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The Complete Beginners' Guide to Artificial Intelligence - Forbes Forbes Artificial intelligence continues to transform the ways we live our lives and run our businesses. However, the meaning and implications of what artificial ... Artificial intelligence as a driver for innovation - FederalNewsRadio ...FederalNewsRadio.com all 2 news articles »

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The Complete Beginners' Guide to Artificial Intelligence - Forbes - Forbes

Ipsos Mori found a two-thirds of the UK public believe the benefits of machine learning outweighed the risks or were balanced. Photograph: Nic Delves-Broughton/PA

Apart from fears of mass unemployment, accidents with machinery, restrictions on freedom, increased economic inequality and a devalued human experience, the public are broadly optimistic about the arrival of artificial intelligence, according to one of the first surveys of British opinions about the technology.

Research by the polling firm Ipsos Mori found nearly a third of people believe the risks of machine learning outweigh the benefits, while 36% believe the risks and benefits are balanced.

Machine learning is technology that underpins internet searches, recommendations on Amazon and Netflix, and voice recognition on smartphones.

The findings provide a snapshot of UK views on what some researchers regard as the early stages of a major revolution that is poised to affect almost every aspect of life.

The research suggests that while people are generally positive about the technology for improving medical treatments, guiding driverless cars and personalising education substantial concerns remain.

With machine learning, computers do not churn out answers by following hard and fast rules that are programmed into them. Instead, they are fed huge amounts of data from which they learn through trial and error.

For example, computers have been given thousands of images of healthy and cancerous cells and told to learn the difference. They can then tell whether a biopsy from a patient is benign or requires treatment.

The Ipsos Mori rsurvey found support for machine learning depended on what it would be used for.

It discovered little enthusiasm for military robots that use the technology to make their own decisions, with only 22% believing the benefits outweighed the risks. People were also wary of computers that learned to play the stock market, with only 18% in favour.

The findings were drawn from face-to-face interviews with 978 people chosen to be representative of the UK population, along with discussions at public meetings in Birmingham, Huddersfield, London and Oxford, and questions put to a community of 244 people online.

The survey found support for facial recognition systems that learn to recognise criminals faces from CCTV footage, with 61% believing the benefits outweighed the risks. Software that recognises speech and answers questions, such as that found on most smartphones, was also seen as beneficial.

But people were more wary of other uses of machine learning. Driverless cars have the potential to reduce traffic accidents substantially, and while some welcomed vehicles programmed to drive at 20mph in 20mph zones, others were sceptical.

One person who took part in the Birmingham event said: There would be twice as many accidents because driverless cars would follow the Highway Code and drivers dont. The transition period would be really dangerous. Wed have to give everyone driverless cars all at once.

The research was commissioned for the Royal Society for a major report published on Tuesday on the power and promise of computers that learn by example.

The report describes the recent rapid progress that computer scientists have made in the field and the possibility of transformative advances in the next five to 10 years.

There is huge potential for machine learning to impact in very positive ways on much of what we do as individuals and as industry and as a society. But there are challenges, said Peter Donnelly, a statistician and geneticist at Oxford University, who led the group that produced the report.

According to the report: Society needs to give urgent consideration to the ways in which the benefits from machine learning can be shared across society.

Among other concerns in the Ipsos Mori research were fears of being replaced by computers.

One person who took part in the London event said: Everybody here is thinking, Well, Im going to lose my job. The primary concern was that machine learning could cause unemployment on a mass scale, the researchers found.

Others feared computers could diminish the human experience by churning out poetry for example, or making it impossible to go for a Sunday drive. More still raised concerns that people would become too reliant on computers and lose key skills, such as the ability to remember information, read maps and so on.

According to the research, some computer systems were already on the verge of fomenting rebellion. Told of a computer system that might try to rein in people who overspend, one participant in Oxford said: I feel like Id want to buy the shoes just to spite it.

The Royal Society report raises a host of other challenges that will come with the arrival of artificial intelligence.

Artificial intelligence systems can pick up biases from training data, making them racist and sexist, and cannot always explain their decisions, both issues that scientists must work on, the report states.

Meanwhile, tech firms are poaching some key UK academics, leaving universities struggling to keep the best minds.

Economists see huge economic growth coming from machine learning but without more companies, and skilled people to work for them, the profits could flow to a handful of major corporations.

Society needs to think about these issues, said Donnelly. We need an open and nuanced discussion to work out what we can do to help ameliorate some of these worries, and what we want to insist on.

