Category Archives: Ai

Musk warns the rise of AI is a greater risk than North Korea as firm he backs bests humans in online combat. Photograph: Ben Macmahon/AAP

Elon Musk has warned again about the dangers of artificial intelligence, saying that it poses vastly more risk than the apparent nuclear capabilities of North Korea does.

The Tesla and SpaceX chief executive took to Twitter to once again reiterate the need for concern around the development of AI, following the victory of Musk-led AI development over professional players of the Dota 2 online multiplayer battle game.

This is not the first time Musk has stated that AI could potentially be one of the most dangerous international developments. He said in October 2014 that he considered it humanitys biggest existential threat, a view he has repeated several times while making investments in AI startups and organisations, including OpenAI, to keep an eye on whats going on.

Musk again called for regulation, previously doing so directly to US governors at their annual national meeting in Providence, Rhode Island.

Musks tweets coincide with the testing of an AI designed by OpenAI to play the multiplayer online battle arena (Moba) game Dota 2, which successfully managed to win all its 1-v-1 games at the International Dota 2 championships against many of the worlds best players competing for a $24.8m (19m) prize fund.

The AI displayed the ability to predict where human players would deploy forces and improvise on the spot, in a game where sheer speed of operation does not correlate with victory, meaning the AI was simply better, not just faster than the best human players.

Musk backed the non-profit AI research company OpenAI in December 2015, taking up a co-chair position. OpenAIs goal is to develop AI in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return. But it is not the first group to take on human players in a gaming scenario. Googles Deepmind AI outfit, in which Musk was an early investor, beat the worlds best players in the board game Go and has its sights set on conquering the real-time strategy game StarCraft II.

Musks latest comments come after a public spat with Facebook chief executive Mark Zuckerberg over the dangers of AI, with Musk dismissing Zuckerberg as having limited understanding of the subject after the social networks head called out Musk for scaremongering over AI.

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Elon Musk: AI 'vastly more risky than North Korea' - The Guardian

Most people dont realize that theyre likely exposed to AI each and every time they shop online whether its on eBay, Nordstrom.com, Warby Parker, or any other retailer. When you are searching for an item and a merchandising strip appears saying something like similar items thats AI in its simplest terms. Its what gives retailers the ability to automatically make informed recommendations.

AI has been around for many years, but recent advancements have moved AI out of the realm of science fiction and made it a business imperative. The game changers: powerful new GPUs, dedicated hardware, new algorithms, and platforms for deep learning. These enable massive data inputs to be calculated quickly and made actionable, as technology powers new algorithms that dramatically increase the speed and depth of learning. In mere seconds, deep learning can reach across billions of data points with thousands of signals and dozens of layers.

We all aspire to a grand vision of AIs role in commerce, and recent developments are creating a fertile environment for new forms of personalization to occur between brands and consumers. Make no mistake about it, the implications of AI will be profound. This is the new frontier of commerce.

As an industry, we are just beginning to scratch the surface of AI. In the next few years, we will see AI-powered shopping assistants embedded across a wide variety of devices and platforms. Shopping occasions will take advantage of camera, voice interfaces, and text.

We are already witnessing the early success of voice-activated assistants like Google Home, Siri, and Cortana. It wont be long before we see virtual and augmented reality platforms commercialized, as well. We see a future rich with voice-activated and social media assistants on platforms such as Messenger, WeChat, WhatsApp, and Instagram. Personal assistants will be everywhere and are already being woven into the fabric of everyday life. This means commerce will become present wherever and whenever the user is engaged on the social, messaging, camera, or voice-activated platforms of their choice.

AI by itself is simply a catalyst for achieving greater levels of personalization with shoppers. Customer data and human intelligence are the critical ingredients needed to run a personal AI engine. As we continue to launch more sophisticated applications, technologists should continue to focus on how to make greater use of our treasure trove of customer data. Looking ahead, the industry will evolve to combine customer data and human expertise into a deep knowledge graph. This will establish a knowledge base to create highly personal and contextual experiences for consumers. For the commerce industry, thiswill allowus to get a clearer understanding of shoppers intent and to service them in a more personalized way.

Keyword search for shopping is not enough anymore. The ability to use text, voice, and photos is becoming the new norm because these avenues provide users with a much richer and more efficient way to express their initial shopping intent. We call this multimodal shopping. And these new types of consumer interactions yield a tremendous amount of user data that can be poured right back into AI algorithms to improve contextual understanding, predictive modeling, and deep learning.

