"We have stability in terms of policy framework, a huge startup ecosystem and a rebalancing of global value chain coming in over the last three years. India is lagging behind in the global robotics race and cannot afford to be left behind in terms of manufacturing. Speaking at the'Building Robotics Ecosystem for the Developing World: Challenges and Opportunities' at the Bengaluru Tech Summit 2022, Volvo Group India president Kamal Bali said that India is currently in its sweet spot to benefit majorly in the manufacturing sector. "We have stability in terms of policy framework, a huge startup ecosystem and a rebalancing of global value chain coming in over the last three years. India stands to gain the most.

India to take over Chair of Global Partnership on AI for 2022-23. India will take over the chair of the Global Partnership on Artificial Intelligence for 2022-23 at a meeting of the body in Tokyo on November 21, the Ministry of Electronics and IT said on Sunday. In the election to the Council Chair, India had received more than a two-thirds majority of first-preference votes while Canada and the United States of America ranked in the next two best places in the tally – so they were elected to the two additional government seats on the Steering Committee, the ministry said in a statement. Minister of State for Electronics and IT Rajeev Chandrasekhar will represent India at the handover ceremony in Tokyo. "Close on the heels of assuming the presidency of G20, a league of world's largest economies, India will take over the chair of the Global Partnership on Artificial Intelligence (GPAI), an international initiative to support responsible and human-centric development and use of Artificial Intelligence (AI)," the statement said.

Multi-robot systems face challenges in reducing human interventions as they are often deployed in dangerous environments. It is therefore necessary to include a methodology to assess robot failure rates to reduce the requirement for costly human intervention. A solution to this problem includes robots with the ability to work together to ensure mission resilience. To prevent this intervention, robots should be able to work together to ensure mission resilience. However, robotic platforms generally lack built-in interconnectivity with other platforms from different vendors. This work aims to tackle this issue by enabling the functionality through a bidirectional digital twin. The twin enables the human operator to transmit and receive information to and from the multi-robot fleet. This digital twin considers mission resilience and autonomous and human-led decision making to enable the resilience of a multi-robot fleet. This creates the cooperation, corroboration, and collaboration of diverse robots to leverage the capability of robots and support recovery of a failed robot.

Recent work in large language modeling (LLMs) has used fine-tuning to align outputs with the preferences of a prototypical user. This work assumes that human preferences are static and homogeneous across individuals, so that aligning to a a single "generic" user will confer more general alignment. Here, we embrace the heterogeneity of human preferences to consider a different challenge: how might a machine help people with diverse views find agreement? We fine-tune a 70 billion parameter LLM to generate statements that maximize the expected approval for a group of people with potentially diverse opinions. Human participants provide written opinions on thousands of questions touching on moral and political issues (e.g., "should we raise taxes on the rich?"), and rate the LLM's generated candidate consensus statements for agreement and quality. A reward model is then trained to predict individual preferences, enabling it to quantify and rank consensus statements in terms of their appeal to the overall group, defined according to different aggregation (social welfare) functions. The model produces consensus statements that are preferred by human users over those from prompted LLMs (>70%) and significantly outperforms a tight fine-tuned baseline that lacks the final ranking step. Further, our best model's consensus statements are preferred over the best human-generated opinions (>65%). We find that when we silently constructed consensus statements from only a subset of group members, those who were excluded were more likely to dissent, revealing the sensitivity of the consensus to individual contributions. These results highlight the potential to use LLMs to help groups of humans align their values with one another.

Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this paper, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several Long Short-Term Memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers for uncertainty estimation. With the proposed uncertainty region and by utilizing a post-processing filter, we can effectively capture the anomaly points. Numerical results show that our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives.

