Time series classification (TSC) aims to predict the class label of a given time series, which is critical to a rich set of application areas such as economics and medicine. State-of-the-art TSC methods have mostly focused on classification accuracy and efficiency, without considering the interpretability of their classifications, which is an important property required by modern applications such as appliance modeling and legislation such as the European General Data Protection Regulation. To address this gap, we propose a novel TSC method - the Randomized-Supervised Time Series Forest (r-STSF). r-STSF is highly efficient, achieves state-of-the-art classification accuracy and enables interpretability. r-STSF takes an efficient interval-based approach to classify time series according to aggregate values of discriminatory sub-series (intervals). To achieve state-of-the-art accuracy, r-STSF builds an ensemble of randomized trees using the discriminatory sub-series. It uses four time series representations, nine aggregation functions and a supervised binary-inspired search combined with a feature ranking metric to identify highly discriminatory sub-series. The discriminatory sub-series enable interpretable classifications. Experiments on extensive datasets show that r-STSF achieves state-of-the-art accuracy while being orders of magnitude faster than most existing TSC methods. It is the only classifier from the state-of-the-art group that enables interpretability. Our findings also highlight that r-STSF is the best TSC method when classifying complex time series datasets.

Smith Willas is a freelance writer, blogger, and digital media journalist. Chinese authorities are testing systems that use AI and facial recognition to detect emotional states. This is reported by the BBC with reference to an unnamed developer of this technology. Experts Boosty Labs, a company that focuses on smart contract development and blockchain app development, share their thoughts of this innovative trend's implications. Beijing is accused by many countries of the genocide of the Uyghur population. The Chinese authorities have flooded the predominantly Muslim Xinjiang Uygur Autonomous Region with surveillance cameras.

In a recent Non-Eventcast, host Jared Correia talks with Nathan Wenzel of SimpleLegal and Alex Smith of iManage to nail down just what "artificial intelligence" refers to, specific ways it's being used, and what lawyers should be doing now to capitalize.

Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by one of the world's largest AI conferences. In 2020, the Conference on Neural Information Processing Systems (NeurIPS) introduced a requirement for submitting authors to include a statement on the broader societal impacts of their research. Drawing insights from similar governance initiatives, including institutional review boards (IRBs) and impact requirements for funding applications, we investigate the risks, challenges and potential benefits of such an initiative. Among the challenges, we list a lack of recognised best practice and procedural transparency, researcher opportunity costs, institutional and social pressures, cognitive biases, and the inherently difficult nature of the task. The potential benefits, on the other hand, include improved anticipation and identification of impacts, better communication with policy and governance experts, and a general strengthening of the norms around responsible research. To maximise the chance of success, we recommend measures to increase transparency, improve guidance, create incentives to engage earnestly with the process, and facilitate public deliberation on the requirement's merits and future. Perhaps the most important contribution from this analysis are the insights we can gain regarding effective community-based governance and the role and responsibility of the AI research community more broadly.

Exactly a year ago, OpenAI unveiled the GPT-3 with a whopping 175 billion parameters, which was made available to developers through an API in private beta. Since then, developers across the globe have been using GPT-3 to create realistic dialogues, summarise complex documents, customer service questions, and make search better than ever before. The company's decision to not open-source GPT-3 gave it more control over the use cases. However, in the recent past, there have been instances of GPT-3 going rogue, like in the case of GPT-3 Dungeon. Microsoft acquired an exclusive license to GPT-3 last year, in the wake of its $1 billion investment in OpenAI.

Coinbase has built the world's leading compliant cryptocurrency platform serving over 30 million accounts in more than 100 countries. With multiple successful products, and our vocal advocacy for blockchain technology, we have played a major part in mainstream awareness and adoption of cryptocurrency. We are proud to offer an entire suite of products that are helping build the cryptoeconomy and increase economic freedom around the world. There are a few things we look for across all hires we make at Coinbase, regardless of role or team. First, we look for signals that a candidate will thrive in a culture like ours, where we default to trust, embrace feedback, disrupt ourselves, and expect sustained high performance because we play as a championship team.

Examining a 20th-century Scandinavian legal theoretical tradition, we can extract an ontological naturalistic, a logical empiristic, and a modern idealistic rationale. We introduce the mathematical syntactic figure present in the `logical empiricism' in a contemporary mathematical logic. A new formal framework for describing explicit purchase statutes (Sweden) is gradually developed and subsequently proposed. This new framework is based on a many-sorted first-order logic (MFOL) approach, where the semantics are grounded in concrete `physical' objects and situations with a legal relevance. Specifically, we present a concrete formal syntactic translation of one of the central statutes of Swedish legislation for the purchase of immovable property. Additionally, we discuss the potential implications that a subsequent development of such formalisations would have for constructing artificial agents (e.g., software) that can be used as `co-creative' legal assistance for solving highly complex legal issues concerning the transfer of property, among others.

InCloudCounsel is seeking a Machine Learning Engineer to help our rapidly growing team deliver automatic negotiation with computable contracts. If you are a data or ML professional looking for a company that is financially-healthy, rapidly scaling, and impacting the trajectory of an industry, we would love to get to know you! InCloudCounsel is an industry leader in modernizing routine legal processes. Our global team works with many of the world's leading companies to offer robust end-to-end contract processing and obligation management solutions that combine cutting edge technology, deep industry expertise, and a world-class roster of legal professionals. The Machine Learning Engineer will report directly to our Director of Machine Learning and the package includes a competitive base salary, equity, and full benefits.

Cass R. Sunstein is one of America's foremost legal scholars; he is also a big fan of science fiction authors such as Isaac Asimov and Arthur C. Clarke. Sunstein thinks that science fiction can be a useful tool to inoculate people against status quo bias--our tendency to resist anything new and unfamiliar. "If you love science fiction, you find it fun, and maybe a good little chill goes down your spine, when you think of things that hadn't been dreamt of until 1990 or 2005, and those things excite you, as well as maybe scaring you," Sunstein says in Episode 468 of the Geek's Guide to the Galaxy podcast. Sunstein's new book Averting Catastrophe lays out an approach for evaluating unpredictable threats such as asteroids, AI, climate change, and pandemics. One of the book's more science fictional ideas is that people might not need to worry so much about the well-being of future generations, an idea that Sunstein attributes to Nobel prize-winning economist Thomas Schelling.

Relativity is banking that Text IQ's technology will help to drive improvements to its own platforms—in particular the AI and machine learning functionality in tools like RelativityOne and Relativity Trace.