Deep Learning creating jobs in Apps, wearable tech and robotics
At the core of the early 21st century technology, with Internet connectivity and data driven by advances in Machine Learning; a sub-domain of what we call Artificial Intelligence, is integral to innovation advances.
A good definition of Artificial Intelligence (or maybe soft or logical AI), as provided by my friendly assistant, Google Now:
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Steve Jurvetson of DFJ said that he believes machine learning, a subset of AI will be one of the most important tech trends over the next 3-5 years for innovation and economic growth. By leveraging big data to allow computers to develop evolving behaviors, machine learning is vastly improving pattern recognition, allowing for broad application such as improved facial and speech recognition for application in many industries, especially national security.
Computer scientists have made significant advances in Machine Learning and soft AI with a particular set of approaches called “deep learning.” Deep Learning algorithms have been extremely successful for applications such as image recognition, speech recognition, and to some extent for natural language processing.
Deep Learning is the application of algorithms and software programming through ‘neural networks’ to develop machines, computers and robots that can do a wide variety of things including driving cars, working in factories, conversing with humans, translating speeches, recognizing and analyzing images and data patterns, and diagnosing complex operational or procedural problems.
One aspect of Deep Learning algorithms, which are also sometimes referred to as learning algorithms, which is receiving much work at major organizations, is providing a machine, computer or robot with the ability to learn from mostly unlabeled data, i.e. to work in a semi-supervised setting, where not all the examples come with complete and correct semantic labels. This was cleverly shown by Google with its ability to identify cats without labels on the photographs (Google builds a brain that can identify cats).
As Professor Yann Le Cunn, now at Facebook says:
The only way to build intelligent machines these days is to have them crunch lots of data — and build models of that data.
Sometimes it’s not who has the best algorithm that wins; it’s who has the most data.
Many Deep Learning scientists and academics are being recruited by Google, Facebook, Microsoft co-founder Paul Allen’s AI organization, Adobe, Amazon, Microsoft (see e.g. Bing), IBM to name a few.
Some of these recruits led the journalist and TV interviewer to quip: “The best minds of my generation are thinking about how to make people click ads.”
As witty (and sad) as that is, there is a degree of truth in it, however deep learning has a far more significant impact and many employers are seeking out people with deep learning capabilities.
Here are a just a few examples of how deep learning is improving how we use computers, wearable tech and robots.
Google Glass – New York Police Department are beta testing Google Glass programmed with Deep Learning. The officer wearing Glass will have access to a database for facial recognition, be able to record the event in real time. With respect to clearing up misunderstandings for law enforcement agents and citizens I see this as a very good move.
One of my favorite uses of Deep Learning can be seen in Amazon’s new Flow App. Flow recognizes items via their shape, size, color, box text, and general appearance. Hold your iPhone up to a row of items at a store, or in your home, and within seconds of “seeing” it with the iPhone’s camera, every recognizable item is placed in queue that can be added to your Amazon cart. You can use Flow to scan a row of competing products, then compare their prices and Amazon ratings once they land in your queue. Unsurprisingly, physical stores are not fans of this.
Deep Learning will be transformational in robotics. Nao, the companion robot created by Aldebaran Robotics, uses deep learning to improve its emotional intelligence, facial recognition and ability to communicate in multiple languages (see video below).
The real innovation challenge it seems will not be to apply deep learning to replace humans but to use it to create new ideas, products and industries that will continue to generate new jobs and opportunities.
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