Two former product wizards from Facebook and Google are combining Silicon Valley’s buzziest buzz words –search, artificial intelligence, and big data — into a new technology service aimed at solving nothing less than the problem of how to provide professional meaning in the modern world.
Founded by chief executive Ashutosh Garg, a former search and personalization expert at Google and IBM research, and chief technology officer Varun Kacholia, who led the ranking team at Google and YouTube search and the News Feed team at Facebook, Eightfold.ai boasts an executive team that has a combined eighty patents and over 6,000 citations for their research.
The two men have come together (in perhaps the most Silicon Valley fashion) to bring the analytical rigor that their former employers are famous for to the question of how best to help employees find fulfillment in the workforce.
“Employment is the backbone of society and it is a hard problem,” to match the right person with the right role, says Garg. “People pitch recruiting as a transaction… [but] to build a holistic platform is to build a company that fundamentally solves this problem,” of making work the most meaningful to the most people, he says.
It’s a big goal and it’s backed $24 million in funding provided by some big time investors — Lightspeed Ventures and Foundation Capital .
The company’s executives say they want to wring all of the biases out of recruiting, hiring, professional development and advancement by creating a picture of an ideal workforce based on publicly available data collected from around the world. That data can be parsed and analyzed to create an almost Platonic ideal of any business in any industry.
That image of an ideal business is then overlaid on a company’s actual workforce to see how best to advance specific candidates and hire for roles that need to be filled to bring a business closer in line with its ideal.
“We have crawled the web for millions of profiles… including data from wikipedia,” says Garg. “From there we have gotten data round how people have moved in organizations. We use all of this data to see who has performed well in an organization or not. Now what we do… we build models over this data to see who is capable of doing what.”
There are two important functions at play, according to Garg. The first is developing a talent network of a business — “the talent graph of a company”, he calls it. “On top of that we map how people have gone from one function to another in their career.”
Using those tools, Garg says Eightfold.ai’s services can predict the best path for each employee to reach their full potential.
The company takes its name from Buddhism’s eightfold path to enlightenment, and while I’m not sure what the Buddha would say about the conflation of professional development with spiritual growth, Garg believes that he’s on the right track.
“Every individual with the right capability and potential placed in the right role is meaningful progress for us,” says Garg.
Eightfold.ai already counts over 100 customers using its tools across different industries. It’s software has processed over 20 million applications to-date, and increased response rates among its customers by 700 percent compared to the industry average all while reducing screening costs and time by 90 percent, according to a statement.
“Eightfold.ai has an incredible opportunity to help people reach their full potential in their careers while empowering the workforces of the future,” said Peter Nieh, a partner at Lightspeed Ventures in a statement. “Ashutosh and Varun are bringing to talent management the transformative artificial intelligence and data science capability that they brought to Google, YouTube and Facebook. We backed Ashutosh previously when he co-founded BloomReach and look forward to partnering with him again.”
The application of big data and algorithmically automated decision making to workforce development is a perfect example of how Silicon Valley approaches any number of problems — and with even the best intentions, it’s worth noting that these tools are only as good as the developers who make them.
Indeed, Kacholia and Garg’s previous companies have been accused on relying too heavily on technology to solve what are essentially human problems.
The proliferation of propaganda, politically-minded meddling by foreign governments in domestic campaigns, and the promotion of hate speech online has been abetted in many cases by the faith technology companies like Google and Facebook have placed in the tools they’ve developed to ensure that their information and networking platforms function properly (spoiler alert: they’re not).
And the application of these tools to work — and workforce development — is noble, but should also be met with a degree of skepticism.
As an MIT Technology Review article noted from last year,
Algorithmic bias is shaping up to be a major societal issue at a critical moment in the evolution of machine learning and AI. If the bias lurking inside the algorithms that make ever-more-important decisions goes unrecognized and unchecked, it could have serious negative consequences, especially for poorer communities and minorities. The eventual outcry might also stymie the progress of an incredibly useful technology (see “Inspecting Algorithms for Bias”).
Algorithms that may conceal hidden biases are already routinely used to make vital financial and legal decisions. Proprietary algorithms are used to decide, for instance, who gets a job interview, who gets granted parole, and who gets a loan.
“Many of the biases people have in recruiting stem from the limited data people have seen,” Garg responded to me in an email. “With data intelligence we provide recruiters and hiring managers powerful insights around person-job fit that allows teams to go beyond the few skills or companies they might know of, dramatically increasing their pool of qualified candidates. Our diversity product further allows removal of any potential human bias via blind screening. We are fully compliant with EEOC and do not use age, sex, race, religion, disability, etc in assessing fit of candidates to roles in enterprises.”
Making personnel decisions less personal by removing human bias from the process is laudable, but only if the decision-making systems are, themselves, untainted by those biases. In this day and age, that’s no guarantee.
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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