Across the three spectrums of multimodal AI, were starting to get much better at understanding our customers and the way they like to interact with us. A few good examples of this have to do with how our personal shopping assistant, eBay ShopBot on Facebook Messenger, remembers you. It can keep track of your shirt size or the brands you like, so it wont keep suggesting Nike when you prefer Adidas. The assistant also uses computer vision it can find similar products it knows you like based on a similar image or an exact photo match.

Innovating on a canvas of AI provides many new opportunities to create highly contextual and personalized shopping experiences. From our perspective, every company should be investing heavily in AI, and it shouldnt just be about using cognitive services. Companies should actually be developing their own models that keep them on the cutting edge of technology. While there is still a lot of work to be done in this area, one thing is clear. The companies that chart the right course in this exciting endeavor will prosper. The ones that dont face extinction.

JapjitTulsi is the VP of Engineering ateBay.

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AI is not optional for retail - VentureBeat

Artificial intelligence (AI) technology has exploded in recent years. Common AI personal assistants such as Siri, Alexa, and Google Assistant are helping people figure out their daily schedules, controlling the lights and thermostats in homes, and helping commuters find the best route to work. And while these recent advancements are amazing, theyre really only a sneak preview of how AI can and will impact society as a whole.

Sales efficiency is an area ripe for AI assistance AI is at the point where it can be just as useful, if not more useful, in the office than at home on ones handheld device. Business AI has evolved from being a sophisticated calculator or database analyzer to an entity that can tell businesses solve their biggest challenges and enhance their market offerings. Although more organizations spanning industries are tuning into the potential of AI, many are still not capitalizing on its potential to transform their sales practices.

For many organizations, consistent lead generation is elusive. Despite the increase in tools, technology, and access to customers, companies still struggle to speak to the right target customers with the right message at the right time. Often, lead generation feels like throwing darts at a board and hoping one will hit the target.

The best lead gen tools will always be people; having the opportunity to spend one on one time with targets, listening to their goals and frustrations is a dream for every sales person. But its not realistic. Luckily, organizations like LeadCrunch are enhancing AI-fueled lead-gen platforms that enable organizations to understand customers on an individual basis so that they can eventually connect with them, person-person throughout the sales cycle. Providing a holistic B2B demand generation solution, it uses an effective combination of AI technology and human verification to engage the best targets for your customer base.

Because AI has such a wide reach, companies can use it to gain and vet more leads, which will ultimately result in more possibilities for contact and upselling while minimizing the need for a large sales team. The Harvard Business Review recently found that when Epson implemented an AI sales assistant, their lead response rate increased by 240%. Furthermore, as the technology continues to improve, companies can scale back or reprioritize its human sales component without the risk of losing potential clients.

In addition to initial contact and lead generation, AI is useful for assisting sales associates with administrative and routine tasks. Rather than having your human sales team be bogged down with paperwork, initial contacts, sorting, scheduling, and other administrative duties, AI is able to complete these quickly and with a high level of accuracy. Administrative tasks dont necessarily need a human component and, as such, are well within the capabilities of modern AI.

Without administrative tasks, a salesperson now has more time to pursue leads sent to them by AI so they can begin to establish a human relationship which is often needed to eventually get the sale. In a recent study, the average American employee spends 40% of their working hours on administrative tasks. With more time to focus on building relationships with clients, sales associates can work with even more clients at once, making the entire process much more efficient.

Moreover, AI can coach your sales force as well. Rather than having your sales team focus on administrative duties and analytics, companies can let AI figure out how and why a particular sales person is struggling. By identifying potential weaknesses, sales associates can rely on AI to prepare them and coach them for future interactions.

The beauty of an AI_driven platform, like LeadCrunchs is that it has the power to adapt and apply new customer insights to future initiatives. Customer behaviors and marketing engagements change on a day-to-day basis, and until now, the audience data that companies rely on has failed to keep up. The implementation of Artificial Intelligence enables LeadCrunch to provide its clients consistnetly evolving data that allows them to grow with their target customers, rather than play catch-up.

