Daily Archives: August 15, 2017

With all that's happening in the world, Elon Musk wants to make sure you don't forget about what he thinks is the biggest danger to humanity.

Over the weekend, Musk returned to tweeting about one of his favorite topics of discussion: artificial intelligence. He referenced the threat of nuclear war with North Korea to help make his point.

Musk's tweets came hours after an A.I. system developed by OpenAI defeated some of the world's best players at a military strategy game called Dota 2. According to a blog post by OpenAI, successfully playing the game involves predicting how an opponent will move, improvising in unfamiliar scenarios, and convincing the opponent's allies to help you instead.

OpenAI is the nonprofit artificial intelligence company Musk co-founded along with Peter Thiel and Sam Altman. The company's purpose is to research and develop A.I. and develop best practices to help ensure that the technology is used for good.

Musk has in the past called A.I. humanity's "biggest existential threat." A known A.I. fear monger, he recently got in a brief public spat with Mark Zuckerberg about the danger that the technology poses to humans. Zuckerberg, whose Facebook--like Tesla--invests heavily in artificial intelligence, referred to Musk's prophesizing about doomsday scenarios as "irresponsible." Musk responded on Twitter the next day by calling Zuckerberg's understanding of the topic "limited."

Comparing the threat of A.I. to that of nuclear war with North Korea is clearly a tactic meant to shock, as Musk has been wont to do on this topic. Earlier this year, he laid out a scenario in which A.I. systems meant to farm strawberries could lead to the destruction of mankind.

Even if Musk is speaking in hyperbole, though, it's not hard to see why an A.I. system that outsmarts humans at military strategy might be cause for concern.

Musk's opinions on the technology have been at odds with those of tech leaders like Zuckerberg, Amazon's Jeff Bezos, and Google co-founders Larry Page and Sergey Brin. All have advocated for A.I. in recent years with few, if any, reservations.

While Tesla relies heavily on artificial intelligence in developing self-driving cars, Musk's opinions have been at odds with those of his fellow tech titans. In July, Musk told a group at the National Governors Association Summer Meeting in Rhode Island that he believes A.I. should be regulated proactively, before the need for such limitations even arise.

"I have exposure to the very cutting-edge A.I.," he said, "and I think people should be really concerned about it."

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Elon Musk Is Very Freaked Out by This Artificial Intelligence System's Victory Over Humans - Inc.com

The operational research unit of the Military Intelligence Unit the software unit of the Israeli armys J6/C4i Directorates Lotem Unit doesnt look like the kind of place where state-of-the art artificial intelligence is being put to work.

There are no espresso machines, brightly colored couches or views of Tel Aviv from the top floors of an office tower. The unit conducts its work in the backwater of Ramat Gan and has the look and feel of any other army office.

But the unit is engaged in the same kind of AI work that the worlds biggest tech companies, like Google, Facebook and Chinas Baidu are doing in a race to apply machine learning to such functions as self-driving cars, analysis of salespeoples telephone pitches and cybersecurity or to fight Israels next war more intelligently.

Maj. Sefi Cohen, 34, is head of the unit, which in effect makes him the armys chief data officer. As he explains it, his units mission is to provide soldiers in the field data-based insights with the help of smart tools. We embed these capabilities in applications that help commanders in the field, he said.

One example is a system for predicting rocket launches from the Gaza Strip. After Operation Protective Edge we developed an app that learns from field sensors and other data we collected what are the most likely areas launchers will be set up and at what hours. That enables us to know in advance what will happen and what areas should be attacked in order to fight them more effectively, he explained.

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In one project the unit built a system based on neural networks whose purpose is to extract from a video a suspicious object and describe it in writing. It wont replace human observers, but instead of looking at five cameras, it will be able to be responsible for dozens, said Cohen.

Cohen said the amount of data at his disposal from the army is endless, reaching into petabytes (one million gigabytes) in some areas. It also makes use of data from outside sources and the apps it develops use open-source code. We return to the world things that we use, Cohen says, Models that are operational obviously do not go out.

