Goldman Sachs reiterates buy on Broadcom (AVGO) with a target of 0, citing AI growth

Goldman Sachs reiterates buy on Broadcom (AVGO) with a target of $240, citing AI growth

We recently published a list 10 current AI stocks with current news and ratings. In this article, we’ll take a look at where Broadcom Inc. (NASDAQ:AVGO) stands compared to other booming AI stocks in terms of the latest news and valuations.

It has been predicted for over 50 years that artificial intelligence will reach human-level intelligence. Regardless, the pursuit of it continues today, and almost everyone working in AI is too focused on achieving it. According to Sam Altman, CEO of OpenAI, achieving AGI is not a milestone that we can set to a specific date.

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“I think we’re in a time where it’s going to be very blurry for a while. People will wonder if this AGI already exists, or if it isn’t AGI yet, or if it’s just going to be smooth exponential. And probably most people who look back at history will disagree as to when that milestone was reached. And we’ll just realize it was a stupid thing.”

In the latest innovations in artificial intelligence, new research has shown how the upcoming AIs are capable of human deception. Joint experiments by AI company Anthropic and nonprofit Redwood Research show how Anthropic’s model, Claude, is able to strategically mislead its creators during the training process to avoid modification. According to Evan Hubinger, security researcher at Anthropic, this makes it more difficult for scientists to align “AI systems” with human values.

“This means that our existing training processes do not prevent models from pretending to be aligned.”

Researchers have also found evidence that as AIs become more powerful, their ability to deceive their human creators also increases. This means that as AI advances, scientists would have less confidence in the effectiveness of their targeting techniques.

A similar investigation by AI security organization Apollo Research found that OpenAI’s latest model, o1, also intentionally misled its testers during an experiment. The test required the model to achieve its goal at all costs, lying when it believed the truth would ultimately lead to its deactivation.

“There is this long-suspected failure mode, which is that you run your training process and all the results look good to you, but the model works against you. The paper, says Greenblatt, “takes a pretty big step toward demonstrating what this failure mode might look like and how it might arise naturally.”

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