Monthly Archives: April 2017

As we get closer and closer to create artificial intelligence that can think and reason in ways that mimic a human brain its becoming increasingly clear that allowing a machine mind to learn from humans is a very bad idea. Weve seen examples of it in the past, but a new studyon AI biases reveals that not only does training an artificial brain create biases, but those leanings reinforce many societal issues regarding race and gender that plague humanity today.

The study, which was conducted by scientists at Princeton University and published in the journal Science, sought to determine not just if the behavior of an AI exhibited specific biases, but whether the machine learning systems that determine the outcome inherently lean one way or the other. To do this, the team trained an AI usingstandard datasets that are popular choices for machine learning. These kinds of sets include millions of words and are often gathered from many sources, including the internet.

The AI studies the words, how their used, and what words theyre used in association with, in order to provide natural language responses and answers in a way that we can understand. It also, as it turns out, learns some of our more unfortunate quirks.

After training the AI, scientists tested how it associates various words with others. For example, flower is more likely to be associated with pleasant than weapon is. That, of course, makes perfect sense. However, the trained AI also had a habit of associatingtypically caucasian-sounding names with other things that it considered to be pleasant, rather than African-American names. The AI also shied away from pairing female pronouns with mathematics, and instead often associated them with artistic terms.

This is obviously a huge issue since, in the name of creating AI that sound and behave more human, some of the standard training materials being used carry with them some of the worst parts of us. Its a very interesting problem, and one that you can bet will get a lot of attention now that evidence seems to be mounting.

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Science proves that training AI to be 'human' makes it sexist and ... - BGR

Executive Summary

A survey by Tata Consultancy Services reveals that while some jobs have been lost to machine intelligence, thats not the major waycompanies are using AI today. Companies are more likely to be using AI to improvecomputer-to-computer tasks while employing the same number of people. The 170-year-old news service Associated Press offers a case in point. In 2013, demand for quarterly earnings stories was insatiable, andstaff reporters could barely keep up. So that year, AP began working with an AI firm to train software to automatically write short earnings news stories. By 2015, APs AI system was writing 3,700 quarterly earnings stories 12 times the number written by its business reporters. No AP business journalist lost a job. In fact, AI has freed up the staff to write more in-depth stories on business trends. Thats the next trend in AI, and one more businesses should try to emulate.

Every few months it seems another study warns that a big slice of the workforce is about to lose their jobs because of artificial intelligence. Four years ago, an Oxford University study predicted 47% of jobs could be automated by 2033. Even the near-term outlook has been quite negative: A 2016 report by the Organization for Economic Cooperation and Development (OECD) said 9% of jobs in the 21 countries that make up its membership could be automated. And in January 2017, McKinseys research arm estimated AI-driven job losses at 5%. My own firm released a survey recently of 835 large companies (with an average revenue of $20 billion) that predicts a net job loss of between 4% and 7% in key business functions by the year 2020 due to AI.

Yet our research also found that, in the shorter term, these fears may be overblown. The companies we surveyed in 13 manufacturing and service industries in North America, Europe, Asia-Pacific, and Latin America are using AI much more frequently in computer-to-computer activities and much less often to automate human activities. Machine-to-machine transactions are the low-hanging fruit of AI, not people-displacement.

For example, our survey, which asked managers of 13 functions, from sales and marketing to procurement and finance, to indicate whether their departments were using AI in 63 core areas, found AI was used most frequently in detecting and fending off computer security intrusions in the IT department. This task was mentioned by 44% of our respondents. Yet even in this case, we doubt AI is automating the jobs of IT security people out of existence. In fact, we find its helping such often severely overloaded IT professionals deal with geometrically increasing hacking attempts. AI is making IT security professionals more valuable to their employers, not less.

How it will impact business, industry, and society.

In fact, although we saw examples of companies using AI in computer-to-computer transactions such as in recommendation engines that suggest what a customer should buy next or when conducting online securities trading and media buying, we saw that IT was one of the largest adopters of AI. And it wasnt just to detect a hackers moves in the data center. IT was using AI to resolve employees tech support problems, automate the work of putting new systems or enhancements into production, and make sure employees used technology from approved vendors. Between 34% and 44% of global companies surveyed are using AI in in their IT departments in these four ways, monitoring huge volumes of machine-to-machine activities.

In stark contrast, very few of the companies we surveyed were using AI to eliminate jobs altogether. For example, only 2% are using artificial intelligence to monitor internal legal compliance, and only 3% to detect procurement fraud (e.g., bribes and kickbacks).

What about the automation of the production line? Whether assembling automobiles or insurance policies, only 7% of manufacturing and service companies are using AI to automate production activities. Similarly, only 8% are using AI to allocate budgets across the company. Just 6% are using AI in pricing.

