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The Three Breakthroughs That Have Finally Unleashed AI on the World
A few months ago I made the trek to the sylvan campus of the IBM research labs in Yorktown Heights, New York, to catch an early glimpse of the fast-arriving, long-overdue future of artificial intelligence.
It no longer exists solely within a wall of cabinets but is spread across a cloud of open-standard servers that run several hundred “instances” of the AI at once.
And instead of one single program, it’s an aggregation of diverse software engines—its logic-deduction engine and its language-parsing engine might operate on different code, on different chips, in different locations—all cleverly integrated into a unified stream of intelligence.
“I believe something like Watson will soon be the world’s best diagnostician—whether machine or human,” says Alan Greene, chief medical officer of Scanadu, a startup that is building a diagnostic device inspired by the Star Trek medical tricorder and powered by a cloud AI.
“At the rate AI technology is improving, a kid born today will rarely need to see a doctor to get a diagnosis by the time they are an adult.” As AIs develop, we might have to engineer ways to prevent consciousness in them—our most premium AI services will be advertised as consciousness-free.
Private investment in the AI sector has been expanding 62 percent a year on average for the past four years, a rate that is expected to continue.
Amid all this activity, a picture of our AI future is coming into view, and it is not the HAL 9000—a discrete machine animated by a charismatic (yet potentially homicidal) humanlike consciousness—or a Singularitan rapture of superintelligence.
Where does that get you?” My unimaginative blindness is solid evidence that predicting is hard, especially about the future, but in my defense this was before Google had ramped up its ad-auction scheme to generate real income, long before YouTube or any other major acquisitions.
Cheap parallel computation Thinking is an inherently parallel process, billions of neurons firing simultaneously to create synchronous waves of cortical computation.
That began to change more than a decade ago, when a new kind of chip, called a graphics processing unit, or GPU, was devised for the intensely visual—and parallel—demands of videogames, in which millions of pixels had to be recalculated many times a second.
Today neural nets running on GPUs are routinely used by cloud-enabled companies such as Facebook to identify your friends in photos or, in the case of Netflix, to make reliable recommendations for its more than 50 million subscribers.
Massive databases, self-tracking, web cookies, online footprints, terabytes of storage, decades of search results, Wikipedia, and the entire digital universe became the teachers making AI smart.
Better algorithms Digital neural nets were invented in the 1950s, but it took decades for computer scientists to learn how to tame the astronomically huge combinatorial relationships between a million—or 100 million—neurons.
When a group of bits in a neural net are found to trigger a pattern—the image of an eye, for instance—that result is moved up to another level in the neural net for further parsing.
It can take many millions of these nodes (each one producing a calculation feeding others around it), stacked up to 15 levels high, to recognize a human face.
In 2006, Geoff Hinton, then at the University of Toronto, made a key tweak to this method, which he dubbed “deep learning.” He was able to mathematically optimize results from each layer so that the learning accumulated faster as it proceeded up the stack of layers.
The code of deep learning alone is insufficient to generate complex logical thinking, but it is an essential component of all current AIs, including IBM’s Watson, Google’s search engine, and Facebook’s algorithms.
Cloud computing obeys the law of increasing returns, sometimes called the network effect, which holds that the value of a network increases much faster as it grows bigger.
Over the past five years, cheap computing, novel algorithms, and mountains of data have enabled new AI-based services that were previously the domain of sci-fi and academic white papers.
That way the autonomous car knows what to expect and simply has to scan the environment with its roof-mounted lasers, cameras, and radar systems to spot anything out of the ordinary.
While old-school image search looks for colors and lines, Clarifai’s AI software understands corners and parallel lines, then can master higher-level concepts like wheels or cars as it studies more and more pictures.
For voice recognition, it breaks down samples of the spoken word, analyzing them until it achieves a sophisticated grasp of the ways sounds combine to form speech.
He’s tasked with improving the social network’s speech and image recognition software to make it more efficient at identifying, say, viral videos that you’ll find funny or photos that you’ll want to see—like your friends in a group snapshot.
You might think that was the end of the story (if not the end of human history), but Kasparov realized that he could have performed better against Deep Blue if he’d had the same instant access to a massive database of all previous chess moves that Deep Blue had.
You can play as your unassisted human self, or you can act as the hand for your supersmart chess computer, merely moving its board pieces, or you can play as a “centaur,” which is the human/AI cyborg that Kasparov advocated.
If AI can help humans become better chess players, it stands to reason that it can help us become better pilots, better doctors, better judges, better teachers.
Most of the commercial work completed by AI will be done by special-purpose, narrowly focused software brains that can, for example, translate any language into any other language, but do little else.
IBM researchers overlaid Watson with a culinary database comprising online recipes, USDA nutritional facts, and flavor research on what makes compounds taste pleasant.
From this pile of data, Watson dreamed up novel dishes based on flavor profiles and patterns from existing dishes, and willing human chefs cooked them.
Over the past 60 years, as mechanical processes have replicated behaviors and talents we thought were unique to humans, we’ve had to change our minds about what sets us apart.
In the grandest irony of all, the greatest benefit of an everyday, utilitarian AI will not be increased productivity or an economics of abundance or a new way of doing science—although all those will happen.
Where VC's Will Invest in 2018: Blockchain, AI, Voice, Pets
Venture capital isn’t a monolith, but startup investors are compared to lemmings for a reason.
(To be safe, if the firm does miss a trend, its partners should privately trash talk it to anyone who will listen.) Investors continue to aggressively pour money into startups.
With the price of virtual currency bitcoin hitting new highs every other day and money raised from “initial coin offerings” for new cryptocurrency projects surpassing that of early-stage venture funding, venture investors are scrambling to develop a cryptocurrency strategy.
His firm is also seeking investment opportunities in services around the cryptocurrency ecosystem, including institutional custody for cryptocurrencies, security, app distribution, and blockchain-based distributed file storage.
She’s interested in startups using blockchains to securely store health records in a centralized database and to track copyrighted and trademarked content and licensing rights.
Other startups that could benefit from the shift include Factory OS, a modular building maker, Kasita.com, a startup making modular housing units, and Katerra, a construction tech company, says Roelof Opperman, a senior associate at Fifth Wall.
Canvas’s Lynn says her firm is looking at companies that use AI to identify the content of videos, an arena where she says the science is “not as far along as you think.” It’s been 18 months since Unilever made a splash with its $1 billion acquisition of subscription-razor company Dollar Shave Club.
“We expect industries like CPG, retail, pharmacies to get more active investing and acquiring companies to help them fend off the threat that Amazon presents,” says Anand Sanwal, CEO of data firm CB Insights.
“They are all feeling the need to reinvent themselves to stay relevant.” The rise of voice-centric devices including Amazon Echo and Google Home has sent tech companies scrambling to develop services for them.
Now that the “hard tech problems” like accuracy are mostly solved, says Canvas’s Lynn (whose partner backed Siri before it sold to Apple), startups are finding new opportunities to use voice, including advertising.
Kaplan named Purch, a startup which owns Reviews.com, as well as General Atlantic portfolio companies A Place for Mom, a referral service for senior living facilities, and Red Ventures, which owns a variety of service-focused content sites, as examples of the trend.
“We have a big ambition to roll some of these things up together and create an Apple-like company that has the aesthetic appeal to pet owners and the true scientific cutting edge with the best and brightest people, with a clear line about what is total bullshit,” he says.
- On Tuesday, January 28, 2020
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