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TypingDNA, a four-year-old startup that was founded in Bucharest, Romania and more recently moved its headquarters to Brooklyn, New York, looks to be raising $ 7 million in funding for something interesting: AI-driven technology that it says can recognize people based on the way they type, both on their laptops and mobile devices.

A new SEC filing that says the company — which graduated from Techstars NYC in late 2018 and early last year closed on €1.3 million in seed funding — has so far raised $ 5.25 million toward that goal.

Typing biometrics — the detailed timing information that describes exactly when each key is pressed and released as way to identify the unique person at the keyboard — is apparently not brand new. A two-year-old, PCWorld article says research in the field dates back 20 years. It also says that inaccuracies have kept the technology from being used as a widespread way to authenticate individuals. TypingDNA meanwhile asserts that the typing pattern recognition technology it has developed has an accuracy rate of between 99% and upwards of 99.9%.

The company’s previous backers include GapMinder Venture Partners, a venture outfit based in Amsterdam. We’ve reached out to cofounder and CEO Raul Popa to learn more, but judging by the filing, the fund backing this new round is Gradient Ventures, which is Google’s nearly three-year-old, AI-focused venture group.

When TypingDNA raised its seed round roughly 11 months ago, it said it planned to use the money to improve its tech and expand its presence in both the financial and enterprise sectors, where it’s been trying to strike partnerships with more companies that are focused on identity and fraud prevention.

According to the startup’s site, it has also been working with educational organizations to help ensure they’re giving the right students credit for the work they receive.


TechCrunch

Apps from Chinese developers have been gaining popularity on Indian app stores for sometime. Last year, as many as 44 of the top 100 Android apps in India were developed by Chinese firms.

But things have changed this year as local developers put on a fight. According to app analytics and marketing firm AppsFlyer, Indian apps as a whole have recaptured their original standing.

41% of top 200 apps in Indian editions of Google’s Play Store and Apple’s App Store in Q2 and Q3 this year were developed by Indian developers and local firms, up from 38% last year, the report said. Data from App Annie, another research firm, corroborates the claim.

“This uptick happened chiefly at the expense of Chinese apps, which fell from their lead position to 38% from 43% in 2018. Altogether, Chinese and Indian apps make up almost four-fifths (79%) of the list,” the report said.

The shift comes as scores of Indian firms have launched payments, gaming, news, and entertainment apps in the last year and a half, said AppsFlyer, which analyzed 6.5 billion installs in the second and third quarters of this year.

But Chinese developers are not giving up and continue to maintain an “impressive” fight in each category, the report said.

India — which is home to more than 450 million smartphone users and maintains relatively lax laws to support an open market — has naturally emerged as an attractive battleground for developers worldwide.

Many Chinese firms including Xiaomi and ByteDance count India as one of their largest markets. TikTok app has amassed over 200 million users in India, for instance. Xiaomi, which leads the Indian smartphone market, is quickly building a portfolio of services for users in India. It launched a lending app in the country earlier this month.

Gaining traction among first time internet users, most of whom have lower financial capacity, can prove challenging. Those developing travel apps had to spend about 170 Indian rupees ($ 2.4) for each install, for instance. Food and drink app makers spent 138 Indian rupees ($ 1.9) per install during the aforementioned period, while games cost 13.5 Indian rupees.

Despite the marketing spends, retention rate for these apps was 23.4% on day 1, a figure that plummeted to 2.6% by the end of the month. (This is still an improvement over retention rates of 22.8% on day 1, and 2.3% on day 30 last year.)


TechCrunch

Alibaba share price increased as much as 7.7% during its first morning of trading on the Hong Kong Stock Exchange. Soon after the market opened, the shares climbed from their listing price of HKD $ 176 (a 2.9% discount from their closing price on the New York Stock Exchange on Tuesday) to HKD $ 189.50.

Each of Alibaba’s American depositary receipts on the NYSE is equivalent to about eight Hong Kong shares. Alibaba issued 500 million new ordinary shares for the secondary offering, plus an overallotment option for 75 million shares that will allow it to raise even more money if exercised. Its Hong Kong shares are trading under the ticker number 9988, a play on the words for “long-term prosperity” in Chinese.

