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.

Sophia was made by Hanson Robotics, based in Hong Kong. It is currently a demonstration product doing a tour of the world's media. Business Insider caught up with it at Web Summit, the gigantic tech conference in Lisbon. We asked it a few unplanned questions and got a variety of answers, ranging in quality from impressive to nonsensical. Sophia delivered its side of the interview while making a series of faces, some eerily appropriate, some grotesquely bizarre. It has a habit of moving its eyebrows and eyelids independently, rather than together, for instance.

We address the problem of bootstrapping language acquisition for an artificial system similarly to what is observed in experiments with human infants. Our method works by associating meanings to words in manipulation tasks, as a robot interacts with objects and listens to verbal descriptions of the interactions. The model is based on an affordance network, i.e., a mapping between robot actions, robot perceptions, and the perceived effects of these actions upon objects. We extend the affordance model to incorporate spoken words, which allows us to ground the verbal symbols to the execution of actions and the perception of the environment. The model takes verbal descriptions of a task as the input and uses temporal co-occurrence to create links between speech utterances and the involved objects, actions, and effects. We show that the robot is able form useful word-to-meaning associations, even without considering grammatical structure in the learning process and in the presence of recognition errors. These word-to-meaning associations are embedded in the robot's own understanding of its actions. Thus, they can be directly used to instruct the robot to perform tasks and also allow to incorporate context in the speech recognition task. We believe that the encouraging results with our approach may afford robots with a capacity to acquire language descriptors in their operation's environment as well as to shed some light as to how this challenging process develops with human infants.

We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the more favorable one. The training relies entirely on datasets of several million chess games, and no further domain specific knowledge is incorporated. The experiments show that the resulting neural network (referred to as DeepChess) is on a par with state-of-the-art chess playing programs, which have been developed through many years of manual feature selection and tuning. DeepChess is the first end-to-end machine learning-based method that results in a grandmaster-level chess playing performance.

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG). We introduce here the first deep learning approach for sleep stage classification that learns end-to-end without computing spectrograms or extracting hand-crafted features, that exploits all multivariate and multimodal Polysomnography (PSG) signals (EEG, EMG and EOG), and that can exploit the temporal context of each 30s window of data. For each modality the first layer learns linear spatial filters that exploit the array of sensors to increase the signal-to-noise ratio, and the last layer feeds the learnt representation to a softmax classifier. Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees. Results obtained on 61 publicly available PSG records with up to 20 EEG channels demonstrate that our network architecture yields state-of-the-art performance. Our study reveals a number of insights on the spatio-temporal distribution of the signal of interest: a good trade-off for optimal classification performance measured with balanced accuracy is to use 6 EEG with 2 EOG (left and right) and 3 EMG chin channels. Also exploiting one minute of data before and after each data segment offers the strongest improvement when a limited number of channels is available. As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals in order to deliver state-of-the-art classification performance with a small computational cost.

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.

The difference between the robots of today and all previous forms of automation is that they are so flexible (Editorial, 25 November). Intelligent robots will be utilised in any new enterprise rather than people now because the financial returns are likely to be so much greater, given that there will be no recruitment difficulties, wage demands, overtime claims, strikes, sickness absence, pensions, transport or housing problems to take care of. Factories can be situated anywhere, and HS2 could be redundant before it becomes operational. In the past, workers displaced by automation could rely on new industries springing up to take them on, but in future these will create far more jobs for robots than people across the board. Our whole economic system, which concentrates on profitability and economics rather than the welfare of the population, can only encourage this trend.

I welcome this opportunity to learn from the vast amount of technical expertise assembled in this room. Market analysts predict that intelligent machines, programmed to think and reason like the human mind, will revolutionize health care in the very near future. In fact, proponents of the transformative power of artificial intelligence usually give two examples: self-driving cars and the delivery of health care. This year's influential Internet Trends Report, released last week in the USA, covers the effects of new technology on health care for the first time, again predicting a huge transformative impact. Artificial intelligence is a new frontier for the health sector.

The proposal to open Café fellatio, an establishment in Geneva, Switzerland where men would be able to get oral sex while drinking their coffee, was met with no uncertain outrage. And city authorities have decided it's also against Swiss law. It's not clear what the robots would look like or what they'd be able to do. The Geneva authorities have also yet to make up their mind whether that's an acceptable solution. On the one hand, you could argue that these sorts of robots, presumably looking as human-like as possible, are nothing more than technologically advanced sex toys--the dildos and fleshlights of the digital age.

Cyber security companies are turning to artificial intelligence and machine learning tools to ward off growing number of attacks on networks, Finland- based internet security firm F-Secure said. As the world is fast moving towards Internet of Things and connected devices, deployment of artificial intelligence (AI) has become inevitable for cyber security firms to analyse huge amount of data to save networks from infiltration attempts, F-Secure's Security Advisor Sean Sullivan said. Networks are persistently exposed to threats like malware, phishing, password breaches and denial of service attacks. On a daily basis, F-Secure Labs on an average receives sample data of 500,000 files from its customers that include 10,000 malware variants and 60,000 malicious URLs for analysis and protection, Sullivan said. For humans, it is a big task to go through such huge amount of data and machine learning tools and AI are lending a helping hand at this stage, he said.