There has been growing interest in using neural networks and deep learning techniques to create dialogue systems. Conversational recommendation is an interesting setting for the scientific exploration of dialogue with natural language as the associated discourse involves goal-driven dialogue that often transforms naturally into more free-form chat. This paper provides two contributions. First, until now there has been no publicly available large-scale dataset consisting of real-world dialogues centered around recommendations. To address this issue and to facilitate our exploration here, we have collected ReDial, a dataset consisting of over 10,000 conversations centered around the theme of providing movie recommendations. We make this data available to the community for further research. Second, we use this dataset to explore multiple facets of conversational recommendations. In particular we explore new neural architectures, mechanisms, and methods suitable for composing conversational recommendation systems. Our dataset allows us to systematically probe model sub-components addressing different parts of the overall problem domain ranging from: sentiment analysis and cold-start recommendation generation to detailed aspects of how natural language is used in this setting in the real world. We combine such sub-components into a full-blown dialogue system and examine its behavior.

Members of the House Judiciary Committee peppered the head of Google about potential bias against conservatives and Russian influence and misinformation; Gillian Turner reports. Google has been forced to shut down and "effectively end" its controversial China search engine project, code-named Project Dragonfly, after members of the company's privacy team raised complaints, according to a new report. The tech giant led by CEO Sundar Pichai was forced to close a data analysis system it was using for the controversial project, according to The Intercept, citing two sources familiar with the matter. The news outlet originally broke the news that Google had been considering launching the app-based search engine. Google has not yet responded to a request for comment from Fox News.

If you're still searching for an ideal gift for the smart home fanatic in your life, consider the Ring Video Doorbell 2. Right now, the Ring sequel is $170 at Best BuyRemove non-product link and many other retailers, but Best Buy sweetens the deal by adding a third-generation Amazon Echo Dot for free. Best Buy had a better early Black Friday deal when this bundle was $140. Still, $170 is a great price and well below the non-holiday pricing of $250. We reviewed the Ring Video Doorbell 2 in August 2017. We found the install process a little frustrating, but overall it's a solid improvement of the original.

Catchy Christmas songs can now be created by a special songwriting AI, taught by studying existing festive tunes. The system came up with catchy jingles with names like'Syllabub Chocolatebell', 'Peaches Twinkleleaves' and'Cocoa Jollyfluff'. Researchers from Made by AI trained a neural network by inputting one hundred Christmas tunes in the form of Musical Instrument Digital Interface (MIDI) files. It then picked out recurring themes, motifs, instruments and rhythms to generate its own hits. Scientists have trained an AI system to write its own catchy Christmas songs by teaching it existing festive tunes.

We've heard from customers that scaling TensorFlow training jobs to multiple nodes and GPUs successfully is hard. TensorFlow has distributed training built-in, but it can be difficult to use. Recently, we made optimizations to TensorFlow and Horovod to help AWS customers scale TensorFlow training jobs to multiple nodes and GPUs. With these improvements, any AWS customer can use an AWS Deep Learning AMI to train ResNet-50 on ImageNet in just under 15 minutes. To achieve this, 32 Amazon EC2 instances, each with 8 GPUs, a total 256 GPUs, were harnessed with TensorFlow. All of the required software and tools for this solution ship with the latest Deep Learning AMIs (DLAMIs), so you can try it out yourself. You can train faster, implement your models faster, and get results faster than ever before. This blog post describes our results and shows you how to try out this easier and faster way to run distributed training with TensorFlow. Figure A. ResNet-50 ImageNet model training with the latest optimized TensorFlow with Horovod on a Deep Learning AMI takes 15 minutes on 256 GPUs.

It's difficult to think of 2018 as a year with anything worth celebrating. But despite all the bad news the year dealt us, there were successes -- if you know where to look. In all corners of tech, we saw wins big and small. There were advances in obvious categories like laptops, smartphones and the connected home, but we also looked outside the mainstream for some of the more surprising gems. Think mini synthesizers for music nerds, retro emulators for nostalgic gamers and e-readers for modern book snobs. Humanity also collectively triumphed, as our space exploration programs broke new frontiers this year and we began to confront the increasingly real question: Should we all just move to Mars? We're just two weeks away from what is hopefully a much better 12 months, and the Engadget team took some time to commemorate our favorite gadgets and trends in tech.

In a world full of smart devices, self-driving cars and voice assistants, Shazam is the closest technology comes to actual magic. The software allows you to hold your phone up to a speaker and answer the age-old question, "what is this song and who's it by?" without the humiliation of having to ask the DJ. And in 2018 the answer, most frequently, was "Solo by Clean Bandit". The song, which features US star Demi Lovato, was tagged 9.1 million times. British artists performed five of Shazam's top 10 songs, with Calvin Harris, Dua Lipa and newcomer Tom Walker all making the chart.

Makes me feel sad for the rest. Actually, that's a movie ("The Spy that Loved Me") that Netflix recommends for me since I'm a James Bond junkie and Netflix knows that. In fact, Netflix knows a lot about me as it knows a lot about all of its viewers, which is one reason why Netflix is a Wall Street darling and has rewarded its stockholders very well over the past couple of years (see Figure 1). But Netflix isn't doing anything that other organizations cannot do. To replicate Netflix's business success starts with thinking differently about the role of data and analytics in powering the organization's business.

In his bestselling book, Up the Organization, former Avis president Robert Townsend captured the problem of automation precisely. Writing at a time when the vast paper systems of corporate America were being transferred to computers, he warned that it was important first to make sure that a company's paper systems are actually effective and accurate. "Otherwise," he quipped, "your new computer will just speed up the mess." Today, we are faced with a new wave of optimism about the prospects of what is called artificial intelligence (AI). It is important to parse these words carefully for they will tell you why artificial intelligence as it is currently conceived will very likely "just speed up the mess."

We consider the problem of matrix completion with side information on an $n\times m$ matrix. We formulate the problem exactly as a sparse regression problem of selecting features and show that it can be reformulated as a binary convex optimization problem. We design OptComplete, based on a novel concept of stochastic cutting planes to enable efficient scaling of the algorithm up to matrices of sizes $n = 10^6$ and $m = 10^5$. We report experiments on both synthetic and real-world datasets that show that OptComplete outperforms current state-of-the-art methods both in terms of accuracy and scalability, while providing insight on the factors that affect the ratings.