Artificial Intelligence (AI) is having a transformative effect on the business world and the $600 billion global legal services market is not immune. As AI automates basic processes, in the legal profession it promises to allow lawyers devote their time to more valuable, cost-effective, and strategic work. Consultants at McKinsey & Company estimate that 22% of a lawyer's job and 35% of a paralegal's job can be automated. However, the common perception among lawyers remains that machines cannot yet match the legal intellect of human lawyers in daily fundamentals of the profession. This assumption was tested in the first of its kind "AlphaGo"-style Study in the legal profession.

The shooting at the school in Florida was devastating, and it appears clear that Russia has been manipulating public opinion in the U.S. to stoke the flames of a divisive argument on guns. What is being missed is a brewing problem that potentially could have an even more devastating impact. Competing for our eyeballs is the news that the U.S. president kissed a woman without her permission. That story has served as a distraction from far more horrendous attacks against women in the tech industry and in government. Intel showcased virtual reality at the Olympics, but almost no one cared. I think that deep learning and artificial intelligence either could make things far better or far, far worse -- and my fear is that we are moving toward the latter and away from the former.

Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.

The rapid growth of storage capacity and processing power has caused machine learning applications to increasingly rely on using immense amounts of labeled data. It has become more important than ever to have fast and inexpensive ways to annotate vast amounts of data. With the emergence of crowdsourcing services, the research direction has gravitated toward putting the wisdom of crowds to better use. Unfortunately, spammers and inattentive annotators pose a threat to the quality and trustworthiness of the consensus. Thus, high quality consensus estimation from crowd annotated data requires a meticulous choice of the candidate annotator and the sample in need of a new annotation. Due to time and budget limitations, it is of utmost importance that this choice is carried out while the annotation collection is in progress. We call this process active crowd-labeling. To this end, we propose an active crowd-labeling approach for actively estimating consensus from continuous-valued crowd annotations. Our method is based on annotator models with unknown parameters, and Bayesian inference is employed to reach a consensus in the form of ordinal, binary, or continuous values. We introduce ranking functions for choosing the candidate annotator and sample pair for requesting an annotation. In addition, we propose a penalizing method for preventing annotator domination, investigate the explore-exploit trade-off for incorporating new annotators into the system, and study the effects of inducing a stopping criterion based on consensus quality. We also introduce the crowd-labeled Head Pose Annotations datasets. Experimental results on the benchmark datasets used in the literature and the Head Pose Annotations datasets suggest that our method provides high-quality consensus by using as few as one fifth of the annotations (~80% cost reduction), thereby providing a budget and time-sensitive solution to the crowd-labeling problem.

We present a generalization of the adversarial linear bandits framework, where the underlying losses are kernel functions (with an associated reproducing kernel Hilbert space) rather than linear functions. We study a version of the exponential weights algorithm and bound its regret in this setting. Under conditions on the eigendecay of the kernel we provide a sharp characterization of the regret for this algorithm. When we have polynomial eigendecay $\mu_j \le \mathcal{O}(j^{-\beta})$, we find that the regret is bounded by $\mathcal{R}_n \le \mathcal{O}(n^{\beta/(2(\beta-1))})$; while under the assumption of exponential eigendecay $\mu_j \le \mathcal{O}(e^{-\beta j })$, we get an even tighter bound on the regret $\mathcal{R}_n \le \mathcal{O}(n^{1/2}\log(n)^{1/2})$. We also study the full information setting when the underlying losses are kernel functions and present an adapted exponential weights algorithm and a conditional gradient descent algorithm.

Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.

Much of the success of single agent deep reinforcement learning (DRL) in recent years can be attributed to the use of experience replay memories (ERM), which allow Deep Q-Networks (DQNs) to be trained efficiently through sampling stored state transitions. However, care is required when using ERMs for multi-agent deep reinforcement learning (MA-DRL), as stored transitions can become outdated because agents update their policies in parallel [11]. In this work we apply leniency [23] to MA-DRL. Lenient agents map state-action pairs to decaying temperature values that control the amount of leniency applied towards negative policy updates that are sampled from the ERM. This introduces optimism in the value-function update, and has been shown to facilitate cooperation in tabular fully-cooperative multi-agent reinforcement learning problems. We evaluate our Lenient-DQN (LDQN) empirically against the related Hysteretic-DQN (HDQN) algorithm [22] as well as a modified version we call scheduled-HDQN, that uses average reward learning near terminal states. Evaluations take place in extended variations of the Coordinated Multi-Agent Object Transportation Problem (CMOTP) [8] which include fully-cooperative sub-tasks and stochastic rewards. We find that LDQN agents are more likely to converge to the optimal policy in a stochastic reward CMOTP compared to standard and scheduled-HDQN agents.

In popular culture, quantum computing is often painted as an ultra-powerful technology that will first outrace, and then replace, its classical counterpart. In reality, though, the two are inextricably linked. We use classical computation and simulations to develop and design today's nascent quantum devices, which are then nested within a much larger framework of classical computers. The effort to advance these different technologies – which both fuel and rely on each other – resembles something like a dance. Movement is required on both sides.

The nation's top lawyers recently battled artificial intelligence in a competition to interpret contracts -- and they lost. A new study, conducted by legal AI platform LawGeex in consultation with law professors from Stanford University, Duke University School of Law, and University of Southern California, pitted twenty experienced lawyers against an AI trained to evaluate legal contracts. Competitors were given four hours to review five non-disclosure agreements (NDAs) and identify 30 legal issues, including arbitration, confidentiality of relationship, and indemnification. They were scored by how accurately they identified each issue. SEE ALSO: Google's new AI can predict heart disease by simply scanning your eyes Unfortunately for humanity, we lost the competition -- badly. The human lawyers achieved, on average, an 85 percent accuracy rate, while the AI achieved 95 percent accuracy.

Linear algebra is a field of mathematics and an important pillar of the field of machine learning. It can be a challenging topic for beginners, or for practitioners who have not looked at the topic in decades. In this post, you will discover how to get help with linear algebra for machine learning. Top Resources for Learning Linear Algebra for Machine Learning Photos by mickey, some rights reserved. Take my free 7-day email crash course now (with sample code).