The named "Cognitive/Artificial Intelligence Systems Market" report is a thorough research performed by analysts on the basis of current industry …

This problem got the attention of a team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), who wondered …

Facebook has been the Uber of 2018. They had a negative breaking headline every other week, centered around issues of privacy, social engineering, and national security. The reason for this is the decade long dissemination of content without any regulation under the legal construct for a platform. By not acknowledging itself as a media company they evaded all the rules that come along with it. Just think, would content flow with such irreverence on any other traditional media platform, and that too without any editorial oversight? Even now the reason for the outrage is because half of the country was pissed at the election result.

First of all, apologies for not posting as frequently as I used to. As you might imagine, blogging is not my full time job and I'm currently extremely involved in a very exciting startup (something I'm going to write about soon). On weekends and evening I'm busy with 7mo infant to help care for and altogether that leaves me with very little time. But I'll try to make it better soon, since a lot is going on in the AI space and signs of cooling are visible now all over the place. In this post I'd like to focus on the recent book by Gary Marcus and Ernest Davis, Rebooting AI.

Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network. Our goal is to incorporate appropriate techniques to mitigate the aforementioned problems of variational autoencoder CF and further improve the recommendation performance. Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible prior gives significant gains. We experiment with the VampPrior, originally proposed for image generation, to examine the effect of flexible priors in CF. We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets (MovieLens & Netflix).

We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. A consideration of these requirements allows us to propose a test of intelligence for trading programs, on the lines of the Turing Test, long the benchmark for intelligent machines. We discuss the application of this methodology to the dilemma in finance, which is whether, when and how much to Buy, Sell or Hold.

Mark Kingwell is a professor of philosophy at the University of Toronto. More than five decades ago, Marshall McLuhan argued that media are ecosystems, extensions of human consciousness. The famous adage that the medium is the message also means, as the often-misquoted title of McLuhan's famous book notes, that the medium is the mass age. We are all immersed in media and technology. Media have changed a lot since McLuhan wrote: less broadcast, more diffusion and unruliness.