Deep learning has been all over the news lately. In a presentation I gave at Boston Data Festival 2013 and at a recent PyData Boston meetup I provided some history of the method and a sense of what it is being used for presently. This post aims to cover the first half of that presentation, focusing on the question of why we have been hearing so much about deep learning lately. The content is aimed at data scientists who might have heard a little about deep learning and are interested in a bit more context. Regardless of your background, hopefully you will see how deep learning might be relevant for you.

Mobileye, the Israeli car automation company that came onto the self-driving car scene as sort of an anti-Google, is now looking at the future in terms that seem a bit closer to Google's than used to be the case. Speaking Friday at a conference organized by Goldman Sachs (which owned a chunk of Mobileye's shares when the company first became publicly traded in 2014), Amnon Shashua, Mobileye's founder and chief technical officer, placed a lot of emphasis on mapping, something Google has done all along.

The achievements of neural nets have been truly astounding. They seem to be setting the bar in terms of performance in many/all ML challenges. I am, however, curious about where they fail. I am trying to understand a sort of meta decision boundary in "problem space" as to which type of algorithm to select for a given problem. I am aware that things like logistic regression are far easier to implement and may perform equally on simple enough tasks.

Leave a human baby with some toys and it'll quickly learn to pick them up. Now a robot with deep-learning capabilities has done the same thing.

Malware has proven increasingly difficult to detect via signature or heuristic-based methods, which means most Antivirus (AV) programs are woefully ineffective against mutating malware, and especially ineffective against APT attacks (Advanced Persistent Threats). Typical malware consists of about 10,000 lines of code. Five to six years ago marked the beginning of the use of machine learning to solve non-linear problems such as facial recognition or understanding malware, and what features one needs to extract to uniquely identify such programs. Other techniques, such as sandboxing and machine-based techniques, are not as fast nor as accurate as Deep Learning. Deep Instinct, founded by Guy Caspi and Eli David, Israeli Defense Force Cybersecurity veterans, applies artificial intelligence Deep Learning algorithms to detect structures and program functions that are indicative of malware.

There is an unreasonable amount of information that can be extracted from what people publicly say on the internet. At Heuritech we use this information to better understand what people want, which products they like and why. This post explains from a scientific point of view what is Knowledge extraction and details a few recent method on how to do it. Highly structured databases make it easy to reason with and can be used for inference. For example in WikiData or YAGO, entities are isolated and linked together with relations.

The recent emergence of a powerful machine-learning technique known as deep learning has made computing giants such as Google, Facebook, and Microsoft even hungrier for data. It's what lets software learn to do things like recognize images or understand language. Yet many problems where deep learning could be most valuable involve data that is hard to come by or is held by organizations that are unwilling to share it. And as Apple CEO Tim Cook puts it, some consumers are already concerned about companies "gobbling up" their personal information. "A lot of people who hold sensitive data sets like medical images are just not going to share them for legal and regulatory concerns," says Vitaly Shmatikov, a professor at Cornell Tech who studies privacy.

Mobileye, the Israeli car automation company that came onto the self-driving car scene as sort of an anti-Google, is now looking at the future in terms that seem a bit closer to Google's than used to be the case. Speaking Friday at a conference organized by Goldman Sachs (which owned a chunk of Mobileye's shares when the company first became publicly traded in 2014), Amnon Shashua, Mobileye's founder and chief technical officer, placed a lot of emphasis on mapping, something Google has done all along. And now Shashua is predicting utterly hands-free driving--if only on the highway--by 2021. Mobileye had always emphasized incremental steps, such as active cruise control and emergency braking, collectively called advanced driver assistance systems (ADAS). It was Google that proposed to skip all half measures and get right to full-bore self-driving cars.

Facebook's AI hardware is now, like its software, open source, joining a broad movement towards outsourcing the world's artificial intelligence intelligence. Facebook also stated it hoped independent AI technicians would develop deep learning tech superior to what the company currently uses, and that it would buy this technology. The tech giant has developed deep learning technology, which it uses for Facebook-related functions like identifying faces in pictures and curating news feeds, but can also apply to a wide range of computing tasks. Through the Open Compute Project, Facebook's custom hardware designs -- a GPU-based server called "Big Sur" -- will join Google's and others' open source deep learning designs. The hope is that more workers will devote themselves to these projects and become familiar with using the technology.

People platform to discuss and share, research, ideas, #deeplearning methods and applications.