A new chatbot start-up from two top artificial intelligence talents lets anyone strike up a conversation with impersonations of Donald Trump, Elon Musk, Albert Einstein and Sherlock Holmes. Registered users type in messages and get responses. They can also create a chatbot of their own on Character.ai, "There were reports of possible voter fraud and I wanted an investigation," the Trump bot said. The start-up's two founders helped create Google's artificial intelligence project LaMDA, which Google keeps closely guarded while it develops safeguards against social risks.

Dublin has long been home to Big Tech's European outposts, drawn by low taxes and Ireland's position as the only English-speaking country in the European Union. Historically, however, this has negatively affected local startups: Big salaries and cushy positions at Big Tech companies made it difficult for smaller, nimbler companies to compete. That situation is finally changing: "Over the past few years, the culture has shifted away from Big Tech," says Nicola McClafferty, chair of the Irish Venture Capital Association and a partner in Molten Ventures, a venture capital firm operating in Ireland. "We're seeing more and more people and talent wanting to come out of those companies, and really thinking about joining earlier-stage and high-growth startups." That's in part down to Irish startup successes like communications platform Intercom and payments system Stripe, which have proven homegrown wins are possible.

Role requiring'No experience data provided' months of experience in None Pay if you succeed in getting hired and start work at a high-paying job first. Get Paid to Read Emails, Play Games, Search the Web, $5 Signup Bonus. At the Space Dynamics Laboratory, we take pride in and highly value our employees. We are seeking mid-level computer vision engineers to work with an agile approach on the next generation of satellite ground systems supporting national defense. SDL offers competitive salaries and fantastic benefits, including: • Flexible work schedules that fit your style-every Friday off, every other Friday off, possible work from home days, or simply traditional hours • Generous paid leisure and sick leave, ensuring you never miss a special event • A 14.2% employer retirement contribution into a 401(a) account-no matching required! Required Qualifications: • Bachelor's degree in computer vision, computer science, aerospace engineering, or a related discipline • 5 years professional experience in design and implementation of computer vision technologies, with emphasis in support of relative navigation • Experience with Mathworks, C, and Python for image processing, computer vision, and deep learning applications • Ability to architect the framework that is used to develop, and deploy computer vision, and deep learning applications • Experience in integrating to GNC and CDH components within embedded architectures • Experience with common software development practices, including: • Agile/Scrum or similar methodologies • Version control and continuous integration • Testing strategies and code testability • Must be a U.S. citizen and be able to obtain a U.S. Government Security Clearance Let us know in your application materials if you possess the following: • Experience designing modular software and communicating/refining designs independently or with a team through whiteboarding, diagrams, UML, etc. • Experience with Atlassian management tools (JIRA, Confluence, Bitbucket, etc.) • Ability to provide mentoring, leadership, and experience sharing with junior engineers • Experience with satellite ground or flight systems • Personal interest in space, space exploration, and space technologies • Active Top Secret security clearance SDL supports a variety of missions, including NASA's vision to reveal the unknown for the benefit of humankind and the Department of Defense's aim to protect our Nation on the ground, in the air, and in space. Our sensors, satellites, software systems, and science and engineering play an essential role in some important missions you've heard of, and others that you haven't. For questions or assistance with the application process or the DoD SkillBridge program, please contact employment@sdl.usu.edu.

Since 1995, the FDA has authorized more than 500 AI/ML-enabled medical devices via 510(k) clearance, granted De Novo request, or approved PMA. This week the FDA published an updated list with 178 new devices that were authorized through July 2022. According to the FDA, their list is based on publicly available information and is not a comprehensive resource of FDA approved AI/ML-enabled medical devices. In today's DeepTech newsletter I'm sharing a high level analysis of the 521 devices on the list, charts to visualize the data, and a summary of milestones. Note: According to the FDA their list is based on publicly available information and is not a comprehensive resource of approved AI/ML-enabled medical devices.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Editor's note: This story contains graphic language. Former first lady Michelle Obama's voting initiative is partnering with a dating app that made a video titled "No Voting No Vucking." The voting initiative, When We All Vote announced that it would be working with the BLK dating app on Oct. 4 and doing "voter registration activations" with the company.