So where should your company look to find such low-hanging fruit applications of AI that wont kill jobs yet could bestow big benefits? From our survey and best-practice research on companies that have already generated significant returns on their AI investments, we identified three patterns that separate the best from the rest when it comes to AI. All three are about using AI first to improve computer-to-computer (or machine-to-machine) activities before using it to eliminate jobs:

Put AI to work on activities that have an immediate impact on revenue and cost. When Joseph Sirosh joined Amazon.com in 2004, he began seeing the value of AI to reduce fraud, bad debt, and the number of customers who didnt get their goods and suppliers who didnt get their money. By the time he left Amazon in 2013, his group had grown from 35 to more than 1,000 people who used machine learning to make Amazon more operationally efficient and effective. Over the same time period, the company saw a 10-fold increase in revenue.

After joining Microsoft Corporation in 2013 as corporate vice president of the Data Group, Sirosh led the charge in using AI in the companys database, big data, and machine learning offerings. AI wasnt new at Microsoft. For example, the company had brought in a data scientist in 2008 to develop machine learning tools that would improve its search engine, Bing, in a market dominated by Google. Since then, AI has helped Bing more than double its share of the search engine market (to 20%); as of 2015, Bing generated more than a $1 billion in revenue every quarter. (That was the year Bing became a profitable business for Microsoft.) Microsofts use of AI now extends far beyond that, including to its Azure cloud computing service, which puts the companys AI tools in the hands of Azure customers. (Disclosure: Microsoft is a TCS client.)

Look for opportunities in which AI could help you produce more products with the same number of people you have today. The AI experience of the 170-year-old news service Associated Press is a great case in point. AP found in 2013 a literally insatiable demand for quarterly earnings stories, but their staff of 65 business reporters could write only 6% of the earnings stories possible, given Americas 5,300 publicly held companies. The earnings news of many small companies thus went unreported on APs wire services (other than the automatically published tabular data). So that year, AP began working with an AI firm to train software to automatically write short earnings news stories. By 2015, APs AI system was writing 3,700 quarterly earnings stories 12 times the number written by its business reporters. This is a machine-to-machine application of AI. The AI software is one machine; the other is the digital data feed that AP gets from a financial information provider (Zacks Investment Research). No AP business journalist lost a job. In fact, AI has freed up the staff to write more in-depth stories on business trends.

Start in the back office, not the front office. You might think companies will get the greatest returns on AI in business functions that touch customers every day (like marketing, sales, and service) or by embedding it in the products they sell to customers (e.g., the self-driving car, the self-cleaning barbeque grill, the self-replenishing refrigerator, etc.). Our research says otherwise. We asked survey participants to estimate their returns on AI in revenue and cost improvements, and then we compared the survey answers of the companies with the greatest improvements (call them AI leaders) to the answers of companies with the smallest improvements (AI followers). Some 51% of our AI leaders predicted that by 2020 AI will have its biggest internal impact on their back-office functions of IT and finance/accounting; only 34% of AI followers said the same thing. Conversely, 43% of AI followers said AIs impact would be greatest in the front-office areas of marketing, sales, and services, yet only 26% of the AI leaders felt it would be there. We believe the leaders have the right idea: Focus your AI initiatives in the back-office, particularly where there are lots of computer-to-computer interactions in IT and finance/accounting.

Computers today are far better at managing other computers and, in general, inanimate objects or digital information than they are at managing human interactions. When companies use AI in this sphere, they dont have to eliminate jobs. Yet the job-destroying applications of AI are what command the headlines: driverless cars and trucks, robotic restaurant order-takers and food preparers, and more.

Make no mistake: Automation and artificial intelligence will eliminate some jobs. Chatbots for customer service have proliferated; robots on the factory floor are real. But we believe companies would be wise to use AI first where their computers already interact. Theres plenty of low-hanging fruit there to keep them busy for years.

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How Companies Are Already Using AI - Harvard Business Review

An artificial intelligence, or AI program has again beaten a group of human poker players to win $792,000 in virtual money.

The AI program won during a recent competition against experienced poker players in China. More than 36,000 hands were played during a 5-day competition on Chinas Hainan Island.

The computer went up against a group of six human players led by Alan Du, a winner in the 2016 World Series of Poker tournament. The human team said it attempted to play against the AI system like a machine, rather than using traditional methods of humans.

The winning system is called Lengpudashi, or cold poker master. It was developed by engineers at Americas Carnegie Mellon University. A previous version of the AI system beat four top poker players in the world in a U.S. competition last January.

Results of the competition between Lengpudashi AI and top poker players in Hainan, China. (Sinovation Ventures)

Artificial intelligence is the capability of a computer to learn to perform human-like operations and make decisions. This can be achieved by putting large amounts of data into a computer for processing.

Algorithms are also used to help computers learn through experiences the same way humans do. This kind of AI technology is used in machine translation systems like Google Translate.

Last year, Googles AI system AlphaGo beat a Korean champion in the ancient Chinese board game Go.

The two wins show how AI development has greatly increased in the ability to succeed against humans. But poker differs from Go in that a player keeps his cards hidden from the opponent. Poker players also use techniques to trick opponents into thinking they have a better hand than they actually do. This is one area where a computer can find it hard to match human thinking and actions.

