A United Nations panel agreed Friday to consider guidelines and potential limitations for military uses of artificial intelligence amid concerns from human rights groups and other leaders that so-called "killer robots" could pose a long-term, lethal threat to humanity. Advocacy groups warned about the threats posed by such "killer robots" and aired a chilling video illustrating their possible uses on the sidelines of the first formal U.N. meeting of government experts on Lethal Autonomous Weapons Systems this week. More than 80 countries took part. Ambassador Amandeep Gill of India, who chaired the gathering, said participants plan to meet again in 2018. He said ideas discussed this week included the creation of legally binding instrument, a code of conduct, or a technology review process.

A U.N. panel agreed yesterday to move ahead with talks to define and possibly set limits on weapons that can kill without human involvement, as human rights groups said governments are moving too slowly to keep up with advances in artificial intelligence that could put computers in control one day. Advocacy groups warned about the threats posed by such "killer robots" and aired a chilling video illustrating their possible uses on the sidelines of the first formal U.N. meeting of government experts on Lethal Autonomous Weapons Systems this week. More than 80 countries took part. Ambassador Amandeep Gill of India, who chaired the gathering, said participants plan to meet again in 2018. He said ideas discussed this week included the creation of legally binding instrument, a code of conduct, or a technology review process.

France offers high-quality education to Saudi youths Driving licenses to boost empowerment of Saudi women Misk Art Institute expands in region Misk Foundation and Siemens to expand collaboration on youth training Major boost for Saudi creative industries at Misk Global Forum RIYADH: While some see artificial intelligence as a leap to the next developmental stage for humankind, many people are worried about jobs, said Riad Hamade, executive editor for the Middle East and Africa, Bloomberg News. The young generation now wonders what type of jobs they should be looking for, especially after talk of smart cities powered by robots became so relevant. "Robotics and artificial intelligence have different meanings to different people," said Esther Baldwin, artificial intelligence strategist for Intel. She argued that artificial intelligence is "nothing new," and that people have had degrees in this topic since the 1980s. Baldwin was speaking on the first day of the MiSK Global Forum, which brings young leaders, creators and thinkers together with established innovators to explore ways to meet challenges of change.

Earlier this year, a US-based company introduced a mobile app for buying and selling cars. Customers are asked to click an image of their vehicle's rear end. Within moments of uploading the image, the car's year, make and model, and the resale value are identified. That done, offering the car, or seeking refinancing and insurance is well, a smooth ride. The technology behind all this does seem like magic.

In today's rapidly growing digital age, long-term survival and success are increasingly being linked with how "smart" a business can be. And for financial services firms that means using technology to become more intelligent, and processes and systems that talk to each other and learn from one another. Banks, insurers and wealth managers are pouring billions of dollars into artificial intelligence (AI), machine learning and other types of technologies that are already not only raising productivity levels, but reducing risk and actually creating new jobs. According to Accenture research, the financial services industry is now the third most impacted by productivity gains achieved with the implementation of AI, behind only the media and telecoms, and manufacturing sectors. The figures show that by 2035 its use should have resulted in an increase of US$1.2 trillion in gross value added, which measures the output value of all goods and services in a sector.

AiBank will focus on lending to individuals and small businesses while leveraging big data and artificial intelligence to build new risk control models, Li Rudong, president of the new bank said at a launch event in Beijing.

Speaking at the Abu Dhabi International Petroleum Exhibition Conference (ADIPEC) on Wednesday, Omar Saleh said technology disruptions over the past three years had been a "wake-up call" for all oil and gas firms.He said AI would be of "massive importance" over the next to five to 10 years, before adding that of any technology, AI would also have the most impact on the oil and gas sector overall.The U.S. shale revolution paved the way for a three-year oil price downturn that sent crude spiraling from more than $100 a barrel in 2014 to about $60 today. That has piled pressure on the oil-dependent economies of OPEC nations and forced a round of production cuts this year. On Tuesday, Baker Hughes GE CEO Lorenzo Simonelli said " " in the oil and gas industry should be viewed positively. Correction: This story has been updated to reflect that Omar Saleh believes AI will have the greatest technological impact on the oil and gas industry over the coming years. Speaking at the Abu Dhabi International Petroleum Exhibition Conference (ADIPEC) on Wednesday, Omar Saleh said technology disruptions over the past three years had been a "wake-up call" for all oil and gas firms.

In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess Champion. This performance gain is achieved by evolving a program that mimics the behavior of a superior expert. The resulting evaluation function of the evolved program consists of a much smaller number of parameters than the expert's. The extended experimental results provided in this paper include a report of our successful participation in the 2008 World Computer Chess Championship. In principle, our expert-driven approach could be used in a wide range of problems for which appropriate experts are available. Keywords Computer chess, Fitness evaluation, Games, Genetic algorithms, Parameter tuning 1 Introduction Since the dawn of modern computer science, game playing has posed a formidable challenge in the field of Artificial Intelligence. A preliminary version of this paper appeared in Proceedings of the 2008 Genetic and Evolutionary Computation Conference [13] and received the Best Paper Award in the conference's Real-World Applications track. John McCarthy, Ken Thompson, Herbert Simon, and others) developed game-playing programs and used games in AI research. The ongoing key role played by and the impact of computer games on AI should not be underestimated.

This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervised learning phase these evolved organisms are further improved upon by means of coevolution. While past attempts succeeded in creating a grandmaster-level program by mimicking the behavior of existing computer chess programs, this paper presents the first successful attempt at evolving a state-of-the-art evaluation function by learning only from databases of games played by humans. Our results demonstrate that the evolved program outperforms a two-time World Computer Chess Champion.

A low rank matrix X has been contaminated by uniformly distributed noise, missing values, outliers and corrupt entries. Reconstruction of X from the singular values and singular vectors of the contaminated matrixY is a key problem in machine learning, computer vision and data science. In this paper, we show that common contamination models (including arbitrary combinations of uniform noise, missing values, outliers and corrupt entries) can be described efficiently using a single framework. We develop an asymptotically optimal algorithm that estimates X by manipulation of the singular values of Y, which applies to any of the contamination models considered. Finally, we find an explicit signal-to-noise cutoff, below which estimation of X from the singular value decomposition of Y must fail, in a well-defined sense.