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Category: robotics/AI – Page 1003

Autonomous aircraft have long been thought of as having the most potential, though not in the realm of glitzy people-carrying drones so much as the more sedate world of cargo. It’s here where the economic savings could be most significant. Large, long-range drones built specifically for cargo have the potential to be faster, cheaper and produce fewer CO2 emissions than conventional aircraft, enabling same-day shipping over very long distances. In fact, the “flying delivering van” is considered the holy grail by many cargo operators.

In this space there are a number of companies operating, and these include: ElroyAir (California, raised $56 million), hybrid electric, VTOL, so so therefore short range; Natilus (California, funding undisclosed) uses a blended wing body, and is a large, longer-term project entailing probably quite high costs in certification and production; and Beta (Vermont, $886 million raised), which is an electric VTOL.

Into this space, out of Bulgaria (but HQ’d in London), comes Dronamics. The startup has already attained a license to operate in Europe, and plans to run a “cargo drone airline” using drones built specifically for the purpose. Dronamics claims its flagship “Black Swan” model will be able to carry 350 kg (770 lb) at a distance of up to 2,500 km (1,550 miles) faster, cheaper and with less emissions than currently available options.

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A second problem is the risk of technological job loss. This is not a new worry; people have been complaining about it since the loom, and the arguments surrounding it have become stylized: critics are Luddites who hate progress. Whither the chandlers, the lamplighters, the hansom cabbies? When technology closes one door, it opens another, and the flow of human energy and talent is simply redirected. As Joseph Schumpeter famously said, it is all just part of the creative destruction of capitalism. Even the looming prospect of self-driving trucks putting 3.5 million US truck drivers out of a job is business as usual. Unemployed truckers can just learn to code instead, right?

Those familiar replies make sense only if there are always things left for people to do, jobs that can’t be automated or done by computers. Now AI is coming for the knowledge economy as well, and the domain of humans-only jobs is dwindling absolutely, not merely morphing into something new. The truckers can learn to code, and when AI takes that over, coders can… do something or other. On the other hand, while technological unemployment may be long-term, its problematicity might be short-term. If our AI future is genuinely as unpredictable and as revolutionary as I suspect, then even the sort of economic system we will have in that future is unknown.

A third problem is the threat of student dishonesty. During a conversation about GPT-3, a math professor told me “welcome to my world.” Mathematicians have long fought a losing battle against tools like Photomath, which allows students to snap a photo of their homework and then instantly solves it for them, showing all the needed steps. Now AI has come for the humanities and indeed for everyone. I have seen many university faculty insist that AI surely could not respond to their hyper-specific writing prompts, or assert that at best an AI could only write a barely passing paper, or appeal to this or that software that claims to spot AI products. Other researchers are trying to develop encrypted watermarks to identify AI output. All of this desperate optimism smacks of nothing more than the first stage of grief: denial.

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A fully-connected annealer extendable to a multi-chip system and featuring a multi-policy mechanism has been designed by Tokyo Tech researchers to solve a broad class of combinatorial optimization (CO) problems relevant to real-world scenarios quickly and efficiently. Named Amorphica, the annealer has the ability to fine-tune parameters according to a specific target CO problem and has potential applications in logistics, finance, machine learning, and so on.

The has grown accustomed to an efficient delivery of goods right at our doorsteps. But did you know that realizing such an efficiency requires solving a mathematical problem, namely what is the best possible route between all the destinations? Known as the “traveling salesman problem,” this belongs to a class of mathematical problems known as “combinatorial optimization” (CO) problems.

As the number of destinations increases, the number of possible routes grows exponentially, and a brute force method based on exhaustive search for the best route becomes impractical. Instead, an approach called “annealing computation” is adopted to find the best route quickly without an exhaustive search.

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Also weighing in with an online essay was the Rev. Russell Moore, formerly head of the Southern Baptist Convention’s public policy division and now editor-in-chief of the evangelical magazine Christianity Today. He confided to his readers that his first sermon, delivered at age 12, was a well-intentioned mess.

“Preaching needs someone who knows the text and can convey that to the people — but it’s not just about transmitting information,” Moore wrote. “When we listen to the Word preached, we are hearing not just a word about God but a word from God.”

“Such life-altering news needs to be delivered by a human, in person,” he added. “A chatbot can research. A chatbot can write. Perhaps a chatbot can even orate. But a chatbot can’t preach.”

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“OpenAI was created as an open source (which is why I named it ‘Open’ AI), non-profit company to serve as a counterweight to Google, but now it has become a closed source, maximum-profit company effectively controlled by Microsoft,” Musk tweeted (Opens in a new window) today. “Not what I intended at all.”

OpenAI this week acknowledged that its process for “fine-tuning” ChatGPT is “imperfect.”

“Sometimes the fine-tuning process falls short of our intent (producing a safe and useful tool) and the user’s intent (getting a helpful output in response to a given input),” OpenAI says (Opens in a new window). “Improving our methods for aligning AI systems with human values is a top priority (Opens in a new window) for our company, particularly as AI systems become more capable.”

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Today, Roblox provides creators with a platform that enables end-to-end tools, services, and support to help them build the most immersive 3D experiences. With Roblox Studio, creators have everything they need, out-of-the-box and for free, to build their experiences and publish immediately on all popular platforms, reaching 61 million people daily worldwide. With the advent of generative AI techniques, however, we are seeing an opportunity to revolutionize creation on the platform, both by augmenting Roblox Studio to make creation dramatically faster and easier, and also by enabling every user on Roblox to be a creator.

As we all know, generative AI learns the underlying patterns and structures of data and generates new content, such as images, audio, code, text, 3D models, or other forms of media, that have not been seen before. With a dramatic acceleration in these tools’ effectiveness for everyday content creation, this technology is at an inflection point. It now has the capability to capture the creator’s intent, provide a broad range of digital editing capabilities, help create the content, and allow for fast iteration. We have already heard from Roblox creators about how they are using this technology to create. However, these off-the-shelf AI systems are not integrated with our platform and they often do not produce “Roblox ready” output that requires substantial follow on work from a creator. We see an incredible opportunity to build generative AI tools and APIs focused on Roblox.

Read more about Generative AI on our official blog: https://blog.roblox.com/2023/02/generative-ai-on…ture-of-creation/

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