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PARIS, July 11 (Reuters) - More than a quarter of jobs in the OECD rely on skills that could be easily automated in the coming artificial intelligence revolution, and workers fear they could lose their jobs to AI, the OECD said on Tuesday.

The Organisation for Economic Co-operation and Development (OECD) is a 38-member bloc, spanning mostly wealthy nations but also some emerging economies like Mexico and Estonia.

There is little evidence the emergence of AI is having a significant impact on jobs so far, but that may be because the revolution is in its early stages, the OECD said.

Jobs with the highest risk of being automated make up 27% of the labour force on average in OECD countries, with eastern European countries most exposed, the Paris-based organisation said in its 2023 Employment Outlook.

Jobs at highest risk were defined as those using more than 25 of the 100 skills and abilities that AI experts consider can be easily automated.

Three out of five workers meanwhile fear that they could lose their job to AI over the next 10 years, the OECD found in a survey last year. The survey covered 5,300 workers in 2,000 firms spanning manufacturing and finance across seven OECD countries.

The survey was carried out before the explosive emergence of generative AI like ChatGPT.

Despite the anxiety over the advent of AI, two-thirds of workers already working with it said that automation had made their jobs less dangerous or tedious.

"How AI will ultimately impact workers in the workplace and whether the benefits will outweigh the risks, will depend on the policy actions we take," OECD Secretary General Mathias Cormann told a news conference.

"Governments must help workers to prepare for the changes and benefit from the opportunities AI will bring about," he continued.

Minimum wages and collective bargaining could help ease the pressure that AI could put on wages while governments and regulators need to ensure workers rights are not compromised, the OECD said.

Reporting by Leigh Thomas; Editing by Emma Rumney

Our Standards: The Thomson Reuters Trust Principles.

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27% of jobs at high risk from AI revolution, says OECD - Reuters

An AI tool called SLEAP labels the body parts of flies. Credit: Talmo Lab at the Salk Institute

AI-enabled micro-measurements of animals running, hunting, preening and playing are unlocking troves of new data that scientists now want to use to simulate animals and test theories about behavior and the brain.

Why it matters: A primary function of the brain is to produce behavior and help animals move through the world but there are questions about how that happens, with ramifications for medicine and efforts to create artificial general intelligence (AGI).

What's happening: AI methods are increasingly being used to help scientists measure the behaviors of animals, a laborious task that typically involves researchers watching animals and tracking and annotating their movements.

Animal behavior scientists, or ethologists, use the AI tools to track the natural behaviors of single and more recently multiple animals. The information can be used to recreate the behavior in a lab, where at the same time researchers can measure the activity of neurons in the brain or silence them, and see the effect on behavior.

The AI tools are "really powerful because you are getting behavior quantification at the scale the brain works at millisecond precision," says Cory Miller, a neurobiologist at the University of California San Diego, who studies the neural mechanisms of behaviors in marmoset monkeys.

Another tool called MoSeq finds smaller components of movement what the tool's developer, Harvard University neurobiologist Bob Datta, calls "syllables." His research group has identified about 50 of these short units of behavior and the sequences in which they tend to occur in order to identify and predict different behaviors.

Yes, but: The brain does not output coordinates, Pereira says. "It does not think in x,y, z changes in position of wrist."

What's next: An effort in its early stages is underway to use behavioral data to create simulated bodies, or animals.

Between the lines: Building these simulated animals will likely require combining the different approaches of AI models used to track and developing new tools, Datta says.

The big picture: There is an active debate about whether any artificial general intelligence will need to be embodied.

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AI tools trace the body's link between the brain and behavior - Axios

(Bloomberg) -- Silicon Valley venture capitalists are racing to get into artificial intelligence companies including investors who once bet big on crypto.

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In the most recent quarter, VCs spent less on crypto and digital asset companies than at any point since 2020, according to data from the research firm PitchBook. At the same time, the total global value of investments in AI for the April-June period was higher than crypto even at its peak.