AI is great for generating leads and for performing routine jobs to help the sales team focus on sales, but its also useful for managing an already-existing customer base as well. For companies with large user bases that offer renewable subscription services or upgrades, AI can help manage a system to both pitch and sell those subscriptions, renewals, or upgrades. After all, the probability of selling more to an existing client is 60% 70%

This is especially useful for Internet companies that want to keep only a few individuals on hand but still be able to manage a large number of clients. For example, rather than having a team of sales representatives standing by to field questions, AI can field and respond to most of these questions instantly. Furthermore, for customer service issues, having an AI platform act as a gatekeeper will facilitate quick responses to frequently asked questions while letting human customer service agents deal with more complicated issues. With AI, a company can meet the needs of an ever-expanding clientele base without spending large amounts of money.

From generating leads, to assisting with administrative tasks, to maintaining and upselling current customers, LeadCrunchs AI lead generation platform is the ultimate sales hack. Its a unique tool that will give a company a much broader reach without any of the traditional overhead, a once unimaginable concept.

What are some other ways AI can help your company with sales?

This post is part of our contributor series. The views expressed are the author's own and not necessarily shared by TNW.

Read next: Google's latest acquisition could reduce your trips to the clinic

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Why AI is the ultimate sales hack - TNW

Andrew Keen is the author of three books: Cult of the Amateur, Digital Vertigo and The Internet Is Not The Answer. He produces Futurecast, and is the host of Keen On.

There are few more credible authorities on artificial intelligence (AI) thanHilary Mason the New York-based founder and chief executive of the data science and machine learning consultancyFast Forward Labs.

So, I asked Mason, who is also theData Scientist in Residenceat Accel Partners and theformer Chief Scientist at Bitly, whether todays AI revolution is for real? Or is it, I wondered, just another catch-all phrase used by entrepreneurs and investors to describe the latest Silicon Valley mania?

Mason who sees AI as theumbrella term to describemachine learning andbig data acknowledges that it has become avery trendy area of start-up activity. That said, she says, there has been such rapid technological progress in machine learning over the last five years to make the fieldlegitimately exciting. This progress has been so profound, Mason insists, that it is making AIclose to the heart of our new innovation economy.

But in contrast withthe fearsof prominent technologists like Elon Musk, Mason doesnt worry about the threat to the human species of super intelligent machines. We humans, she says, use machines as tools and the advent of AI doesnt change this.Machines arent rational, she thus argues, implying that there are many more important things for us to worry about than an imminent singularity.

What does concern Mason, however, are questions about the role of women in tech. Thats a question interviewers like myself should be asking men rather than women, she insists. It just createsextra burden for female technologists and thus isnt something that she wants to publicly discuss.

Many thanks to the folks at theGreater Providence Chamber of Commercefor their help in producing this interview.

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Why AI is now at the heart of our innovation economy | TechCrunch - TechCrunch

AI, machine learning, and the internet of things are rapidly permeating every aspect of our lives. Dubbed the fourth industrial revolution, it seems everywhere we turn companies are touting the AI capabilities of their products. Everything from self-driving cars to what shows up on Facebook newsfeeds are driven, in part, by artificial intelligence.

As AI rapidly exerts itself in every industry under the sun, it has also had to contend with the growing pains of an over eager market and a deeply complex technology. The popularity of AI is such that the term is in danger of becoming mere marketing parlance, with companies commandeering it to lend excitement and credence to their products. Everyone wants a piece of the AI pie a pie estimated to be worth $100 billion by 2025.

Truly intelligent, adaptive, and cutting-edge AI technology is rare, but if any program has nurtured such technology, it would be NASAs Deep Space program. For decades, the program has operated out of Caltechs Jet Propulsion Laboratory (JPL), where scientists have developed proven technology for unmanned robotics and space exploration missions.

In the most desolate of environments, these truly intelligent and autonomous AI systems have learned to analyze situations, think critically, and solve difficult problems with human-like reasoning. Now Caltech and NASA are ready to commercialize this technology. Southern California company, Beyond Limits, which was granted an exclusive license by Caltech to improve and commercialize this technology, recently closed a Series B of $20 million from BP Ventures, and is rapidly establishing itself as the leader in industrial-grade Artificial General Intelligence (AGI) software.

According to Beyond Limits CEO AJ Abdallat, Artificial General Intelligence represents a massive shift from qualitative machine learning to a bio-inspired human-like cognition to understand, learn, reason and solve problems. Not just the rapid crunching of huge amounts of data that is the hallmark of many would-be AI solutions, but truly cognitive computing.

According to Abdallat, AGI is fast, lightweight, and flexible, without the huge amounts of server, power, and human resources requirement of current AI offerings. The smaller infrastructure and cost makes Beyond Limits AI solution more scalable than something like Watson or DeepMind.

