PIBT is a computationally lightweight algorithm that can be applied to a variety of multi-agent pathfinding (MAPF) problems, generating the next collision-free locations of agents given another. Because of its simplicity and scalability, it is becoming a popular underlying scheme for recent large-scale MAPF methods involving several hundreds or thousands of agents. Vanilla PIBT makes agents behave greedily towards their assigned goals, while agents typically have multiple best actions, since the graph shortest path is not always unique. Consequently, tiebreaking about how to choose between these actions significantly affects resulting solutions. This paper studies two simple yet effective techniques for tiebreaking in PIBT, without compromising its computational advantage. The first technique allows an agent to intelligently dodge another, taking into account whether each action will hinder the progress of the next timestep. The second technique is to learn, through multiple PIBT runs, how an action causes regret in others and to use this information to minimise regret collectively. Our empirical results demonstrate that these techniques can reduce the solution cost of one-shot MAPF and improve the throughput of lifelong MAPF. For instance, in densely populated one-shot cases, the combined use of these tiebreaks achieves improvements of around 10-20% in sum-of-costs, without significantly compromising the speed of a PIBT-based planner.

Bill Gates has claimed that artificial intelligence will be more of a help than a hindrance in achieving climate goals, despite growing concern that a surge in new datacentres could drain green energy supplies. The philanthropist and Microsoft co-founder told journalists that AI would enable countries to use less energy, even as they require more data centres, by making technology and electricity grids more efficient. Gates downplayed fears over AI's climate impact after mounting concerns that the tech breakthrough could lead to a surge in energy demand and require more fossil fuels as a result. "Let's not go overboard on this," Gates said. "Datacentres are, in the most extreme case, a 6% addition [in energy demand] but probably only 2 to 2.5%. The question is, will AI accelerate a more than 6% reduction? And the answer is: certainly," he said.

Back-support exoskeletons are commonly used in the workplace to reduce low back pain risk for workers performing demanding activities. However, for the assistance of tasks differing from lifting, back-support exoskeletons potential has not been exploited extensively. This work focuses on the use of an active back-support exoskeleton to assist carrying. Two control strategies are designed that modulate the exoskeleton torques to comply with the task assistance requirements. In particular, two gait phase detection frameworks are exploited to adapt the assistance according to the legs' motion. The two strategies are assessed through an experimental analysis on ten subjects. Carrying task is performed without and with the exoskeleton assistance. Results prove the potential of the presented controls in assisting the task without hindering the gait movement and improving the usability experienced by users. Moreover, the exoskeleton assistance significantly reduces the lumbar load associated with the task, demonstrating its promising use for risk mitigation in the workplace.

For the second year in a row, Canada has refused visas to dozens of researchers - most of them from Africa - who were hoping to attend an artificial intelligence (AI) conference in Vancouver. The hassles have caused at least one other AI conference to choose a different country for their next event. The Neural Information Processing Systems conference (NeurIPS), which brings together thousands of experts and researchers from all over the world, will be held in Vancouver next month. Last week, NeurIPS began hearing that several attendees had had their visas denied. It was the second year in a row the conference has had visa troubles.

Filing a patent is the clerical equivalent of pulling teeth -- at least in the U.S. It first requires inventors to determine the type of intellectual property (IP) protection they require (i.e., utility, design, or plant). Then they're on the hook to conduct a United States Patent and Trademark Office (USPTO) database search for similar inventions. If and only if the novelty of their idea passes muster are they allowed to proceed to the next step, which is preparing an application and fees. The system has motivated people like former aerospace engineer Dr. Stephen Thaler to turn to AI in pursuit of a better way. He, along with a team of legal experts and engineers, developed DABUS, a "creativity machine" that's able to generate ideas without human intervention.

Disclaimer: The challenges discussed in this blog are when the Eye Tracking study is done with the help of glasses. Eye Tracking study is about knowing a person's gaze behavior in regards to the real world environment consumer. The technology is well known in the retail market research, where companies are eager to find out the insights of consumer behavior in accordance with their purchasing attributes. While the study is essential for understanding the market, there are some challenges associated with setting up of Eye Tracking study. Some of them are highlighted in this article.

As we indicated in our article on AI applications in Africa, keeping up with the changes in our current fast-paced world where tech solutions are developed by the day and requires formidable structures aimed at leveraging long term objectives and solutions. It's no doubt that AI offers remedies to Africa's most pervasive problems not just in healthcare but also in reducing poverty, elevating inclusion in societies, solutions to food crises, addressing sustainability challenges as well as enhancing the quality of education. Artificial intelligence is crucial in an African setting due to how it democratizes access to pioneering and productivity-boosting innovations that fuel the continent's growth towards sustaining its needs. In the past few years, several governments (as illustrated in our article mentioned above) across Africa have started mobilizing with the aim of promoting the growth of AI in the continent. Having a vibrant AI ecosystem requires integration of precise policies but most importantly, a coming together of progressive lawmakers, global technology partners, governmental institutions and civil society groups.

If you've been studying artificial intelligence and its growth, you'll know that the industry is well past its nascent stage now. There is significant maturity in its growth, and companies from diverse backgrounds are realizing the impact of incorporating data and AI into their ecosystems. In a bid to understand the dynamics of this data-centered growth, I teamed up with Hewlett Packard Enterprise (HPE) to do an analysis of their international survey on the present and the future of AI within the industrial sector. What percentages of companies are working on AI? How can AI transform industrial IoT for the better? Will it prove to be a job killer for us humans?

The secret of success is honesty and fair dealing. If you can fake those, you've got it made.

This model of abstraction, call it the N&S model, is, what I will call a "context pro-functional" accumulator. It sets up a checkpoint for the selection of candidates in a given class of situation, call it [S1], to moving up the ladder of abstraction. It allows items to climb that meet certain functional requirements for an AI construction goal. It may even cull out an [S1]-dys-functional elements, if they are of concern. But such dysfunctional elements may be overlooked or disregarded, as being, for example of low probability of happening, or of low enough cost in the long run to allow to pass through.