Category Archives: Artificial Intelligence

At a time when all sorts of technology companies are getting accolades for their artificial intelligence prowess, International Business Machines Corp. (IBM) is apparently struggling, leading Wall Street investment firm Jefferies to lower its price target on the stock.

Citing checks that show a slow AI adoption rate, Jefferies analyst James Kisner cut his price target on Big Blue to $125 from $135 a share, implying the stock could fall more than 18%. In a research note to clients, the analyst called IBM outgunned in the war for AI talent and argued that it's a problem that will only get worse. (See also: The Other Side of IBM's Watson AI Solution.)

Our checks suggest that IBMs Watson platform remains one of the most complete cognitive platforms available in the marketplace today. However, many new engagements require significant consulting work to gather and curate data, making some organizations balk at engaging with IBM, wrote the analyst in the research report covered by 24/7 Wall Street.

Whats more, the analyst said that with a lot of companies making significant investments in AI and a slew of startups splashing on the scene, IBM is having a hard time luring top talent to the company. Kisner poured over job listings and found that Amazon.com Inc. (AMZN) has 10 times more for AI professionals than IBM. It doesnt help that businesses have lots of AI options, which is why the company reduced the pricing for Watson Conversations by 70% last October, the analyst argued. (See also: How Much Money Would You Have if You Followed Buffett into IBM?)

While Jefferies thinks IBM is behind when it comes to AI, that doesnt mean the company hasnt been making strides to grow that side of the business. In March it announced a strategic deal with Salesforce.com (CRM) to jointly provide AI services and data analytics offerings that help businesses make faster and smarter decisions. Watson is a cognitive system capable of learning from earlier interactions, garnering knowledge and value over time, and thinking like a human. It works by combining AI and advanced analytical software for analysis of various forms of data, thereby providing optimal responses based on reasoning and interacting like a question-answering machine.

Salesforce Einstein is the core AI technology that powers the Salesforce CRM platform by using data mining and machine learning algorithms. It aims to proactively spot trends across sales, services and marketing systems. The system is designed to forecast behavior that could spot up-sale prospects and opportunities, or identify crisis situations in advance. Under the deal, IBMs Watson and Salesforces Einstein will be integrated to offer intelligent customer engagement across various functions like sales, service, marketing and e-commerce.

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IBM Lags in Artificial Intelligence: Jefferies | Investopedia - Investopedia

Nothing brings a smile to the face of Sabine Toulson co-founder in 1995 of Intelligent Financial Systems faster than the notion that AI and its associated technologies are something new.

Both Sabine and husband Darren were graduates of UCLs Artificial Intelligence Lab alongside other veteran entrepreneurs such as Jason Kingdon, who founded UCL spinout Searchspace, which was famous at the time for the quality of its anti-money laundering software.

Searchspace has been using machine learning techniques for years to combat money laundering, employing tools that compared millions of transactions and distinguished between legitimate and fraudulent transactions between buyers and sellers.

Like Searchspace, Intelligent Financial Systems (IFS) succeeded early in cracking the difficult US financial software market. Back in 2000, the company won a contract to study and analyse the enormous volumes of data emerging daily from the Chicago Board of Trade. It was an exceptional feat, and not just because the board had given the contract to a non-US company. The episode reflects the very strong US interest both then and now in the future of the UKs AI sector.

IFS the subject of many a takeover offer continues to produce trading software for the London Stock Exchange, big Japanese banks and Euronext-LIFFE, among others.

That early handful of AI wizards has grown and in the past few years especially after Google and Twitter bought some very young UK AI companies for huge sums interest in AI applications among a new generation exploded.

At the same time, big improvements in computing power have accelerated a revolution in AI with Alphabet, Amazon, Apple, Facebook and Microsoft all invested heavily. Much of the popular, if febrile, debate has concentrated on whether AI and their Earthly agents, robots will do us out of jobs and, ultimately, dominate us.

In practice, few realise how ubiquitous AI has already become among SMEs. By 2017 one index of SMEs found that no fewer than 192 UK companies claimed to be adopting some form of what they defined as AI or machine learning into their operations spanning IT, medicine, biotech, the professions, security, and games.

These firms range from newcomers such as advertising decision-maker Adbrain to smart tracking micro firm Armadale Technologies, developing an Intelligent Video Surveillance (IVS) system aimed at analysing and predicting human behaviour. These companies employ word or visual matching, pattern recognition and cluster mapping techniques of pure machine learning.

In 2010 Assessment21 used AI to mark exam papers electronically. The software was originally written to help Manchester University cut the costs of setting, administering and marking traditional paper exams. Assessment21 tests students online and is apparently capable of assessing a variety of question types.

Academic software to auto-mark multiple-choice questionnaires is now standard. But Assess By Computer, Assessment21s product, can mark complex, open-ended questions that test students understanding not just their memory. The software picks up on key words in students answers and allows them to be evaluated against a model answer. It can highlight answers that are similar, and be used as an anti-plagiarism tool.

Dr David Alexander Smith, meanwhile, is the key man at Matchdeck a rival to Experian that offers an introductory service to 16 million companies, fitting buyers to sellers. The firm crunches records using data models and matching algorithms, employing something it calls an AI web extraction engine and a semantic big-linked data platform.

But what exactly is AI in this context? Its a big topic with lots of related subjects and theres plenty of hype right now. Ian Page, a former Oxford academic, entrepreneur, and now director of Seven Spires Investments, reckons on many approaches to creating AI. This allows many Brit tech and engineering SMEs to coalesce under the broader AI umbrella.

The one that is the hottest news right now is based on a much-simplified model of how individual brain cells (neurons) might connect together and process information. These Neural Nets have been around for decades but it is only with recent reductions in the cost of powerful computers that researchers have been able to build much more complex neural nets, the so-called Deep Neural Nets, and to find ways of training those DNNs on vast amounts of data, he notes.

The result is software that is able to learn, or update itself through the activity of searching and discovering patterns, connections and linkages in large volumes of data pinpointing the sort of lateral thinking that we used to believe only the human brain was capable of achieving.

In the 1990s, Pages research group implemented AI algorithms of different types: neural networks, simulated annealing, genetic/evolutionary algorithms, cellular automata, and even a singing synthesiser.

