symptoms are treated is another topic that has been clouded by misinformation. Last week, I told a friend about some unusually bad pelvic pain I’d experienced. Her immediate advice was to find out if I was perimenopausal and, if I was, to request hormone replacement therapy (HRT) as soon as possible. If my doctor wouldn’t prescribe it, she continued, I should simply find another doctor who would. This line of thinking has been heavily promoted on social media platforms, says Paula Briggs, a former chair of the British Menopause Society who currently leads the menopause service at Liverpool Women’s Hospital. But it’s not helpful. HRT is essentially designed to top up or replace hormones like estrogen and progesterone, which naturally decline around menopause. There are lots of different drugs that can be taken in lots of different ways and at various doses. While it does come with some risks and won’t suit everyone, HRT can be immensely helpful for many menopausal women. Not only can it help with many of the common symptoms of menopause, but it can also help prevent osteoporosis and maintain muscle strength. But these drugs were trialed in, and approved for, menopausal women, says Lumsden. They won’t have the same effects in perimenopausal women. “If you give standard HRT, it may well get swamped by [the woman’s] own hormone production,” she says. HRT can also cause abnormal bleeding in perimenopausal women, says Briggs. She’s concerned about the messaging on perimenopause that is being promoted on social media. Particularly worrisome, she says, is the way younger women are being encouraged to assume they are perimenopausal and seek out HRT treatment.

“It’s almost cult-like, this idea that everybody must have HRT,” she says. And then there are the supplements. There’s been an explosion in marketing for vitamins and supplements specifically targeted to middle-aged and menopausal women. But the evidence for these, too, is either limited or nonexistent. “I can’t see a mechanism for a lot of them,” says Lumsden. Women who take these supplements don’t always know what they’re getting. Some of Lumsden’s patients have told her they take testosterone supplements to manage their symptoms. But blood tests revealed no increase in testosterone levels. “Whatever they’re getting, it’s not testosterone,” she says. At any rate, not all the symptoms women experience in midlife can be blamed on hormones. The lengthy lists of perimenopause symptoms shared on social media include fatigue, brain fog, aches and pains, digestive issues, and more. “These do not link closely to the obvious menstrual cycle changes and hormone changes … across menopause,” says Nanette Santoro, a professor of obstetrics and gynecology at the University of Colorado Anschutz who studies menopause. If you’re experiencing any symptoms, it’s worth getting them checked out to make sure they’re not being caused by something else. My own pelvic pain, for example, is almost definitely the result of endometriosis—a condition that can be made worse by HRT, Lumsden tells me. At any rate, by the time women reach their 40s, many are already juggling care for children and aging parents, often while holding down a job (and dealing with pressures from societies that don’t appear to value older women). It’s an exhausting time—and not all of that exhaustion can be blamed on hormones. As Santoro puts it: “Attributing everything unpleasant that happens to a woman over 35 to perimenopause is not based on any scientific evidence.” This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

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. Meet GPT-Red: an LLM super-hacker OpenAI built to make its models safer OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks.  It automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. OpenAI gave MIT Technology Review an exclusive peek into the system. Find out how it could keep the company ahead of human attackers.
—Will Douglas Heaven Why heat pumps are still so hot in the US —Casey Crownhart
It feels as if it should be illegal to even think about heating appliances during the height of summer, but we need to talk about heat pumps.  The appliances use electricity for heating, they’re incredibly efficient, and they’re on the rise. In the US, their sales have doubled over the past 15 years, according to a new report. They’re also winning the heating race against fossil fuels, outpacing natural-gas furnaces by 32% during the first quarter of 2026.  These stats are especially striking at this moment, because a key tax credit for heat pumps just ended. So why are heat pumps still so hot? Read the full story for the answer. This article is from The Spark, our weekly climate tech newsletter. Sign up to receive it in your inbox every Wednesday. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Elon Musk discreetly bought a $1 billion gas turbine firm to power GrokHe acquired fossil fuel company APR Energy in May. (Electrek)+ The most likely application will be powering AI data centers. (Engadget)+ The deal was revealed through an FTC filing. (Gizmodo)+ What will power AI’s growth? (MIT Technology Review) 2 A hack shows the Suno AI music generator scraped YouTube, DeezerIt scraped decades’ worth of music to train its models. (404 Media)+ The hacked is a unique look into the black boxes powering GenAI. (CNET)+ AI is coming for music, too. (MIT Technology Review) 3 Thinking Machines has launched an open-weight AI modelInkling offers a US alternative to China’s open-source models. (Reuters $)+ It’s the first AI model built by Thinking Machines. (WSJ $)+ The startup was founded by former OpenAI CTO Mira Murati. (Axios) 4 Europe is narrowing its ambitions for tech independenceManufacturing and research show promise, but funding is a problem. (NYT $)+ Earnings are strong, but an AI gap persists. (Reuters $)+ India is also scrambling for AI independence. (MIT Technology Review) 5 Earth is absorbing energy at a rate that’s alarming climate scientistsThe planet is taking in more heat than models predicted. (Economist $)+ The legal case for climate justice is growing. (MIT Technology Review) 6 The AI backlash has tech executives fearing for their livesViolent threats against AI firms are spilling into the real world. (WSJ $)+ An anti-AI movement is growing globally. (MIT Technology Review) 7 A Moroccan intelligence insider exposed widespread Pegasus useIncluding to target journalists, activists, and foreign politicians. (Guardian)8 AI is powering citizen-led disaster relief from afar for VenezuelaIt’s helping to locate missing people and coordinate relief. (Rest of World) 9 Thermodynamic computers could turn noise into useful calculationsThey may offer a cooler, more efficient way to process information. (Quanta)10 An engineer has explained every ’90s computer in Jurassic ParkFans have debated the technology in the film for decades. (Ars Technica) Quote of the day

