Engineers at The University of Manchester have unlocked the secrets to designing a robot capable of jumping 120 meters—higher than any other jumping robot designed to date.

Using a combination of mathematics, computer simulations, and laboratory experiments, the researchers have discovered how to design a robot with the optimum size, shape, and arrangement of its parts, allowing it to jump high enough to clear obstacles many times its own size.

The current highest-jumping robot can reach up to 33 meters, which is equivalent to 110 times its own size. Now, researchers have designed a robot that could jump more than 120 meters in the air—or 200 meters on the moon, which is more than twice the height of Big Ben’s tower.

The advancement, published in the journal Mechanism and Machine Theory, will revolutionize applications ranging from planetary exploration to disaster rescue to surveillance of hazardous or inaccessible spaces.

Co-author Dr. John Lo, Research Associate in Space Robotics at The University of Manchester, said, “Robots are traditionally designed to move by rolling on wheels or using legs to walk, but jumping provides an effective way of traveling around locations where the terrain is very uneven, or where there are a lot of obstacles, such as inside caves, through forests, over boulders, or even the surface of other planets in space.

“While jumping robots already exist, there are several big challenges in the design of these jumping machines, the main one being to jump high enough to overcome large and complicated obstacles. Our design would dramatically improve the energy efficiency and performance of spring-driven jumping robots.”

The researchers found that traditional jumping robots often take off before fully releasing their stored spring energy, resulting in inefficient jumps and limiting their maximum height. They also found that they wasted energy by moving side to side or rotating instead of moving straight up.

The new designs must focus on removing these undesirable movements while maintaining the necessary structural strength and stiffness.

Co-author Dr. Ben Parslew, Senior Lecturer in Aerospace Engineering, said, “There were so many questions to answer and decisions to make about the shape of the robot, such as should it have legs to push off the ground like a kangaroo, or should it be more like an engineered piston with a giant spring? Should it be a simple symmetrical shape like a diamond, or should it be something more curved and organic?

“Then, after deciding this we need to think about the size of the robot—small robots are light and agile, but then large robots can carry bigger motors for more powerful jumps, so is the best option somewhere in the middle?

“Our structural redesigns redistribute the robot’s component mass towards the top and taper the structure towards the bottom. Lighter legs, in the shape of a prism and using springs that only stretch are all properties that we have shown to improve the performance and most importantly, the energy efficiency of the jumping robot.”

Although the researchers have found a practicable design option to significantly improve performance, their next goal is to control the direction of the jumps and find out how to harness the kinetic energy from its landing to improve the number of jumps the robot can do in a single charge. They will also explore more compact designs for space missions, making the robot easier to transport and deploy on the moon.

Artificial intelligence built on mountains of potentially biased information has created a real risk of automating discrimination, but is there any way to re-educate the machines?

The question for some is extremely urgent. In this ChatGPT era, AI will generate more and more decisions for health care providers, bank lenders or lawyers, using whatever was scoured from the internet as source material.

AI’s underlying intelligence, therefore, is only as good as the world it came from, as likely to be filled with wit, wisdom, and usefulness, as well as hatred, prejudice and rants.

“It’s dangerous because people are embracing and adopting AI software and really depending on it,” said Joshua Weaver, Director of Texas Opportunity & Justice Incubator, a legal consultancy.

“We can get into this feedback loop where the bias in our own selves and culture informs bias in the AI and becomes a sort of reinforcing loop,” he said.

Making sure technology more accurately reflects human diversity is not just a political choice.

Other uses of AI, like facial recognition, have seen companies thrown into hot water with authorities for discrimination.

This was the case against Rite-Aid, a US pharmacy chain, where in-store cameras falsely tagged consumers, particularly women and people of color, as shoplifters, according to the Federal Trade Commission.

‘Got it wrong’

ChatGPT-style generative AI, which can create a semblance of human-level reasoning in just seconds, opens up new opportunities to get things wrong, experts worry.

The AI giants are well aware of the problem, afraid that their models can descend into bad behavior, or overly reflect a western society when their user base is global.

“We have people asking queries from Indonesia or the US,” said Google CEO Sundar Pichai, explaining why requests for images of doctors or lawyers will strive to reflect racial diversity.

But these considerations can reach absurd levels and lead to angry accusations of excessive political correctness.

This is what happened when Google’s Gemini image generator spat out an image of German soldiers from World War Two that absurdly included a black man and Asian woman.

“Obviously, the mistake was that we over-applied… where it should have never applied. That was a bug and we got it wrong,” Pichai said.

But Sasha Luccioni, a research scientist at Hugging Face, a leading platform for AI models cautioned that “thinking that there’s a technological solution to bias is kind of already going down the wrong path.”

Generative AI is essentially about whether the output “corresponds to what the user expects it to” and that is largely subjective, she said.

The huge models on which ChatGPT is built “can’t reason about what is biased or what isn’t so they can’t do anything about it,” cautioned Jayden Ziegler, head of product at Alembic Technologies.

For now at least, it is up to humans to ensure that the AI generates whatever is appropriate or meets their expectations.

‘Baked in’ bias

But given the frenzy around AI, that is no easy task.

Hugging Face has about 600,000 AI or machine learning models available on its platform.

“Every couple of weeks a new model comes out and we’re kind of scrambling in order to try to just evaluate and document biases or undesirable behaviors,” said Luccioni.

One method under development is something called algorithmic disgorgement that would allow engineers to excise content, without ruining the whole model.

But there are serious doubts this can actually work.

Another method would “encourage” a model to go in the right direction, “fine tune” it, “rewarding for right and wrong,” said Ram Sriharsha, chief technology officer at Pinecone.

Pinecone is a specialist of retrieval augmented generation (or RAG), a technique where the model fetches information from a fixed trusted source.

For Weaver of the Texas Opportunity & Justice Incubator, these “noble” attempts to fix bias are “projections of our hopes and dreams for what a better version of the future can look like.”

But bias “is also inherent into what it means to be human and because of that, it’s also baked into the AI as well,” he said.

© 2024 AFP