AI is being applied across the board in many industries worldwide, and its scope of influence is only likely to continue to expand as Kaifu Lee, a noted AI expert who was formerly head of Google China, recently told TechCrunch.
The main battle appears to be between companies in the U.S. and China, but this week a startup in Southeast Asia came out of stealth mode to show that innovation is present elsewhere in the world.
Ucare.ai is focused on applying AI on the healthcare system to increase efficiencies and help patient coverage. It focuses on three distinct audiences: patients, health providers and those who pay the bills.
In particular, the company uses deep learning and neural network algorithms to predict healthcare patterns in patients, and beyond, to reduce preventable hospitalization, and, in turn, save on costs and hassles. That also allows medical professionals and insurers to focus on the more obvious risk patients, Ucare.ai said.
The company was founded in 2016 by Neal Liu, an MIT graduate who career includes six years with Google and stints with Microsoft, eBay and others. The company picked up seed funding in 2016, finance executive Christina Teo came on board as CEO (Liu is CTO) a year later and this week Ucare.ai came out of stealth with the announcement of its $8.2 million Series A round from backers that include Walden International and Singapore’s Great Eastern.
Singapore is gaining ground as startup destination that locates founders within striking distance of Greater China whilst also giving them access to Southeast Asia, a nascent but fast-growing market where the ‘internet economy’ is tipped to reach $200 billion by 2025 according to a recent report co-authored by Google.
Ucare.ai spent its initial two years developing its core AI smarts, the backbone of its service, by stitching together de-identified healthcare data using a mix of publicly available information and data from private partners, before then building out products for the health sector.
“Healthcare costs are only going in one direction as people are living longer and chronic diseases become more prevalent,” Teo told TechCrunch an interview. “That means that costs are going up, and payers are paying more, while corporate health is receiving a lot of attention with corporate clients expecting cost coverage and intervention programs.”
Ucare.ai CEO Christina Teo (left) and CTO Neal Liu (right)
That’s the ecosystem Ucare.ai has set out to impact. With hospitalization one of the most significant costs, the startup wants to reduce that through AI-powered predictive services. Healthcare provider Parkway Shenton, which has over 1,000 clinics, is one public name that signed on with Ucare.ai with other partners as-yet-undisclosed. Clients like Parkway pay for various different products which can provide real-time predictions, or more regular report-like information, Teo explained.
Liu had been based in Singapore while at Google, and he saw an opportunity to develop the startup there whilst tapping into the unique features of the city-state.
“Singapore is ideal,” Teo, herself a Singaporean, told TechCrunch. “It has a robust healthcare system, is well audited, there’s tech adoption such as cashless payments, and data privacy is taken seriously.”
“It’s also a country where you can study people of different backgrounds and lifestyles, which makes it fairly good for scientists. The cost of businesses is reasonable, there are government grants and there’s talent,” she added.
There’s also the potential to expand the business. Ucare.ai has focused its efforts on Singapore, to date, but Teo said there are opportunities to move into neighboring markets to both improve the systems by adding more data and grow the business from a revenue perspective.
“The heavy lifting has been done in the last two years, now we’re looking at opportunities to scale and repeat the business models in other parts of Southeast Asia,” she said, adding that Greater China is also a focus of interest.
Right now, the startup has less than 20 staff with a blend of nationalities, but Teo said the headcount is climbing on “a near-daily basis.”
Other notable healthcare-focused startups in Southeast Asia include fellow Singapore-based CXA, which helps corporates provide quality healthcare to employees, and mClinica, which maps healthcare sales and data in the region.
News Source = techcrunch.com
With at least $1.3 billion invested globally in 2018, VC funding for blockchain blows past 2017 totals
Although bitcoin and blockchain technology may not take up quite as much mental bandwidth for the general public as it did just a few months ago, companies in the space continue to rake in capital from investors.
One of the latest to do so is Circle, which recently announced a $110 million Series E round led by bitcoin mining hardware manufacturer Bitmain. Other participating investors include Tusk Ventures, Pantera Capital, IDG Capital Partners, General Catalyst, Accel Partners, Digital Currency Group, Blockchain Capital and Breyer Capital.
This round vaults Circle into an exclusive club of crypto companies that are valued, in U.S. dollars, at $1 billion or more in their most recent venture capital round. According to Crunchbase data, Circle was valued at $2.9 billion pre-money, up from a $420 million pre-money valuation in its Series D round, which closed in May 2016. According to Crunchbase data, only Coinbase and Robinhood — a mobile-first stock-trading platform which recently made a big push into cryptocurrency trading — were in the crypto-unicorn club, which Circle has now joined.
But that’s not the only milestone for the world of venture-backed cryptocurrency and blockchain startups.
Back in February, Crunchbase News predicted that the amount of money raised in old-school venture capital rounds by blockchain and blockchain-adjacent startups in 2018 would surpass the amount raised in 2017. Well, it’s only May, and it looks like the prediction panned out.
In the chart below, you’ll find worldwide venture deal and dollar volume for blockchain and blockchain-adjacent companies. We purposely excluded ICOs, including those that had traditional VCs participate, and instead focused on venture deals: angel, seed, convertible notes, Series A, Series B and so on. The data displayed below is based on reported data in Crunchbase, which may be subject to reporting delays, and is, in some cases, incomplete.
A little more than five months into 2018, reported dollar volume invested in VC rounds raised by blockchain companies surpassed 2017’s totals. Not just that, the nearly $1.3 billion in global dollar volume is greater than the reported funding totals for the 18 months between July 1, 2016 and New Year’s Eve in 2017.
And although Circle’s Series E round certainly helped to bump up funding totals year-to-date, there were many other large funding rounds throughout 2018:
- $118 million raised by Orbs, a purported “consumer-ready blockchain” service set to launch in June.