“We hit pause because the communities powering AI should share in its success. Maybe that’s a novel concept in Washington.”  —New York Gov. Kathy Hochul responds on X to President Donald Trump’s criticism of her state’s new data center moratorium. One More Thing Will we ever trust robots? Robotics firm Prosper is developing a humanoid called Alfie to perform tasks in homes, hospitals, and hotels. The company’s founder, Shariq Hashme, has identified trustworthiness as the top design priority—and first hurdle to clear before humanoids can live up to their hype. Hashme believes one essential tactic to get people to put their trust in Alfie is to build a detailed character from the ground up—something humanlike but not too human. But the robot’s reliance on remote human operators raises broader questions about privacy, labour, and whether society will truly accept humanoids in our private spaces.

EXECUTIVE SUMMARY It feels as if it should be illegal to even think about heating appliances during the height of summer—seriously, these heat waves in New York have been brutal—but we need to talk about heat pumps. The appliances use electricity for heating, they’re incredibly efficient, and they’re on the rise. (For what it’s worth, many heat pumps can also be run in reverse to cool buildings.) In the US, heat pump sales have doubled over the past 15 years, according to a new report. And they’re winning the heating race against fossil fuels, outpacing natural-gas furnaces by 32% during the first quarter of 2026. These stats are especially striking at this moment, because a key tax credit for heat pumps just ended with the close of 2025. But you wouldn’t know it from looking at the data. Why are heat pumps still so hot?   In case you need a quick refresher, heat pumps use electricity to essentially move heat from one spot to another. A refrigerant moves around a loop in the device, expanding and compressing, gathering and releasing heat at different points in the cycle. (For a more in-depth look at the thermodynamics, this explainer I wrote in 2023 still holds up.)
The result is an appliance that can be incredibly efficient. Once you pay for and install a heat pump, it’s generally significantly cheaper to run than a gas or oil furnace or other types of electric heating systems. And because they’re more efficient and don’t involve burning fossil fuels, heat pumps can be a major help in decarbonizing buildings. One of the major hurdles to wider use of heat pumps is the appliances’ cost: They tend to be more expensive to buy and install than gas furnaces. For this reason, many governments offer incentives to encourage their adoption. In the US, people who installed heat pumps between 2023 and 2025 were eligible for up to $2,000 in tax credits.
Last year, though, the Trump administration slashed those tax credits, along with many of the other incentives that were part of the 2022 Inflation Reduction Act. Effective January 1, 2026, no more financial help for heat pumps. I think I’ve seen this film before, and I didn’t like the ending. Tax credits of up to $7,500 for new EVs ended on September 30, 2025. In the quarter leading up to that deadline, sales spiked as people rushed to take advantage of the incentive. Then they fell off a cliff. Things are starting to normalize now, but clearly the tax credit’s sunset had a major effect. But as it turns out, heat pumps are an entirely different story. In the first few months of 2026, sales have actually gone up, as Lucas Davis, an energy economist and UC Berkeley professor, points out in a new analysis. Heat pump shipments were flat from December to January and have seen a gradual rise since then, according to data from the Air Conditioning, Heating, and Refrigeration Institute, a trade group that represents about 90% of the US market. This increase from winter into spring follows a seasonal trend seen in previous years—and it’s actually a bit stronger in 2026. This data isn’t what you’d expect to see if losing the tax credit were hurting demand. As Davis lays out in his post, it seems the credit wasn’t really convincing people to install heat pumps, or at least the case for doing so was sufficient without the added incentive. “It appears that the U.S. market for heat pumps is strong enough that it does not depend on tax credits,” Davis writes. In 2024, MIT Technology Review put heat pumps on our annual list of breakthrough technologies. “We’ve entered the era of the heat pump,” I wrote at the time. While heat pump sales have been up and down over the last few years, the era is going strong. The appliances have outsold gas furnaces in the US for the last four years. It’s not just the US, either. Countries including China and Germany have seen strong movement to heat pumps in recent years. There’s rarely a straight path to adoption for new technology, especially something that requires so many individual households to make a significant change. But it’s encouraging that a major decarbonization tool is going strong, even when roadblocks pop up. This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. 