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Artificial intelligence survey finds UK public broadly optimistic - The Guardian

ZURICH ABB has sealed a collaboration agreement with International Business Machines Corp, the Swiss engineering company said on Tuesday, the latest step in its efforts to ramp up its presence in digital technology and the internet of things.

In a joint statement ABB said it would combine its digital offering, which gathers information from machinery, with IBM's expertise in artificial intelligence featured in its Watson data analytics software. The two companies will jointly develop and sell new products.

"This powerful combination marks truly the next level of industrial technology, moving beyond current connected systems that simply gather data, to industrial operations and machines that use data to sense, analyze, optimize and take actions that drive greater uptime, speed and yield for industrial customers," ABB Chief Executive Ulrich Spiesshofer said in a statement.

For example, instead of manual machinery inspections, ABB and IBM intend to use Watson's artificial intelligence to help find defects via real-time images collected by an ABB system, and then analyzed using IBM Watson.

ABB has identified digital technology where machinery communicates with control centers to increase productivity and reduce downtime as a driver of growth. It now gets around 55 percent of sales from products that are digitally enabled.

As part of the drive, the company last year signed a strategic partnership with Microsoft Corp to roll out digital products for customers in the robotics, marine and ports, electric vehicles and renewable energy sectors.

To spearhead its strategy, it appointed former Cisco executive Guido Jouret as its first chief digital officer last year. {nL8N1BH09N]

(Reporting by John Revill, Editing by Michael Shields)

BEIJING/SHANGHAI China is targeting 35 million vehicle sales by 2025 and wants new energy vehicles (NEVs) to make up at least one-fifth of that total, the industry ministry said on Tuesday.

TOKYO Japan's Toshiba Corp will start taking bids for Landis+Gyr, its Swiss smart meter unit, as early as June, Kyodo news agency reported on Tuesday.

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ABB, IBM team up on industrial artificial intelligence - Reuters

CNNMoney, citing a PwC report, declared that 38 percent of USA jobs will be lost due to robots and artificial intelligence over the coming 15 years. Jobs that perform routine, repetitive tasks and are in industries include manufacturing, banking, education, retail and hospitality. The same warning bells are being rung by The Economist, New York Times, The Guardian and others. The World Economic Forum cites a net loss of over 5 million jobs by 2020 in 15 major developed and emerging economies.

The mainstream media headlines around automation related job loss are akin to Chicken Littles warning that the sky was falling. The sensationalism overstates reality.

The impression is that job loss due to automation is a recent phenomenon. Its not. The ATM was created in 1967 and has taken over 30 years to evolve into what we take for granted today. Did it significantly reduce bank teller jobs? Yes, over a long period of time. But it did not eliminate the position; it evolved.

There are countless examples of how technology has changed economies, professions and industries. This has been going long before the steam engine and electricity was invented. The pace of technological change today is, however, increasingly faster. PwCs 15 years forecast for job loss is challenging to believe when we look at the reality of what it takes to implement artificial intelligence.

Spoiler alert - our ability to forecast the timing of future events with a measurable degree of accuracy isnt particularly good.

Rurik Bradbury, Head of Research at LivePerson, a mobile and online messaging company, has a different perspective. The prospect of replacing entire jobs with just technology is unlikely, shares Bradbury. There is a lot of confusion about AI with more talk than actual deployment.

Todays artificial intelligence technologies are capable of performing tasks at the atomic level. These are very narrowly defined tasks that operate within a clearly defined set of responses.

Based on LivePersons customer experience, Bradbury strongly believes that AI can perform, on average, 40 percent of the tasks comprising customer care jobs. In other words, AI driving the level of unemployment forecasted by CNN within the next 15 to 20 years is unlikely. Even if we just focus on customer care jobs across all industries.

Jobs are comprised of a multitude of tasks as well as a wide range of problem solving situations that require lateral thinking and complex, emotion-based human interactions. Bringing in AI to a customer support position, for example, requires the job to be broken down into its detailed components.

On average approximately 40 to 50 percent of tasks in a call center are good candidates for automation. These are tasks that a call center agent or manager can trigger updating your address, for example. The dialog between the AI and the customer is controlled by how the AI application is programmed and closely measured with human oversight. AI does not run without tight controls in place. The analytics include sentiment analysis that tells management which AI-conducted customer interactions were positive or negative. Negative interactions can result in shifting that task back to a human or reprogramming the AI software.