Alibaba’s debut on the New York Stock Exchange in 2014 raised a total of $ 25 billion, making it the largest public offering in history. The company had initially considered holding its IPO in Hong Kong, but at the time, its stock exchange did not allow dual-class shares, a structure often used by tech startups because it allows holders of one class of shares to have more voting rights than common shareholders, ensuring companies continue to have control even after they go public.

Last year, the Hong Kong Stock Exchange changed its rules to accommodate dual-class share, enabling tech companies, including Meituan and Xiaomi, to debut there.

Listing on Hong Kong will also make it easier for more Chinese investors to buy and sell Alibaba shares, once it is included in the Stock Connect, a collaboration between the Hong Kong, Shanghai and Shenzhen stock exchanges.

This is not the first time Alibaba has had a presence on the Hong Kong stock market. In 2007, its B2B e-commerce platform, Alibaba.com, went public there, before the company took the unit private again in 2012.

Alibaba’s Hong Kong debut comes after months of tumultuous pro-democracy demonstrations (the stock exchange has stayed stable despite the protests), and the day after more than half the 452 seats up for vote in local district council elections flipped from pro-Beijing to pro-democracy candidates. Demonstrators have called for more transparency from the government and police, and the election results send a clear signal about public sentiment to chief executive Carrie Lam.


TechCrunch

Amazon Pay users in India can now use voice command with Alexa to pay their utility, internet, mobile, and satellite cable TV bills, the e-commerce giant said on Wednesday. This is the first time, the company said, it is pairing these functionalities with Amazon Pay in any market.

The e-commerce giant, which competes with Walmart’s Flipkart in India, said any Alexa-enabled device such as the Echo Dot smart speaker, the Fire TV Stick dongle, or headphones from third-party vendors will support the aforementioned feature in India.

To be sure, Amazon has long allowed users in many markets to purchase items using voice command with Alexa. But this is the first time the American company is letting users pay their electricity, water, cooking gas, broadband, and satellite TV bills with voice and Amazon Pay.

Amazon Pay is available in many markets, but the service has become especially popular in India, where the concept of parking money to a digital wallet skyrocketed in usage in late 2016 after the Indian government invalidated much of the paper bills in circulation in the country.

Without disclosing specific figures, Amazon said “3X more customers” compared to last year’s event used Amazon Pay service to pay during the recent six-day festive sales. It said a quarter of all digital transactions during the event was carried out on its Pay service.

To boost Amazon Pay engagements in India, the company has offered lofty cashback on Pay on a number of purchases over the years. Users can also enjoy hefty discount if they use Amazon Pay to pay for their food, tickets, and other things on select popular third-party services.

During the holiday season, the company said, “customers booked flight tickets worth 300 trips around the earth.”

Amazon Pay makes it much more convenient for users to pay their digital purchases especially those that are recurring in nature, said Puneesh Kumar, country manager of Alexa Experiences and Devices.

The company says users can engage with Pay through voice commands like “Alexa, what’s my balance,” which will reveal the amount they have available for purchase in their Amazon Pay wallet. Users can also initiate the process of topping money to their mobile wallet using a voice command. They can say something like, “Alexa, add Rs 1000 to my Amazon Pay balance,” which will send a link as a text on their phones to complete the transaction.


TechCrunch

Yesterday, we had a chance to talk with longtime venture investor Brad Feld of Foundry Group, whose book “Venture Deals” was recently republished for the fourth time, and for good reason. It’s a storehouse of knowledge, from how venture funds really work to term sheet terms, from negotiation tactics to how to choose (and pay for) the right investment banker.

Feld was generous with his time and his advice to founders, many dozens of whom had dialed in, conference-call style. In fact, you can find a full transcript of our conversation right here if you’re a member of Extra Crunch.

In the meantime, we thought we’d highlight some of our favorite parts of the conversation. One of these touches on SoftBank, an organization that Feld knows a little better than many other investors. We also discussed what happened at WeWork and specifically the difference between a cult-like leader and a visionary — and why it’s not always clear right away whether a founder is one or the other.  These excerpts have been edited for length and clarity.

TC: We were just talking about startups raising too much money, and speaking of which, you were involved with SoftBank long ago. Your software company had raised capital from SoftBank, then you later worked for the company as an investor. This way predates the Vision Fund, but you did know Masayoshi Son, which makes me wonder: what do you think of how they’ve been investing their capital?

