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June 25, 2019
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Famed founder Daphne Koller tells it straight: “With most drugs, we do not understand why they work”

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Daphne Koller doesn’t mind hard work. She joined Stanford University’s computer science department in 1995, spending the next 18 years there in a full-time capacity before cofounding the online education giant Coursera, where she spent the following four years and remained co-chairman until last month. Koller then spent a little less than two years at Alphabet’s longevity lab, Calico, as its first chief computing officer.

It was there that Koller was reminded of her passion for applying machine learning to improve human health. She was also reminded of what she doesn’t like, which is wasted effort, something that the drug development industry — slow to understand the power of computational methods for analyzing biological data sets — as been plagued by for years.

In fairness, those computational methods have also gotten a whole lot better more recently. Little wonder that last year, Koller spied the opportunity to start another company, a drug development startup called Insitro that has since raised $100 million in Series A funding, including from GV, Andreessen Horowitz and Bezos Expeditions, among others. As notably, the company recently partnered with Gilead Sciences to find medicines to treat a liver disease called nonalcoholic steatohepatitis (NASH) because of all the human data on the disease that Gilead has amassed over the years.

Later, Insitro may target even bigger epidemics, including perhaps Alzheimer’s disease or Type 2 diabetes. Certainly, it has reason to feel optimistic about what it can accomplish. As Koller told a group of rapt attendees at an event hosted by this editor a few days ago, “We’re now at a moment in history where a confluence of technologies emerged all at around the same time allow really large and interesting and disease-relevant data sets to be produced in biology. In parallel, we see  . . . machine learning technologies that are able to make sense of that data and come up with novel insights that can hopefully cure disease.”

It all sounds like talk we’ve heard before in recent years, but coming from Koller, one gets the sense that we’re finally getting close, despite the mysteries of human biology. Below are some excerpts from Koller’s interview with journalist Sarah McBride of Bloomberg. You can also watch their conversation below.

On why Insitro struck a partnership with Gilead (beyond that it could prove lucrative, with up to $1 billion in milestones attached to successfully developing targets for NASH):

There are fairly broad categories that our technology is well-suited for. We’re really interested in creating what you might call disease-in-a-dish models — places where diseases are complex, where we really haven’t had a good model system, where typical animal models that have been used [for years, including testing on mice] just aren’t very effective — and creating those ‘in vitro’ models to generate very large amounts of data that can be interpreted using machine learning.

There’s a whole slew of diseases that lend themselves to this type of approach. NASH was one of them, so partly it was the suitability of our technology to this disease, and partly it was that Gilead was just a really good partner for it because they have a whole bunch of human data from some of the clinical trials that have been running [which give us] access to two complementary data sources. One is what happens to the disease in large human cohorts, and one is what happens when you look at what the disease does in vitro, in the dish, then see if we can use what we see in the dish using machine learning to predict what we see in the human.

On how Insitro views data differently than big pharma companies:

Pharma companies say, ‘We have lots of data.’ And you say, ‘What kinds of data do you have?’ And it turns out they have dribs and drab of data, each stored on a separate spreadsheet in someone else’s laptop. There’s metadata that isn’t even recorded. For them, it’s like, ‘Yeah, I did the experiment and obviously I recorded what I had to because it doesn’t make sense to throw it away,’ but they don’t think of it as something you build a company on top of.

We come at it a completely different way. We say, ‘This is the problem that you’d like to solve. If only we had a model that could tell us the result of this experiment without having to do the experiment, because it’s costly or complicated or even impossible [because it would involve perturbing a living human’s gene].’  Well, machine learning has gotten really good at building predictive models if you give it the right data to train the model. So we’re in the business of actually building data for the sole purpose of training machine learning models. We think of [these models] like little crystal balls that would allow you to avoid doing [these more expensive or complicated] experiments.

On the impact of the National Institutes of Health’s “All of Us” research program, which is an effort to gather data from one million or more people living in the U.S. to accelerate research and improve health in part by logging individual differences in lifestyle, environment, and biology:

I would say if anything that the U.S. is a little late to the game on this one. There have been a number of national cohorts have already been generated in different countries; the two that are currently best developed are in Iceland and in the U.K, but there’s also one in Finland and one in Ireland and even in Estonia, where they’ve taken a large population from within that country and measured their genetics, but also measured a whole lot of properties about those people, including blood biomarkers and urine biomarkers and behavioral aspects and physical aspects and imaging. And so what you have now (in these countries) is a dataset that tells you, ‘Nature perturbed this gene,’ and, ‘We see this effect on the human.’

[In the UK, specifically, where they started their program five years ago and recruited 500,000 volunteers who agreed to physical and cognitive and blood pressure testing and images of the brain and the abdomen, among other things] it’s an incredibly rich data set [from which] discoveries are coming along on pretty much a weekly basis.

