Teleoperation of humanoid robots enables the integration of the cognitive skills and domain expertise of humans with the physical capabilities of humanoid robots. The operational versatility of humanoid robots makes them the ideal platform for a wide range of applications when teleoperating in a remote environment. However, the complexity of humanoid robots imposes challenges for teleoperation, particularly in unstructured dynamic environments with limited communication. Many advancements have been achieved in the last decades in this area, but a comprehensive overview is still missing. This survey paper gives an extensive overview of humanoid robot teleoperation, presenting the general architecture of a teleoperation system and analyzing the different components. We also discuss different aspects of the topic, including technological and methodological advances, as well as potential applications. A web-based version of the paper can be found at https://humanoid-teleoperation.github.io/.

This two part paper argues that seemingly "technical" choices made by developers of machine-learning based algorithmic tools used to inform decisions by criminal justice authorities can create serious constitutional dangers, enhancing the likelihood of abuse of decision-making power and the scope and magnitude of injustice. Drawing on three algorithmic tools in use, or recently used, to assess the "risk" posed by individuals to inform how they should be treated by criminal justice authorities, we integrate insights from data science and public law scholarship to show how public law principles and more specific legal duties that are rooted in these principles, are routinely overlooked in algorithmic tool-building and implementation. We argue that technical developers must collaborate closely with public law experts to ensure that if algorithmic decision-support tools are to inform criminal justice decisions, those tools are configured and implemented in a manner that is demonstrably compliant with public law principles and doctrine, including respect for human rights, throughout the tool-building process.

As previously reported in this space, the Court of Appeal for the Federal Circuit has ruled that an AI machine cannot be an inventor because it is not a "natural person." You can read those posts here and here. On November 11, 2022, a group of plaintiffs filed suit in the Northern District of California against several defendants, including GitHub, Inc., Microsoft Corporation, and OpenAI, Inc. and related companies to OpenAI. The issue stems from a product called Copilot and a product integrated into Copilot called Codex. To provide some context of the issue, some backstory may help.

Early this week, tractor maker John Deere said it had signed a memorandum of understanding with the American Farm Bureau Federation, an agricultural trade group, promising to make it easier for farmers to access tools and software needed to repair their own equipment. The deal looked like a concession from the agricultural equipment maker, a major target of the right-to-repair movement, which campaigns for better access to documents and tools needed for people to repair their own gear. But right-to-repair advocates say that despite some good points, the agreement changes little, and farmers still face unfair barriers to maintaining equipment they own. Kevin O'Reilly, a director of the right-to-repair campaign run by the US Public Interest Research Group, a grassroots lobbying organization, says the timing of Deere's deal suggests the company may be trying to quash recent interest in right-to-repair laws from state legislators. In the past two years, corn belt states including Nebraska and Missouri, and also Montana, have considered giving farmers a legal right to tools needed to repair their own equipment. But no laws have been passed.

A US government research lab has built a tool for finding and summarising knowledge in scientific paper abstracts. It uses the same AI that powers the recently publicly released chatbot ChatGPT. "There is a line of sight to the time when we will have research assistants that are AIs that have access to incredible amounts of information," says John Dagdelen at Lawrence Berkeley National Laboratory in California.

A few posts ago we talked about A/B testing with multiple metrics. We used the famous Bonferroni Correction to control our family-wise error rate when performing multiple tests. The Bonferroni method is widely used for its simplicity and broad applicability. We know its biggest drawback is that it is a conservative test. A finding that survives a Bonferroni adjustment is a credible trial outcome.

China will begin enforcing its strict new rules around the creation of deepfakes from today. Deepfakes are increasingly being used for manipulation and humiliation. We've seen deepfakes of figures like disgraced FTX founder Sam Bankman-Fried to commit fraud, Ukrainian President Volodymyr Zelenskyy to spread disinformation, and US House Speaker Nancy Pelosi to make her appear drunk. Last month, the Cyberspace Administration of China (CAC) announced rules to clampdown on deepfakes. "In recent years, in-depth synthetic technology has developed rapidly. While serving user needs and improving user experiences, it has also been used by some criminals to produce, copy, publish, and disseminate illegal and bad information, defame, detract from the reputation and honour of others, and counterfeit others," explains the CAC.

Check out all the on-demand sessions from the Intelligent Security Summit here. The onslaught of endpoint attacks delivers more and more data -- data that DevOps teams need to fine-tune existing products and invent new ones. Mining attack data to identify new threat patterns and correlations, then fine-tuning machine learning (ML) models and new products, is the goal. Gleaning new insights from endpoint attack data is a high strategic priority for market leaders. During his keynote at Palo Alto Networks' Ignite '22 Conference, Nikesh Arora, Palo Alto Networks chairman and CEO, said, "we collect the most amount of endpoint data in the industry from our XDR. We collect almost 200 megabytes per endpoint, which is, in many cases, 10 to 20 times more than most of the industry participants. Because we take that raw data and cross-correlate or enhance most of our firewalls; we apply attack surface management with applied automation using XDR."

STR's Analytics Division develops novel technologies to solve challenging national security problems through advanced analytics. Our team consists of passionate, motivated individuals with degrees in engineering, computer science, mathematics, physics, and data science. We use our expertise and creativity to take innovative ideas from conception to mature implementation in order to improve mission success of our customers. The Analysis and Decisions Systems (ADS) Group in the Analytics Division works to help humans make the best decisions they can. By leveraging expertise with machine learning, advanced algorithms, and software development best practices, we build tools that can make a difference in mission planning, autonomous systems reasoning, tracking of illicit activities, and more.

After a year in which war ravaged Ukraine, stubbornly high inflation brought the global economy to the brink of recession, a "tripledemic" revived pandemic fears, and limited progress was made on the climate crisis, it would be understandable to approach 2023 with a sense of unease. And yet a series of scientific breakthroughs in 2022 are bringing reasons for optimism for the new year. From fusion energy to improved vaccines and organ transplants, an artificial intelligence revolution to diverted asteroids, technologies previously found only in science fiction came to fruition. Those landmark discoveries, some the culmination of decades of work, offer grounds for hope. Why do heatwaves in the UK feel hotter than abroad?