When used as a surrogate objective for maximum likelihood estimation in latent variable models, the evidence lower bound (ELBO) produces state-of-the-art results. Inspired by this, we consider the extension of the ELBO to a family of lower bounds defined by a particle filter's estimator of the marginal likelihood, the filtering variational objectives (FIVOs). FIVOs take the same arguments as the ELBO, but can exploit a model's sequential structure to form tighter bounds. We present results that relate the tightness of FIVO's bound to the variance of the particle filter's estimator by considering the generic case of bounds defined as log-transformed likelihood estimators. Experimentally, we show that training with FIVO results in substantial improvements over training the same model architecture with the ELBO on sequential data.

In 1983, a curious teenager named David Lightman found a backdoor in a military computer system and very nearly caused the United States to start a nuclear war. If this sounds familiar, it isn't because it happened in real life. This was the plot of a movie called War Games, and it very realistically portrayed some common hacking techniques of the day like war dialing and phone phreaking. What was most surprising about this movie was that, unlike some science fiction movies, the problems it described were real and needed immediate solutions. But it would take five years for the first theoretical paper to be published on a term that would be eventually become well-known outside of the cybersecurity community: the firewall.

The main driver for the shift to a robotic workforce is the rise of Artificial Intelligence.

I have a fairly dumb question about Tensorflow: Every NN example I've come across has described network layer creation and training and computing accuracy but they all seem to leave off the step where you utilize the trained model to predict your outputs -- How is this accomplished? After training it, I try to pass in a single X value to produce a single Y value, expecting Y sin(X) but what I actually receive as output is garbage. Can someone show me method definition to have this NN operate as a replacement for sin(X)?

It was an early form of machine learning based on rules. The difference was that we were "bold enough" to claim that the program incorporated …

BBC America's science fiction show Dirk Gently's Holistic Detective Agency is based on a pair of novels by Douglas Adams, author of The Hitchhiker's Guide to the Galaxy. Science fiction author Tom Gerencer loves the idea of Dirk Gently--a detective who trusts in fate and leaves everything up to chance. "He's not a brilliant detective," Gerencer says in Episode 281 of the Geek's Guide to the Galaxy podcast, "but in a way he's making these other brilliant realizations that step completely off logic and go into the realm of'let go of all that stuff and get into the flow of things, and you're going to find that things work out a lot better for you that way.'" The show has a lot going for it, including an original voice, brilliant writing, and complex characters, but it's failed to connect with many viewers. Writer Leah Schnelbach loves how the show's many mysteries slowly come together, but acknowledges that Dirk Gently can be a challenge for newcomers.

This reminds me a bit of what /u/sixwings used to say. I think the idea was that (most) neural networks were still basically just rule-based systems and that they all used supervised learning (even the reinforcement/unsupervised learning ones). I will also note that often the network's inputs and outputs are symbolic in the sense that we associate them with local and (somewhat) interpretable meanings (although this is a bit more debatable for things like pixels). Under all of this lies a question of what "a GOFAI approach" is. Neural networks have certainly been around for a very long time, so someone could say they're old(-fashioned), good and AI...

Almost 12,000 people in 139 countries have taken our AI survey -- Do You Speak Human? We are living in the age of artificial intelligence and we scarcely even realise it. AI explains what we see in our Facebook news feed, how Netflix determines what we should watch next, and why Google Maps can predict where we're heading when we jump in the car. And this is just the beginning. Increasingly we will see computer-based life forms in our homes, our cars, our household goods -- technology woven into the very fabric of our lives, living alongside us and making decisions on our behalf.

Researchers have ventured into uncharted territory to find ways to improve computer algorithms -- the brains of fruit flies. While search algorithms work by analyzing users' previous searches, a fruit fly searches for fruits by remembering the odor of the fruit they have fed on. "This is a problem that pretty much every technology company with any kind of information retrieval system has to solve, so it's been something that computer scientists have studied for years. Now, we have this new approach to similarity searches thanks to the fly," said Saket Navlakha, assistant professor at Salk's Integrative Biology Laboratory and lead author of the research paper titled "A neural algorithm for a fundamental computing problem." The paper was published in the Science Journal on Thursday.