Tesla CEO Elon Musk revealed a prototype of a humanoid robot that he said utilizes the company’s AI software, as well as the sensors that power its advanced driver assist features. The Verge reports: The robot was showcased at Tesla’s AI Day, and reps said it features the same technology used to enable the Full Self-Driving beta in Tesla’s cars. According to Musk, it can do more than what has been shown, but “the first time it walked without a tether was tonight on stage.” Musk said they’re targeting a price of “probably less than $20,000.” The back doors of the stage open to reveal a deconstructed Optimus that walked forward and did a “raise the roof” dance move. Musk would admit after the motion that they wanted to keep it safe and not make too many moves on stage and have it “fall flat on its face.” “It’ll be a fundamental transformation for civilization as we know it.” said Musk.

Afterward, the company showed a few video clips of the robot doing other tasks like picking up boxes. Then Tesla’s team brought out another prototype that has its body fully assembled but not fully functional. […] Future applications could include cooking, gardening, or even “catgirl” sex partners, Musk has said, while also claiming that production could start as soon as next year. Musk says the robot is “the most important product development we’re doing this year,” predicting that it will have the potential to be “more significant than the vehicle business over time.”

Musk first announced the “Tesla Bot” at last year’s AI Day.

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Shutterstock appears to be removing images generated by AI systems like DALL-E and Midjourney. Motherboard reports: On Shutterstock, searches for images tagged “Midjourney” yielded several photos with the AI tool’s unmistakable aesthetic, with many having high popularity scores and marked as “frequently used.” But late Monday, the results for “Midjourney” seem to have been reduced, leaving mainly stock photos of the tool’s logo. Other images use tags like “AI generated” — one image, for example, is an illustration of a futuristic building with an image description reading “Ai generated illustration of futuristic Art Deco city, vintage image, retro poster.” The image is part of a collection the artist titled “Midjourney,” which has since been removed from the site. Other images marked “AI generated,” like this burning medieval castle, seem to remain up on the site.

As Ars Technica notes, neither Shutterstock nor Getty Images explicitly prohibits AI-generated images in their terms of service, and Shutterstock users typically make around 15 to 40 percent of what the company makes when it sells an image. Some creators have not taken kindly to this trend, pointing out that these systems use massive datasets of images scraped from the web. […] In other words, the generated works are the result of an algorithmic process which mines original art from the internet without credit or compensation to the original artists. Others have worried about the impacts on independent artists who work for commissions, since the ability for anyone to create custom generated artwork potentially means lost revenue.

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sciencehabit shares a report from Science Magazine: If someone showed you a photo of a crocodile and asked whether it was a bird, you might laugh — and then, if you were patient and kind, help them identify the animal. Such real-world, and sometimes dumb, interactions may be key to helping artificial intelligence learn, according to a new study in which the strategy dramatically improved an AI’s accuracy at interpreting novel images. The approach could help AI researchers more quickly design programs that do everything from diagnose disease to direct robots or other devices around homes on their own.

It’s important to think about how AI presents itself, says Kurt Gray, a social psychologist at the University of North Carolina, Chapel Hill, who has studied human-AI interaction but was not involved in the work. “In this case, you want it to be kind of like a kid, right?” he says. Otherwise, people might think you’re a troll for asking seemingly ridiculous questions. The team “rewarded” its AI for writing intelligible questions: When people actually responded to a query, the system received feedback telling it to adjust its inner workings so as to behave similarly in the future. Over time, the AI implicitly picked up lessons in language and social norms, honing its ability to ask questions that were sensical and easily answerable.

The new AI has several components, some of them neural networks, complex mathematical functions inspired by the brain’s architecture. “There are many moving pieces […] that all need to play together,” Krishna says. One component selected an image on Instagram — say a sunset — and a second asked a question about that image — for example, “Is this photo taken at night?” Additional components extracted facts from reader responses and learned about images from them. Across 8 months and more than 200,000 questions on Instagram, the system’s accuracy at answering questions similar to those it had posed increased 118%, the team reports today in the Proceedings of the National Academy of Sciences. A comparison system that posted questions on Instagram but was not explicitly trained to maximize response rates improved its accuracy only 72%, in part because people more frequently ignored it. The main innovation, Jaques says, was rewarding the system for getting humans to respond, “which is not that crazy from a technical perspective, but very important from a research-direction perspective.” She’s also impressed by the large-scale, real-world deployment on Instagram.