BF: Just for factual reference, I was initially affiliated with SoftBank with a couple of other VCs; Fred Wilson, Rich Levandov and at the time Jerry Colonna, who now runs a company called Reboot. During that period of time, a subset of us ended up starting a fund that eventually became called Mobius Venture Capital, but it was originally called SoftBank Venture Capital or SoftBank Technology Ventures. We were essentially a fund sponsored by SoftBank, so we had SoftBank money. The partners ran the fund, but we were a central part of the SoftBank ecosystem at the time. I’d say that was probably ’95, ’96 to ’99, 2000. We changed the name of the firm to Mobius in 2001 because it was endlessly getting confused with the other [SoftBank] fund activity.

I do know a handful of the senior principals at SoftBank today very well, and I have enormous respect for them. Ron Fisher [the vice chairman of SoftBank Group] is the person I’m closest to. I have enormous respect for Ron. He’s one of my mentors and somebody I have enormous affection for.

There are endless piles of ink spilled on SoftBank, and there are loads of perspectives on Masa and about the Vision Fund. I would make the observation that the biggest dissonance in everything that’s talked about is timeframe, because even in the 1990s, Masa was talking about a 300-year vision. Whether you take it literally or figuratively, one of Masa’s powers is this incredible long arc that he operates on. Yet the analysis that we have on a continual basis externally is very short term — it’s days, weeks, months.

What Masa and the Vision Fund conceptually are playing is a very, very long-term game. Is the strategy an effective strategy? I have no idea . . .  but when you start being a VC, it takes a long time to know whether you’re any good at it out or not. It takes maybe a decade really before you actually know. You get a signal in five or six years. The Vision Fund is very young . . . It’s [also] a different strategy than any strategy that’s ever been executed before at that magnitude, so it will take a while to know whether it’s a success or not. One of the things that could cause that success to be inhibited would be having too short a view on it.

If a brand-new VC or a brand new fund is measured two years in in terms of its performance, and investors look at that and that’s how they decide what to do with the VC going forward, there would be no VCs. They’d all be out of business because the first two years of a brand-new VC, with very few exceptions, is usually a time period that it’s completely indeterminate as to whether or not they’re going to be successful.

TC: So many funds — not just the Vision Fund — are deploying their funds in two years, where it used to be four or five years, that it’s a bit harder. When you deploy all your capital, you then need to raise funding and it’s [too soon] to know how your bets are going to play out.

BF: One comment on that, Connie, because I think it’s a really good one: When I started, in the ’90s, it used to be a five-year fund cycle, which is why most LP docs have a five-year commitment period for VC funds. You literally have five years to commit the capital. In the internet bubble, it’s shortened to about three years, and in some cases it shortened to 12 months. At Mobius, we raised a fund in 1999 and a fund in 2000, so we had the experience of that compression.

When we set out the raise Foundry, we decided that our fund cycle would be three years and we would be really disciplined about that. We had a model for how we were going to deploy capital from each of our funds over that period of time. It turned out that when we look back in hindsight, we raised a new fund every three years and eventually we lost a year in that cycle. We have a 2016 vintage and a 2018 vintage and it’s because we really deployed the capital over 2.75 to three years . . .It eventually caught up with us.

I think the discipline of trying to have time diversity against the capital that you have is super important. If you talk to LPs today, there is a lot of anxiety about the increased pace at which funds have been deployed, and there has been a two year cycle in the last kind of two iterations of this. I think you’re going to start seeing that stretch back out to three years. From a time diversity perspective three years is plenty [of time] against portfolio construction. When it gets shorter, you actually don’t get enough time diversity in the portfolio and it starts to inhibit you.

TC: Very separately, you wrote a post about WeWork where you used the term cult of personality. For those who didn’t read that post — even for those who did — could you explain what you were saying?

BF: What I tried to abstract was the separation between cults of personality and thought leadership. Thought leadership is incredibly important. I think it’s important for entrepreneurs. I think it’s important for CEOs. I think it’s important for leaders, and I think it’s important for people around the system.

