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In late 2015, a commuter-shuttle startup caught the attention of Ford Motor Co.executive John Casesa, who runs global strategy for the auto maker. The startup, called Chariot, was growing fast and had an interesting crowdsourced reservation model, a staffer told him, suggesting a meeting.

One year and a $65 million deal later, the San Francisco van service is owned by the Detroit giant—part of an acquisition-fueled push into new areas as an uncertain and perhaps driverless future looms.

“We are in an era in our industry where M&A will be a frequently used instrument,” Mr. Casesa said.

For much of the past half-century, U.S. corporations in industries from manufacturing to retail generally eschewed Silicon Valley startups, instead choosing to build their own new products or buy established companies.

But a combination of factors—fear of seeing business disrupted, struggles to find new growth, changes that require new skills—are leading more of these companies to hunt for tech deals.

In recent months, a burst of old-line companies have swallowed tech upstarts, including Wal-Mart Stores Inc.’s $3.3 billion purchase of web-discount retailer Jet.com Inc., General Motors Co.’s more than $1 billion acquisition of self-driving tech company Cruise Automation and Unilever PLC’s $1 billion purchase of online razor seller Dollar Shave Club.

Nontech companies spent nearly $10 billion buying venture-backed U.S. startups this year, nearly double the amount last year and the highest total in at least five years, according to PitchBook.

Venture-capital investors and analysts expect a flurry of deals in 2017, particularly given that funding is harder to come by in the private and public markets.

Good software is “becoming the oxygen” for established companies, said Barry Jaruzelski, a principal at PricewaterhouseCoopers LLP who focuses on research and development and technology. “People are saying, ‘I need to acquire.’ ”

The result is that Silicon Valley conferences, networking events and office buildings are gradually being infiltrated by the old guard of retail, manufacturing, insurance and other traditional sectors.

New venture-capital arms of large corporations seem to sprout up every few months, enabling their executives to mingle and hunt for partnerships and acquisitions. Entrants this year include Campbell Soup Co., Kellogg Co., JetBlue Airways Corp. and Airbus Group SE, the last two located in Silicon Valley.

Rich Wong, a partner at Accel Partners, an investor in Jet.com, said that until recently potential buyers for startups were generally confined to giants in the tech space, like Oracle Corp. and Microsoft Corp.—so much so that traditional companies in a startup’s sector didn’t come up as candidates.

Accel Partners recently counseled the founders of its invested startups to become more familiar with these older companies, an apparent gesture to the disdain startup founders often express about their corporate counterparts.

But these generally cautious corporate buyers face a dilemma: Startups typically carry high valuations that are largely a product of excitement around their growth potential. That makes them extremely expensive based on traditional metrics like revenue—and therefore inherently risky bets that can be tough for shareholders to stomach.

Wal-Mart has a market capitalization about 65 times that of what it paid for Jet.com, and it has roughly $480 billion in revenue compared with what it said was $1 billion in annualized revenue for Jet.com.

But Wal-Mart is facing ever-more competition from Amazon.com and other retailers, and executives at the company have said Jet.com will allow them to expand their e-commerce at a much faster rate.

For these older companies buying startups, “generally speaking, you are overpaying for assets right now,” said Jason Gere, a consumer-products analyst at KeyBanc Capital Markets. The deals often are a bet on the future as they try to connect with a younger generation, he said. “They’re hoping that what they’re doing is creating another avenue for growth.”

Another option is to write smaller checks, making earlier bets on young companies like Ford did with Chariot in September.

Scotts Miracle-Gro Co., the 148-year-old lawn-products company based outside Columbus, Ohio, wanted more tech-focused products and gadgets to help with lawn care, but quickly realized it would have to look outside its ranks for help, said Peter Supron, the company’s chief of staff.

“To the best of our knowledge, we don’t have an electrical engineer doing electrical engineering in the company,” he said.

After charting out the lawn-care startup landscape and many flights to Southern California—where many are located—Scotts bought two startups this month. One, called Blossom, helps make a digitally connected sprinkler system, and the other, PlantLink, makes sensors that tell gardeners when to water. Both are small acquisitions—under $10 million, Mr. Supron said—but they saved Scotts the headache of building products on its own.

“These startups can get there faster and cheaper than us,” he said.

After decades of trying to push into healthier foods, Nestlé SA is elevating a new chief executive—plucked straight from the health-care industry—to kick its effort into higher gear.