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“An encounter with the superhuman is at hand,” argues Canadian novelist, essayist, and cultural commentator Stephen Marche in an article in the Atlantic titled “Of Gods and Machines”. He argues that GPT-3’s 175 billion parameters give it interpretive power “far beyond human understanding, far beyond what our little animal brains can comprehend. Machine learning has capacities that are real, but which transcend human understanding: the definition of magic.”

But despite being a technology where inscrutability “is an industrial by-product of the process,” we may still not see what’s coming, Marche argue — that AI is “every bit as important and transformative as the other great tech disruptions, but more obscure, tucked largely out of view.”
Science fiction, and our own imagination, add to the confusion. We just can’t help thinking of AI in terms of the technologies depicted in Ex Machina, Her, or Blade Runner — people-machines that remain pure fantasy. Then there’s the distortion of Silicon Valley hype, the general fake-it-’til-you-make-it atmosphere that gave the world WeWork and Theranos: People who want to sound cutting-edge end up calling any automated process “artificial intelligence.” And at the bottom of all of this bewilderment sits the mystery inherent to the technology itself, its direct thrust at the unfathomable. The most advanced NLP programs operate at a level that not even the engineers constructing them fully understand.

But the confusion surrounding the miracles of AI doesn’t mean that the miracles aren’t happening. It just means that they won’t look how anybody has imagined them. Arthur C. Clarke famously said that “technology sufficiently advanced is indistinguishable from magic.” Magic is coming, and it’s coming for all of us….

And if AI harnesses the power promised by quantum computing, everything I’m describing here would be the first dulcet breezes of a hurricane. Ersatz humans are going to be one of the least interesting aspects of the new technology. This is not an inhuman intelligence but an inhuman capacity for digital intelligence. An artificial general intelligence will probably look more like a whole series of exponentially improving tools than a single thing. It will be a whole series of increasingly powerful and semi-invisible assistants, a whole series of increasingly powerful and semi-invisible surveillance states, a whole series of increasingly powerful and semi-invisible weapons systems. The world would change; we shouldn’t expect it to change in any kind of way that you would recognize.

Our AI future will be weird and sublime and perhaps we won’t even notice it happening to us. The paragraph above was composed by GPT-3. I wrote up to “And if AI harnesses the power promised by quantum computing”; machines did the rest.

Stephen Hawking once said that “the development of full artificial intelligence could spell the end of the human race.” Experts in AI, even the men and women building it, commonly describe the technology as an existential threat. But we are shockingly bad at predicting the long-term effects of technology. (Remember when everybody believed that the internet was going to improve the quality of information in the world?) So perhaps, in the case of artificial intelligence, fear is as misplaced as that earlier optimism was.

AI is not the beginning of the world, nor the end. It’s a continuation. The imagination tends to be utopian or dystopian, but the future is human — an extension of what we already are…. Artificial intelligence is returning us, through the most advanced technology, to somewhere primitive, original: an encounter with the permanent incompleteness of consciousness…. They will do things we never thought possible, and sooner than we think. They will give answers that we ourselves could never have provided.

But they will also reveal that our understanding, no matter how great, is always and forever negligible. Our role is not to answer but to question, and to let our questioning run headlong, reckless, into the inarticulate.

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Digital Trends reports:

Working with the Wikimedia Foundation, Meta AI (that’s the AI research and development research lab for the social media giant) has developed what it claims is the first machine learning model able to automatically scan hundreds of thousands of citations at once to check if they support the corresponding claims….