Cohen got his start in combat signals corps. Near the end of his compulsory service he completed a course in Lotem and spent another 10 years at its command and control systems unit. Ive always loved algorithms. I was already involved with them in high school and worked in the field. When I saw drafted I wanted to combine the technology with a combat, he recalls.

Cohen set up the unit he now leads with the help of local high-tech executives. I convinced my commanders that we could use machine learning in combat, and from there I started to bring in more and more people, he said. The unit now comprises about 20 officers, all of them in the career army and holding advanced degrees in computer science, focusing on AI.

The units only female member left recently, so for the moment its an all-male team. Cohen says most are graduates of the armys elite Talpiot program; the one who isnt has a masters from the Technion Israel Institute of Technology. Everyone whos here is the tops. I learn a lot from them, he said.

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Tiny IDF Unit Is Brains Behind Israeli Army Artificial Intelligence - Haaretz

The world is on the brink of the fourth industrial revolution, and it could change the way we use everything from cars to shoes.

The first three industrial revolutions brought us mechanisation, mass production and automation. Now, more than half a century after the first robots worked on production lines, artificial intelligence (AI) and machine learning are shaking things up again.

Manufacturing is becoming less about muscle and more about brainsGreg Kinsey, VP, Hitachi Insight Group

Industry 4.0 uses technologies such as the internet of things to make manufacturing smarter allowing companies to revolutionise the way they make and ship goods. Manufacturing is becoming less about muscle and more about brains, says Greg Kinsey, vice president of Hitachi Insight Group.

It becomes less place-specific. You start to look at 3D printing. The shoe industry is contemplating: do we actually need to produce all these shoes in lots of variations in southeast Asia, ship them around the world, only to go to the shop and it doesnt have your size? Why not produce them at the point of sale put your foot in the scanner, measure the size and shape, swipe your credit card and pick your shoes up later that day?

The digital transformation of manufacturing and supply chains means that data from factories is directly analysed using technologies such as machine learning and AI. The process can lead to drastic efficiency gains up to 10pc, says Mr Kinsey. Companies can also see manufacturing lead times slashed in half.

Consumers will see a wider variety of products, to the point of mass customisation, where you can design your own, says Mr Kinsey. Product will become linked to emerging demand, so well never be in a position where things are just out of stock.

The first stage, says Mr Kinsey, is to get rid of paper-based processes something that many factories still rely on. Once digitised, the data can be crunched to ensure factories are operating efficiently. But the idea isnt to get rid of people; its to augment what they do.

When I graduated from university, I was heavily into industrial robots, says Mr Kinsey. Everyone said that robots were going to take our jobs. But the companies that invested heavily in robots like German car makers are now world leaders, employing many more people than they would otherwise have done.

When we use AI tools to predict bad quality, or to optimise the settings for a production line, we can manage it with more confidence. We have had a lot of clients tell us that this technology helps them improve the way they work. This is should be the real driver of innovation.

European companies are currently leading the charge in the digital transformation of industry, says Mr Kinsey. Many are also working closely with start-ups to enhance industrial processes.

Theres a lot of interest in working with start-ups, Mr Kinsey explains. When you embark on innovation, you dont always know what the solutions are.

Companies that invested heavily in robots are now world leaders, and employ more peopleGreg Kinsey, VP, Hitachi Insight Group

The resulting Industry 4.0 may change the way we all think about products, Mr Kinsey says and the first signs are already here.

In Europe, you have a lot of people thinking: Do I need to own a car? That would have been unthinkable 20 or 30 years ago. Michelin already has aircraft tyres that are on a pay-per-use basis: people pay based on the number of times the jet takes off.

You need to embrace this technology; if you dont, because you fear that you might lose some jobs, you are going to lose all the jobs, as your company will no longer be competitive. In fact, digital technologies can improve the workplace and quality of work.

Modern life is saturated with data, and new technologies are emerging nearly every day but how can we use these innovations to make a real difference to the world?

Hitachi believes that Social Innovation should underpin everything they do, so they can find ways to tackle the biggest issues we face today.

Visit social-innovation.hitachi to learn how Social Innovation is helping Hitachi drive change across the globe.