I’m a participant in the system, right? I’m a VC. There are lots of different ways for me to contribute, and I think personally, rather than creating a cult of personality around myself, as a contribution factor, I think it’s much better to try to provide thought leadership, including running lots of experiments, trying lots of things, being wrong a lot, and learning from it. One of the things about thought leadership that’s so powerful from my frame of reference is that people who exhibit thought leadership are truly curious, are trying to learn, are looking for data, and are building feedback loops from what they’re learning that then allows them to be more effective leaders in whatever role they have.

Cult of personality a lot of times masquerades as thought leadership . . . [but it tends] to be self-reinforcing around the awesomeness that is that person or the importance that is that person, or the correctness of the vision that person has. And what happens with cult of personality is that you very often, not always, but very often, lose the signal that allows you to iterate and change and evolve and modify so that you build something that’s stronger over time.

In some cases, it goes totally off the rails. I mean, just call it what it is: what business does a private company have, regardless of how much revenue it has, to buy a Gulfstream V or whatever [WeWork] bought? It’s crazy. ..

From an entrepreneurial perspective, I think being a leader with thought leadership and introspection around what’s working and what’s not working is much, much more powerful over a long period of time than the entrepreneur or the leader who gets wrapped in the cult of personality [and is] inhaling [his or her] own exhaust.

TC: Have you been in that situation yourself as a VC? Could VCs have done something sooner in this case or is that not possible when dealing with a strong personality?

BF: One of the difficult things to do, not just as an investor, but as a board member — and it’s frankly also difficult for entrepreneurs — is to deal with the spectrum that you’re on, where one end of the spectrum as an investor or board member is dictating to the charismatic, incredibly hard-driving founder who is the CEO  what they should do, and, at the other end, letting them be unconstrained so that they do whatever they want to do.

One of the challenges of a lot of VCs is that, when things are going great, it’s hard to be internally critical about it. And so a lot of times, you don’t focus as much on the character. Every company, as it’s growing the leadership, the founders, the CEO, the other executives, have to evolve. [Yet] a lot of times for various reasons, and it’s a wide spectrum, there are moments in time where it’s easier to not pay attention to that as an investor or board member. There’s a lot of investors and board members who are afraid to confront it. And there’s a lot of situations where, because you don’t set up the governance structure of the company in a certain way, because as an investor you wanted to get into the deal, or the entrepreneurs insist on [on a certain structure], or you don’t have enough influence because of when you invested, it’s very, very hard. If the entrepreneur is not willing to engage collaboratively, it’s very hard to do something about it.

Again, if you’re an Extra Crunch subscriber, you can read our unedited and wide-ranging conversation here.


TechCrunch

The boom in popularity for podcasting has given a new voice to the world of spoken word content that had been largely left for dead with the decline of broadcast radio. Now riding the wave of that growth, a startup called Descript that’s building tools to make the art of creating podcasts — or any other content that involves working with audio — a little easier with audio transcription and editing tools, has a trio of news announcements: funding, an acquisition, and the launch of a new tool that brings some of the magic of natural language processing and AI to the medium by letting people create audio of their own voices based on text that they type.

Descript, the latest startup from Groupon founder Andrew Mason, created as a spinoff of his audio-guide business Detour (which got acquired by Bose last year), is today announcing $ 15 million in funding, a Series A for expanding the business (including hiring more  people) that’s coming from Andreessen Horowitz (it also funded the startup’s seed round in 2017) and Redpoint.

Along with that, the company has acquired a small Canadian startup, Lyrebird — which had, like Descript, also built audio editing tools. Together, the two are rolling out a new feature for Descript called Overdub: people will now be able to create “templates” of their voices that they can in turn use to create audio based on words that they type, part of a bigger production suite that will also let users edit multiple voices on multiple tracks. The audio can be standalone, or the audio track for a video.

(The video transcription works a little differently: when you add in words, or take them out, the video makes jumps to account for the changes in timing.)

Overdub is the latest addition to a product that lets users create instant transcriptions of audio text that can then be cut and potentially augmented with music other audio using drag-and-drop tools that take away the need for podcasters to learn sound engineering and editing software. The non-technical emphasis of the product has given Descript a following among podcasters and others that use transcription software as part of their audio production suites. The product is priced in a freemium format: no charge for up to four hours of voice content, and $ 10 per month after that.

In the age of market-defining, election-winning fake news aided and abetted by technology, you’d be forgiven for wondering if Overdub might not be a highway to Deep Fake City, where you could use the technology to create any manner of “statements” by famous voices.