Boston Dynamics, the DARPA-backed robotics company known for uncomfortable videos where nearly 200-pound humanoid robots perform backflips, uncomfortable dances, and various forms of horrifyingly aggressive parkour, says it isn't interested in weaponizing its robots. In an open letter this week, Boston Dynamics Dynamics joined five other robotics makers in a pledge not to weaponize their advanced-mobility, general-purpose robots, or the software that makes them tick. The companies said they would carefully review their customers' intended application for the bots "when possible" and pledged to explore features that could somehow mitigate risks. Stating the obvious, the companies wrote that weaponization of advanced robotics "raises new risks of harm and serious ethical issues," and could harm public trust in the technology. The robot makers went on to encourage policymakers to explore ways to promote the safe use of robots and encouraged other researchers and developers to join the pledge. "We are convinced that the benefits for humanity of these technologies strongly outweigh the risk of misuse, and we are excited about a bright future in which humans and robots work side by side to tackle some of the world's challenges," the companies wrote.

Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability, however, is challenged in this paper. We identify a novel threat model named stability attack, which aims to hinder robust availability by slightly manipulating the training data. Under this threat, we show that adversarial training using a conventional defense budget $\epsilon$ provably fails to provide test robustness in a simple statistical setting, where the non-robust features of the training data can be reinforced by $\epsilon$-bounded perturbation. Further, we analyze the necessity of enlarging the defense budget to counter stability attacks. Finally, comprehensive experiments demonstrate that stability attacks are harmful on benchmark datasets, and thus the adaptive defense is necessary to maintain robustness. Our code is available at https://github.com/TLMichael/Hypocritical-Perturbation.

Multi-label learning has attracted significant attention from both academic and industry field in recent decades. Although existing multi-label learning algorithms achieved good performance in various tasks, they implicitly assume the size of target label space is not huge, which can be restrictive for real-world scenarios. Moreover, it is infeasible to directly adapt them to extremely large label space because of the compute and memory overhead. Therefore, eXtreme Multi-label Learning (XML) is becoming an important task and many effective approaches are proposed. To fully understand XML, we conduct a survey study in this paper. We first clarify a formal definition for XML from the perspective of supervised learning. Then, based on different model architectures and challenges of the problem, we provide a thorough discussion of the advantages and disadvantages of each category of methods. For the benefit of conducting empirical studies, we collect abundant resources regarding XML, including code implementations, and useful tools. Lastly, we propose possible research directions in XML, such as new evaluation metrics, the tail label problem, and weakly supervised XML.

Developed and used responsibly Artificial Intelligence (AI) is a force for global sustainable development. Given this opportunity, we expect that the many of the existing guidelines and recommendations for trustworthy or responsible AI will provide explicit guidance on how AI can contribute to the achievement of United Nations' Sustainable Development Goals (SDGs). This would in particular be the case for the AI strategies of the Nordic countries, at least given their high ranking and overall political focus when it comes to the achievement of the SDGs. In this paper, we present an analysis of existing AI recommendations from 10 different countries or organisations based on topic modelling techniques to identify how much these strategy documents refer to the SDGs. The analysis shows no significant difference on how much these documents refer to SDGs. Moreover, the Nordic countries are not different from the others albeit their long-term commitment to SDGs. More importantly, references to \textit{gender equality} (SDG 5) and \textit{inequality} (SDG 10), as well as references to environmental impact of AI development and use, and in particular the consequences for life on earth, are notably missing from the guidelines.

The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for data-to-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often employed methods for generating Brazilian Portuguese texts from structured data. Results suggest that explicit intermediate steps in the generation process produce better texts than the ones generated by neural end-to-end architectures, avoiding data hallucination while better generalizing to unseen inputs. Code and corpus are publicly available.