Deep Neural Networks (DNN) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time. Therefore, reducing the energy consumption has become a major design consideration in DNN training. This paper proposes the first end-to-end DNN training framework that provides quantitative energy guarantees. The key idea is to formulate the DNN training as an optimization problem in which the energy budget imposes a previously unconsidered optimization constraint. We integrate the quantitative DNN energy estimation into the DNN training process to assist the constraint optimization. We prove that an approximate algorithm can be used to efficiently solve the optimization problem. Compared to the best prior energy-saving techniques, our framework trains DNNs that provide higher accuracies under same or lower energy budgets.

When Azeem asked me to give a talk at CogX, he asked me to focus on just a single point that I wanted the audience to take away. A few years ago my priority would have been convincing people that deep learning was a real revolution, not a fad, but there have been enough examples of shipping products that that question seems answered. I knew this was true before most people not because I'm any kind of prophet with deep insights, but because I'd had a chance to spend a lot of time running hands-on experiments with the technology myself. I could be confident of the value of deep learning because I had seen with my own eyes how effective it was across a whole range of applications, and knew that the only barrier to seeing it deployed more widely was how long it takes to get from research to deployment. Instead I chose to speak about another trend that I am just as certain about, and will have just as much impact, but which isn't nearly as well known.

Brazilian startup Cobli has specialized in technological solutions for vehicle fleet monitoring and management. It is currently focusing on safety and refining a tool to identify driver behavioral patterns by analyzing data collected by a solar-powered tracker. The project is based on machine learning, an application of artificial intelligence, and had the support from the São Paulo Research Foundation--FAPESP through its Innovative Research in Small Business Program (PIPE). "The algorithm uses the data collected to establish a driving profile with more than 90% accuracy," says engineer Rodrigo Mourad, a partner and co-founder of Cobli. According to Mourad, in one or two weeks of use, the system can glean a sufficient amount of data--on speed, acceleration, braking and curve angles--to produce a profile of the driver's vehicle handling habits. Directly linked to the question of traffic safety, these data also have an economic and financial impact on the fleet owner's business since aggressive driving increases fuel consumption and the cost of vehicle maintenance.

Drones have become the ultimate option for monitoring, surveying and inspection. Enel, the multinational energy company has implemented this strategy and selected Percepto's Sparrow system to monitor the Torrevaldaliga Nord power plant in Italy. The Sparrow's AI and computer vision technology will allow it to operate as independently as possible, and the collected aerial footage, photography is transmitted to Enel in real-time. "While drones are touted as the technology of the future, the ability to act autonomously unlocks their true potential, enabling them to act as a responsible, independent and smart team member that provides not only a bird's eye view of facilities, but real, actionable insights," said Percepto CEO, Dor Abuhasira. The goal is to introduce cost-effective and practical drone support to a business model attempting to continuously refine itself.

Last week, IBM announced, in conjunction with the U.S. Department of Energy's Oak Ridge National Laboratory (ORNL), some impressive AI performance numbers from the Summit supercomputer. ORNL is hailing Summit as the world's "most powerful and smartest supercomputer" currently in existence, unseating the previous record-holder, a Chinese supercomputer named Sunway TaihuLight. Based on its self-published, mixed precision performance numbers, it is the new AI beast. Summit was designed with 4,608 of IBM's newest generation POWER9 Systems (which I've written previously on here and here) and 27,648 of NVIDIA's Volta GPUs. IBM says this is the first supercomputer to be designed expressly for the purpose of AI which wasn't an accident, by the way- it was by design.

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

We present KBLRN, a framework for end-to-end learning of knowledge base representations from latent, relational, and numerical features. KBLRN integrates feature types with a novel combination of neural representation learning and probabilistic product of experts models. To the best of our knowledge, KBLRN is the first approach that learns representations of knowledge bases by integrating latent, relational, and numerical features. We show that instances of KBLRN outperform existing methods on a range of knowledge base completion tasks. We contribute a novel data sets enriching commonly used knowledge base completion benchmarks with numerical features. The data sets are available under a permissive BSD-3 license. We also investigate the impact numerical features have on the KB completion performance of KBLRN.

Researchers from American University of Sharjah's (AUS) College of Engineering are benefitting from the university's new research agenda, with students and faculty working on research projects likely to have a global impact.

The US just took back the title for the world's fastest supercomputer. On Friday, the US Department of Energy's Oak Ridge National Laboratory (ORNL) in Tennessee unveiled the'Summit' supercomputer that can deliver a peak performance of 200 petaflops, or about 200 quadrillion calculations per second. It managed to beat out the previous record holder that was China's Sunway TaihuLight supercomputer. Summit is 60% faster than the TaihuLight supercomputer, which could achieve a peak performance of 93 petaflops. The feat puts the US at the front of the top 500 supercomputers in the world -- the first time it has held such ranking since June 2013. Summit has been in development for several years now and is made up of thousands of chips.

Computers can beat chess champions, simulate star explosions, and forecast global climate. We are even teaching them to be infallible problem-solvers and fast learners. And now, physicists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and their collaborators have demonstrated that computers are ready to tackle the universe's greatest mysteries. The team fed thousands of images from simulated high-energy particle collisions to train computer networks to identify important features. The researchers programmed powerful arrays known as neural networks to serve as a sort of hivelike digital brain in analyzing and interpreting the images of the simulated particle debris left over from the collisions.