In addition, Beyond Limits claims its AI can make 1.5 billion calculations per second, 50 times faster than the current market leader. It isnt coupled to any core platform components, and its software requires a much smaller footprint and is deployable in the cloud, on a chip, or embedded in a device.

Our technology has tested its mettle in space, which is exponentially more proven deployment than most AI technologies can claim, says Abdallat. And it was in space that the core benefits of todays Beyond Limits AI was honed.

Space is at a premium for these unmanned missions, so this technology was made to be exceptionally light and efficient, Abdallat explained. In addition, on missions where the tiniest error can mean the scrapping of millions of dollars in R&D and years of work, failure of unmanned components is not an option. We cant easily call back a rover that has already been deployed, so the AI had to be developed in such a way that it could solve even unexpected and unforeseen scenarios. For almost two decades, all NASA missions have utilized the technology Beyond Limits is now introducing to a wider audience.

The industrial applications are numerous. As evidenced by BPs investment, oil and gas exploration can see huge improvements aided by Beyond Limits software, helping locate and develop reservoirs, make unmanned repairs possible for dangerous maintenance, even manage operations at refineries.

Abdallat sees application in everything from transportation to healthcare to finance and beyond. We are confident that the technology that allows Rovers to land, explore, and bring back information on faraway planets can help humans thrive here on earth. For decades, weve been promised an AI revolution, and Beyond Limits is finally fulfilling that promise.

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Beyond Limits is Spreading NASA's AI to the World - HuffPost

OpenAI used the action at this years Dota 2 championships as an opportunity to show off its work by having top players lose repeatedly to its in-game bot.

Dotas normally a team game with a heavy emphasis on coordination and communication, but for players interested in beefing up their pure, technical ability, the game also has a 1v1 mode. Thats what tech company OpenAI used to show off its programming of a bot against one of the games most famous and beloved players, Danil Dendi Ishutin.

That mode has both players compete in the games mid-lane, with only the destruction of that first tower or two enemy kills earning either side a win. In addition, for purposes of this particular demonstration, specific items like Bottle and Soul Ring, which help players manage health and mana regeneration, were also restricted. Dendi decided to play Shadow Fiend, a strong but fragile hero who excels at aggressive plays, and to make it a mirror match the OpenAI bot did the same.

Rarely do you hear a crowd of people cheering over creep blocking, but thats what the fans in Key Arena did last night while watching the exhibition match. The earliest advantage in a 1v1 Dota face-off comes with one side slowing down their support wave of AI creeps enough to force the opponent farther into enemy territory and thats exactly what the bot managed to do within the first thirty seconds of the bout.

After that, things seemed to even out but Dendi, lacking a good read on his AI rival, played cautiously and ended up losing out on experience and gold as the bot was given space to land more last-hits. By three minutes in, OpenAI had already harassed Dendis tower and gained double the CS. The former TI winner suffered his first death as a result shortly after. At that point, with the AI unlikely to make a crucial mistake and Dendi falling further and further behind in experience points, the game match was all but over. The pro tried to change things around with last ditch attempt at a kill but he ended up sacrificing his own life to do it.

In a rematch, Dendi admitted that he was going to try and mimic the AIs strategy of pushing his lane early, explaining how the dynamic of a 1v1 fight in Dota is counter-intuitive since it relies on purely outplaying your opponent rather than trying to out think them. Switching sides from Radiant to Dire for game two, Dendi got off to an even worse. He and the opposing AI exchanged blows early, and within the first two minutes he as forced to retreat only to die along the way.

The OpenAI bot was trained, accroding to company CTO Greg Brockman, by playing many lifetimes worth of matches and only limited coaching along the way. Earlier in the week it had defeated other pros renowned for their technical play, including SumaiL and Arteezy, learning each time and improving itself. But these matches were more to test how far the bot had come than anything else. Self-playing was what got it to that point, with Brockman explaining in a blog post that the AIs learning style requires playing against opponents very close in skill level so it can make incremental adjustments to improve over time.

The company, funded in part by Elon Musk, is working on a number of different AI projects, including impersonating Reddit commenters, but games have always been an important part of designing and testing computer learning. From checkers and chess to StarCraft and now Dota, the well defined rule systems and clear win conditions are a natural fit.

