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Facebook has a new job posting calling for chip designers

Facebook has posted a job opening looking for an expert in ASIC and FPGA, two custom silicon designs that companies can gear toward specific use cases — particularly in machine learning and artificial intelligence.

There’s been a lot of speculation in the valley as to what Facebook’s interpretation of custom silicon might be, especially as it looks to optimize its machine learning tools — something that CEO Mark Zuckerberg referred to as a potential solution for identifying misinformation on Facebook using AI. The whispers of Facebook’s customized hardware range depending on who you talk to, but generally center around operating on the massive graph Facebook possesses around personal data. While a camera might have a set of data points as a series of pixels, Facebook’s knowledge of you goes well beyond your list of friends and down to minute preferences you have — a set of data so large that it demands a new approach to speed up the process.

Most in the industry speculate that it’s being optimized for Caffe2, an AI infrastructure deployed at Facebook, that would help it tackle those kinds of complex problems. Customized silicon generally tends to be around optimizing inference (the “is that a cat” part of machine learning) or machine training (“this is what a cat is”). On either end, it’s based on speeding up relatively simple math operations based in a field called linear algebra. But we’ve been hearing about this for a bit now, and it seems like Facebook is about to be much more overt about the process.

FPGA is designed to be a more flexible and modular design, which is being championed by Intel as a way to offer the ability to adapt to a changing machine learning-driven landscape. The downside that’s commonly cited when referring to FPGA is that it is a niche piece of hardware that is complex to calibrate and modify, as well as expensive, making it less of a cover-all solution for machine learning projects. ASIC is similarly a customized piece of silicon that a company can gear toward something specific, like mining cryptocurrency.

Facebook’s director of AI research tweeted about the job posting this morning, noting that he previously worked in chip design:

Interested in designing ASIC & FPGA for AI?
Design engineer positions are available at Facebook in Menlo Park.

I used to be a chip designer many moons ago: my engineering diploma was in Electrical… https://t.co/D4l9kLpIlV

Yann LeCun (@ylecun) April 18, 2018

While the whispers grow louder and louder about Facebook’s potential hardware efforts, this does seem to serve as at least another partial data point that the company is looking to dive deep into custom hardware to deal with its AI problems. That would mostly exist on the server side, though Facebook is looking into other devices like a smart speaker. Given the immense amount of data Facebook has, it would make sense that the company would look into customized hardware rather than use off-the-shelf components like those from Nvidia.

Most of the other large players have found themselves looking into their own customized hardware. Google has its TPU for its own operations, while Amazon is also reportedly working on chips for both training and inference. Apple, too, is reportedly working on its own silicon, which could potentially rip Intel out of its line of computers. Microsoft is also diving into FPGA as a potential approach for machine learning problems.

Still, that it’s looking into ASIC and FPGA does seem to be just that — dipping toes into the water for FPGA and ASIC. Nvidia has a lot of control over the AI space with its GPU technology, which it can optimize for popular AI frameworks like TensorFlow. And there are also a large number of very well-funded startups exploring customized AI hardware, including Cerebras Systems, SambaNova Systems, Mythic, and Graphcore (and that isn’t even getting into the large amount of activity coming out of China). So there are, to be sure, a lot of different interpretations as to what this looks like.

One significant problem Facebook may face is that this job opening may just sit up in perpetuity. Another common criticism of FPGA as a solution is that it is hard to find developers that specialize in FPGA. While these kinds of problems are becoming much more interesting, it’s not clear if this is more of an experiment than Facebook’s full all-in on custom hardware for its operations.

But nonetheless, this seems like more confirmation of Facebook’s custom hardware ambitions, and another piece of validation that Facebook’s data set is becoming so increasingly large that if it hopes to tackle complex AI problems like misinformation, it’s going to have to figure out how to create some kind of specialized hardware to actually deal with it.

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BenevolentAI, which uses AI to develop drugs and energy solutions, nabs $115M at $2B valuation

In the ongoing race to build the best and smartest applications that tap into the advances of artificial intelligence, a startup out of London has raised a large round of funding to double down on solving persistent problems in areas like healthcare and energy. BenevolventAI announced today that it has raised $115 million to continue developing its core “AI brain” as well as different arms of the company that are using it specifically to break new ground in drug development and more.