Ulf Mark Schneider, 51 years old, takes the reins at Nestlé on Jan. 1. He spent the past 13 years heading German health-care giant Fresenius SE, which runs hospitals, makes medical equipment and supplies drugs and nutritional products.

Nestlé, the world’s largest packaged-food company by revenue, is tapping that experience as it works to reinvigorate its drive to diversify away from the slower-growing food and beverage products—like Stouffer’s frozen TV dinners, KitKat chocolate bars and Nescafé instant coffee—that have long been its mainstay.

PepsiCo Inc., General Mills Inc., Kraft Heinz Co. and others have pumped money into creating healthier products, or reformulating existing ones to make them less bad for you, with mixed results. So has Nestlé, but the company has simultaneously taken a more radical path: For years, it has also been investing in a bevy of health-care firms in an effort to profit from the intersection of food and pharmaceuticals.

Nestlé, which declined to comment for this article, also declined to make Mr. Schneider available to comment.

In September, Nestlé agreed to buy a British maker of a device to treat dysphagia, a swallowing disorder. Earlier in the year, it teamed up with a U.S. biotech to develop products aimed at restoring bacterial balance in the digestive system. It is also expanding aggressively into “medical foods,” intended to help prevent, treat or manage conditions like metabolism issues, cancer and obesity.

The effort has stretched back decades—Nestlé made its first health-care-nutrition investment in 1986—but it has gained more prominence in recent years. In 2010, Nestlé promised to “be a pioneer in the new industry that we are helping to shape in the space between a fast-moving consumer-goods company and a pharma company.” It created a separate health-science division the next year and since has made a series of acquisitions aimed at beefing it up. But so far, these haven’t delivered significant sales increases.

Last year, Nestlé health-science revenue was about 2 billion Swiss francs ($1.95 billion), or just 2.25% of overall revenue. The unit absorbed Nestlé’s prior health-care-nutrition business, which had sales of 1.6 billion francs in 2009, but is distinct from the much-larger general nutrition business.

Despite an array of bets in fields like oncology and depression, the health-science unit has no blockbuster products. Its biggest revenue generator is Boost, a flavored protein drink, rather than any of its disease-focused products, according to a person familiar with the matter.

Critics have said Nestlé doesn’t disclose enough about the business and its trajectory. “This is something of a black box to us,” said RBC Capital Markets analyst James Edwardes Jones.

Shares of Nestlé have declined 1.7% so far this year.

Nestlé’s elevation of a health-care veteran, in Mr. Schneider, could help. He will take direct control of Nestlé’s health-sciences and skin-health units.

His financial record at Fresenius—before becoming CEO, he was CFO of its medical-care business—also could help Nestlé win over investors long accustomed to poor communication by management and thin margins.

Nestlé recently warned it would miss its own sales-growth target for the fourth straight year.

The company’s earnings-before-interest-and-tax margin is forecast to rise by just 0.57 percentage points between fiscal 2016 and 2018, compared with estimates of 4.58% for Kraft Heinz, 2.43% for Mondelez International Inc. and 1.56% for Kellogg Co., according to Jefferies LLC.

Investors hope this will change under Mr. Schneider who at Fresenius was known for his deal making and cost-cutting. Fresenius shares rose 1,246% during his CEO tenure. He is credited with boosting revenue through geographic expansion and a number of big acquisitions. Mr. Schneider “really knows the details and can communicate and explain it all in an easy-to-understand way,” said Marcus Luttgen, a portfolio manager at Alecta Pension Insurance Mutual, which holds shares in Fresenius and Nestlé.

Mr. Schneider has been working at the company since September learning the ropes. He is the youngest pick for the top job in 50 years and the first outsider CEO since 1922.

Mr. Schneider has already been suggesting new cost-savings initiatives, according to a person familiar with the plans, and pushing to accelerate existing cost-cutting measures—like centralizing companywide purchasing.

But Mr. Schneider lacks experience dealing with food and beverages, still Nestlé’s biggest businesses. He will be stepping up to manage a company significantly bigger than his old firm, with almost twice as many markets. Nestlé has 335,000 employees and sells products in 189 countries, compared with Fresenius’s 220,000 employees and operations in 100 countries.