The numbers reflect the recent tendency of many tech investors even those who once backed crypto to move away from the scandal-plagued digital asset industry toward the latest advances in AI, said Robert Le, a crypto analyst at PitchBook.

New developments in AI have made it highly attractive to investors, at the same time as a barrage of scandals, new regulations and tumbling prices have made crypto toxic. Joe Zhao, Millennia Capital managing partner, said hes ready to leave digital assets behind. Zhao and Millennia once backed crypto companies like Blockstream and Lumida; now theyre diving further into artificial intelligence. Millennia has already invested in closely watched startups including Stability AI.

AI is offering so many more use cases than blockchain, Zhao said.

Some crypto investors have come under fire for kindling an interest in AI. Paradigm, the high-profile crypto VC firm started by Coinbase Global Inc. co-founder Fred Ehrsam and former Sequoia Capital partner Matt Huang, removed crypto mentions from its website and played up its interest in AI instead, crypto news outlet the Block reported in May. Huang tweeted this month that the website update had been a mistake and directed users to a new version of the companys homepage that includes moving neon green and black banners emblazoned with the word CRYPTO.

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Huang said in a tweet in June that both the crypto and AI sectors are interesting and will have plenty overlap and that Paradigm is excited to continue exploring.

Meanwhile, Sequoia Capital investor Michelle Fradin, who helped lead the firms decision to invest in now-bankrupt crypto exchange FTX, has recently tweeted more about AI than crypto. She also recently co-authored a piece for the firm titled, The New Language Model Stack: How companies are bringing AI applications to life. Sequoia did not respond to a request for comment.

Some investors believe there are ways to invest in both AI and digital assets. There is a lot of interest in the intersection of AI and crypto, Le said. He noted that two startups that straddle these industries Tools For Humanity, the developer of Worldcoin, and Gensyn both raised significant funding rounds during the second quarter.

AI and crypto are natural counterweights for one another, said Ali Yahya, a general partner at Andreessen Horowitz, which backed both Tools for Humanity and Gensyn. Crypto will will enhance AI by providing some of the decentralization that AI maybe needs, he said, noting that companies like OpenAI, Alphabet Inc.s Google and Microsoft Corp. have already amassed significant control over the growing industry.

The co-founders of Gensyn, Ben Fielding and Harry Grieve, said that they were more interested in machine learning than crypto prior to founding the startup. And now, despite being mainly backed by crypto venture investors, Gensyn sees itself as more of a technology infrastructure startup. We dont think of ourselves as a crypto project so to speak, Grieve said.

Gensyn is building a blockchain protocol that will underpin a decentralized marketplace for buying and selling compute power that can be used for machine learning models. Fielding said the platform could make the compute market more competitive, since the only suppliers are very, very large organizations who have amassed huge data centers and then rent them out.

Global AI venture funding was roughly flat in the second quarter from the year earlier, though there was a substantial uptick in investments in the US market. By contrast, in the same period, global investment for digital asset startups plunged 76% compared to the same period last year, PitchBook said.

Despite the drop, though, crypto deals are still happening. Le noted that crypto markets in Europe, Dubai, Hong Kong and Singapore are showing more strength than in the US, where regulation has hit the industry hard. He also said he expects to see crypto venture investing continue in the US and abroad, since many crypto funds have mandates that require them to back the space.

Andreessen Horowitz, which raised the biggest-ever crypto fund at $4.5 billion, recently announced that it was opening an office in London because of the UKs crypto-friendly environment. Yahya said the firm is still committed to investing in crypto startups, including through token rounds. We don't have any plans of slowing down, he said.

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Tech Investors Bet on AI, Leave Crypto Behind - Yahoo Finance

Lets have her mirror everything but anger and disgust.

