No matter what your job is, it's likely that a machine will someday do it better. And, for many American workers, that day may come sooner than later. According to a paper published by researchers at Ball State University, roughly half of the jobs American workers perform could be automated in the near future; the study also found low-income work as the category most susceptible to automation. While change is inevitable in the more physical professions, it's increasingly apparent that knowledge-based jobs -- those requiring some special skills or expertise -- are equally at risk. In fact, some of those positions are already being replaced. As a college professor, I spend most of my days reading, writing and thinking about theoretical topics.

After the last post where carlos demonstrated his utter lack of knowledgability in statistics while attempting to comment on stats fundamentals, I was already disinclined to pay him any mind, and this post is equally devoid of real insight. Why are people like this writing textbooks?

I love it when I get feedback from a blog that I've written. I appreciate the different perspectives and insights that others bring to a topic of interest. And no blog that I've written has drawn more comments than my blog, "Isaac Asimov: The 4th Law of Robotics." The section of the blog that fueled the most comments stem from a scene in the movie I, Robot where Detective Spooner (played by Will Smith) is explaining to Doctor Calvin (who is responsible for giving robots human-like behaviors) why he distrusts and hates robots. He is describing an incident where his police car crashed into another car and both cars were thrown into a cold and deep river – certain death for all occupants.

Artificial Intelligence technology – which British physicist Stephen Hawking says can "outperform humans" – could be joining the gymnastic judges' …

At least that was one wine critic's opinion of the new Sumika brand from Marks & Spencer. After all, sometimes you just want to have fun at home. In fact, companies have been developing all kinds of interesting products and services to make the leisurely side of our home lives more connected and more enjoyable. Streaming music services like Pandora and Spotify, as well as multi-room wireless speaker systems like Sonos, for example, allow people to enjoy the pleasures of synchronized whole home audio without the costs and hassles of running wires all over your house. Thanks to recent upgrades from Amazon and Google, households with multiple Echo or Google Home smart speakers can also start to benefit from this surprisingly enjoyable feature--once limited to high-end custom homes. For this to work, you'll need to configure your speakers to function together as a multiroom group in the respective Alexa and Google Home apps, but the process is straightforward.

Since our bio-inspired machine learning technology "Dynamic Boltzmann Machine (DyBM)" debuted in the fall of 2015, we received many comments on the music demo and human evolution image that we used to show how an artificial neural network learns about different topics in different formats. Many developers expressed interest in using the code to let DyBM learn other music or animation. This request for more "openness" made us wonder if we should dramatically change DyBM's bio-oriented design. It learns patterns like neurons: at each moment of a song or an image, DyBM adjusts its internal parameters. The more data fed into DyBM, the better it will master what it's trying to understand.

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?

The creative process will be democratized by artificial intelligence. All of Sud's workshop hypotheticals were based on analysis of where tech trends …

Recommender systems have been widely adopted by electronic commerce and entertainment industries for individualized prediction and recommendation, which benefit consumers and improve business intelligence. In this article, we propose an innovative method, namely the recommendation engine of multilayers (REM), for tensor recommender systems. The proposed method utilizes the structure of a tensor response to integrate information from multiple modes, and creates an additional layer of nested latent factors to accommodate between-subjects dependency. One major advantage is that the proposed method is able to address the "cold-start" issue in the absence of information from new customers, new products or new contexts. Specifically, it provides more effective recommendations through sub-group information. To achieve scalable computation, we develop a new algorithm for the proposed method, which incorporates a maximum block improvement strategy into the cyclic blockwise-coordinate-descent algorithm. In theory, we investigate both algorithmic properties for global and local convergence, along with the asymptotic consistency of estimated parameters. Finally, the proposed method is applied in simulations and IRI marketing data with 116 million observations of product sales. Numerical studies demonstrate that the proposed method outperforms existing competitors in the literature.

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.