The personnel changes at Alphabet continue, this time with Mustafa Suleyman — one of the three co-founders of the company’s influential AI lab DeepMind — moving to Google.

Suleyman announced the news on Twitter, saying that after a “wonderful decade” at DeepMind, he would be joining Google to work with the company’s head of AI Jeff Dean and its chief legal officer Kent Walker. The exact details of Suleyman’s new role are unclear but a representative for the company told The Verge it would involve work on AI policy.

The move is notable, though, as it was reported earlier this year that Suleyman had been placed on leave from DeepMind. (DeepMind disputed these reports, saying it was a mutual decision intended to give Suleyman “time out … after 10 hectic years.”) Some speculated that Suleyman’s move was the fallout of reported tensions between DeepMind and Google, as the former struggled to commercialize its technology.DEEPMIND BREAKS GROUND IN AI RESEARCH, BUT SPENDS A LOT OF MONEY DOING IT

Although DeepMind has achieved a number of research milestones in the AI world, most notably the success of its AlphaGo program in 2016, the lab has also recorded significant financial losses. In 2018, it doubled its revenues to £102.8 million ($135 million), but its expenditures also rose to £470.2 million ($618 million) and it recorded a total debt of more than £1 billion ($1.3 billion).

Suleyman, who founded DeepMind in 2010 along with Demis Hassabis (now CEO) and Shane Legg (now chief scientist), had spearheaded the company’s health team, which offered the lab one avenue to monetize its research. DeepMind’s engineers designed a number of health algorithms that broke new ground, and its team built an assistant app for nurses and doctors that promised to save time and money. But the venture was also criticized strongly for its mishandling of UK medical data, and in 2018 was absorbed into Google Health.

In addition to this, Suleyman also led the “DeepMind for Google” team, which aimed to put the company’s research to practical uses in Google products, delivering tangible commercial benefits like improved battery life on Android devices and a more natural voice for Google Assistant.

It’s difficult to parse the meaning behind Suleyman’s move to Google without more details on his new role, but it’s clear that DeepMind is still working out how to position itself for the future — as highlighted by the publication of a blog post by Hassabis timed with the announcement of Suleyman’s departure.

In the post, Hassabis charts the journey of DeepMind “from unlikely start-up to major scientific organization.” And although he highlights collaborations the lab has made with other parts of Alphabet, he ultimately focuses on the “fundamental breakthroughs” and “grand challenges” that DeepMind hopes to tackle — most notably, using artificial intelligence to augment scientific research. It seems clear that long-term research, not short-term profits, are still the priority for DeepMind’s scientists.

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Google on Thursday announced Google Research India, an artificial intelligence (AI)-based research lab out of Bengaluru. “We look forward to engaging with everything from academic institutions, to government to the industry and startup ecosystem in the country in the field of AI,” said Jay Yagnik, vice president and engineering fellow.

The Indian AI lab will be led by Dr. Manish Gupta, a renowned computer scientist in the country and an ACM (Association for Computing Machinery) fellow. “We are making a long term commitment and this will be one of our large investments,” Yagnik said, though he didn’t confirm how many people will be employed at the AI research lab in India.

Yagnik also said the company hopes to go deeper in its interactions with academics and students in the country in the AI space through this initiative. “We already give out phd fellowships to students in the country, we also fund top academics through various programmes. We are hoping to go much deeper in those relationships,” he said.

“The ecosystem here is ripe for fostering and taking to the next level and we would love to play a key role in that,” he added. Yagnik says what form the AI community in the country takes will depend on Google’s interactions with various stakeholders, like IITs (Indian Institute of Technology) and more.

Google already has various AI research labs across the world, under its Google AI arm. The Bengaluru lab will be the first in India and the company doesn’t foresee the need to have another one, though Yagnik said it will revaluate this once the lab reaches a certain size.

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Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they’re still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries.

One common cause of false positives are so-called “high-risk” lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time. This means that every year thousands of women go through painful, expensive, scar-inducing surgeries that weren’t even necessary.

How, then, can unnecessary surgeries be eliminated while still maintaining the important role of mammography in cancer detection? Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts General Hospital, and Harvard Medical School believe that the answer is to turn to artificial intelligence (AI).