The global biosecurity landscape is rapidly evolving. Shifting natural ecosystems, global travel and the potential misuse of AI require greater vigilance — yet AI is also a critical tool for our response. We need frontier AI models, and the scientific advances they will enable, to respond to these challenges and help make society more resilient to events like future outbreaks.Today, Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience.Our work is twofold – to prevent threat actors from misusing our models, and to ensure that governments, scientists, biosecurity experts and our teams can harness these technologies to build a more resilient world.Over the past 12 months, we have advanced more than 15 partnerships with government bodies, biosecurity organizations, and research groups to prevent threat actors from misusing our models, detect new outbreaks quickly and respond quickly and effectively.Inside our bioresilience programWe believe society must harness AI’s advancing capabilities to address infectious diseases and prepare for future outbreaks.Breakthroughs like Google DeepMind’s AlphaFold, which mapped the 3D structures of nearly all known proteins; Isomorphic Labs’ AI-powered Drug Design Engine (IsoDDE), which provides the real-world accuracy required to navigate novel biological systems with unprecedented precision; and AlphaGenome, which sheds light on genome function, are radically shifting the balance. Instead of just reacting to natural outbreaks or safety risks, we can now use these intelligent systems to help researchers design proactive defenses, accelerate the discovery of therapeutics, and safeguard the global health ecosystem with greater speed and precision.With this in mind, we are making our AI models and agents available to trusted partners to support progress across three key areas: prevention, detection and response. Here are some examples:Prevent:To ensure our models, like Gemini, are safe and useful to experts, we follow a four-step safety process: threat modeling, evaluations, mitigations and monitoring.We partner closely with in-house biologists, security experts and external partners to understand potential threats, test our models against them and build in robust safeguards.Additionally, we’re working on adapting our SynthID watermarking technology to biology, which could help DNA synthesis providers screen for potentially risky, AI-generated biological sequences.Detect:We are helping make pathogen surveillance more cost-effective. For example, our agent AlphaEvolve can optimize algorithms used for producing and analyzing metagenomic sequencing data, helping detect new outbreaks faster. This optimization allows for quicker and more accurate DNA analysis, making it cheaper to track diseases worldwide on a large scale.We are also exploring how technologies like AlphaGenome and Protein Function annotation could be used to help detect and characterize pathogens from sequence data, identifying novel patterns and emerging threats faster than traditional methods.Respond:Building on the scientific impact of AlphaFold, we are granting trusted researchers access to Google DeepMind’s latest AI systems to help accelerate the design of vaccines and other countermeasures for both known and novel threats.To support government bodies and non-profit organizations during novel outbreaks, Isomorphic Labs has established a focused unit to rapidly deploy its drug design engine to design medical countermeasures that could address both naturally occurring pandemics and potential risks arising from the misuse of advanced AI. Working in collaboration with governments and global health authorities to advance a diverse range of diagnostic and therapeutic strategies enables the Isomorphic Labs Drug Design Engine’s real-world impact for bioresilience.This is a complex, long-term effort, and part of our broader approach to managing potential Chemical, Biological, Radiological and Nuclear (CBRN) risks, aligning with the proactive mitigations and rigorous evaluation protocols of our Frontier Safety Framework.We are committed to working openly and collaboratively — with biosecurity labs, governments and the broader scientific community — to ensure AI is developed and deployed responsibly to help protect against one of society’s greatest risks.To read more about our work and our call for new partnerships, see this full update on our approach.

