Elon Musk proposes interplanetary rocket and plans Mars colonization
Speaking at the...
SpaceX founder and CEO Elon Musk has revealed his plans to colonize Mars by pumping all of its resources and revenue into the project.
Speaking at the Internation Astronautical Congress - which he preempted by tweeting that he would reveal "something really special" - Musk detailed his idea of an interplanetary rocket that could travel around the world in under an hour.
Musk's Interplanetary Transport System (nicknamed BFR) that would be used to travel to Mars and the Moon, as well as travel a variety of routes across Earth for the same price as an economy airline ticket.
In a video demonstration, passengers boarded a boat in New York City that took them to an offshore launch platform, before travelling at 27,000km/h and landing at their destination in Shanghai 39 minutes later.
Musk himself said, however, that the most important point of the whole project is figuring out how to pay for it.
"We think we've found a way to do it. We think we can fund this," Musk announced.
"We believe we can build BFR with the revenue we receive from launching satellites and servicing the space station."
"This system would cannibalise our own products. It would make the Falcon 9, the Falcon Heavy and the Dragon all redundant. All of that resource would then be applied to this system. That is fundamental."
The project timeline for a Mars mission has actually been boosted and accelerated - last year, Musk predicted a mission to the Red Planet would happen in 2024. Now, he says the first ITS ships would launch in 2022.
SpaceX would also use the new rocket system to run missions to the Moon, with US Vice President Mike Pence recently hinting that NASA may return there soon.
Beginning his speech by talking about becoming a multiplanet species, Musk said it is all about "believing that the future will be better than the past."
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.