Released in 2015, Domingos's book is an introduction to machine learning and how it relates to everyday life. The answer to all the learning problems of AI technology, argues Domingos, a professor of computer science at the University of Washington, is an ultimate "master" algorithm that gives itself feedback to develop endlessly. He writes: "If it exists, the Master Algorithm can derive all knowledge in the world--past, present, and future--from data. Inventing it would be one of the greatest advances in the history of science."

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Recently, I reached out to 17 thought leaders -- AI experts, computer engineers, roboticists, physicists, and social scientists -- with a single question: "How worried should we be about artificial intelligence?" Disagreement about the appropriate level of concern, and even the nature of the problem, is broad. Some experts consider AI an urgent danger; many more believe the fears are either exaggerated or misplaced. Here is what they told me. I am infinitely excited about artificial intelligence and not worried at all.

In today's world, children as young as pre-schoolers have already started using tablets while top executive education programmes boast high-tech facilities where corporate leaders can learn in new ways. We have also seen the rise of e-learning and distance learning for many university degrees, with students learning online without ever having to step into a classroom.

When a machine learning model misses something, its really easy to just think its a bug, or perhaps a defect in the model. It is vitally important you understand that that is not the case. False positives and false negatives are part and parcel of what machine learning is. It makes mistakes sometimes, just like we do. Every business has to be prepared for occasional false positives and negatives in machine learning.

There is increasing interest in learning algorithms that involve interaction between human and machine. Comparison-based queries are among the most natural ways to get feedback from humans. A challenge in designing comparison-based interactive learning algorithms is coping with noisy answers. The most common fix is to submit a query several times, but this is not applicable in many situations due to its prohibitive cost and due to the unrealistic assumption of independent noise in different repetitions of the same query. In this paper, we introduce a new weak oracle model, where a non-malicious user responds to a pairwise comparison query only when she is quite sure about the answer. This model is able to mimic the behavior of a human in noise-prone regions. We also consider the application of this weak oracle model to the problem of content search (a variant of the nearest neighbor search problem) through comparisons. More specifically, we aim at devising efficient algorithms to locate a target object in a database equipped with a dissimilarity metric via invocation of the weak comparison oracle. We propose two algorithms termed WORCS-I and WORCS-II (Weak-Oracle Comparison-based Search), which provably locate the target object in a number of comparisons close to the entropy of the target distribution. While WORCS-I provides better theoretical guarantees, WORCS-II is applicable to more technically challenging scenarios where the algorithm has limited access to the ranking dissimilarity between objects. A series of experiments validate the performance of our proposed algorithms.

We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level, e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods.

The difficulties in matching the latent posterior to the prior, balancing powerful posteriors with computational efficiency, and the reduced flexibility of data likelihoods are the biggest challenges in the advancement of Variational Autoencoders. We show that these issues arise due to struggles in marginal divergence minimization, and explore an alternative to using conditional distributions that is inspired by Generative Adversarial Networks. The class probability estimation that GANs offer for marginal divergence minimization uncovers a family of VAE-GAN hybrids, which offer the promise of addressing these major challenges in variational inference. We systematically explore the solutions available for distribution matching, but show that these hybrid methods do not fulfill this promise, and the trade-off between generation and inference that they give rise to remains an ongoing research topic.

Does having a smaller batch size, perhaps even size 1, help in a sequential learning problem ( where there is an output corresponding to each input) where the dataset is imbalanced? Suppose my batch size is 4 and timesteps are 10. Class 2 rarely occurs in the dataset. In such cases, can the presence of multiple samples in minibatch adversarially impact training of each sample (especially those which contain label 2)? Since label 2 may not even occur in some samples of a minibatch, the network may not learn to predict 2 for samples in which it does occur.