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First look at Instagram’s self-policing Time Well Spent tool

Are you Overgramming? Instagram is stepping up to help you manage overuse rather than leaving it to iOS and Android’s new screen time dashboards. Last month after TechCrunch first reported Instagram was prototyping a Usage Insights feature, the Facebook sub-company’s CEO Kevin System confirmed its forthcoming launch.

Tweeting our article, Systrom wrote “It’s true . . . We’re building tools that will help the IG community know more about the time they spend on Instagram – any time should be positive and intentional . . . Understanding how time online impacts people is important, and it’s the responsibility of all companies to be honest about this. We want to be part of the solution. I take that responsibility seriously.”

Now we have our first look at the tool via Jane Manchun Wong, who’s recently become one of TechCrunch’s favorite sources thanks to her skills at digging new features out of apps’ Android APK code. Though Usage Insights might change before an official launch, these screenshots give us an idea of what Instagram will include. We’ve reached out to Instagram for comment, and will update if we hear back.

This unlaunched version of Instagram’s Usage Insights tool offers users a daily tally of their minutes spent on the app. They’ll be able to set a time spent daily limit, and get a reminder once they exceed that. There’s also a shortcut to manage Instagram’s notifications so the app is less interruptive. Instagram has been spotted testing a new hamburger button that opens a slide-out navigation menu on the profile. That might be where the link for Usage Insights shows up, judging by this screenshot.

Instagram doesn’t appear to be going so far as to lock you out of the app after your limit, or fading it to grayscale which might annoy advertisers and businesses. But offering a handy way to monitor your usage that isn’t buried in your operating system’s settings could make users more mindful.

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VCs serve up a large helping of cash to startups disrupting food

Joanna Glasner Contributor More posts by this contributor Scaling startups are setting up secondary hubs in these cities Here is where CEOs of heavily funded startups went to school

Here is what your daily menu might look like if recently funded startups have their way.

You’ll start the day with a nice, lightly caffeinated cup of cheese tea. Chase away your hangover with a cold bottle of liver-boosting supplement. Then slice up a few strawberries, fresh-picked from the corner shipping container.

Lunch is full of options. Perhaps a tuna sandwich made with a plant-based, tuna-free fish. Or, if you’re feeling more carnivorous, grab a grilled chicken breast fresh from the lab that cultured its cells, while crunching on a side of mushroom chips. And for extra protein, how about a brownie?

Dinner might be a pizza so good you send your compliments to the chef — only to discover the chef is a robot. For dessert, have some gummy bears. They’re high in fiber with almost no sugar.

Sound terrifying? Tasty? Intriguing? If you checked tasty and intriguing, then here is some good news: The concoctions highlighted above are all products available (or under development) at food and beverage startups that have raised venture and seed funding this past year.

These aren’t small servings of capital, either. A Crunchbase News analysis of venture funding for the food and beverage category found that startups in the space gobbled up more than $3 billion globally in disclosed investment over the past 12 months. That includes a broad mix of supersize deals, tiny seed rounds and everything in-between.

Spending several hours looking at all these funding rounds leaves one with a distinct sense that eating habits are undergoing a great deal of flux. And while we can’t predict what the menu of the future will really hold, we can highlight some of the trends. For this initial installment in our two-part series, we’ll start with foods. Next week, we’ll zero in on beverages.

Chickenless nuggets and fishless tuna

For protein lovers disenchanted with commercial livestock farming, the future looks good. At least eight startups developing plant-based and alternative proteins closed rounds in the past year, focused on everything from lab meat to fishless fish to fast-food nuggets.

N

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UK report warns DeepMind Health could gain ‘excessive monopoly power’

DeepMind’s foray into digital health services continues to raise concerns. The latest worries are voiced by a panel of external reviewers appointed by the Google-owned AI company to report on its operations after its initial data-sharing arrangements with the U.K.’s National Health Service (NHS) ran into a major public controversy in 2016.

The DeepMind Health Independent Reviewers’ 2018 report flags a series of risks and concerns, as they see it, including the potential for DeepMind Health to be able to “exert excessive monopoly power” as a result of the data access and streaming infrastructure that’s bundled with provision of the Streams app — and which, contractually, positions DeepMind as the access-controlling intermediary between the structured health data and any other third parties that might, in the future, want to offer their own digital assistance solutions to the Trust.

