Researchers take major step forward in Artificial Intelligence

The long-standing dream of using Artificial Intelligence (AI) to build an artificial brain has taken a significant step forward, as a team led by Professor Newton Howard from the University of Oxford has successfully prototyped a nanoscale, AI-powered, artificial brain in the form factor of a high-bandwidth neural implant.

Professor Newton Howard (pictured above and below) holding parts of the implant device

In collaboration with INTENT LTD, Qualcomm Corporation, Intel Corporation, Georgetown University and the Brain Sciences Foundation, Professor Howard’s Oxford Computational Neuroscience Lab in the Nuffield Department of Surgical Sciences has developed the proprietary algorithms and the optoelectronics required for the device. Rodents’ testing is on target to begin very soon.

This achievement caps over a decade of research by Professor Howard at MIT’s Synthetic Intelligence Lab and the University of Oxford, work that resulted in several issued US patents on the technologies and algorithms that power the device, 

• the Fundamental Code Unit of the Brain (FCU), 
• the Brain Code (BC) and the Biological Co-Processor (BCP) 

are the latest advanced foundations for any eventual merger between biological intelligence and human intelligence. Ni2o (pronounced “Nitoo”) is the entity that Professor Howard licensed to further develop, market and promote these technologies.

The Biological Co-Processor is unique in that it uses advanced nanotechnology, optogenetics and deep machine learning to intelligently map internal events, such as neural spiking activity, to external physiological, linguistic and behavioral expression. The implant contains over a million carbon nanotubes, each of which is 10,000 times smaller than the width of a human hair. Carbon nanotubes provide a natural, high-bandwidth interface as they conduct heat, light and electricity instantaneously updating the neural laces. They adhere to neuronal constructs and even promote neural growth. Qualcomm team leader Rudy Beraha commented, 'Although the prototype unit shown today is tethered to external power, a commercial Brain Co-Processor unit will be wireless and inductively powered, enabling it to be administered with a minimally-invasive procedures.'

The device uses a combination of methods to write to the brain, including 

• pulsed electricity, 
• light and 
• various molecules that simulate or inhibit the activation of specific neuronal groups. 

These can be targeted to stimulate a desired response, such as releasing chemicals in patients suffering from a neurological disorder or imbalance. The BCP is designed as a fully integrated system to use the brain’s own internal systems and chemistries to pattern and mimic healthy brain behavior, an approach that stands in stark contrast to the current state of the art, which is to simply apply mild electrocution to problematic regions of the brain. 

Therapeutic uses

The Biological Co-Processor promises to provide relief for millions of patients suffering from neurological, psychiatric and psychological disorders as well as degenerative diseases. Initial therapeutic uses will likely be for patients with traumatic brain injuries and neurodegenerative disorders, such as Alzheimer’s, as the BCP will strengthen the weak, shortening connections responsible for lost memories and skills. Once implanted, the device provides a closed-loop, self-learning platform able to both determine and administer the perfect balance of pharmaceutical, electroceutical, genomeceutical and optoceutical therapies.

Dr Richard Wirt, a Senior Fellow at Intel Corporation and Co-Founder of INTENT, the company’s partner of Ni2o bringing BCP to market, commented on the device, saying, 'In the immediate timeframe, this device will have many benefits for researchers, as it could be used to replicate an entire brain image, synchronously mapping internal and external expressions of human response. Over the long term, the potential therapeutic benefits are unlimited.'

The brain controls all organs and systems in the body, so the cure to nearly every disease resides there.- Professor Newton Howard

Rather than simply disrupting neural circuits, the machine learning systems within the BCP are designed to interpret these signals and intelligently read and write to the surrounding neurons. These capabilities could be used to reestablish any degenerative or trauma-induced damage and perhaps write these memories and skills to other, healthier areas of the brain. 