This venture round values the company at $2.1 billion post-money, its founder and executive chairman Ken Mulvaney confirmed to TechCrunch. Investors in this round include previous backer Woodford Investment Management, and while Mulvaney said the company was not disclosing the names of any other investors, he added it was a mix of family offices and some strategic backers, with a majority coming from the U.S., but would not specify any more. Notably, BenevolentAI does not have any backing from more traditional VCs, which more generally have been doubling down on investments in AI startups. Founded in 2013, the company has now raised more than $200 million to date.

The core of BenevolentAI’s business is focused around what Mulvaney describes as a “brain” built by a team of scientists — some of whom are disclosed, and some of whom are not, for competitive reasons; Mulvaney said: There are 155 people working at the startup in all, with 300 projected by the end of this year. The brain has been created to ingest and compute billions of data points in specific areas such as health and material science, to help scientists better determine combinations that might finally solve persistently difficult problems in fields like medicine.

The crux of the issue in a field like drug development, for example, is that even as scientists identify the many permutations and strains of, say, a particular kind of cancer, each of these strains can mutate, and that is before you consider that each mutation might behave completely differently depending on which person develops the mutation.

This is precisely the kind of issue that AI, which is massive computational power and “learning” from previous computations, can help address. (And BenevolventAI is not the only one taking this approach. Specifically in cancer, others include Grail and Paige.AI.)

But even with the speed that AI brings to the table, it’s a very long, long game for BenevolentAI. The division of BenevolentAI that is focused on drugs, called Benevolent Bio, currently has two drugs in more advanced stages of development, Mulvaney said, although neither of those happen to be in the area of cancer. A Parkinson’s drug is currently in Phase 2B clinical trials, after years of work.

And an ALS medication currently in development — which Mulvaney says will aim to significantly extend the prospects for those who have been diagnosed with ALS — is about five years away from trials. It’s worth the effort to try, though: The best ALS medications on the market today at best only add about three months to a patient’s life expectancy.

Some of the long period of development is because with drugs, there is a large regulatory framework a company must go through. “But we benefit from that,” Mulvaney said, “because it means you can actually then offer something in the market.” (Blood tests à la Theranos are very different in terms of regulatory requirements, he said.)

In part because of that long cycle, and also because BenevolentAI has spotted an adjacent opportunity, the company has more recently also been extending applications from its “brain” to other adjacent areas that also tap into chemistry and biology, such as material science.

One area Mulvaney said is of particular interest is to see if Benevolent can create materials that can both withstand extreme heat — to allow engines to work at higher rates without risks — as well as chemicals that could essentially create the next generation of efficient batteries that could provide more power in smaller formats for longer periods.

“There has been little development beyond a lithium-ion battery,” he noted, which may be fine for the Teslas of the world today. “But there is not enough lithium on this planet for us all to go electric, and there is not nearly enough energy density there unless you have thousands of batteries working together. We need other technology to provide more energy donation. That tech doesn’t exist yet because chemically it’s very difficult to do that.” And that spells opportunity for BenevolentAI.

Other areas where the startup hopes to move into over the coming months and years include agriculture, veterinary science, and other categories that sit alongside those BenevolentAI is already tapping.

 

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Apple, in a very Apple move, is reportedly working on its own Mac chips

Apple is planning to use its own chips for its Mac devices, which could replace the Intel chips currently running on its desktop and laptop hardware, according to a report from Bloomberg.

Apple already designs a lot of custom silicon, including its chipsets like the W-series for its Bluetooth headphones, the S-series in its watches, its A-series iPhone chips, as well as customized GPU for the new iPhones. In that sense, Apple has in a lot of ways built its own internal fabless chip firm, which makes sense as it looks for its devices to tackle more and more specific use cases and remove some of its reliance on third parties for their equipment. Apple is already in the middle of in a very public spat with Qualcomm over royalties, and while the Mac is sort of a tertiary product in its lineup, it still contributes a significant portion of revenue to the company.

Creating an entire suite of custom silicon could do a lot of things for Apple, the least of which bringing in the Mac into a system where the devices can talk to each other more efficiently. Apple already has a lot of tools to shift user activities between all its devices, but making that more seamless means it’s easier to lock users into the Apple ecosystem. If you’ve ever compared connecting headphones with a W1 chip to the iPhone and just typical Bluetooth headphones, you’ve probably seen the difference, and that could be even more robust with its own chipset. Bloomberg reports that Apple may implement the chips as soon as 2020.

