The Commander-in-Chief of Russia's air force Viktor Bondarev has told a gathering at the MAKS-2017 international airshow his aircraft would soon be getting cruise missiles with artificial intelligence capable of analysing its environment and opponents and make "decisions" about altitude, speed, course -- and targets. "Work in this area is underway," Russian news agency TASS reports Tactical Missiles Corporation CEO Boris Obnosov as adding. "As of today, certain successes are available, but we'll still have to work for several years to achieve specific results." While neither indicated which missiles were slated to get such enhanced artificial intelligence, there are two apparent contenders among the "super weapons" President Vladimir Putin bragged about last year: the "Avangard" hypersonic glide vehicle and the "Burevestnik" nuclear-powered cruise missile. RELATED: Why the world's most holy place sends people crazy RELATED: Earth's magnetic pole is on the move and we don't know why Much modern weaponry is already capable of making choices -- such as the automated Gatling guns designed to react and shoot-down incoming missiles in the blink of an eye.

The current crop also includes applications of on-demand manufacturing, augmented reality, music-assisted learning, interactive video, and online music creation. The Aussie team got the nod for their knack at using AI to separate, classify and up-res audio "for the purpose of audio search, discovery, recommendation, personalization, and quality enhancement," which works with studio, UGC audio and, maybe, live mobile communication, a statement from Techstars Music reads. Software engineer and chief of Mawson and Popgun Stephen Phillips paid tribute to his fellow Brisbanites with a tweet. Two more AI startups from Mawson are ready for take off. Both Replica and SuperRes have joined the Techstars Music 2019 program in LA. White says she's "excited and super grateful" to be a member of Techstars' 2019 class of music-based startups.

Nearly a third of the world's food supply gets thrown out -- from produce surplus in farmers' fields to expired products discarded by retailers to leftovers. That's the issue Timothy Richards, the Morrison Chair of Agribusiness in the W. P. Carey School of Business at Arizona State University, will be trying to solve with a new grant from the USDA's Agriculture and Food Research Initiative (NIFA). "Food waste occurs at virtually all stages of the supply chain from the farmer to the retailer to the consumer -- resulting in the disposal of potentially usable food in nearly every sector of the food system in the distribution channel between farmers and consumers," Richards said. The goal of the research is to combine grocers' inventory with machine learning algorithms to develop a better system for matching supply to consumer demand fluctuations. This would ensure customers get what they want without the need for excess food.

Generative Adversarial Networks, or GANs, are an architecture for training generative models, such as deep convolutional neural networks for generating images. The GAN architecture is comprised of both a generator and a discriminator model. The generator is responsible for creating new outputs, such as images, that plausibly could have come from the original dataset. The generator model is typically implemented using a deep convolutional neural network and results-specialized layers that learn to fill in features in an image rather than extract features from an input image. Two common types of layers that can be used in the generator model are a upsample layer (UpSampling2D) that simply doubles the dimensions of the input and the transpose convolutional layer (Conv2DTranspose) that performs an inverse convolution operation. In this tutorial, you will discover how to use UpSampling2D and Conv2DTranspose Layers in Generative Adversarial Networks when generating images. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.

Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Wimbledon is one of the most prestigious major events in the world. With over 675 matches played and over 147,000 tennis points played, its size and scale are substantial. In fact, even if fans diligently watch their favorite players, they will miss a high proportion of the played points. Wimbledon uses IBM digital and AI capabilities to provide rapid access to match highlights to serve up the best content to fans.

Artificial intelligence (AI) programs have bested humans in checkers, chess, go and two-player poker, but multiplayer poker was always believed to be a bigger ask. Researchers at Carnegie Mellon University, working with Facebook's AI initiative, on Thursday announced that their program defeated a group of top professionals in six-player no-limit Texas Hold'em. The program, Pluribus, and its big wins were described in the US journal Science. "Pluribus achieved superhuman performance at multiplayer poker, which is a recognized milestone in artificial intelligence and in game theory," Carnegie Mellon computer science professor Tuomas Sandholm said. Sandholm worked with Noam Brown, who is working at Facebook AI while completing his doctorate at the Pittsburgh-based university.

When it comes to longevity, it's helpful to think of your personal expiration date as something more than just the day you'll die. Religion and spirituality have a variety of takes on mortality, as does the rapidly progressing world of science. A common medical view is that there are two types of age: your chronological age and your biological age. Chronologically, the number of years you lived is considered your age, and when you pass away, that total becomes your age of death. It's simple math, and the averages are used to determine a generalized sense of life expectancy for massive populations.

UNESCO has published the Beijing Consensus on Artificial Intelligence (AI) and Education, the first ever document to offer guidance and recommendations on how best to harness AI technologies for achieving the Education 2030 Agenda. It was adopted during the International Conference on Artificial Intelligence and Education, held in Beijing from 16 – 18 May 2019, by over 50 government ministers, international representatives from over 105 Member States and almost 100 representatives from UN agencies, academic institutions, civil society and the private sector. The Beijing Consensus comes after the Qingdao Declaration of 2015, in which UNESCO Member States committed to efficiently harness emerging technologies for the achievement of SDG 4. Ms Stefania Giannini, Assistant Director-General for Education at UNESCO, stated that ''we need to renew this commitment as we move towards an era in which artificial intelligence – a convergence of emerging technologies – is transforming every aspect of our lives (…) we need to steer this revolution in the right direction, to improve livelihoods, to reduce inequalities and promote a fair and inclusive globalization.'' The Consensus affirms that the deployment of AI technologies in education should be purposed to enhance human capacities and to protect human rights for effective human-machine collaboration in life, learning and work, and for sustainable development. The Consensus states that the systematic integration of AI in education has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. In summary, the Beijing Consensus recommends governments and other stakeholders in UNESCO's Member States to: The Consensus also details its ambitions for UNESCO to act as a support system for the capacity building of education policy-makers to implement the recommended measures, and to act as a convener for financing, partnership and international cooperation together with other international organizations and partners active in the field of AI in education.

Apple Park, the corporate HQ of Apple Inc., located in California. Digitalization is evolving from an economic challenge to a governance and political problem. Some studies suggest that by 2030, Artificial Intelligence (AI) might contribute up to EUR 13.33 trillion to the global economy (more than the current output of China and India combined). The essence of the political conflict that raises the issue of global governance is what type of actor (a state or a digital corporation) will lead this process, creating global asymmetry in terms of trade, information flows, social structures and political power. This means challenging the international system as we know it. AI is generating new large-scale systems based on (1) services (such as traffic management and smart vehicles, international banking systems, and new healthcare ecosystems); (2) global value chains, the Internet of things (IoT) and robotics (Industry 4.0); and (3) electronics with a new generation of microprocessors and highly specialized chips.

Mixtures-of-Experts (MoE) are conditional mixture models that have shown their performance in modeling heterogeneity in data in many statistical learning approaches for prediction, including regression and classification, as well as for clustering. Their estimation in high-dimensional problems is still however challenging. We consider the problem of parameter estimation and feature selection in MoE models with different generalized linear experts models, and propose a regularized maximum likelihood estimation that efficiently encourages sparse solutions for heterogeneous data with high-dimensional predictors. The developed proximal-Newton EM algorithm includes proximal Newton-type procedures to update the model parameter by monotonically maximizing the objective function and allows to perform efficient estimation and feature selection. An experimental study shows the good performance of the algorithms in terms of recovering the actual sparse solutions, parameter estimation, and clustering of heterogeneous regression data, compared to the main state-of-the art competitors.