“Nestlé has some material structural growth problems on things like U.S. prepared meals, ice cream, coffee and confectionery that have manifested themselves in its consistent guidance misses,” said Jefferies analyst Martin Deboo. Mr. Schneider, he said, will jump-start growth only by “moving the needle on brands like Nescafé and Maggi, not by repositioning Nestlé as a health company.”

The euro is on course to hit parity with the dollar for the first time in more than 14 years, helping the eurozone maintain its recovery by making exports more competitive, according to a Financial Times poll of economists.

More than two-thirds of the 28 experts polled said the dollar would attain the same value as the euro during the course of the year, buoyed by higher US interest rates, in accordance with president-elect Donald Trump’s plans to shift from monetary to fiscal stimulus.

The economists expressed relative confidence that after a year of upsets — including Mr Trump’s election and Britain’s vote to leave the EU — the eurozone would avoid major shocks.

But they also worried about Mr Trump’s policies once in office and what they saw as low-probability, high-risk, possibilities, such as a victory for the far-right Marine Le Pen in France’s presidential elections next year.

The single currency traded at $1.05 on Thursday evening — far below its 2008 peak of $1.5979. It has not fallen to $1 or below since December 2002, the year euro notes and coins were introduced.

While some EU countries, such as Italy, have cheered on the euro’s fall in value, precisely because of its impact on exports, the currency’s recent weakness and the low interest rate policy that underpins it, is much more controversial in Germany, the eurozone’s economic powerhouse.

Throughout 2016, the single currency has lost 4 per cent against the dollar, with many market participants expecting further weakness as US interest rates head upwards.

Most economists polled said the US central bank would raise rates at least twice next year after a 25 basis point increase this month.

“Higher rates in the US will raise the attractiveness of European exports and help the ECB’s goal of achieving higher inflation,” said Danae Kyriakopoulou, head of research at Omfif, a forum for central bankers. “But they risk causing problems to emerging markets with vulnerable external balances.”

By contrast with Mr Trump’s plans for a big infrastructure spending plan to boost the economy, the eurozone is likely to still rely on the European Central Bank, which the economists believed would keep to existing plans to roll out its landmark quantitative easing package.

More than three-quarters of those polled said the ECB would stick to its plan to buy €780bn in mostly government bonds to lift growth and inflation. The central bank is now buying €80bn worth of bonds a month, but, in accordance with a decision this month, will trim that figure to €60bn from March.

“The ECB is likely to play it safe, but the risk of a policy mistake has increased slightly following the December 2016 decision to scale down the pace of QE,” said Frederik Ducrozet, economist at Pictet Wealth Management.

On average, economists expected eurozone growth of 1.47 per cent and inflation of 1.26 per cent next year. Twenty-six of 28 respondents said the recovery would outlast 2017.

“The weak euro, expansionary monetary policy and the absence of fiscal consolidation will be supportive [of the eurozone’s recovery],” said Peter Bofinger, a professor at the University of Würzburg.

However, those polled acknowledged threats from the uncertain political climate and the problems of Italian banks.

“Italy is today the most fragile economy in the eurozone with low potential growth, one of the highest public debt levels, a high stock of non-performing loans and a weak banking sector,” said Laurence Boone, chief economist at Axa Group. “As well as an institutional framework which makes reforms difficult to get through.”

Highlighting another possible disruptive factor, almost 43 per cent of those polled said they expected a hard Brexit — a clean break between the UK and the EU — with just 14 per cent expecting a soft Brexit.

While most did not expect to see a win for Ms Le Pen in next year’s French election, many said if she did prevail it would weigh on the economy by fuelling the climate of uncertainty. Some said it would trigger an existential crisis for the euro.

“By raising the possibility of ‘Frexit’, Le Pen will naturally lead to an intensification of political uncertainty not just in France but across the region as a whole, and at the minimum dampen euro area investment and consumer spending in the short term,” said Chris Williamson, chief business economist at IHS Markit.

Some expressed concern over whether Mr Trump would follow through on his incendiary campaign rhetoric with policies to match. “[The impact of a Trump presidency] is ambiguous,” said John Llewellyn, a partner at Llewellyn Consulting. “The dollar’s strength would be positive for the eurozone’s economy. But protectionism, global uncertainty and financial volatility would not be.”

Over the past several years, Jack Gallant’s neuroscience lab has produced a string of papers that sound absurd.