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What does AI mean for the future of manufacture? - Telegraph.co.uk

As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. Its perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, Matrix-like, as some sort of human battery.

And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become the destroyer of worlds, as Oppenheimer lamented after spearheading the construction of the first nuclear bomb?

I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldnt avoid asking: As an AI expert, what do I fear about artificial intelligence?

Fear of the unforeseen

The HAL 9000 computer, dreamed up byscience fiction author Arthur C. Clarkeand brought to life bymovie director Stanley Kubrickin 2001: A Space Odyssey, is a good example of a system that fails because of unintended consequences. In many complex systems the RMS Titanic, NASAs space shuttle, the Chernobyl nuclear power plant engineers layer many different components together. The designers may have known well how each element worked individually, but didnt know enough about how they all worked together.

That resulted in systems that could never be completely understood, and could fail in unpredictable ways. In each disaster sinking a ship, blowing up two shuttles and spreading radioactive contamination across Europe and Asia a set of relatively small failures combined together to create a catastrophe.

I can see how we could fall into the same trap in AI research. We look at the latest research from cognitive science, translate that into an algorithm and add it to an existing system. We try to engineer AI without understanding intelligence or cognition first.

Systems like IBMs Watson and Googles Alpha equip artificial neural networks with enormous computing power, and accomplish impressive feats. But if these machines make mistakes, they lose on Jeopardy! or dont defeat a Go master. These are not world-changing consequences; indeed, the worst that might happen to a regular person as a result is losing some money betting on their success.

But as AI designs get even more complex and computer processors even faster, their skills will improve. That will lead us to give them more responsibility, even as the risk of unintended consequences rises. We know that to err is human, so it is likely impossible for us to create a truly safe system.

Fear of misuse

Im not very concerned about unintended consequences in the types of AI I am developing, using an approach called neuroevolution. I create virtual environments and evolve digital creatures and their brains to solve increasingly complex tasks. The creatures performance is evaluated; those that perform the best are selected to reproduce, making the next generation. Over many generations these machine-creatures evolve cognitive abilities.

Right now we are taking baby steps to evolve machines that can do simple navigation tasks, make simple decisions, or remember a couple of bits. But soon we will evolve machines that can execute more complex tasks and have much better general intelligence. Ultimately we hope to create human-level intelligence.

Along the way, we will find and eliminate errors and problems through the process of evolution. With each generation, the machines get better at handling the errors that occurred in previous generations. That increases the chances that well find unintended consequences in simulation, which can be eliminated before they ever enter the real world.

Another possibility thats farther down the line is using evolution to influence the ethics of artificial intelligence systems. Its likely that human ethics and morals, such as trustworthiness and altruism, are a result of our evolution and factor in its continuation. We could set up our virtual environments to give evolutionary advantages to machines that demonstrate kindness, honesty and empathy. This might be a way to ensure that we develop more obedient servants or trustworthy companions and fewer ruthless killer robots.

While neuroevolution might reduce the likelihood of unintended consequences, it doesnt prevent misuse. But that is a moral question, not a scientific one. As a scientist, I must follow my obligation to the truth, reporting what I find in my experiments, whether I like the results or not. My focus is not on determining whether I like or approve of something; it matters only that I can unveil it.

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What An Artificial Intelligence Researcher Fears About AI - IFLScience

Analysts predict that by 2020, artificial intelligence technologies will be in almost every new software and service release. And if they're not actually in them, technology vendors will probably use smoke and mirrors marketing tactics to make users believe they are.

Many tech vendors already shoehorn the AI label into the marketing of every new piece of software they develop, and it's causing confusion in the market. To muddle things further, major software vendors accuse their competitors of egregious mislabeling, even when the products in question truly do include artificial intelligence technologies.

AI mischaracterization is one of the three major problems in the AI market, as highlighted by Gartner recently. More than 1,000 vendors with applications and platforms describe themselves as artificial intelligence products vendors, or say they employ AI in their products, according to the research firm. It's a practice Gartner calls "AI washing" -- similar to the cloudwashing and greenwashing, which have become prevalent over the years as businesses overexaggerate their association to cloud computing and environmentalism.