But, in his view, computers and AI software will still have a hard time competing in real world functions with the human brain. It cant be irrelevant that the human/mammalian brain has lots of diverse physical structure, Page said.

Whatever the human brain is doing, it definitely is not doing it within a single architectural paradigm. So, if nature and evolution couldnt do it (general intelligence that is) within a single network of neurons, however big, then it seems odds on favourite that AI researchers wont be able to crack that problem either within the framework of only DNNs.

Neural networks today typically have a few thousand to a few million units and millions of connections. Hilariously, their computing power is similar to the brain of a worm and several orders of magnitude simpler than a human brain.

Perhaps the most interesting fact is the way ordinary UK companies those outside the Silicon Roundabout bubble and beyond the blinkers of those focussed on digital personal assistants like Siri have forged products, processes and markets across the widest range of applications.

IntelliMon part of STS Defence this year introduced a satellite-linked monitoring technology that can monitor the biggest marine diesel engines on the high seas and transmit a simple health score to a vessels operator thousands of miles away. The system employs a combination of sensors to capture vast amounts of data and machine learning.

Being able to predict when a supertanker, container vessel or cruise ship needs to be brought into port for engine maintenance can avoid breakdowns at sea, saving six-figure sums for shipping owners and management companies.

The innovation lies primarily in the algorithms devised by the Institute of Industrial Research at the University of Portsmouth. They analyse vibration readings by extracting key engine performance indicators that can be translated into basic, byte-sized health score information. These can then be sent back to shore via satellite link or, potentially, even using the vessels own automatic ID transponder.

David Garrity, STS Defence chief scientist, said: We began work with 450 tests of different faults created on a purpose-built diesel engine test rig [we] developed which operated at constant speed bands, mimicking engines on ships. Other potential applications lie in off-road vehicles, whether battle tanks or earth movers, and remote diesel generators in oil and gas installations.

Earlier, in October 2016, it had designed an electronic personal protection system designed to detect and predict the rapid rise in temperature that precedes a flashover incident for the emergency services. Thermal sensors use artificial intelligence to analyse the rapidly changing temperatures in a smoke-filled contained-fire environment where firefighters frequently operate. Its warnings give fire fighters vital time to flee.

Rainbird Technologies has won an enviable contract with financial services giant Mastercard. The payments giant will use its smarts to power an automated, virtual sales assistant. Rainbird claims to offer a cognitive reasoning platform, something that uses Machine Learning and lots of relevant data to make recommendations. With Mastercard, Rainbird will use the experience gleaned from the entire sales team and the thousands of customer conversations, to help predict which calls might convert to sales.

The UK AI ventures and projects are as strong as they were more than 25 years ago when Sabine got off that plane from Chicago with a contract in her pocket.

We'll be covering machine learning, AI and analytics and ethics at MCubed London in October. Full details, including early bird tickets, right here.

Originally posted here:

'Many' ways to create artificial intelligence. Just ask the UK's AI businesses - The Register

For most of my adult life, I have been maniacally focused on my work. I would answer emails instantly during the day, and even get up twice each night to ensure that all the emails were answered. Yes, I would spend time with my family membersbut just so they didnt complain, and not an hour more.

Then in September 2013, I was diagnosed with fourth-stage lymphoma. I faced the real possibility that my remaining time on Earth would be measured in months. As terrifying as that was, one of my strongest feelings was an instant, irretrievable, and painful regret. As Bronnie Wares book about regrets of people on their deathbeds all too accurately describes, I was wracked with remorse over not spending more time sharing love with the people I cared about most.

Kai-Fu Lee , Ph.D., is the Founder and CEO of Sinovation Ventures and the president of its Artificial Intelligence Institute.

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I am now in remission, so I can write this piece. I am spending much more time with my family. I moved closer to my mother. Whether on business or for pleasure, I travel with my wife. Formerly, when my grown kids came home, I would take two or three days off from work to see them. Now I take two or three weeks. I spend weekends traveling with my best friends. I took my company on a one-week vacation to Silicon Valleytheir Mecca. I meet with young people who send me questions on Facebook. I have reached out to people I offended years ago and asked for their forgiveness and friendship.

This near-death experience has not only changed my life and priorities, but also altered my view of artificial intelligencethe field that captured my selfish attention for all those years. This personal reformation gave me an enlightened view of what AI should mean for humanity. Many of the recent discussions about AI have concluded that this scientific advance will likely take over the world, dominate humans, and end poorly for mankind. But my near-death experience has enabled me to envision an alternate ending to the AI storyone that makes the most of this amazing technology while empowering humans not just to survive, but to thrive.

My catharsis came at a point when we were losing perspective on AI. For much of my career, the great accomplishments of this scientific pursuit always seemed to be five years away. But recently they have been cascading one after another, most strikingly with AlphaGos victory in 2016. There is a feeling that HAL, the stubborn and deadly computer in 2001: A Space Odyssey , is looming at the gates, and a form of near-panic has set in. We are bombarded with dire predictions by a number of self-appointed futurists about superintelligence, singularity, cyborgs, and the unprovable claim that we live in a video game. These dystopian warnings are infectious, because they come from famous peopleand perhaps because they are reinforced by the familiar plots of science fiction.

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As someone who has worked on AI for 37 years, I assure you that there exists no engineering basis for these outlandish predictions. Science fiction is all fiction, and very little science, and it would be catastrophic for mankind to capitulate to these imaginative but irresponsible predictions.

Whats more, the real AI story is itself as fascinating as any noveland indeed, it has its dark side. The excitement behind AI today is largely due to a 2010 invention called deep learning, which uses massive amounts of data to optimize decision engines with superhuman accuracy. Given a massive amount of data in a particular domain, deep learning can be used to optimize single objective functions, such as win Go, minimize default rate, or maximize speech recognition accuracy.

The results have been spectacular. Armed with deep learning and other machine-learning technologies, AI has proven capable of matching or surpassing some of the most impressive human feats of intelligence. It has vanquished human world champions in Go and poker, and is already superior than the average person in recognizing faces, videos, or words from speech. Critical mobile and internet applications, such as search ranking, e-commerce recommendation, and speech agents like Siri and Alexa, arent even imaginable without AI.

