The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. In April 2020, news broke that Banjo CEO Damien Patton, once the subject of profiles by business journalists, was previously convicted of crimes committed with a white supremacist group. According to OneZero's analysis of grand jury testimony and hate crime prosecution documents, Patton pled guilty to involvement in a 1990 shooting attack on a synagogue in Tennessee. Amid growing public awareness about algorithmic bias, the state of Utah halted a $20.7 million contract with Banjo, and the Utah attorney general's office opened an investigation into matters of privacy, algorithmic bias, and discrimination. But in a surprise twist, an audit and report released last week found no bias in the algorithm because there was no algorithm to assess in the first place.

Kabul – It was the last known missile fired by the United States in its 20-year war in Afghanistan, and the military called it a "righteous strike" -- a drone attack after hours of surveillance Aug. 29 against a vehicle that U.S. officials thought contained an Islamic State bomb and posed an imminent threat to troops at Kabul's airport. But a New York Times investigation of video evidence, along with interviews with more than a dozen of the driver's co-workers and family members in Kabul, raises doubts about the U.S. version of events, including whether explosives were present in the vehicle, whether the driver had a connection to the Islamic State group and whether there was a second explosion after the missile struck the car. Military officials said they did not know the identity of the car's driver when the drone fired but deemed him suspicious because of how they interpreted his activities that day, saying that he possibly visited an Islamic State group safe house and, at one point, loaded what they thought could be explosives into the car. Times reporting has identified the driver as Zemari Ahmadi, a longtime worker for a U.S. aid group. The evidence, including extensive interviews with family members, co-workers and witnesses, suggests that his travels that day actually involved transporting colleagues to and from work.

Special Forces veteran reacts to Gitmo detainees now leading the Taliban on'Fox News Primetime' The United States' account of a drone strike launched against a suspected terrorist in Afghanistan toward the end of the military withdrawal from Kabul is being challenged by a report suggesting the victim was not a threat to the United States. According to a New York Times report, the drone attack that American officials said killed an ISIS terrorist carrying a bomb in a car toward U.S. troops may have killed a man with no ties to ISIS and who was carrying water to family members. American military officials announced late last month that the drone strike, carried out the day after a suicide bombing killed 13 U.S. service members, killed an alleged "ISIS-K planner" and an "associate." The New York Times says that after reviewing video evidence and interviewing more than a dozen of the driver's friends and family members in Kabul, it has doubts about the U.S. version of events. "Times reporting has identified the driver as Zemari Ahmadi, a longtime worker for a U.S. aid group," the report states.

Sequential modelling entails making sense of sequential data, which naturally occurs in a wide array of domains. One example is systems that interact with users, log user actions and behaviour, and make recommendations of items of potential interest to users on the basis of their previous interactions. In such cases, the sequential order of user interactions is often indicative of what the user is interested in next. Similarly, for systems that automatically infer the semantics of text, capturing the sequential order of words in a sentence is essential, as even a slight re-ordering could significantly alter its original meaning. This thesis makes methodological contributions and new investigations of sequential modelling for the specific application areas of systems that recommend music tracks to listeners and systems that process text semantics in order to automatically fact-check claims, or "speed read" text for efficient further classification.

Numerous government initiatives (e.g. the EU with GDPR) are coming to the conclusion that the increasing complexity of modern software systems must be contrasted with some Rights to Explanation and metrics for the Impact Assessment of these tools, that allow humans to understand and oversee the output of Automated Decision Making systems. Explainable AI was born as a pathway to allow humans to explore and understand the inner working of complex systems. But establishing what is an explanation and objectively evaluating explainability, are not trivial tasks. With this paper, we present a new model-agnostic metric to measure the Degree of eXplainability of correct information in an objective way, exploiting a specific model from Ordinary Language Philosophy called the Achinstein's Theory of Explanations. In order to understand whether this metric is actually behaving as explainability is expected to, we designed a few experiments and a user-study on two realistic AI-based systems for healthcare and finance, involving famous AI technology including Artificial Neural Networks and TreeSHAP. The results we obtained are very encouraging, suggesting that our proposed metric for measuring the Degree of eXplainability is robust on several scenarios and it can be eventually exploited for a lawful Impact Assessment of an Automated Decision Making system.

As artificial intelligence technologies become more complex and better integrated with new services and products, executives worldwide are concerned about cyber security vulnerabilities. While AI is a strong tool for security, security experts also predict that malicious actors will utilize artificial intelligence to unleash a global cyber incident in the near future. Today, unauthorized users can get easy access to AI-powered systems to create sophisticated cyber threats. For example, AI chatbots have emerged as a novel doorway to cyber attackers, and the Emotet Trojan malware is hyped as an AI-based cyber threat prototype directed at the financial services sector. A recent global study of early adopters found that over 40 percent of executives have "extreme" or "major" concerns about AI threats, with cybersecurity vulnerabilities leading that list.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Women in the AI field are making research breakthroughs, launching exciting companies, spearheading vital ethical discussions, and inspiring the next generation of AI professionals. And that's why we created the VentureBeat Women in AI Awards -- to emphasize the importance of their voices, work, and experiences, and to shine a light on some of these leaders. We first announced the six winners at Transform 2021 in July, and ever since, we've been catching up with each of them for deeper discussions around their work and emerging challenges in the field. Our conversations have touched on everything from regulation and dealing with messy real world data to how to approach AI more responsibly.

Association Rules is one of the very important concepts of machine learning being used in market basket analysis. This course covers the working Principle of Association Mining and its various concepts like Support, Confidence, and Life in a very simplified manner. All of these algorithms has been explained by taking working examples. Parteek Bhatia is Professor in the Department of Computer Science and Engineering and Former Associate Dean of Student Affairs at Thapar Institute of Engineering and Technology, Patiala. At present he is on sabbatical at Tel Aviv University, Israel and acting as Visiting Professor at LAMBDA Lab, TAU.

TechRepublic's Karen Roby spoke with Chris Ford, VP of product for Threat Stack, about supervised and unsupervised machine learning. The following is an edited transcript of their conversation. Christopher Ford: Supervised and unsupervised learning are techniques that help to facilitate different use cases within the sphere of machine learning. As your viewers know, machine learning is used to gain insights out of data sets. I would say that the crucial difference between unsupervised learning and supervised learning is that the former, unsupervised learning, it's easier to get started with because it does not require labeled data.

As researchers and engineers race to develop new artificial intelligence systems for the U.S. military, they must consider how the technology could lead to accidents with catastrophic consequences. In a startling, but fictitious, scenario, analysts at the Center for Security and Emerging Technology -- which is part of Georgetown University's Walsh School of Foreign Service -- lay out a potential doomsday storyline with phantom missile launches. In the scenario, U.S. Strategic Command relies on a new missile defense system's algorithms to detect attacks from adversaries. The system can quickly and autonomously trigger an interceptor to shoot down enemy missiles which might be armed with nuclear warheads. "One day, unusual atmospheric conditions over the Bering Strait create an unusual glare on the horizon," the report imagined.