Lifeboat Foundation

Summary.

What will artificial intelligence do to industries and jobs? For a preview, look to the finance industry which has been incorporating data and algorithms for a long time, and which is always a canary in the coal mine for new technology. The experience of finance suggests that AI will transform some industries (sometimes very quickly) and that it will especially benefit larger players. But it may not leave the overall system better off.

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Hypnotized LLMs can help leak confidential financial information, generate malicious code and even cross red lights.

Tech pundits worldwide have been fluctuating between marking artificial intelligence as the end of all of humanity and calling it the most significant thing humans have ever touched since the internet.

We are in a phase where we are unsure what the AI Pandora’s box will reveal. Are we heading for doomsday or utopia?

The AI tool allows people to create songs using the voices of various artists. What will this do to the music industry?

Months after an outburst over AI-engineered songs that used the voice of artists, it seems like the world’s largest record label – Universal Music Group (UMG) – is getting on board to ride the AI wave before it washes out the company.

In collaboration with Google, UMG will soon develop a tool allowing fans to create AI-generated music using musicians’ voices, reported Financial Times. The deal involves paying copyright holders their share in using their melody and allows the artists a choice to opt in.

“Frank Sinatra’s voice has been used on a version of the hip-hop song ”Gangsta’s Paradise,” while Johnny Cash’s has been deployed on the pop single ”Barbie Girl.” A YouTube user called PluggingAI offers songs imitating the voices of the deceased rappers Tupac and Notorious B.I.G.

”An artist’s voice is often the most valuable part of their livelihood and public persona, and to steal it, no matter the means, is wrong,” Universal Music general counsel Jeffrey Harleston told US lawmakers last month.

Discussions between Google and Universal Music are at an early stage, and no product launch is imminent, but the goal is to develop a tool for fans to create these tracks legitimately, and pay the owners of the copyrights for it, said people close to the situation. Artists would have the choice to opt in, the people said.

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On the twelfth challenge: Suppose that in the next decade or two, we meet all the engineering challenges in a single system. Will we then have a conscious AI systems? Not everyone will agree that we do. But if someone disagrees, we can ask once again: what is the X that is missing? And could that X be built into an AI system?

My conclusion is that within the next decade, even if we don’t have human-level artificial general intelligence, we may well have systems that are serious candidates for consciousness. There are many challenges on the path to consciousness in machine learning systems, but meeting those challenges yields a possible research program toward conscious AI.

I’ll finish by reiterating the ethical challenge.⁴ I’m not asserting that we should pursue this research program. If you think conscious AI is desirable, the program can serve as a sort of roadmap for getting there. If you think conscious AI is something to avoid, then the program can highlight paths that are best avoided. I’d be especially cautious about creating agent models. That said, I think it’s likely that researchers will pursue many of the elements of this research program, whether or not they think of this as pursuing AI consciousness. It could be a disaster to stumble upon AI consciousness unknowingly and unreflectively. So I hope that making these possible paths explicit at least helps us to think about conscious AI reflectively and to handle these issues with care.

Empirical applications of the free-energy principle entail a commitment to a particular process theory. Here, the authors reverse engineered generative models from neural responses of in vitro networks and demonstrated that the free-energy principle could predict how neural networks reorganized in response to external stimulation.

Summary: A recent study reveals that everyday pleasures like music and coffee can significantly enhance cognitive performance.

Utilizing groundbreaking brain-monitoring technology, the study examined brain activity during cognitive tests under various stimulants, including music, coffee, and perfume. Results indicated increased “beta band” brain wave activity, linked to peak cognitive performance, when subjects engaged with music or consumed coffee.

AI-generated music, in particular, showed significant performance boosts, opening new avenues for exploration.

That’s how Andrew Feldman, CEO of Silicon Valley AI computer maker Cerebras, begins his introduction to his company’s latest achievement: An AI supercomputer capable of 2 billion billion operations per second (2 exaflops). The system, called Condor Galaxy 1, is on track to double in size within 12 weeks. In early 2024, it will be joined by two more systems of double that size. The Silicon Valley company plans to keep adding Condor Galaxy installations next year until it is running a network of nine supercomputers capable of 36 exaflops in total.

If large-language models and other generative AI are eating the world, Cerebras’s plan is to help them digest it. And the Sunnyvale, Calif., company is not alone. Other makers of AI-focused computers are building massive systems around either their own specialized processors or Nvidia’s latest GPU, the H100. While it’s difficult to judge the size and capabilities of most of these systems, Feldman claims Condor Galaxy 1 is already among the largest.

Condor Galaxy 1—assembled and started up in just 10 days—is made up of 32 Cerebras CS-2 computers and is set to expand to 64. The next two systems, to be built in Austin, Texas, and Ashville, N.C., will also house 64 CS-2s each.

AI singularity refers to the future point where artificial intelligence becomes so advanced that it surpasses human intelligence and undergoes rapid, unpredictable self-improvement, leading to an exponential increase in capabilities. At this stage, AI could potentially reshape society, science, and civilization in profound and transformative ways, and its behavior might become difficult for humans to comprehend or control.

The AI alignment problem refers to the challenge of ensuring that artificial intelligence systems are designed and programmed to act in accordance with human values, goals, and intentions. It involves developing AI systems that align with human interests, do not produce harmful outcomes, and operate transparently and predictably, so they can be trusted and reliably controlled. Addressing the AI alignment problem is crucial to avoid potential risks and negative consequences associated with AI development and deployment.

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