As a first project to apply AI to improving detection and diagnosis, the teams collaborated to develop an AI system that uses machine learning to predict if a high-risk lesion identified on needle biopsy after a mammogram will upgrade to cancer at surgery.

When tested on 335 high-risk lesions, the model correctly diagnosed 97 percent of the breast cancers as malignant and reduced the number of benign surgeries by more than 30 percent compared to existing approaches.

“Because diagnostic tools are so inexact, there is an understandable tendency for doctors to over-screen for breast cancer,” says Regina Barzilay, MIT’s Delta Electronics Professor of Electrical Engineering and Computer Science and a breast cancer survivor herself. “When there’s this much uncertainty in data, machine learning is exactly the tool that we need to improve detection and prevent over-treatment.”

Trained on information about more than 600 existing high-risk lesions, the model looks for patterns among many different data elements that include demographics, family history, past biopsies, and pathology reports.

“To our knowledge, this is the first study to apply machine learning to the task of distinguishing high-risk lesions that need surgery from those that don’t,” says collaborator Constance Lehman, professor at Harvard Medical School and chief of the Breast Imaging Division at MGH’s Department of Radiology. “We believe this could support women to make more informed decisions about their treatment, and that we could provide more targeted approaches to health care in general.”

A recent MacArthur “genius grant” recipient, Barzilay is a co-author of a new journal article describing the results, co-written with Lehman and Manisha Bahl of MGH, as well as CSAIL graduate students Nicholas Locascio, Adam Yedidia, and Lili Yu. The article was published today in the medical journal Radiology.

How it works

When a mammogram detects a suspicious lesion, a needle biopsy is performed to determine if it is cancer. Roughly 70 percent of the lesions are benign, 20 percent are malignant, and 10 percent are high-risk lesions.

Doctors manage high-risk lesions in different ways. Some do surgery in all cases, while others perform surgery only for lesions that have higher cancer rates, such as “atypical ductal hyperplasia” (ADH) or a “lobular carcinoma in situ” (LCIS).

The first approach requires that the patient undergo a painful, time-consuming, and expensive surgery that is usually unnecessary; the second approach is imprecise and could result in missing cancers in high-risk lesions other than ADH and LCIS.

“The vast majority of patients with high-risk lesions do not have cancer, and we’re trying to find the few that do,” says Bahl, a fellow doctor at MGH’s Department of Radiology. “In a scenario like this there’s always a risk that when you try to increase the number of cancers you can identify, you’ll also increase the number of false positives you find.”

Using a method known as a “random-forest classifier,” the team’s model resulted in fewer unnecessary surgeries compared to the strategy of always doing surgery, while also being able to diagnose more cancerous lesions than the strategy of only doing surgery on traditional “high-risk lesions.” (Specifically, the new model diagnosed 97 percent of cancers compared to 79 percent.)

“This work highlights an example of using cutting-edge machine learning technology to avoid unnecessary surgery,” says Marc Kohli, director of clinical informatics in the Department of Radiology and Biomedical Imaging at the University of California at San Francisco. “This is the first step toward the medical community embracing machine learning as a way to identify patterns and trends that are otherwise invisible to humans.”

Lehman says that MGH radiologists will begin incorporating the model into their clinical practice over the next year.

“In the past we might have recommended that all high-risk lesions be surgically excised,” Lehman says. “But now, if the model determines that the lesion has a very low chance of being cancerous in a specific patient, we can have a more informed discussion with our patient about her options. It may be reasonable for some patients to have their lesions followed with imaging rather than surgically excised.”

The team says that they are still working to further hone the model.

“In future work we hope to incorporate the actual images from the mammograms and images of the pathology slides, as well as more extensive patient information from medical records,” says Bahl.

Moving forward, the model could also easily be tweaked to be applied to other kinds of cancer and even other diseases entirely.

“A model like this will work anytime you have lots of different factors that correlate with a specific outcome,” says Barzilay. “It hopefully will enable us to start to go beyond a one-size-fits-all approach to medical diagnosis.”

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