Facebook today is publishing an academic paper and a blog post detailing Torchnet, a new piece of open-source software that's designed to streamline deep learning, a type of artificial intelligence. Deep learning is a trendy approach that involves training artificial neural networks on lots of data, like photos, and then getting the neural networks to make predictions about new data. Rather than build a completely new deep learning framework, of which there are many, Facebook chose to build on top of Torch, an open-source library to which Facebook has previously contributed. "It makes it really easy to, for instance, completely hide the costs for I/O [input/output], which is something that a lot of people need if you want to train a practical large-scale deep learning system," Laurens van der Maaten, a research scientist in Facebook's Artificial Intelligence Research (FAIR) lab, told VentureBeat in an interview. Torchnet, which is written in Lua and can run on standard x86 chips or graphics processing units (GPUs), also lets programmers reuse certain code, which means doing less work and lowering the chances of introducing bugs, said van der Maaten.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

TOKYO (Reuters) – Toyota Motor Corp is targeting developing in the next five years driver assistance systems that integrate artificial intelligence to improve vehicle safety, the head of its advanced research division said. Gill Pratt, CEO of recently set up Toyota Research Institute (TRI), the Japanese automaker's research and development company that focuses on AI, said it aims to improve car safety by enabling vehicles to anticipate and avoid potential accident situations. Toyota has said the institute will spend 1 billion over the next five years, as competition to develop self-driving cars intensifies. Earlier this month, home rival Honda Motor Co said it was setting up a new research body which would focus on artificial intelligence, joining other global automakers which are investing in robotics research, including Ford and Volkswagen AG. "Some of the things that are in car safety, which is a near-term priority, I'm very confident that we will have some advances come out during the next five years," Pratt told reporters late last week in comments embargoed for Monday.

Adele's record-breaking album 25 is coming to Spotify, Apple Music, and other streaming services tomorrow (or right now if you happen to reading from New Zealand or Australia). While there are other artists who are absent from most music subscription services--Prince and Neil Young come to mind--Adele's 25 is a unique for two reasons: First, it's the best-selling album since 2001, when music sales began their epic collapse. Secondly, 25 has not been made available on any streaming service until now. Other big name recent albums have either limited their release to certain services (Tidal, more often than not) or delayed their streaming debut all together, but these windows and exclusives typically don't last seven months. While the subscription services are undoubtedly thrilled to finally offer Adele's latest, the stunning success she achieved without their help doesn't bode well for the streaming music model.

In part 1, we went over how to use data visualization and data analysis prior to machine learning. For example, we discussed how to visualize the data to identify potential issues in the dataset, examine the variable distributions, etc. In this blog post, we'll continue by building a very simple model and using data visualization to examine that model. Just a quick reminder: as I noted in part 1, we're working with a very simple model. This is deliberately a "toy" model, which allows us to focus on the visualization/analysis aspect of the task without the added level of complexity that we'd inject by using a more advanced machine learning algorithm.

Chat bots seem to be extremely popular these days, every other tech company is announcing some form of intelligent language interface. The truth is that language is everywhere, it's the way we communicate and the way we manage our thoughts. Most, if not all, of our culture & knowledge is encoded and stored in some language. One can think that if we manage to tap to that source of information efficiently then we are a step closer to create ground breaking knowledge systems. Of course, chat-bots are not even close to "solving" the language problem, after all language is as broad as our thoughts.

AI and linked data can be leveraged to perform a search on a client. The resulting data garnered from both free and paid sites can then be analyzed, categorized, and filtered to identify any issues of concern. Performing these tasks manually is time consuming for a single client. If the client is a company then similar checks must be performed on the company's officers and board members. Employing AI to perform the "heavy lifting"greatly reduces manual effort and frees up the onboarding specialist to focus on the results.

The artificial intelligence (AI) system is "based on deep learning, a machine-learning algorithm used for a range of applications including speech recognition and image recognition," explains Andrew Beck, an associate professor in pathology at Harvard Medical School, who heads the team developing the new system at Beth Israel Deaconess Medical Center (BIDMC), in Boston, MA. Prof. Beck and colleagues demonstrated the new AI system in a competition held at the annual meeting of the International Symposium of Biomedical Imaging (ISBI 2016) in Prague in April. He and his colleagues are developing AI methods that train computers to interpret pathology images to improve the accuracy of diagnoses. The approach they are using teaches computers to interpret the complex patterns seen in such images by "building multi-layer artificial neural networks," says Prof. Beck. The process is thought to be similar to the way learning takes place in the layers of neurons in the neocortex of the brain, the region where thinking occurs.

In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amount of time on data analysis. The traditional statement is that data scientists "spend 80% of their time on data preparation." While I think that this statement is essentially correct, a more precise statement is that you'll spend 80% of your time on getting data, cleaning data, aggregating data, reshaping data, and exploring data using exploratory data analysis and data visualization. And ultimately, the importance of data analysis applies not only to data science generally, but machine learning specifically.

IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. By the time the course began in October, 160,000 people in 190 countries had signed up for it. At the same time Andrew Ng, also a Stanford professor, made one of his courses, on machine learning, available free online, for which 100,000 people enrolled. Both courses ran for ten weeks. Such online courses, with short video lectures, discussion boards for students and systems to grade their coursework automatically, became known as Massive Open Online Courses (MOOCs).