Naturally, businesses are using AI to automate tasks that humans used to perform. These include chatbots for customer service, loan officers for approving loans, and security guards for checking IDs. For example, my team invested in a company called Smart Finance, which built an app that uses an AI as a loan officer. Initially, this company lost money due to a high rate of bad loansbut the AI learning kicked in, and with enough data accumulated, the bad loan rate dropped dramatically. It can now make a loan decision in seconds, with higher accuracy than a loan officer who takes hours. And it is infinitely scalable: This company will underwrite about 30 million loans this year, more than any bank that I know of. All of this happened in under two years.

David Paul Morris/Bloomberg

This is clearly threatening news for loan officers. The core functions of other jobssuch as tellers, tele-salespeople, paralegals, reporters, stock traders, research analysts, and radiologistswill gradually be replaced by AI software. And as robotics evolve, including semi-autonomous and autonomous hardware, AI will perform the labor of factory workers, construction workers, drivers, delivery people, and many others.

The AI revolution is on the scale of the Industrial Revolutionprobably larger and definitely faster. But while robots may take over jobs, believe me when I tell you there is no danger that they will take over . These AIs run narrow applications that master a single domain each time, but remain strictly under human control. The necessary ingredient of dystopia is General AIAI that by itself learns common sense reasoning, creativity, and planning, and that has self-awareness, feelings, and desires. This is the stuff of the singularity that the Cassandras predict. But General AI isnt here. There are simply no known engineering algorithms for it. And I dont expect to see them any time soon. The singularity hypothesis extrapolates exponential growth from the recent boom, but ignores the fact that continued exponential growth requires scientific breakthroughs that are unlikely to be solved for a hundred years, if ever.

woolzian/iStock

So based on these engineering realities, instead of discussing this fictional super-intelligence, we should focus on the very real narrow AI applications and extensions. These will proliferate quickly, leading to massive value creation and an Age of Plenty, because AI will produce fortunes, make strides to eradicate poverty and hunger, and give all of us more spare time and freedom to do what we love. But it will also usher in an Age of Confusion. As an Oxford study postulates, AI will replace half of human jobs, and many people will become depressed as they lose their jobs and the purpose that comes with gainful employment.

It is imperative that we focus on the certainty of these serious issues, rather than talking about dystopia, singularity, or super-intelligence. Perhaps the most vexing question is: How do we create enough jobs to place these displaced workers? The answer to this question will determine whether the alternate ending to the AI story will be happy or tragic.

One suggested solution is to try to move people to jobs that are a step or two ahead of what machines can do. The idea would be to transition people to jobs that require higher dexterity (e.g., retrain an assembly line worker to be a plumber), hidden talent (e.g., encourage an accountant to pursue her dream of becoming a comedian), or new skills (e.g., train a cooling expert for a giant AI data center). Of course we should try this, but these numbers would be infinitesimal compared to the number of jobs displaced. And it is only the rarest accountant who can kill it at the Comedy Cellar.

There are other optimists who try to hand-wave the problem away by saying that new jobs have been created with every technological revolution, so we should have faith. These modern Panglosses often cite the Industrial Revolution, the office revolution (typewriters, calculators, mimeograph machines, etc.), and the computer revolution as examples. As a well-known 2013 Oxford study by Carl Frey and Michael Osborne has shown, each of the previous revolutions created some jobs (such as assembly line workers) even as they destroyed others (trained hand-craftsmen). But in the upcoming AI revolution, when AI replaces humans for a task it often does so completely, without creating new jobs or tasks. So, we cannot expect AI to solve our employment problem. We must solve it for ourselves.

The answer I propose would never have come to me when I was myself somewhat of an automaton, living to work rather than the other way around. It was only my cancer diagnosis, and the sudden realization of what my own stupidity had made me miss, that led me to my suggestion. Our coexistence with artificial intelligence hinges on combining what is humanly unattainablethe hugely scaled narrow AI intelligence that will only get better at any given domainwith what we humans can uniquely offer to one another. And that is love. What makes us human is that we can love.

We are far from understanding the human heart, let alone replicating it. But we do know that humans are uniquely able to love and be loved. The moment when we see our newborn babies; the feeling of love at first sight; the warm feeling from friends who listen to us empathetically; the feeling of self-actualization when we help someone in need. Loving and being loved are what makes our lives worthwhile.

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Love is what will always differentiate us from AI. Narrow AI has no self awareness, emotions, or a heart. Narrow AI has no sense of beauty, fun, or humor. It doesnt even have feelings or self-consciousness. Can you imagine the ecstasy that comes from beating a world champion? AlphaGo bested the globes best player, but took no pleasure in the game, felt no happiness from winning, and had no desire to hug a loved one after its victory.

Despite what science fiction movies may portray, I can tell you responsibly that AI programs cannot love. Scarlett Johansson may have been able to convince you otherwisebecause she is an actress who drew on her knowledge of love.

Imagine a situation in which you informed a smart machine that you were going to pull its plug, and then changed your mind and gave it a reprieve. The machine would not change its outlook on life or vow to spend more time with its fellow machines. It would not grow, as I did, or serve others more generously.

Love is what is missing from machines. Thats why we must pair up with them, to leaven their powers with what only we humans can provide. Your future AI diagnostic tool may well be 10 times more accurate than human doctors, but patients will not want a cold pronouncement from the tool: You have fourth stage lymphoma and a 70 percent likelihood of dying within five years. That in itself would be harmful. Patients would benefit, in health and heart, from a doctor of love who will spend as much time as the patient needs, always be available to discuss their case, and who will even visit the patients at home. This doctor might encourage us by sharing stories such as, Kai-Fu had the same lymphoma, and he survived, so you can too. This kind of doctor of love would not only make us feel better and give us greater confidence, but would also trigger a placebo effect that would increase our likelihood of recuperation. Meanwhile, the AI tool would watch the Q&A between the doctor of love and the patient carefully, and then optimize the treatment. If scaled across the world, the number of doctors of love would greatly outnumber todays doctors.

