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Illustration: Sarah Grillo/Axios
Data is AI's jet fuel — amassing as much as possible allows tech companies to precisely target ads, or medical AI to differentiate between a benign tumor and a malignant one, Kaveh reports.
No problem for Facebook and its gobs of data — but hard for a small clinic with few patients to learn from. Now, new AI methods are allowing companies to benefit from the collective wisdom of peers and competitors, without giving up sensitive data or trade secrets.
Why it matters: This could help improve health care, among the country’s most stubborn problems, by clearing a key hurdle for medical AI — gathering a big and diverse enough dataset to help doctors diagnose difficult problems or choose better treatments.
What's happening: Privacy-preserving AI techniques like federated learning are powering new systems that can benefit from multiple companies' data — without even having to know what the data is.
Perhaps the most obvious application for federated learning is in health care, where strict rules prevent sharing patient data — but the benefit of gathering lots is potentially very high.
VIA, a Boston-area AI startup, uses federated learning to pool data about the condition of power transformers, such that a utility in Europe can learn from one in Thailand or New Zealand.
What's next: Intel is working on methods that will allow companies to apply an AI model to data without even decrypting it, which would open new doors to cooperation even among the most privacy-conscious companies and industries.
The bottom line: Sharing is just one way to solve one of the biggest problems still ahead of AI — figuring out how to slake computers' unending thirst for data. Researchers are also experimenting with bolstering small datasets with synthetic training data, or creating algorithms that can learn from far fewer examples.
Illustration: Rebecca Zisser/Axios
When the Trump administration slapped tariffs on washing machines imported from China last year, American consumers started paying 12% more for them, Erica writes.
The big picture: For the past few quarters, retailers have warned analysts that the U.S.-China trade war could push prices up and turn shoppers away. But those effects have been invisible so far because the first two rounds of tariffed products largely excluded consumer goods.
Now things are changing: Tariffs are being imposed on the third and fourth "lists" of goods set out by the Trump administration.
Where it stands: In recent earnings calls, big retailers like Kohl's, Nordstrom and JCPenney have reported disappointing sales numbers and said that the impact of tariffs could make things worse.
The latest: As many as 12,000 stores could close this year if tariffs become a tipping point for smaller or struggling retailers, writes UBS analyst Jay Sole.
Photo: Dan Kitwood/Getty
Month by month, the tight U.S. jobs market is pushing up wages beyond the rate of inflation. But not if you are a fresh college graduate.
If you graduate this year, your annual pay on average will be $51,347. That's just 1.9% higher than last year's starting salary of $50,390 — and just keeping up with inflation, according to recruiting firm Korn Ferry, per the WSJ's Kelsey Gee.
Economists have struggled to explain why, despite the historically tight market, wage hikes are so sluggish.
Go deeper: Salaries for selected cities and occupations
Illustration: Aïda Amer/Axios
Photo: Katherine Frey/Washington Post via Getty
Things people do while stuck in traffic: Listen to music, text (super dangerous), gab on the phone. But in Mexico City, you can also order a burger — right to your car.
How it works: If you are no more than 1.8 miles from a Burger King, and a restaurant app determines that you will be stuck in traffic for at least 30 minutes, you can use a voice command to place an order. A motorcycle will weave its way through the traffic to your car window with your burger.
What's next: Burger King says it is considering expanding the offering to other super-trafficky cities like Los Angeles, São Paulo and Shanghai.