David Beyer > Quotes

“In the ’80s, a number of psychologists, computer scientists and linguists developed the Connectionist approach to cognitive psychology. Using neural nets, this community cast a new light on human thought and learning, anchored in basic ingredients from neuroscience. Indeed, backpropagation and some of the other algorithms in use today trace back to those efforts.”
― David Beyer, The Future of Machine Intelligence

“How do you even think about unsupervised learning? How do you benefit from it? Once our understanding improves and unsupervised learning advances, this is where we will acquire new ideas, and see a completely unimaginable explosion of new applications. (Ilya Sutskever)”
― David Beyer, The Future of Machine Intelligence

“Modern machine learning naturally occurs in a world of higher dimensions, generating lots of multivariate data in the process, including a large amount of noise. (Anima Anandkumar)”
― David Beyer, The Future of Machine Intelligence

“Tensors are higher dimensional extensions of matrices. Just as matrices can represent pairwise correlations, tensors can represent higher order correlations (Anima Anandkumar)”
― David Beyer, The Future of Machine Intelligence

“whereas Chomsky focused on an innate grammar and the use of logic, deep learning looks to meaning. Grammar, it turns out, is the icing on the cake. Instead, what really matters is our intention: it’s mostly the choice of words that determines what we mean, and the associated meaning can be learned. (Yoshua Bengio)”
― David Beyer, The Future of Machine Intelligence

“In a recommender system, the hidden information represents users’ unknown interests and the observed data consist of products they have purchased thus far. If a user recently bought a bike, she is interested in biking/outdoors, and is more likely to buy biking accessories in the near future. We can model her interest as a hidden variable and infer it from her buying pattern. To discover such relationships, however, we need to observe a whole lot of buying patterns from lots of users—making this problem a big data one. (Anima Anandkumar)”
― David Beyer, The Future of Machine Intelligence

“The mathematical and technical virtuosity of achievements in this field evoke the qualities that make us human: Everything from intuition and attention to planning and memory.”
― David Beyer, The Future of Machine Intelligence

“Since humans can solve perception problems very quickly, despite our neurons being relatively slow, moderately deep and large neural networks have enabled machines to succeed in a similar fashion. (Ilya Sutskever)”
― David Beyer, The Future of Machine Intelligence

“There is a compelling argument that large, deep neural networks should be able to represent very good solutions to perception problems. It goes like this: human neurons are slow, and yet humans can solve perception problems extremely quickly and accurately. If humans can solve useful problems in a fraction of a second, then you should only need a very small number of massively-parallel steps in order to solve problems like vision and speech recognition. (Ilya Sutskever)”
― David Beyer, The Future of Machine Intelligence

“But I can’t even articulate what it is we want from unsupervised learning. You want something; you want the model to understand...whatever that means. (Ilya Sutskever)”
― David Beyer, The Future of Machine Intelligence

“Machine learning has been through several transition periods starting in the mid-90s. From 1995–2005, there was a lot of focus on natural language, search, and information retrieval. The machine learning tools were simpler than what we’re using today; they include things like logistic regression, SVMs (support vector machines), kernels with SVMs, and PageRank. Google became immensely successful using these technologies, building major success stories like Google News and the Gmail spam classifier using easy-to-distribute algorithms for ranking and text classification—using technologies that were already mature by the mid-90s. (Reza Zadeh)”
― David Beyer, The Future of Machine Intelligence