When a technology is labelled AI, the vendor must provide information that makes it clear how AI is used as a differentiator and what problems it solves that can't be solved by other technologies, explained Jim Hare, a research VP at Gartner, who focuses on analytics and data science.

You have to go in with the assumption that it isn't AI, and the vendor has to prove otherwise. Jim Hareresearch VP, Gartner

"You have to go in with the assumption that it isn't AI, and the vendor has to prove otherwise," Hare said. "It's like the big data era -- where all the vendors say they have big data -- but on steroids."

"What I'm seeing is that anything typically called machine learning is now being labelled AI, when in reality it is weak or narrow AI, and it solves a specific problem," he said.

IT buyers must hold the vendor accountable for its claims by asking how it defines AI and requesting information about what's under the hood, Hare said. Customers need to know what makes the product superior to what is already available, with support from customer case studies. Also, Hare urges IT buyers to demand a demonstration of artificial intelligence products using their own data to see them in action solving a business problem they have.

Beyond that, a vendor must share with customers the AI techniques it uses or plans to use in the product and their strategy for keeping up with the quickly changing AI market, Hare said.

The second problem Gartner highlights is that machine learning can address many of the problems businesses need to solve. The buzz around more complicated types of AI, such as deep learning, gets so much hype that businesses overlook simpler approaches.

"Many companies say to me, 'I need an AI strategy' and [after hearing their business problem] I say, 'No you don't,'" Hare said.

Really, what you need to look for is a solution to a problem you have, and if machine learning does it, great," Hare said. "If you need deep learning because the problem is too gnarly for classic ML, and you need neural networks -- that's what you look for."

When to use AI versus BI tools was the focus of a spring TDWI Accelerate presentation led by Jana Eggers, CEO of Nara Logics, a Cambridge, Mass., company, that describes its "synaptic intelligence" approach to AI as the combination of neuroscience and computer science.

BI tools use data to provide insights through reporting, visualization and data analysis, and people use that information to answer their questions. Artificial intelligence differs in that it's capable of essentially coming up with solutions to problems on its own, using data and calculations.

Companies that want to answer a specific question or problem should use business analytics tools. If you don't know the question to ask, use AI to explore data openly, and be willing to consider the answers from many different directions, she said. This may involve having outside and inside experts comb through the results, perform A/B testing, or even outsource via platforms such as Amazon's Mechanical Turk.

With an AI project, you know your objectives and what you are trying to do, but you are open to finding new ways to get there, Eggers said.

A third issue plaguing AI is that companies don't have the skills on staff to evaluate, build and deploy it, according to Gartner. Over 50% of respondents to Gartner's 2017 AI development strategies survey said the lack of necessary staff skills was the top challenge to AI adoption. That statistic appears to coincide with the data scientist supply and demand problem.

Companies surveyed said they are seeking artificial intelligence products that can improve decision-making and process automation, and most prefer to buy one of the many packaged AI tools rather than build one themselves. Which brings IT buyers back to the first problem of AI washing; it's difficult to know which artificial intelligence products truly deliver AI capabilities, and which ones are mislabeled.

After determining a prepackaged AI tool provides enough differentiation to be worth the investment, IT buyers must be clear on what is required to manage it, Hare said; what human services are needed to change code and maintain models over the long term? Is it hosted in a cloud service and managed by the vendor, or does the company need knowledgeable staff to keep it running?

"It's one thing to get it deployed, but who steps in to tweak and train models over time?" he said. "[IBM] Watson, for example, requires a lot of work to stand up and you need to focus the model to solve a specific problem and feed it a lot of data to solve that problem."

Companies must also understand the data and compute requirements to run the AI tool, he added; GPUs may be required and that could add significant costs to the project. And cutting-edge AI systems require lots and lots of data. Storing that data also adds to the project cost.