Intel may be the clear loser here, and the market is reflecting that. Intel’s stock is down nearly 8% after the report came out, as it would be a clear shift away from the company’s typical architecture where it has long held its ground as Apple moves on from traditional silicon to its own custom designs. Apple, too, is not the only company looking to design its own silicon, with Amazon looking into building its own AI chips for Alexa in another move to create a lock-in for the Amazon ecosystem. And while the biggest players are looking at their own architecture, there’s an entire suite of startups getting a lot of funding building custom silicon geared toward AI.

Apple declined to comment.

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The Linux Foundation launches a deep learning foundation

Despite its name, the Linux Foundation has long been about more than just Linux. These days, it’s a foundation that provides support to other open source foundations and projects like Cloud Foundry, the Automotive Grade Linux initiative and the Cloud Native Computing Foundation. Today, the Linux Foundation is adding yet another foundation to its stable: the LF Deep Learning Foundation.

The idea behind the LF Deep Learning Foundation is to “support and sustain open source innovation in artificial intelligence, machine learning, and deep learning while striving to make these critical new technologies available to developers and data scientists everywhere.”

The founding members of the new foundation include Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa and ZTE. Others will likely join in the future.

“We are excited to offer a deep learning foundation that can drive long-term strategy and support for a host of projects in the AI, machine learning, and deep learning ecosystems,” said Jim Zemlin, executive director of The Linux Foundation.

The foundation’s first official project is the Acumos AI Project, a collaboration between AT&T and Tech Mahindra that was already hosted by the Linux Foundation. Acumos AI is a platform for developing, discovering and sharing AI models and workflows.

Like similar Linux Foundation-based organizations, the LF Deep Learning Foundation will offer different membership levels for companies that want to support the project, as well as a membership level for non-profits. All LF Deep Learning members have to be Linux Foundation members, too.

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Here are the top Midwestern states and cities for startups

The American Midwest has a long history of making stuff. During the 20th century, it was the manufacturing center for the nation, and indeed much of the world. It’s still where a surpassing majority of agricultural commodities are grown and processed. But is it also a major producer of technology startups? Maybe not as much as the coasts, but the Midwest’s bustling metropoli and vast expanses of rural land prove to be fertile ground for quite a bit of startup activity.

And that’s what we’re going to take a look at here. In a similar vein to our recent analysis of startup fundraising in the South, we’ll break down the region into its constituent parts, assessing deal and dollar volume trends in the Midwest’s two primary sub-regions, some of its individual states and the most active metropolitan areas in the U.S.’s midsection.

And, to be clear, this is not Crunchbase News’s first foray into the region. We’ve covered the region’s seed-stage interest in AI and hard tech, a few notable rounds and have always included the Midwest in all manner of data-spelunking expeditions. And to this, we’ll add a deep dive into the numbers.

Defining the midwest

Borders and boundaries are a deep well of disputes. To preempt debate, we use the U.S. Census Bureau’s definition of the Midwest region which, unlike its definition of the South, shouldn’t be too controversial. If you have something against Kansas or Ohio being included in this group, take it up with the Feds.

The good folks at the Census Bureau split the Midwest into two distinct — and rather unimaginatively named — sub-regions: the West North Central and East North Central states, which are separated by the Mississippi River. We’ve included the map below.

By splitting the Midwest into two distinct parts, we’ll be able to see where most of the startup and funding activity is concentrated. Spoiler alert: The farther west you go, the startup population (and the population itself) grows more scattered.

Capital flows into Midwestern startups

Based only on reported data in Crunchbase, the Midwest appears to be affected by the same phenomenon as the rest of the country. Crunchbase News has previously found that the number of seed and early-stage deals has gone off a cliff in the U.S., resulting in a top-heavy market featuring many large, late-stage deals. And this wouldn’t be a problem if it weren’t for a shortfall in new startups to fill the next cycle of early-stage funding. The “hollowing out” of the Midwestern venture deal pipeline becomes readily apparent when you look at funding data for the past several years, which you can find in the chart below.

To wit, deal volume is down markedly since 2014, as Crunchbase News reported in its Q4 2017 report of startup funding activity in the U.S. and Canada. But somewhat counterintuitively, the amount of money being invested into startups is on the rise in the Midwest and throughout many other parts of the country, reaching fresh multi-year highs in 2017. Almost one full quarter into 2018, the trend appears to continue unabated.

But this chart abstracts away a lot of nuance, so let’s take a closer look at the region and its states.

Focusing in on Midwestern deal and dollar volume

We’ll start first with deal volume, because that’s a fairly decent indicator of a geographic region’s level of startup activity. Below, we’ve plotted venture deal volume, divided by sub-region.