… This is valuable not just primarily for gene therapies but just as a way of identifying targets that actually make a difference, because most drugs that go into clinical trials fail. And by most, I mean 95 percent. And most drugs fail because they are targeting the wrong things. They are targeting proteins or genes that do not affect the disease they are supposed to affect. The recent, very visible failures of Alzheimer’s drug trials — actually several of them in a row — were almost certainly because the protein they were targeting, called amyloid beta, is just not the right causal factor in the disease.

On what researchers can do now with stem cells that would have been impossible even a few years ago:

[There are now] tools that have enabled the creation of not only large amounts of data but large amounts of biologically relevant data. So we used to do experiments on cancer cell lines . . . but it’s not a very disease relevant model. Today, we can take a small sample of skin cells and use what’s called the Yamanaka factor, to reprogram those cells to stem cell status, which are the cells that exist effectively in the womb. And those cells are capable of differentiating themselves into neural cells or liver cells or cardiac cells, and those are very disease relevant because they represent human biology; you can take those cells now from patients and from healthy people and see if there are differences in how they appear.

Readers, we could feature more of the transcript here, but we highly suggest watching the conversation with Koller. If you use this text as a leaping off point, you’ll want to start listening at around the 13-minute mark. It’s definitely worth the time to hear what she has to say, including about cystic fibrosis, spinal muscular dystrophy in babies, and why the “mouse models” we’ve long relied on for a wide number of seemingly ubiquitous diseases “range from bad to really, really bad.” Hope you enjoy it.

Where top VCs are investing in media, entertainment & gaming

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Most of the strategy discussions and news coverage in the media & entertainment industry is concerned with the unfolding corporate mega-mergers and the political implications of social media platforms.

These are important conversations, but they’re largely a story of twentieth-century media (and broader society) finally responding to the dominance Web 2.0 companies have achieved.

To entrepreneurs and VCs, the more pressing focus is on what the next generation of companies to transform entertainment will look like. Like other sectors, the underlying force is advances in artificial intelligence and computer power.

In this context, that results in a merging of gaming and linear storytelling into new interactive media. To highlight the opportunities here, I asked nine top VCs to share where they are putting their money.

Here are the media investment theses of: Cyan Banister (Founders Fund), Alex Taussig (Lightspeed), Matt Hartman (betaworks), Stephanie Zhan (Sequoia), Jordan Fudge (Sinai), Christian Dorffer (Sweet Capital), Charles Hudson (Precursor), MG Siegler (GV), and Eric Hippeau (Lerer Hippeau).

Cyan Banister, Partner at Founders Fund

In 2018 I was obsessed with the idea of how you can bring AI and entertainment together. Having made early investments in Brud, A.I. Foundation, Artie and Fable, it became clear that the missing piece behind most AR experiences was a lack of memory.

Bad PR ideas, esports, and the Valley’s talent poaching war

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Sending severed heads, and even more PR DON’Ts

I wrote a “master list” of PR DON’Ts earlier this week, and now that list has nearly doubled as my fellow TechCrunch writers continued to experience even more bad behavior around pitches. So, here are another 12 things of what not to do when pitching a startup:

DON’T send severed heads of the writer you want to cover your story

Heads up! It’s weird to send someone’s cranium to them.

This is an odd one, but believe it or not, severed heads seem to roll into our office every couple of months thanks to the advent of 3D printing. Several of us in the New York TechCrunch office received these “gifts” in the past few days (see gifts next), and apparently, I now have a severed head resting on my desk that I get to dispose of on Monday.

Let’s think linearly on this one. Most journalists are writers and presumably understand metaphors. Heads were placed on pikes in the Middle Ages (and sadly, sometimes recently) as a warning to other group members about the risk of challenging whoever did the decapitation. Yes, it might get the attention of the person you are sending their head to, in the same way that burning them in effigy right in front of them can attract eyeballs.

Now, I get it — it’s a demo of something, and maybe it might even be funny for some. But, why take the risk that the recipient is going to see the reasonably obvious metaphorical connection? Use your noggin — no severed heads.

Why your CSO — not your CMO — should pitch your security startup

Meet GV investors at the TechCrunch Include March Office Hours

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GV (formerly Google Ventures) is partnering with TechCrunch Include to host Office Hours for underserved and underrepresented founders on March 5th. From 10:30am – 12:30pm, GV investors Dave Munichiello, Graham Spencer, Laura Melahn, Brian Bendett and Barkha Gvalani will meet for one-on-one sessions with founders. Apply here.

In 2014, TechCrunch launched the Include program, which facilitates opportunities for underserved and underrepresented founders in tech through our vast network and resources. Include Office Hours is one of TechCrunch’s initiatives. TechCrunch partners with VC firms to give founders access to investors for guidance as well as product and business model feedback. Investors host private 20-minute one-on-one meetings with founders, roundtables or lunches.