AI doesnt always replace workers; it augments their ability to be more effective and productive. That doesnt mean that the nature of work will not change, it will. The operative word is augmented Bradbury calls it job sharing.

Routine, data-driven, narrowly defined subtasks will be automated freeing the human worker to engage in higher level, most sophisticated tasks such as creative problem solving, strategic thinking and relationship building. The latter being things humans are much better suited for.

Based on Bradburys research and LivePerson customers experience, the rate of AI taking over human tasks is slower than popular media would lead you to believe.

First, to effectively employ AI to drive a positive, productive customer experience requires a clear plan based on gradual automation over time. Secondly, the current rate of automating tasks is one percent a year. In the case of the 40 percent of call center agent tasks that could be candidates for automation, companies would be extremely hard pressed to achieve that level of automation within ten to fifteen years. So much for predictions.

That doesnt mean ignore artificial intelligence. Approach it with a solid plan based on best practices. Here are a few of Bradburys suggestions:

1. Collect a data set of good (read: successful) customer interactions and categorize them, identifying the most frequent interactions.

2. Pick candidates for automation based on opportunities to improve the interaction. Start with a very small group of interactions to experiment with.

3. Take a subset of these identified interactions and create a chatbot or AI interface that is specific to the atomic task being automated. The more granular the definition and automation of the task, the higher the success. 70 percent of current AI tasks fail because they are too general.

4. Put the AI task into production aside a team of call center agents and test. That means collect data, perform A/B testing, and analyze the conversations and their outcomes. Evolve the AI software over time based on the results of the analysis.

5. As success is realized, automate additional tasks based on the same testing and analysis approach. Set performance thresholds for each AI task. Keep in mind that AI applications work in tandem with employees and need to be orchestrated are part of a companys ecosystem.

How we look at technology directly influences how we fit it into our lives. Dont think of artificial intelligence as a separate project or technology. Think of it as part of a job and measure it accordingly.

Ignore the chicken littles. Leverage AI where it enhances the customer experience and delivers measurable value add. Start small, get granular, and go slow.

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The Chicken Littles of Artificial Intelligence - Huffington Post

Artificial intelligence will create more jobs than it terminates, this VC says Silicon Valley Business Journal Ash Fontana invests with Zetta Venture Partners, a fund that only backs companies working in artificial intelligence startups. He explains that focus and his optimism about the future with AI in this TechFlash Q&A.

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Artificial intelligence will create more jobs than it terminates, this VC says - Silicon Valley Business Journal

Jack Ma issued the warning to encourage businesses to adapt or face problems in the future. Photograph: VCG via Getty Images

Artificial intelligence and other technologies will cause people more pain than happiness over the next three decades, according to Jack Ma, the billionaire chairman and founder of Alibaba.

Social conflicts in the next three decades will have an impact on all sorts of industries and walks of life, said Ma, speaking at an entrepreneurship conference in China about the job disruptions that would be created by automation and the internet. A key social conflict will be the rise of artificial intelligence and longer life expectancy, which will lead to an aging workforce fighting for fewer jobs.

Ma, who is usually more optimistic in his presentations, issued the warning to encourage businesses to adapt or face problems in the future. He said that 15 years ago he gave hundreds of speeches warning about the impact of e-commerce on traditional retailers and few people listened because he wasnt as well-known as he is now.

Machines should only do what humans cannot, he said. Only in this way can we have the opportunities to keep machines as working partners with humans, rather than as replacements.

Even so, Ma acknowledged that in the future companies will likely be run by robots.

Machines should only do what humans cannot

Thirty years later, the Time magazine cover for the best CEO of the year very likely will be a robot, he said. Robots can make calculations more quickly and rationally than humans, Ma added, and wont be swayed by emotions, for example by getting angry at competitors.

Leaders who dont understand that cloud computing and artificial intelligence are essential for business should identify young people in their companies to explain it to them, he said.

His comments echo a number of studies suggesting that automation will eliminate jobs, including a Forrester study that suggested 6% of all jobs in the US would be eliminated by 2021. The job displacement will start with customer service representatives and eventually move to truck and taxi drivers, the report read.

Current technologies in this field include virtual assistants, such as Alexa, Cortana, Siri and Google Now, as well as chatbots and automated robotic systems. For now they are quite simple, but over the next five years they will become much better at making decisions on our behalf in more complex scenarios, which will enable mass adoption of breakthroughs, such as self-driving cars.

