Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms. Learning forward models is an interesting and important challenge in order to address problems where a model is not available. Building upon previous work on the Neural GPU, this paper introduces the Neural Game Engine, as a way to learn models directly from pixels. The learned models are able to generalise to different size game levels to the ones they were trained on without loss of accuracy. Results on 10 deterministic General Video Game AI games demonstrate competitive performance, with many of the games models being learned perfectly both in terms of pixel predictions and reward predictions. The pre-trained models are available through the OpenAI Gym interface and are available publicly for future research here: \url{https://github.com/Bam4d/Neural-Game-Engine}

A new primary torque control concept for hydrostatics mobile machines was introduced in 2018. The mentioned concept controls the pressure in a closed circuit by changing the angle of the hydraulic pump to achieve the desired pressure based on a feedback system. Thanks to this concept, a series of advantages are expected. However, while working in a Y cycle, the primary torque-controlled wheel loader has worse performance in efficiency compared to secondary controlled earthmover due to lack of recuperation ability. Alternatively, we use deep learning algorithms to improve machines' regeneration performance. In this paper, we firstly make a potential analysis to show the benefit by utilizing the regeneration process, followed by proposing a series of CRDNNs, which combine CNN, RNN, and DNN, to precisely detect Y cycles. Compared to existing algorithms, the CRDNN with bi-directional LSTMs has the best accuracy, and the CRDNN with LSTMs has a comparable performance but much fewer training parameters. Based on our dataset including 119 truck loading cycles, our best neural network shows a 98.2% test accuracy. Therefore, even with a simple regeneration process, our algorithm can improve the holistic efficiency of mobile machines up to 9% during Y cycle processes if primary torque concept is used.

Healthcare is one of the major success stories of our times. Medical science has improved rapidly, raising life expectancy around the world, but as longevity increases, healthcare systems face growing demand for their services, rising costs and a workforce that is struggling to meet the needs of its patients. Demand is driven by a combination of unstoppable forces: population aging, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation being but a few. By 2050, one in four people in Europe and North America will be over the age of 65--this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management.

A challenge on the data science community site Kaggle is asking great minds to apply machine learning to battle the COVID-19 coronavirus pandemic. As COVID-19 continues to spread uncontrolled around the world, shops and restaurants have closed their doors, information workers have moved home, other businesses have shut down entirely, and people are social distancing and self-isolating to "flatten the curve." It's only been a few weeks, but it feels like forever. If you listen to the scientists, we have a way to go still before we can consider reopening and reconnecting. The worst is yet to come for many areas.

The explosion of breakthroughs, investments, and entrepreneurial activity around artificial intelligence over the last decade has been driven exclusively by deep learning, a sophisticated statistical analysis technique for finding hidden patterns in large quantities of data. A term coined in 1955--artificial intelligence--was applied (or mis-applied) to deep learning, a more advanced version of an approach to training computers to perform certain tasks--machine learning--a term coined in 1959. The recent success of deep learning is the result of the increased availability of lots of data (big data) and the advent of Graphics Processing Units (GPUs), significantly increasing the breadth and depth of the data used for training computers and reducing the time required for training deep learning algorithms. The term "big data" first appeared in computer science literature in an October 1997 article by Michael Cox and David Ellsworth, "Application-controlled demand paging for out-of-core visualization," published in the Proceedings of the IEEE 8th conference on Visualization. They wrote that "Visualization provides an interesting challenge for computer systems: data sets are generally quite large, taxing the capacities of main memory, local disk, and even remote disk. We call this the problem of big data. When data sets do not fit in main memory (in core), or when they do not fit even on local disk, the most common solution is to acquire more resources."

In the modern era, each industry seems to grow with the technology that supports it. Looking to the cannabis business of today, it's amazing to see how sophisticated and modernized this once grassroots and obscure industry has become. To this end, the cannabis industry of 2019 is beginning to mirror more mainstream businesses, as well as share in the technological advancements that support them. Of the novel technologies being entertained in the cannabis space, artificial intelligence shows some promising potential on the cybersecurity front. In any U.S. state with a legal cannabis market, compliance and security are some of the most integral features of successful business operations.

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An artificial intelligence can accurately translate thoughts into sentences, at least for a limited vocabulary of 250 words. The system may bring us a step closer to restoring speech to people who have lost the ability because of paralysis. Joseph Makin at the University of California, San Francisco, and his colleagues used deep learning algorithms to study the brain signals of four women as they spoke. The women, who all have epilepsy, already had electrodes attached to their brains to monitor seizures. Each woman was asked to read aloud from a set of sentences as the team measured brain activity.

In our early 20s, we could spend whole days immersed in epic role-playing video games, sacrificing months to the demands of the latest Final Fantasy or Dragon Quest adventure. But, in our 30s and 40s, we're lucky to catch 10 minutes of Fortnite here and there. However, now that many of us are finding we have time on our hands, it could be the opportunity we need to attempt some of the more chronologically demanding narrative video game masterpieces of the last decade. Here are 15 that should see you through the next six months – and beyond. Ubisoft's tale of vengeance-seeking mercenaries battling through the chaos of the Peloponnesian war gives us a vast reproduction of ancient Greece, almost Homeric in its depth and detail.

New technology that can read minds and turn thoughts into complete sentences with the help of AI is giving hope to people who can't speak. Researchers from the University of California say their technology is able to translate brain activity into English word by word with the help of machine learning. The technology could revolutionise the way people who can't speak or move are able to communicate, as it is more natural than existing tools, the team say. It has an accuracy rate of 97 per cent - more than twice as high as other brain-signal decoding devices and works by mapping activity of neurons to words. Translating neurons to words enables it to type word sequences on a computer interface in real time - which can then be read out by a synthetic voice.