AI & Robotics News and Discussions

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Yuli Ban
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Delivery robots set to roll out in Bloomfield, Lawrenceville this week
First came electric scooters, now come the robots.

The Pittsburgh Department of Mobility and Infrastructure announced Tuesday the next phase of a pilot program that would see delivery robots, or personal delivery devices (PDDs), on the streets of Bloomfield, Garfield and Lawrenceville delivering books, medicine and food to residents as early as this week.

Kiwibot, a Los Angeles-based sidewalk delivery startup, is sending 10 of their robots to Pittsburgh in a partnership funded by the Knight Foundation’s Autonomous Vehicle Initiative. According to a statement from the city, the pilot “will focus on bringing residents to the center of the conversation about this emerging technology.”

The city said the pilot will also help explore affordable delivery options for “last mile deliveries” to businesses, pharmacies and libraries. In supply chain management, last mile deliveries are the movement of goods from a transportation hub to their final destination.
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Yuli Ban
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UK publishes 10-year plan to become ‘A.I. superpower’, seeking to rival U.S. and China
The U.K. government on Wednesday released its 10-year plan to make the country a global “artificial intelligence superpower”, seeking to rival the likes of the U.S. and China.

The so-called “National Artificial Intelligence Strategy” is designed to boost the use of AI among the nation’s businesses, attract international investment into British AI companies and develop the next generation of homegrown tech talent.

“Today we’re laying the foundations for the next ten years’ growth with a strategy to help us seize the potential of artificial intelligence and play a leading role in shaping the way the world governs it,” Chris Philp, a minister of the Department for Digital, Culture, Media and Sport, said in a statement.
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Set and Meet Goals
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Yuli Ban wrote: Thu Sep 23, 2021 10:44 pm UK publishes 10-year plan to become ‘A.I. superpower’, seeking to rival U.S. and China
The U.K. government on Wednesday released its 10-year plan to make the country a global “artificial intelligence superpower”, seeking to rival the likes of the U.S. and China.

The so-called “National Artificial Intelligence Strategy” is designed to boost the use of AI among the nation’s businesses, attract international investment into British AI companies and develop the next generation of homegrown tech talent.

“Today we’re laying the foundations for the next ten years’ growth with a strategy to help us seize the potential of artificial intelligence and play a leading role in shaping the way the world governs it,” Chris Philp, a minister of the Department for Digital, Culture, Media and Sport, said in a statement.
I like the increased competition but dare I say I think the UK is delusional if they believe they can compete with the US or China. XD
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Neuroactivity of one million mouse brain neurons. (Alipasha Vaziri)
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A recent New York Times article concludes that new AI-powered automation tools such as Codex for software developers will not eliminate jobs but simply be a welcome aid to augment programmer productivity. This is consistent with the argument we’re increasingly hearing that people and AI have different strengths and there will be appropriate roles for each.

As discussed in a Harvard Business Review story: “AI-based machines are fast, more accurate, and consistently rational, but they aren’t intuitive, emotional, or culturally sensitive.” The belief is that “AI plus humans” is something of a centaur, greater than either one operating alone.

This idea of humans plus AI producing better outcomes has become a tenant of faith in technology. Everyone talks about humans being freed up to perform higher-level functions, but no one seems to know just what those high-level functions are, how they translate into real work and jobs, or the number of people needed to perform them.

A corollary of this augmented-workforce narrative is that not only will AI-augmented work enable people to pursue a higher level of abstract thinking, it will — according to some — also lift all of society to a higher standard of living. This is certainly an optimistic vision, and we can hope for that. However, this could also be a story imbued with magical thinking, with the true end-game being fully automated work.


Gonna make a long post about how I feel the near future of labor is going to go down. Basically up to 2029, and focusing more on the American reaction to what's coming. It's been a while since I made an extended post.
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Deep Learning’s Diminishing Returns
DEEP LEARNING IS NOW being used to translate between languages, predict how proteins fold, analyze medical scans, and play games as complex as Go, to name just a few applications of a technique that is now becoming pervasive. Success in those and other realms has brought this machine-learning technique from obscurity in the early 2000s to dominance today.

