Picture the corniest sci-fi '80s TV you can imagine: filled with cheesy one-liners, fast cars, a tough action hero, and retro technology that probably felt cool at the time but now seems incredibly dated. Now, what if I told you that same show may have predicted a 21st-century technology that could revolutionize the world? That show is none other than Knight Rider, a 1980s NBC TV show featuring former detective Michael Knight, who takes on bad guys with the help of a superpowered artificially intelligent car known as Knight 2000, or KITT. As a self-driving car, KITT beat out Elon Musk's Tesla and other autonomous vehicles by decades -- even if only on the small screen. But is the portrayal of KITT on Knight Rider something more than science fiction concocted by Hollywood screenwriters?

The study of artificial intelligence (AI) and neuroscience have many things in common. At its core, neurosciences aim to better understand the brain by deciphering its complex networks and processes. Complimentarily, many AI-focused research projects involve constructing synthetic components of the human brain. The connection of these fields benefits both computer scientists and biology-focused neuroscientists as they help us understand natural and artificial learning systems. These domains of research lend themselves to be inspired by one another.

Two major trends Artificial Intelligence and Machine Learning are going to define the future of fintechs, said Soumya Rajan, Founder & CEO, Waterfield Advisors, at the FE Modern BFSI Summit. As for the emerging trends in the financial sector, Rajan noted two big themes, connectivity and computing, which are going to shape up the future. As far as connectivity is concerned, India has 750 million smartphone users, which is likely to become 1 billion by 2026. Rajan said that on the demographics front, the Gen Ys, and the Gen Zs are digital natives, which rely more on the technology for their financial services. In 2021, around 770 billion digital transactions happened globally, of which around 40 billion were with regard to mobile money.

The use of artificial intelligence (AI)-style'sewbots' that can replace human sewers and other robotics look set to transform the apparel supply chain and facilitate reshoring or near-shoring to developed countries currently reliant on lower income outsourcing hubs, maybe thousands of kilometres away from buyers. Online merchants and small new brands owned by millennials are already driving reshoring by claiming it involves a lower environmental footprint through reduced transport and less inventory waste. Palaniswamy'Raj' Rajan, chairman and CEO of Softwear Automation, based in Atlanta, Georgia, US, whose sewbots can manufacture t-shirts in a fully automated process, warned that major and more established brands will remain "laggards not leaders" in technologically-driven reshoring. They will wait for a critical mass to develop reshored production, before taking the plunge and disrupting established supply chain routes to market, he predicted. But, stressed Rajan, the artificial intelligence (AI) technology that can deliver this vision is becoming available for the apparel sector: "Our sewbots take cut fabric pieces as input, then put them through a series of automatic steps, and output a finished t-shirt," he told Just Style. He explained that software automation technology is a combination of "proprietary advanced robotics, computer vision, AI, and IoT [internet of things] technologies" enabling on-demand production at scale.

Artificial Intelligence (AI) has moved from "when will we do it?" AI passed some important tests during the pandemic, says David Tareen, director of AI and analytics at SAS. "The pandemic put AI and chatbots in place to answer a flood of pandemic-related questions. Computer vision supported social distancing efforts. Machine learning models have become indispensable for modeling the effects of the reopening process." But the future upside of AI is still considerable.

As Rajan prepares for his second attempt to climb Mount Everest and wears his Oculus VR 108.1, he feels better prepared this time compared to his first climb two years ago. Having paid $20K to repair and upgrade his avatar from previous damages, he also had acquired the state of the art virtual gear recommended by the best AI Trainer available in the market -- The Nimbus 314 which he hired for $50K. With 80% of his biannual earnings invested in this attempt, Rajan feels he has left no stone unturned this time and he is all set to be the Everest climber 209,441 in the Metaverse 005 and will have rightfully earned the right to his first Everest summit peak flag NFT(whose current market value was going at $149K). However, as he rests in his rented NFT tent in the Everest Base camp, looking through his VR lens, he remembers his first failed attempt. It was the year 3008, and he had completed two years at his lucrative job as an AI Model for a top tech company specializing in building state-of-the-art AI avatars for humans and owner of one of the 5 most popular metaverses. As an employee, he was eligible for a 50% discount for his first avatar purchase which he utilized immediately by going for 14Trekker 89.0 avatar as he wanted to be an avid virtual trekker like his parents.

Last weekend, in what the BBC clearly regarded as important news, the corporation announced that its media editor, Amol Rajan, had been granted an interview with Sundar Pichai, the current CEO of Alphabet (which basically means Google). It was billed as "the first of a series of interviews with global figures". If the boss of Google counts as a global figure, one wonders who else is on the list, the CEO of ExxonMobil? Simply this: Mr Pichai is a nice guy. He comes from a modest background in India, dropped out of Stanford in the time-honoured manner, has an MBA from Wharton and has worked for Google since 2004.

A new artificial intelligence graduate degree program at Iowa State University will be the first of its kind in the state. The Iowa Board of Regents approved the two-year master's of science degree program Thursday through consent agenda after being presented with the program Wednesday in committee. The graduate program is expected to begin this fall. Hridesh Rajan, a professor and chairperson of ISU's Department of Computer Science, said the new program seeks to produce graduates that can work on building and enhancing components of artificial intelligence -- not only to be able to understand and make practical use of machine learning and big data, but also be able to communicate the capabilities and limitations of AI. Artificial intelligence, or AI, is the study of techniques that help incorporate intelligence into software, Rajan said.

The deep neural nets of modern artificial intelligence (AI) have not achieved defining features of biological intelligence, including abstraction, causal learning, and energy-efficiency. While scaling to larger models has delivered performance improvements for current applications, more brain-like capacities may demand new theories, models, and methods for designing artificial learning systems. Here, we argue that this opportunity to reassess insights from the brain should stimulate cooperation between AI research and theory-driven computational neuroscience (CN). To motivate a brain basis of neural computation, we present a dynamical view of intelligence from which we elaborate concepts of sparsity in network structure, temporal dynamics, and interactive learning. In particular, we suggest that temporal dynamics, as expressed through neural synchrony, nested oscillations, and flexible sequences, provide a rich computational layer for reading and updating hierarchical models distributed in long-term memory networks. Moreover, embracing agent-centered paradigms in AI and CN will accelerate our understanding of the complex dynamics and behaviors that build useful world models. A convergence of AI/CN theories and objectives will reveal dynamical principles of intelligence for brains and engineered learning systems. This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.

Improving and optimizing oceanographic sampling is a crucial task for marine science and maritime resource management. Faced with limited resources in understanding processes in the water-column, the combination of statistics and autonomous systems provide new opportunities for experimental design. In this work we develop efficient spatial sampling methods for characterizing regions defined by simultaneous exceedances above prescribed thresholds of several responses, with an application focus on mapping coastal ocean phenomena based on temperature and salinity measurements. Specifically, we define a design criterion based on uncertainty in the excursions of vector-valued Gaussian random fields, and derive tractable expressions for the expected integrated Bernoulli variance reduction in such a framework. We demonstrate how this criterion can be used to prioritize sampling efforts at locations that are ambiguous, making exploration more effective. We use simulations to study and compare properties of the considered approaches, followed by results from field deployments with an autonomous underwater vehicle as part of a study mapping the boundary of a river plume. The results demonstrate the potential of combining statistical methods and robotic platforms to effectively inform and execute data-driven environmental sampling.