Polanyi's paradox, named in honor of the philosopher and polymath Michael Polanyi, states that "we know more than we can tell."[1] He means that most of our knowledge is tacit and cannot be easily formalized with words.[2] In The Tacit Dimension, Polanyi gives the example of recognizing a face without being able to tell what facial features humans use to make such a distinction. The example describes Gestalt psychology which emerged in the early twentieth century as a theory of perception that rejected the basic principles of elementalist and structuralist psychology as well as functionalist and behavioralist theories of the mind. Gestalt theory emphasizes that conscious humans perceive entire patterns or configurations, not individual components, and cannot always explain what they know. Consider the ancient Chinese game Go, where nobody can define a good move.

Artificial intelligence (AI) has the potential to help every sector of the economy. There is a challenge, though, in sectors that have fuzzier analysis and the potential to train with data that can continue human biases. A couple of years ago, I described the problem with bias in an article about machine learning (ML) applied to criminal recidivism. It's worth revisiting the sector as time have changed in how bias is addressed. One way is to look at sectors in the legal profession where bias is a much smaller factor.

We've been losing the war on cybercrime for some time. Research firm Forrester reports over a billion accounts stolen in 2016 alone, and these data breaches are going up, not down. We are having to wade through more incident data, and people cannot keep up. Could machine learning help solve the problem? For years, researchers hoped that artificial intelligence would produce human-like machines.

Autonomous vehicles are already making profound choices about whose lives matter, according to experts, so we might want to pay attention. "Every time the car makes a complex manoeuvre, it is implicitly making trade-off in terms of risks to different parties," Iyad Rahwan, an MIT cognitive scientist, wrote in an email. The most well-known issues in AV ethics are trolly problems -- moral questions dating back to the era of trollies that ask whose lives should be sacrificed in an unavoidable crash. For instance, if a person falls onto the road in front of a fast-moving AV, and the car can either swerve into a traffic barrier, potentially killing the passenger, or go straight, potentially killing the pedestrian, what should it do? Rahwan and colleagues have studied what humans consider the moral action in no-win scenarios (you can judge your own cases at their crowd-sourced project, Moral Machine).

Autonomous vehicles are already making profound choices about whose lives matter, according to experts, so we might want to pay attention. "Every time the car makes a complex maneuver, it is implicitly making trade-off in terms of risks to different parties," Iyad Rahwan, an MIT cognitive scientist, wrote in an email. The most well-known issues in AV ethics are trolly problems--moral questions dating back to the era of trollies that ask whose lives should be sacrificed in an unavoidable crash. For instance, if a person falls onto the road in front of a fast-moving AV, and the car can either swerve into a traffic barrier, potentially killing the passenger, or go straight, potentially killing the pedestrian, what should it do? Rahwan and colleagues have studied what humans consider the moral action in no-win scenarios (you can judge your own cases at their crowd-sourced project, Moral Machine).

Uber is creating a new AI research lab dedicated to exploring the frontiers of machine learning and applying key advances to its business. The lab will be based in Silicon Valley and will be led by Gary Marcus, a professor at NYU and the CEO of Geometric Intelligence, a company Uber is acquiring for an undisclosed sum. The Uber AI lab will also employ another big-name AI researcher, Zoubin Ghahramani, who will retain a part-time post as a professor at the University of Cambridge in the U.K. The company's other cofounders are Ken Stanley, an associate professor at the University of Central Florida, and Doug Bemis, a recent NYU graduate with a PhD in neurolinguistics. The new lab will have 15 founding members, and it will explore a range of fundamental challenges, including developing forms of machine learning that need less data; training AI systems using not only data but also explicit rules; and designing machine-learning systems that explain their decisions. Advances in these areas could be vital to self-driving cars but might also help improve Uber's existing business by, for instance, helping route cars or match customers in an Uber pool more efficiently.

With the increasingly vast volumes of data generated by enterprises, relying on static rule-based decision systems is no longer competitive; instead, there is an unprecedented opportunity to optimize decisions, and adapt to changing conditions, by leveraging patterns in real-time and historical data. The very size of the data however makes it impossible for humans to find these patterns, and this has lead to an explosion of industry interest in the field of Machine Learning, which is the science and practice of designing computer algorithms that, broadly speaking, find patterns in large volumes of data. ML is particularly important in digital marketing: understanding how to leverage vast amounts of data about digital audiences and the media they consume can be the difference between success and failure for the world's largest brands. MediaMath's vision is for every addressable interaction between a marketer and a consumer to be driven by ML optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes. In this series of blog posts we will present a very basic, non-technical introduction to Machine Learning.