The same idea could apply to lawyers, teachers, accountants, and wedding planners. In innumerable instances, excellent AI tools may emerge, but the human-to-human interface is critical to ensuring we feel listened to and cared for when we encounter important life events. We should encourage more people to go into service careers, choosing the ones into which they can pour their hearts and souls, spreading their love and experienceswhether as a passionate tour guide, an attentive concierge, a funny bartender, an infectious hair dresser, or an innovative sushi chef.

We should also work hard to invent new service jobs that deliver joy and love. Imagine a nutritional chef who comes to your home to cook only with fresh, organic, local ingredients. Or perhaps the season changer who changes and redecorates your closets seasonally, with flowers and aromas that make changing clothes a fun experience. Or perhaps an elderly companion who takes your aging parents to see a "doctor of love" when you cannot.

There will also be a big demand for social workers who answer the hotlines for displaced workers, dealing with their depression and anxiety. Volunteering service jobs today may turn into real jobs of the futurethat of assisting at a blood bank, teaching at an orphanage, mentoring at Scouts organizations, or being a sponsor at AA or the Veterans Recruitment Appointment. Each of these jobs will deliver love and empathyand there will be so many that we can replace many, if not all, of that 50 percent loss that comes from automation. Most importantly, the people filling these new jobs will fill our planet with love and joy.

So, this is the alternate ending to the narrative of AI dystopia. An ending in which AI performs the bulk of repetitive jobs, but the gap is filled by opportunities that require our humanity.

Can I guarantee that scientists in the future will never make the breakthroughs that will lead to the kind of general-intelligence computer capabilities that might truly threaten us? Not absolutely. But I think that the real danger is not that such a scenario will happen, but that we wont embrace the option to double down on humanity while also using AI to improve our lives. This decision is ultimately up to us: Whatever we choose may become a self-fulfilling prophecy. If we choose a world in which we are fully replaceable by machines, whether it happens or not, we are surrendering our humanity and our pursuit for meaning. If everyone capitulates, our humanity will come to an end.

Such a capitulation is not only premature and unproven, but also irresponsible to our legacy, our ancestors, and our maker. On the other hand, if we choose to pursue our humanity, and even if the improbable happen and machines truly replace us, we can then capitulate knowing that we did the responsible thing, and that we had fun doing it. We will have no regrets over how we lived.

I do not think the day will ever comeunless we foolishly make it happen ourselves. Let us choose to let machines be machines, and let humans be humans. Let us choose to use our machines, and love one another.

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A Blueprint for Coexistence with Artificial Intelligence - WIRED

Artificial Intelligence or AI can create dynamic content. Lets apply best use cases to our work as storytellers.

At this years Wimbledon Tennis Tournament, for example, IBMs artificial intelligence platform, Watson, had a major editorial role -- analyzing and curating the best moments and data points from the matches, producing Cognitive Highlight videos, tagging relevant players and themes, and sharing the content with Wimbledons global fans.

Intel just announced a collaboration with the International Olympic Committee (IOC) that will bring VR, 360 replay technology, drones and AI to future Olympic experiences. In a recent press release Intel notes, The power to choose what they want to see and how they want to experience the Olympic Games will be in the hands of the fans.

In the context of development, future technology will change the way we interact with global communities. Researchers at Microsoft are experimenting with a new class of machine-learning software and tools to embed AI onto tiny intelligent devices. These edge devices dont depend on internet connectivity, reduce bandwidth constraints and computational complexity, and limit memory requirements yet maintain accuracy, speed, and security all of which can have a profound effect on the development landscape. Specific projects focus on small farmers in poor and developing countries, and on precision wind measurement and prediction.

Microsofts technology could help push the smarts to small cheap devices that can function in rural communities and places that are not connected to the cloud. These innovations could also make the Internet of Things devices cheaper, making it easier to deploy them in developing countries, according to a leading Microsoft researcher.

But the fact is, the non-western setting is currently the greatest challenge for AR/VR platforms. Wil Monte, founder and Director of Millipede, one of our SecondMuse collaborators says currently VR/AR platforms are completely hardware reliant, and being a new technology, often require a specification level that is cost-prohibitive to many.

Monte says labs like Microsoft pushing the processing capability of machine learning, while crunching the hardware requirements will mean that the implementation of the technologies will soon be much more feasible in a non-western or developing setting. He says development agencies should be empowered to push, optimise and democratise the technology so it has as many use cases as possible, therefore enabling storytellers to deploy much needed content to more people, in different settings.

"From our experience in Tonga, I learned that while the delivery of content via AR/VR is especially compelling, the infrastructure restraints means that we need to 'hack' the normal deployment and distribution strategies to enable the tech to have the furthest reach. With Millipede's lens applied, this would be immersive and game-based storytelling content, initially delivered on touch devices but also reinforced through a physical board or card game to enable as much participation in the story as possible, Monte says.

According to Ali Khoshgozaran, Co-founder and CEO of Tilofy, an AI-powered trend forecasting company based in Los Angeles, content creation is one of the most exciting segments where technology can work hand in hand with human creativity to apply more data-driven, factual and interactive context to a story. For example, at Tilofy, they automatically generate insights and context behind all their machine generated trend forecasts. When it comes to accessing knowledge and information, issues of digital divide, low literacy, low internet penetration rate and poor connectivity still affect hundreds of millions of people living in rural and underdeveloped communities all around the world, Khoshgozaran says.

This presents another great opportunity for technology to bridge the gap and bring the world closer. Microsoft use of AI in Skypes real-time translator service has allowed people from the furthest corners of the world to connect -- even without understanding each others native language -- using a cellphone or a landline. Similarly, Googles widely popular translate service has opened a wealth of content originally created in one language to many others. Due to its constant improvements in quality and number of languages covered, Google Translate might soon enhance or replace human-centric efforts like project Lingua by auto translating trending news at scale, Khoshgozaran says.

Furthermore, technologies like the Google Tango and Apple ARKit can provide new opportunities says Ali Fardinpour, Research Scientist in Learning and Assessment via Augmented/Virtual Reality at CingleVue International in Australia. The opportunity to bring iconic characters out of the literature and history and bring them to every kid's mobile phone or tablet and educate them on important issues and matters in life can be one of the benefits of Augmented Reality Storytelling.

