Fujitsu World Tour 2017: the future of Artificial Intelligence

Ravi Krishnamoorthi, Senior Vice President, Fujitsu EMEIA

Artificial intelligence and cyber security were among the topic of 2017 Fujitsu World Tour held in Brussels the 8th of June 2017.

In a world of constant digital transformation, Cyber security and Artificial intelligence are actual topics. Organizations and citizens need to learn fast, act quickly and where possible, scale rapidly. No-one can do this alone.

We had an exclusive interview with Mr. Ravi Krishnamoorthi Senior Vice President & Head of Business Consulting, at Fujitsu on the theme of Artificial intelligence.

Mr. Krishnamoorthi which are going to be, in the future, the biggest facts or events supporting the development of artificial intelligence?

The world today is in a very right moment for the emerging of artificial intelligence (AI).  In one hand, we have resources, very high skilled people and on the other, we have huge technologies upswing that is happening. We started to have new technologies, faster technologies, more optimal technologies. Where there is a need for decision support to happen, it can be in any industry, manufactory, public service, health care, transportation, you will start to see artificial intelligence everywhere. In the next 25 years, I expect to see artificial intelligence as a commodity platform. People will buy AI as they buy utilities or services today. Al will probably will become a platform where people will start using the solution that are built in AI, as a web utility and web services. For example in the field of health care now, we have doctors who are not able to meet the demand of patient for actually going and intervene. Imagine if we have AI, which is actually helping them to meet more people so they can intervene much ahead of time. Artificial intelligence is already there.

Basically artificial intelligence could give something to human development ?

Absolutely. That is one of Fujitsu principles. The human centric innovations. We need to deliver something to the human being. It is all about how we can enable technology to deliver absolute value to human being. This is what we call as human centric innovation through digital creation.

The bigger cyber-attack of some week ago is a clear signal that we need more integrated solutions. How can we avoid or limit such attacks?

From a cyber perspective, the more you come up with interventions the more people are going to become intelligent and anti-social elements are going to become more intelligent to actually attack more. This is a long journey. I do not think there is any particular way of absolutely stopping the cyber-attack ever, that is not possible. However, there is a way to minimize it. That is exactly what Fujitsu coming to play where there is a clear mandate, not just cyber security but also physical security. How can we physically stop the wrong person touching a laptop or accessing a server for example? Right from there to actually testing, helping customer on new threats that are coming in. Therefore, the idea is to provide a consulting and professional service to help customer, besides providing ongoing very high end security services to deliver to customers.

We need a European level strategy or International strategy?

I think we need a multi prompt strategy and not a single strategy, which can actually solve the problem of cyber security.  We need to have a personal strategy, community strategy, regional strategy, country strategy moving on to European strategy and international and global strategy because the threats levels are different. In the cyber world, while there are no boundaries, there are every possibility to escalate fast from bring a regional threats level to a country level and global level. I think there are already standard available like the GDPR that is probably the best instrument enable enforcement many guidelines for companies and countries. The penalties are becoming very heavy, very strict and countries are executing an implementing the GDPR quite extensively. I think this is a first step. Cyber-security is a process an ongoing evolving exercise. It is all about how fast and agile you are and how ahead off any one of these threats you are.

Fujitsu World Tour is the largest roadshow of its kind, stopping in more than 25 cities around the globe.

Optical computing for deep learning with a programmable nanophotonic processor

programmable nanophotonic processor

Researchers at MIT and elsewhere has developed a new approach to deep learning AI computing, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations

Soljačić says that many researchers over the years have made claims about optics-based computers, but that “people dramatically over-promised, and it backfired.” While many proposed uses of such photonic computers turned out not to be practical, a light-based neural-network system developed by this team “may be applicable for deep-learning for some applications,” he says.

Traditional computer architectures are not very efficient when it comes to the kinds of calculations needed for certain important neural-network tasks. Such tasks typically involve repeated multiplications of matrices, which can be very computationally intensive in conventional CPU or GPU chips.