Again, based on the reported data from Crunchbase, we found that deal counts have been on a downward trend for several years. And though some of this may be attributable to reporting delays, projected deal volume data for the whole of the U.S. and Canada (fourth chart down in the Q4 quarterly report) shows a years’-long downtrend. There’s no reason to believe that startup activity in the Midwest is materially different from the rest of the U.S. and Canada.

But what about the relative “balance of power” between the two sub-regions? At least when it comes to deal volume, has one sub-region waxed while the other waned? To a limited extent, the answer is yes. Between 2012 and 2017, the percentage share of all Midwestern dealflow going to West North Central states like the Dakotas, Minnesota and Missouri has grown from 25.4 percent to 31.2 percent, up by nearly one-fifth in relative terms.

Now let’s check out dollar volume. The chart below displays aggregate reported venture capital dollar volume raised by startups in the Midwest.

As far as the amount of money Midwestern startups have raised over time, the trendline is generally up and to the right. But that’s not the only way this differs from the deal volume data we looked at earlier. For dollar volume, there appears to be no appreciable change in the “balance of power” between the two sub-regions since 2012. Depending on the year, East North Central states like Illinois, Michigan and Ohio raked in between 70 and 78 percent of total dollar volume, but that variance doesn’t appear in an orderly trend.

Where are most Midwestern deals done?

We started first at the regional level, then compared smaller groupings of states. Now, let’s see how deal and dollar volume is distributed on a state-by-state level. Doing so will point to the states that lead the region in venture-backed startup activity. Below, you’ll find a chart of how deal volume is split between the top five Midwestern states.

And here is how dollar volume is distributed.

As we saw with our analysis of the South, the top five Midwestern states for deal volume are the same five top-ranked states for dollar volume. But there is some notable variation in how these states rank among each other and the amount of deal and dollar volume they account for.

Considering that Illinois is home to Chicago and a number of downstate universities with deep tech startup roots, the fact that it places first for both metrics shouldn’t come as much of a surprise.

What might be more of a head-scratcher is Minnesota, which ranks third in deal volume but second in dollar volume. Why does it switch places with Ohio? The answer could lie in the industrial mix which, in the case of Minnesota, includes a disproportionately high number of medical device and other life sciences companies, which typically take a lot of capital to get off the ground.

The top Midwestern startup cities

Longtime readers of Crunchbase News may remember a ranking of Midwestern startup cities we wrote back in August 2017. However, here we’re just focusing on deal and dollar volume over the past 15 months, since the start of 2017.

Let’s start first with the top 10 Midwestern cities as measured by number of startup funding rounds.

And in the chart below, you can see the top cities, as ranked by venture dollar volume, from the same period of time.

In both rankings, four of the top five cities are the same, but the odd one out appears to be Columbus, Ohio. Although there were a fairly large number of rounds raised by startups in that metro area, most of the rounds were fairly small by national standards. And one of the main reasons why Kansas City, Missouri jumped so much in the dollar volume rankings was a $100 million Series F round raised by C2FO.

But, again, as far as the Midwest goes, everything pales in comparison to Chicago alone.

For many, the Midwest is in a kind of Goldilocks zone. The East and West coasts seem to hold more or less equal sway over the culture and economy and most of its cities are neither too big nor too small. The only extreme it seems to occupy is its winter weather.

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Clari raises $35M for its AI-based sales platform, expands into marketing and supply chain management

Clari — a startup that has built a predictive sales tool that provides just-in-time assistance for sales people close deals and for those who work in the bigger chain of command to monitor the progress of the sales operation — is capitalising on the big boom in interest for all things AI in the business world. The company is today announcing that it has closed a Series B round of $35 million, funding that it will be using to build out its own sales and marketing team and expand its platform capabilities.

The round was led by Tenaya Capital, the VC fund that started its life as a part of Lehman Brothers, along with participation from other new investors Thomvest Ventures and Blue Cloud Ventures, and previous investors Sequoia Capital, Bain Capital Ventures and Northgate Capital. It brings the total raised by Clari to $61 million.

Andy Byrne, the founder and CEO who is a repeat entrepreneur and has been involved in several exits, said the funding closed “definitely at an upround, and much bigger than we thought it was going to be,” but declined to give a number. For some context, Clari, according to Pitchbook, had a relatively modest post-money valuation of $83.5 million in its last round in 2014, so my guess is that it’s now comfortably into hundred-million territory, once you add in this latest $35 million.

The funding comes at an interesting time for AI startups, particularly those aimed at enterprise IT.

