Samsung is still two days away from officially unveiling its new Galaxy device, but a series of leaks surrounding the Note 9 in recent weeks means there's not much left to unveil. The 9 August "Unpacked" event has been preceded by numerous images and specs sheets revealing key features and details about the premium smartphone, including a possible price. Depending on the variant, the Galaxy Note 9 will cost between £899 (128GB version) and £1,099 (512GB version), according to the latest leak from Roland Quandt. So what can customers expect to get for this price? The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

News about Machine Learning (ML), Artificial Intelligence (AI) and related research areas. IBM believes that blockchains can help reduce carbon emissions by turning carbon credits into crypto-tokens. The world moving towards a "token-driven economy" is supposedly part of a scheme to create a massive marketplace of novel digital assets. IBM hopes to build software platforms for trading these tokens. And one of the first things it plans to help digitize has a bonus feature: it benefits the environment.

Organizations have been tracking and reporting on their assets – including people, processes, and physical things such as computers – for years. Companies use the information they get to help lower operating costs, reduce risk and outages, improve regulatory compliance and take control of capital expenditure planning. But, like other fields, the rapid advance of technology is disrupting enterprise asset intelligence. Artificial intelligence technology like machine learning has been particularly influential. In this post, we'll review what asset intelligence is today and how machine learning will impact it going forward.

While it is relatively easy to start an online advertising campaign, obtaining a high Key Performance Indicator (KPI) can be challenging. A large body of work on this subject has already been performed and platforms known as DSPs are available on the market that deal with such an optimization. From the advertiser's point of view, each DSP is a different black box, with its pros and cons, that needs to be configured. In order to take advantage of the pros of every DSP, advertisers are well-advised to use a combination of them when setting up their campaigns. In this paper, we propose an algorithm for advertisers to add an optimization layer on top of DSPs. The algorithm we introduce, called SKOTT, maximizes the chosen KPI by optimally configuring the DSPs and putting them in competition with each other. SKOTT is a highly specialized iterative algorithm loosely based on gradient descent that is made up of three independent sub-routines, each dealing with a different problem: partitioning the budget, setting the desired average bid, and preventing under-delivery. In particular, one of the novelties of our approach lies in our taking the perspective of the advertisers rather than the DSPs. Synthetic market data is used to evaluate the efficiency of SKOTT against other state-of-the-art approaches adapted from similar problems. The results illustrate the benefits of our proposals, which greatly outperforms the other methods.

Multi-output Gaussian processes (MOGPs) are recently extended by using spectral mixture kernel, which enables expressively pattern extrapolation with a strong interpretation. In particular, Multi-Output Spectral Mixture kernel (MOSM) is a recent, powerful state of the art method. However, MOSM cannot reduce to the ordinary spectral mixture kernel (SM) when using a single channel. Moreover, when the spectral density of different channels is either very close or very far from each other in the frequency domain, MOSM generates unreasonable scale effects on cross weights which produces an incorrect description of the channel correlation structure. In this paper, we tackle these drawbacks and introduce a principled multi-output convolution spectral mixture kernel (MOCSM) framework. In our framework, we model channel dependencies through convolution of time and phase delayed spectral mixtures between different channels.

Artificial intelligence is no different than the cotton gin, telecommunication satellites or nuclear power plants. It's a technology, one with the potential to vastly improve the lives of every human on Earth, transforming the way that we work, learn and interact with the world around us. But like nuclear science, AI technology also carries the threat of being weaponized -- a digital cudgel with which to beat down the working class and enshrine the current capitalist status quo. Just look at how Amazon's automated facial recognition system is being marketed to law enforcement and government agencies, despite its obvious racial biases, or Wisconsin's automated sentencing tool, Compas, which determines a defendant's prison time via a proprietary and secret algorithm. It just so happens to sentence black and brown defendants to longer terms than their white counterparts for similar crimes.

As convolutional neural networks (CNNs) enable state-of-the-art computer vision applications, their high energy consumption has emerged as a key impediment to their deployment on embedded and mobile devices. Towards efficient image classification under hardware constraints, prior work has proposed adaptive CNNs, i.e., systems of networks with different accuracy and computation characteristics, where a selection scheme adaptively selects the network to be evaluated for each input image. While previous efforts have investigated different network selection schemes, we find that they do not necessarily result in energy savings when deployed on mobile systems. The key limitation of existing methods is that they learn only how data should be processed among the CNNs and not the network architectures, with each network being treated as a blackbox. To address this limitation, we pursue a more powerful design paradigm where the architecture settings of the CNNs are treated as hyper-parameters to be globally optimized. We cast the design of adaptive CNNs as a hyper-parameter optimization problem with respect to energy, accuracy, and communication constraints imposed by the mobile device. To efficiently solve this problem, we adapt Bayesian optimization to the properties of the design space, reaching near-optimal configurations in few tens of function evaluations. Our method reduces the energy consumed for image classification on a mobile device by up to 6x, compared to the best previously published work that uses CNNs as blackboxes. Finally, we evaluate two image classification practices, i.e., classifying all images locally versus over the cloud under energy and communication constraints.

Machine learning, combined with satellite imagery and Cloud computing, is enabling understanding of the world and making the food supply chain more efficient. There are more than 7 billion people on Earth now, and roughly one in eight people do not have enough to eat. According to the World Bank, the human population will hit an astounding 9 billion by 2050. With rapidly increasing population, the growing need for food is becoming a grave concern. The burden is now on technology to make up for the looming food crises in the coming decades.

Don't be surprised if you soon see VR video from some of the darker corners of the ocean. Marine Imaging Technologies has launched a new camera, the Hydrus VR, that promises 360-degree 8K video (higher-resolution than many current headsets) at depths of up to 984 feet, even in lighting conditions as dim as 0.004 lux. The 10-camera array takes advantage of new ultra-sensitive Sony sensors to capture video at up to ISO 409,600 -- the result will be noisy, but might be the key to spotting an elusive fish hiding in a cave. The system is also helpful for undersea movie makers willing to wait to get the perfect shot. It normally has enough battery power and storage to record two hours of footage, but a subsea control module can extend that capture time to eight hours. You can integrate it with robotic vehicles to keep humans far away from the action.

Venezuela President Nicolas Maduro may be the first major leader to have faced a drone-based assassination attempt. Information Minister Jorge Rodriguez claimed that at least one bomb-laden drone had exploded close to Maduro while he was giving a speech at a Caracas event on August 4th. The authoritarian leader was unhurt in the incident, but seven National Guard soldiers were reportedly injured. Video from state TV showed Maduro and officials looking up in a moment of panic before the camera cut away, with the assembled troops running soon afterward. The country has seen mounting unrest over recent years due to both Maduro's anti-democratic policies and an economic collapse triggered in part by plummeting oil prices.