While the underlying FHIR (aka, fast healthcare interoperability resource) deployed by DeepMind for Streams uses an open API, the contract between the company and the Royal Free Trust funnels connections via DeepMind’s own servers, and prohibits connections to other FHIR servers. A commercial structure that seemingly works against the openness and interoperability DeepMind’s co-founder Mustafa Suleyman has claimed to support.

“There are many examples in the IT arena where companies lock their customers into systems that are difficult to change or replace. Such arrangements are not in the interests of the public. And we do not want to see DeepMind Health putting itself in a position where clients, such as hospitals, find themselves forced to stay with DeepMind Health even if it is no longer financially or clinically sensible to do so; we want DeepMind Health to compete on quality and price, not by entrenching legacy position,” the reviewers write.

Though they point to DeepMind’s “stated commitment to interoperability of systems,” and “their adoption of the FHIR open API” as positive indications, writing: “This means that there is potential for many other SMEs to become involved, creating a diverse and innovative marketplace which works to the benefit of consumers, innovation and the economy.”

“We also note DeepMind Health’s intention to implement many of the features of Streams as mod

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Crown a new app from Tinder’s parent company turns dating into a game

If you’re already resentful of online dating culture and how it turned finding companionship into a game, you may not be quite ready for this: Crown, a new dating app that actually turns getting matches into a game. Crown is the latest project to launch from Match Group, the operator of a number of dating sites and apps including Match, Tinder, Plenty of Fish, OK Cupid, and others.

The app was thought up by Match Product Manager Patricia Parker, who understands first-hand both the challenges and the benefits of online dating – Parker met her husband online, so has direct experience in the world of online dating.

Crown won Match Group’s internal “ideathon,” and was then developed in-house by a team of millennial women, with a goal of serving women’s needs in particular.

The main problem Crown is trying to solve is the cognitive overload of using dating apps. As Match Group scientific advisor Dr. Helen Fisher explained a few years ago to Wired, dating apps can become addictive because there’s so much choice.

“The more you look and look for a partner the more likely it is that you’ll end up with nobody…It’s called cognitive overload,” she had said. “There is a natural human predisposition to keep looking—to find something better. And with so many alternatives and opportunities for better mates in the online world, it’s easy to get into an addictive mode.”

Millennials are also prone to swipe fatigue, as they spend an average of 10 hours per week in dating apps, and are being warned to cut down or face burnout.

Crown’s approach to these issues is to turn getting matches into a game of sorts.

While other dating apps present you with an endless stream of people to pick from, Crown offers a more limited selection.

Every day at noon, you’re presented with 16 curated matches, picked by some mysterious algorithm. You move through the matches by choosing who you like more between two people at a time.

That is, the screen displays two photos instead of one, and you “crown” your winner. (Get it?) This process then repeats with two people shown at a time, until you reach your “Final Four.”

Those winners are then given

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Teaching computers to plan for the future

As humans, we’ve gotten pretty good at shaping the world around us. We can choose the molecular design of our fruits and vegetables, travel faster and further and stave off life threatening diseases with personalized medical care. However, what continues to elude our molding grasp is the airy notion of “time” – how to see further than our present moment, and ultimately how to make the most of it. As it turns out, robots might be the ones who can answer this question.

Computer scientists from the University of Bonn in Germany wrote this week that they were able to design a software that could predict a sequence of events up to five minutes in the future with accuracy between 15 and 40 percent. These values might not seem like much on paper, but researcher Dr. Juergen Gall says it represents a step toward a new area of machine learning that goes beyond single step prediction.

Although Gall’s goal of teaching a system how to understand a sequence of events is not new (after all, this is a primary focus of the fields of machine learning and computer vision) it is unique in its approach. Thus far, research in these fields has focused on the interpretation of a current action or the prediction of an anticipated next action. This was seen recently in the news when a paper from Stanford AI researchers reported designing an algorithm that could achieve up to 90 percent accuracy in its predictions regarding end-of-life care.

When researchers provided the algorithm with data from over two million palliative care patient records, it was able to analyze patterns in the data and predict when the patient would pass with high levels of accuracy. However, unlike Gall’s research, this algorithm focused on a retrospective, single prediction.

Accuracy itself is a contested question in the field of machine learning. While it appears impressive on paper to report accuracies ranging upwards of 90 percent, there is debate about the over-inflation of these values through cherry picking “successful” data in a process called p-hacking.

In their experiment, Gall and his team used hours of video data demonstrating different cooking actions (e.g. frying an egg or tossing a salad) and presented the software with only portions of the action and tasked it with predicting the remaining sequence based on what it had “learned.” Through their approach, Gall hopes the field can take a st

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