One day, these capabilities could also be used in healthy patients to radically augment human ability and proactively improve health. As Professor Howard points out: 'The brain controls all organs and systems in the body, so the cure to nearly every disease resides there.' Speaking more broadly, Professor Howard sees the merging of man with machine as our inevitable destiny, claiming it to be 'the next step on the blueprint that the author of it all built into our natural architecture.'

With the resurgence of neuroscience and AI enhancing machine learning, there has been renewed interest in brain implants. This past March, Elon Musk and Bryan Johnson independently announced that they are focusing and investing in for the brain/computer interface domain. 

When asked about these new competitors, Professor Howard said he is happy to see all these new startups and established names getting into the field - he only wonders what took them so long, stating: 'I would like to see us all working together, as we have already established a mathematical foundation and software framework to solve so many of the challenges they will be facing. We could all get there faster if we could work together - after all, the patient is the priority.'

© 2017 Nuffield Department of Surgical Sciences, John Radcliffe Hospital, Headington, Oxford, OX3 9DU

ORIGINAL: NDS Oxford

2 June 2017 

Inside Vicarious, the Secretive AI Startup Bringing Imagination to Computers

By reinventing the neural network, the company hopes to help computers make the leap from processing words and symbols to comprehending the real world.

Life would be pretty dull without imagination. In fact, maybe the biggest problem for computers is that they don’t have any.

That’s the belief motivating the founders of Vicarious, an enigmatic AI company backed by some of the most famous and successful names in Silicon Valley. Vicarious is developing a new way of processing data, inspired by the way information seems to flow through the brain. The company’s leaders say this gives computers something akin to imagination, which they hope will help make the machines a lot smarter.

Vicarious is also, essentially, betting against the current boom in AI. Companies including Google, Facebook, Amazon, and Microsoft have made stunning progress in the past few years by feeding huge quantities of data into large neural networks in a process called “deep learning.” When trained on enough examples, for instance, deep-learning systems can learn to recognize a particular face or type of animal with very high accuracy (see “10 Breakthrough Technologies 2013: Deep Learning”). But those neural networks are only very crude approximations of what’s found inside a real brain.

Illustration by Sophia Foster-Dimino

Vicarious has introduced a new kind of neural-network algorithm designed to take into account more of the features that appear in biology. An important one is the ability to picture what the information it’s learned should look like in different scenarios—a kind of artificial imagination. The company’s founders believe a fundamentally different design will be essential if machines are to demonstrate more human like intelligence. Computers will have to be able to learn from less data, and to recognize stimuli or concepts more easily.

Despite generating plenty of early excitement, Vicarious has been quiet over the past couple of years. But this year, the company says, it will publish details of its research, and it promises some eye-popping demos that will show just how useful a computer with an imagination could be.

The company’s headquarters don’t exactly seem like the epicenter of a revolution in artificial intelligence. Located in Union City, a short drive across the San Francisco Bay from Palo Alto, the offices are plain—a stone’s throw from a McDonald’s and a couple of floors up from a dentist. Inside, though, are all the trappings of a vibrant high-tech startup. A dozen or so engineers were hard at work when I visited, several using impressive treadmill desks. Microsoft Kinect 3-D sensors sat on top of some of the engineers’ desks.

D. Scott Phoenix, the company’s 33-year-old CEO, speaks in suitably grandiose terms. “We are really rapidly approaching the amount of computational power we need to be able to do some interesting things in AI,” he told me shortly after I walked through the door. “In 15 years, the fastest computer will do more operations per second than all the neurons in all the brains of all the people who are alive. So we are really close.”

Vicarious is about more than just harnessing more computer power, though. Its mathematical innovations, Phoenix says, will more faithfully mimic the information processing found in the human brain. It’s true enough that the relationship between the neural networks currently used in AI and the neurons, dendrites, and synapses found in a real brain is tenuous at best.

One of the most glaring shortcomings of artificial neural networks, Phoenix says, is that information flows only one way. “If you look at the information flow in a classic neural network, it’s a feed-forward architecture,” he says. “There are actually more feedback connections in the brain than feed-forward connections—so you’re missing more than half of the information flow.”