When Clari first emerged from stealth in April 2014, the idea of applying AI to solve pain points for non-technical people in organizations was a fairly nascent and still-novel concept.

Fast forward to today, things have moved very fast, as is often the case in the tech world. Now, you can’t seem to move for all the enterprise IT startups that are either using or claiming to use AI in their solutions. There are so many startup hopefuls, and so many organizations looking for the best way to use AI to improve their business and operations, that there are even startups being founded to manage that opportunity of connecting the two pieces together, such as Element AI.

“I’m not saying we were clairvoyant for targeting the idea of using AI for sales in 2013,” Byrne said. “There has been a large macro trend and if you happen to be a small company that is along for the ride. When we first launched, we had this thesis about AI for sales. Now it’s not the number three or two priority for sales teams, it’s number one. It’s everywhere. Businesses want to invest and spend more money on AI and making things more efficient.”

Clari says that its customer base has tripled in the last year, with customers including Adobe, Audi, Check Point Software, Equinix, Epicor Software Corporation, GE, and PerkinElmer.

Clari’s approach for using AI for the sales team comes in two main areas. First, the company’s system is aimed to reduce some of the busywork that salespeople have in maintaining and updating files on people, by bringing in a number of different data sources and using them to provide composite pictures of target companies that salespeople might have had to otherwise compile with more manual means. Second, Clari puts a lot of focus on its “Opportunity-to-Close (OTC) solutions” — a type of risk-analysis for salespeople and their managers to help them figure out which leads and strategic directly would be the most likely to produce sales.

“Working with Clari since inception, we have been impressed with its growth and strong execution,” said Aaref Hilaly, Partner at Sequoia Capital, in a statement. “Clari has fast become indispensable to many of the most successful sales teams, giving them visibility into their most important metrics: rep productivity, pipeline health, and forecast accuracy.”

Indeed, risk and outcome is a smart area to be in: using AI to help model this is a key area of focus in enterprise IT at the moment, according to feedback I’ve had from a number of others in the enterprise world.

“If you have 150 opportunities presented to you as a salesperson, how do you choose 10 where you should spend your time?” Byrne asked. “A more traditional CRM platform has never showcased your risk and outcomes.”

While up to now Clari has focused on providing intelligence on what is already in a company’s account database, the next step, Byrne noted, is to draw on data from around the web, providing completely new business leads to the sales team.

When we last covered a funding round for Clari, we noted that the company’s laser focus on sales was something that made the company stand out for investors: nailing one aspect of a business’s operations without distractions from other parts of the organization and what it could be spending time solving elsewhere (in fact, when you think about it, the very goal that Clari has been aiming to achieve for salespeople through its product).

But four years on, the company is now widening that ambition. It’s applying its AI engine now to help marketeers weigh up the best opportunities for reaching out to prospective customers; and interestingly it sounds like it will also be applying its engine to product development and specifically supply chain management.

Byrne described one customer, a medical device maker, that was encountering “inefficiencies” around what they should build and when to meet market demand. “Now that they can predict and forecast order bookings and revenue targets, and what’s happened is that their supply chain has become more efficient,” he said. “It is great example of how our AI is now being expanded.”

“The Clari team has leveraged its deep AI expertise to build a unique platform that surfaces predictive insights for sales reps, managers, and execs during the opportunity-to-close process,” said Brian Paul, MD at Tenaya Capital, in a statement. “We see a massive opportunity for AI to transform how sales teams operate which is clearly validated by Clari’s customers and the impressive growth the team has achieved.”

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ELSA raises $3.2M for its A.I.-powered English pronunciation assistant

 ELSA, an app whose name stands for “English Language Speech Assistant” (and not the popular Disney character!), has raised $3.2 million for its A.I.-assisted language learning platform that teaches people how to speak English. Unlike other courses that focus mainly on teaching grammar and vocabulary, ELSA uses artificial intelligence and speech recognition technology to help… Read More

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Intelligo is using AI to make background checks relevant again

 To realize that the background check industry needs an overhaul look no further than the backlog of 700,000 background checks faced by the federal agency that handles all background checks for sensitive government positions. This backlog has essentially rendered background checks useless, as many agencies are able to give security clearances on a temporary basis before a background check is… Read More

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SafeToNet demos anti-sexting child safety tool

 With rising concern over social media’s ‘toxic’ content problem, and mainstream consumer trust apparently on the slide, there’s growing pressure on parents to keep children from being overexposed to the Internet’s dark sides. Yet pulling the plug on social media isn’t exactly an option.  Read More

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