Did you ever watch the 2015 movie Chappie? Set in the 22nd Century in South Africa, it's about a robotic police force fighting crime, the development of an artificial intelligence chip, and a robot named Chappie who begins to think like a human. Even since 2001 when Steven Spielberg's science fiction drama AI hit theaters, humanoid robots and artificial intelligence have been considered synonymous. In reality, though, the artificial intelligence industry is much broader. This essential part of the technology sector aims to create intelligent machines of all kinds that work and react like humans.

Recent studies on knowledge base completion, the task of recovering missing facts based on observed facts, demonstrate the importance of learning embeddings from multi-step relations. Due to the size of knowledge bases, previous works manually design relation paths of observed triplets in symbolic space (e.g. random walk) to learn multi-step relations during training. However, these approaches suffer some limitations as most paths are not informative, and it is prohibitively expensive to consider all possible paths. To address the limitations, we propose learning to traverse in vector space directly without the need of symbolic space guidance. To remember the connections between related observed triplets and be able to adaptively change relation paths in vector space, we propose Implicit ReasoNets (IRNs), that is composed of a global memory and a controller module to learn multi-step relation paths in vector space and infer missing facts jointly without any human-designed procedure. Without using any axillary information, our proposed model achieves state-of-the-art results on popular knowledge base completion benchmarks.

Second, our regulatory regime makes it more difficult to build things in the United States and sell them to other countries, creating a market for foreign competitors who would otherwise not stand a chance. For years, the United States curbed exports of encryption technology and basic processors. This only led international competitors to fulfill demand, creating a market for themselves. When U.S. allies like Saudi Arabia, Pakistan, the United Arab Emirates, and Turkey needed access to unmanned aerial systems to prosecute the war on terror, these requests were delayed or denied. We have since lost almost all of these markets to Chinese exports and indigenous development.

What would our computers tell us if we gave them a voice? We'll soon find out thanks to Natural Language Generation which gives computers a written opinion on virtually anything. For now, we must program their responses, but soon they'll form their own opinions and develop a creative voice. This may seem a long way off, so let's consider their progression as a writer in comparison to a human. A child progresses as a writer by starting with basic creative writing exercises: What did you do over summer break?

We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capable of conversing with humans on popular small talk topics through both speech and text. The system consists of an ensemble of natural language generation and retrieval models, including template-based models, bag-of-words models, sequence-to-sequence neural network and latent variable neural network models. By applying reinforcement learning to crowdsourced data and real-world user interactions, the system has been trained to select an appropriate response from the models in its ensemble. The system has been evaluated through A/B testing with real-world users, where it performed significantly better than many competing systems. Due to its machine learning architecture, the system is likely to improve with additional data.

Can you tell us a little bit about your background and why you set up Artificial Lawyer? About 10 years ago, I started working at a large US consultancy as a strategy consultant to law firms. I then worked for a smaller boutique consultancy in the City of London. Two years ago, I set up my own business, Tromans Consulting, and I've been advising law firms on strategy and business decisions since. It became quite clear to me about 18 months ago that AI was going to have a profound impact as it evolves and spreads through the market, and I needed to take this into account when advising my clients.

The boss of a Manchester-based data analytics and artificial intelligence firm believes it has the potential to become a tech unicorn. Richard Potter is the chief executive of Peak, which secured £2.5 million in a Series A funding round led by London-based venture capital firm MMC Ventures in September. The firm has since unveiled a new website and brand identity which places a stronger focus on its AI and machine learning offering. In an interview with BusinessCloud, Potter said the company's ambition is to be a global technology champion in the AI and data analytics-as-a-service space. "Specifically for what we do, we think it's a nascent market with no other operators in this space because we're offering a service powered by our own technology platform," he said.

So, what's Yann LeCun talking about when he says "he's ready to throw Probability Theory under the bus"? This article attempts to explore this sentiment. The problem with Probability Theory has to do with its efficacy in making predictions. It's obvious that the distributions are different, unfortunately the statistical measures are identical! Said differently, if the basis of your predictions are expectations calculated from probability distributions, then you can very easily be fooled.

Most of us followed the exciting introduction of the new iPhone X, and there you also saw in the press conference, it's all about machine learning now for face recognition, applied also, machine learning in face recognition to unlock your phone. So, I think we all experience it already with our smartphones, and going forward, we'll see much more of it. Michael Chui: What we're starting to see is these AI technologies underpinning a lot of the things, all the online and mobile services that we're now increasingly taking advantage of. So, for instance, in e-commerce or media, when systems are providing you with suggestions for things you might be interested in, things you might be interested in reading or things you might be interested in buying--the next-product-to-buy use case, as we describe it--increasingly, those types of systems are powered not only by statistical methods, but by some of these AI technologies as well, hopefully bringing consumers closer to the things that they'd be most interested in. Simon London: I'm going to throw one more into the pot there. I'm lucky enough to live in the city of Mountain View in Silicon Valley. There are a surprising number of self-driving cars out on the road.

Renowned physicist Prof Stephen Hawking has said robots will eventually completely replace humanity. Prof Hawking said that he believes artificial intelligence (AI) will eventually reach a level where it will essentially be a "new form of life that will outperform humans" in an interview with WIRED magazine. He said: "I fear that AI may replace humans altogether. If people design computer viruses, someone will design AI that improves and replicates itself. This will be a new form of life that outperforms humans."