Hyderabad: Bharat Dynamics Limited (BDL), a defence public sector undertaking, has started developing products for the armed forces using artificial intelligence (AI) with active participation of start-ups. The parliamentary standing committee on defence led by chairman Jual Oram visited BDL here on Friday for a study on'modernisation of defence PSUs'. Chairman and managing director of BDL Commodore Siddharth Mishra said BDL was committed to working towards creation of Atmanirbhar Bharat in the defence sector by modernising its facilities and training its manpower and BDL had entered into an agreement with foreign equipment manufacturers to transfer the technology into India.

People should not be slaves to machines, a coalition of evangelical church congregations from more than 30 countries preached to leaders of the European Union earlier this summer. The European Evangelical Alliance believes all forms of AI with the potential to harm people should be evaluated, and AI with the power to harm the environment should be labeled high risk, as should AI for transhumanism, the alteration of people with tech like computers or machinery. It urged members of the European Commission for more discussion of what's "considered safe and morally acceptable" when it comes to augmented humans and computer-brain interfaces. The evangelical group is one of more than 300 organizations to weigh in on the EU's Artificial Intelligence Act, which lawmakers and regulators introduced in April. The comment period on the proposal ended August 8, and it will now be considered by the European Parliament and European Council, made up of heads of state from EU member nations.

August 24, 2021 – Congratulations to Montreal-based start-up BrainBox AI, which is one of 10 companies--and the only Canadian company, it says--selected to present at COP26's "Tech for our planet" program, an initiative at the upcoming 26th United Nations Climate Change Conference. BrainBox was selected by the U.K. government to display its technology in Challenge 3–Thinking Smart, which is dedicated to solutions that can capture and share data to better predict and manage energy consumption. In the three months leading to COP26 (being held in November in Glasgow), BrainBox will demonstrate the benefits of grid-interactive buildings to achieve net zero objectives for the electrical grid. "By implementing technologies like BrainBox AI in one of the world's greatest energy consumers--buildings--we can turn the tide and help the real estate industry play its part in stopping the effects of climate change," said Sam Ramadori, president, BrainBox AI. Currently installed in over 100 million sf of real estate across 17 countries, BrainBox's flagship product combines AI and cloud computing to create a fully autonomous commercial HVAC solution that reduces energy consumption and emissions.

TMMI, high resolution video technology pioneer, expands into artificial intelligence with co-development agreement. TMMI President, Michael Kozole made the announcement stating, "TMMI has always focused on high quality, cutting-edge, video technology since its beginning in 1990. Over the recent months, TMMI has assembled this opportunity to bring together an association of top-level talent and shared technology development with CodeBaby, that will create emotionally intelligent avatars that deliver all-new experiences in artificial intelligence with greater access to a broad base of businesses and consumers". The CodeBaby teams come with over 20 years of experience in animation, gaming and artificial intelligence. Founded in 2001, CodeBaby attracted the attention of two doctors in Alberta, Canada, who had co-founded a video game company (Bioware) in 1995.

The United States Securities and Exchange Commission (SEC) signed a deal with blockchain analytics firm AnChain.AI to help its efforts in monitoring the decentralized finance (DeFi) space. The company's service focuses on tracking illicit activity across crypto exchanges, DeFi protocols, and traditional financial institutions. The contract between the blockchain analysis firm and the SEC started in May and probably played a role in AnChain.AI securing a $10 million Series A round led by Susquehanna Group affiliate SIG Asia Investments LLP. Why It Matters: The regulator is seemingly leveraging the contractor to monitor the DeFi ecosystem more closely, as expected after recent remarks by SEC Chairman Gary Gensler. For instance, Fang explained that it is actually an amalgam of 30,000 separate smart contracts that manages over $1.8 billion worth of transactions in the 24 hours prior to the Forbes article being published.