A look at deep learning vs. machine learning

The power, promise and controversy of AI

AI apps need a reality check

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AI washing muddies the artificial intelligence products market - TechTarget

Chris Catliff, president and CEO, BlueShore Financial

Artificial intelligence (AI) is all around us, largely unseen, but it is fundamentally changing our world and our perceptions. The algorithms that Facebook or other apps use to hook us with our preferred news are based on our past clicks and preferences on our apps. We no longer receive the news thats fit to print, but information thats not necessarily grounded in fact or objectively true the mind candy and click bait we have shown we have an appetite for. The deep divisions of political tribalism that are gripping our neighbour down South are in part a reflection of individuals chosen feedback loop.

We are in an Information Age era where AI will affect and shape how we absorb information. Stephen Hawking and Elon Musk have said the development of full artificial intelligence could spell the end of the human race. While regulation and ethical considerations need to be factored in as AI continues to rapidly evolve, there are ways that AI can be used effectively in business today.

AI is simply the mixture of machine learning, language processing and adaptive or cognitive computing. With this sophisticated blend, AI simulates the intelligence of humans into machines. Trained humans can perform tasks but never at the speed or scale of machines, so AI plays a major role in overcoming this unfortunate limitation to human intelligence.

Technology can open up a world of limitless possibilities. Yet it can be hard to separate hype from reality. Getting a realistic picture of AIs current practical application and its most relevant benefits can be challenging. In the past, AI has stumbled in pinning down the subtle nuances of images or voice recognition. Despite the limitations, 8 out of 10 businesses have already implemented, or are planning to adopt, AI solutions. Nowhere is this truer than in banking and wealth management.

As the financial services industry is so data intensive and the ability to analyze all of the data has become more crucial, financial institutions have been early adopters. Increasing work-force productivity, identifying opportunities and accelerating innovation are critical to meeting clients changing needs and maintaining competitive advantage. AI is also levelling the playing field between small and large businesses. At BlueShore Financial, a full-service financial boutique with $5-billion in assets under administration, we use AI to pro-actively assist our client service. We are also exploring the launch of a new robo-advice platform in wealth management next year.

High-tech and high-touch: the dynamic duo

According to Gartner, 85 per cent of all customer interactions with a business will be managed without human interaction by 2020. But despite the incredible potential of AI to transform the customer service experience, teaching a machine human intuition is not yet feasible, so AI will only serve to enhance the human element rather than eliminate it.

Instead of viewing AI as a threat, businesses need to embrace this disruptor and leverage its benefits to increase efficiency and provide more customized service. There are a multitude of ways most businesses can leverage AI but here are three:

1. Customizing the client experience

Recommendation engines (think Netflix) can personalize the customer experience, especially for front-line employees interacting with clients. Using data about our preferences, algorithms suggest and then employees filter with their emotional intelligence to offer highly customized recommendations. Recommendation engines boost revenue and will continue to play a pivotal role. For employees, AI simplifies decisions and eases work flow a case of automation complementing the human element.

2. Accuracy in detecting fraud

AI-based systems, compared with traditional software systems used for detecting fraud, are more accurate in detecting fraudulent patterns. By using machine learning algorithms, companies can spot emerging anomalies in the data. Financial institutions are particularly vulnerable to cybercrime, where global losses from card fraud are expected to reach $31-billion in three years, and cyberattacks are becoming increasingly sophisticated. Security goals and customer experience goals need to be in sync for fraud prevention technologies to be effective.

3. Increasing client engagement

While chat bots are AI-based automated chat systems that can simulate human chat without human intervention, they are being extensively applied to revolutionize customer interactions. By identifying context and emotions in a text chat by the human end-user, chat bots respond with the most appropriate reply. In addition, chat bots, over time, collect data on the behaviour and habits of that individual and can learn their preferred behaviour, adapting even more to their needs and moods. By improving customized communication, customers are more likely to be far more engaged with your company.

While AI and data analytics can appear daunting, the strategic benefits of investing in AI are a no-brainer for larger companies and those looking to scale fast. AI is the portal to a future that will continue to improve our lives faster than we appreciate. I look forward to it.

Executives, educators and human resources experts contribute to the ongoing Leadership Lab series.

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Three ways your business can leverage artificial intelligence - The Globe and Mail