FRANKFURT, GERMANY – Can performing Beethoven symphonies together help employees team up on projects at work, too? Some companies -- above all in Germany and Asia -- seem to think so. A conspicuous number of big German corporate names -- along with a handful in Japan and South Korea -- have their own company-linked symphony orchestra. That means 60 or so accountants, engineers, sales reps and computer specialists bring violins, cellos, oboes and trombones and gather in their spare time to rehearse and perform lengthy, complex pieces of classical music. The orchestras serve as public relations tools, playing charity concerts and livening up corporate events.

Smart speakers like the Amazon Echo and Google Home have proved useful -- but they tend not to sound very good. Sure, they're serviceable in a pinch, and are better than most cheap Bluetooth speakers, but they don't compare to options like the entire Sonos lineup, let alone a nice set of bookshelf speakers like the Audioengine A5 . That's slowly changing, though: The Alexa-powered Sonos One speaker performs well and is affordable, while Apple's forthcoming HomePod sounded excellent in a brief demo we saw earlier this year. Google's Home Max is the company's first attempt to join the HiFi audio space -- it does everything that the smaller Home speakers do, but with significantly larger and higher-caliber components. Of course, that higher quality comes at a significantly higher price. At $399, the Home Max is more comparable with dedicated, higher-quality speakers.

Ring founder and chief executive Jamie Siminoff holds a Ring Video Doorbell, left, that has a video camera so residents can see who's knocking with a smartphone app. Ring founder and chief executive Jamie Siminoff holds a Ring Video Doorbell, left, that has a video camera so residents can see who's knocking with a smartphone app. Before you buy any "smart" gadgets, make sure they're not dumb. This holiday season, a third of Americans plan to buy a smart home device, according to the Consumer Technology Assn. The trick is figuring out which ones are worth the cost, trouble and inevitable security risks.

Connectionist temporal classification (CTC) is widely used for maximum likelihood learning in end-to-end speech recognition models. However, there is usually a disparity between the negative maximum likelihood and the performance metric used in speech recognition, e.g., word error rate (WER). This results in a mismatch between the objective function and metric during training. We show that the above problem can be mitigated by jointly training with maximum likelihood and policy gradient. In particular, with policy learning we are able to directly optimize on the (otherwise non-differentiable) performance metric. We show that joint training improves relative performance by 4% to 13% for our end-to-end model as compared to the same model learned through maximum likelihood. The model achieves 5.53% WER on Wall Street Journal dataset, and 5.42% and 14.70% on Librispeech test-clean and test-other set, respectively.

Regularization is important for end-to-end speech models, since the models are highly flexible and easy to overfit. Data augmentation and dropout has been important for improving end-to-end models in other domains. However, they are relatively under explored for end-to-end speech models. Therefore, we investigate the effectiveness of both methods for end-to-end trainable, deep speech recognition models. We augment audio data through random perturbations of tempo, pitch, volume, temporal alignment, and adding random noise.We further investigate the effect of dropout when applied to the inputs of all layers of the network. We show that the combination of data augmentation and dropout give a relative performance improvement on both Wall Street Journal (WSJ) and LibriSpeech dataset of over 20%. Our model performance is also competitive with other end-to-end speech models on both datasets.

The legacy of fake news has created renewed demand for reliable online news distribution. This is where Flipboard, one of the first digital news curators, is finding a niche. Launched in 2010, the Palo Alto-based news aggregator provides a digital magazine-style content platform that it claims is used by more than 100 million people each month. Flipboard expects to hit profitability in 2018, a result the company says, of connecting millions of people to publishers--a high percentage of whom are affluent (and human) audiences that advertisers crave. Flipboard is depending on cloud applications and ad tech analytics to make sure the content from its advertisers and publishers is being read by real people.

GitHub is much more than a software versioning tool, which it was originally meant to be. Now people from different backgrounds and not just software engineers are using it to share their tools / libraries they developed on their own, or even share resources that might be helpful for the community. Following the best repos on GitHub can be an immense learning experience. You not only see what are the best open contributions, but also see how their code was written and implemented. Being an avid data science enthusiast, I have curated a list of repositories that have been particularly famous in the year 2017.

Google Executive Chairman Eric Schmidt has suggested machine learning would be the one commonality for every big startup over the next five years. The inference here is that machine learning, or AI, will be as revolutionary as the Internet, the mobile phone, the personal computer; heck, I'll say it, as game changing as sliced bread. AI is responsible for many simple experiences we already take for granted: the Netflix "recommended for you" and the Facebook feeds that happen to show travel deals for places we've been searching. Another opportunity is the soon-to-be-ubiquitous self-driving cars and the growth of devices that can help plan your life. Here, AI will be based on what machines have "learned" about your habits, preferences, and any other data they can connect with that might pick up on things like traffic or consumer information.

Adding further understanding, a companion study confirms empirically that ES (with a large enough perturbation size parameter) acts differently than SGD would, because it optimizes for the expected reward of a population of policies described by a probability distribution (a cloud in the search space), whereas SGD optimizes reward for a single policy (a point in the search space). In practice, SGD in RL is accompanied by injecting parameter noise, which turns points in the search space into clouds (in expectation).

Microsoft on Wednesday rolled out new features on its Bing search engine powered by artificial intelligence, including one that summarizes the two opposing sides of contentious questions, and another that measures how many reputable sources are behind a given answer. Tired of delivering misleading information when their algorithms are gamed by trolls and purveyors of fake news, Microsoft and its tech-company rivals have been going out of their way to show they can be purveyors of good information either by using better algorithms or hiring more human moderators. Microsoft is also trying to distinguish its second-place search engine from long-dominant Google and position itself as an innovator in finding real-world applications for the latest advances in artificial intelligence. "As a search engine we have a responsibility to provide answers that are comprehensive and objective," said Mr. Jordi Ribas, Microsoft's corporate vice president for AI products. Bing's new capabilities are designed to give users more confidence that an answer is correct and save them time so they don't have to click through multiple links to validate it themselves.