After years of research, the MIT team has come up with a way of performing these operations optically instead. “This chip, once you tune it, can carry out matrix multiplication with, in principle, zero energy, almost instantly,” Soljačić says. “We’ve demonstrated the crucial building blocks but not yet the full system.”
By way of analogy, Soljačić points out that even an ordinary eyeglass lens carries out a complex calculation (the so-called Fourier transform) on the light waves that pass through it. The way light beams carry out computations in the new photonic chips is far more general but has a similar underlying principle. The new approach uses multiple light beams directed in such a way that their waves interact with each other, producing interference patterns that convey the result of the intended operation. The resulting device is something the researchers call a programmable nanophotonic processor.

This futuristic drawing shows programmable nanophotonic processors integrated on a printed circuit board and carrying out deep learning computing. Image: RedCube Inc., and courtesy of the researchers

Nature Photonics – Deep learning with coherent nanophotonic circuits

Abstract

Artificial neural networks are computational network models inspired by signal processing in the brain. These models have dramatically improved performance for many machine-learning tasks, including speech and image recognition. However, today’s computing hardware is inefficient at implementing neural networks, in large part because much of it was designed for von Neumann computing schemes. Significant effort has been made towards developing electronic architectures tuned to implement artificial neural networks that exhibit improved computational speed and accuracy. Here, we propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks. We experimentally demonstrate the essential part of the concept using a programmable nanophotonic processor featuring a cascaded array of 56 programmable Mach–Zehnder interferometers in a silicon photonic integrated circuit and show its utility for vowel recognition.

Better definitions and metrics around human level AI are needed

IEEE Spectrum asked AI experts about when human level AI will be achieved and as usual there was a massive range of dates from a few years to hundreds of years.

Gary Marcus talks about many decades before AI’s can process inputs and situations with the flexibility and adaptability of humans.

As usual the questions are how can sufficient flexibility and extensibility be solved. Also can the AI’s be placed with usefully broad constrained environments. Can sensors and other solutions be used to solve the adaptability issues.

We see this with the self driving cars in the wild problems and robotics in the home, hospital and factory.

Super precise (millimeter) or better global mapping can be used to help robots and AIs to usefully get around the world. General global maps might be only centimeter or meter precise. Then even more precise maps can be in the factory or across the city.

AI is already tens of billions of dollars of industry and investment. AI will reach across most of the trillion dollar IT industry within ten years.

Entire industries will be AI dominated

– Transportation
– Factories
etc…

Rodney Brooks talked about 50-100 years until we have AI with dog level intelligence and consciousness. He also thinks those AI dogs will have bad noses.

This is meaningless and pointless.

AIs could dominate the global economy without consciousness. AIs will tap into global networks of sensors and soon low orbit global satellites.

Artificial intelligence and the coming health revolution

Some the same artificial intelligence techniques used in the Google DeepMind Challenge to defeat a grandmaster in the board game Go can be adapted for medical uses — AFP

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YOUR next doctor could very well be a bot. And bots, or automated programs, are likely to play a key role in finding cures for some of the most difficult-to-treat diseases and conditions.

Artificial intelligence is rapidly moving into health care, led by some of the biggest technology companies and emerging startups using it to diagnose and respond to a raft of conditions.

Consider these examples:

— California researchers detected cardiac arrhythmia with 97 percent accuracy on wearers of an Apple Watch with the AI-based Cariogram application, opening up early treatment options to avert strokes.

— Scientists from Harvard and the University of Vermont developed a machine learning tool — a type of AI that enables computers to learn without being explicitly programmed — to better identify depression by studying Instagram posts, suggesting "new avenues for early screening and detection of mental illness."

— Researchers from Britain's University of Nottingham created an algorithm that predicted heart attacks better than doctors using conventional guidelines.

While technology has always played a role in medical care, a wave of investment from Silicon Valley and a flood of data from connected devices appear to be spurring innovation.

"I think a tipping point was when Apple released its Research Kit," said Forrester Research analyst Kate McCarthy, referring to a program letting Apple users enable data from their daily activities to be used in medical studies.

McCarthy said advances in artificial intelligence has opened up new possibilities for "personalized medicine" adapted to individual genetics.

"We now have an environment where people can weave through clinical research at a speed you could never do before," she said.

