As the use of AI accelerates around the world, policymakers are asking questions about what frameworks should guide the design and use of AI, and how it can benefit society. The EU is the first institution to take a major step to answer these questions through a proposed legal framework for AI released on 21 April 2021. In doing so, the EU is seeking to establish a safe environment for AI innovation and to position itself as a leader in setting "the global gold standard" for regulating AI. This is a positive aspect of the proposal as AI is a broad set of technology, tools and applications. Shifting the focus away from AI technology, which can have significantly different impacts depending on the application for which it is used, helps to mitigate the risk of divergent requirements for AI products and services.

It never happened--but not for lack of effort. Research teams around the world stepped up to help. The AI community, in particular, rushed to develop software that many believed would allow hospitals to diagnose or triage patients faster, bringing much-needed support to the front lines--in theory. In the end, many hundreds of predictive tools were developed. None of them made a real difference, and some were potentially harmful.

An algorithm can turn photos of wooden objects into a 3D model that is detailed enough for a skilled carpenter to replicate

There were three workshops held at AIIDE-20, held virtually October 19-23, 2020, including Experimental AI in Games, Intelligent Narrative Technologies, and Artificial Intelligence for Strategy Games. For more information the AIIDE conference, please see aiide.org. INT returned for its 12th meeting in 2020 with two excellent keynote talks and a wide variety of topics on applying AI to games and other interactive stories. The 12th workshop on Intelligent Narrative Technologies was held this year as part of the AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment. INT brings together a multidisciplinary team of researchers interested in artificial intelligence, narrative theory, game development, psychology, social justice, and many other topics. This year's workshop featured two keynotes.

The EU's forthcoming regulation on Artificial Intelligence could cost the bloc's economy up to €31 billion over the next 5 years and cause investments to shrink by as much as 20%, according to a study published on Monday (26 July). The assessment by the Centre for Data Innovation looked into the administrative costs of the Artificial Intelligence Act (AIA), a horizontal EU regulation to introduce increasing obligations based on the level of risk associated with the application of the groundbreaking technology. The study author stresses the administrative burden the new legislation is expected to create, which they say will disincentivise innovation and technology uptake. "The Commission has repeatedly asserted that the draft AI legislation will support growth and innovation in Europe's digital economy, but a realistic economic analysis suggests that argument is disingenuous at best," said senior policy analyst and report author Ben Mueller. That goal would require roughly 10 times the level of current investment in the technology, yet the study author says compliance costs would eat up just under 20% of those investments.

This article provides an organization of various kinds of biases that can occur in the AI pipeline starting from dataset creation and problem formulation to data analysis and evaluation. It highlights the challenges associated with the design of bias-mitigation strategies, and it outlines some best practices suggested by researchers. Finally, a set of guidelines is presented that could aid ML developers in identifying potential sources of bias, as well as avoiding the introduction of unwanted biases. The work is meant to serve as an educational resource for ML developers in handling and addressing issues related to bias in AI systems.

A new alliance of professional and research organisations is aiming to deliver a set of professional standards for data scientists. If widely adopted, the framework could go a long way to ensuring those working on advanced AI and machine learning systems (AI/ML) do so in a way that mitigates the emerging technology's risk to society. It could eventually lead to anyone unethically implementing AI being'struck off', or banned from the profession, one expert told Tech Monitor. The Alliance for Data Science Professionals has been formed by organisations including the BCS, the chartered institute for IT, and the Alan Turing Institute for AI research, along with the Royal Statistical Society, the Institute of Mathematics and the National Physical Laboratory. It aims to set the standards "needed to ensure an ethical and well-governed approach so the public, organisations and governments can have confidence in how their data is used".

Researchers in Switzerland and the US have devised a new machine learning approach to the detection of website advertising material that's based on the way such material interacts with the browser, rather than by analyzing its content or network behavior – two approaches which have proved ineffective in the long term in the face of CNAME cloaking (see below). Dubbed WebGraph, the framework uses a graph-based AI ad-blocking approach to detect promotional content by concentrating on such essential activities of network advertising – including telemetry attempts and local browser storage – that the only effective evasion technique would be to not conduct these activities. Though previous approaches have achieved slightly higher detection rates than WebGraph, all of them are prone to evasive techniques, while WebGraph is able to approach 100% integrity in the face of adversarial responses, including more sophisticated hypothesized responses that may emerge in the face of this novel ad-blocking method. The paper is led by two researchers from the Swiss Federal Institute of Technology, in concert with researchers from University of California, Davis and the University of Iowa. The work is a development from a 2020 research initiative with Brave browser called AdGraph, which featured two of the researchers from the new paper.

Conversational agents, or chatbots, providing question-answer assistance on smart devices, have proliferated in recent years and are poised to transform online customer services of corporate sectors.1,6 Implemented through dialogue management systems, chatbots converse through voice-based and textual dialogue, and harness natural language processing and artificial intelligence to recognize requests, provide responses, and predict user behavior.5,28 Market analysts concur on current adoption trends and the magnitude of growth and impact of chatbots anticipated in the next five years. According to a report by Grand View Research, for instance, already 45% of users prefer chatbots as the primary point of communications for customer service enquiries, translating into a global'chatbot' market of $1.23 billion by 2025, at a compounded annual growth rate (CAGR) of 24.3%.9 The strategy for conducting conversations using chatbots requires an efficient resolution of two key aspects. First, user queries or automatically perceived needs through user interactions have to be interpreted and mapped into categories, or user intents. This is based on historical processing of queries and needs, and the use of intent classification techniques.12 Second, conversations must be constructed for specific intents using frame-based dialogue management2 and neural response generation techniques.15 In frame-based dialogue management, the chatbot needs to converse with the user to have a fully filled frame (for example, flight information) in which all slot values are provided by the user (for example, airline carrier, departure time, departure location, and arrival location). The dialogue flow is constructed through an ordered sequence of frames.

In 2016, three veterans of the still young autonomous vehicle industry formed Aurora, a startup focused on developing self-driving cars. Partnerships followed with major automakers, including Hyundai and Volkswagen. CEO Chris Urmson said at the time that the link-ups would help the company bring "mobility as a service" to urban areas--Uber-like rides without a human behind the wheel. But by late 2019, Aurora's emphasis had shifted. It said self-driving trucks, not cars, would be quicker to hit public roads en masse. Its executives, who had steadfastly refused to provide a timeline for their self-driving-car software, now say trucks equipped with its "Aurora Driver" will hit the roads in 2023 or 2024, with ride-hail vehicles following a year or two later.