Although deep learning's rise to fame is relatively recent, its origins are not. In 1958, back when mainframe computers filled rooms and ran on vacuum tubes, knowledge of the interconnections between neurons in the brain inspired Frank Rosenblatt at Cornell to design the first artificial neural network, which he presciently described as a "pattern-recognizing device." But Rosenblatt's ambitions outpaced the capabilities of his era—and he knew it. Even his inaugural paper was forced to acknowledge the voracious appetite of neural networks for computational power, bemoaning that "as the number of connections in the network increases...the burden on a conventional digital computer soon becomes excessive."
gwern
My comment: this is a rehash of that MIT Arxiv paper which was circulating a while ago. The paper in question uses a very dumb methodology where instead of doing actual scaling law research (where you directly measure how much compute it takes to increase a specific model's performance on some error metric), they just dump a bunch of random Arxiv papers (all by different researchers, model architectures, goals etc) into a blender and try to deduce some sort of trend between compute and error rates. Unsurprisingly, because people are always publishing very disparate papers examining many different topics or aspects of something (many quite bad), this implies you need ~∞ compute to do much better. They do not recover known scaling laws and at least in the version I read, completely ignore the entire scaling literature. Garbage. (That Marcus is cheering on Twitter tells you everything you need to know.)

starspawn0
Seems like I've been reading this worry for years now, but the limits haven't yet been reached. And systems are doing pretty well, so far. Speech recognition is pretty good, for example; so is machine translation and image recognition. How much more improvement do we really need on these tasks??

Regarding systems built on "expert knowledge" that use less compute, that hides the amount of effort it took for humans to discover the rules used in those models. No fair to leave that out! Human effort + compute cycles may be greater than for a system trained from scratch using much less human effort, even though it learns inefficiently.

Quote:
Our analysis of this phenomenon also allowed us to compare what's actually happened with theoretical expectations. Theory tells us that computing needs to scale with at least the fourth power of the improvement in performance. In practice, the actual requirements have scaled with at least the ninth power.

This ninth power means that to halve the error rate, you can expect to need more than 500 times the computational resources.
It's heavily dependent on the type of data being used, though; and probably also depends heavily on the choice of loss function. I seem to recall some people from OpenAI giving a talk, where they said that scaling curves for image processing changed as you change the resolution of the images. Also, if the data has less noise, learning is usually quicker. Finally, there's possibility of new datasets arriving with the coming boom in BCIs -- these may produce even better scaling curves, still.

I would need to look at this article again, but I don't recall seeing mention of transfer learning. Mention is made of meta-learning, but I don't recall seeing transfer learning (I read it yesterday and may have forgotten). Transfer learning could severely reduce the amount of compute needed to learn new tasks.

I don't think shrinking the neural nets is the answer. That will just make them less robust -- and adding symbolic processing also won't help. The human brain still uses far more compute than any of these neural net models, and if we want to make AI that emulates it, we probably are going to have to use at least as much in AI models.
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The rumors were true. Amazon is working on an Alexa-powered robot on wheels. At its fall hardware event, the company showed off Astro. Set to initially cost $1,000 when it becomes available later this year, it's essentially an Alexa display that can roam around your home.

The robot features a periscope camera that allows it to expand its field of view beyond floor level. It can extend that camera to check on things like stovetops and sleeping pets. With Ring's Protect Pro subscription service, you can also program Astro to patrol your home while you're away. It can detect the sound of a smoke alarm, carbon monoxide detector or breaking glass. It will send you notifications when it notices something usual, and you can save what it records to your Ring account.

If only it could do something that useful.
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How Google plans to improve web searches with multimodal AI
During a livestreamed event today, Google detailed the ways it’s using AI techniques — specifically a machine learning algorithm called multitask unified model (MUM) — to enhance web search experiences across different languages and devices. Beginning early next year, Google Lens, the company’s image recognition technology, will gain the ability to find objects like apparel based on photos and high-level descriptions. Around the same time, Google Search users will begin seeing an AI-curated list of things they should know about certain topics, like acrylic paint materials. They’ll also see suggestions to refine or broaden searches based on the topic in question, as well as related topics in videos discovered through Search.

The upgrades are the fruit of a multiyear effort at Google to improve Search and Lens’ understanding of how language relates to visuals from the web. According to Google VP of Search Pandu Nayak, MUM, which Google detailed at a developer conference last June, could help better connect users to businesses by surfacing products and reviews and improving “all kinds” of language understanding, whether at the customer service level or in a research setting.

“The power of MUM is its ability to understand information on a broad level. It’s intrinsically multimodal — that is, it can handle text, images, and videos all at the same time,” Nayak told VentureBeat in a phone interview. “It holds out the promise that we can ask very complex queries and break them down into a set of simpler components, where you can get results for the different, simpler queries and then stitch them together to understand what you really want.”
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