Daily Archives: July 13, 2017

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

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

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.

More:

A Blueprint for Coexistence with Artificial Intelligence - WIRED