Predictive analytics

AI is better known in the tech field for uses such as autonomous driving, or defeating experts in the board game Go.

But it can also be used to glean new insights from existing data such as electronic health records and lab tests, says Narges Razavian, a professor at New York University's Langone School of Medicine who led a research project on predictive analytics for more than 100 medical conditions.

"Our work is looking at trends and trying to predict (disease) six months into the future, to be able to act before things get worse," Razavian said.

— NYU researchers analyzed medical and lab records to accurately predict the onset of dozens of diseases and conditions including type 2 diabetes, heart or kidney failure and stroke. The project developed software now used at NYU which may be deployed at other medical facilities.

— Google's DeepMind division is using artificial intelligence to help doctors analyze tissue samples to determine the likelihood that breast and other cancers will spread, and develop the best radiotherapy treatments.

— Microsoft, Intel and other tech giants are also working with researchers to sort through data with AI to better understand and treat lung, breast and other types of cancer.

— Google parent Alphabet's life sciences unit Verily has joined Apple in releasing a smartwatch for studies including one to identify patterns in the progression of Parkinson's disease. Amazon meanwhile offers medical advice through applications on its voice-activated artificial assistant Alexa.

IBM has been focusing on these issues with its Watson Health unit, which uses "cognitive computing" to help understand cancer and other diseases.

When IBM's Watson computing system won the TV game show Jeopardy in 2011, "there were a lot of folks in health care who said that is the same process doctors use when they try to understand health care," said Anil Jain, chief medical officer of Watson Health.

Systems like Watson, he said, "are able to connect all the disparate pieces of information" from medical journals and other sources "in a much more accelerated way."

"Cognitive computing may not find a cure on day one, but it can help understand people's behavior and habits" and their impact on disease, Jain said.

It's not just major tech companies moving into health.

Research firm CB Insights this year identified 106 digital health startups applying machine learning and predictive analytics "to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images."

Maryland-based startup Insilico Medicine uses so-called "deep learning" to shorten drug testing and approval times, down from the current 10 to 15 years.

"We can take 10,000 compounds and narrow that down to 10 to find the most promising ones," said Insilico's Qingsong Zhu.

Insilico is working on drugs for amyotrophic lateral sclerosis (ALS), cancer and age-related diseases, aiming to develop personalized treatments.

Finding depression

Artificial intelligence is also increasingly seen as a means for detecting depression and other mental illnesses, by spotting patterns that may not be obvious, even to professionals.

A research paper by Florida State University's Jessica Ribeiro found it can predict with 80 to 90% accuracy whether someone will attempt suicide as far off as two years into the future.

Facebook uses AI as part of a test project to prevent suicides by analyzing social network posts.

And San Francisco's Woebot Labs this month debuted on Facebook Messenger what it dubs the first chatbot offering "cognitive behavioral therapy" online — partly as a way to reach people wary of the social stigma of seeking mental health care.

New technologies are also offering hope for rare diseases.

Boston-based startup FDNA uses facial recognition technology matched against a database associated with over 8,000 rare diseases and genetic disorders, sharing data and insights with medical centers in 129 countries via its Face2Gene application.

Cautious optimism

Lynda Chin, vice chancellor and chief innovation officer at the University of Texas System, said she sees "a lot of excitement around these tools" but that technology alone is unlikely to translate into wide-scale health benefits.

One problem, Chin said, is that data from sources as disparate as medical records and Fitbits is difficult to access due to privacy and other regulations.

More important, she said, is integrating data in health care delivery where doctors may be unaware of what's available or how to use new tools.

"Just having the analytics and data get you to step one," said Chin. "It's not just about putting an app on the app store." — AFP

Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology — AFP

Elon Musk wants to link computers to our brains to prevent an existential threat to humanity

Elon Musk is a busy guy.

He's developing electric vehicles, self-driving cars, tunnel-boring infrastructure technology, and re-usable rockets (with the goal of getting humans to Mars). 

Given Musk's ambitiousness, it's not totally surprising that he is also launching a company that will look into ways to link human brains to computers. Musk reportedly plans to spend 3-5% of his work time on Neuralink, which will develop technology to integrate brains and computers as a way to fix medical problems and eventually supercharge human cognition.

