The unsuccessful experiment of Microsoft with its AI algorithm Tay (Tay), which within 24 hours after the beginning of interaction with people from Twitter turned into an inveterate racist, showed that the AI systems that are being created today can become victims of human prejudices and, in particular, stereotyped Thinking. Why this happens – tried to find out a small group of researchers from Princeton University. In addition, they developed an algorithm capable of predicting the manifestation of social stereotypes based on an intensive analysis of how people communicate with each other on the Internet. Many AI systems undergo training in understanding the human language using massive collections of text data. They are also called corps.

In this article, we have listed some of the best free machine learning books that you should consider going through (no order in particular). Based on the Stanford Computer Science course CS246 and CS35A, this book is aimed for Computer Science undergraduates, demanding no pre-requisites. This book has been published by Cambridge University Press. This book holds the prologue to statistical learning methods along with a number of R labs included. This Deep Learning textbook is designed for those in the early stages of Machine Learning and Deep learning in particular.

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base. In this work, we introduce a Neural Symbolic Machine, which contains (a) a neural "programmer", i.e., a sequence-to-sequence model that maps language utterances to programs and utilizes a key-variable memory to handle compositionality (b) a symbolic "computer", i.e., a Lisp interpreter that performs program execution, and helps find good programs by pruning the search space. We apply REINFORCE to directly optimize the task reward of this structured prediction problem. To train with weak supervision and improve the stability of REINFORCE, we augment it with an iterative maximum-likelihood training process. NSM outperforms the state-of-the-art on the WebQuestionsSP dataset when trained from question-answer pairs only, without requiring any feature engineering or domain-specific knowledge.

Though the deep learning is pushing the machine learning to a new stage, basic theories of machine learning are still limited. The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing neural transfer function and cost function, etc., are still far from clear. In this paper, we present a general theoretical framework for machine learning. We classify the prior knowledge into common and problem-dependent parts, and consider that the aim of learning is to maximally incorporate them. The principle we suggested for maximizing the former is the design risk minimization principle, while the neural transfer function, the cost function, as well as pretreatment of samples, are endowed with the role for maximizing the latter. The role of the neuron bias is explained from a different angle. We develop a Monte Carlo algorithm to establish the input-output responses, and we control the input-output sensitivity of a learning machine by controlling that of individual neurons. Applications of function approaching and smoothing, pattern recognition and classification, are provided to illustrate how to train general learning machines based on our theory and algorithm. Our method may in addition induce new applications, such as the transductive inference.

This work presents a novel objective function for the unsupervised training of neural network sentence encoders. It exploits signals from paragraph-level discourse coherence to train these models to understand text. Our objective is purely discriminative, allowing us to train models many times faster than was possible under prior methods, and it yields models which perform well in extrinsic evaluations.

Existing algorithms for subgroup discovery with numerical targets do not optimize the error or target variable dispersion of the groups they find. This often leads to unreliable or inconsistent statements about the data, rendering practical applications, especially in scientific domains, futile. Therefore, we here extend the optimistic estimator framework for optimal subgroup discovery to a new class of objective functions: we show how tight estimators can be computed efficiently for all functions that are determined by subgroup size (non-decreasing dependence), the subgroup median value, and a dispersion measure around the median (non-increasing dependence). In the important special case when dispersion is measured using the average absolute deviation from the median, this novel approach yields a linear time algorithm. Empirical evaluation on a wide range of datasets shows that, when used within branch-and-bound search, this approach is highly efficient and indeed discovers subgroups with much smaller errors.

Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -- even millions -- of null hypotheses. For high-dimensional multivariate distributions, these hypotheses may concern a wide range of parameters, with complex and unknown dependence structures among variables. In analyzing such hypothesis testing procedures, gains in efficiency and power can be achieved by performing variable reduction on the set of hypotheses prior to testing. We present in this paper an approach using data-adaptive multiple testing that serves exactly this purpose. This approach applies data mining techniques to screen the full set of covariates on equally sized partitions of the whole sample via cross-validation. This generalized screening procedure is used to create average ranks for covariates, which are then used to generate a reduced (sub)set of hypotheses, from which we compute test statistics that are subsequently subjected to standard multiple testing corrections. The principal advantage of this methodology lies in its providing valid statistical inference without the \textit{a priori} specifying which hypotheses will be tested. Here, we present the theoretical details of this approach, confirm its validity via a simulation study, and exemplify its use by applying it to the analysis of data on microRNA differential expression.

A new study by AI firm CrowdFlower reveals that a majority of data scientists feel as though they've landed this century's sexiest job. While the sexiness is debatable, it's clear that job satisfaction rates are high within this burgeoning career path. According to the study, more than 90 percent of data scientists surveyed said they were happy doing their jobs, and nearly 50 percent said they were thrilled. Data scientists are effectively the human engine behind today's most pivotal technologies, including artificial intelligence, machine learning, and algorithms and analytics. This report suggests most practicing data scientists are well aware of their importance and relish the job stability.

Tesla founder and Chief Executive Elon Musk said his latest company Neuralink is working to link the human brain with a machine interface by creating micron-sized devices. Neuralink is aiming to bring to the market a product that helps with certain severe brain injuries due to stroke, cancer lesions, and so on, in about four years, Musk said in an interview with website Wait But Why. "If I were to communicate a concept to you, you would essentially engage in consensual telepathy," Musk said in the interview published on Thursday. The interview was part of a nearly 40,000-word article that explains in detail the science of the human brain and how Neuralink will aim to connect to it. Artificial intelligence and machine learning will create computers so sophisticated and godlike that humans will need to implant "neural laces" in their brains to keep up, Musk said in a tech conference last year. "There are a bunch of concepts in your head that then your brain has to try to compress into this incredibly low data rate called speech or typing," Musk said in the latest interview.

When an Icelander arrives at an office building and sees "Solarfri" posted, they need no further explanation for the empty premises: The word means "when staff get an unexpected afternoon off to enjoy good weather." The people of this rugged North Atlantic island settled by Norsemen some 1,100 years ago have a unique dialect of Old Norse that has adapted to life at the edge of the Artic. Hundslappadrifa, for example, means "heavy snowfall with large flakes occurring in calm wind." But the revered Icelandic language, seen by many as a source of identity and pride, is being undermined by the widespread use of English, both for mass tourism and in the voice-controlled artificial intelligence devices coming into vogue. Linguistics experts, studying the future of a language spoken by fewer than 400,000 people in an increasingly globalized world, wonder if this is the beginning of the end for the Icelandic tongue.