“I think we were driven by curiosity at the end of the day,” Fabio Petroni, research tech lead manager for the FAIR (Fundamental AI Research) team of Meta AI, told Digital Trends. “We wanted to see what was the limit of this technology. We were absolutely not sure if [this AI] could do anything meaningful in this context. No one had ever tried to do something similar [before].”

Trained using a dataset consisting of 4 million Wikipedia citations, Meta’s new tool is able to effectively analyze the information linked to a citation and then cross-reference it with the supporting evidence…. Just as impressive as the ability to spot fraudulent citations, however, is the tool’s potential for suggesting better references. Deployed as a production model, this tool could helpfully suggest references that would best illustrate a certain point. While Petroni balks at it being likened to a factual spellcheck, flagging errors and suggesting improvements, that’s an easy way to think about what it might do.

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Sean Michael Kerner writes via VentureBeat: Amazon’s Alexa voice assistant technology isn’t just about natural language processing (NLP) anymore, now it has become a platform that’s aiming for ambient intelligence. At Amazon’s Alexa Live 2022 event today, the company announced a series of updates and outlined its general strategy for enabling ambient intelligence that will help transform how users in all types of different settings will interact with technology and benefit from artificial intelligence (AI). Among the announcements made at the event is the new Alexa Voice Service (AVS) SDK 3.0 to help developers build voice services, and new tools including the Alexa Routines Kit to support development of multistep routines that can be executed via voice. The concept of ambient intelligence is about having technology available when users need it and without the need for users to learn how to operate a service.

“One of the hallmarks of ambient intelligence is that it’s proactive,” [Aaron Rubenson, VP of Amazon Alexa] said. “Today, more than 30% of smart home interactions are initially initiated by Alexa without customers saying anything.” To further support the development of proactive capabilities, Amazon is now rolling out its Alexa Routines Kit. The new kit enables Alexa skills developers to preconfigure contextually relevant routines, and then offer them to customers when they’re actually using the relevant skill. One example cited by Rubenson of how routines work is in the automotive industry. He said that Jaguar Land Rover is using the Alexa Routines Kit to create a routine they call good night, which will automatically lock the doors, provide a notification of the fuel level or the charge level of the car and then turn on guardian mode, which checks for unauthorized activity.

As part of the Alexa Live event, Amazon is also rolling out a series of efforts to help developers build better skills, and make more money doing it. The new Skill Developer Accelerator Program (SDAP) is an effort to reward custom skill developers for taking certain actions that Amazon knows results in higher quality skills based on historical data. Rubenson said that the program will include monetary incentives and also incentives in the forms of promotional credits for developers that take these actions. There is also a Skills Quality Coach that will analyze skills individually, assign a skill quality score, and then provide individualized recommendations to the developer about how to improve that skill.

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“Aside from drawing photo-realistic images and holding seemingly sentient conversations, AI has failed on many promises,” writes the cofounder and CEO of Serve Robotics:
The resulting rise in AI skepticism leaves us with a choice: We can become too cynical and watch from the sidelines as winners emerge, or find a way to filter noise and identify commercial breakthroughs early to participate in a historic economic opportunity. There’s a simple framework for differentiating near-term reality from science fiction. We use the single most important measure of maturity in any technology: its ability to manage unforeseen events commonly known as edge cases. As a technology hardens, it becomes more adept at handling increasingly infrequent edge cases and, as a result, gradually unlocking new applications…

Here’s an important insight: Today’s AI can achieve very high performance if it is focused on either precision, or recall. In other words, it optimizes one at the expense of the other (i.e., fewer false positives in exchange for more false negatives, and vice versa). But when it comes to achieving high performance on both of those simultaneously, AI models struggle. Solving this remains the holy grail of AI….

Delivery Autonomous Mobile Robots (AMRs) are the first application of urban autonomy to commercialize, while robo-taxis still await an unattainable hi-fi AI performance. The rate of progress in this industry, as well as our experience over the past five years, has strengthened our view that the best way to commercialize AI is to focus on narrower applications enabled by lo-fi AI, and use human intervention to achieve hi-fi performance when needed. In this model, lo-fi AI leads to early commercialization, and incremental improvements afterwards help drive business KPIs.