In 2011, the lab showed it was possible to recreate movie clips just from observing the brain activity of people watching movies. Using a computer to regenerate the images of a film just by scanning the brain of a person watching one is, in a sense, mind reading. Similarly, in 2015, Gallant’s team of scientists predicted which famous paintings people were picturing in their minds by observing the activity of their brains.

This year, the team announced in the journal Nature that they had created an “atlas” of where 10,000-plus individual words reside in the brain — just by having study participants listen to podcasts.

How did they do all this? By using machine learning tools — a type of artificial intelligence — to mine huge troves of brain data and find the patterns of brain activity that predict our perception.

The goal here isn’t to build a mind-reading machine (although it’s often confused for that). Neuroscientists aren’t interested in stealing your passwords right out of your head. Nor are they interested in your darkest secrets. The real goal is a lot bigger. By turning neuroscience into a “big data” science, and using machine learning to mine that data, Gallant and others in the field have the potential to revolutionize our understanding of the brain.

The human brain, after all, is the most complicated object we know of in the universe, and we barely understand it. The wild idea of Gallant’s lab — an idea that could lift the field of neuroscience out of its infancy — is this: Maybe we have to build machines to figure out the brain for us. The hope is if we can decipher the intensely intricate patterns of the brain, we can figure out how to fix the brain when it’s suffering from disease.

The functional MRI — the main tool we use to peer into and analyze the function of the brain and its anatomy — has only been around since the 1990s, and it gives us only a cruddy view.

To put that view into perspective, the smallest unit of brain activity an fMRI can detect is called a voxel. Usually, these voxels are smaller than a millimeter cubed. And there might be 100,000 neurons in a voxel. As University of Texas neuroscientist Tal Yarkoni put it to me, fMRI is “like flying over a city and seeing where the lights are on.”

Traditional fMRI images can show where broad areas crucial to a behavior exist — for instance, you can see where we process negative emotions, or the areas that light up when we see a familiar face.

But you don’t know exactly what role that area plays in the behavior or whether other, less active areas play a crucial role as well. The brain isn’t like a Lego set, with each brick serving a specific function. It’s a mesh of activity. “Every area of the brain will have a 50 percent chance to be connected to every other area in the brain,” Gallant says.

That’s why simple experiments — to identify a “hunger” area or a “vigilance” area of the brain — can’t really yield satisfying conclusions.

“For the last 15 years, we’ve been looking at these blobs of activity and thinking that’s all the information that’s there — just these blobs,” Peter Bandettini, chief of the department on fMRI methods at the National Institute of Mental Health, told me in July. “And it turns out every nuance of the blob, every little nuance of fluctuation, contains information about what the brain is doing that we haven’t really tapped into completely yet. That’s why we need these machine learning techniques. Our eyes see the blobs, but we don’t see the patterns. The patterns are too complicated.”

Here’s an example. The traditional view of how the brain processes language is that it takes place in the left hemisphere, and two specific areas — Broca’s area and Wernicke's area — are the centers of language activity. If those areas are damaged, you can’t produce language.

But Alex Huth, a postdoc in Gallant’s Lab, recently showed that’s too simplistic an understanding. Huth wanted to know if the whole brain was involved in language comprehension.

In an experiment, he had several participants listen up to two hours of the storytelling podcast The Moth while he and colleagues recorded their brain activity in fMRI scanners. The goal was to correlate distinct areas of brain activity with hearing individual words.

That produces an enormous amount of data, more than any human can possibly deal with, Gallant says. But a computer program trained to look for patterns can find them. And the program Huth designed was able to reveal an “atlas” of where individual words “live” in the brain.

“Alex’s study showed huge parts of the brain are involved in semantic comprehension,” Gallant says. He also showed that words with similar meanings — like “poodle” and “dog” — are located near one another in the brain.

So what’s the significance of a project like this? In science, prediction is power. If scientists can predict how a dizzying flurry of brain activity translates to language comprehension, they can build a better model of how the brain works. And if they can build a working model, they can better understand what’s happening when the variables change — when the brain is sick.

What is machine learning?

“Machine learning” is a broad term that encompasses a huge array of software. In consumer tech, machine learning technology is accelerating by leaps and bounds — identifying learning how to “see” objects in photos, for instance, at near human levels. Using a machine learning technique called “deep learning,” Google’s Translate service has gone from a rudimentary (often humorously so) translation tool to a machine that can translate Hemingway in a dozen languages with a style that rivals the pros.