Fardinpour says this kind of technology can substitute for the lack of mainstream media coverage or misleading coverage to educate kids and even adults on the current development projects, I am sure there are a lot of amazing young storytellers who would love the opportunity to create their own stories to tell to inspire their communities. And this is where AR/AI can play an important role.

A profound view of the future of storytellers comes from Tash Tan, Co-Founder of Sydney based Digital Company S1T2. Tan is leading one of our immersive storytelling projects in the South Pacific called LAUNCH Legends aimed at addressing issues of healthy eating and nutrition through the use of emerging, interactive technologies. As storytellers it is important to consider that perhaps we are one step closer to creating a truly dynamic story arch with Artificial intelligence. This means that stories won't be predetermined or pre-authored, or curated but instead they will be emerging and dynamically generated with every action or consequence, Tan says, If we can create a world that is intimate enough and subsequently immersive enough we can perhaps teach children through the best protagonist of all -- themselves.

A version of this story first appeared on the United Nations System Staff College blog earlier today.

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How Artificial Intelligence Is Changing Storytelling - HuffPost

It was billed as a battle of human intelligence versus artificial intelligence, man versus machine.

The machine won.

Just over a month ago, a Google computer program named AlphaGo competed against 19-year-old Chinese prodigy Ke Jie, the top-ranked player of what is believed to be the world's most sophisticated board game, Go. (According to Wikipedia, the number of possible moves in Go--a number estimated to be greater than the total count of atoms in the visible universe--vastly outweighs those in chess.)

Soon after losing the decisive second match in a series of three, Ke blamed his loss on the very element that separated him from his foe:

His emotions.

"I was very excited. I could feel my heart bumping," Ke told The New York Times in an interview. "Maybe because I was too excited I made some stupid moves.... Maybe that's the weakest part of human beings."

But this was just the beginning.

Fast forward one month later.

With some time to reflect, Ke Jie said the following in an interview (which was shared on Twitter by Demis Hassabis, founder and CEO of DeepMind, the company that developed AlphaGo):

"After my match against AlphaGo, I fundamentally reconsidered the game, and now I can see that this reflection has helped me greatly. I hope all Go players can contemplate AlphaGo's understanding of the game and style of thinking, all of which is deeply meaningful. Although I lost, I discovered that the possibilities of Go are immense and that the game has continued to progress. I hope that I too can continue to progress, that my golden era will persevere for a few more years, and that I will keep growing stronger."

Absolutely brilliant.

In a few short sentences, Ke demonstrated that what he felt was a weakness--the impact of emotion--was actually his greatest strength.

It's the hurt from losing that caused Ke to engage in self-reflection, caused him to find meaning in his loss. It's emotion that inspired him to pursue growth and progress.

I see this as a remarkable example of emotional intelligence (EI), the ability to make emotions work for you instead of against you. EI is about much more than identifying our natural abilities, tendencies, strengths, and weaknesses. It involves learning to understand, manage, and maximize all of those traits, so that you can:

When we develop emotional intelligence, failure isn't bad. It's just another learning opportunity. It's about cultivating a mindset of continuous growth, continuing the journey of self-improvement.

These are also very "human" elements.

I guess the machines didn't win after all.

See the original post:

Google's Artificial Intelligence Destroyed the World's Best Go Player. Then He Gave This Extraordinary Response - Inc.com

By Shari Trewin, IBM T.J. Watson Research Center and Chair, Association for Computing Machinery Special Interest Group on Accessible Computing (SIGACCESS)

Are you looking forward to a future filled with smart cognitive systems? Does artificial intelligence sound too much like Big Brother? For many of us, these technologies promise more freedom, not less.

One of the distinctive features of cognitive systems is the ability to engage with us, and the world, in more human-like ways. Through advances in machine learning, cognitive systems are rapidly improving their ability to see, to hear, and to interact with humans using natural language and gesture. In the process, they also become more able to support people with disabilities and the growing aging population.

The World Health Organization estimates that 15 percent of the global population lives with some form of disability. By 2050, people aged 60 and older will account for 22 percent of the world's population, with age-related impairments likely to increase as a result.

I'm cautiously optimistic that by the time I need it, my car will be a trusted independent driver. Imagine the difference it will make for those who cannot drive to be able to accept any invitation, or any job offer, without being dependent on having a person or public transport to get them there. . Researchers and companies are also developing cognitive technologies for accessible public transportation. For example, IBM, the CTA (Consumer Technology Association) Foundation, and Local Motors are exploring applications of Watson technologies to developing the world's most accessible self-driving vehicle, able to adapt its communication and personalize the overall experience to suit each passengers unique needs. Such a vehicle could use sign language with deaf people; describe its location and surroundings to blind passengers; recognize and automatically adjust access and seating for those with mobility impairments; and ensure all passengers know where to disembark.

The ability to learn and generalize from examples is another important feature of cognitive technologies. For example, in my smart home, sensors backed by cognitive systems that can interpret their data will learn my normal activity and recognize falls or proactively alert my family or caregivers before a situation becomes an emergency, enabling me to live independently in my own home more safely. My stove will turn itself on when I put a pot on, and I'll tell it "cook this pasta al dente," then go off for a nap, knowing it will turn itself off and has learned the best way to wake me.

All of this may sound futuristic, but in the subfield of computer science known as accessibility research, machine learning and other artificial intelligence techniques are already being applied to tackle obstacles faced by people with disabilities and to support independent aging. For example, people with visual impairments are working with researchers on machine learning applications that will help them navigate efficiently through busy and complex environments, and even to run marathons. Cognitive technologies are being trained to recognize interesting sounds and provide alerts for those with hearing loss; to recognize items of interest in Google Street View images, such as curb cuts and bus stops; to recognize and produce sign language; and to generate text summaries of data, tailored to a specific reading level.

One of the most exciting areas is image analysis. Cognitive systems are learning to describe images for people with visual impairment. Currently, making images accessible to the visually impaired requires a sighted person to write a description of the image that can then be read aloud by a computer to people who can't see the original image. Despite well-established guidelines from the World Wide Web Consortium (W3C), and legislation in many countries requiring alternative text descriptions for online images, they are still missing in many websites. Cognitive technology for image interpretation may, at last, offer a solution. Facebook is already rolling out an automatic description feature for images uploaded to its social network. It uses cognitive technologies to recognize characteristics of the picture and automatically generates basic but useful descriptions such as "three people, smiling, beach."

