AI researchers at Chinese tech beast Baidu have attempted to teach virtual bots English in a two-dimensional maze-like world. The study "paves the way for the idea of a family robot," a smart robo-butler that can understand orders given by its owner, it is claimed. This ability to handle normal language is essential to creating machines with human-level intelligence, the researchers argue in a paper now available on arXiv. Teaching bots language by describing the simulated world around them gives the software knowhow and knowledge that can be transferred from task to task – that's surprisingly hard to do correctly and a sign of general intelligence. The researchers compare their method to parents using language to coach a baby who is learning to walk and talk.

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A little more than a decade ago, Mike Mendl developed a new test for gauging a laboratory rat's level of happiness. Mendl, an animal welfare researcher in the veterinary school at the University of Bristol in England, was looking for an objective way to tell whether animals in captivity were suffering. Specifically, he wanted to be able to measure whether, and how much, disruptions in lab rats' routines--being placed in an unfamiliar cage, say, or experiencing a change in the light/dark cycle of the room in which they were housed--were bumming them out. He and his colleagues explicitly drew on an extensive literature in psychology that describes how people with mood disorders such as depression process information and make decisions: They tend to focus on and recall more negative events and to judge ambiguous things in a more negative way. You might say that they tend to see the proverbial glass as half-empty rather than half-full. "We thought that it's easier to measure cognitive things than emotional ones, so we devised a test that would give us some indication of how animals responded under ambiguity," Mendl says.

Humans look to the power of artificial intelligence (AI) to make better and unbiased decisions. However, a new study has found that the technology is becoming racist and sexist as it learns, thus hindering its ability to make balanced resolutions. Researchers discovered that the better AI becomes at interpreting the human language, the more likely it will adopt human bias about race and gender that lurks within the data it is fed. A Study found that AI is becoming racists and sexist as it learns, hindering its ability to make unbiased decisions. Princeton University conducted a word associate task with the algorithm GloVe, an unsupervised AI that uses online text to understand human language.

We prove a \emph{query complexity} lower bound on rank-one principal component analysis (PCA). We consider an oracle model where, given a symmetric matrix $M \in \mathbb{R}^{d \times d}$, an algorithm is allowed to make $T$ \emph{exact} queries of the form $w^{(i)} = Mv^{(i)}$ for $i \in \{1,\dots,T\}$, where $v^{(i)}$ is drawn from a distribution which depends arbitrarily on the past queries and measurements $\{v^{(j)},w^{(j)}\}_{1 \le j \le i-1}$. We show that for a small constant $\epsilon$, any adaptive, randomized algorithm which can find a unit vector $\widehat{v}$ for which $\widehat{v}^{\top}M\widehat{v} \ge (1-\epsilon)\|M\|$, with even small probability, must make $T = \Omega(\log d)$ queries. In addition to settling a widely-held folk conjecture, this bound demonstrates a fundamental gap between convex optimization and "strict-saddle" non-convex optimization of which PCA is a canonical example: in the former, first-order methods can have dimension-free iteration complexity, whereas in PCA, the iteration complexity of gradient-based methods must necessarily grow with the dimension. Our argument proceeds via a reduction to estimating the rank-one spike in a deformed Wigner model. We establish lower bounds for this model by developing a "truncated" analogue of the $\chi^2$ Bayes-risk lower bound of Chen et al.

We consider two stage estimation with a non-parametric first stage and a generalized method of moments second stage, in a simpler setting than (Chernozhukov et al. 2016). We give an alternative proof of the theorem given in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample splitting and $n^{1/4}$-consistency of the first stage, imply $\sqrt{n}$-consistency and asymptotic normality of second stage estimates. Our proof is for a variant of their estimator, which is based on the empirical version of the moment condition (Z-estimator), rather than a minimization of a norm of the empirical vector of moments (M-estimator). This note is meant primarily for expository purposes, rather than as a new technical contribution.

This paper describes a simple new semantics for logic rules, founded semantics, and its straightforward extension to another simple new semantics, constraint semantics. The new semantics support unrestricted negation, as well as unrestricted existential and universal quantifications. They are uniquely expressive and intuitive by allowing assumptions about the predicates and rules to be specified explicitly. They are completely declarative and easy to understand and relate cleanly to prior semantics. In addition, founded semantics can be computed in linear time in the size of the ground program.

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail.

BENGALURU: The humongous amount of digital data being generated, and companies' need to glean insights and make predictions from them have made skills in data visualisation, data science, and machine learning among the most valued for technology recruiters today. This is reflected in the number of working professionals signing up for specialised courses in these spaces. Candidates who complete the courses tend to get between 20% and 50% increase in salaries. Kashyap Dalal, chief business officer at online learning platform Simplilearn, says that big data and analytics courses were the big growth drivers in the past three years. While data science continues to remain popular, accounting for 30% of all learners, courses on visualisation tools and machine learning have become very attractive over the past six months, he said.

Over 60% of Britons believe artificial intelligence (AI) and robots will soon have the potential to malfunction and even kill humans. This concern rises to 72% when those who understand and are interested in AI are asked about their fears over future robots. Participants were asked: "Films and TV series...portray intelligent robots who malfunction and kill humans. How far away do you feel we are from the technology depicted in the films?" Just 9% believed such a scenario is "very unlikely", while 45% said it was likely and 17% considered a future of robots killing humans as "very likely".