Sohrob Kazerounian is a Distinguished AI Researcher at Vectra AI where he develops and applies novel machine learning architectures in the domain of cybersecurity. After realizing that his goal of becoming a skilled hacker was not meant to be, he focused his studies on Artificial Intelligence, with a particular interest in neural networks. After receiving his Ph.D. in Cognitive and Neural Systems at Boston University, he held a postdoctoral fellowship at the Swiss AI Lab (IDSIA) working on Deep Learning, Recurrent Neural Networks, and Reinforcement Learning.
In just the last few years, numerous studies have been published and institutes inaugurated that are dedicated to studying which jobs of the future will remain in the hands of humans, and which will be doled out to the machines.
The use of AI in cybersecurity not only expands the scope of what a single security expert is able to monitor, but importantly, it also enables the discovery of attacks that would have otherwise been undetectable by a human. Just as it was nearly inevitable that AI would be used for defensive purposes, it is undeniable that AI systems will soon be put to use for attack purposes.
In the last blog post, we alluded to the No-Free-Lunch (NFL) theorems for search and optimization. While NFL theorems are criminally misunderstood and misrepresented in the service of crude generalizations intended to make a point, I intend to deploy a crude NFL generalization to make just such a point.
Despite the recent explosion in machine learning and artificial intelligence (AI) research, there is no singular method or algorithm that works best in all cases. In fact, this notion has been formalized and shown mathematically in a result known as the No Free Lunch theorem (Wolpert and Macready 1997).
Deep learning refers to a family of machine learning algorithms that can be used for supervised, unsupervised and reinforcement learning. These algorithms are becoming popular after many years in the wilderness. The name comes from the realization that the addition of increasing numbers of layers typically in a neural network enables a model to learn increasingly complex representations of the data.
There are numerous techniques for creating algorithms that are capable of learning and adapting over time. Broadly speaking, we can organize these algorithms into one of three categories—supervised, unsupervised, and reinforcement learning.
“The original question ‘Can machines think?’ I believe to be too meaningless to deserve discussion. Nevertheless, I believe that at the end of the century, the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.” – Alan Turing
Can machines think? The question itself is deceptively simple in so far as the human ability to introspect has made each of us intimately aware of what it means to think.
It is difficult to tell the history of AI without first describing the formalization of computation and what it means for something to compute. The primary impetus towards formalization came down to a question posed by the mathematician David Hilbert in 1928.
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