There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a new method to identify topics in a corpus and represent documents as topic sequences. Discourse Atom Topic Modeling draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on the distinct capabilities of each. We first identify a set of vectors ("discourse atoms") that provide a sparse representation of an embedding space. Atom vectors can be interpreted as latent topics: Through a generative model, atoms map onto distributions over words; one can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the U.S. National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender, and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.

Earlier this year in April, the European Commission led the way in this area suggesting a legal framework for the regulation of facial recognition and certain types of AI systems. The draft legislation (also explained in a press release here) looks to create "trustworthy AI" which protects the fundamental rights of citizens while strengthening AI investment and innovation across the EU. The proposal also outlines a risk-based approach to AI, with AI use cases ranked from unacceptable risk to high risk, through to minimal risk uses. Unacceptable risk AI (such as social scoring practices) would be banned, while high risk AI (e.g. As with GDPR, it is clear that the legislation (if adopted) would create much to be considered by those companies creating and marketing AI systems.

I have got a new doorbell. It should be; it cost £89. It's a Ring video doorbell; you'll have seen them around. There are others available, made by other companies, with other four-letter names such as Nest and Arlo. When someone rings my doorbell, I'm alerted on my smartphone. I can see who is there, and speak to them. C major first inversion chord, arpeggiated, repeated, for the musically trained – you'll recognise it if you've heard it. Amazon, as it happens; Amazon acquired Ring in 2018, reportedly for more than $1bn.

The various institutions of the EU aim to be the rule makers and standard bearers for artificial intelligence and associated technology ("AI"). One AI use case which has come under particular scrutiny is that of facial recognition. Since we last wrote on the subject, it has become increasingly clear that the European Commission will take a restrictive approach to the use of facial recognition technology, especially when such use is in public areas. Earlier this year in April, the European Commission led the way in this area suggesting a legal framework for the regulation of facial recognition and certain types of AI systems. The draft legislation (also explained in a press release here) looks to create "trustworthy AI" which protects the fundamental rights of citizens while strengthening AI investment and innovation across the EU. The measures would restrict the use of live facial recognition to a very narrow set of scenarios where this would be deemed essential from a public interest perspective; such as the search for missing children or the policing of terrorist incidents.

A total of 34% of AI research and development projects fails or are abandoned, according to a recent survey by Rackspace Technology of 1,870 companies. We propose a new strategic framework, aiSTROM, that empowers managers to create a successful AI strategy based on a thorough literature review. This provides a unique and integrated approach that guides managers and lead developers through the various challenges in the implementation process. In the aiSTROM framework, we start by identifying the top n potential projects (typically 3-5). For each of those, seven areas of focus are thoroughly analysed. These areas include creating a data strategy that takes into account unique cross-departmental machine learning data requirements, security, and legal requirements. aiSTROM then guides managers to think about how to put together an interdisciplinary artificial intelligence (AI) implementation team given the scarcity of AI talent. Once an AI team strategy has been established, it needs to be positioned within the organization, either cross-departmental or as a separate division. Other considerations include AI as a service (AIaas), or outsourcing development. Looking at new technologies, we have to consider challenges such as bias, legality of black-box-models, and keeping humans in the loop. Next, like any project, we need value-based key performance indicators (KPIs) to track and validate the progress. Depending on the company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities, and threats) can help further classify the shortlisted projects. Finally, we should make sure that our strategy includes continuous education of employees to enable a culture of adoption. This unique and comprehensive framework offers a valuable, literature supported, tool for managers and lead developers.

The need to explain the output from Machine Learning systems designed to predict the outcomes of legal cases has led to a renewed interest in the explanations offered by traditional AI and Law systems, especially those using factor based reasoning and precedent cases. In this paper we consider what sort of explanations we should expect from such systems, with a particular focus on the structure that can be provided by the use of issues in cases.

Artificial intelligence may be the technology of the future, but it can still be utterly befuddled by the mistakes of the past. During the "Learnings from Legal Industry's Foray into AI" panel hosted by Wednesday's edition of the 2021 Virtual ACC Xchange conference, panelists discussed the many dangers of treating AI like a magic wand rather than a strategic tool. "There's such a plethora of tools out there, it's easy to get very, I think, enamored by those bright shiny objects.

Thus, Big Data and Machine learning in the current scenario are very closely interrelated. India has diverse and large amounts of data given the population. AI remains a crucial element of innovation, infrastructure, jobs, skill market and strategic interests of the country whose need has escalated even more due to the pandemic. However, there is an absence of any big data repository, guidelines for usage of big data and the Regulation of AI in India, thus, making it hard to deduce a trend of AI friendly technological ecosystem in the country. It's impossible to approach AI and Big data trends without considering legal implications and accountability for its application.

AI has the potential to deliver real value in business, creating unprecedented efficiencies across countless processes. But is South Africa's industry ready? In the 1964 children's novel by British author Roald Dahl Charlie and the Chocolate Factory, Charlie's dad, Mr Bucket, loses his job when the factory where he works is mechanised. Once responsible for screwing the lids onto tubes of toothpaste, he gets laid off because a robot is able to perform his job more cheaply and efficiently. This trope of man being replaced by machine often comes up in discussions about artificial intelligence (AI) and its potential impact on our world and workforce.

Google is delaying its plan to phase out third-party cookies in Chrome to mid-2023 amid pushback from regulators and the wider ad industry. The web giant first announced its Privacy Sandbox initiative in 2019 and the following year it revealed a 2022 implementation date. As part of the sweeping changes, Google will effectively replace individual user tracking with group-based ad targeting. Others, including Apple, Microsoft and Mozilla, have already done so in their respective browsers. Though Google was keen to catch up -- even testing its Federated Learning of Cohorts (FLoC) tech on some users around the world in March -- it's now slowing down the pace.