Founders from diverse backgrounds are encouraged to apply. Underrepresented and underserved founders include, but are not limited to, veteran, female, Latino/a, Black, LGBTQ and founders with handicaps.

The March Include Office Hours will be hosted by GV (formerly Google Ventures) on March 5th from 10:30am – 12:30pm PT. Founded in 2009, GV is a venture fund based in California with more than 300 investments. Apply here.

Meet the participating investors:

Dave Munichiello – General Partner

Dave is a general partner at GV and leads the team’s investments in data, platforms and infrastructure. Prior to GV, Dave built and led enterprise software sales and operations teams for highly technical products, under pressure in rapidly changing markets.

As a senior executive at Kiva Systems, he helped grow the enterprise-enabling robotics and software platform to $120 million in annual revenue before it was purchased by Amazon. Dave’s career prior to Kiva included management consulting for The Boston Consulting Group and leading teams as a Captain in the U.S. military’s most elite units. His military leadership roles ranged from running a high-tech organization in Europe to serving as an aide-de-camp to the four-star general responsible for U.S. forces in Europe, Africa and Afghanistan to deploying with elite special operations teams worldwide, ensuring they were enabled by the world’s most advanced technologies.

Dave is a combat veteran and former paratrooper.

Graham Spencer – Managing Partner

Graham Spencer is a managing partner at GV. He was an engineering director at Google following the 2006 acquisition of JotSpot, which he co-founded with Joe Kraus. Graham was one of the original six founders of Excite.com and was the chief technology officer of the company until its sale to @Home.

In 1999, Graham left Excite@Home to co-found DigitalConsumer.org, a 50,000-member nonprofit consumer organization dedicated to protecting fair-use rights for digital media. Graham is also on the board of the Santa Fe Institute.

Laura Melahn – Investing Partner

Laura joined GV in 2011 and is a partner on the investing team. Previously, she established GV’s marketing function, working with their portfolio on branding and growth.

Laura named Calico, Alphabet’s company aiming to slow aging and counteract age-related diseases. Prior to joining GV, Laura was a product marketing manager at Google, where she worked on Search, Maps, Analytics and the brand. She developed the Street View snowmobile for the 2010 Winter Olympics and helped bring Search Stories to TV. Previously, Laura conducted research at the Cancer Research Center of Hawaii and in the University of Oxford biochemistry department.

Brian Bendett – Investing Partner

Brian is a partner on the GV investing team focusing on investments in platforms, machine learning and infrastructure.

Prior to joining GV, Brian managed projects at Google across people operations, finance, marketing and corporate development. In a former life, Brian worked in private equity and spent time in Washington, D.C. supporting the White House Council of Economic Advisers and the Office of the Vice President.

Barkha Gvalani – Engineering Partner

Barkha works on investing operations, product management and analytics at GV. She also helps portfolio companies scale their operations through analytics, data-warehousing, and business intelligence.

Prior to joining GV, Barkha worked extensively with Google’s Ads and Hardware finance teams solving their hard data problems. She was also chief of staff on the team overseeing Google’s financial systems strategy. Before Google, Barkha worked at Tata Consultancy Services, where she specialized in the leasing business and consulted for GE Commercial Finance.

If you are a partner/managing director of a firm and are interested in supporting underserved and underrepresented founders, email neesha@techcrunch.com.

Helen Yiang and Andy Wheeler will be speaking at TC Sessions: Robotics + AI April 18 at UC Berkeley

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We’re just under two months out from this year’s TC Sessions: Robotics + AI event, and we’ve still got a lot left to announce. As noted, we’ll have Anca Dragan, Marc Raibert, Alexei Efros, Hany Farid, Melonee Wise, Peter Barrett, Rana el Kaliouby, Arnaud Thiercelin and Laura Major at the April event, and today we’ve got a pair of names to add to the ever-growing speaker list.

Today we’re excited to announce to additions to our VC panel, who will be discussing the wild world of robotics investments.

Founding and Managing Partner of FoundersX Ventures, Helen Liang will be joining us at the event to discuss the 20 early stage robotics and AI startups she has invested in. Liang brings a decade of product development to her work at her early-stage capital fund and also serves as Founding President at Tech for Good.

Andy Wheeler is a founding partner at GV (formerly Google Ventures), focusing on bringing early-stage tech to market. He is a co-founder of Ember Corporation and a veteran of MIT Media Lab. His list of early investments include Carbon, Farmer’s Business Network, Abundant Robotics and Orbital Insight.

Early bird tickets are now on sale – book your $249 ticket today and save $100 before prices go up. Students, did you know that you can save $45 with a heavily-discounted student ticket? Book your student ticket here.

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