The main contribution of this paper is a novel sensory feedback control law for an octopus arm. The control law is inspired by, and helps integrate, several observations made by biologists. The proposed control law is distinct from prior work which has mainly focused on open-loop control strategies. Several analytical results are described including characterization of the equilibrium and its stability analysis. Numerical simulations demonstrate life-like motion of the soft octopus arm, qualitatively matching behavioral experiments. Quantitative comparison with bend propagation experiments helps provide the first explanation of such canonical motion using a sensory feedback control law. Several remarks are included that help draw parallels with natural pursuit strategies such as motion camouflage or classical pursuit.

As advances in computer graphics and natural language AI have made it easier to create realistic-seeming virtual humans, some companies are attempting to turn these artificial personas into influencers that can work on behalf of brands and cultivate a fan base. One such company is New Zealand-based startup Uneeq, maker of a digital human called Sophie, who has partnered with brands like BMW, Deutsche Telekom and IBM. Uneeq recently launched a new collection of nonfungible token (NFT) art as a way for Sophie to build her profile and better integrate her into the constellation of emerging technologies known as Web3. Sophie isn't the first AI persona to sell NFTs; that distinction likely belongs to similarly named Sophia the robot, who managed to sell a "self-portrait" for nearly $700,000 last year. But Uneeq CEO Danny Tomsett said Sophie's NFTs aren't necessarily as much about raising revenue as building a fan community around Sophie and boosting her status as a tech-savvy influencer.

In December 2021 and January 2022, we asked recipients of our Data and AI Newsletters to participate in our annual survey on AI adoption. We were particularly interested in what, if anything, has changed since last year. Are companies farther along in AI adoption? Do they have working applications in production? Are they using tools like AutoML to generate models, and other tools to streamline AI deployment? We also wanted to get a sense of where AI is headed. The hype has clearly moved on to blockchains and NFTs. AI is in the news often enough, but the steady drumbeat of new advances and techniques has gotten a lot quieter. Compared to last year, significantly fewer people responded. This year's survey ran during the holiday season (December 8, 2021, to January 19, 2022, though we received very few responses in the new year); last year's ran from January 27, 2021, to February 12, 2021. Pandemic or not, holiday schedules no doubt limited the number of respondents.

The thriving industry of Data Science is continuously evolving with the technological advancements in Machine Learning and Artificial intelligence. This has opened up whole new avenues for Data Scientists worldwide. Professionals who can handle Big Data and have the necessary knowledge required for understanding, analysing and processing data are in high demand in the job market. However, there is one important thing that also needs to be addressed is the raging problem caused by the gender gap in this sector. As per the statistical report from the Boston Consulting Group, only 15 to 22 per cent of the Data Science-related professional roles are occupied by women.

Previous partial permutation synchronization (PPS) algorithms, which are commonly used for multi-object matching, often involve computation-intensive and memory-demanding matrix operations. These operations become intractable for large scale structure-from-motion datasets. For pure permutation synchronization, the recent Cycle-Edge Message Passing (CEMP) framework suggests a memory-efficient and fast solution. Here we overcome the restriction of CEMP to compact groups and propose an improved algorithm, CEMP-Partial, for estimating the corruption levels of the observed partial permutations. It allows us to subsequently implement a nonconvex weighted projected power method without the need of spectral initialization. The resulting new PPS algorithm, MatchFAME (Fast, Accurate and Memory-Efficient Matching), only involves sparse matrix operations, and thus enjoys lower time and space complexities in comparison to previous PPS algorithms. We prove that under adversarial corruption, though without additive noise and with certain assumptions, CEMP-Partial is able to exactly classify corrupted and clean partial permutations. We demonstrate the state-of-the-art accuracy, speed and memory efficiency of our method on both synthetic and real datasets.

People who grew up in rural areas have better sense of direction than those raised in cities, particularly cities with grid-pattern streets, a new study says. Researchers say it may be because the countryside has more disorderly road layouts, which effectively primes the brain for remembering and navigating environments. The scientists from France and London tested nearly 400,000 people from 38 countries on their spatial navigation, using a video game called Sea Hero Quest. The mobile game, designed to help research into dementia, involves directing a virtual boat around certain routes that players have had to memorise. The authors found that individuals who grew up in more structured, grid-like cities, such as Chicago, performed better on game levels with a similar grid-like layout.

Mood is a unique way for researchers to try to measure the impact of natural or unnatural disasters on people. However, it's simply impractical to ask every single person in the world how they're feeling in the aftermath of a sweeping event. But scientists from the Massachusetts Institute of Technology, the Chinese Academy of Sciences, and the Max Planck Institute for Human Development found a workaround. They used machine learning techniques to scan social media for sentiment shifts following the first wave of COVID-19 in 100 different countries and get real-time reads on how happy or sad the events related to the pandemic made people across the world. Think of the process as an AI-powered mood ring, but for millions of people.

Data streams are ubiquitous in modern business and society. In practice, data streams may evolve over time and cannot be stored indefinitely. Effective and transparent machine learning on data streams is thus often challenging. Hoeffding Trees have emerged as a state-of-the art for online predictive modelling. They are easy to train and provide meaningful convergence guarantees under a stationary process. Yet, at the same time, Hoeffding Trees often require heuristic and costly extensions to adjust to distributional change, which may considerably impair their interpretability. In this work, we revisit Model Trees for machine learning in evolving data streams. Model Trees are able to maintain more flexible and locally robust representations of the active data concept, making them a natural fit for data stream applications. Our novel framework, called Dynamic Model Tree, satisfies desirable consistency and minimality properties. In experiments with synthetic and real-world tabular streaming data sets, we show that the proposed framework can drastically reduce the number of splits required by existing incremental decision trees. At the same time, our framework often outperforms state-of-the-art models in terms of predictive quality -- especially when concept drift is involved. Dynamic Model Trees are thus a powerful online learning framework that contributes to more lightweight and interpretable machine learning in data streams.

What if there was a magical robot that could cure any disease? Everyone knows there's no one machine that could do that. But maybe a swarm made up of tens of thousands of tiny autonomous micro-bots could? That's the premise laid out by proponents of nanobot medical technology. In science fiction, the big idea usually involves creating tiny metal robots via some sort of magic-adjacent miniaturization technology.

The report also provides the analysis of import/export, production and consumption ratio, supply and demand, cost, price, estimated revenue, and gross margins. The global Artificial Intelligence (AI) & advanced Machine Learning (ML) market size is expected to reach USD 471.39 Billion at a steady CAGR of 35.2% in 2028, according to latest analysis by Emergen Research. Artificial Intelligence (AI) and advanced Machine Learning (ML) technologies are witnessing increasing demand and deployment across various fields, such as in leading-edge medical diagnostics, advanced quantum computer systems, consumer electronics, and smart personal assistants. Machine Learning is a type of AI, which enables computers to learn without being initially programmed. Rising focus on development of computer programs that can teach themselves and change and evolve when exposed to new data, is a factor driving demand for these technologies.