It’s undeniably alluring to think that imagination—a capability so fundamentally human it sounds almost mystical in a computer—could be the key to the next big advance in AI.

Vicarious has so far shown that its approach can create a visual system capable of surprisingly deft interpretation. In 2013 it showed that the system could solve any captcha (the visual puzzles that are used to prevent spam-bots from signing up for e-mail accounts and the like). As Phoenix explains it, the feedback mechanism built into Vicarious’s system allows it to imagine what a character would look like if it weren’t distorted or partly obscured (see “AI Startup Says It Has Defeated Captchas”).

Phoenix sketched out some of the details of the system at the heart of this approach on a whiteboard. But he is keeping further details quiet until a scientific paper outlining the captcha approach is published later this year.

In principle, this visual system could be put to many other practical uses, like recognizing objects on shelves more accurately or interpreting real-world scenes more intelligently. The founders of Vicarious also say that their approach extends to other, much more complex areas of intelligence, including language and logical reasoning.

Phoenix says his company may give a demo later this year involving robots. And indeed, the job listings on the company’s website include several postings for robotics experts. Currently robots are bad at picking up unfamiliar, oddly arranged, or partly obscured objects, because they have trouble recognizing what they are. “If you look at people who are picking up objects in an Amazon facility, most of the time they aren’t even looking at what they’re doing,” he explains. “And they’re imagining—using their sensory motor simulator—where the object is, and they’re imagining at what point their finger will touch it.”

While Phoenix is the company’s leader, his cofounder, Dileep George, might be considered its technical visionary. George was born in India and received a PhD in electrical engineering from Stanford University, where he turned his attention to neuroscience toward the end of his doctoral studies. In 2005 he cofounded Numenta with Jeff Hawkins, the creator of Palm Computing. But in 2010 George left to pursue his own ideas about the mathematical principles behind information processing in the brain, founding Vicarious with Phoenix the same year.

I bumped into George in the elevator when I first arrived. He is unassuming and speaks quietly, with a thick accent. But he’s also quite matter-of-fact about what seem like very grand objectives.

George explained that imagination could help computers process language by tying words, or symbols, to low-level physical representations of real-world things. In theory, such a system might automatically understand the physical properties of something like water, for example, which would make it better able to discuss the weather. “When I utter a word, you know what it means because you can simulate the concept,” he says.

This ambitious vision for the future of AI has helped Vicarious raise an impressive $72 million so far. Its list of investors also reads like a who’s who of the tech world. Early cash came from Dustin Moskovitz, ex-CTO of Facebook, and Adam D’Angelo, cofounder of Quora. Further funding came from Peter Thiel, Mark Zuckerberg, Jeff Bezos, and Elon Musk.

Many people are itching to see what Vicarious has done beyond beating captchas. “I would love it if they showed us something new this year,” says Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence in Seattle.

In contrast to the likes of Google, Facebook, or Baidu, Vicarious hasn’t published any papers or released any tools that researchers can play with. “The people [involved] are great, and the problems [they are working on] are great,” says Etzioni. “But it’s time to deliver.”

For those who’ve put their money behind Vicarious, the company’s remarkable goals should make the wait well worth it. Even if progress takes a while, the potential payoffs seem so huge that the bet makes sense, says Matt Ocko, a partner at Data Collective, a venture firm that has backed Vicarious. A better machine-learning approach could be applied in just about any industry that handles large amounts of data, he says. “Vicarious sat us down and demonstrated the most credible pathway to reasoning machines that I have ever seen.”

Ocko adds that Vicarious has demonstrated clear evidence it can commercialize what it’s working on. “We approached it with a crapload of intellectual rigor,” he says.

It will certainly be interesting to see if Vicarious can inspire this kind of confidence among other AI researchers and technologists with its papers and demos this year. If it does, then the company could quickly go from one of the hottest prospects in the Valley to one of its fastest-growing businesses.