But a co-creator of the Lengpudashi program, Noam Brown, said the computer even performed well in this part of the competition.

People think that bluffing is very human -- it turns out that's not true, said Brown, a computer scientist and student. A computer can learn from experience that if it has a weak hand and it bluffs, it can make more money, Brown told Bloomberg.

Noam brown, a Ph.D. candidate at Carnegie Mellon University and co-creator of the Lengpudashi AI system, confers with Alan Du, head of the human team in the competition. (Sinovation Ventures)

Brown and Carnegie Mellon professor Tuomas Sandholm won $290,000 in the competition. The money will go to Strategic Machine Inc., a company started by the two to develop AI.

The company is involved in many other areas besides games. These include AI solutions that can be applied to business, negotiation, cybersecurity, political campaigns, and medical treatment.

Im Bryan Lynn.

Bryan Lynn wrote this story for VOA Learning English. Hai Do was the editor.

We want to hear from you. What are your thoughts on AI? Have you had any personal experiences with it? Write to us in the Comments section, and visit our Facebook page.

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poker n. card game in which players bet money of the value of their cards

virtual adj. representing something without actually being it

hand n. the cards held by a player in a card game

master n. person who becomes very skilled at something

algorithm n. set of steps that are followed in order to solve a mathematical problem or to complete a computer process

card n. small piece of stiff paper used to play games

bluff n. pretend to do or know something to trick someone into doing what you want

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AI Computer Beats Human Poker Players by Nearly $800000 - Voice of America

Artificial Intelligence has been around for sixty years, and through this long time it has had many ups and downs, but mostly downs. The AI winters, as they are commonly known, were caused because of the insurmountable obstacles that declarative programming presented when building the knowledge base of an intelligent system. Hand coding a complete description of the world proved to be an impossible task. Systems were limited by the knowledge that could be coded into them, and therefore unable to cope with the unexpected. And then, sometime around the late 1990s, the unexpected happened: a mostly discredited notion in AI, that one should emulate the human brain and its neural intricacies, came back in vogue. This resurrection of old ideas coincided with the cost of parallel processing dropping significantly thanks to Graphic Processing Units (GPUs) used to process video game graphics. The AI everyone is talking about is this new kind of brain-like AI, where you do not code the world; instead you teach the machine how to learn about the world.

Brain-like AI, in the guise of machine learning or deep learning systems, is the heart of the debate on the future of work and humans, as intelligent machines begin to take over many cognitive tasks that used to require human intelligence. This new technology is truly unlike anything else we have ever invented. As we ponder the future, and how we will live and collaborate with those machines, it is important to reduce what is particular and unique about AI. I would like to suggest that there are three unique characteristics of AI that we should consider; self-improvement, prescience and autonomy.

Lets look first at the ability of AI for self-improvement. No other technological artefact has ever had such ability before. For every machine that humans created in the past its performance was forever determined in advance; with wear and tear that performance was reduced over time, and it was always necessary for humans to undertake regular maintenance, just to keep the performance of those machine level. Improvements would only occur when humans replaced parts of the machine, or produced new, improved, versions. In AI we have machines that do not need humans in order to improve their performance. With machine learning, AI systems become better every time they ingest and process a new set of data.

The second unique characteristic of AI is prescience, or its ability to predict. This ability is sometimes based on mathematical approaches that pre-existed the advent of hardware capable of executing them. But the fact that those predictive mathematical algorithms have become executable in machines is a great achievement in itself. More sophisticated approaches, such as reinforcement learning combined with convoluted neural networks, are delivering systems capable of strategizing in complex situations. Just think of what AlphaGo achieved a few months ago, beating a highly-skilled and experienced human in the most difficult game of strategy every invented by humans. Prescience is the prerequisite for strategy. In the biological world only highly-advanced predators are capable of strategies that require prediction. AIs prescience furnishes this technology with the ability to adapt its behaviour according to unexpected events so that it achieves a final goal. No other technology is capable of such outcomes.

Finally, and not least because of self-improvement and prescience, AI is also capable of autonomy. Autonomy means that the system can take decisions about its future actions based on its internal states changing according to perceived sensory data. This makes AI systems similar to biological systems. The military is already testing a number of Lethal Autonomous Weapon Systems (LAWS) that can be perform complex combat missions without the need of human guidance. Many of the AI systems currently in use require a human-in-the-loop; mostly because of the necessity for clean and tagged data sets for use in supervised learning. But AI systems are becoming increasingly independent. Soon they will be able to explore the world for themselves, purely out of curiosity.

In combination, the 3 characteristics of AI point to a logical eventuality: a system that can learn and therefore self-improve, and is also prescient, will eventually maximize its autonomy. And this is why it is absolutely necessary to think of AI ethics now. The autonomous, intelligent machines of the future, must have a code of ethics that limits the autonomy of their decisions.

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The 3 things that make AI unlike any other technology - Huffington Post