This week, Elon Musk unveiled, "Optimus," Tesla's humanoid robot. Most of the demonstration of Optimus' abilities was via videos of his performance in laboratory conditions. Live, on stage, he merely stood still and waved his arms. Musk joked that he didn't want the robot to do anything more in front of the audience because he might "fall on his face." Perhaps the most impressive thing about the robot was that it had a human-like, five-fingered hand and a human-shaped head, so, except for the metal and wires, it did resemble a human.

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The White House issued a call for artificial intelligence systems to be developed with built-in protections Tuesday, even as the tech industry barrels forward in an AI free-for-all. Why it matters: Automated systems can influence or even determine important aspects of Americans' lives, including healthcare, employment, housing and education. In the U.S., government regulations covering the new technology remain minimal or nonexistent. Driving the news: The Blueprint for an AI Bill of Rights, released Tuesday by the Office of Science & Technology Policy, describes 5 principles that should be incorporated into AI systems to insure their safety and transparency, limit the impact of algorithmic discrimination, and give users control over data. The report details real-world consequences of failures to put such principles into practice.

Robot revolution to transform human workplaces Feature 18 April 2017 Over the last few months, the idea that a robotic revolution is just around the corner has become commonplace Nick Ismail Robots and artificial intelligence is set to intrude into many of the spheres of our lives – driverless cars are about be tested on Manchester roads, self-driving delivery robots are being trialled in London while a New York firm has developed a robot which can lay six times as many bricks in a day as its human counterpart A recent report from The International Bar Association, a global organisation for lawyers, said Governments could be forced to legislate for quotas of human workers, traditional working practices would be transformed over the coming years and that legal frameworks regulating employment and safety were becoming rapidly outdated. A third of graduate level jobs around the world may eventually be replaced by machines or software, the report said. See also: Robots: better saved for Sci-Fi believe UK consumers An estimate by PWC earlier this year said that 10 million UK workers were at high risk of being replaced by robots over the next 15 years. In some sectors half the jobs could go, it warned. The speed with which change is occurring and the broadness of impact being brought about by AI and robotics is incredible.

On May 17, 2021, CAIDP awarded certificates to several individuals who "successfully completed a comprehensive program, including research, writing, and policy analysis, in ARTIFICIAL INTELLIGENCE POLICY." The CAIDP AI Policy Certification requires completion of a detailed multiple-choice test in Ai History, AI Issues and Institutions, AI Regulation, and Research Methods. Candidates are also required to complete a Statement of Professional Ethics for AI Policy and a policy analysis assignment. The recipients of the CAIDP 2021 AI Policy Certificate will be known as the Giovanni Buttarelli Inaugural Class, in memory of the former European Data Protection Supervisor. In Il futuro della privacy e la vivacità della democrazia in Privacy 2030: Una nuova visione per l'Europa (in English), Giovanni warned that a digital underclass has emerged.

Far from being a futuristic concept relegated to the realms of science fiction, the use of artificial intelligence (AI) in the workplace is becoming more common. The benefits of using AI are often cited by reference to time and productivity savings. However, the challenges of implementing AI into HR practice and procedures should not be underestimated. AI technologies are already being used across a broad range of industries, at every stage in the employment cycle. From recruitment to dismissal, their use has significant implications.

The Blueprint for an AI Bill of Rights notably does not set out specific enforcement actions, but instead is intended as a White House call to action for the U.S. government to safeguard digital and civil rights in an AI-fueled world, officials said. "This is the Biden-Harris administration really saying that we need to work together, not only just across government, but across all sectors, to really put equity at the center and civil rights at the center of the ways that we make and use and govern technologies," said Alondra Nelson, deputy director for science and society at the White House Office of Science and Technology Policy. "We can and should expect better and demand better from our technologies." The office said the white paper represents a major advance in the administration's agenda to hold technology companies accountable, and highlighted various federal agencies' commitments to weighing new rules and studying the specific impacts of AI technologies. The document emerged after a year-long consultation with more than two dozen different departments, and also incorporates feedback from civil society groups, technologists, industry researchers and tech companies including Palantir and Microsoft.

Retrieval Augment Generation (RAG) is a recent advancement in Open-Domain Question Answering (ODQA). RAG has only been trained and explored with a Wikipedia-based external knowledge base and is not optimized for use in other specialized domains such as healthcare and news. In this paper, we evaluate the impact of joint training of the retriever and generator components of RAG for the task of domain adaptation in ODQA. We propose \textit{RAG-end2end}, an extension to RAG, that can adapt to a domain-specific knowledge base by updating all components of the external knowledge base during training. In addition, we introduce an auxiliary training signal to inject more domain-specific knowledge. This auxiliary signal forces \textit{RAG-end2end} to reconstruct a given sentence by accessing the relevant information from the external knowledge base. Our novel contribution is unlike RAG, RAG-end2end does joint training of the retriever and generator for the end QA task and domain adaptation. We evaluate our approach with datasets from three domains: COVID-19, News, and Conversations, and achieve significant performance improvements compared to the original RAG model. Our work has been open-sourced through the Huggingface Transformers library, attesting to our work's credibility and technical consistency.

Extracting relevant urban patterns from multiple data sources can be difficult using classical clustering algorithms since we have to make a suitable setup of the hyperparameters of the algorithms and deal with outliers. It should be addressed correctly to help urban planners in the decision-making process for the further development of a big city. For instance, experts' main interest in criminology is comprehending the relationship between crimes and the socio-economic characteristics at specific georeferenced locations. In addition, the classical clustering algorithms take little notice of the intricate spatial correlations in georeferenced data sources. This paper presents a new approach to detecting the most relevant urban patterns from multiple data sources based on tensor decomposition. Compared to classical methods, the proposed approach's performance is attested to validate the identified patterns' quality. The result indicates that the approach can effectively identify functional patterns to characterize the data set for further analysis in achieving good clustering quality. Furthermore, we developed a generic framework named TensorAnalyzer, where the effectiveness and usefulness of the proposed methodology are tested by a set of experiments and a real-world case study showing the relationship between the crime events around schools and students performance and other variables involved in the analysis.

The audio spectrogram is a time-frequency representation that has been widely used for audio classification. The temporal resolution of a spectrogram depends on hop size. Previous works generally assume the hop size should be a constant value such as ten milliseconds. However, a fixed hop size or resolution is not always optimal for different types of sound. This paper proposes a novel method, DiffRes, that enables differentiable temporal resolution learning to improve the performance of audio classification models. Given a spectrogram calculated with a fixed hop size, DiffRes merges non-essential time frames while preserving important frames. DiffRes acts as a "drop-in" module between an audio spectrogram and a classifier, and can be end-to-end optimized. We evaluate DiffRes on the mel-spectrogram, followed by state-of-the-art classifier backbones, and apply it to five different subtasks. Compared with using the fixed-resolution mel-spectrogram, the DiffRes-based method can achieve the same or better classification accuracy with at least 25% fewer temporal dimensions on the feature level, which alleviates the computational cost at the same time. Starting from a high-temporal-resolution spectrogram such as one-millisecond hop size, we show that DiffRes can improve classification accuracy with the same computational complexity.