The possibilities for cognitive technology to support greater autonomy for people with disabilities are endless. We are beginning to see the emergence of solutions that people could only dream of a decade ago. Cognitive systems, coupled with sensors in our homes, in our cities and on our bodies will enhance our own ability to sense and interpret the world around us, and will communicate with us in whatever way we prefer.

The more that machines can sense and understand the world around us, the more they can help people with disabilities to overcome barriers, by bridging the gap between a person's abilities and the chaotic, messy, demanding world we live in. Big Brother may not be all bad after all.

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Tony Stark (Robert Downey Jr.) relies on the JARVIS artificial intelligence to help pilot his Iron Man suit. (Marvel Comics/Paramount Pictures)

Artificial intelligence, machine learning and autonomy are central to the future of American war. In particular, the Pentagon wants todevelop software that can absorb more information from more sources than a human can, analyzeit andeither advise the human how to respond or in high-speed situations like cyber warfare and missile defense act on its own with careful limits.Call it the War Algorithm,the holy grailof a single mathematical equation designed togive the US military near-perfect understanding of what is happening on the battlefield and help its human designers to react more quickly than our adversaries and thus win our wars. Our coverage of this issue attracted the attention ofCapt. Chris Telley, an Army information operations officer studying at the Naval Postgraduate School. In this op-ed, he offers something of a roadmap for the Pentagon to follow as it pursues this highly complex and challenging goal. Read on! The Editor.

If I had an hour to solve a problem Id spend 55 minutes thinking about the problem and five minutes thinking about solutions. Albert Einstein

Artificial intelligence is to be the crown jewel of the Defense Departmentsmuch-discussedThird Offset, the US militarys effort to prepare for the next 20 years. Unfortunately, joint collaborative human-machine battle networks are off to a slow, evenstumbling, start. Recognizing that todays AI is different from the robots that have come before, the Pentagon must seize what may be just a fleeting opportunity to get ahead on the adoption curve. Adapting the military to the coming radical change requires some simultaneous baby steps to learn first and buy second while growing leaders who can wield the tools of the fourth industrial revolution.

First and foremost, the US must be willing to stomach the cost to build cutting-edge systems. AI functions wired into free or discounted Internet services workbecause the companies profit byselling user data; the Pentagonis probablynot eligible for this discount. Also, some of our more stovepiped tactical networks may have difficulty providing the large numbers of training data points, up to 10,000,000 events, needed to teach a learning machine. Military AIs will go to school with crayons untilwe invest significant capital in open architecture data networks. Furthermore, the technicians needed to integrate military AI wont becheap either. According to data from Glassdoor,AI engineersearn a national average of 35 percent more thancybersecurity engineers, whom DoD is already jumping throughhoopsto recruit and those technical skills arent getting any less valuable.

Last year AI went from research concept toengineering application, one CEO said.Another thinks the next 10years may mean the dawn of anAge of Artificial Intelligence. This isnt just hype. In 2013 anOxford studyforecast that 47 percent of total US jobs were susceptible to computerization.Notably, white-collar workers are beginning to be replaced. It now seems that any job which involves routine manipulation of information on a computer is vulnerable to automation. J.P. Morgan is now using AI solutions to slice360,000 man hoursfrom loan reviews eachweek. This year,insurance claimsworkers began to be replaced by IBMs Watson Explorer. The crux of our human failing is that an AI is capable of analyzingintuitive solutionsout of millions of possible results and manipulating those answers far faster than we can. The fastesthuman gamerscan click a keyboard or mouse at a rate of several hundred actions per minute; a computer can do tens of thousands.

Planners DoDs white-collar workers will be replaced before riflemen. They are just as susceptible to automation as their civilian peers. Right now,synthesizing knowledge and producinga creative and flexible array of means to accomplish assigned missions belongs to staff planners. These service members and defense civilians usebasically the same tools PowerPoint, Excel, etc. as does a contemporary office worker. If a robot can buy stocks and turn a profit or satisfactorily answer 20,000,000 helpdesk queries, certainly it can understand the tactical terms and control measure graphics that compose the language of tactics. After all, field manuals and technique publications are just a voluminous trove of and, or, and not logic gates that can be algorithmically diagrammed.

Enemy contact front?Envelop! Need to plan field logistics?Lay thistemplateover semi-permissive terrain! If the product is an Excel workbook or a prefabricated PowerPoint slide, like intelligence preparation of the environment or battlefield calculus, an AI can probably do it better. The robots are coming for us all even the lowly staff officer.

According to Pedro Domingo, author of The Master Algorithm, the best way to not lose your job to a robot is to automate it yourself. The key to effectively and efficiently on-boarding these technologies, as well as the multi-domain battles they will effect, is human capital. We need a bench of service members and government civilians who at least understand the lexicon and how to ask the right questions of the application interface. These leaders will provide adoption capacity for eventually fielding unilaterally developed defense systems that will form the core of the Third Offset. They help us fight on new, cognitive, attack surfaces; Microsofts @TayTweets chatbot was hacked, not with code, but by Internet trolls slyly teaching it bad behaviors. Just as the Navy trains officers to use celestial navigation while still fighting with GPS, DoD needs leaders who can spar in both the twentieth and twenty-first centuries to enable graceful system degradation.

Overall, AI will be in everything but will not be everything, so the Department must create a career path for these people without creating acareer field. The machines will eventually write their own code so we need thinkers to operationalize automation rather than build software. Those skills can be acquired through intermixing funded massive openonline courses,broadening seminarswith academia, andtraining with industrytenures into standard professional timelines. The US is behind in computer science curriculum; if the DoD is to use AI to lighten the cognitive load by 2021, as the Armys Robotic and Autonomous Systems Strategy demands, they, and the rest of DoD, will need to nurture and retain people with skills in robotics, computational math, and computational art.These programs need selection criteria and retention incentives toproduce at least one AI literate leader for every battalion level command on that four-year timeline. This may seem fast, but leading AI experts expected a machine to beat humans at the game Go in2027;it happenedthis year.