That’s something the company’s founders would certainly like to imagine.

ORIGINAL: MIT Tech Review

by Will Knight. Senior Editor, AI

May 19, 2016

An executive’s guide to machine learning

An executive’s guide to machine learning

It’s no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix.

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning—and the need for it.

Stanford's Fei-Fei Li

In 2007 Fei-Fei Li, the head of Stanford’s Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 Last November, Li’s team unveiled a program that identifies the visual elements of any picture with a high degree of accuracy. IBM’s Watson machine relied on a similar self-generated scoring system among hundreds of potential answers to crush the world’s best Jeopardy! players in 2011.

Dazzling as such feats are, machine learning is nothing like learning in the human sense (yet). But what it already does extraordinarily well—and will get better at—is relentlessly chewing through any amount of data and every combination of variables. Because machine learning’s emergence as a mainstream management tool is relatively recent, it often raises questions. In this article, we’ve posed some that we often hear and answered them in a way we hope will be useful for any executive. Now is the time to grapple with these issues, because the competitive significance of business models turbocharged by machine learning is poised to surge. Indeed, management author Ram Charan suggests that “any organization that is not a math house now or is unable to become one soon is already a legacy company.2

1. How are traditional industries using machine learning to gather fresh business insights?

Well, let’s start with sports. This past spring, contenders for the US National Basketball Association championship relied on the analytics of Second Spectrum, a California machine-learning start-up. By digitizing the past few seasons’ games, it has created predictive models that allow a coach to distinguish between, as CEO Rajiv Maheswaran puts it, “a bad shooter who takes good shots and a good shooter who takes bad shots”—and to adjust his decisions accordingly.

You can’t get more venerable or traditional than General Electric, the only member of the original Dow Jones Industrial Average still around after 119 years. GE already makes hundreds of millions of dollars by crunching the data it collects from deep-sea oil wells or jet engines to optimize performance, anticipate breakdowns, and streamline maintenance. But Colin Parris, who joined GE Software from IBM late last year as vice president of software research, believes that continued advances in data-processing power, sensors, and predictive algorithms will soon give his company the same sharpness of insight into the individual vagaries of a jet engine that Google has into the online behavior of a 24-year-old netizen from West Hollywood.

2. What about outside North America?

In Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine-learning techniques and, in some cases, experienced 10 percent increases in sales of new products, 20 percent savings in capital expenditures, 20 percent increases in cash collections, and 20 percent declines in churn. The banks have achieved these gains by devising new recommendation engines for clients in retailing and in small and medium-sized companies. They have also built microtargeted models that more accurately forecast who will cancel service or default on their loans, and how best to intervene.

Closer to home, as a recent article in McKinsey Quarterly notes,3 our colleagues have been applying hard analytics to the soft stuff of talent management. Last fall, they tested the ability of three algorithms developed by external vendors and one built internally to forecast, solely by examining scanned résumés, which of more than 10,000 potential recruits the firm would have accepted. The predictions strongly correlated with the real-world results. Interestingly, the machines accepted a slightly higher percentage of female candidates, which holds promise for using analytics to unlock a more diverse range of profiles and counter hidden human bias.

As ever more of the analog world gets digitized, our ability to learn from data by developing and testing algorithms will only become more important for what are now seen as traditional businesses. Google chief economist Hal Varian calls this “computer kaizen.” For “just as mass production changed the way products were assembled and continuous improvement changed how manufacturing was done,” he says, “so continuous [and often automatic] experimentation will improve the way we optimize business processes in our organizations.”4

3. What were the early foundations of machine learning?

Machine learning is based on a number of earlier building blocks, starting with classical statistics. Statistical inference does form an important foundation for the current implementations of artificial intelligence. But it’s important to recognize that classical statistical techniques were developed between the 18th and early 20th centuries for much smaller data sets than the ones we now have at our disposal. Machine learning is unconstrained by the preset assumptions of statistics. As a result, it can yield insights that human analysts do not see on their own and make predictions with ever-higher degrees of accuracy.

