Artificial intelligence programs are extremely good at finding subtle patterns in enormous amounts of data, but don't understand the meaning of anything. Whether you are searching the Internet on Google, browsing your news feed on Facebook, or finding the quickest route on a traffic app like Waze, an algorithm is at the root of it. Algorithms have permeated our daily lives; they help to simplify, distill, process, and provide insights from massive amounts of data. According to Ernest Davis, a professor of computer science at New York University's Courant Institute of Mathematical Sciences whose research centers on the automation of common-sense reasoning, the technologies that currently exist for artificial intelligence (AI) programs are extremely good at finding subtle patterns in enormous amounts of data. "One way or another," he says, "that is how they work."

Fox News Flash top headlines for Oct. 24 are here. Check out what's clicking on Foxnews.com The owner of the Rubik's Cube has lost an appeal to regain the European Union trademark rights to the classic puzzle's iconic shape in a new twist to the ongoing legal drama. Rubik's Brand Ltd. lost the protection rights to the puzzle's shape in 2017, after the EU's top court ruled that law prevents the firm from having "a monopoly on technical solutions or functional characteristics of a product," Bloomberg reported. The EU General Court in Luxembourg upheld that decision on Thursday.

I have always been drawn to books and argumentation. In primary school, I was an avid debater, representing my school in competitions and winning laurels. In my early teens, my parents deemed it necessary to ban me from reading novels, as I would do little else. Looking back now, I guess this background foreshadowed my choice of a career as a lawyer. No one particularly influenced my decision; law seemed a natural fit and an easy choice for me.

Machine learning is a race. Those companies that can put machine learning models into production, on a large scale, first, will gain a huge advantage over their competitors and billions in potential revenue. But, there is a huge issue with the usability of machine learning -- there is a significant challenge around putting machine learning models into production at scale. Organisations can create incredibly complex machine learning models, but it's problematic to take huge datasets, apply them to different iterations of ML models and then deploy those successful iterations into production. Machine learning is becoming the phrase that data scientists hide from CVs, putting a data science model into production is the biggest data challenge, and companies are still not getting it.

AI-powered legal software has been proven to boost efficiency in analyzing legal documents. Artificial intelligence is continuing to play an essential role in bringing automation and driving precision in the decision-making process across many industries. Recently, the legal profession has also been subject to AI-based innovations. Some app developers already built future-ready AI tools for lawyers and law firms. This has prompted many industry experts to predict the upcoming AI uses for law firms and the legal industry. As per the prediction of Deloitte, by 2036 a whopping 100,000 professional roles in the legal sector will be automated.

AI-powered legal software has been proven to boost efficiency in analyzing legal documents. Artificial intelligence is continuing to play an essential role in bringing automation and driving precision in the decision-making process across many industries. Recently, the legal profession has also been subject to AI-based innovations. Some app developers already built future-ready AI tools for lawyers and law firms. This has prompted many industry experts to predict the upcoming AI uses for law firms and the legal industry. As per the prediction of Deloitte, by 2036 a whopping 100,000 professional roles in the legal sector will be automated.

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accuracy, when subdivided by specific sensitive variable subgroups. The reasons for these discrepancies are varied and not limited to the influence of mitigating variables, institutional bias, underlying population distributions as well as sampling bias. Among the numerous definitions of fairness that exist, we argue that at a minimum, principled ML practices should ensure that classification predictions are able to mirror the underlying sub-population distributions. However, as the number of sensitive variables increase, populations meeting at the intersectionality of these variables may simply not exist or may not be large enough to provide accurate samples for classification. In these increasingly likely scenarios, we make the case for human intervention and applying situational and individual definitions of fairness. In this paper we present lower bounds of subgroup sample complexity for metric-fair learning based on the theory of Probably Approximately Metric Fair Learning. We demonstrate that for a classifier to approach a definition of fairness in terms of specific sensitive variables, adequate subgroup population samples need to exist and the model dimensionality has to be aligned with subgroup population distributions. In cases where this is not feasible, we propose an approach using individual fairness definitions for achieving alignment. We look at two commonly explored UCI datasets under this lens and suggest human interventions for data collection for specific subgroups to achieve approximate individual fairness for linear hypotheses.

Veena Dubal is an unlikely star in the tech world. A scholar of labor practices regarding the taxi and ride-hailing industries and an Associate Professor at San Francisco's U.C. Hastings College of the Law, her work on the ethics of the gig economy has been covered by the New York Times, NBC News, New York Magazine, and other publications. She's been in public dialogue with Naomi Klein and other famous authors, and penned a prominent op-ed on facial recognition tech in San Francisco -- all while winning awards for her contributions to legal scholarship in her area of specialization, labor and employment law. At the annual symposium of the AI Now Institute, an interdisciplinary research center at New York University, Dubal was a featured speaker. The symposium is the largest annual public gathering of the NYU-affiliated research group that examines AI's social implications.

The third Tallinn Digital Summit was recently held in Tallinn with a focus on AI for public value and to mark the occasion we spoke to Dr Julien Cornebise, who leads AI for Good at Element AI. He is an awarded scientist who has worked with Amnesty International and he was an early employee at DeepMind. We talked about the hype around AI, but also the all the good it could be used for with the right incentives. We have a government team within Element AI and we've had some requests, but like every contact we get – whether it's from NGOs, agencies or governments – we make very sure to explain to the people who reach out to separate the hype from reality. In some cases, we've said that it's not feasible now, but maybe after a few more years of research. More generally, yes, we want to work with governments around AI for good, because the sustainable development goals are not just for NGOs.