Developing a new drug can cost billions of dollars and take a dozen or more years to bring to market. Two Israeli researchers have applied artificial intelligence (AI) and deep learning to shave time and money off the drug-discovery process. Instead of searching for the appropriate molecules to use in a new medicine, as is done today, they enabled a computer to make smart predictions without human guidance. Shahar Harel and Kira Radinsky at the Technion-Israel Institute of Technology fed into their computer system hundreds of thousands of known molecules as well as the chemical composition of all FDA-approved drugs up until 1950. Aided by AI, the computer came up with new potential molecules by making sometimes unexpected correlations from within this massive sample.

China will be the world's dominant player in artificial intelligence by 2030. A State Council document, issued in July last year, resolved to position China as the world's pre-eminent practitioner of artificial intelligence (AI) in both research and application within the next 12 years. Governments across the world are rushing to support innovation in AI, but none has published as coherent a plan as China and – more importantly – has the ability to get things done: the Chinese government can implement policy in ways that are impossible in western democracies. Intent, however, is one thing: to paraphrase the boxer Mike Tyson, everyone has a plan until they get punched in the mouth. The Chinese not only have a strategy, they have a track record of delivering on large-scale, ambitious projects.

A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. A World Food Programme convoy carries humanitarian aid to Aleppo, Syria. Getting food into conflict zones is a major hurdle -- and a topic of discussion at the WFP's Innovation Accelerator. Let's figure out how to end hunger forever.

Companies and cities all over world are experimenting with using artificial intelligence to reduce and prevent crime, and to more quickly respond to crimes in progress. The ideas behind many of these projects is that crimes are relatively predictable; it just requires being able to sort through a massive volume of data to find patterns that are useful to law enforcement. This kind of data analysis was technologically impossible a few decades ago, but the hope is that recent developments in machine learning are up to the task. There is good reason why companies and government are both interested in trying to use AI in this manner. As of 2010, the United States spent over $80 billion a year on incarations at the state, local, and federal levels. Estimates put the United States' total spending on law enforcement at over $100 billion a year. Law enforcement and prisons make up a substantial percentage of local government budgets.

Another option is at the very least to cooperate in order to increase the power of negotiation. A lot of the development of AI is based on the accumulation of enormous amount of data. This data is being accumulated from throughout the world, from countries like India, Brazil or Mexico, and is centralized in places like California or China. You can say that to some extent what we see today in the world is a kind of data colonization. Just as in the 19th century (when) raw material from South America, Africa, Asia, was used to fuel the industrial revolution in Europe and the countries that provided the raw material gained almost nothing in return, a similar thing may be happening now with data.

Elon Musk is one of the world's most prominent business figures. His latest headline-grabber is the announcement of the first paying passenger his SpaceX venture plans to fly around the Moon. There are also his extraordinary pioneering achievements include creating online payment platform PayPal, running electric car maker Tesla, as well as SpaceX. His innovative and wide-ranging interests include solar energy and artificial intelligence, and he has promised the Hyperloop, a super-high speed magnetic train travel, in an underground tube and he has designs on Mars. But this year, things seem to have turned sour for the tech entrepreneur.

SAP has created an AI ethics panel to guide its use of machine-learning technology. If only it had a similar committee for fraud allegations: it might have avoided the corruption scandal engulfing it in South Africa. The German ERP giant – which is accused of kicking back $2m to secure state contracts – claimed it is the first European biz to create a external artificial intelligence ethics board: a five-person committee that includes technical experts and specialists in public policy, ethics, and bioethics. However, while several of them possess solid IT credentials, there's no one with a background in AI. Rather, expertise in the evolving field will come from inside SAP.

Most studies about the impact of artificial intelligence (AI) on jobs and the economy have focused on developed countries such as the United States and Britain. Through my work as a scientist, technology executive and venture capitalist in the US and China, I have come to believe that the gravest threat AI poses is to emerging economies. In recent decades, China and India have presented the world with two different models on how countries can climb the development ladder. In the China model, the nation leveraged its large population and low costs to build a base of blue-collar manufacturing. The country then steadily worked its way up the value chain by producing better and more technology-intensive goods.

Abstract--Drought forecasting and prediction is a complicated process due to the complexity and scalability of the environmental parameters involved. Hence, it required a high level of expertise to predict. In this paper, we describe the research and development of a rule-based drought early warning expert systems (RB-DEWES) for forecasting drought using local indigenous knowledge obtained from domain experts. The system generates inference by using rule set and provides drought advisory information with attributed certainty factor (CF) based on the user's input. The system is believed to be the first expert system for drought forecasting to use local indigenous knowledge on drought. The architecture and components such as knowledge base, JESS inference engine and model base of the system and their functions are presented. The intricate complexity of drought has always been a stumbling block for drought forecasting and prediction systems [1]. This is mostly due to the web of environmental events (such as climate variability) that directly/indirectly triggers this environmental phenomenon. There are six broad categories of drought: meteorological, climatological, atmospheric, agricultural, hydrologic and water drought [1]. Nevertheless, irrespective of the category of drought, there is a consensus amongst scientist that drought is a disastrous condition of lack of moisture caused by a deficit in precipitation in a certain geographical region over some time period [2]. The effect of drought can be quantified based on the frequency, duration and intensity in the affected region subject to established timescales.

Machines and algorithms in the workplace are expected to create 133 million new roles, but cause 75 million jobs to be displaced by 2022 according to a new report from the World Economic Forum (WEF) called "The Future of Jobs 2018." This means that the growth of artificial intelligence could create 58 million net new jobs in the next few years. With this net positive job growth, there is expected to be a major shift in quality, location and permanency for the new roles. And companies are expected to expand the use of contractors doing specialized work and utilize remote staffing. In 2025, machines are expected to perform more current work tasks than humans compared to 71% being performed by humans as of now.