More recently, in the 1930s and 1940s, the pioneers of computing (such as Alan Turing, who had a deep and abiding interest in artificial intelligence) began formulating and tinkering with the basic techniques such as neural networks that make today’s machine learning possible. But those techniques stayed in the laboratory longer than many technologies did and, for the most part, had to await the development and infrastructure of powerful computers, in the late 1970s and early 1980s. That’s probably the starting point for the machine-learning adoption curve. New technologies introduced into modern economies—the steam engine, electricity, the electric motor, and computers, for example—seem to take about 80 years to transition from the laboratory to what you might call cultural invisibility. The computer hasn’t faded from sight just yet, but it’s likely to by 2040. And it probably won’t take much longer for machine learning to recede into the background.

4. What does it take to get started?

C-level executives will best exploit machine learning if they see it as a tool to craft and implement a strategic vision. But that means putting strategy first. Without strategy as a starting point, machine learning risks becoming a tool buried inside a company’s routine operations: it will provide a useful service, but its long-term value will probably be limited to an endless repetition of “cookie cutter” applications such as models for acquiring, stimulating, and retaining customers.

We find the parallels with M&A instructive. That, after all, is a means to a well-defined end. No sensible business rushes into a flurry of acquisitions or mergers and then just sits back to see what happens. Companies embarking on machine learning should make the same three commitments companies make before embracing M&A. Those commitments are,

• first, to investigate all feasible alternatives; 
• second, to pursue the strategy wholeheartedly at the C-suite level; and, 
• third, to use (or if necessary acquire) existing expertise and knowledge in the C-suite to guide the application of that strategy.

The people charged with creating the strategic vision may well be (or have been) data scientists. But as they define the problem and the desired outcome of the strategy, they will need guidance from C-level colleagues overseeing other crucial strategic initiatives. More broadly, companies must have two types of people to unleash the potential of machine learning.

• “Quants” are schooled in its language and methods. 
• “Translators” can bridge the disciplines of data, machine learning, and decision making by reframing the quants’ complex results as actionable insights that generalist managers can execute.

Access to troves of useful and reliable data is required for effective machine learning, such as Watson’s ability, in tests, to predict oncological outcomes better than physicians or Facebook’s recent success teaching computers to identify specific human faces nearly as accurately as humans do. A true data strategy starts with identifying gaps in the data, determining the time and money required to fill those gaps, and breaking down silos. Too often, departments hoard information and politicize access to it—one reason some companies have created the new role of chief data officer to pull together what’s required. Other elements include putting responsibility for generating data in the hands of frontline managers.

Start small—look for low-hanging fruit and trumpet any early success. This will help recruit grassroots support and reinforce the changes in individual behavior and the employee buy-in that ultimately determine whether an organization can apply machine learning effectively. Finally, evaluate the results in the light of clearly identified criteria for success.

5. What’s the role of top management?

Behavioral change will be critical, and one of top management’s key roles will be to influence and encourage it. Traditional managers, for example, will have to get comfortable with their own variations on A/B testing, the technique digital companies use to see what will and will not appeal to online consumers. Frontline managers, armed with insights from increasingly powerful computers, must learn to make more decisions on their own, with top management setting the overall direction and zeroing in only when exceptions surface. Democratizing the use of analytics—providing the front line with the necessary skills and setting appropriate incentives to encourage data sharing—will require time.

C-level officers should think about applied machine learning in three stages: machine learning 1.0, 2.0, and 3.0—or, as we prefer to say,

1. description, 
2. prediction, and 
3. prescription. 

They probably don’t need to worry much about the description stage, which most companies have already been through. That was all about collecting data in databases (which had to be invented for the purpose), a development that gave managers new insights into the past. OLAP—online analytical processing—is now pretty routine and well established in most large organizations.

