Dive in — Sam Altman, Demis Hassabis and Elon Musk — had sought private audiences with the Pope, recognizing the need for ethical frameworks, and more
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Sam Altman, Demis Hassabis and Elon Musk — had sought private audiences with the Pope, recognizing the need for ethical frameworks
Pope Francis’ moral guidance spoke beyond religious boundaries. Rooted in a deep concern for the common good, his message has resonated with policymakers, technologists, and citizens alike, regardless of religious belief or views on organized religion.
Consider how Pope Francis’ teachings can guide a future where technological progress enhances, rather than diminishes, human dignity.
His teachings challenge us to evaluate progress not by technical capability or profit, but by how well technology serves humanity—placing people at the center of innovation, not its margins.
Read more | BROOKINGS
AI Is Using Your Likes to Get Inside Your Head
What is the future of the like button in the age of artificial intelligence? Max Levchin—the PayPal cofounder and Affirm CEO—sees a new and hugely valuable role for liking data to train AI to arrive at conclusions more in line with those a human decisionmaker would make.
“I would argue that one of the most valuable things Facebook owns is that mountain of liking data,” Levchin told us. Indeed, at this inflection point in the development of artificial intelligence, having access to “what content is liked by humans, to use for training of AI models, is probably one of the singularly most valuable things on the internet.”
Read more | WIRED
Satya Nadella says as much as 30% of Microsoft code is written by AI
“I’d say maybe 20%, 30% of the code that is inside of our repos today and some of our projects are probably all written by software,” Nadella said during a with Meta CEO Mark Zuckerberg.
Nadella asked Zuckerberg how much of Meta’s code was coming from AI. “Our bet is sort of that in the next year probably … maybe half the development is going to be done by AI, as opposed to people, and then that will just kind of increase from there,” Zuckerberg said.
Read more | CNBC
We need to start thinking of AI as “normal”
The core point, Sayash Kapoor says, is that we need to start differentiating between the rapid development of AI methods—the flashy and impressive displays of what AI can do in the lab—and what comes from the actual applications of AI, which in historical examples of other technologies lag behind by decades.
“Much of the discussion of AI’s societal impacts ignores this process of adoption,” Kapoor told me, “and expects societal impacts to occur at the speed of technological development.” In other words, the adoption of useful artificial intelligence, in his view, will be less of a tsunami and more of a trickle.
Read more | MIT TECHNOLOGY REVIEW
Google launches AI tools for practicing languages through personalized lessons
Google is releasing three new AI experiments aimed at helping people learn to speak a new language in a more personalized way. The first experiment helps you quickly learn specific phrases you need in the moment, while the second experiment helps you sound less formal and more like a local.
The third experiment allows you to use your camera to learn new words based on your surroundings.
Read more | TECH CRUNCH
Big banks ramp up AI hiring as gains materialize
With JPMorgan Chase, Wells Fargo and Citigroup leading the charge, the industry increased its pool of AI model development, platform engineering and project management technicians by nearly 13%. The increase was the largest six-month jump in two years, Evident said.
“Away from the market noise and volatility, the leading banks are quietly but relentlessly pressing forward with AI transformation,” Alexandra Mousavizadeh, Evident co-founder and co-CEO, said in the report. “AI roles may be the only safe jobs in banking right now.”
Read more | CIO DIVE
When it comes to risk, AI is the new cloud
It’s not hard to see the parallels between the cloud and AI. Like the cloud, AI has democratized access to resources that were previously difficult to come by for many organizations. The widespread availability of generative AI models like ChatGPT means organizations no longer need to hire costly AI engineers to create, manage, and fine-tune their own models.
Instead, they can put an application layer on top of an existing model and deliver a compelling service to their customers at a relatively low cost of ownership.
Read more | FAST COMPANY
Google search’s made-up AI explanations for sayings no one ever said, explained
Last week, the phrase "You can't lick a badger twice" unexpectedly went viral on social media. The nonsense sentence—which was likely never uttered by a human before last week—had become the poster child for the newly discovered way Google search's AI Overviews makes up plausible-sounding explanations for made-up idioms (though the concept seems to predate that specific viral post by at least a few days).
Google users quickly discovered that typing any concocted phrase into the search bar with the word "meaning" attached at the end would generate an AI Overview with a purported explanation of its idiomatic meaning. Even the most nonsensical attempts at new proverbs resulted in a confident explanation from Google's AI Overview, created right there on the spot.
Read more | ARS TECHNICA
Anthropic following OpenAI, forms group looking into economic impact of AI
The group, known as the Economic Advisory Council, will aid the maker of the Claude chatbot with “expert guidance” on the economic implications of AI development and deployment, Anthropic said.
“The Council will advise Anthropic on AI's impact on labor markets, economic growth, and broader socioeconomic systems,” Anthropic said in a statement. “This work will inform the research agenda for the Anthropic Economic Index, an initiative that aims to understand AI’s impact on the labor market and global economy over time.”
Read more | SEEKING ALPHA
Sycophancy in GPT-4o: What happened
When shaping model behavior, we start with baseline principles and instructions outlined in our Model Spec. We also teach our models how to apply these principles by incorporating user signals like thumbs-up / thumbs-down feedback on ChatGPT responses.
However, in this update, we focused too much on short-term feedback, and did not fully account for how users’ interactions with ChatGPT evolve over time. As a result, GPT‑4o skewed towards responses that were overly supportive but disingenuous.
Read more | OpenAI
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