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| The Next Quantum Leap: Artificial Intelligence |
But when cutting-edge sectors evolve at lightning pace, it is not always possible for traditional sectors to change at the same speed.
My company, Gravity4, has been intensively exploring Deep Learning in our development lab to advance our understanding with the ad technology platforms. Recently, I did a mentorship series with the youth in a struggling education system. The fascination of all great things possible, through the cutting edge revolution of AI, brought much energy in the room. Sure AI will enable us to operate smart cars in smart cities, empower us to predict when a particular disease may affect a region or individual, and even predict an election results - with precision!
A bit too much of a sci-fi for you? Well, much of the above is already being tested with machines learning, as it does the heavy computation and analysis to form the best probability for an event. If we look at the recent ‘emerging technologies’, the list would include: self-driving cars, biometrics, chatbots, drones, 3D printing, and VR, just to name but a few.
As with all-things-exciting, the subject sometimes leads to open invitation for the ‘hype’. Perhaps the entrepreneurs all around know the ‘next biggest breakthrough’ facing our human evolution is, “The Artificial Intelligence.” So, let’s work on dispelling the myth, understand what “is” possible today, and ways in which to prepare for this revolution.
What Is Machine Learning?
Physicists
define Machine Learning as “a theory of a field that incorporates the
statistical, probabilistic, computer science and complex algorithms
which result from learning interactively from data and hidden insights,
which can then be used to produce intelligent applications.”
Ok. Let me break this down into simple terms: Machine Learning enables computers to learn without being explicitly programmed. As the inflow of data changes, the computer detects a change in data patterns, and modifies itself in order to accommodate that change. While the underlying process is similar to data mining, Machine Learning does not extract data for human comprehension. It does it automatically in order to respond to pattern change.
How does this affect us? The days when we need to hire individuals who are familiar with basic statistical and data modeling interpretation - in excel - will be soon long gone. In the case of Machine Learning, we now need individuals with a solid understanding in Linear Algebra, Probability Theory and Statistics, Multivariate Calculus and Computational Optimizations.
To that end, in our schools, Science, Technology, Engineering and Mathematics (STEM), need to have a strong focus NOW – possibly one which is heavily weighted in the core curriculum. As industry and technology continue to evolve, it is said that Linear Algebra may very well be the basic requirement to begin understand Machine Learning, simply because it is the necessary foundation. Furthermore, a strong understanding of Vector Spaces and Norms are crucial to formulate interpretations and understand the optimization methods used in ML.
