Hunt brilliantly describes how these processes work in "Sheep Logic"; a masterpiece. My chapter 20 contains a short version of the theory.

Hewlett Packard Enterprise (HPE) today announced new offerings to help customers ramp up, optimize and scale artificial intelligence (AI) usage across business functions to drive outcomes such as better demand forecasting, improved operational efficiency and increased sales. PricewaterhouseCoopers predicts the global GDP to grow 14 percent – the equivalent of $15.7 trillion – by 2030 as a result of AI, with increased labor productivity and consumer demand being the most impactful business outcomes.(2) However, while AI holds great promise, current adoption rates are low. According to Gartner's 2018 CIO Agenda Survey, four percent of CIOs globally have implemented AI, while a further 46 percent have developed plans to do so.(3) "Global tech giants are investing heavily in AI, but the majority of enterprises are struggling both with finding viable AI use cases and with building technology environments that support their AI workloads. As a result, the gap between leaders and laggards is widening," said Beena Ammanath, Global Vice President, Artificial Intelligence, HPE Pointnext.

It took Ernest Cline 10 years to write Ready Player One. There were times he thought he would never finish the manuscript, let alone publish it. But the novel, mostly set in a global online pleasure world called Oasis, went on to become a bestseller and was translated into more than 20 languages. Now a film adaptation by Steven Spielberg is in cinemas – a real-life geek-to-riches drama so reflective of the book's plot it seems almost unfeasible. The sci-fi story's setup is simple.

Once upon a time, advertising was one of the most creative industries in the country. It was ruled by the Mad Men of Madison Avenue, whose imaginations and creativity were valued above all else. In today's age of big data and artificial intelligence (AI), however, the industry is now ruled by data scientists and numbers nerds. Some of this is for the better. Certainly, many functions in the advertising industry have benefited from AI. From bid rate management and budget allocation to programmatic targeting and A/B optimization, activities that require heavy data processing are now being done much faster and more effectively by machines.

In December 2017, a user named "DeepFakes" posted realistic looking explicit videos of famous celebrities on Reddit. He generated these fake videos using deep learning, the latest in AI, to insert celebrities' faces into adult movies. In the following weeks, the internet exploded with articles about the dangers of face swapping technology: harassing innocents, propagating fake news, and hurting the credibility of video evidence forever. It's true that bad actors will use this technology for harm; but given that the genie is out of the bottle, shouldn't we pause to consider what else DeepFakes could be used for? In this post, I explore the capabilities of this tech, describe how it works, and discuss potential applications.

Animals execute goal-directed behaviours despite the limited range and scope of their sensors. To cope, they explore environments and store memories maintaining estimates of important information that is not presently available. Recently, progress has been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep neural networks, and the excitement surrounding these results has led to the pursuit of related ideas as explanations of non-human animal learning. However, we demonstrate that contemporary RL algorithms struggle to solve simple tasks when enough information is concealed from the sensors of the agent, a property called "partial observability". An obvious requirement for handling partially observed tasks is access to extensive memory, but we show memory is not enough; it is critical that the right information be stored in the right format. We develop a model, the Memory, RL, and Inference Network (MERLIN), in which memory formation is guided by a process of predictive modeling. MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. Our model demonstrates a single learning agent architecture that can solve canonical behavioural tasks in psychology and neurobiology without strong simplifying assumptions about the dimensionality of sensory input or the duration of experiences.

The entertainment and media (E&M) industry is a diverse sector composed of multiple segments including film, television and media streamed online. By 2021, the U.S. E&M industry is projected to reach $759 billion in revenue, increasing at a compound annual growth rate (CAGR) of 3.6 percent. Despite the anticipated growth, there are concerns about a revenue declines in more traditional market segments. As a result, industry analysts such as PwC argue that user experience must take increasing priority and AI is among leading emerging technologies poised to positively contribute to this effort. In this article we break down applications of artificial intelligence in the entertainment and media industry market to provide business leaders with an understanding of current and emerging trends that may impact their sector. We'll begin with a synopsis of the sectors we covered: In the full article below, we'll explore the AI applications of each application by section and provide representative examples.

Obviously not the paper author, but this looks quite interesting. Mostly the fact that it finds all the local minima and can thus select the global minimum from them is nice. Though it would have been nice to see what the tradeoff is in terms of computational space and time complexity compared to error backpropagation.

In some applications of machine learning, predictive performance is all that matters. Indeed, in these cases it is often possible to build a large number of alternative models that, while different in structure, all make excellent near-optimal functional predictions. In biology, however, prediction is not enough. The key additional question is which of the many excellent predictive models is the one actually used by the living system, as optimized not by computation but by evolution. DCell provides proof-ofconcept of a system that, while optimizing functional prediction, respects biological structure.

Huawei may be best known for US retailers not stocking its wares, but regardless, the company continues to ramp up its flagship smartphones. In the past few years, phones like the P9 made a lot of us stand up and take notice, thanks to classy design touches and Huawei's own imaging tricks. Its next phones, the P20 and P20 Pro, take that latter part even further as the company tries to spar with Samsung and the rest with a tapestry of AI skills and so very many camera sensors. There's so much going on when it comes to imaging (both in terms of hardware and software) that, at least during my short time with both phones, I couldn't test out all the modes and use cases. I'll say this, though: Huawei is taking its smartphone cameras very seriously.