And the 1v1 mode of Valves MOBA takes that logic even further, offering a way of limiting the number of variables operating in the form of other players. Rather than worry about what nine other people are doing and exponentially increasing the number of options and possibilities the AI has to contend with, 1v1 allows it to focus the games core elements, similar a beginner chess player practicing openings. The OpenAI teams ambitions dont stop there, however. The bots designers hope to see it perform in full-fledged 5v5 matches by next year.

You can watch the entire demo below.

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An AI Is Beating Some Of The Best Dota Players In The World - Kotaku

Regulations, sanctions, rules they are not always pure evil as some might suggest. The regulations about how to keep a commuter rail safe or the sanctions the U.S. government uses to manage relations with foreign countries are necessary, not evil.

Yet, when it comes to AI, do we really need to worry?

Elon Musk has gone on the offensive attempting to convince us that AI needs to be more regulated because it could spin out of control. He tweeted that the dangers we face from AI and machine learning has vastly more risk than North Korea. He followed up that tweet by saying that everything thats a danger to the public is regulated, including cars and planes.

The problem with this line of thinking, of course, is that an AI is a piece of software. A plane weighs over 350,000 pounds and can fall out of the sky. Where are we in the continuum of machines taking over? In an infant stage not even crawling or walking. We might want to avoid hysterics.

Still, some of the reactions have been quite interesting.

One user said it was inappropriate to compare a nuclear threat to AI. One said the real danger is humans creating AI that doesnt work. Another pointed out the obvious if there is a nuclear war, it might not matter if the machines take over. Well all be dead.

The problem with the end of the world thanks to AI discussion is that we never get into specifics. Its a random tweet comparing machine intelligence to nuclear war. Its another random tweet talking about regulation. But what kind of AI should be regulated? By whom and where? What are the actual dangers? The problem with fear-mongering about AI is that there are no obvious examples of a machine actually causing mass destructionyet. We hear about failed automations, of cars driving themselves off the road, of a chatbot app crashing.

Musk has noted before that we should regulate now before it gets out of hand. Again, he hasnt explained what should be regulated Microsoft Word? Chatbots? The subroutines in a home sensor that shuts off your sprinkler system? Satellites? Autonomous trucks? Lets get the subject out in the open and get into the specifics of regulation and see where that takes us, because my guess is that the companies making chatbots dont need to be regulated as much as they need to be told to make better and more useful bots with the funding they already have.

Or is this all about the laws of robotics? If thats the case, we get into a brand new problem what is a robot? Im sure Isaac Asimov never predicted that there would be a catbot that tells us the weather forecast (if he did, I apologize to all science fiction fans everywhere). Lets regulate the catbots before they get out of hand, right? Next up the dogbots.

The issue is pretty clear: When you start talking about specific regulations and dangers, they become a bit laughable. What are we really asking Congress to do anyway? And, when you start talking about machines taking over because they want to destroy humanitywell, its too late. Youre a piece of toast and the bots won. We need to get granular, not broad.

Do you agree? Disagree? If you have a reasonable argument to make about the dangers (or maybe the catbots) please send them to me. I promise to respond if youre interested in a civil discourse.

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Sorry, Elon Musk. AI is not a bigger threat than North Korea - VentureBeat

I had just sat down to work one Saturday afternoon, when the familiar sound of the clothes washer, starting its spin cycle like an airplane taking off, started humming in the background. It was that sort of familiar noise that was both comforting and quickly drowns out the background, allowing me to sink quickly into a nice flow with some engineering work.

Suddenly, with a loud thump, the sound of a rattle, and something too awful to describe, the spinning machine came to a dramatic halt. I knew immediately it was the washing machine, as that peaceful hum was no longer softly blanketing the background. An uncomfortable silence was left in its void.

I walked over to the machine and made a quick inspection. Sure enough, there was a dim indicator on the front panel that readErras my clothes sat in a soapy swamp. My first indication was to go online and seek some machine first-aid atifixit.com. As an engineer myself, its almost a reflex to begin the troubleshooting process, no matter the medium.

Down the rabbit hole I went, educating myself on condenser units, evacuation pumps, controller computers, and the impressive array of components used to assemble these machines. Eventually some sort of alarm went off in my mind, and I was hit with the heavy reality of having wasted several hours attempting to gain expertise in a field I barely knew. So, I called the repair line and booked a repair.