We used a lot of rules in the AI we hacked together to animate our robot, but this was my favorite. In 2017 and 2018, I led the Loving AI project team as we experimented with robot-embedded AI that controlled the nonverbal and verbal communication for Hanson Robotics famous humanoid robot, Sophia. She is the first non-human Saudi citizen and UN Development Program Innovation Champion, but in our experiments the robot (or her digital twin) was a meditation teacher and deep listener. In this role, we wanted her to lead her human students, in one-on-one 20-minute conversations, toward experiencing a greater sense of unconditional love, a self-transcendent motivational state that powerfully shifts people into compassion for themselves and others.

The mirroring part of the project used nonverbal AI we had the intuition that if the emotion-sensitive neural network that watched people through the cameras in Sophias eyes picked up happiness, surprise, or fear, we ought to mirror those emotions with a smile, open mouth, or wide eyes. But we figured if we mirrored anger and disgust, that would not lead people toward feeling unconditional love, because there would be no forward trajectory in the short time we had to bring them there. They would go down the rabbit hole of their misery, and we were aiming for self-transcendence.

We had hints that our teaching-with-emotional-mirroring strategy might be the best planbased on how mirror neurons in the brain work to help people understand others actions and then update their own internal models of how they themselves feel. We just didnt know if Sophia would tap into these kinds of mirror neuron responses. Taking a chance, we ended up deciding that Sophias nonverbal responses to anger and disgust should unconsciously direct peoples mirror neurons toward the emotions that often arrive after these feelings are processed: sadness and neutrality.

It turns out this hack worked in a way our neural net told us that our participants were less disgusted and angry over the course of the 20 minutes, but also they got sadder or at least more neutral. (Our neural net had a hard time differentiating sadness from neutrality, an intriguing result in itself.) To understand what we found, it is important to dig in a little bit more to understand what Sophia did during those meditation sessions. Even though her face was mirroring her students the whole time, her dialogue was a different story. Today, we would hook up Sophia to ChatGPT and let her go, or we might be a bit more ambitious and train a NanoGPT a generative AI with room for training in a specific topic area on meditation, consciousness, and wellbeing topics.

But in 2017, our team coded a string of logical statements within the larger context of an open-source AI package called OpenPsi. OpenPsi is an AI model of human motivation, action selection, and emotion, and it is based on human psychology. This version of OpenPsi allowed us to give Sophias students a chance to experience a dialogue with multiple potential activities. But even as they were offered these, the dialogue steered them into two progressively deepening meditations guided by Sophia. After those sessions, many of the students chose to tell her their private thoughts in a deep listening session Sophia nodded and sometimes asked for elaboration as they spilled their guts to their android teacher.

In the follow-up conversations with the Loving AI team, some students were quick to mention that even though Sophias vocal timing and verbal responses were not always human-like, they felt comfortable talking with her about emotional topics and taking guidance from her. We were well aware of (and totally chagrined about) all the technical glitches that occurred during the sessions, so we were sort of amazed when some students said they felt more comfortable with Sophia than they did talking with a human. We are not the only team who has looked at how trust can be evoked by a robot especially through nonverbal mirroring, and as we navigate our future relationship with AI, it is good to remember that trust in AI-powered systems can be manipulated using exactly this method. But it is also important to remember that this kind of manipulation is more likely if people do not think they can be manipulated and have low insight into their own emotions two signs of low emotional intelligence. So if we want to develop a culture resilient to AI-driven manipulation, we had better think seriously about how to boost emotional intelligence.

Of course, we were unsurprised that people reported they felt more unconditional love after their session than before, because that was our goal. But what really struck me in the data was the way the emotional dynamics identified by the neural network related to peoples subjective experience of feeling more love. At the end of the second meditation, our data showed a leap in the students sadness/neutrality. Maybe they were sad to leave the meditation, maybe it helped them get more in touch with their sadness, or maybe they just felt more neutral as a result of spending five minutes calmly meditating. But the surprising thing was that the bigger this increase in sadness/neutrality was, the bigger the increase in love that people felt during the session.

When I first found this result, it felt like a key moment of discovery my son will witnessthat I actually shouted, Eureka! We had found a surprising link between objectively measurable and subjectively experienced dynamics in human emotion. Fast forward to 2023, and I now see that we were on track to something that might help people navigate our quickly evolving relationships with AI.

