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#431165 Intel Jumps Into Brain-Like Computing ...
The brain has long inspired the design of computers and their software. Now Intel has become the latest tech company to decide that mimicking the brain’s hardware could be the next stage in the evolution of computing.
On Monday the company unveiled an experimental “neuromorphic” chip called Loihi. Neuromorphic chips are microprocessors whose architecture is configured to mimic the biological brain’s network of neurons and the connections between them called synapses.
While neural networks—the in vogue approach to artificial intelligence and machine learning—are also inspired by the brain and use layers of virtual neurons, they are still implemented on conventional silicon hardware such as CPUs and GPUs.
The main benefit of mimicking the architecture of the brain on a physical chip, say neuromorphic computing’s proponents, is energy efficiency—the human brain runs on roughly 20 watts. The “neurons” in neuromorphic chips carry out the role of both processor and memory which removes the need to shuttle data back and forth between separate units, which is how traditional chips work. Each neuron also only needs to be powered while it’s firing.
At present, most machine learning is done in data centers due to the massive energy and computing requirements. Creating chips that capture some of nature’s efficiency could allow AI to be run directly on devices like smartphones, cars, and robots.
This is exactly the kind of application Michael Mayberry, managing director of Intel’s research arm, touts in a blog post announcing Loihi. He talks about CCTV cameras that can run image recognition to identify missing persons or traffic lights that can track traffic flow to optimize timing and keep vehicles moving.
There’s still a long way to go before that happens though. According to Wired, so far Intel has only been working with prototypes, and the first full-size version of the chip won’t be built until November.
Once complete, it will feature 130,000 neurons and 130 million synaptic connections split between 128 computing cores. The device will be 1,000 times more energy-efficient than standard approaches, according to Mayberry, but more impressive are claims the chip will be capable of continuous learning.
Intel’s newly launched self-learning neuromorphic chip.
Normally deep learning works by training a neural network on giant datasets to create a model that can then be applied to new data. The Loihi chip will combine training and inference on the same chip, which will allow it to learn on the fly, constantly updating its models and adapting to changing circumstances without having to be deliberately re-trained.
A select group of universities and research institutions will be the first to get their hands on the new chip in the first half of 2018, but Mayberry said it could be years before it’s commercially available. Whether commercialization happens at all may largely depend on whether early adopters can get the hardware to solve any practically useful problems.
So far neuromorphic computing has struggled to gain traction outside the research community. IBM released a neuromorphic chip called TrueNorth in 2014, but the device has yet to showcase any commercially useful applications.
Lee Gomes summarizes the hurdles facing neuromorphic computing excellently in IEEE Spectrum. One is that deep learning can run on very simple, low-precision hardware that can be optimized to use very little power, which suggests complicated new architectures may struggle to find purchase.
It’s also not easy to transfer deep learning approaches developed on conventional chips over to neuromorphic hardware, and even Intel Labs chief scientist Narayan Srinivasa admitted to Forbes Loihi wouldn’t work well with some deep learning models.
Finally, there’s considerable competition in the quest to develop new computer architectures specialized for machine learning. GPU vendors Nvidia and AMD have pivoted to take advantage of this newfound market and companies like Google and Microsoft are developing their own in-house solutions.
Intel, for its part, isn’t putting all its eggs in one basket. Last year it bought two companies building chips for specialized machine learning—Movidius and Nervana—and this was followed up with the $15 billion purchase of self-driving car chip- and camera-maker Mobileye.
And while the jury is still out on neuromorphic computing, it makes sense for a company eager to position itself as the AI chipmaker of the future to have its fingers in as many pies as possible. There are a growing number of voices suggesting that despite its undoubted power, deep learning alone will not allow us to imbue machines with the kind of adaptable, general intelligence humans possess.
What new approaches will get us there are hard to predict, but it’s entirely possible they will only work on hardware that closely mimics the one device we already know is capable of supporting this kind of intelligence—the human brain.
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#431155 What It Will Take for Quantum Computers ...
Quantum computers could give the machine learning algorithms at the heart of modern artificial intelligence a dramatic speed up, but how far off are we? An international group of researchers has outlined the barriers that still need to be overcome.
This year has seen a surge of interest in quantum computing, driven in part by Google’s announcement that it will demonstrate “quantum supremacy” by the end of 2017. That means solving a problem beyond the capabilities of normal computers, which the company predicts will take 49 qubits—the quantum computing equivalent of bits.
As impressive as such a feat would be, the demonstration is likely to be on an esoteric problem that stacks the odds heavily in the quantum processor’s favor, and getting quantum computers to carry out practically useful calculations will take a lot more work.
But these devices hold great promise for solving problems in fields as diverse as cryptography or weather forecasting. One application people are particularly excited about is whether they could be used to supercharge the machine learning algorithms already transforming the modern world.
