Human beings have a remarkable ability to make inferences based on their surroundings. Where might I find a parking spot? Am I more likely to get to a gas station by taking a left or a right at this stoplight? Such decisions require us to look beyond our "visual scene" and weigh an exceedingly complex set of understandings and real-time judgments. This begs the question: Can we teach computers to "see" in the same way?

For decades, neuroscientists have been trying to design computer networks that can mimic visual skills such as recognizing objects, which the human brain does very accurately and quickly. Until now, no computer model has been able to match the primate brain at visual object recognition during a brief glance. However, a new study from MIT neuroscientists has found that one of the latest generation of these so-called "deep neural networks" matches the primate brain. Because these networks are based on neuroscientists' current understanding of how the brain performs object recognition, the success of the latest networks suggest that neuroscientists have a fairly accurate grasp of how object recognition works, says James DiCarlo, a professor of neuroscience and head of MIT's Department of Brain and Cognitive Sciences and the senior author of a paper describing the study in the Dec. 18 issue of the journal PLoS Computational Biology. "The fact that the models predict the neural responses and the distances of objects in neural population space shows that these models encapsulate our current best understanding as to what is going on in this previously mysterious portion of the brain," says DiCarlo, who is also a member of MIT's McGovern Institute for Brain Research.

In recent years, some of the most exciting advances in artificial intelligence have come courtesy of convolutional neural networks, large virtual networks of simple information-processing units, which are loosely modeled on the anatomy of the human brain. Neural networks are typically implemented using graphics processing units (GPUs), special-purpose graphics chips found in all computing devices with screens. A mobile GPU, of the type found in a cell phone, might have almost 200 cores, or processing units, making it well suited to simulating a network of distributed processors. At the International Solid State Circuits Conference in San Francisco this week, MIT researchers presented a new chip designed specifically to implement neural networks. It is 10 times as efficient as a mobile GPU, so it could enable mobile devices to run powerful artificial-intelligence algorithms locally, rather than uploading data to the Internet for processing.

The appropriate venue for seeking help depends on the kind of question you have. Please do take some time to search for similar questions that were asked and answered in the past. If you find something similar that doesn't fully answer your question, it can be helpful to say something like "I found X but it doesn't address facet Y" and link to the previous discussion. When asking questions on StackOverflow, please use the theano tag, so your question can be found, and follow StackOverflow's guidance on asking questions. Consider also using the python and numpy tags, especially if you are unsure which library your problem relates to.

The big news on March 12 of this year was of the Go-playing AI-system AlphaGo securing victory against 18-time world champion Lee Se-dol by winning the third straight game of a five-game match in Seoul, Korea. After Deep Blue's victory against chess world champion Gary Kasparov in 1997, the game of Go was the next grand challenge for game-playing artificial intelligence. Go has defied the brute-force methods in game-tree search that worked so successfully in chess. In 2012, Communications published a Research Highlight article by Sylvain Gelly et al. on computer Go, which reported that "Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players." AlphaGo combines tree-search techniques with search-space reduction techniques that use deep learning.

In this article, I cover the 12 types of AI problems i.e. I address the question: in which scenarios should you use Artificial Intelligence (AI)? We cover this space in the Enterprise AI course. Recently, I conducted a strategy workshop for a group of senior executives running a large multinational corporation. In the workshop, one person asked the question: How many cats does it need to identify a Cat?

This week's top Machine Learning stories, with a special focus on how deep learning is changing the world! Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning!

This week we are celebrating Computer Science Education Week around the globe. In this "age of acceleration," in which advances in technology and the globalization of business are transforming entire industries and society itself, it's more critical than ever for everyone to be digitally literate, especially our kids. This is particularly true for women and girls who, while representing roughly 50 percent of the world's population, account for less than 20 percent of computer science graduates in 34 OECD countries, according to this report. This has far-reaching societal and economic consequences. One issue sometimes cited for the dearth of women in computing fields is the lack of professional role models who could inspire girls to pursue their STEM dreams. We've attempted to counteract this by asking 17 women within Microsoft's global research organization their views on what's likely to occur in their fields in 2017.

Microsoft kicked off 2017 by acquiring Canadian artificial intelligence (AI) startup Maluuba. Many more AI acquisitions are likely to occur throughout the year, but this one is special. Maluuba's acquisition signals Microsoft's interest in artificial general intelligence (AGI). While AGI is a pipe dream for many, including Paul Allen, one of Microsoft's founders who has said that AGI is a long way off, Maluuba is already working on achieving AGI in the specific domain of language. By acquiring Maluuba, Microsoft will have its own DeepMind.

We've seen the rise of multiple big data solutions in the past few years. Building on top of the volume, variety and velocity of data, we've seen the growing need for automating business decisions based on the knowledge coming from online systems, sensors and connected devices. In order to take advantage of this wealth of data we're seeing the rapid rise of a wide range of machine and deep learning SDKs, tools, frameworks, systems, services, and libraries. This blog post highlights some of the available machine learning and deep learning SDKs available from leading platform vendors, hardware vendors, researchers, and open source projects. It's a great time to be a software engineer and to have all of these technologies provided by developer relations programs.