Bacteria and other small creatures squirming inside bodies often have to propel themselves through thick, viscous environments. For a human, this would look like someone awkwardly trying to swim their way through a pool of honey. Nature has already come up with creative solutions to this sticky problem. E.coli, for example, uses a corkscrew motion to cut through the muck while flagella contort their frames and whip themselves forward. Now, using this natural adaptation as inspiration, researchers from Tampere University and Anhui Jianzhu University have created a new doughnut-shaped micro-robot capable of autonomously navigating its way through mucus and other goopy substances.

The number of nonconsensual deepfake porn videos online has exploded since 2017. As the harmful videos have spread, thousands of women--including Twitch streamers, gamers, and other content creators--have complained to Google websites hosting the videos and tried to get the tech giant to remove them from its search results. Two of the most prominent deepfake video websites have been the subject of more than 6,000 and 4,000 complaints each, data published by Google and Harvard University's Lumen database shows. Millions of people find and access deepfake video websites by searching for deepfakes, often alongside the names of celebrities or content creators. WIRED is not naming the specific websites to limit the exposure they receive.

Machine learning and artificial intelligence have recently represented a popular paradigm for designing and optimizing robotic systems across various scales. Recent studies have showcased the innovative application of large language models (LLMs) in industrial control [1] and in directing legged walking robots [2]. In this study, we utilize an LLM, GPT-4, to train two prototypical microrobots for swimming in viscous fluids. Adopting a few-shot learning approach, we develop a minimal, unified prompt composed of only five sentences. The same concise prompt successfully guides two distinct articulated microrobots -- the three-link swimmer and the three-sphere swimmer -- in mastering their signature strokes. These strokes, initially conceptualized by physicists, are now effectively interpreted and applied by the LLM, enabling the microrobots to circumvent the physical constraints inherent to micro-locomotion. Remarkably, our LLM-based decision-making strategy substantially surpasses a traditional reinforcement learning method in terms of training speed. We discuss the nuanced aspects of prompt design, particularly emphasizing the reduction of monetary expenses of using GPT-4.

Many robotic systems locomote using gaits - periodic changes of internal shape, whose mechanical interaction with the robot's environment generate characteristic net displacements. Prominent examples with two shape variables are the low Reynolds number 3-link "Purcell swimmer" with inputs of 2 joint angles and the "ideal fluid" swimmer. Gait analysis of these systems allows for intelligent decisions to be made about the swimmer's locomotive properties, increasing the potential for robotic autonomy. In this work, we present comparative analysis of gait optimization using two different methods. The first method is variational approach of "Pontryagin's maximum principle" (PMP) from optimal control theory. We apply PMP for several variants of 3-link swimmers, with and without incorporation of bounds on joint angles. The second method is differential-geometric analysis of the gaits based on curvature (total Lie bracket) of the local connection for 3-link swimmers. Using optimized body-motion coordinates, contour plots of the curvature in shape space give visualization that enables identifying distance-optimal gaits as zero level sets. Combining and comparing results of the two methods enables better understanding of changes in existence, shape and topology of distance-optimal gait trajectories, depending on the swimmers' parameters.

Just when you thought it was safe to go back to normal -- are you ready for round two? "There are big changes ahead," says Forrester VP Brandon Purcell. "There are a lot of changes that have been brought about by what happened over the last 2 years. The pace of change is very rapid. There are pretty big things happening." Purcell spoke with InformationWeek about the predictions for AI in 2022 and beyond.

"Unfortunately, there's no way to quantify the size of this problem," said Brandon Purcell, a Forrester vice president, principal analyst, and co-author of the report, adding "… it's true that we are far from artificial general intelligence, but AI is being used to make critical decisions about people at scale today--from credit decisioning, to medical diagnoses, to criminal sentencing. These could include business leaders, lawyers, security and risk specialists, as well as activists, nonprofits, members of the community and consumers. Accounting for intersectionality or how different elements of a person's identity combine to compound the impacts of bias or privilege. "The key is in adopting best practices across the AI lifecycle from the very conception of the use case, through data understanding, modeling, evaluation, and into deployment and monitoring," Purcell said. "Unfortunately, there's no way to quantify the size of this problem," said Brandon Purcell, a Forrester vice president, principal analyst, and co-author of the report, adding "… it's true that we are far from artificial general intelligence, but AI is being used to make critical decisions about people at scale today--from credit decisioning, to medical diagnoses, to criminal sentencing.

As AI becomes more pervasive, AI-based discrimination is getting the attention of policymakers and corporate leaders but keeping it out of AI-models in the first place is harder than it sounds. According to a new Forrester report, Put the AI in "Fair" with the Right Approach to Fairness, most organizations adhere to fairness in principle but fail in practice. "Fairness" has multiple meanings: "To determine whether or not a machine learning model is fair, a company must decide how it will quantify and evaluate fairness," the report said. "Mathematically speaking, there are at least 21 different methods for measuring fairness." Sensitivity attributes are missing: "The essential paradox of fairness in AI is the fact that companies often don't capture protected attributes like race, sexual orientation, and veteran status in their data because they're not supposed to base decisions on them," the report said.

AI systems sometimes run amok. One chatbot, designed by Microsoft to mimic a teenager, began spewing racist hate speech within hours of its release online. Microsoft immediately took the bot down. Another system, which Amazon designed to help its recruiting efforts but ultimately didn't release, inadvertently discriminated against female applicants. Other so-called "smart" systems have led to false arrests, biased bail amounts for criminal defendants, and even fatal car crashes. Experts expect to see more cases of problematic AI as organizations increasingly implement intelligent technology, sometimes doing so without adopting the proper governance in place.

About 10 undisclosed companies in Europe, Canada and the U.S. are using Centricity's software platform in sectors such as grocery, nonfood retail, apparel and consumer electronics, said Chief Executive Michael Brackett, who founded the company in late 2019. Centricity employs about 50, up from less than 10 last April, and its planned fundraise could bring total venture-capital investment to $12.5 million. Startups such as Centricity, which build software and services aimed directly at large enterprise customers, have been capitalizing on the increased demand for their services during the coronavirus pandemic, as companies have been forced to accelerate their digital initiatives to remain competitive. Companies use Centricity's AI-based insights to help predict what customers will want to buy in around one to three months, depending on the client, so they can stock their shelves accordingly. Its technology can also be used by research and development divisions at companies interested in launching new products.

A report issued by technology research firm Forrester, AI Aspirants: Caveat Emptor, highlights the growing need for third-party accountability in artificial intelligence tools. The report found that a lack of accountability in AI can result in regulatory fines, brand damage, and lost customers, all of which can be avoided by performing third-party due diligence and adhering to emerging best practices for responsible AI development and deployment. The risks of getting AI wrong are real and, unfortunately, they're not always directly within the enterprise's control, the report observed. "Risk assessment in the AI context is complicated by a vast supply chain of components with potentially nonlinear and untraceable effects on the output of the AI system," it stated. Most enterprises partner with third parties to create and deploy AI systems because they don't have the necessary technology and skills in house to perform these tasks on their own, said report author Brandon Purcell, a Forrester principal analyst who covers customer analytics and artificial intelligence issues.