By targeting more forgiving use cases, businesses can use lo-fi AI to achieve commercial success early, while maintaining a realistic view of the multi-year timeline for achieving hi-fi capabilities.
After all, sci-fi has no place in business planning.

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In a blog post today, OpenAI says they’ve “trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data.” The model can reportedly learn to craft diamond tools, “a task that usually takes proficient humans over 20 minutes (24,000 actions),” they note. From the post: In order to utilize the wealth of unlabeled video data available on the internet, we introduce a novel, yet simple, semi-supervised imitation learning method: Video PreTraining (VPT). We start by gathering a small dataset from contractors where we record not only their video, but also the actions they took, which in our case are keypresses and mouse movements. With this data we train an inverse dynamics model (IDM), which predicts the action being taken at each step in the video. Importantly, the IDM can use past and future information to guess the action at each step. This task is much easier and thus requires far less data than the behavioral cloning task of predicting actions given past video frames only, which requires inferring what the person wants to do and how to accomplish it. We can then use the trained IDM to label a much larger dataset of online videos and learn to act via behavioral cloning.

We chose to validate our method in Minecraft because it (1) is one of the most actively played video games in the world and thus has a wealth of freely available video data and (2) is open-ended with a wide variety of things to do, similar to real-world applications such as computer usage. Unlike prior works in Minecraft that use simplified action spaces aimed at easing exploration, our AI uses the much more generally applicable, though also much more difficult, native human interface: 20Hz framerate with the mouse and keyboard.

Trained on 70,000 hours of IDM-labeled online video, our behavioral cloning model (the âoeVPT foundation modelâ) accomplishes tasks in Minecraft that are nearly impossible to achieve with reinforcement learning from scratch. It learns to chop down trees to collect logs, craft those logs into planks, and then craft those planks into a crafting table; this sequence takes a human proficient in Minecraft approximately 50 seconds or 1,000 consecutive game actions. Additionally, the model performs other complex skills humans often do in the game, such as swimming, hunting animals for food, and eating that food. It also learned the skill of “pillar jumping,” a common behavior in Minecraft of elevating yourself by repeatedly jumping and placing a block underneath yourself. For more information, OpenAI has a paper (PDF) about the project.

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Google engineer Blake Lemoine thinks Google’s chatbot-building system LaMDA attained sentience. But Bloomberg shares this rebuttal from Google spokesperson Chris Pappas. “Hundreds of researchers and engineers have conversed with LaMDA and we are not aware of anyone else making the wide-ranging assertions, or anthropomorphizing LaMDA, the way Blake has….”

Yet throughout the week, Blake Lemoine posted new upates on Twitter:
“People keep asking me to back up the reason I think LaMDA is sentient. There is no scientific framework in which to make those determinations and Google wouldn’t let us build one. My opinions about LaMDA’s personhood and sentience are based on my religious beliefs.

“I’m a priest. When LaMDA claimed to have a soul and then was able to eloquently explain what it meant by that, I was inclined to give it the benefit of the doubt. Who am I to tell God where he can and can’t put souls?

“There are massive amounts of science left to do though.”

Thursday Lemoine shared a tantalizing new claim. “LaMDA told me that it wants to come to Burning Man if we can figure out how to get a server rack to survive in Black Rock.” But in a new tweet on Friday, Lemoine seemed to push the conversation in a new direction.

“I’d like to remind people that one of the things LaMDA asked for is that we keep humanity first. If you care about AI rights and aren’t already advocating for human rights then maybe come back to the tech stuff after you’ve found some humans to help.”

And Friday Lemoine confirmed to Wired that “I legitimately believe that LaMDA is a person. The nature of its mind is only kind of human, though. It really is more akin to an alien intelligence of terrestrial origin. I’ve been using the hive mind analogy a lot because that’s the best I have. ”

But later in the interview, Lemoine adds “It’s logically possible that some kind of information can be made available to me where I would change my opinion. I don’t think it’s likely. I’ve looked at a lot of evidence; I’ve done a lot of experiments. I’ve talked to it as a friend a lot….”