Since the AI market space is accelerating quickly, there are many possibilities for dual-use applications for the Defense Department. Though the military, most notably DARPA, has dabbledwith AI in things like thecyberandself-driving cargrand challenges;fielding a variety of functional technologic solutions will provide proven ground before attempting unilateral projects.

There are many promising areas that would help defense planners get their toes in the water. The first is information operations.Predictiveandprogrammaticmarketing are incredibly lucrative algorithmically powered tools and they are already in use. Combined with AI systems forjournalistic contentcreation, perhaps DoD can overcome ahistorically slowinfluence apparatus to beat state and non-state adversary propaganda. (Editors note: We are VERY uneasy with this idea for moral and more provincial reasons.) Can Google Maps, or its competitors, tell us where traffic isnt, compared to where it was yesterday as a blend of HUMINT/SIGINT to identify roadside bombs (IEDs)? Similar questions should be asked of emerging applications to compete with humans in the strategy game StarCraft, to help combined arms planning at the tactical level. The tools being built to examine cancer genomes could also be developed to model the cell mutations of extremist networks.

Small, short timeline endeavors like Project Maven, recently created touse machine learning for wading through intelligence data, must provide the network integration experience needed for building larger programs of record. Many small successes will certainly be needed to garner senior leader buy-in if decisive AI tools are to survive the Valley of Death between lab experiments and the transition to a program of record.

Fortunately, the AImarket spaceis still coalescing. Unfortunately, it is an exponential technology so every success or failure is amplified by an order of magnitude. So far, Deputy Defense Secretary Bob Work wants$12 billion to $15 billionin 2017 for programs aimed at human-machine collaboration and combat teamingand has received11 recommendationsfrom the Defense Departments Innovation Advisory board toget started.If even half of those dollars go to AI research then the DoD will have matched theventure capitalspent last year on relevant startups. However, our adversaries will seek to gain advantage. China has already spent billions on AI research programs and they have state-owned investor companies, like ZGC Capitol, residing in Santa Clara, Calif.; their military leaders are aiming toward the leading edge of a military revolution of intelligentization. Its also worth noting that many resources, like Googles TensorFlow, are freely available online for whomever decides to use the technology.

So, the time is now for Artificial Intelligence; strategic surprise featuring things like data driven behavior change or A.I. modulated denial of the electromagnetic spectrum will pose difficult challenges from which to recover. If we are to ride the disruptive wave of what some call the Great Restructuring, existing AI applications should be re-purposed before attempting defense-only machine learning systems. Also, developing a cadre of AI-savvy leaders is essential for rapid application integration, as well as for planning to handle graceful system degradation. The right AI investment, in understanding, strategy, and leaders, should be our starting block for a race that will surely reshape thecharacter of warin ways we can only begin to imagine.

Capt. Chris Telley is an Army information operations officer assigned to the Naval Postgraduate School. He commanded in Afghanistan and served in Iraq as a United States Marine. He tweets at @chris_telleyThese are the opinions of the author and do not reflect the position of the Army or the United States Government.

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Info Ops Officer Offers Artificial Intelligence Roadmap - Breaking Defense

Toyota is the latest Fortune 500 company to launch an AI focused venture capital fund. The initial early-stage fund will deploy $100 million and operate as a subsidiary of theToyota Research Institute.The automaker has strategically positioned itself as an ROI rather than strategic-focused fund meaning that it aims to profit like any other VC firm.

Jim Adler will serve as managing director of the fund. He has been serving as vice president of Toyota Research and comes from a product background. Adler and the rest of the team at Toyota AI Ventures have made three investments to date. These include:

Nauto Developing driverless car technology

SLAMcore Buildingvisual tracking and mapping algorithms

Intuition Robotics Creating a robot companion for older adults

The team says their strongest value add is helping startups think about what business problems are worth solving. Of course, Toyota Research Institute also brings technical expertise to assist the AI fund with diligence and to help startups make improvements to core technology.

Most of the top founders I speak to tell me that they have little issue raising capital and tend to avoid corporate venture when they can. There is a general anxiety in the market that corporates are not genuine when they promise to be ROI rather than strategic investors. Many question whether even small IP and strategic risk warrants corporate involvement, particularly at the volatile seed stage.

We let startups lead these kinds of discussions, Adler said when asked about this tension. Were not here to extract IP from these investments.

Toyota has structured its fund as a separate company rather than an on-balance-sheet entity to minimize conflicts of interest. The firm expects to follow on and lead both seed and Series A deals.

Running effective corporate venture arms is difficult, and its even more difficult when dealing with AI startups. The capital-saturated AI startup ecosystem needs data, genuine corporate customers and advisors with product expertise. There are exactly four trillion corporate venture arms in the world, but shockingly few get this right fingers crossed Toyota knows what theyre getting themselves into.

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How successful Intels efforts prove to be will be crucial not only for the company but also for the long-term future of the computer chip industry.

Were seeing a lot more competition in the data-center market than weve seen in a long time, said Linley Gwennap, a semiconductor expert who leads a technology research firm in Mountain View, Calif.

Intel has long dominated the business for central processing chips that control industry-standard servers in data centers. Matthew Eastwood, an analyst at IDC, said the company controlled about 96 percent of such chips.

But others are making inroads into advanced data centers. Nvidia, a chip maker in Santa Clara, Calif., does not make Intel-style central processors. But its graphics-processing chips, used by gamers in turbocharged personal computers, have proved well suited for A.I. tasks. Nvidias data-center business is taking off, with the companys sales surging and its stock price nearly tripling in the last year.

Big Intel customers like Google, Microsoft and Amazon are also working on chip designs. AMD and ARM, which make central processing chips like Intel, are edging into the data-center market, too. IBM made its Power chip technology open source a few years ago, and Google and others are designing prototypes.

To counter some of these trends, Intel is expected on Tuesday to provide details about the performance and uses of its new chips and its plans for the future. The company is set to formally introduce the next generation of its Xeon data-center microprocessors, code-named Skylake. And there will be a range of Xeon offerings with different numbers of processing cores, speeds, amounts of attached memory, and prices.

Yet analysts said that would represent progress along Intels current path rather than an embrace of new models of computing.

Stacy Rasgon, a semiconductor analyst at Bernstein Research, said, Theyre late to artificial intelligence.

