Darktrace was named winner in the AI & Machine Learning category, an award which showcases pioneers in the artificial intelligence space.

This is the EOptomizer application that will be presented for Nokia and Huawai next week.

Automatic detection of fake news is a highly important task in the contemporary world. This study reports the 2nd shared task called UrduFake@FIRE2021 on identifying fake news detection in Urdu. The goal of the shared task is to motivate the community to come up with efficient methods for solving this vital problem, particularly for the Urdu language. The task is posed as a binary classification problem to label a given news article as a real or a fake news article. The organizers provide a dataset comprising news in five domains: (i) Health, (ii) Sports, (iii) Showbiz, (iv) Technology, and (v) Business, split into training and testing sets. The training set contains 1300 annotated news articles -- 750 real news, 550 fake news, while the testing set contains 300 news articles -- 200 real, 100 fake news. 34 teams from 7 different countries (China, Egypt, Israel, India, Mexico, Pakistan, and UAE) registered to participate in the UrduFake@FIRE2021 shared task. Out of those, 18 teams submitted their experimental results, and 11 of those submitted their technical reports, which is substantially higher compared to the UrduFake shared task in 2020 when only 6 teams submitted their technical reports. The technical reports submitted by the participants demonstrated different data representation techniques ranging from count-based BoW features to word vector embeddings as well as the use of numerous machine learning algorithms ranging from traditional SVM to various neural network architectures including Transformers such as BERT and RoBERTa. In this year's competition, the best performing system obtained an F1-macro score of 0.679, which is lower than the past year's best result of 0.907 F1-macro. Admittedly, while training sets from the past and the current years overlap to a large extent, the testing set provided this year is completely different.

Despite the progress observed with model-agnostic explainable AI (XAI), it is the case that model-agnostic XAI can produce incorrect explanations. One alternative are the so-called formal approaches to XAI, that include PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. The computation of relevant features serves to trade off probabilistic precision for the number of features in an explanation. However, even for very simple classifiers, the complexity of computing sets of relevant features is prohibitive. This paper investigates the computation of relevant sets for Naive Bayes Classifiers (NBCs), and shows that, in practice, these are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained with NBCs.

Malav Shah is a Data Scientist II at DIRECTV. He joins DIRECTV from AT&T, where he worked on multiple consumer businesses – including broadband, wireless, and video – and deployed machine learning (ML) models across a wide array of use cases spanning the full customer lifecycle from acquisition to retention. Malav holds a Master's Degree in Computer Science with a specialization in Machine Learning from Georgia Tech, a degree he puts to good use every day at DIRECTV by applying modern ML techniques to help the company deliver innovative entertainment experiences. Can you outline your career journey and why you first got into machine learning? It has been an interesting journey.

Memorial University will have a new artificial intelligence centre come the fall.

Recently, Bill Meyer interviewed Walter Bradley Center director Robert J. Marks on his Oregon-based talk show about "Why computers will never understand what they are doing," in connection with his new book, Non-Computable You: What You Do That Artificial Intelligence Never Will (Discovery Institute Press, 2022). We are rebroadcasting it with permission here as (Episode 194). Meyer began by saying, "I started reading a book over the weekend that I am going to continue to eagerly devour because it cut against some of my preconceived notions": A partial transcript, notes, and Additional Resources follow. Meyer and Marks began by discussion the recent flap at Google where software engineer Blake Lemoine claimed that the AI he was working with was sentient, like a human being. Google has dismissed this claim out of hand and put him on leave. There are so many ways to push back on that claim and it's hard to choose which one to go down.

The Uber Files is an international investigation into the ride-hailing company's aggressive entrance into cities around the world -- while frequently challenging the reach of existing laws and regulations. Documents illuminate how Uber used stealth technology to thwart regulators and law enforcement and how the company courted prominent political leaders as it sought footholds outside the United States. The project is based on more than 124,000 emails, text messages, memos and other records. They were obtained by the Guardian and shared with the International Consortium of Investigative Journalists, which helped lead the project, and dozens of other news organizations, including The Washington Post. 'Hit the kill switch': Regulators entered Uber's offices only to see computers go dark before their eyes

Roles that involve building relationships with clients, customers or patients can never be replaced by automation. Download The Economic Times News App to get Daily Market Updates & Live Business News. How will India achieve its steep renewable-energy goals? Solar plant that floats is one way. Unfriended: What Sheryl Sandberg's sign out from Facebook means for the tech giant in India

It's not just that one uncle who's not allowed at Thanksgiving anymore who's been spreading misinformation online. The practice began long before the rise of social media -- governments around the world have been doing it for centuries. But it wasn't until the modern era, one fueled by algorithmic recommendation engines built to infinitely increase engagement, that nation-states have managed to weaponize disinformation to such a high degree. In his new book Tyrants on Twitter: Protecting Democracies from Information Warfare, David Sloss, Professor of Law at Santa Clara University, explores how social media sites like Facebook, Instagram, and TikTok have become platforms for political operations that have very real, and very dire, consequences for democracy while arguing for governments to unite in creating a global framework to regulate and protect these networks from information warfare. Excerpted from Tyrants on Twitter: Protecting Democracies from Information Warfare, by David L. Sloss, published by Stanford University Press, 2022 by the Board of Trustees of the Leland Stanford Junior University.