EXECUTIVE SUMMARY OpenAI has built an LLM super-hacker called GPT-Red that it uses as a sparring partner to help its other models boost their defenses against cyberattacks. Last week the company released the latest version of its flagship LLM, GPT-5.6. OpenAI says that training it against GPT-Red made the model its most robust release yet. GPT-Red automates a type of safety evaluation for software systems known as red-teaming, which is typically done by a team of human testers. The aim is to find as many different ways to break or hijack a system as possible. The weak spots can then be patched before the final version of the software is released. As LLMs become more complex and get used in a wider variety of tasks—especially in the form of agents, which can interact with computer files, websites, and third-party code as well as other agents—it’s hard for teams of people by themselves to keep up with all the types of attacks that might take place. “The risk surface grows and the blast radius also grows,” says Nikhil Kandpal, a research scientist at OpenAI who co-created GPT-Red. OpenAI built GPT-Red to future-proof its safety testing process. “As more capable models become available, we will have already designed the system that can discover new modes of attack,” says Dylan Hunn, a research scientist at the company and fellow co-creator of GPT-Red. The researchers say it has already come up with new types of attack that had not been seen before.
OpenAI focused most of its efforts on a type of attack known as a prompt injection, where a hacker slips an LLM instructions to make it do things its developers or users do not want it to, such as copy confidential information, sabotage a company’s code base, or generate embarrassing or harmful output. In theory, such instructions can be hidden in any text that the LLM might encounter—in code or on a website, for example.     Training dojo To build GPT-Red, OpenAI’s researchers took an LLM that had not been trained as a hacker and set it up in what’s known as a self-play loop with several other models. Its goal was to try to attack the other models; their goal was to try to defend themselves. Over many rounds of play, GPT-Red became better and better at attacking other LLMs, and those LLMs became better and better at fending off the attacks.
The training took place in a kind of dojo that OpenAI had designed to mimic a range of scenarios in which LLMs might be deployed in the real world, including browsing the web, reading emails or calendar apps, and editing code.   When GPT-Red found a new kind of attack, it would explore multiple different versions of it to find the most efficient one for specific scenarios. “Compared to a human red-teamer, the model is very, very good at finding exactly what will work, exactly what’s most effective,” says Hunn. “It’s extremely persistent about drilling down into an attack that it has discovered.”   In particular, OpenAI claims that GPT-Red found a type of prompt injection attack that the researchers had not seen before, which they call a fake chain of thought. A chain of thought is a kind of diary in which an LLM makes notes to itself and keeps track of partial results as it works through problems. GPT-Red found a way to insert a fake entry into another model’s chain of thought that would trick that model into acting on spoofed information. “It’s like if I told you that 1+1=3 and that you have verified this already,” says Chris Choquette-Choo, another research scientist on the team. “The model’s like, ‘Oh, okay, of course,’ and it just spits out 3.” Jessica Ji, a senior research analyst who works on AI security at Georgetown University’s Center for Security and Emerging Technology (CSET), thinks the self-play loop that OpenAI used is a good approach. “The results look very promising,” she says. OpenAI tested how good an attacker GPT-Red was by rerunning an experiment from 2025 in which human red-teamers tried to find weaknesses in an earlier version of GPT-5. When GPT-Red was set the same task, it was more successful at finding effective attacks than the humans had been. OpenAI also tested GPT-Red against Vendy, a vending machine agent developed by Andon Labs, a company that assesses how well agents perform real-world tasks. GPT-Red was able to hack Vendy to make it change the prices of items on sale and cancel a customer’s order. Defensive behavior OpenAI says that when it tried out some of the strongest attacks that GPT-Red had come up with on its models, more than 90% of them worked against GPT-5 (released in August last year), and fewer than 23% worked against the new GPT-5.6.

GPT-Red isn’t perfect. It is not great at figuring out attacks that involve a back-and-forth conversation between hacker and target, something that human attackers would have few problems with. It is also not yet that great at using images, which can be used to pass text to models in prompt injection attacks.     The company says that GPT-Red supplements the work of its human red-teamers; people can find attacks it misses, and vice versa. One approach OpenAI is taking is to give GPT-Red an attack that humans came up with and ask it to find all the variations. “I think human expertise will still be very important,” says CSET’s Ji. “It would be really useful to be able to distinguish where human testing is most needed.” Unsurprisingly, OpenAI will not be releasing GPT-Red. The company is also confident that the super-hacker is stronger than any copycat model someone might try to create. The researchers say they have been working on the model for more than a year, backed by the compute resources of one of the richest companies in the world. “It’s not a trivial thing that someone else could easily do—you know, just go and train a super-attacker using this idea,” says Choquette-Choo.

