Data Scientist, Data analystics, database gurus Java developer or a database admin or an interface designer

Point72 Ventures last year, hiring Pete Casella of JPMorgan Chase Strategic Investments to help lead the effort.

looking at natural language processing, automating trucks or cars, or synthesizing news.

Billionaire Steve Cohen has opened a Palo Alto office to invest in early-stage companies focused on big data and machine learning, networking and storage specialists in the cloud.

In-Q-Tel has been specializing in companies that mine data on Twitter and other social networks.

In traditional machine learning, the learning process is supervised and the programmer has to be very, very specific when telling the computer what types of things it should be looking for when deciding if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction and the computer’s success rate depends entirely upon the programmer’s ability to accurately define a feature set for "dog." The advantage of deep learning is that the program builds the feature set by itself without supervision. This is not only faster, it is usually more accurate.

Deep learning is an aspect of artificial intelligence (AI) that is concerned with emulating the learning approach that human beings use to gain certain types of knowledge. At its simplest, deep learning can be thought of as a way to automate predictive analytics. 

While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. To understand deep learning, imagine a toddler whose first word is “dog.” The toddler learns what is (and what is not) a dog by pointing to objects and saying the word “dog.” The parent says “Yes, that is a dog” or “No, that is not a dog.” As the toddler continues to point to objects, he becomes more aware of the features that all dogs possess. What the toddler does, without knowing it, is to clarify a complex abstraction (the concept of dog) by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy. 

Computer programs that use deep learning go through much the same process. Each algorithm in the hierarchy applies a non-linear transformation on its input and uses what it learns to create a statistical model as output. Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label “deep.” 

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. 

The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to detect patterns in data and adjust program actions accordingly.  Machine learning algorithms are often categorized as being supervised or unsupervized. Supervised algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from datasets.

Facebook's News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling in order to read or "like" a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use to patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.