Easy enough. In modern times, we have access to nearly immediate service only a phone call or screen-tap away. Though as I sat back down at my computer, I began to wonder if there was another, more efficient way to allow the manufacturer to diagnose and service my washer. After all, the selfish side of me reasoned it would save me, as the consumer, some additional TCO in the life of the appliance.

On the flip side, what if I wouldve just declared the device defective, irreparable, or obsolete? Would it have made its way to a recycling yard or trash heap as I enjoyed the delivery of a shiny new product? Appliance manufacturers are producing products withshorter lifespans than everand higher failure rates than their legacy counterparts.

This drives earlier whole-unit replacements and generates more waste. However, I would add that the millennial generation has a distaste for such environmental or corporate villainy, quickly sniffing out its presence and choosing the more sustainable option instead.

Consumer electronics are attractive, and the provocation of lust for the next best thing is always innate in their marketing strategies. However, what if we had another option where our devices could detect or predict failure, suggesting and even ordering replacement parts for us in the meantime? What if we could then be guided by the manufacturer through a mobile app, giving us the opportunity to save time and money by walking us through the replacement process?

If we were constrained for time, at least the manufacturer could realize savings, both environmentally and in labor cost, by invoking only one trip for the service technician. Even better, what if the device could fix itself?

Though the idea, at some levels, seems trivial, one critical piece to construct such a tool has been missing: visual cognition. Though computers have been getting better at recognizing individual parts, an ensemble of image recognition, cognition, and communication (chat-bot) are necessary for this type of automation.

Its at the intersection of these three that we can begin to create fully automated solutions where we can rapidly decrease our ejection of defective technology to landfills and simultaneously reduce our environmental impact, in the end saving money for both ourselves, and the producers of the products we enjoy.

Taking this one step further, with accurate failure reporting and detection, manufacturers can design products that perform their tasks more reliably and efficiently. Instead of ending up in the trash,as 70.8 percent of consumer electronics eventually do, fully functional hand me downs could enrich less affluent regions, with the maintenance cost also reduced through the above means.

Through I regularly defend the positive impact of AI to my friends who entertain a dystopian viewpoint, at the intersection of industries are possibilities that I gloss over on a daily basis. To a larger degree, even the receipt of defective machine parts for recycling could then be automated, allowing the return path to be optimized in a way that isnt currently possible.

My washing machine is now fixed, and I have some fresh, clean clothes. However, next time an appliance breaks and Im tempted to discard it, I would love to have Artificial Intelligence take care of the process, saving both environment and resources at the same time.

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AI, responsible sustainability, and my broken washing machine - TNW

LONDON (Reuters) - Artificial intelligence robots are turbo-charging the race to find new drugs for the crippling nerve disorder ALS, or motor neurone disease.

The condition, also known as Lou Gehrig's disease, attacks and kills nerve cells controlling muscles, leading to weakness, paralysis and, ultimately, respiratory failure.

There are only two drugs approved by the U.S. Food and Drug Administration to slow the progression of ALS (amyotrophic lateral sclerosis), one available since 1995 and the other approved just this year. About 140,000 new cases are diagnosed a year globally and there is no cure for the disease, famously suffered by cosmologist Stephen Hawking.

"Many doctors call it the worst disease in medicine and the unmet need is huge," said Richard Mead of the Sheffield Institute of Translational Neuroscience, who has found artificial intelligence (AI) is already speeding up his work.

Such robots - complex software run through powerful computers - work as tireless and unbiased super-researchers. They analyze huge chemical, biological and medical databases, alongside reams of scientific papers, far quicker than humanly possible, throwing up new biological targets and potential drugs.

One candidate proposed by AI machines recently produced promising results in preventing the death of motor neurone cells and delaying disease onset in preclinical tests in Sheffield.

Mead, who aims to present the work at a medical meeting in December, is now assessing plans for clinical trials.

He and his team in northern England are not the only ones waking up to the ability of AI to elucidate the complexities of ALS.

In Arizona, the Barrow Neurological Institute last December found five new genes linked to ALS by using IBM's Watson supercomputer. Without the machine, researchers estimate the discovery would have taken years rather than only a few months.

Mead believes ALS is ripe for AI and machine-learning because of the rapid expansion in genetic information about the condition and the fact there are good test-tube and animal models to evaluate drug candidates.

That is good news for ALS patients seeking better treatment options. Famous sufferers include Lou Gehrig, the 1923-39 New York Yankees baseball player; actor and playwright Sam Shepard, who died last month; and Hawking, a rare example of someone living for decades with the condition.