But on the most basic level, machine learning programs look for patterns — what’s the likelihood that X variable will correlate with Y.

Typically, machine learning programs need to be “trained” on a data set first. In training, these programs look for patterns in the data. The more training data, typically, the “smarter” and more accurate these programs become. After training, the machine learning programs are given brand new sets of data they’ve never seen before. And with those new sets of data, they can start to make predictions.

A good simple example is your email’s spam filter. Machine learning programs have scanned enough pieces of junk mail — learning the patterns of language contained within them — to know a piece of spam when they see a new email.

Machine learning can be very simple programs that just calculate mathematical regressions. (You remember those from middle school math, right? Hint: It’s about finding the slope of a line that explains the patterns of a smattering of data points.) Or it can be like Google DeepMind, which feeds off millions of data points and is the reason why Google was able to build a computer to beat a human at Go, a game so complicated that its board and pieces have more possible configurations than there are atoms in the universe.

Neuroscientists are using machine learning for several different ends. Here are the two basic ones: encoding and decoding.

With “encoding,” machine learning tries to predict the pattern of brain activity a stimulus will produce.

“Decoding” is the opposite: looking at areas of brain activity and predicting what the participants are looking at.

(Note: Neuroscientists can use machine learning on other forms of brain scans — like EEGs and MEGS — in addition to fMRI.)

Brice Kuhl, a neuroscientist at the University of Oregon, recently used decoding to reconstruct faces that participants were looking at from fMRI data alone.

The brain regions Kuhl targeted in the MRI have been long known to be related to vivid memories. “Is that region representing details of what you saw — or just [lighting up] because you were just confident in the memory?” Kuhl says. That the machine learning program could predict features of the face from the brain activity in that region suggests that’s where the information on the “details of what you saw” lives.

Similarly, Gallant’s experiment to predict what works of art participants were thinking about unveiled a small secret about the mind: We activate the same brain areas to remember visual features as we do when we’re seeing them.

The neuroscientists I spoke to all said that machine learning isn’t revolutionizing their field drastically yet. The big reason why is that they don’t have enough data. Brain scans take a lot of time and are very costly, and studies typically use a few dozen participants, not a few thousand.

“In the ’90s, when neuroimaging was just taking off, people were looking at category-level representation — what part of the brain is looking at faces versus words versus houses versus tools, large-scale questions,” says Avniel Ghuman, a neurodynamic researcher at the University of Pittsburgh. “Now we’re able to ask more refined questions. Like, ‘Is this memory someone is recalling right now the same thing they were thinking about 10 minutes ago?’”

This progress is “is more evolutionary than revolutionary,” he says.

Neuroscientists hope machine learning could help diagnose and treat mental disorders

To this day, psychiatrists can’t put a patient in an fMRI and determine if she has a mental disorder like schizophrenia from brain activity alone. They have to rely on clinical conversations with the patient (which has great value, no doubt). But a more machine-driven diagnostic approach could differentiate one form of the disease from another, which could have implications for treatment. To solve this, Bandettini at NIMH says neuroscientists will need to be able to access huge 10,000-subject databases of blobs — fMRI scans.

Machine learning programs could mine those data sets looking for telltale patterns of mental disorders. “You can then go back and start using this more clinically — put a person in a scanner and say, ‘Based on this biomarker that was generated by this 10,000-person database, we can now make the diagnosis of, let’s say, schizophrenia,’” he says. Efforts here are still preliminary, and have not yet yielded blockbuster results.

But with enough understanding of how the networks of the brain work with one another, it could be possible “to design more and more sophisticated kinds of interventions to fix things in the brain when they go wrong,” Dan Yamins, a computational neuroscientist at MIT, says. “It might be something like you put an implant into the brain that corrects Alzheimer’s in some way, or corrects Parkinson’s in some way.”

Machine learning could also help psychiatrists predict how an individual patient’s brain will respond to a drug treatment for depression. “Right now psychiatrists have to guess which medication is likely to be effective from a diagnostic point of view,” Yamins says. “Because what presents symptomatically is not a strong enough picture of what’s happening in the brain.”

He stressed that this may not happen until well into the future. But scientists are starting to think through these problems now. The journal NeuroImage just devoted a whole issue to papers on predicting individual brain differences and diagnoses from neuroimaging data alone.

