New legislation is expected to open the door to the use of facial recognition within a range of surveillance technologies in Ireland, including CCTV cameras and police body cams, automatic number plate recognition (ANPR or LPR in the U.S.), according to reports by the Irish Times. Backlash to the facial recognition element has been significant from civil society and academics. They find the move premature given the current stage of the EU AI Act and call for a moratorium on facial recognition technology. An amendment to the Garda Síochána (Digital Recording) Bill (Garda Síochána being the National Police), expected in the autumn after further scrutiny by government, will clarify the law in light of national and European Union legislation such as GDPR for these technologies to be used with face biometrics. It could be enacted by the end of the year.

Microsoft inadvertently learned the risks of creating racist AI, but what happens if you deliberately point the intelligence at a toxic forum? As Motherboard and The Verge note, YouTuber Yannic Kilcher trained an AI language model using three years of content from 4chan's Politically Incorrect (/pol/) board, a place infamous for its racism and other forms of bigotry. After implementing the model in ten bots, Kilcher set the AI loose on the board -- and it unsurprisingly created a wave of hate. In the space of 24 hours, the bots wrote 15,000 posts that frequently included or interacted with racist content. They represented more than 10 percent of posts on /pol/ that day, Kilcher claimed.

Talent management's many challenges in keeping employees engaged are helping to define the future of work. Every organization is struggling to meet its need for experts who bring new skills, made more difficult by high attrition rates and a competitive job market. Chief human resources officers (CHROs) and the organizations they lead are looking to build the expertise they need by upskilling talent. Add to those challenges getting internal mobility right, providing employees with learning and growth opportunities, coaching managers to be talent champions, achieving less bias in hiring decisions and the future of work's growing challenges become clear. A data-driven approach to solving these challenges using AI delivers results, as the interviews and presentations at the Eightfold Cultivate 22 Summit showed.

Fox News correspondent David Spunt has the latest on Congress' response to the failed assassination attempt of Justice Brett Kavanaugh on'Special Report.' Washington Post deputy editorial editor Ruth Marcus wants to make sure people are aware "deranged individuals do deranged things" on "both ends of the political spectrum" before assigning blame for the man who was arrested near the Maryland home of Supreme Court Justice Brett Kavanaugh. On Wednesday, an armed California man identified as Nicholas John Roske was carrying a gun, knife and pepper spray when arrested outside Kavanaugh's home. He told officers that he wanted "to give his life purpose" and purchased the gun and other items for the purpose of breaking into Kavanaugh's home and killing the justice and then himself. A piece published Thursday night by Marcus headlined, "The Kavanaugh threat exposed weaknesses in judicial security -- and our discourse," admitted the incident "could have ended in unfathomable tragedy" but urged readers not to assign blame or dismiss people who created the environment that "fueled" the assassination attempt.

One of the essential factors for widespread robotics adoption, especially in the inspection and maintenance area, is the regulatory landscape. Regulatory and legal issues should be addressed to establish effective robotics deployment legal frameworks. Common goals of boosting the widespread adoption of robotics can only be achieved by creating networks between the robotics community and regulators. On the 23rd of March, Maarit Sandelin, Peter Voorhans and Dr Carlos Cuevas Garcia were invited by Robotics4EU and RIMA network to discuss how cooperation among regulators and the robotics community can be fostered and what are the most pressing legal challenges for the inspection & maintenance application area of robotics. Maarit Sandelin and Peter Voorhans from Robotic Innovation Department in SPRINT Robotics have opened the workshop with the question of why robotics are important in inspection and maintenance?

In our understanding, the question of who owns intellectual property rights on AI-related works is also important when determining who is liable for AI-caused harm. For that reason, further development of legislation in that direction is expected. As we mentioned before, one of the main characteristics of AI is the use (collection, analysis) of data. Personal data is included in this. Some experts have opined that AI systems can develop more quickly in jurisdictions where there is less regulation on the use and protection of personal data--or where it is not regulated at all. This is related to the fact that AI needs to use data to achieve the established tasks. The EU is the realm of the General Data Protection Regulation (GDPR), which aims to protect personal data against illegal use. The EU, in light of the GDPR, has already prepared a list of prohibited practices of AI.

The Artificial Intelligence Act was introduced to the European Union in April 2021, and is rapidly progressing through comment periods and rewrites. When it goes into effect, which experts say could occur at the beginning of 2023, it will have a broad impact on the use of AI and machine learning for citizens and companies around the world. The AI law aims to create a common regulatory and legal framework for the use of AI, including how it's developed, what companies can use it for, and the legal consequences of failing to adhere to the requirements. The law will likely require companies to receive approval before adopting AI for some use cases, outlaw certain other AI uses deemed too risky, and create a public list of other high-risk AI uses. At a broad level, the law seeks to codify the EU's trustworthy AI paradigm, according to an official presentation on the law by the European Commission, the continent's governing body.

This article originally appeared in issue 28 of IT Pro 20/20, available here. The business world is fascinated with artificial intelligence (AI), with most of the excitement centred around customer-facing services. The technologies used to make a Tesla stop or embedded in the soothing voice of your virtual assistant have grabbed most of the headlines, but what about AI's role in supporting relatively unglamorous back-office functions? According to industry figures, the use of AI in supporting the HR, IT, legal and financial departments within organisations is not only well underway but maturing. In the last few years, these automations have become increasingly widespread, with some organisations now assessing whether it's viable to automate vast swathes of the business.

As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible. The current lack of interpretability often undermines the deployment of accurate machine learning tools in sensitive settings. In this work, we present a model-agnostic explanation method for image classification based on a hierarchical extension of Shapley coefficients--Hierarchical Shap (h-Shap)--that resolves some of the limitations of current approaches. Unlike other Shapley-based explanation methods, h-Shap is scalable and can be computed without the need of approximation. Under certain distributional assumptions, such as those common in multiple instance learning, h-Shap retrieves the exact Shapley coefficients with an exponential improvement in computational complexity. We compare our hierarchical approach with popular Shapley-based and non-Shapley-based methods on a synthetic dataset, a medical imaging scenario, and a general computer vision problem, showing that h-Shap outperforms the state of the art in both accuracy and runtime. Code and experiments are made publicly available.

Microsoft inadvertently learned the risks of creating racist AI, but what happens if you deliberately point the intelligence at a toxic forum? As Motherboard and The Verge note, YouTuber Yannic Kilcher trained an AI language model using three years of content from 4chan's Politically Incorrect (/pol/) board, a place infamous for its racism and other forms of bigotry. After implementing the model in ten bots, Kilcher set the AI loose on the board -- and it unsurprisingly created a wave of hate. In the space of 24 hours, the bots wrote 15,000 posts that frequently included or interacted with racist content. They represented more than 10 percent of posts on /pol/ that day, Kilcher claimed. Nicknamed GPT-4chan (after OpenAI's GPT-3), the model learned to not only pick up the words used in /pol/ posts, but an overall tone that Kilcher said blended "offensiveness, nihilism, trolling and deep distrust."