The potential is summarized in a recent review paper in the journal Nature written by a group of experts from the emerging field of quantum machine learning.
“Classical machine learning methods such as deep neural networks frequently have the feature that they can both recognize statistical patterns in data and produce data that possess the same statistical patterns: they recognize the patterns that they produce,” they write.
“This observation suggests the following hope. If small quantum information processors can produce statistical patterns that are computationally difficult for a classical computer to produce, then perhaps they can also recognize patterns that are equally difficult to recognize classically.”
Because of the way quantum computers work—taking advantage of strange quantum mechanical effects like entanglement and superposition—algorithms running on them should in principle be able to solve problems much faster than the best known classical algorithms, a phenomenon known as quantum speedup.
Designing these algorithms is tricky work, but the authors of the review note that there has been significant progress in recent years. They highlight multiple quantum algorithms exhibiting quantum speedup that could act as subroutines, or building blocks, for quantum machine learning programs.
We still don’t have the hardware to implement these algorithms, but according to the researchers the challenge is a technical one and clear paths to overcoming them exist. More challenging, they say, are four fundamental conceptual problems that could limit the applicability of quantum machine learning.
The first two are the input and output problems. Quantum computers, unsurprisingly, deal with quantum data, but the majority of the problems humans want to solve relate to the classical world. Translating significant amounts of classical data into the quantum systems can take so much time it can cancel out the benefits of the faster processing speeds, and the same is true of reading out the solution at the end.
The input problem could be mitigated to some extent by the development of quantum random access memory (qRAM)—the equivalent to RAM in a conventional computer used to provide the machine with quick access to its working memory. A qRAM can be configured to store classical data but allow the quantum computers to access all that information simultaneously as a superposition, which is required for a variety of quantum algorithms. But the authors note this is still a considerable engineering challenge and may not be sustainable for big data problems.
Closely related to the input/output problem is the costing problem. At present, the authors say very little is known about how many gates—or operations—a quantum machine learning algorithm will require to solve a given problem when operated on real-world devices. It’s expected that on highly complex problems they will offer considerable improvements over classical computers, but it’s not clear how big problems have to be before this becomes apparent.
Finally, whether or when these advantages kick in may be hard to prove, something the authors call the benchmarking problem. Claiming that a quantum algorithm can outperform any classical machine learning approach requires extensive testing against these other techniques that may not be feasible.
They suggest that this could be sidestepped by lowering the standards quantum machine learning algorithms are currently held to. This makes sense, as it doesn’t really matter whether an algorithm is intrinsically faster than all possible classical ones, as long as it’s faster than all the existing ones.
Another way of avoiding some of these problems is to apply these techniques directly to quantum data, the actual states generated by quantum systems and processes. The authors say this is probably the most promising near-term application for quantum machine learning and has the added benefit that any insights can be fed back into the design of better hardware.
“This would enable a virtuous cycle of innovation similar to that which occurred in classical computing, wherein each generation of processors is then leveraged to design the next-generation processors,” they conclude.
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#431110 Soft robotics: self-contained soft ...
Researchers at Columbia Engineering have solved a long-standing issue in the creation of untethered soft robots whose actions and movements can help mimic natural biological systems. A group in the Creative Machines lab led by Hod Lipson, professor of mechanical engineering, has developed a 3D-printable synthetic soft muscle, a one-of-a-kind artificial active tissue with intrinsic expansion ability that does not require an external compressor or high voltage equipment as previous muscles required. The new material has a strain density (expansion per gram) that is 15 times larger than natural muscle, and can lift 1000 times its own weight. Continue reading
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#431000 Japan’s SoftBank Is Investing Billions ...
Remember the 1980s movie Brewster’s Millions, in which a minor league baseball pitcher (played by Richard Pryor) must spend $30 million in 30 days to inherit $300 million? Pryor goes on an epic spending spree for a bigger payoff down the road.
One of the world’s biggest public companies is making that film look like a weekend in the Hamptons. Japan’s SoftBank Group, led by its indefatigable CEO Masayoshi Son, is shooting to invest $100 billion over the next five years toward what the company calls the information revolution.
The newly-created SoftBank Vision Fund, with a handful of key investors, appears ready to almost single-handedly hack the technology revolution. Announced only last year, the fund had its first major close in May with $93 billion in committed capital. The rest of the money is expected to be raised this year.
The fund is unprecedented. Data firm CB Insights notes that the SoftBank Vision Fund, if and when it hits the $100 billion mark, will equal the total amount that VC-backed companies received in all of 2016—$100.8 billion across 8,372 deals globally.
The money will go toward both billion-dollar corporations and startups, with a minimum $100 million buy-in. The focus is on core technologies like artificial intelligence, robotics and the Internet of Things.