In terms of artificial intelligence, there are several security and privacy deficiencies in the traditional centralized training methods of machine learning models by a server. To address this limitation, federated learning (FL) has been proposed and is known for breaking down ``data silos" and protecting the privacy of users. However, FL has not yet gained popularity in the industry, mainly due to its security, privacy, and high cost of communication. For the purpose of advancing the research in this field, building a robust FL system, and realizing the wide application of FL, this paper sorts out the possible attacks and corresponding defenses of the current FL system systematically. Firstly, this paper briefly introduces the basic workflow of FL and related knowledge of attacks and defenses. It reviews a great deal of research about privacy theft and malicious attacks that have been studied in recent years. Most importantly, in view of the current three classification criteria, namely the three stages of machine learning, the three different roles in federated learning, and the CIA (Confidentiality, Integrity, and Availability) guidelines on privacy protection, we divide attack approaches into two categories according to the training stage and the prediction stage in machine learning. Furthermore, we also identify the CIA property violated for each attack method and potential attack role. Various defense mechanisms are then analyzed separately from the level of privacy and security. Finally, we summarize the possible challenges in the application of FL from the aspect of attacks and defenses and discuss the future development direction of FL systems. In this way, the designed FL system has the ability to resist different attacks and is more secure and stable.

Related to advancement challenges, experts say that since around 90% of AI and machine learning deployments fail to get off the ground, …

The Artemis 1 Orion crew vehicle has set a new record for a NASA flight. At approximately 8:40AM ET on Saturday, Orion flew farther than any spacecraft designed to carry human astronauts had ever before, surpassing the previous record set by Apollo 13 back in 1970. As of 10:17AM ET, Orion was approximately 249,666 miles ( from 401,798 kilometers) from Earth. "Artemis I was designed to stress the systems of Orion and we settled on the distant retrograde orbit as a really good way to do that," said Jim Geffre, Orion spacecraft integration manager. "It just so happened that with that really large orbit, high altitude above the moon, we were able to pass the Apollo 13 record. But what was more important though, was pushing the boundaries of exploration and sending spacecraft farther than we had ever done before."

In May 2022, the Future of Privacy Forum (FPF) launched a comprehensive Report analyzing case-law under the General Data Protection Regulation (GDPR) applied to real-life cases involving Automated Decision-Making (ADM). Our research highlighted that the GDPR's protections for individuals against forms of ADM and profiling go significantly beyond Article 22 – which provides for the right of individuals not to be subject to decisions based solely on automated processing that produces legal effects or significantly impacts them, and are currently being applied by courts and Data Protection Authorities (DPAs) alike. These range from detailed transparency obligations to applying the fairness principle to avoid situations of discrimination and strict conditions for valid consent in ADM cases. As EU lawmakers are now discussing the amendments they would like to include in the European Commission (EC)'s Artificial Intelligence (AI) Act Proposal, what lessons can be drawn from GDPR enforcement precedents–as outlined in the Report–when deciding on the scope and obligations of the Act? This blog will explore: the link between the GDPR's provisions as relevant for ADM and the AI Act Proposal (1); how the AI Act's concepts of providers and users fare compared to the GDPR's controllers and processors (2); how the AI Act facilitates GDPR compliance for the deployers of AI systems (3); the opportunities to enhance or clarify obligations under the AI Act through the lens of ADM jurisprudence (4); the overlaps between GDPR enforcement precedents and the AI Act's prohibited practices or high-risk use cases (5); the issue of redress under the GDPR and the AI Act (6); and a compilation of lessons learned from the FPF Report in the context of the debates around the AI Act (7). Note: when referring to case numbers in this blog, the author is using the numbering of cases in the FPF Report.

Lithuania's Foreign Minister, Gabrielius Landsbergis, talked with Fox News Digital about Russia, China and the'global rules-based order' on the 20th anniversary of his country joining NATO. Lithuania commemorated its entry into NATO this last week and its long-standing partnership with the U.S. as leaders look ahead to the increasingly complex security landscape developing around the world. President George W. Bush visited the Lithuanian capital of Vilnius 20 years ago to welcome the country into the still-growing NATO alliance, applauding the character of member states to "stand in the face of evil, to have the courage to always face danger." "President [George W.] Bush made the most famous speech any American has ever made in Lithuania exactly 20 years ago," Lithuanian Foreign Minister Gabrielius Landsbergis told Fox News Digital in an exclusive interview. "That was even before we were a member of NATO, and it was probably the most important security guarantee that we got before Article Five started covering us with its umbrella."