It’s important work. Because when it comes to health care, prediction offers new paths for treatment and prevention.

Machine learning could predict epileptic seizures

With epilepsy, patients never know when a debilitating seizure will strike. “It’s a huge impairment in your life — you can’t drive a car, it’s a burden, you don’t participate in everyday life as you would,” Christian Meisel, a neuroscientist at the National Institutes of Health, tells me on a recent afternoon. “Ideally you would have a warning system.”

Treatment options for epilepsy aren’t perfect either. Some patients are medicated with anti-convulsants 24/7, but those drugs have serious side effects. And for some 20 to 30 percent of epileptics, there’s no drug that works for them.

Prediction could change the game.

If epileptics knew a seizure was imminent, they could at least get themselves to a safe place. Prediction could also change treatment options: A warning could cue a device to give a patient a fast-acting epileptic drug, or send an electrical signal to stop the seizure in its tracks.

This is an EEG — electroencephalogram — Meisel shared of an epileptic patient. “There's no seizure there,” Meisel says. “The question is, though, is this activity one hour away from a seizure, or is it more than four hours away?”

It would be very hard for a clinician to predict, “if not impossible,” he says.

But information about a coming seizure may be hidden in that flurry. To test this possibility, Meisel’s lab recently took part in a contest hosted by Kaggle, a data science community hub on the web. Kaggle provided months and years’ worth of EEG recordings on three epilepsy patients. Meisel used deep learning to analyze the data and look for patterns.

How good is it at predicting seizures based on EEG scans in the lead-up to one? “If you have a perfect system that predicts everything, you get a score of 1,” Meisel says. “If you have a random system that flips a coin, you get a 0.5. We get a 0.8 now. This means we’re not at perfect prediction. But we’re much better than random.” (That sounds great, but this approach is more theoretical than practical for now. These patients were monitored by intercranial EEG, an invasive procedure.)

Meisel is a neurological theorist, drafting models for how epileptic seizures grow from small pockets of neural activity to total debilitating storms. He says machine learning is becoming a useful tool to help him refine the theory. He can include his theory in the machine learning model and see if that makes the system more predictive or less predictive. “If it works, then my theory is right,” he says.

For machine learning to really make a difference, neuroscience will need to become a big data science

Machine learning won’t solve all the big problems in neuroscience. It may be limited by the quality of data coming in from fMRI and other brain scanning techniques. (Recall that fMRI paints a fuzzy picture of the brain, at best.) “If we had an infinite amount of imaging data, you would not get perfect prediction because those imaging procedures are very imperfect,” Gael Varoquaux, a computational scientist who has developed machine learning toolkits for neuroscientists, says.

But at the very least, the neuroscientists I spoke to were excited about machine learning because it makes for cleaner science. Machine learning combats a problem called “multiple comparison problem,” wherein researchers essentially go fishing for a statistically significant result in their data (with enough brain scans, some region is bound to “light up” somewhere). With machine learning, either your prediction about brain behavior is correct or it’s not. “Prediction,” Varoquaux says, “is something you can control.”

A big data approach also means neuroscientists may be able to start studying behavior outside the confines of the lab. “All of our traditional models of how brain activity works are based on these very artificial experimental settings,” Ghuman says. “We’re not entirely clear that it works the same way when you get to the real-world setting.” If you have enough data on brain activity (perhaps from a wearable EEG monitor) and behavior, machine learning could start to find the patterns that connect the two without the need for contrived experiments.

And there’s one last possibility for the use of machine learning in neuroscience, and it sounds like science fiction: We can use machine learning on the brain to build better machine learning programs. The biggest advance in machine learning in the past 10 years is an idea called “convolutional neural networks.” This is what Google uses to recognize objects in photos. These neural nets are based on theories in neuroscience. So as machine learning gets better at understanding the brain, it may pick up new tricks and grow smarter. Those improved machine learning programs can then be turned back on the brain, and we can learn even more about neuroscience.

(Researchers can also pull insights from machine learning programs trained to reproduce a human behavior like vision. Perhaps in learning the behavior, the program will reproduce the way the brain actually does it.)

“I don’t want people to think we’re suddenly going to get brain-reading devices; that’s not true,” Varoquaux says. “The promises are to get richer computational models to better understand the brain. I think we’re going to get there.”