Aside from being Japan’s richest man, Son is also a futurist who has predicted the singularity, the moment in time when machines will become smarter than humans and technology will progress exponentially. Son pegs the date as 2047. He appears to be hedging that bet in the biggest way possible.
Show Me the Money
Ostensibly a telecommunications company, SoftBank Group was founded in 1981 and started investing in internet technologies by the mid-1990s. Son infamously lost about $70 billion of his own fortune after the dot-com bubble burst around 2001. The company itself has a market cap of nearly $90 billion today, about half of where it was during the heydays of the internet boom.
The ups and downs did nothing to slake the company’s thirst for technology. It has made nine acquisitions and more than 130 investments since 1995. In 2017 alone, SoftBank has poured billions into nearly 30 companies and acquired three others. Some of those investments are being transferred to the massive SoftBank Vision Fund.
SoftBank is not going it alone with the new fund. More than half of the money—$60 billion—comes via the Middle East through Saudi Arabia’s Public Investment Fund ($45 billion) and Abu Dhabi’s Mubadala Investment Company ($15 billion). Other players at the table include Apple, Qualcomm, Sharp, Foxconn, and Oracle.
During a company conference in August, Son notes the SoftBank Vision Fund is not just about making money. “We don’t just want to be an investor just for the money game,” he says through a translator. “We want to make the information revolution. To do the information revolution, you can’t do it by yourself; you need a lot of synergy.”
Off to the Races
The fund has wasted little time creating that synergy. In July, its first official investment, not surprisingly, went to a company that specializes in artificial intelligence for robots—Brain Corp. The San Diego-based startup uses AI to turn manual machines into self-driving robots that navigate their environments autonomously. The first commercial application appears to be a really smart commercial-grade version that crosses a Roomba and Zamboni.
A second investment in July was a bit more surprising. SoftBank and its fund partners led a $200 million mega-round for Plenty, an agricultural tech company that promises to reshape farming by going vertical. Using IoT sensors and machine learning, Plenty claims its urban vertical farms can produce 350 times more vegetables than a conventional farm using 1 percent of the water.
Round Two
The spending spree continued into August.
The SoftBank Vision Fund led a $1.1 billion investment into a little-known biotechnology company called Roivant Sciences that goes dumpster diving for abandoned drugs and then creates subsidiaries around each therapy. For example, Axovant Sciences is devoted to neurology while Urovant focuses on urology. TechCrunch reports that Roivant is also creating a tech-focused subsidiary, called Datavant, that will use AI for drug discovery and other healthcare initiatives, such as designing clinical trials.
The AI angle may partly explain SoftBank’s interest in backing the biggest private placement in healthcare to date.
Also in August, SoftBank Vision Fund led a mix of $2.5 billion in primary and secondary capital investments into India’s largest private company in what was touted as the largest single investment in a private Indian company. Flipkart is an e-commerce company in the mold of Amazon.
The fund tacked on a $250 million investment round in August to Kabbage, an Atlanta-based startup in the alt-lending sector for small businesses. It ended big with a $4.4 billion investment into a co-working company called WeWork.
Betterment of Humanity
And those investments only include companies that SoftBank Vision Fund has backed directly.
SoftBank the company will offer—or has already turned over—previous investments to the Vision Fund in more than a half-dozen companies. Those assets include its shares in Nvidia, which produces chips for AI applications, and its first serious foray into autonomous driving with Nauto, a California startup that uses AI and high-tech cameras to retrofit vehicles to improve driving safety. The more miles the AI logs, the more it learns about safe and unsafe driving behaviors.
Other recent acquisitions, such as Boston Dynamics, a well-known US robotics company owned briefly by Google’s parent company Alphabet, will remain under the SoftBank Group umbrella for now.
This spending spree begs the question: What is the overall vision behind the SoftBank’s relentless pursuit of technology companies? A spokesperson for SoftBank told Singularity Hub that the “common thread among all of these companies is that they are creating the foundational platforms for the next stage of the information revolution.All of the companies, he adds, share SoftBank’s criteria of working toward “the betterment of humanity.”
While the SoftBank portfolio is diverse, from agtech to fintech to biotech, it’s obvious that SoftBank is betting on technologies that will connect the world in new and amazing ways. For instance, it wrote a $1 billion check last year in support of OneWeb, which aims to launch 900 satellites to bring internet to everyone on the planet. (It will also be turned over to the SoftBank Vision Fund.)
SoftBank also led a half-billion equity investment round earlier this year in a UK company called Improbable, which employs cloud-based distributed computing to create virtual worlds for gaming. The next step for the company is massive simulations of the real world that supports simultaneous users who can experience the same environment together(and another candidate for the SoftBank Vision Fund.)
Even something as seemingly low-tech as WeWork, which provides a desk or office in locations around the world, points toward a more connected planet.
In the end, the singularity is about bringing humanity together through technology. No one said it would be easy—or cheap.
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