Mason tells me that the company has built a way to keep that from being able to happen.

The demo on the company’s home page is created with a special proprietary voice just for illustrative purposes, but to actually activate the editing and augmenting feature for a piece of their own audio, users have to first record a number of statements that repeated-back, based on text created on the fly and in real time. These audio clips are then used to shape your digital voice profile.

This means that you can’t, for example, feed audio of Donald Trump into the system to create a version of the President saying that he is awfully sorry for suggesting that building walls between the US and Mexico was a good idea, and that this would not, in fact, make America Great Again. (Too bad.)

But if you subscribe to the idea that tech advances in NLP and AI overall are something of a Pandora’s Box, the cat’s already out of the bag, and even if Descript doesn’t allow for it, someone else will likely hack this kind of technology for more nefarious ends. The answer, Mason says, is to keep talking about this and making sure people understand the potentials and pitfalls.

“People have already have created the ability to make deep fakes,” Mason said. “We should expect that not everybody is going to follow the same constrants that we have followed. But part of our role is to create awareness of the possibilities. Your voice is your identity, and you need to own that voice. It’s an issue of privacy, basically.”

The developments underscore the new opportunity that has opened up in tapping some of the developments in artificial intelligence to address what is a growing market. On one hand, it’s a big market: based just on ad revenues alone, podcasting is expected to bring in some $ 679 million this year, and $ 1 billion by 2021, according to the IAB — one reason why companies like Spotify and Apple are betting big on it as a complement to their music streaming businesses.

On the other, the area of production tools for podcasters is a very crowded market, with a number of startups and others putting out a lot of tools that all work quite well in identifying what people are saying and transcribing it accurately.

On the front of transcription and the area where Descript is working, rivals include the likes of Trint, Wreally and Otter, among many others. Decript itself doesn’t even create its basic NLP software; it uses Google’s, since basic NLP is now an area that has essentially become “commoditized,” said Mason in an interview.

That makes creating new features, tapping into AI and other advances, all the more essential, as we look to see if one tool emerges as a clear leader in this particular area of SaaS.

“In live multiuser collaboration, there is still no other tool out there that has done what we have done with large uncompressed audio files. That is no small feat, and it has taken time to get it right,” said Mason. “I have seen this transition manifest from documents to spreadsheets to product design. No one would have thought of something like product design to be huge space but just by taking these tools for collaboration and successfully porting them to the cloud, companies like Figma have emerged. And that’s how we got involved here.”


TechCrunch

AMD announced that Google and Twitter are among the companies now using EPYC Rome processors during a launch event for the 7nm chips today. The release of EPYC Rome marks a major step in AMD’s processor war with Intel, which said last month that its own 7nm chips, Ice Lake, won’t be available until 2021 (though it is expected to release its 10nm node this year).

Intel is still the biggest datacenter processor maker by far, however, and also counts Google and Twitter among its customers. But AMD’s latest releases and its strategy of undercutting competitors with lower pricing have quickly transformed it into a formidable rival.

Google has used other AMD chips before, including in its “Millionth Server,” built in 2008, and says it is now the first company to use second-generation EPYC chips in its datacenters. Later this year, Google will also make virtual machines that run on the chips available to Google Cloud customers.

In a press statement, Bart Sano, Google vice president of engineering, said “AMD 2nd Gen Epyc processors will help us continue to do what we do best in our datacenters: innovate. Its scalable compute, memory and I/O performance will expand out ability to drive innovation forward in our infrastructure and will give Google Cloud customers the flexibility to choose the best VM for their workloads.”

Twitter plans to begin using EPYC Rome in its datacenter infrastructure later this year. Its senior director of engineering, Jennifer Fraser, said the chips will reduce the energy consumption of its datacenters. “Using the AMD EPYC 7702 processor, we can scale out our compute clusters with more cores in less space using less power, which translates to 25% lower [total cost of ownership] for Twitter.”

In a comparison test between 2-socket Intel Xeon 6242 and AMD EPYC 7702P processors, AMD claimed that its chips were able to reduce total cost of ownership by up to 50% across “numerous workloads.” AMD EPYC Rome’s flagship is the 64-core, 128-thread 7742 chip, with a 2.25 base frequency, 225 default TDP and 256MB of total cache, starts at $ 6,950.


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