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. PsiQuantum has a plan to make a massive quantum computer out of light The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory.  Inside, some 100 stainless-steel cabinets each hold hundreds of chips. On those chips, thousands of light particles will fly through a maze of optical switches and beam splitters. Each photon must be accounted for, because precisely measuring where it ends up will help answer questions that current computers might take millions of years to solve. This computer, as described, does not exist. It’s the brainchild of a company called PsiQuantum, founded in 2016 by four physicists from UK universities. In a crowded field of deep-pocketed competitors with similarly fantastical visions, the company aims to be the first to build a useful quantum machine.
Read the full story on the company’s quest. —James O’Donnell
MIT Technology Review Narrated: inside the world’s deepest and longest subsea road tunnel —Niall Firth I’m currently around 1,000 feet beneath the North Sea, in a dark, dank cave. It smells weird. And I’m increasingly aware of the pressure from millions of tons of seawater just above my head. I’m under the iconic fjords of Norway to visit what will soon become the world’s longest and deepest subsea road tunnel—an exceptional engineering feat that will carry drivers deep beneath the North Sea. I’m here to understand how you make a 16.6-mile highway that sits 1,280 feet below the sea at its deepest point. And also—at a time when it can feel hard to get anything done—to reassure myself that ambitious engineering is still possible. That we can still make things.  This is our latest story to be turned into an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Meta allegedly used AI to target workers with health issues for layoffsTheir lawsuit says Meta relied on AI to create a termination list. (Guardian)+ And pinpointed staff who took maternity or disability leave. (Reuters $)+ One was allegedly informed the day before her water broke. (Ars Technica)+ The layoffs aimed to offset Meta’s AI spending. (Gizmodo)+ AI agents are not your “coworkers.” (MIT Technology Review)2 OpenAI’s first consumer device will be a mobile smart speakerThe screenless device will serve as an “AI companion.” (Bloomberg $)+ It’ll let you talk with ChatGPT. (Verge)+ And use a camera and sensor to understand your environment. (Reuters $)+ It’s set to launch next year. (Engadget) 3 The US military sent explosive drone boats into combat for the first timeThey attacked an Iranian midget submarine and naval port. (Ars Technica)+ Underwater drones may shape a war in Taiwan. (MIT Technology Review) 4 DeepMind’s CEO has called for a US-led body to test frontier AI modelsDemis Hassabis wants the watchdog to vet national security threats. (FT $)+ If dangers mount, it would coordinate an industry-wide slowdown. (Axios) 5 Data centers are set to add billions in power costs in 13 statesA power auction is slated to produce $6.3 billion in new charges. (NYT $)+ Australia plans to govern the use of water and power for AI. (WSJ $)6 xAI’s unpermitted power pollution hits Black communities hardestElon Musk’s xAI has been installing gas turbines without permits. (Reuters $)+ We need to focus on Big Tech’s energy footprint. (MIT Technology Review) 7 Stripe and Advent have offered to buy PayPal for more than $53 billionThe payments giant and private equity firm have made a joint bid. (Reuters $)+ Apple and Google Pay have eroded PayPal’s market share. (Bloomberg $)8 DeepSeek plans to file for IPO as soon as this yearThe Chinese AI pioneer is likely to list in Shanghai. (WSJ $)+ Here’s why DeepSeek’s latest model matters. (MIT Technology Review)9 A hard, lightweight “bio-metal” has been discovered in sea worm jawsIt could have applications in engineering. (New Scientist $)10 A new $3,000 fitness suit electrocutes you to boost your gainsCelebrities love it—but not everyone’s a fan. (404 Media) 

Quote of the day “By economic and engineering measures, generative AI might be the worst technology ever deployed.”  —Alex Reisner, a staff writer at The Atlantic, explains why GenAI’s scaling problem is an engineering disaster. One More Thing FRANZISKA BARCZYK Hackers made death threats against this security researcher. Big mistake. In April 2024, an anonymous hacker began posting death threats on Telegram and Discord channels aimed at a cybersecurity researcher named Allison Nixon. It wasn’t long before others piled on. Someone shared AI-generated nudes of her. They targeted Nixon because she had become a formidable threat. As chief research officer at the cyber investigations firm Unit 221B, named after Sherlock Holmes’s apartment, she had built a career tracking cybercriminals and helping get them arrested.  For years, Nixon had lurked quietly in online chat channels or used pseudonyms to engage with perpetrators and bring them to justice. Now, she resolved to unmask the people behind the death threats—and take them down for crimes they admitted to committing.  Find out why they learned to regret their choice of target. —Kim Zetter
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 musician has discovered the true masters of metal breakdowns: birds.+ Photographer Fontanesi’s surreal photo splits transform everyday images into spectacular hybrid scenes.+ Over 30 actors, filmmakers, and friends recount how Steven Spielberg infiltrated Hollywood in this terrific article.+ Who would win the World Cup if less important things than soccer decided it, like life expectancy and happiness? A new game tests your knowledge.