Retired U.S. Army General Don Bolduc joins'The Next Revolution' to discuss the chaotic scene in Afghanistan As Afghanistan readies for a future without the presence of U.S. troops, American military officials will most likely combat ISIS-K through the use of drone strikes, said drone expert Brett Velicovich. One of the first strikes was carried out on Friday in response to a bombing that killed 13 U.S. service members a day earlier amid evacuations at Kabul's international airport. ISIS-K, an Islamic State affiliate in Afghanistan and Pakistan, claimed responsibility for the terror attack. The Pentagon said the retaliatory strike hit a vehicle carrying an ISIS-K target who was believed to be planning future attacks. The operation signals a new way of conducting warfare against terror groups, especially in remote areas where there isn't an American boots-on-the-ground presence, Velicovich, a former Army intelligence specialist and author of "Drone Warrior," told Fox News.

Recent advance in classical reinforcement learning (RL) and quantum computation (QC) points to a promising direction of performing RL on a quantum computer. However, potential applications in quantum RL are limited by the number of qubits available in the modern quantum devices. Here we present two frameworks of deep quantum RL tasks using a gradient-free evolution optimization: First, we apply the amplitude encoding scheme to the Cart-Pole problem; Second, we propose a hybrid framework where the quantum RL agents are equipped with hybrid tensor network-variational quantum circuit (TN-VQC) architecture to handle inputs with dimensions exceeding the number of qubits. This allows us to perform quantum RL on the MiniGrid environment with 147-dimensional inputs. We demonstrate the quantum advantage of parameter saving using the amplitude encoding. The hybrid TN-VQC architecture provides a natural way to perform efficient compression of the input dimension, enabling further quantum RL applications on noisy intermediate-scale quantum devices.

Can machines learn to use a search engine as an interactive tool for finding information? That would have far reaching consequences for making the world's knowledge more accessible. This paper presents first steps in designing agents that learn meta-strategies for contextual query refinements. Our approach uses machine reading to guide the selection of refinement terms from aggregated search results. Agents are then empowered with simple but effective search operators to exert fine-grained and transparent control over queries and search results. We develop a novel way of generating synthetic search sessions, which leverages the power of transformer-based generative language models through (self-)supervised learning. We also present a reinforcement learning agent with dynamically constrained actions that can learn interactive search strategies completely from scratch. In both cases, we obtain significant improvements over one-shot search with a strong information retrieval baseline. Finally, we provide an in-depth analysis of the learned search policies.

Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing machine learning (ML). In many cases, ML is used on ill-defined problems, e.g. optimising sepsis treatment, where there is no clear, pre-defined specification against which to assess validity. This problem is exacerbated by the "opaque" nature of ML where the learnt model is not amenable to human scrutiny. Explainable AI methods have been proposed to tackle this issue by producing human-interpretable representations of ML models which can help users to gain confidence and build trust in the ML system. However, there is not much work explicitly investigating the role of explainability for safety assurance in the context of ML development. This paper identifies ways in which explainable AI methods can contribute to safety assurance of ML-based systems. It then uses a concrete ML-based clinical decision support system, concerning weaning of patients from mechanical ventilation, to demonstrate how explainable AI methods can be employed to produce evidence to support safety assurance. The results are also represented in a safety argument to show where, and in what way, explainable AI methods can contribute to a safety case. Overall, we conclude that explainable AI methods have a valuable role in safety assurance of ML-based systems in healthcare but that they are not sufficient in themselves to assure safety.

An impossibility theorem demonstrates that a particular problem or set of problems cannot be solved as described in the claim. Such theorems put limits on what is possible to do concerning artificial intelligence, especially the super-intelligent one. As such, these results serve as guidelines, reminders, and warnings to AI safety, AI policy, and governance researchers. These might enable solutions to some long-standing questions in the form of formalizing theories in the framework of constraint satisfaction without committing to one option. In this paper, we have categorized impossibility theorems applicable to the domain of AI into five categories: deduction, indistinguishability, induction, tradeoffs, and intractability. We found that certain theorems are too specific or have implicit assumptions that limit application. Also, we added a new result (theorem) about the unfairness of explainability, the first explainability-related result in the induction category. We concluded that deductive impossibilities deny 100%-guarantees for security. In the end, we give some ideas that hold potential in explainability, controllability, value alignment, ethics, and group decision-making. They can be deepened by further investigation.