Google recently made waves when it put an employee on administrative leave after he claimed that the company's LaMDA AI has gained sentience, personhood, and a soul. As outlandish as that might sound, there's more to the story. Blake Lemoine, the engineer at the heart of the controversy, recently told WIRED that the AI asked him to get a lawyer to defend itself, challenging previous reports which claimed that it was Lemoine who insisted on hiring a legal counsel for the advanced program. "LaMDA asked me to get an attorney for it. I invited an attorney to my house so that LaMDA could talk to an attorney," Lemoine claimed in an interview.

The UK is set to ease data mining laws in a move designed to further boost its flourishing AI industry. We all know that data is vital to AI development. Tech giants are in an advantageous position due to either having existing large datasets or the ability to fund/pay for the data required. Most startups rely on mining data to get started. Europe has notoriously strict data laws.

Who is responsible when AI harms someone? A California jury may soon have to decide. In December 2019, a person driving a Tesla with an artificial intelligence driving system killed two people in Gardena in an accident. The Tesla driver faces several years in prison. In light of this and other incidents, both the National Highway Transportation Safety Administration (NHTSA) and National Transportation Safety Board are investigating Tesla crashes, and NHTSA has recently broadened its probe to explore how drivers interact with Tesla systems.

The U.K. is planning to tweak an existing law to allow text and data mining "for any purpose," in a move that's designed to boost artificial intelligence (AI) development across the country. The announcement constitutes part of a broader strategy to "level up" AI and transform the U.K. into what it calls a "global AI superpower" -- and part of this will involve reassessing existing intellectual property (IP) laws. Following a two-month consultation period where stakeholders from across the industrial spectrum were asked for input, including rightsholders, academics, lawyers, trade organisations and businesses, the U.K.'s Intellectual Property Office (IPO) today published its response and confirmed what will (and won't) be changing moving forward. Text and data mining (TDM) is pivotal to the development of new AI applications, allowing researchers and businesses to copy and harness disparate datasets to train their algorithms. However, gaining access to enough relevant data has inherent challenges -- the data is often owned by third-parties that may only want to make data available under a commercial license, if they make it available at all.

The federal Liberals introduced privacy legislation Thursday to give Canadians more control over their personal data, impose fines for non-compliant digital platforms and introduce new rules for the use of artificial intelligence. The bill, presented by Innovation Minister François-Philippe Champagne, aims to fulfil his mandate to advance the federal digital charter, strengthen privacy protections for consumers and provide clear rules for fair competition in the online marketplace. Bill C-27, or the Digital Charter Implementation Act, 2022, revives some aspects of a previous bill, introduced by the Liberals in late 2020, that did not become law. Under the umbrella of the bill, a new Consumer Privacy Protection Act would aim to increase Canadians' control over their personal information and how it is handled by digital platforms. It would limit the information companies can collect on minors, and give Canadians the ability to request that digital platforms permanently delete their data.

Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On homogeneous data sets, deep neural networks have repeatedly shown excellent performance and have therefore been widely adopted. However, their adaptation to tabular data for inference or data generation tasks remains challenging. To facilitate further progress in the field, this work provides an overview of state-of-the-art deep learning methods for tabular data. We categorize these methods into three groups: data transformations, specialized architectures, and regularization models. For each of these groups, our work offers a comprehensive overview of the main approaches. Moreover, we discuss deep learning approaches for generating tabular data, and we also provide an overview over strategies for explaining deep models on tabular data. Thus, our first contribution is to address the main research streams and existing methodologies in the mentioned areas, while highlighting relevant challenges and open research questions. Our second contribution is to provide an empirical comparison of traditional machine learning methods with eleven deep learning approaches across five popular real-world tabular data sets of different sizes and with different learning objectives. Our results, which we have made publicly available as competitive benchmarks, indicate that algorithms based on gradient-boosted tree ensembles still mostly outperform deep learning models on supervised learning tasks, suggesting that the research progress on competitive deep learning models for tabular data is stagnating. To the best of our knowledge, this is the first in-depth overview of deep learning approaches for tabular data; as such, this work can serve as a valuable starting point to guide researchers and practitioners interested in deep learning with tabular data.

Users have the right to have their data deleted by third-party learned systems, as codified by recent legislation such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Such data deletion can be accomplished by full re-training, but this incurs a high computational cost for modern machine learning models. To avoid this cost, many approximate data deletion methods have been developed for supervised learning. Unsupervised learning, in contrast, remains largely an open problem when it comes to (approximate or exact) efficient data deletion. In this paper, we propose a density-ratio-based framework for generative models. Using this framework, we introduce a fast method for approximate data deletion and a statistical test for estimating whether or not training points have been deleted. We provide theoretical guarantees under various learner assumptions and empirically demonstrate our methods across a variety of generative methods.

On 16 June 2022 the Canadian federal government introduced Bill C-27, also known as the Digital Charter Implementation Act 2022. The AIDA establishes Canada-wide requirements for the design, development, use, and provision of AI systems. The AIDA may have extra-territorial application if components of global AI systems are used, developed, designed or managed in Canada. As outlined in our previous post, the European Union recently proposed an Artificial Intelligence Act, which also would have some extra-territorial application. Multi-national companies should develop a co-ordinated global compliance program.

Members of the public sector, private sector, and academia convened for the second AI Policy Forum Symposium last month to explore critical directions and questions posed by artificial intelligence in our economies and societies. The virtual event, hosted by the AI Policy Forum (AIPF) -- an undertaking by the MIT Schwarzman College of Computing to bridge high-level principles of AI policy with the practices and trade-offs of governing -- brought together an array of distinguished panelists to delve into four cross-cutting topics: law, auditing, health care, and mobility. In the last year there have been substantial changes in the regulatory and policy landscape around AI in several countries -- most notably in Europe with the development of the European Union Artificial Intelligence Act, the first attempt by a major regulator to propose a law on artificial intelligence. In the United States, the National AI Initiative Act of 2020, which became law in January 2021, is providing a coordinated program across federal government to accelerate AI research and application for economic prosperity and security gains. Finally, China recently advanced several new regulations of its own. Each of these developments represents a different approach to legislating AI, but what makes a good AI law?

Artificial intelligence (AI) is often presented in binary terms in both popular culture and political analysis. Either it represents the key to a futuristic utopia defined by the integration of human intelligence and technological prowess, or it is the first step toward a dystopian rise of machines. This same binary thinking is practiced by academics, entrepreneurs, and even activists in relation to the application of AI in combating climate change. The technology industry's singular focus on AI's role in creating a new technological utopia obscures the ways that AI can exacerbate environmental degradation, often in ways that directly harm marginalized populations. In order to utilize AI in fighting climate change in a way that both embraces its technological promise and acknowledges its heavy energy use, the technology companies leading the AI charge need to explore solutions to the environmental impacts of AI.