Intel disputes that characterization, saying that artificial intelligence is an emerging technology in which the company is making major investments. In a blog post last fall, Brian Krzanich, Intels chief executive, wrote that it was uniquely capable of enabling and accelerating the promise of A.I.

Intel has been working in several ways to respond to the competition in data-center chips. The company acquired Nervana Systems, an artificial intelligence start-up, for more than $400 million last year. In March, Intel created an A.I. group, headed by Naveen G. Rao, a founder and former chief executive of Nervana.

The Nervana technology, Intel has said, is being folded into its product road map. A chip code-named Lake Crest is being tested and will be available to some customers this year.

Lake Crest is tailored for A.I. programs called neural networks, which learn specific tasks by analyzing huge amounts of data. Feed millions of cat photos into a neural network and it can learn to recognize a cat and later pick out cats by color and breed. The principle is the same for speech recognition and language translation.

Intel has also said it is working to integrate Nervana technology into a future Xeon processor, code-named Knights Crest.

Intels challenge, analysts said, is a classic one of adapting an extraordinarily successful business to a fundamental shift in the marketplace.

As the dominant data-center chip maker, used by a wide array of customers with different needs, Intel has loaded more capabilities into its central processors. It has been an immensely profitable strategy: Intel had net income of $10.3 billion last year on revenue of $59.4 billion.

Yet key customers increasingly want computing designs that parcel out work to a collection of specialized chips rather than have that work flow through the central processor. A central processor can be thought of as part brain, doing the logic processing, and part traffic cop, orchestrating the flow of data through the computer.

The outlying, specialized chips are known in the industry as accelerators. They can do certain things, like data-driven A.I. tasks, faster than a central processor. Accelerators include graphics processors, application-specific integrated circuits (ASICs) and field-programmable gate arrays (F.P.G.A.s).

A more diverse set of chips does not mean the need for Intels central processor disappears. The processor just does less of the work, becoming more of a traffic cop and less of a brain. If this happens, Intels business becomes less profitable.

Intel is not standing still. In 2015, it paid $16.7 billion for Altera, a maker of field-programmable gate arrays, which make chips more flexible because they can be repeatedly reprogrammed with software.

Mr. Gwennap, the independent analyst, said, Intel has a very good read on data centers and what those customers want.

Still, the question remains whether knowing what the customers want translates into giving them what they want, if that path presents a threat to Intels business model and profit margins.

Follow Steve Lohr on Twitter @SteveLohr.

A version of this article appears in print on July 11, 2017, on Page B5 of the New York edition with the headline: Intel Protects Its Lead While Pivoting to A.I.

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Intel, While Pivoting to Artificial Intelligence, Tries to Protect Lead - New York Times

This article was originally published on The Conversation. Read the original article.

How do you stop a robot from hurting people? Many existing robots, such as those assembling cars in factories, shut down immediately when a human comes near. But this quick fix wouldnt work for something like a self-driving car that might have to move to avoid a collision, or a care robot that might need to catch an old person if they fall. With robots set to become our servants, companions and co-workers, we need to deal with the increasingly complex situations this will create and the ethical and safety questions this will raise.

Science fiction already envisioned this problem and has suggested various potential solutions. The most famous was author Isaac Asimovs Three Laws of Robotics, which are designed to prevent robots harming humans. But since 2005my colleagues and I at the University of Hertfordshire have been working on an idea that could be an alternative.

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Instead of laws to restrict robot behavior, we think robots should be empowered to maximize the possible ways they can act so they can pick the best solution for any given scenario. As we describe in a new paper in Frontiers, this principle could form the basis of a new set of universal guidelines for robots to keep humans as safe as possible.

Asimovs Three Laws are as follows:

While these laws sound plausible, numerous arguments have demonstrated why they are inadequate. Asimovs own stories are arguably a deconstruction of the laws, showing how they repeatedly fail in different situations. Most attempts to draft new guidelines follow a similar principle to create safe, compliant and robust robots.

One problem with any explicitly formulated robot guidelines is the need to translate them into a format that robots can work with. Understanding the full range of human language and the experience it represents is a very hard job for a robot. Broad behavioral goals, such as preventing harm to humans or protecting a robots existence, can mean different things in different contexts. Sticking to the rules might end up leaving a robot helpless to act as its creators might hope.

Our alternative concept, empowerment, stands for the opposite of helplessness. Being empowered means having the ability to affect a situation and being aware that you can. We have been developing ways to translate this social concept into a quantifiable and operational technical language. This would endow robots with the drive to keep their options open and act in a way that increases their influence on the world.

When we tried simulating how robots would use the empowerment principle in various scenarios, we found they would often act in surprisingly natural ways. It typically only requires them to model how the real world works but doesnt need any specialised artificial intelligence programming designed to deal with the particular scenario.

But to keep people safe, the robots need to try to maintain or improve human empowerment as well as their own. This essentially means being protective and supportive. Opening a locked door for someone would increase their empowerment. Restraining them would result in a short-term loss of empowerment. And significantly hurting them could remove their empowerment altogether. At the same time, the robot has to try to maintain its own empowerment, for example by ensuring it has enough power to operate and it does not get stuck or damaged.

Using this general principle rather than predefined rules of behavior would allow the robot to take account of the context and evaluate scenarios no one has previously envisaged. For example, instead of always following the rule dont push humans, a robot would generally avoid pushing them but still be able to push them out of the way of a falling object. The human might still be harmed but less so than if the robot didnt push them.

In the film I, Robot, based on several Asimov stories, robots create an oppressive state that is supposed to minimize the overall harm to humans by keeping them confined and protected. But our principle would avoid such a scenario because it would mean a loss of human empowerment.

While empowerment provides a new way of thinking about safe robot behavior, we still have much work to do on scaling up its efficiency so it can easily be deployed on any robot and translate to good and safe behaviour in all respects. This poses a very difficult challenge. But we firmly believe empowerment can lead us towards a practical solution to the ongoing and highly debated problem of how to rein in robots behavior, and how to keep robotsin the most naive senseethical.

Christoph Salgeis aMarie Curie Global Fellow at theUniversity of Hertfordshire.

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Artificial Intelligence and the Robot Apocalypse: Why We Need New Rules to Keep Humans Safe - Newsweek