Ever wondered what would happen if we made an artificial intelligence programme question its own merits and demerits? Wonder no further, for scientists have made it happen. Since AI is built by humans and thrives on data fed to it by human calculations, the creators' bias has a tendency to seep into the programme, regardless of the intention. This is evident in how AI programmes recreate real inequalities in what is called "coded bias". This bias may stem from discrimination based on race, gender, etc. The "Megatron Transformer", built by the Applied Deep Research team at Nvidia was invited by the researchers to self-assess the merits of artificial intelligence.

Self-driving cars and autonomous driving research has been receiving considerable attention as major promising prospects in modern artificial intelligence applications. According to the evolution of advanced driver assistance system (ADAS), the design of self-driving vehicle and autonomous driving systems becomes complicated and safety-critical. In general, the intelligent system simultaneously and efficiently activates ADAS functions. Therefore, it is essential to consider reliable ADAS function coordination to control the driving system, safely. In order to deal with this issue, this paper proposes a randomized adversarial imitation learning (RAIL) algorithm. The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions. The proposed method is able to train the decision maker that deals with the LIDAR data and controls the autonomous driving in multi-lane complex highway environments. The simulation-based evaluation verifies that the proposed method achieves desired performance.

Entrepreneur, business magnate and billionaire Elon Musk believes Neuralink's sophisticated implantable brain-machine interfaces and not Mark Zuckerberg's VR-driven metaverse could soon put humans "fully into virtual reality." Neuralink co-founder Musk took a dig at Zuckerberg and other proponents of virtual reality and metaverse in an interview with the conservative and Christian satire site Babylon Tree on Tuesday. During the interview, the billionaire revealed his lack of support for the metaverse and Web3, two trends believed to radically change the tech world. For the Time Magazine's 2021 Person of the Year, his neurotechnology company, Neuralink, is more capable of providing a better environment for people to fully immerse in virtual reality. "In the long term, a sophisticated Neuralink could put you fully into virtual reality," Musk noted.

I'm guessing that using an artificial intelligence computer program to write part of a review may be a first in the 125-year history of the Book …

Renewable energy resources (RERs) have been increasingly integrated into modern power systems, especially in large-scale distribution networks (DNs). In this paper, we propose a deep reinforcement learning (DRL)-based approach to dynamically search for the optimal operation point, i.e., optimal power flow (OPF), in DNs with a high uptake of RERs. Considering uncertainties and voltage fluctuation issues caused by RERs, we formulate OPF into a multi-objective optimization (MOO) problem. To solve the MOO problem, we develop a novel DRL algorithm leveraging the graphical information of the distribution network. Specifically, we employ the state-of-the-art DRL algorithm, i.e., deep deterministic policy gradient (DDPG), to learn an optimal strategy for OPF. Since power flow reallocation in the DN is a consecutive process, where nodes are self-correlated and interrelated in temporal and spatial views, to make full use of DNs' graphical information, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for spatial-temporal graph information extraction, preparing for its sequential DDPG. We validate our proposed DRL-based approach in modified IEEE 33, 69, and 118-bus radial distribution systems (RDSs) and show that our DRL-based approach outperforms other benchmark algorithms. Our experimental results also reveal that MG-ASTGCN can significantly accelerate the DDPG training process and improve DDPG's capability in reallocating power flow for OPF. The proposed DRL-based approach also promotes DNs' stability in the presence of node faults, especially for large-scale DNs.

In recent years, the rapid advance of artificial intelligence has evoked cries of alarm from billionaire entrepreneur Elon Musk and legendary physicist Stephen Hawking. Others, including the eccentric futurist Ray Kurzweil, have embraced the coming of true machine intelligence, suggesting that we might merge with the computers, gaining superintelligence and immortality in the process. It turns out, we may not have to wait much longer. This morning, a group of research scientists at Google DeepMind announced that they had inadvertently solved the riddle of artificial general intelligence (AGI). Their approach relies upon a beguilingly simple technique called symmetrically toroidal asyncronomous bisecting convolutions.

IT TOOK decades for scientists to unlock the structure of just 17 per cent of the proteins in the human body. But UK-based AI company DeepMind raised the bar to 98.5 per cent in July when it announced that its AlphaFold model could quickly and reliably calculate the way proteins fold. This could lead to targeted drugs that bind to specific parts of molecules. We caught up with Pushmeet Kohli at DeepMind to see how work is progressing with mapping almost every one of the more than 100 million known proteins that have been sequenced from across the tree of life. Were you surprised at the success of AlphaFold, considering that figuring out protein folding previously required vast supercomputers?

Kahneman, a Nobel Prize winner in Economic Sciences and the author of Thinking, Fast and Slow, noted an instance in which humans use judgment heuristics--shortcuts, essentially--to answer questions they don't know the answer to. In the example, people are given a small amount of information about a student: She's about to graduate, and she was reading fluently when she was four years old. From that, they're asked to estimate her grade point average.

The current issues of the lending industry can be solved through technology and digitalizing the operations as much as possible. A thorough research and analyses on this matter has led Rishabh Goel to the idea of launching Credgenics, a technological solution to digitize a largely manual collections workflow. Rishabh was soon joined by Anand and Mayank, who are currently the CTO and COO of Credgenics respectively. Analytics Insight has engaged in an exclusive interview with Rishabh to discuss about his vision of creating a technology-based solution for the lending industry. After graduating from IIT Delhi, I worked first with Deutsche Bank and then with Blackrock, where I understood the nuances of the lending industry and observed the problems with the current collections practices.

Many of us who have uploaded images of our faces and the faces of our friends and family to openly-licensed platforms on the Web may have inadvertently contributed to a massive and growing database for AI facial recognition. So how are our faces being used? So have we all thrown away our privacy and assumption of innocence for a selfie? The film is Web Monetized, with all streaming payments going to the Surveillance Technology Oversight Project (S.T.O.P.) On the GftW Community Forum, we have been streaming funds to S.T.O.P. since July. So far, we have generated almost $200 in micropayments to support their work.