If the research goes on to deliver new medicines, it would mark a notable victory for AI in drug discovery, bolstering the prospects of a growing batch of start-up companies focused on the technology.

Those firms are based on the premise that while AI robots won't replace scientists and clinicians, they should save time and money by finding drug leads several times faster than conventional processes.

Mead from Sheffield is working with BenevolentAI, one of a handful of British "unicorns" - private companies with a market value above $1 billion, in this case $1.7 billion - which is rapidly expanding operations at its offices in central London.

Others in the field include Scotland's Exscientia and U.S.-based firms Berg, Numerate, twoXAR, Atomwise and InSilico Medicine - the last of which recently launched a drug discovery platform geared specifically to ALS.

"What we are trying to do is find relationships that will give us new targets in disease," said Jackie Hunter, a former drug hunter at GlaxoSmithKline (GSK) who now heads Benevolent's pharma business.

"We can do things so much more dynamically and be really responsive to what essentially the information is telling us."

Unlike humans, who may have pet theories, AI scans through data and generates hypotheses in an unbiased way.

Conventional drug discovery remains a hit-and-miss affair and Hunter believes the 50 percent failure rates seen for experimental compounds in mid- and late-stage clinical trials due to lack of efficacy is unsustainable, forcing a shift to AI.

A key test will come with a Phase IIb study by Benevolent to assess a previously unsuccessful compound from Johnson & Johnson in a new disease area - this time for treating Parkinson's disease patients with excessive daytime sleepiness.

Big pharmaceutical companies like GSK, Sanofi and Merck are now exploring the potential of AI through deals with start-ups.

They are treading cautiously, given the failure of "high throughput screening" in the early 2000s to improve efficiency by using robots to test millions of compounds. Yet AI's ability to learn on the job means things may be different this time.

CPR Asset Management fund manager Vafa Ahmadi, for one, believes it is a potential game-changer.

"Using artificial intelligence is going to really accelerate the way we produce much better targeted molecules. It could have a dramatic impact on productivity, which in turn could have a major impact on the valuation of pharmaceutical stocks," he said.

Drugmakers and start-ups are not the only ones chasing that value. Technology giants including Microsoft, IBM and Google's parent Alphabet are also setting up life sciences units to explore drug R&D.

For Benevolent's Hunter, today's attempts to find new drugs for ALS and other difficult diseases marks an important test-bed for the future of AI, which is already being deployed in other high-tech areas such as autonomous cars.

"The aim is to show that we can deliver in a very difficult and complex area. I believe if you can do it in drug discovery and development, you can show the power of AI anywhere."

Reporting by Ben Hirschler; Editing by Pravin Char

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How AI robots hunt new drugs for crippling nerve disease - Reuters

CXOs: Participate in Constellation's digital transformation survey by Aug.18, 2017 and receive a summary of the results.

The combination of machine learning, deep learning, natural language processing, and cognitive computing will change the ways that humans and machines interact. AI-driven smart services will sense one's surroundings, learn one's preferences are from past behavior, and subtly guide people and machines through their daily lives in ways that will truly feel frictionless. This quest to deliver AI-driven smart services across all industries and business processes will usher the most significant shift in computing and business this decade and beyond.

Organizations can expect AI-driven smart services to impact future of work flows, IoT services, customer experience journeys, and synchronous ledgers (blockchain). Success requires the establishment of AI outcomes (see Figure 1). Once the outcomes are established, organizations can craft AI-driven smart services that orchestrate, automate, and deliver mass personalization at scale.

The disruptive nature of AI comes from the speed, precision, and capacity of augmenting humanity. When AI is defined through seven outcomes, the business value of AI projects gain meaning and can easily show business value through a spectrum of outcomes:

Because AI-driven smart services require offloading the decision-making responsibility to atomic driven smart services, the foundation of any AI-driven smart service is trust. Below an explanation of how the five key components of an AI-driven smart service orchestrate trust.

Fears of robots taking over the world have been overblown. Successful AI-driven smart services will augment human intelligence just as machines augmented physical capabilities. AI driven smart services play a key role in defining business models for synchronous ledger technologies (blockchain), Internet of Things, customer experience, and future of work by reducing errors, improving decision-making speed, identifying demand signals, predicting outcomes, and preventing "disasters".

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How to build any AI-driven smart service - ZDNet