Lifeboat Foundation

It’s no surprise that machines have the same problem. Although they’re armed with a myriad of sensors, self-driving cars are still trying to live up to their name. They perform well under perfect weather conditions and roads with clear traffic lanes. But ask the cars to drive in heavy rain or fog, smoke from wildfires, or on roads without streetlights, and they struggle.

This month, a team from Purdue University tackled the low visibility problem head-on. Combining thermal imaging, physics, and machine learning, their technology allowed a visual AI system to see in the dark as if it were daylight.

Continue reading “New Night Vision Tech Lets AI See in Pitch Darkness Like It’s Broad Daylight” »

The term ‘Expensive optimization problem’ (EOP) refers to any problem that requires expensive or even unaffordable costs to evaluate candidate solutions. These problems exist in many significant real-world applications.

On the one hand, the “expensive cost” can refer that an evaluation itself that requires abundant time, money and so on. On the other hand, the “expensive cost” is a relative concept rather than an absolute concept in many real-world problems.

For instance, when encountering emergencies like epidemics or natural disasters, transportation and dispatching can be urgent for supporting daily operations and saving lives, where the time cost of optimization will become too expensive to accept at this time.

Warren Buffett missed a trick when he passed on Tesla early on, Elon Musk said.

“He could’ve invested in Tesla when we were worth basically nothing and didn’t,” the SpaceX and Tesla CEO posted on X, the website formerly called Twitter, on Thursday.

He was responding to a post highlighting Buffett’s vast wealth and the enormous value of his Berkshire Hathaway conglomerate.

A subsidy-fueled boom helped build China into an electric-car giant, but with an economic slowdown and hundreds of ride-hailing companies going bust, the country is facing a excess of unwanted batter-powered vehicles. Linda Lew reports on Bloomberg Television.
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Nvidia (NVDA) will report its second quarter earnings after the closing bell next Wednesday, setting up what will be the AI hype cycle’s biggest test yet. During this AI gold rush, companies around the world looking to profit have turned to Nvidia’s graphics processors to power new AI software and platforms.

Currently, tech firms of all sizes are doing everything they can to get their hands on Nvidia chips. During Tesla’s (TSLA) Q2 earnings call, CEO Elon Musk told analysts that the automaker will take as many Nvidia graphics processors as the company can produce.

Nvidia is widely expected to have a blowout earnings report. A miss could derail the AI hype train.

Continue reading “Nvidia’s earnings will be the AI hype cycle’s biggest test” »

Called “Pibot,” this humanoid robot integrates large language models to help it fly any aircraft as well as, if not better than, a human pilot.

Researchers at the Korea Advanced Institute of Science & Technology (KAIST) are working to develop a humanoid pilot that can fly an aircraft without modifying the cockpit. Called “Pibot,” the robot has articulated arms and fingers that can interact with flight controls with great precision and dexterity. It also comes with camera “eyes” that help the robot monitor the internal and external conditions of the aircraft while in control.

Continue reading “Meet Pibot: Korea’s LLM-powered smart robotic pilot” »

The robot’s memory is so large that it can memorise all Jeppesen navigation charts, a task that is impossible for human pilots.

Both artificial intelligence (AI) and robotics have made significant strides in recent years, meaning most human jobs could soon be overtaken by technology — on the ground and even in the skies above us.

A team of engineers and researchers from the Korea Advanced Institute of Science & Technology (KAIST) is currently developing a humanoid robot that can fly aircraft without needing to modify the cockpit.

We’ve seen a lot about large learning models in general, and a lot of that has been elucidated at this conference, but many of the speakers have great personal takes on how this type of process works, and what it can do!

For example, here we have Yoon Kim talking about statistical objects, and the use of neural networks (transformer-based neural networks in particular) to use next-word prediction in versatile ways. He uses the example of the location of MIT:

“You might have a sentence like: ‘the Massachusetts Institute of Technology is a private land grant research university’ … and then you train this language model (around it),” he says. “Again, (it takes) a large neural network to predict the next word, which, in this case, is ‘Cambridge.’ And in some sense, to be able to accurately predict the next word, it does require this language model to store knowledge of the world, for example, that must store factoid knowledge, like the fact that MIT is in Cambridge. And it must store … linguistic knowledge. For example, to be able to pick the word ‘Cambridge,’ it must know what the subject, the verb and the object of the preceding or the current sentence is. But these are, in some sense, fancy autocomplete systems.”

A new airplane seat concept that allows wheelchair users to stay in their own chair throughout a flight was revealed this week by a subsidiary of US airline Delta, a move welcomed as a “huge step” by potential customers.

“Unbelievably excited,” is how power wheelchair user and avid traveler Cory Lee described his reaction after a working prototype of the design was demonstrated by Delta Flight Products (DFP) at the Aircraft Interiors Expo (AIX) in Hamburg, Germany, a symposium spotlighting airplane cabin innovations.

DFP’s concept seamlessly converts to and from a traditional airplane seat. The built-in seat folds up to allow a wheelchair to be docked into place. The seat would be installed into pre-existing aircraft seat track systems, so would not involve any structural change to the airplane.