Existing brain-computer interfaces, which are relatively simple compared to Musks's goals, can connect to a few hundred brain cells at a time. Those are already helping the deaf hear, the blind see, and the paralyzed move robotic arms. Once researcher are able to understanding and connect interfaces to the 100 billion neurons in the brain, these linkages could essentially give people superpowers.

That potential has clearly captured Musk's interest, but this new project also seems to stem from his concerns about super-intelligent artificial intelligence (AI).

Tim Urban of Wait But Why has a relationship with Musk that gives him unique access to insight into the tech mogul. Urban suggests Musk is betting on the possibility that melding human and artificial intelligence will make us more likely to survive the emergence of super-intelligent (and super-powerful) AI.

Urban wrote an excellent 38,000 word post about Neuralink and AI's existential threat to humanity, but he gave a short version of this idea to author Virginia Heffernan in a conversation hosted by Heleo:

"Elon is very nervous about AI, and rightly so. Intelligence gives humans this God-like power over all animals just because we're more intelligent. We're building something more intelligent than we are, that's a concern. He believes that the solution to reduce existential risk is to be able to high bandwidth interface with AI. He thinks that if we can think with AI, it allows AI to function as a third layer in our brain, where we could have AI that's built for us. So we have human intelligence and then we have artificial intelligence, and they're both us and so we become AI in a way.

That sounds kind of creepy but it makes sense if all of us are AI, there's not really anyone that can get control over all the AI in the world, monopolize it, and maybe do bad things with it because they are contending with a millions and billions of people who have access to AI. It's much safer in a weird way, even though it gives us all a lot more power. It's like you don't want one Superman on earth, but if you have a billion Supermen then everything is okay because they check and balance each other."

The threat Urban is referring to is that AI could in theory lead to intelligent machines that become exponentially "smarter" than humans. In the hands of a bad or insane actor — or in a situation in which AI were to somehow go rogue — that could pose an existential threat to humanity.

Musk isn't the only one with these fears — people like Stephen Hawking and Bill Gates have expressed similar concerns. 

We're still working on understanding the human brain. Human Connectome Project, Science, March 2012.

Connecting our own brains to the digital world, however, could allow individual humans to make use of the same sort of computing power and intelligence. In that case, there'd be many equally intelligent and powerful actors out there. Basically anyone with sufficient means and desire to harness the power of AI could do so. In that sense, Musk's venture could be seen as a sort of mass deterrence system.

Musk sees the emergence of AI as "inevitable," and has another company working to develop a safe path towards artificial intelligence. But the rise of super-intelligent AI is most likely still far away.

Brain-computer interfaces still have a ways to go, too. The relatively simple systems that already exist can send movement signals to prosthetic arms, or function as ears or eyes for people unable to see or hear. But more complex interfaces could one day allow the brain to directly connect to the cloud or to the mind of another person in a given network. Urban mentions these applications as potential far-future goals, though they're completely beyond modern technology.

In fact, we're still in the early stages of understanding the brain, whichChristof Koch, chief scientific officer of the Allen Institute for Brain Science, has described as the "most complex object in the universe."

Furthermore, even if the technology were to become available, most Americans are not enthusiastic about brain computer interfaces,according to a recent survey by Pew.

But as Musk sees it, we're closer to that future than we think.

"We already have a digital tertiary layer in a sense, in that you have your computer or your phone or your applications," Musk told Urban. "The thing that people, I think, don’t appreciate right now is that they are already a cyborg. You’re already a different creature than you would have been twenty years ago, or even ten years ago ... If you leave your phone behind, it’s like missing limb syndrome."

Musk also told Urban that he thinks healthy people will be able to start using some sort of brain-computer interface for cognitive enhancement within the next 8 to 10 years. If that happens, the minds working on understanding our own brain and trying to develop new  interfaces will grow even smarter, quicker, and more powerful.

A.I. will create more jobs that can’t be filled, not mass unemployment, Alphabet’s Eric Schmidt says

Hero Images | Getty Images

AI assistants can provide alternatives and present tradeoffs while human asset managers ultimately decide the course of action.