- $75 million closed in a Series B round for Paris-based secure hardware wallet-maker Ledger.
- $32 million raised by Project Shivom, which claims to use two hyped technologies — blockchain and artificial intelligence — to analyze and protect genomic data.
- $16 million in a Benchmark-led Series A round for Chainalysis, a blockchain analysis platform.
There were, of course, many other large rounds over the past five months. After all, we had to get to $1.3 billion somehow.
All of this is to say that investor interest in the blockchain space shows no immediate signs of slowing down, even as the price of bitcoin, ethereum and other cryptocurrencies hover at less than half of their all-time highs. Considering that regulators are still figuring out how to treat most crypto assets, massive price volatility and dubious real-world utility of the technology, it may surprise some that investors at the riskiest end of the risk capital pool invest as much as they do in blockchain.
Notes on methodology
Like in our February analysis, we first created a list of companies in Crunchbase’s bitcoin, ethereum, blockchain, cryptocurrency and virtual currency categories. We added to this list any companies that use those keywords, as well as “digital currency,” “utility token” and “security token” that weren’t previously included in the above categories. After de-duplicating this list, we merged this set of companies with funding rounds data in Crunchbase.
Please note that for some entries in Crunchbase’s round data, the amount of capital raised isn’t known. And, as previously noted, Crunchbase’s data is subject to reporting delays, especially for seed-stage companies. Accordingly, actual funding totals are likely higher than reported here.
News Source = techcrunch.com
AI will save us from yanny/laurel, right? Wrong
If you haven’t taken part in the yanny/laurel controversy over the last couple days, allow me to sincerely congratulate you. But your time is up. The viral speech synth clip has met the AI hype train and the result is, like everything in this mortal world, disappointing.
Sonix, a company that produces AI-based speech recognition software, ran the ambiguous sound clip through Google, Amazon, and Watson’s transcription tools, and of course its own.
Google and Sonix managed to get it on the first try — it’s “laurel,” by the way. Not yanny. Laurel.
But Amazon stumbled, repeatedly producing “year old” as its best guess for what the robotic voice was saying. IBM’s Watson, amazingly, got it only half the time, alternating between hearing “yeah role” and “laurel.” So in a way, it’s the most human of them all.
Sonix CEO Jamie Sutherland told me in an email that he can’t really comment on the mixed success of the other models, not having access to them.
“As you can imagine the human voice is complex and there are so many variations of volume, cadence, accent, and frequency,” he wrote. “The reality is that different companies may be optimizing for different use cases, so the results may vary. It is challenging for a speech recognition model to accommodate for everything.”
My guess as an ignorant onlooker is it may have something to do with the frequencies the models have been trained to prioritize. Sounds reasonable enough!
It’s really an absurd endeavor to appeal to a system based on our own hearing and cognition to make an authoritative judgement in a matter on which our hearing and cognition are demonstrably lacking. But it’s still fun.
News Source = techcrunch.com
What we know about Google’s Duplex demo so far
The highlight of Google’s I/O keynote earlier this month was the reveal of Duplex, a system that can make calls to set up a salon appointment or a restaurant reservation for you by calling those places, chatting with a human and getting the job done. That demo drew lots of laughs at the keynote, but after the dust settled, plenty of ethical questions popped up because of how Duplex tries to fake being human. Over the course of the last few days, those were joined by questions about whether the demo was staged or edited after Axios asked Google a few simple questions about the demo that Google refused to answer.
We have reached out to Google with a number of very specific questions about this and have not heard back. As far as I can tell, the same is true for other outlets that have contacted the company.
If you haven’t seen the demo, take a look at this before you read on.
So did Google fudge this demo? Here is why people are asking and what we know so far:
During his keynote, Google CEO Sundar Pichai noted multiple times that we were listening to real calls and real conversations (“What you will hear is the Google Assistant actually calling a real salon.”). The company made the same claims in a blog post (“While sounding natural, these and other examples are conversations between a fully automatic computer system and real businesses.”).
Google has so far declined to disclose the name of the businesses it worked with and whether it had permission to record those calls. California is a two-consent state, so our understanding is that permission to record these calls would have been necessary (unless those calls were made to businesses in a state with different laws). So on top of the ethics questions, there are also a few legal questions here.
We have some clues, though. In the blog post, Google Duplex lead Yaniv Leviathan and engineering manager Matan Kalman posted a picture of themselves eating a meal “booked through a call from Duplex.” Thanks to the wonder of crowdsourcing and a number of intrepid sleuths, we know that this restaurant was Hongs Gourmet in Saratoga, California. We called Hongs Gourmet last night, but the person who answered the phone referred us to her manager, who she told us had left for the day. (We’ll give it another try today.)
Sadly, the rest of Google’s audio samples don’t contain any other clues as to which restaurants were called.
What prompted much of the suspicion here is that nobody who answers the calls from the Assistant in Google’s samples identifies their name or the name of the business. My best guess is that Google cut those parts from the conversations, but it’s hard to tell. Some of the audio samples do however sound as if the beginning was edited out.
Google clearly didn’t expect this project to be controversial. The keynote demo was clearly meant to dazzle — and it did so in the moment because, if it really works, this technology represents the culmination of years of work on machine learning. But the company clearly didn’t think through the consequences.
My best guess is that Google didn’t fake these calls. But it surely only presented the best examples of its tests. That’s what you do in a big keynote demo, after all, even though in hindsight, showing the system fail or trying to place a live call would have been even better (remember Steve Job’s Starbucks call?).
For now, we’ll see if we can get more answers, but so far all of our calls and emails have gone unanswered. Google could easily do away with all of those questions around Duplex by simply answering them, but so far, that’s not happening.
News Source = techcrunch.com
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