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Reinforcement learning is important for understanding behavior because it tells us how actions are guided by reward. But the topic also has a broader significance---as an example of the happy marriage that can come from blending computer science, psychology and neuroscience. In this way, RL is a poster child for what's known as Marr's levels analysis, an approach to understanding computation that essentially asks why, how, and where. On this episode we first define some of the basic terms of rei

Microsoft is privileged to work with leading-edge customers and partners who are...

Welcome to How AI Transforms Business, a new series featuring insights from conversations with...

This post is authored by Anusua Trivedi, Senior Data Scientist at Microsoft. This post builds on...

A special guest post by cricket legend and founder of Spektacom Technologies, Anil Kumble. This post...

Announcing new open source contributions to the Apache Spark community for creating deep,...

This post is authored by Tara Shankar Jana, Senior Technical Product Marketing Manager at...

By Joseph Sirosh, Corporate Vice President and CTO of AI, and Sumit Gulwani, Partner Research...

In part two of our conversation on what counts as an explanation in science, we pickup with special guest David Barack giving his thoughts on the "model–mechanism–mapping" criteria for explanation. This leads us into a lengthy discussion on explanatory versus phenomenological (or "descriptive") models. We ask if there truly is a distinction between these model classes or if a sufficiently good descript

Based on a recent conversation between Joseph Sirosh, CTO for AI at Microsoft, and Roger Magoulas,...

This post is co-authored by Chun Ming Chin, Technical Program Manager, and Max Kaznady, Senior Data...

Earlier this week, MIT, in collaboration with Boston Consulting Group, released their second global...

As scientists, we throw around words like "explanation" a lot. We assume explaining stuff is part of what we're doing when we make and synthesize discoveries. But what does it actually take for something to be an explanation? Can a theory or model be successful without truly being one? How do these questions play out in computational neuroscience specifically? We bring in philosopher-neuroscientist David B

Since biological neural network are not feedforward but connect in both forward and backward directions, they have a different structure from ANNs (artificial neural networks)

Machine learning has been making big strides in a lot of straightforward tasks, such as taking an image and labeling the objects in it. But what if you want an algorithm that can, for example, generate an image of an object? That's a much vaguer and more difficult request. And it's where generative models come in! We discuss the motivation for making generative models (in addition to making cool images) and how they help us understand the core components of our data. We also get into the

A powerful, under-explored tool for neural network visualizations and art.

Because of the sheer number of neurons in the gut, the enteric nervous system is sometimes called the second brain. What're all those neurons doing down there? And what, or who, is controlling them? Science has recently revealed that the incredibly large population of microorganisms in the gut have a lot to say to the brain, by acting on these neurons and other mechanisms, and can impact everything from stress to obesity to autism. In this episode, we give the basic stats and facts about the ent

Today, OpenAI released a new result . We used PPO (Proximal Policy Optimization), a general reinforcement learning algorithm invented by OpenAI, to train a team of 5 agents to play Dota and beat semi-pros. This is the game that to me feels closest to the real world and complex decision making (combining strategy, tactics, coordinating, and real-time action) of any game AI had made real progress against so far. The agents we train consistently outperform two-week old agents with a win rate of 90-

You may have heard of predictive coding; it's a theory that gets around. In fact, it's been used to understand everything from the retina to consciousness. So, before we get into the details, we start this episode by describing our impressions of predictive coding. Where have we encountered it? Has it influenced our work? Why do philosophers like it? And, finally, what does it actually mean? Eventually we settle on a two-tiered definition: "hard" predictive coding refers to a very specific hypot

Join Survey Design and Analysis Services and Provalis Research founder and President Normand Peladeau for a 2-day training course in Sydney and Brisbane on the unstructured data analytics tool WordStat. The training in Sydney will be held from July 19 to 20 at the University of Sydney and the training in Brisbane will be held […]

New product: Stack Overflow for Teams lets you direct questions to members of your own team, company, or organization. Read more "Announcing Stack Overflow for Teams"

There is a tension when it comes to the study of behavior in neuroscience. On the one hand, we would love to understand animals as they behave in the wild---with the full complexity of the stimuli they take in and the actions they emit. On the other hand, such complexity is almost antithetical to the scientific endeavor, where control over inputs and precise measurement of outputs is required. Throw in the constraints that come when trying to record from and manipulate neurons and you've got a r

There's this popular idea among developers that when you face a problem with code, you should get out a rubber duck and explain, to the duck, exactly how your code was supposed to work. Read more "Strange and maddening rules"

UPDATE: Unfortunately my Pull-Request to Keras that changed the behaviour of the Batch Normalization layer was not accepted. You can read the details here. For those of you who are brave enough to mess with custom implementations, you can find the code in my branch. I might maintain it and merge it with the latest […]

I had to think for a minute to realize that Stack Overflow has “gamification" too. Not a ton. Maybe a dusting of gamification, most of it around reputation. Read more "A Dusting of Gamification"

On this unique episode of Unsupervised Thinking, we talk not about a particular area of science, but about the process of doing science itself. In particular, we're discussing how scientists take in information from their niche research areas and beyond. The topic for this free-form conversation stemmed from interviews we collected at the latest Computational and Systems Neuroscience Conference (Cosyne), where we asked people to tell us about a research finding from outside their area that they

At the most recent Computational and Systems Neuroscience Conference ("Cosyne"), held in Denver, we collected some interviews from attendees. The goal was to get people talking about work outside of their own immediate research area. Cosyne is a great conference to get exposed to ideas from other areas, particularly across the experiment-theory boundary. In total we collected 11 interviews from grad students, postdocs, and professors. We prompted people by asking them to first give

This is an archive of the original "fsyncgate" email thread. This is posted here because I wanted to have a link that would fit on a slide for a talk on file safety with a mobile-friendly non-bloated format . From:Craig Ringer Subject:Re: PostgreSQL's handling of fsync() errors is unsafe and risks data loss at least on XFS Date:2018-03-28 02:

Sleep is such a ubiquitous part of our lives we may forget just how weird of a thing it is to spend a third of our days laying in darkness. In this episode on the science of sleep, we start by describing types of sleep (while appreciating its strangeness) and the negative cognitive effects of missing out on it. We also discuss the potential role of rapid eye movement (REM) sleep in training neural connections and how that idea has been ported to artificial intelligence. We then take a rare (for

Provalis Research is holding a three-day training workshop for QDA Miner 5 and WordStat 7 in Montreal, Quebec, Canada. The training will be held from June 6 to June 8, 2018 at the Provalis Research head office. QDA Miner training: June 6 and June 7 – 9am to 5pm, 9am to 12pm WordStat training: June 7 and June 8 – […]

We have a new trainer/consultant Christopher Tomanek. You can access his profile on our training page: https://provalisresearch.com/trainers-christopher-tomanek/ Chris is based in Poland and an expert in QDA Miner, WordStat, Simstat. He used our software for more than 8 years. He speaks English and Polish and is available for training on our software and consulting projects.