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Can AI Replace Doctors? Discover 5 Artificial Intelligence Applications in Healthcare


After revolutionizing various industry sectors, the introduction of artificial intelligence in healthcare is transforming how we diagnose and treat critical disorders. A team of experts in the Laboratory for Respiratory Diseases at the Catholic University of Leuven, Belgium, trained an AI-based computer algorithm using good quality data. Dr. Marko Topalovic, a postdoctoral researcher in the team, announced that AI was found to be more consistent and accurate in interpreting respiratory test results and in suggesting diagnoses, as compared to lung specialists. Likewise, Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University developed the BioMind AI system, which correctly diagnosed brain tumor in 87% of 225 cases in about 15 minutes, whereas the results of a team of 15 senior doctors displayed only 66% accuracy. With further improvements and the support of other advanced technologies like machine learning, AI is getting smarter with time.

The Flemish Scrollers, 2021


Every meeting of the flemish government in Belgium is live streamed on a youtube channel. When a livestream starts the software is searching for phones and tries to identify a distracted politician. This is done with the help of AI and face recognition. The video of the distracted politician are then posted to a Twitter and Instagram account with the politician tagged. The software is written in Python and is using machine learning to detect phones and facial recognition to identify the politician.

Demoting Outdated 'Truth' With Machine Learning


Sometimes the truth has an expiry date. When a time-limited claim (such as'masks are obligatory on public transport') emerges in search engine rankings, its apparent'authoritative' solution can outstay its welcome even by many years, outranking later and more accurate content on the same topic. This is a by-product of search engine algorithms' determination to identify and promote'long-term' definitive solutions, and of their proclivity to prioritize well-linked content that maintains traffic over time – and of an increasingly circumspect attitude to newer content in the emerging age of fake news. Alternately, devaluing valuable web content simply because the timestamp associated with it has passed an arbitrary'validity window' risks that a generation of genuinely useful content will be automatically demoted in favor of subsequent material that may be of a lower standard. Towards redressing this syndrome, a new paper from researchers in Italy, Belgium and Denmark has used a variety of machine learning techniques to develop a methodology for time-aware evidence ranking.

Less communication among robots allows them to make better decisions


New research that could help us use swarms of robots to tackle forest fires, conduct search and rescue operations at sea and diagnose problems inside the human body, has been published by engineers at the University of Sheffield. The study, led by Dr. Andreagiovanni Reina from the University's Department of Computer Science, could improve how swarms of robots work together, adapt to changes in their environment and make more sophisticated decisions much quicker. Published in the journal Science Robotics, the research has found that robot swarms are able to respond more effectively to changes in their environment when communication between the robots is reduced. The study disproves the widely accepted theory that more connections between robots leads to more effective information exchange. The team, which included researchers from UCL and IRIDIA, Université Libre de Bruxelles, Belgium, discovered their findings by studying how a swarm of tiny robots moved around and reached a consensus on the best area (e.g.

Demystifying the Draft EU Artificial Intelligence Act Artificial Intelligence

Thanks to Valerio De Stefano, Reuben Binns, Jeremias Adams-Prassl, Barend van Leeuwen, Aislinn Kelly-Lyth, Lilian Edwards, Natali Helberger, Christopher Marsden, Sarah Chander, Corinne Cath-Speth for comments and/or discussion; substantive and editorial input by Ulrich Gasper; and the conveners and participants of several workshops including one convened by Margot Kaminski, one by Burkhard Schäfer, one part of the 2nd ELLIS Workshop in Human-Centric Machine Learning; one between Lund University and the Labour Law Community; and one between Oxford, KU Leuven and UCL. A CC-BY 4.0 license applies to this article after 3 calendar months from publication have elapsed.

AI Tool Tracks the Time Politicians Spend on Their Phones


If you have been frustrated by the lack of interest your local representative shows during their work hours, here's a way to flag it now. Belgium-based developer, Dries Depoorter has created an artificial intelligence (AI) tool that calculates how much time are politicians distracted by their phones during meetings. Called the Flemish Scrollers, the tool is written in Python and uses machine learning and face recognition technologies. The law of the land requires that all meetings of the Flemish government be in the public domain. The government broadcasts it live on its YouTube channel. The code uses face recognition to identify the politician and then tracks the amount of time they spend on their phones during the broadcast.

Learning to Delegate for Large-scale Vehicle Routing Artificial Intelligence

Vehicle routing problems (VRPs) are a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions on small problem instances of up to 100 customers, their performance does not scale to large problems. This article presents a novel learning-augmented local search algorithm to solve large-scale VRP. The method iteratively improves the solution by identifying appropriate subproblems and $\textit{delegating}$ their improvement to a black box subsolver. At each step, we leverage spatial locality to consider only a linear number of subproblems, rather than exponential. We frame subproblem selection as a regression problem and train a Transformer on a generated training set of problem instances. We show that our method achieves state-of-the-art performance, with a speed-up of up to 15 times over strong baselines, on VRPs with sizes ranging from 500 to 3000.

AI bot trolls politicians with how much time they're looking at phones


But if you're working on the taxpayer's dime, you'd better be ready for artificial intelligence to call you out for gawping at the black mirror in the legislature when you should be, you know, legislating. That's what digital artist Dries Depoorter did for his latest installation "The Flemish Scrollers." His software that uses facial recognition to automatically call out politicians in the Flemish province of Belgium who are distracted by their phones when its parliament is in session. The project comes almost two years after Flemish Minister-President Jan Jambon caused public outrage after playing Angry Birds during a policy discussion. Launched Monday, Depoorter's system monitors daily livestreams of government meetings on YouTube to assess how long a representative has been looking at their phone versus the meeting in progress.

Market for Emotion Recognition Projected to Grow as Some Question Science - AI Trends


The emotion recognition software segment is projected to grow dramatically in coming years, spelling success for companies that have established a beachhead in the market, while causing some who are skeptical about its accuracy and fairness to raise red flags. The global emotion detection and recognition market is projected to grow to $37.1 billion by 2026, up from an estimated $19.5 billion in 2020, according to a recent report from MarketsandMarkets. North America is home to the largest market. Software suppliers covered in the report include: NEC Global (Japan), IBM (US), Intel (US), Microsoft (US), Apple (US), Gesturetek (Canada), Noldus Technology (Netherlands), Google (US), Tobii (Sweden), Cognitec Systems (Germany), Cipia Vision Ltd (Formerly Eyesight Technologies) (Israel), iMotions (Denmark), Numenta (US), Elliptic Labs (Norway), Kairos (US), PointGrab (US), Affectiva (US), nViso (Switzerland), Beyond Verbal (Israel), Sightcorp (Holland), Crowd Emotion (UK), Eyeris (US), Sentiance (Belgium), Sony Depthsense (Belgium), Ayonix (Japan), and Pyreos (UK). Some question whether emotion recognition software is effective, and whether its use is ethical.

Senior Data Engineer [Core Team] (M/W)


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