Add Eric Schmidt to the list of tech luminaries concerned about the dangers of AI. The former Google chief tells guests at The Wall Street Journal's CEO Council Summit that AI represents an "existential risk" that could get many people "harmed or killed." He doesn't feel that threat is serious at the moment, but he sees a near future where AI could help find software security flaws or new biology types. It's important to ensure these systems aren't "misused by evil people," the veteran executive says.
Schmidt doesn't have a firm solution for regulating AI, but he believes there won't be an AI-specific regulator in the US. He participated in a National Security Commission on AI that reviewed the technology and published a 2021 report determining that the US wasn't ready for the tech's impact.
Schmidt doesn't have direct influence over AI. However, he joins a growing number of well-known moguls who have argued for a careful approach. Current Google CEO Sundar Pichai has cautioned that society needs to adapt to AI, while OpenAI leader Sam Altman has expressed concern that authoritarians might abuse these algorithms. In March, numerous industry leaders and researchers (including Elon Musk and Steve Wozniak) signed an open letter calling on companies to pause AI experiments for six months while they rethought the safety and ethical implications of their work.
Speaking of the existential threat of AI is science fiction, and bad science fiction for that matter because it is not based on anything we know about science, logic, and nothing that we even know about ourselves:
Despite their apparent success, LLMs are not (really) 'models of language' but are statistical models of the regularities found in linguistic communication. Models and theories should explain a phenomenon (e.g., F = ma) but LLMs are not explainable because explainability requires structured semantics and reversible compositionality that these models do not admit (see Saba, 2023 for more details). In fact, and due to the subsymbolic nature of LLMs, whatever 'knowledge' these models acquire about language will always be buried in billions of microfeatures (weights), none of which is meaningful on its own. In addition to the lack of explainability, LLMs will always generate biased and toxic language since they are susceptible to the biases and toxicity in their training data (Bender et. al., 2021). Moreover, and due to their statistical nature, these systems will never be trusted to decide on the "truthfulness" of the content they generate (Borji, 2023) – LLMs ingest text and they cannot decide which fragments of text are true and which are not. Note that none of these problematic issues are a function of scale but are paradigmatic issues that are a byproduct of the architecture of deep neural networks (DNNs) and their training procedures. Finally, and contrary to some misguided narrative, these LLMs do not have human-level understanding of language (for lack of space we do not discuss here the limitations of LLMs regarding their linguistic competence, but see this for some examples of problems related to intentionality and commonsense reasoning that these models will always have problems with). Our focus here is on the now popular theme of how dangerous these systems are to humanity.
The article goes on to provide a statistical argument as to why we are many, many years away from AI being an existential threat, ending with:
So enjoy the news about "the potential danger of AI". But watch and read this news like you're watching a really funny sitcom. Make a nice drink (or a nice cup of tea), listen and smile. And then please, sleep well, because all is OK, no matter what some self-appointed god fathers say. They might know about LLMs, but they apparently never heard of BDIs.
The author's conclusion seems to be that although AI may pose a threat to certain professions, it doesn't endanger the existence of humanity.
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