There are likely to be more jobs that can't be fulfilled in the age of automation, according to Alphabet's Executive Chairman Eric Schmidt,striking a contrary tone to many who've warned of large-scale unemployment as a result of artificial intelligence (AI).

Humans will need to work alongside computers in order to be more productive, Schmidt argued.

Schmidt cited a study by McKinsey released at the Viva Tech conference in Paris on Thursday, which suggested 90 percent of jobs are not fully automatable. The Alphabet chairman said that while some of the routine of a job could be replaced, much of what a human does cannot.

"So what that tells me is that your future is you with a computer, not you replaced by a computer," Schmidt told an audience during a talk at Viva Tech.

The former Google CEO said populations are getting older so the number of people working has decreased. So Schmidt said working alongside computers will be key to get those in work to be more productive.

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"We have to make them more productive through automation, through tools. So I'm convinced that there is in fact going to be a jobs shortage. There is going to be jobs that are unfulfilled, and that the way we'll fill them is to take people plus computers, and the computers will make people smarter. If you make the people smarter, their wages go up. They don't go down, and the number of jobs go up, not down, if you see my point."

"People keep saying, what happens to jobs in the era of automation? I think there will be more jobs, not fewer."

At Viva Tech, Jeff Immelt, the outgoing chief executive of General Electric, also spoke out against people predicting widespread unemployment as a result of automation, saying that the idea robots will completely run factories in five years is "bulls--t".

"There's 330,000 people that work for GE and none of them had a productive day yesterday, none of them had a completely productive day. So my own belief is that when it comes to digital tools and things like that, that first part of the revolution, is going to be to make your existing workforce productive," Immelt said during a talk at the Viva Tech conference in Paris on Thursday.

Fierce debate is raging around the impact that automation could have on jobs. Around a third of jobs in the U.K. could be affected by artificial intelligence and automation, while this figure rises to 38 percent in the U.S. by the 2030s, according to a report by accountancy firm PWC released in March.

Some technologists such as Elon Musk warned humans may have to merge somehow with machines to prevent becoming irrelevant in the age of AI. Others in Silicon Valley have suggested a universal basic income could be necessary to help cushion the blow of unemployment resulting from automation.

New role of machine learning engineers focused on creating data products, making data science work

We’ve been talking about data science and data scientists for a decade now. While there’s always been some debate over what “data scientist” means, we’ve reached the point where many universities, online academies, and bootcamps offer data science programs: master’s degrees, certifications, you name it. The world was a simpler place when we only had statistics. But simplicity isn’t always healthy, and the diversity of data science programs demonstrates nothing if not the demand for data scientists.

As the field of data science has developed, any number of poorly distinguished specialties have emerged. Companies use the terms “data scientist” and “data science team” to describe a variety of roles, including:

• individuals who carry out ad hoc analysis and reporting (including BI and business analytics)
• people who are responsible for statistical analysis and modeling, which, in many cases, involves formal experiments and tests
• machine learning modelers who increasingly develop prototypes using notebooks

And that listing doesn’t include the people DJ Patil and Jeff Hammerbacher were thinking of when they coined the term “data scientist”: the people who are building products from data. These data scientists are most similar to the machine learning modelers, except that they’re building something: they’re product-centric, rather than researchers. They typically work across large portions of data products. Whatever the role, data scientists aren’t just statisticians; they frequently have doctorates in the sciences, with a lot of practical experience working with data at scale. They are almost always strong programmers, not just specialists in R or some other statistical package. They understand data ingestion, data cleaning, prototyping, bringing prototypes to production, product design, setting up and managing data infrastructure, and much more. In practice, they turn out to be the archetypal Silicon Valley “unicorns”: rare and very hard to hire.

What’s important isn’t that we have well-defined specialties; in a thriving field, there will always be huge gray areas. What made “data science” so powerful was the realization that there was more to data than actuarial statistics, business intelligence, and data warehousing. Breaking down the silos that separated data people from the rest of the organization—software development, marketing, management, HR—is what made data science distinct. Its core concept was that data was applicable to everything. The data scientist’s mandate was to gather, and put to use, all the data. No department went untouched.

Read the source article at O’Reilly.com.