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Alibaba founder Jack Ma: AI will cause people 'more pain than happiness' - The Guardian

Artificial Intelligence breakthroughs and developments entail new challenges and problems. As AI algorithms grow more advanced, it becomes more difficult to make sense of their inner workings. Part of this is because the companies that develop them do not allow the scrutiny of their proprietary algorithms. But a lot of it has to do with the mere fact that AI is becoming opaque due to its increasing complexity.

And this can turn into a problem as we move forward and Artificial Intelligence becomes more prominent in our lives.

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By a long range, AI algorithms perform much better than their human counterparts at the tasks they master. Self driving cars, for instance, which rely heavily on machine learning algorithms, will eventually reduce 90 percent of road accidents. AI diagnosis platforms spot early signs of dangerous illnesses much better than humans, and help save lives. And predictive maintenance can detect signs of wear in machinery and infrastructure in ways that are impossible for humans, preventing disasters and reducing costs.

But AI is not flawless, and does make mistakes, albeit at a lower rate than humans. Last year, the AI-powered opponent in the game Elite Dangerous went berserk and started creating super-weapons to hunt players. In another case, Microsofts AI chatbot Tay started spewing out racist comments within a day of its launch. And remember that time Google face recognition started making some offending labeling of pictures?

None of these mistakes are critical, and the damage can be shrugged off without much thought. However, neural networks, machine learning algorithms, and other subsets of AI are finding their way into more critical domains. Some of these fields include healthcare, transportation and law, where mistakes can have critical and sometimes fatal consequences.

We humans make mistakes all the time, including fatal ones. But the difference here is that we can explain the reasons behind our actions and bear the responsibility. Even the software we used before the age of AI was code and rule-based logic. Mistakes could be examined and reasoned out, and culpability could be well-defined.

The same cant be said of Artificial Intelligence. In particular, neural networks, which are the key component in many AI applications, are something of a black box. Often, not even the engineers can explain why their algorithm made a certain decision. Last year, Googles Go-playing AI stunned the world by coming up with moves that professionals couldnt think of.

As Nils Lenke, Senior Director of Corporate Research at Nuance, says about neural networks, Its not always clear what happens inside you let the network organize itself, but that really means it does organize itself: it doesnt necessarily tell you how it did it.

This can cause problems if those algorithms havefull control in making decisions. Who will be responsible if a self-driving car causes a fatal accident? You cant hold any of the passengers accountable for something they didnt control. And the manufacturers will have a hard time explainingan event that involves so many complexities and variables. And dont expect the car itself to start explaining its actions.

The same can be said of an AI application that has autonomous control over a patients treatment process. Or a risk assessment algorithm that decides whether convicts stay in prison or are free to go.

So can we trust Artificial Intelligence to make decisions on its own? For non-critical tasks, such as advertising, games and Netflix suggestions, where mistakes are tolerable, we can. But for situations where the social, legal, economic and political repercussion can be disastrous, we cant not yet. The same goes for scenarios where human lives are at stake. Were still not ready to forfeit control to the robots.

As Lenke says, [Y]ou need to look at the tasks at hand. For some, its not really critical if you dont fully understand what happens, or even if the network is wrong. A system that suggests music, for example: all that can go wrong is, you listen to boring piece of music. But with applications like enterprise customer service, where transactions are involved, or computer-assisted clinical documentation improvement, what we typically do there is, we dont put the AI in isolation, but we have it co-work with a human being.

For the moment Artificial Intelligence will show its full potential in complementing human efforts. Were already seeing inroads in fields such as medicine and cybersecurity. AI takes care of data-oriented research and analysis and presents human experts with invaluable insights and suggestions. Subsequently, the experts make the decisions and assume responsibility for the possible consequences.

In the meantime, firms and organization must do more to make Artificial Intelligence more transparent and understandable. An example is OpenAI, a nonprofit research company founded by Teslas Elon Musk and YCombinators Sam Altman. As the name suggests, OpenAIs goal is to open AI research and development to everyone, independent of financial interests.

Another organization, Partnership on AI, aims to raise awareness on and deal with AI challenges such as bias. Founded by tech giants including Microsoft, IBM and Google, the Partnership will also work on AI ethics and best practices.

Eventually, well achieve for better or worse Artificial General Intelligence, AI that is on par with the human brain. Maybe then, our cars and robot will be able to go to court and stand trial for their actions. But then, well be dealing with totally different problems.

Thats for the future. In the present, human-dominated world, to make critical decisions, you either have to be flawless or accountable. For the moment, AI falls within none of those categories.

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Artificial Intelligence has to deal with its transparency problems - TNW