There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.

We propose a framework to automatically generate descriptive comments for source code blocks. While this problem has been studied by many researchers previously, their methods are mostly based on fixed template and achieves poor results. Our framework does not rely on any template, but makes use of a new recursive neural network called Code-RNN to extract features from the source code and embed them into one vector. When this vector representation is input to a new recurrent neural network (Code-GRU), the overall framework generates text descriptions of the code with accuracy (Rouge-2 value) significantly higher than other learning-based approaches such as sequence-to-sequence model. The Code-RNN model can also be used in other scenario where the representation of code is required.

In this paper, we study the product title summarization problem in E-commerce applications for display on mobile devices. Comparing with conventional sentence summarization, product title summarization has some extra and essential constraints. For example, factual detail errors or loss of the key information are intolerable for E-commerce applications. Therefore, we abstract two more constraints for product title summarization: (i) do not introduce irrelevant information; (ii) retain the key information (e.g., brand name and commodity name). To address these issues, we propose a novel multi-source pointer network by adding a new knowledge encoder for pointer network. The first constraint is handled by pointer mechanism, generating the short title by copying words from the source title. For the second constraint, we restore the key information by copying words from the knowledge encoder with the help of the soft gating mechanism. For evaluation, we build a large collection of real-world product titles along with human-written short titles. Experimental results demonstrate that our model significantly outperforms the other baselines.Finally, online deployment of our proposed model has yielded a significant business impact, as measured by the click-through rate.

A common question when studying a class of context-free grammars (CFGs) is whether equivalence is decidable within this class. We answer this question positively for the class of Clark-congruential grammars, which are of interest to grammatical inference. We also consider the problem of checking whether a given CFG is Clark-congruential, and show that it is decidable given that the CFG is a deterministic CFG.

Aug 20 (Reuters) - Nvidia Corp on Monday released a new generation of gaming chips that combine its latest "ray tracing" technology and artificial intelligence to give gamers access to more realistic graphics. At a Gamescom 2018 press conference in Cologne, Germany, Nvidia rolled out its newest generation of gaming chips - the RTX 2070, RTX 2080 and RTX 2080 Ti. They are based on its recently launched chip blueprint called "Turing," which Nvidia is also using for higher-priced chips announced last week for game designers, movie makers and other computer graphics professionals. The chips will range from $499 to $999 and will be available in retail stores starting Sept. 20, Chief Executive Jensen Huang said at the event.

At a Gamescom 2018 press conference in Cologne, Germany, Nvidia rolled out its newest generation of gaming chips - the RTX 2070, RTX 2080 and RTX 2080 Ti. They are based on its recently launched chip blueprint called "Turing," which Nvidia is also using for higher-priced chips announced last week for game designers, movie makers and other computer graphics professionals. The chips will range from $499 to $999 and will be available in retail stores starting Sept. 20, Chief Executive Jensen Huang said at the event. The biggest selling point of the gaming chips released on Monday is an improvement in so-called "real-time ray tracing," or the ability for the chip to simulate how light rays will bounce around in a visual scene, which helps video games and other computer graphics more closely resemble shadows and reflections in the real world. Part of how the new chip achieves high resolution graphics quickly is a special section of the chip that finishes most of the image but then uses artificial intelligence to guess what the unfinished pixels should look like.

Frozen water has been found on the surface of the moon for the first time following the use of high tech satellite scans. Experts say they have detected ice in areas of permanent shadow in the moon's polar regions. They used an imaging technique that can tell different types of water apart, including that on the surface, absorbed into the soil, or bound in minerals. Water has previously been found in the moon's soil, but this is believed to be the first time it has been detected on the surface. Surface water ice was only located in around 3.5 per cent of the moon's shadow covered areas.

An Apple Store in Amsterdam had to be cleared after an iPad exploded in the shop, according to local reports. The battery inside the tablet appears to have caught fire and create a dangerous situation inside the store. It was quickly secured and placed into a bucket of sand to ensure that any fire was contained. But because of the potential for dangerous vapours to be released the store was shut down and three people were treated for breathing difficulties, the local fire department tweeted. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

During the AI boom of the 1980s, the field also enjoyed a great deal of hype and rapid investment. Rather than considering the value of individual startups' ideas, investors were looking for interesting technologies to fund. This is why most of the first generation of AI companies have already disappeared. Companies like Symbolics, Intellicorp, and Gensym -- AI companies founded in the '80s -- have all transformed or gone defunct. And here we are again, nearly 40 years later, facing the same issues.

Artificial intelligence will change the world. Because so many people and companies believe this, AI and the entire technological ecosystem in which it functions are highly valuable to private-sector organizations and nation-states. That means that nations will try to identify, steal, and corrupt or otherwise counteract the AI and related assets of others, and will use AI against each other in pursuit of their own national interests. And that presents the United States and its allies with a classic counterintelligence problem in a novel and high-stakes context: How do we protect a valuable national asset against a range of threats from hostile foreign actors, and how do we protect ourselves against the threat from AI in the hands of adversaries? In the broad and diverse discussion of artificial intelligence in the global technological and economic infrastructure of the future, this question has received remarkably little attention. In this post and others to follow, I will endeavor to explore some of the counterintelligence risks and problems presented by AI and the AI ecosystem. I'll first talk about the general problem of AI and counterintelligence and then, in later posts, dive into some of the specific areas that cause me the greatest concern in this sphere. Technological advancements often change society.