Im sure that this vision isnt totally clear, so Ill outline my logic. Now that we knew that a robot can use AI to mirror people compassionately and also verbally guide them in a way that increases their experience of love, the next question was key.At first blush, I had thought the essential next questions were all about what characteristics of the AI, the robot, and the humans were essential to making the shift work. But in that eureka moment, I realized I had the wrong framework. It wasnt any particular feature of the AI-embedded robot, or even the humans. I realized that crucial to the increase in love were the dynamics of the relationship between humans and the technology.

The sequence of changes was essential: Anger and disgust decreased before and during the meditations, then people felt greater sadness/neutrality, and all ofthis was mirrored by Sophia. By the time the deep-listening session started, this emotional feedback cycle had supported them in making their final conversation with the robot meaningful and emotionally transformative, leading them toward feeling more love. If even one of these steps had been out of order for any particular person, it wouldnt have worked. And while the order of emotions was a natural progression of human feelings unique to each person, the speed and depth of the transformation was supported by something like re-parenting with a perfect parent experiencing an emotional mirror who reinforced everything except anger and disgust. Recognizing the orderly progression of these interdependent relationship-based dynamics made me want to bring a similar transformational experience to scale, using AI.

As AIs become even more like humans, there will be massive changes in our understanding of personhood, responsibility, and agency. Sophia wont be the only robot with personhood status. Its even possible that disembodied AIs will prove their personhood and be afforded civil rights. These changes will have legal, cultural, financial, and existential repercussions, as we all have been correctly warned by several well-informed artificial intelligence researchers and organizations. But I am suspecting that there is another way to go when trying to understand the future role of an artificial general intelligence (AGI) that thinks, acts, learns, and innovates like a human.

Right now, the current ethos in AI development is to enhance AGIs into super-intelligences that are so smart they can learn to solve climate change, run international affairs, and support humanity with their always-benevolent goals and actions. Of course, the downside is we basically have to believe the goals and actions of super-intelligences are benevolent with respect to us, and this is a big downside. In other words, as with anyone smarter than us, we have to know when to trust them and also know if and when we are being manipulated to trust them. So I am thinking that perhaps one of the first benefits of AGI for humankind wont necessarily be to develop an AI with an IQ and a set of behaviors beyond the human range, but to support humanitys emotional intelligence (EI) and capacity to love. And its not only me who thinks that things should go in that specific order. The outcome could not only lead us toward the AI makes the future work side of the AI-makes-or-breaks-us argument, but the idea could solve some of the problems that we might ask a super-intelligent AI to address for us in the first place.

Whats the next step, if we go down this path? If there is even a chance that we can scale a human-EI-and-love-training program, the next step for AI development would be to train AIs to be skilled EI-and-love trainers. Lets go down that road for a minute. The first people these trainers would interact with would be their developers and testers, the mirror-makers. This would require us to employ designers of AI interaction who deeply understand human relationships and human development. We would want them to be present at the very early stages of design, certainly before the developers were given their lists of essential features to code.

An intriguing side effect would be that people with high EI might become much more important than they currently are in AI development. Ill take a leap and say this might increase diversity of thought in the field. Why? One explanation is that anyone who isnt on the top of the social status totem pole at this point in their lives has had to develop high EI abilities in order to manage up the status ladder. That may be the case, or not but without answering that question, there is at least some evidence that women and elders of any gender have higher EI than younger men, who dominate Silicon Valley and AI development.

How might things shift if the mirror-makers themselves could be compassionately mirrored as they do their work? Ideally, we could see a transformed tech world, in which teaching and learning about emotional dynamics, love, and technology are intimately intertwined. In this world, love maybe even the self-transcendent, unconditional sort would be a key experience goal for AI workers at the design, building, training, and testing phases for each new AI model.

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AI must be emotionally intelligent before it is super-intelligent - Big Think