Artificial Intelligence is not just a project for the biggest names in tech. Starting in 2018 1 million US dollars will be invested in the African AI start-up as part of the AI Revolution Challenge.

The fight against malaria has been improving, but there's still lots more work to do. For one thing, anti-larval sprays are both expensive and time-consuming -- you can't always afford to spray an entire area. Thankfully, a mix of technology is making that mosquito battle more practical. Wales' Aberystwyth University and Tanzania's Zanzibar Malaria Elimination Programme have partnered on an initiative that uses drones to survey malaria hot zones and identify the water-laden areas where malaria-carrying mosquitoes are likely to breed. An off-the-shelf drone (in this case, DJI's Phantom 3) can cover a large rice paddy in 20 minutes, and the data can be processed in the space of an afternoon.

We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL agent learns to determine which survey question to ask next and when to stop to make a prediction about their likelihood of malaria based on their responses hitherto. The agent incurs a small penalty for each question asked, and a large reward/penalty for making the correct/wrong prediction; it thus has to learn to balance the length of the survey with the accuracy of its final predictions. Our RL agent is a Deep Q-network that learns a policy directly from the responses to the questions, with an action defined for each possible survey question and for each possible prediction class. We focus on Kenya, where malaria is a massive health burden, and train the RL agent on a dataset of 6481 households from the Kenya Malaria Indicator Survey 2015. To investigate the importance of having survey questions be adaptive to responses, we compare our RL agent to a supervised learning (SL) baseline that fixes its set of survey questions a priori. We evaluate on prediction accuracy and on the number of survey questions asked on a holdout set and find that the RL agent is able to predict with 80% accuracy, using only 2.5 questions on average. In addition, the RL agent learns to survey adaptively to responses and is able to match the SL baseline in prediction accuracy while significantly reducing survey length.

One major theme that's been running through science fiction recently is the rise of artificial intelligence and the impact that might have on humanity. As we continue to improve upon and refine machine learning, it seems inevitable that the development of a true AI will occur at some point. And consensus is that, once it does, humans will probably be in a bit of trouble. The four books on this list deal with common themes: intelligent robots that are contemplating the nature of their existence, and malevolent AI that seek the destruction of humanity (and the link between the two). When it comes to machine intelligence, we will reap what we sow, as these novels make evidently clear. Thirty years ago, humans lost the war with their servants, robots they created.

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.

The Libyan National Army has been battling ISIS in the cities of Sirte and Benghazi. The U.S. military has launched airstrikes this month in Yemen, Somalia, Iraq, Syria, Afghanistan and Friday, for the first time since September, in Libya. According to a defense official, the drone strike in the desert of central Libya Friday killed "several" ISIS militants in a sign the Pentagon may be ramping up pressure on terror groups in Africa. The most recent strike comes a year after the military launched nearly 500 airstrikes against ISIS in the coastal city of Sirte, located halfway between Tripoli and Benghazi. The September strike killed 17 ISIS fighters.

We describe a method to identify poor households in data-scarce countries by leveraging information contained in nationally representative household surveys. It employs standard statistical learning techniques---cross-validation and parameter regularization---which together reduce the extent to which the model is over-fitted to match the idiosyncracies of observed survey data. The automated framework satisfies three important constraints of this development setting: i) The prediction model uses at most ten questions, which limits the costs of data collection; ii) No computation beyond simple arithmetic is needed to calculate the probability that a given household is poor, immediately after data on the ten indicators is collected; and iii) One specification of the model (i.e. one scorecard) is used to predict poverty throughout a country that may be characterized by significant sub-national differences. Using survey data from Zambia, the model's out-of-sample predictions distinguish poor households from non-poor households using information contained in ten questions.

Hamas has blamed the Israeli national intelligence agency Mossad for the assassination of one of its Tunisian members after conducting an 11-month-long investigation. The Palestinian group said Mohammed al-Zawari, a commander of its armed wing the Qassam Brigades since 2006, was fatally shot outside his home multiple times while in his car near Sfax, 270km southeast of Tunis, on December 15, 2016. Hamas had set up an investigative committee in the immediate aftermath of the assassination. Speaking at a press conference in Beirut on Thursday, Mohammed Nazzal, Hamas politburo member, called the Mossad operation a "terrorist act". "Mossad is officially accused of being behind the assassination, which is not only a terrorist act, but a violation of state sovereignty," he said.

Artificial intelligence (AI) poses the greatest threat to the oil and gas industry over the next five to 10 years, according to Microsoft's oil and gas director for the Middle East and Africa.

'A stable bright light, larger than anything practical shined into my room on the second floor, not making any noise; it disappeared.' It's one of nearly 60,000 unsettling stories revealed in a new map of the contiguous United States, compiling UFO sightings from every state, dating back to 1995. While these mysterious encounters may largely have slipped out of the public eye after the Cold War-era UFO craze died down, the map shows reports have steadily grown in the last two decades, hitting a mid-summer peak each year. The map shows these reports are concentrated in major cities and dense population hubs, making places like New York and the surrounding metropolitan area hotspots for UFO sightings, along with southern and central California. The new map of reported UFO sightings in the US was created by Data Solutions Engineer Adam Crahen of the Data Duo, using data from Kaggle UFO sightings. There's little doubt that the internet has played a role in the growth of UFO reports in recent years, though most can be explained by natural or human-caused phenomena.