Reinforcement learning methods have been used for learning dialogue policies from the experience of conversations. However, learning an effective dialogue policy frequently requires prohibitively many conversations. This is partly because of the sparse rewards in dialogues, and the relatively small number of successful dialogues in early learning phase. Hindsight experience replay (HER) enables an agent to learn from failure, but the vanilla HER is inapplicable to dialogue domains due to dialogue goals being implicit (c.f., explicit goals in manipulation tasks). In this work, we develop two complex HER methods providing different trade-offs between complexity and performance. Experiments were conducted using a realistic user simulator. Results suggest that our HER methods perform better than standard and prioritized experience replay methods (as applied to deep Q-networks) in learning rate, and that our two complex HER methods can be combined to produce the best performance.

Machine intelligence--also known as artificial intelligence--is the ability of machines to perform tasks that would normally require human intelligence. These new machine capabilities both inspire and unnerve: trucks that drive themselves, computer programs that develop drug therapies, software that writes news articles and composes music. But before you can imagine the possibilities, you need to know the basics. This Machine Intelligence Primer will give you a foundational understanding of complex concepts. In it, we discuss where MI came from, how it got to where it is today, and where it's likely going. We are honored to accompany you in your effort to understand and harness MI.

Machine learning aims to create artificial intelligence systems that make decisions with little intervention from people.

Carnegie Mellon University's School of Computer Science announced that a new artificial intelligence (AI) major will be launched this year, the first …

It's been a week of risk in the tech world, and I don't just mean Elon Musk's recent Twitter-on-acid experiment. Best Buy is wagering $800 million on a company that teaches your grandparents how gadgets work, Saint Louis University is peppering its dorms with Echos for some reason, and Reebok is hoping folks won't be too tempted to eat their vegetable-based sneakers. That's why Best Buy is shelling out nearly a billion dollars for GreatCall, makers of the senior-friendly JitterBug phones and purveyors of an emergency tech support service. But what will the yutes do with all that extra time now that we're not explaining to our parents how IG works? In an upcoming sequel to the forthcoming film, 1st Born, Kaye plans to hire (or at least build) a robotic actor capable of starring in a feature length film next to A-List talent like Val Kilmer and Tom Berenger.

Alphabet is broken out into its core Google business and a number of other subsidiaries, which it deems "Other Bets." The majority of Google's business comes from advertising revenues, which the company generates through its search engine as well as a number of other Google-affiliated and partnership websites. Outside of search and advertising, Google generates revenue from products including cloud and enterprise, consumer hardware, mapping, and YouTube. In addition to Google, Alphabet encompasses a host of other subsidiaries called "Other Bets." These companies are more experimental in nature, and as a result are not material to Alphabet's bottom line.

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.

Back in 2011, IBM's new Watson technology was the focus of a lot of excited expectation about what its artificial intelligence platform – having famously bested human opponents on Jeopardy! Seven years later, some media reports are wondering whether Watson's potential was over-promised. Recent stories, in Stat News, the Wall Street Journal and elsewhere have taken aim at the AI technology's diagnostic track record, drawn attention to potential patient safety risks, and questioned whether the billions of dollars Big Blue has spent building and training the platform have worth it. The Stat News story reported on internal IBM documents that showed "multiple examples of unsafe and incorrect treatment recommendations" from the Watson for Oncology system. The Wall Street Journal pointed out that "more than a dozen IBM partners and clients have halted or shrunk Watson's oncology-related projects."

Alexander Gerst will test the technology demonstrator aboard the ISS Watson AI (IBM's artificial intelligence technology) is designed to support space flight crews Friedrichshafen / Bremen, 26/02/2018 – Airbus, in cooperation with IBM, is developing CIMON (Crew Interactive MObile CompanioN), an AI-based assistant for astronauts for the DLR Space Administration. The technology demonstrator, which is the size of a medicine ball and weighs around 5 kg, will be tested on the ISS by Alexander Gerst during the European Space Agency's Horizons mission between June and October 2018. "In short, CIMON will be the first AI-based mission and flight assistance system," said Manfred Jaumann, Head of Microgravity Payloads from Airbus. "We are the first company in Europe to carry a free flyer, a kind of flying brain, to the ISS and to develop artificial intelligence for the crew on board the space station." Pioneering work was also being done in the area of manufacturing, Jaumann continued, with the entire structure of CIMON, which is made up of plastic and metal, created using 3D printing.