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Lnk of the following text: http://www.bbc.com/news/technology-41668701
Google's DeepMind says it has made another big advance in artificial intelligence by getting a machine to master the Chinese game of Go without help from human players. This has turned out to be far more efficient way of addressing the problem. Whereas AlphaGo took months to get to the point where it could take on a professional, AlphaGo Zero got there in just three days, using a fraction of the processing power The AlphaGo program, devised by the tech giant's Al division, has already beaten two of the world's best players "It shows it's the novel algorithms that count, not the compute power or the data," says Mr Silver It had started by learning from thousands of games played by humans But the new AlphaGo Zero began with a blank Go board and no data apart from the rules, and then played itself. He enthuses about an idea some may find rather scary. that in just a few days a machine has surpassed the knowledge of this game acquired by humanity over thousands ofyears. Within 72 hours it was good enough to beat the original program by 100 games to zero. DeepMind's chief executive, Demis Hassabis, said the system could now have more general applications in scientific research "Weve actually removed the constraints of human knowledge and it's able therefore, to create knowledge itself from first principles, from a blank slate," he said We're quite excited because we think this is now good enough to make some real progress on some real problems even though were obviously a long way from full A," he told the BBC and other journalists Whereas earlier versions quickly learned from and improved upon human strategies, AlphaGo Zero developed techniques which the professional player who advised DeepMind said he had never seen before The London-based artificial intelligence company's software defeated leading South Korean Go player Lee Se-dol by four games to one last year Many of the team have now moved on to new projects where they are trying to take this technique to new areas. Demis Hassabis mentions drug design and the discovery of new materials as areas of interest In a game where there are more possible legal board positions than there are atoms in the universe, it was a triumph for machine over man and one that came much earlier than many in the Al world had expected. Whereas some see a threat from Al, he looks ahead with optimism AlphaGo followed this with the defeat of the world's number one Go player, China's Ke Jie, in May. "I hope these kind of algorithms will be routinely working with us as scientific experts medical experts on advancing the frontiers of science and medicine- that's what I hope, he says As with many advances in this field, the achievements required the combination ofvast amounts of data in this case records of thousands of games- and a lot of computer-processing power David Silver, who led that effort, says the team took a very different approach with AlphaGo Zero. But he and his colleagues are cautious about how rapidly we will see the wider application of these Al techniques a game with clear rules and no element of luck is one thing, the messy, random, unpredictable real world quite another The newversion starts from a neural network that knows nothing at all about the game of Go," he explained I wrote earlier this week about the tidal wave of Al hype pouring into my email inbox·Alpha Go Zero is at the other end of the spectrum . proper peer-reviewed science with a real advance in computer intelligence The only knowledge it has is the rules of the game. Apart from that, it figures everything out just by playing games against itsel. We need to keep a close eye on the ethical dilemmas involved in developing a machine that, by some definitions, can think for itself- especially when it is controlled by a giant like Google Go is thought to date back to ancient China, several thousand years ago. Using black and white stones on a grid, players gain the upper hand by surrounding their opponents' pieces with their own. But for now, there are few signs that AlphaGo Zero and its ilk will either steal our jobs or threaten to make humanity obsolete The rules are simpler than those of chess, but a player typically has a choice of 200 moves at most points in the game, compared with about 20 in chess. It can be very difficult to determine who is winning, and many of the top human players rely on instinct.Explanation / Answer
a) Some of the ethical dilemma about machines that can think by themselves are:
1. Unemployment.
What happens after the end of jobs?
The hierarchy of labour is concerned primarily with automation. As we’ve invented ways to automate jobs, we could create room for people to assume more complex roles, moving from the physical work that dominated the pre-industrial globe to the cognitive labour that characterizes strategic and administrative work in our globalized society.
Look at trucking: it currently employs millions of individuals in the United States alone. What will happen to them if the self-driving trucks promised by Tesla’s Elon Musk become widely available in the next decade? But on the other hand, if we consider the lower risk of accidents, self-driving trucks seem like an ethical choice. The same scenario could happen to office workers, as well as to the majority of the workforce in developed countries.
Most people still rely on selling their time to have enough income to sustain themselves and their families. We can only hope that this opportunity will enable people to find meaning in non-labour activities, such as caring for their families, engaging with their communities and learning new ways to contribute to human society.
2. Inequality.
How do we distribute the wealth created by machines?
Our economic system is based on compensation for contribution to the economy, often assessed using an hourly wage. The majority of companies are still dependent on hourly work when it comes to products and services. But by using artificial intelligence, a company can drastically cut down on relying on the human workforce, and this means that revenues will go to fewer people. Consequently, individuals who have ownership in AI-driven companies will make all the money.
3. Artificial stupidity.
How can we guard against mistakes?
Intelligence comes from learning, whether you’re human or machine. Systems usually have a training phase in which they "learn" to detect the right patterns and act according to their input. Once a system is fully trained, it can then go into test phase, where it is hit with more examples and we see how it performs.
Obviously, the training phase cannot cover all possible examples that a system may deal with in the real world. These systems can be fooled in ways that humans wouldn't be. For example, random dot patterns can lead a machine to “see” things that aren’t there. If we rely on AI to bring us into a new world of labour, security and efficiency, we need to ensure that the machine performs as planned, and that people can’t overpower it to use it for their own ends.
4. Security.
How do we keep AI safe from adversaries?
The more powerful a technology becomes, the more can it be used for nefarious reasons as well as good. This applies not only to robots produced to replace human soldiers, or autonomous weapons, but to AI systems that can cause damage if used maliciously. Because these fights won't be fought on the battleground only, cybersecurity will become even more important. After all, we’re dealing with a system that is faster and more capable than us by orders of magnitude.
5. Evil genies.
How do we protect against unintended consequences?
It’s not just adversaries we have to worry about. What if artificial intelligence itself turned against us? This doesn't mean by turning "evil" in the way a human might, or the way AI disasters are depicted in Hollywood movies. Rather, we can imagine an advanced AI system as a "genie in a bottle" that can fulfill wishes, but with terrible unforeseen consequences.
In the case of a machine, there is unlikely to be malice at play, only a lack of understanding of the full context in which the wish was made. Imagine an AI system that is asked to eradicate cancer in the world. After a lot of computing, it spits out a formula that does, in fact, bring about the end of cancer – by killing everyone on the planet. The computer would have achieved its goal of "no more cancer" very efficiently, but not in the way humans intended it.
b) While emerging technologies can improve the speed, quality, and cost of available goods and services, they may also displace large numbers of workers. This possibility challenges the traditional benefits model of tying health care and retirement savings to jobs. In an economy that employs dramatically fewer workers, we need to think about how to deliver benefits to displaced workers.
The impacts of automation technologies are already being felt throughout the economy. The worldwide number of industrial robots has increased rapidly over the past few years. The falling prices of robots, which can operate all day without interruption, make them cost-competitive with human workers. In the service sector, computer algorithms can execute stock trades in a fraction of a second, much faster than any human. As these technologies become cheaper, more capable, and more widespread, they will find even more applications in an economy.
The recent trend towards increased automation stems in part from the Great Recession, which forced many businesses to operate with fewer workers. After growth resumed, many businesses continued automating their operations rather than hiring additional workers. This echoes a trend among technology companies that receive massive valuations with relatively few workers.
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