Chinese internet giant Baidu says it plans to leverage advanced cloud-computing to analyse the online data of millions of its users to help companies improve their marketing campaigns. The Chinese search engine giant, which has real-time search data on more than 700 million internet users, is able to analyse individual users through its cloud arm's artificial intelligence (AI), big data and cloud computing technologies, Yin Shiming, vice president and general manager of Baidu Cloud Computing, said in Shenzhen. "AI is bringing in new ways of thinking for many traditional industries," said Yin, who cited the recent battle between Google DeepMind's AlphaGo computer program and Chinese Go master Ke Jie as supporting his view that the development of AI technology has stepped up. "Our Marketing Cloud, backed by Baidu Cloud's data and technology, is not just saving resources and costs, but making marketing easier," Yin said. Despite challenges from other local search brands such as Sogou and Qihoo 360, Baidu's dominance in online search has hardly swayed over the years, accounting for about 75 per cent of the search market.

Notably, the same way is passed by the product owner's company. In his session at 20th Cloud Expo, Oleg Lola, CEO of MobiDev, will provide a generalized overview of the evolution of a software product, the product owner, the needs that arise at various stages of this process, and the value brought by a software development partner to the product owner as a response to these needs. All in all, the key value that can be added to the success of your product does not comprise technical expertise and corresponding development resources only; true success is possible when the software development company becomes a reliable long-time partner that keeps developing the product further and making it continuously relevant. Speaker Bio Oleg Lola, MobiDev CEO, is a talented business manager and a skilled mobile developer who has a 10-year experience in software engineering. He understands the needs of startups and tech companies.

LG has had a busy week at CES already, and the show hasn't even started yet. Over the past seven days, the firm has announced new wireless headphones, a levitating speaker and today it unveiled its latest smart home products including a household robot that is a cute rival to Amazon Echo and Google Home. Called Hub Robot, the household robot is a cross between Bicentennial Man (in function, rather than looks), and looks like it comes straight out of a Pixar film. It connects to other smart appliances in the home and uses Amazon Alexa's voice recognition technology to carry out tasks. LG's examples include turning on the air conditioner or changing a dryer cycle.

Joy Buolamwini is a graduate researcher at the MIT Media Lab and founder of the Algorithmic Justice League – an organisation that aims to challenge the biases in decision-making software. She grew up in Mississippi, gained a Rhodes scholarship, and she is also a Fulbright fellow, an Astronaut scholar and a Google Anita Borg scholar. Earlier this year she won a $50,000 scholarship funded by the makers of the film Hidden Figures for her work fighting coded discrimination. How did you become interested in that area? When I was a computer science undergraduate I was working on social robotics – the robots use computer vision to detect the humans they socialise with.

Humanoid robots are more than just sci-fi novelties in South Korea, which holds the top spot for the world's highest robot density – beating out China, Japan, Germany and the United States. Robot density refers to the ratio of industrial robots to employees. To maintain its lead, the South Korean government has committed $450 million to robotics over the next five years. 'The Highs' is a series which looks at countries that rank the highest in certain categories – for better or worse.

DataRobot, the leader in automated machine learning, announced it has acquired Nutonian, Inc., a data science software company specializing in time series analytical modeling. The terms of the acquisition were not disclosed, and the deal is officially closed. Developed in Cornell's Artificial Intelligence Lab by two of the "World's Most Powerful Data Scientists," Nutonian's A.I.-powered modeling engine, Eureqa, powers predictive and prescriptive analytics at global companies, including Audi, Beck's Hybrids, NASA, and RealPage. Eureqa is renowned for its success in time series analytics and for creating easy-to-interpret predictive models in minutes rather than weeks or months. The addition of the Nutonian team and technology helps DataRobot accelerate its mission to bring machine learning to the masses by bolstering its capabilities across different types of modeling problems, particularly those involving time series data.

By Paul Mozur HONG KONG: Google computer program called AlphaGo beat the world's best player in the second game. It's all over for humanity - at least in the game of Go. For the second game in a row, a Google computer program called AlphaGo beat the world's best player of what many consider the world's most sophisticated board game. AlphaGo is scheduled to play its human opponent, the 19-year-old Chinese prodigy Ke Jie, one more time on Saturday in the best-of-three contest. But with a score of 2-0 heading into that final game, and earlier victories against other opponents already on the books, AlphaGo has proved its superiority.

We have always believed in the potential for AI to help society discover new knowledge and benefit from it, and AlphaGo has given us an early glimpse that this may indeed be possible. More than a competitor, AlphaGo has been a tool to inspire Go players to try new strategies and uncover new ideas in this 3,000 year-old game. The creative moves it played against the legendary Lee Sedol in Seoul in 2016 brought completely new knowledge to the Go world, while the unofficial online games it played under the moniker Magister (Master) earlier this year have influenced many of Go's leading professionals - including the genius Ke Jie himself. Events like this week's Pair Go, in which two of the world's top players partnered with AlphaGo, showed the great potential for people to use AI systems to generate new insights in complex fields. This week's series of thrilling games with the world's best players, in the country where Go originated, has been the highest possible pinnacle for AlphaGo as a competitive program.

The excited states of polyatomic systems are rather complex, and often exhibit meta-stable dynamical behaviors. Static analysis of reaction pathway often fails to sufficiently characterize excited state motions due to their highly non-equilibrium nature. Here, we proposed a time series guided clustering algorithm to generate most relevant meta-stable patterns directly from ab initio dynamic trajectories. Based on the knowledge of these meta-stable patterns, we suggested an interpolation scheme with only a concrete and finite set of known patterns to accurately predict the ground and excited state properties of the entire dynamics trajectories. As illustrated with the example of sinapic acids, the estimation error for both ground and excited state is very close, which indicates one could predict the ground and excited state molecular properties with similar accuracy. These results may provide us some insights to construct an excited state force field with compatible energy terms as traditional ones.

As of May 2016, there are 24.34 million Indonesian, or around 10% of the population being active monthly on Twitter [1], sharing news, events, as well as their personal feelings and experiences including healthrelated information. Twitter offers a potential for data mining of public information flows [2] and these massive data sources may be exploited for public health monitoring and surveillance purposes [3]. Previous studies have explored the use of Twitter, for example, to track levels of disease activity [4], to predicts heart disease mortality [5], and for measuring health-related quality of life [6]. However, the validity of twitter mining protocols to correctly detect health and disease events is one methodological challenge of this media. This study seeks to validate a search protocol of ill health-related terms using real-time Twitter data which can later be used to understand if, and how, twitter can reveal information on the current health situation in Indonesia. In this validation study of mining protocols, we: 1) extracted geo-located conversations related to health and disease postings on Twitter using a set of predefined keywords, 2) assessed the prevalence, frequency and timing of such content in these conversations, and 3) validated how this search protocol was able to detect relevant disease tweets.