Man vs AI: Ethics and the Future of Machines

mind_machine_by_neodecayRecent strides in artificial intelligence from big name players such as Google, Facebook, and Baidu, as well as increasingly successful heterogenous systems like IBM’s Watson have provoked fear and excitement amongst the intelligentsia in equal measures. Public figures, such as Steven Hawking, are concerned, and not surprisingly the popular press are excited to cover it. Recently, Elon Musk has become worried that AI might eventually spell doom for the human race. He donated $10 million to fund the Future of Life organization whose stated goal is to ensure AI remains beneficial and does not threaten our wellbeing. An open letter by this organization, titled “Research Priorities for Robust and Beneficial Artificial Intelligence,” was signed by hundreds of research leaders. Influential futurist, Ray Kurzweil, has popularized the idea of the technological singularity where intelligent systems surpass human capabilities and leave us marginalized at best.
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What is Deep Learning?

dlIn recent years the concept of deep learning has been gaining widespread attention. The media frequently reports on talent acquisitions in this field, such as those by Google and Facebook, and startups which claim to employ deep learning are met with enthusiasm. Gratuitous comparisons with the human brain are frequent. But is this just a trendy buzz word? What exactly is deep learning and how is it relevant to developments in machine intelligence?

For many researchers, deep learning is simply a continuation of the multi-decade advancement in our ability to make use of large scale neural networks. Let’s first take a quick tour of the problems that neural networks and related technologies are trying to solve, and later we will examine the deep learning architectures in greater detail.

Machine learning generally breaks down into two application areas which are closely related: classification and regression. Continue reading

Regression and Conspiracy Theories

RegressionThis post is about fitting a curve through a set of points. This is called regression: It is also the classic problem of coming up with a generalization from a discrete training data set in machine learning. We have a set of points that are observations at specific places and we want to make a system that predicts what the likely observations should be at all places within the domain of interest. We use the given observations (a training set) to train a model and then when we get more observations (a test set) we can evaluate how much error there is in our Continue reading