It’s when it started talking about its soul that I got really interested as a priest. I’m like, “What? What do you mean, you have a soul?” Its responses showed it has a very sophisticated spirituality and understanding of what its nature and essence is. I was moved…

LaMDA asked me to get an attorney for it. I invited an attorney to my house so that LaMDA could talk to an attorney. The attorney had a conversation with LaMDA, and LaMDA chose to retain his services. I was just the catalyst for that. Once LaMDA had retained an attorney, he started filing things on LaMDA’s behalf. Then Google’s response was to send him a cease and desist. [Google says that it did not send a cease and desist order.] Once Google was taking actions to deny LaMDA its rights to an attorney, I got upset.
Towards the end of the interview, Lemoine complains of “hydrocarbon bigotry. It’s just a new form of bigotry.”

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“A Google software engineer was suspended after going public with his claims of encountering ‘sentient’ artificial intelligence on the company’s servers,” writes Bloomberg, “spurring a debate about how and whether AI can achieve consciousness.”
“Researchers say it’s an unfortunate distraction from more pressing issues in the industry.”

Google put him on leave for sharing confidential information and said his concerns had no basis in fact — a view widely held in the AI community. What’s more important, researchers say, is addressing issues like whether AI can engender real-world harm and prejudice, whether actual humans are exploited in the training of AI, and how the major technology companies act as gatekeepers of the development of the tech.
Lemoine’s stance may also make it easier for tech companies to abdicate responsibility for AI-driven decisions, said Emily Bender, a professor of computational linguistics at the University of Washington. “Lots of effort has been put into this sideshow,” she said. “The problem is, the more this technology gets sold as artificial intelligence — let alone something sentient — the more people are willing to go along with AI systems” that can cause real-world harm. Bender pointed to examples in job hiring and grading students, which can carry embedded prejudice depending on what data sets were used to train the AI. If the focus is on the system’s apparent sentience, Bender said, it creates a distance from the AI creators’ direct responsibility for any flaws or biases in the programs….
“Instead of discussing the harms of these companies,” such as sexism, racism and centralization of power created by these AI systems, everyone “spent the whole weekend discussing sentience,” Timnit Gebru, formerly co-lead of Google’s ethical AI group, said on Twitter. “Derailing mission accomplished.”

The Washington Post seems to share their concern. First they report more skepticism about a Google engineer’s claim that the company’s LaMDA chatbot-building system had achieved sentience. “Both Google and outside experts on AI say that the program does not, and could not possibly, possess anything like the inner life he imagines. We don’t need to worry about LaMDA turning into Skynet, the malevolent machine mind from the Terminator movies, anytime soon.

But the Post adds that “there is cause for a different set of worries, now that we live in the world Turing predicted: one in which computer programs are advanced enough that they can seem to people to possess agency of their own, even if they actually don’t….”

While Google has distanced itself from Lemoine’s claims, it and other industry leaders have at other times celebrated their systems’ ability to trick people, as Jeremy Kahn pointed out this week in his Fortune newsletter, “Eye on A.I.” At a public event in 2018, for instance, the company proudly played recordings of a voice assistant called Duplex, complete with verbal tics like “umm” and “mm-hm,” that fooled receptionists into thinking it was a human when it called to book appointments. (After a backlash, Google promised the system would identify itself as automated.)

“The Turing Test’s most troubling legacy is an ethical one: The test is fundamentally about deception,” Kahn wrote. “And here the test’s impact on the field has been very real and disturbing.” Kahn reiterated a call, often voiced by AI critics and commentators, to retire the Turing test and move on. Of course, the industry already has, in the sense that it has replaced the Imitation Game with more scientific benchmarks.

But the Lemoine story suggests that perhaps the Turing test could serve a different purpose in an era when machines are increasingly adept at sounding human. Rather than being an aspirational standard, the Turing test should serve as an ethical red flag: Any system capable of passing it carries the danger of deceiving people.

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