Predictions for the potential of artificial intelligence wax poetic -- solutions from climate change to curing disease -- but the everyday applications make it seem far more mundane, like a glorified clock radio. Thankfully, the future may be closer than we think. And the miraculous feats are not happening in Silicon Valley X-Labs -- in a plot twist, nonprofits are leading the charge in creating human-centered applications of the hottest AI technologies. From the simplest automated communications to contextual learnings based on analysis of deep data, these technologies have the potential to rapidly scale and improve the lives of our most underserved communities. Take chatbots for example, a new spin on mobile messaging that has historically been human-powered.

While there are plenty of dire-sounding discussions taking place these days around artificial intelligence (AI) and machine learning--and their potential to disrupt the world as we know it--this isn't technology of the future. New technologies are promising to upend the traditional ways in which content is conceived, produced, and disseminated. A Copyblogger article from as far back as 2015 noted that both Forbes and the Associated Press were producing machine-generated content. These examples are likely to both thrill and chill content marketers, depending on where they're perched along the content creation continuum--including the need to generate an increasing volume of content and to make a living from creating that content. For now, though, there is fortunately less to fear than there is to cheer, says Natalia Markova, senior web content strategist with Jellyfish, a global digital agency.

Everyone is already expecting Samsung to announce the Galaxy Note 8 soon, but it looks like the South Korean phone maker will also be announcing a new app along with it. A recent report is claiming that Samsung is planning to release its very own news app that will be called News Today. Information on the new Samsung app was first reported by SamMobile, which cited "trusted sources." The app will not only provide the latest news articles, but it will also apparently be able to provide users with podcasts. It's believed that users will be able to subscribe to podcasters and channels, and they should also be able to search for podcasts as well.

Hello, I suspect this isn't the right subreddit for this kind of thing, bu MLQuestions is really quiet. I'm trying to implement a FRBM based on these papers: Transactions on Fuzzy Systems 1 A Fuzzy Restricted Boltzmann Machine: Novel Learning Algorithms Based on Crisp Possibilistic Mean Value of Fuzzy Numbers, pages 5 and 7, and Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning, page 6. I am looking for a recommendation for a good starting implementation of an RBM that can be modified in order to acomplish this. Is there a framework with an implementation that can be adapted, or any (easy to read) code in Python or MatLab.

As an artificial intelligence researcher, I often come across the idea that many people are afraid of what AI might bring. It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldn't avoid asking: As an AI expert, what do I fear about artificial intelligence?

Officially, Pachet will head up Spotify's new Creator Technology Research Lab in Paris. The lab "will focus on making tools to help artists in their creative process," according to a blog post from Spotify. The blurb doesn't go into any more detail than that, but a rundown of Pachet's previous work invites a few educated guesses. Until recently, Pachet led Sony's Computer Science Laboratory in Paris, which he helped found 20 years ago. In that capacity, he worked on a range of music intelligence technologies, including a project called Flow Machines that aims to teach computers how to understand musical style and composition.

It's perhaps unsurprising, given both history and the entertainment industry, that we might be afraid of a cybernetic takeover that forces us to live locked away, "Matrix"-like, as some sort of human battery. And yet it is hard for me to look up from the evolutionary computer models I use to develop AI, to think about how the innocent virtual creatures on my screen might become the monsters of the future. Might I become "the destroyer of worlds," as Oppenheimer lamented after spearheading the construction of the first nuclear bomb? I would take the fame, I suppose, but perhaps the critics are right. Maybe I shouldn't avoid asking: As an AI expert, what do I fear about artificial intelligence?

I'm a little bewildered here. Note, that the softmax is not included in the table for the very simple reason that it gave miserable results on this NN configuration. Softmax Cross Entropy is the de facto output activation in FCNs. They don't specify if that test was with CE error or MSE, but even if it was with MSE (as a later experiment is), that just speaks to the incredibly poorly designed network they used (392-50-10 neurons is truly weird). The idea bears some resemblance to momentum, where we gradually speed things up when the error gradients are consistent.

We are a nation hurtling toward a dark dystopian future, in which robots fulfill an endless need to be entertained and women are enslaved as reproductive machines. Or we are going to be just fine despite racial tensions, fat shaming, alien invasion or government conspiracy because family, love and loyal friendship will always win the day. The Emmy nominations mean many things to many people, but this year, with an unprecedented number of popular new shows occupying berths in the categories of outstanding drama and comedy, they offer a surprisingly sharp guide to the fractured American psyche. It's been a rough year. The country's divided along every line imaginable.

VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, the data we model possesses continuous attributes that we would like to take into account at generation time. We propose in this paper GLSR-VAE, a Geodesic Latent Space Regularization for the Variational AutoEncoder architecture and its generalizations which allows a fine control on the embedding of the data into the latent space. When augmenting the VAE loss with this regularization, changes in the learned latent space reflects changes of the attributes of the data. This deeper understanding of the VAE latent space structure offers the possibility to modulate the attributes of the generated data in a continuous way. We demonstrate its efficiency on a monophonic music generation task where we manage to generate variations of discrete sequences in an intended and playful way.