There’s a much more urgent need to embrace the prediction stage, which is happening right now. Today’s cutting-edge technology already allows businesses not only to look at their historical data but also to predict behavior or outcomes in the future—for example, by helping credit-risk officers at banks to assess which customers are most likely to default or by enabling telcos to anticipate which customers are especially prone to “churn” in the near term (exhibit).

Exhibit

A frequent concern for the C-suite when it embarks on the prediction stage is the quality of the data. That concern often paralyzes executives. In our experience, though, the last decade’s IT investments have equipped most companies with sufficient information to obtain new insights even from incomplete, messy data sets, provided of course that those companies choose the right algorithm. Adding exotic new data sources may be of only marginal benefit compared with what can be mined from existing data warehouses. Confronting that challenge is the task of the “chief data scientist.”

Prescription—the third and most advanced stage of machine learning—is the opportunity of the future and must therefore command strong C-suite attention. It is, after all, not enough just to predict what customers are going to do; only by understanding why they are going to do it can companies encourage or deter that behavior in the future. Technically, today’s machine-learning algorithms, aided by human translators, can already do this. For example, an international bank concerned about the scale of defaults in its retail business recently identified a group of customers who had suddenly switched from using credit cards during the day to using them in the middle of the night. That pattern was accompanied by a steep decrease in their savings rate. After consulting branch managers, the bank further discovered that the people behaving in this way were also coping with some recent stressful event. As a result, all customers tagged by the algorithm as members of that microsegment were automatically given a new limit on their credit cards and offered financial advice.

The prescription stage of machine learning, ushering in a new era of man–machine collaboration, will require the biggest change in the way we work. While the machine identifies patterns, the human translator’s responsibility will be to interpret them for different microsegments and to recommend a course of action. Here the C-suite must be directly involved in the crafting and formulation of the objectives that such algorithms attempt to optimize.

6. This sounds awfully like automation replacing humans in the long run. Are we any nearer to knowing whether machines will replace managers?

It’s true that change is coming (and data are generated) so quickly that human-in-the-loop involvement in all decision making is rapidly becoming impractical. Looking three to five years out, we expect to see far higher levels of artificial intelligence, as well as the development of distributed autonomous corporations. These self-motivating, self-contained agents, formed as corporations, will be able to carry out set objectives autonomously, without any direct human supervision. Some DACs will certainly become self-programming.

One current of opinion sees distributed autonomous corporations as threatening and inimical to our culture. But by the time they fully evolve, machine learning will have become culturally invisible in the same way technological inventions of the 20th century disappeared into the background. The role of humans will be to direct and guide the algorithms as they attempt to achieve the objectives that they are given. That is one lesson of the automatic-trading algorithms which wreaked such damage during the financial crisis of 2008.

No matter what fresh insights computers unearth, only human managers can decide the essential questions, such as which critical business problems a company is really trying to solve. Just as human colleagues need regular reviews and assessments, so these “brilliant machines” and their works will also need to be regularly evaluated, refined—and, who knows, perhaps even fired or told to pursue entirely different paths—by executives with experience, judgment, and domain expertise.

The winners will be neither machines alone, nor humans alone, but the two working together effectively.

7. So in the long term there’s no need to worry?

It’s hard to be sure, but distributed autonomous corporations and machine learning should be high on the C-suite agenda. We anticipate a time when the philosophical discussion of what intelligence, artificial or otherwise, might be will end because there will be no such thing as intelligence—just processes. If distributed autonomous corporations act intelligently, perform intelligently, and respond intelligently, we will cease to debate whether high-level intelligence other than the human variety exists. In the meantime, we must all think about what we want these entities to do, the way we want them to behave, and how we are going to work with them.

About the authors

Dorian Pyle is a data expert in McKinsey’s Miami office, and Cristina San Jose is a principal in the Madrid office.

ORIGINAL: McKinsey

by Dorian Pyle and Cristina San Jose

June 2015