crypto420

Plenty of technological innovation came from the Soviet Union. NASA was created as a direct consequence of Sputnik. America knew they were falling behind in space technology and needed an internal jolt to catch up

Carlini

CNNs excel in vision tasks where you have limited compute, limited memory, limited data, and want something that works super well and quick. People usually don't hook CNNs up to a transformer to get language understanding either, you have to train bespoke CNNs for specific tasks

ViTs excel where you're unbounded in compute + data and also want text understanding or have a conversation about an image

I spent part of my life growing up in a major Asian city where communal ties are very strong, and spent a very significant fraction of my childhood outside of my house, just because there was so much to do - whether it was playing soccer, playing hide and seek, or taking the local bus/train with friends to shows, coffee shops or restaurants. All of this was before cell phones. If I got lost or needed help I'd ask a stranger for directions or navigation.

Nothing bad ever happened to me, and most people were actually quite kind and helpful to a 14 year old kid asking for help and directions. That sense of confidence in myself - that I could figure out how to pretty much do anything - has stuck with me in a way that a lot of my friends who grew up in sanitized suburban neighborhoods just don't have.

Kids really don't know what they're missing.

My PhD advisor found out that my English writing skills are quite good and the rest of their lab were Chinese internationals, so they started making me write all of their research grants. 30 pages every 2 months, pre-ChatGPT days.

Unfortunately, consciousness is deceptively hard to define, and so any benchmark to measure or quantify it can be endlessly debated.

You can argue that it's a property that all living beings have in common - and even among *unconscious* beings there's a form of consciousness and self-awareness that's ever present, but definitions are elusive and vague and tough to pin down.

The mechanistic argument against LLMs - that they're just matrix multiplications - breaks down because they can clearly pass the Turing Test which was the gold standard for what intelligent behavior really meant, thus breaking the old notion that intelligence has to have some form of biological basis. Yet its clear that there are forms of intelligence that rats have which the frontier LLMs don't possess (is that consciousness? or a different kind of intelligence), and its hard to pinpoint what exactly that is, so we probably need the philosophy departments of major universities to come up with newer definitions of intelligence and consciousness.

I personally believe that intelligence and consciousness are 2 separate forms of emergence from simple automata that may occur together (such as in humans) or not (such as consciousness in plants and intelligence in LLMs)

> There hasn't been real product innovation at Apple in over a decade.

Everyone says this but forgets that AirPods were released in 2016, Pros in 2020, and they're now the most popular headphones in the world

> Well, both, obviously.

So, 95 with 1k?

The whole point of Apple intelligence is that it shouldn’t feel like you’re talking with an LLM, it’s that it fades into the OS but delivers new features that make the OS more intelligent. I’m actually very bullish on it

I'm not sure if people here even read the entirety of the article. From the article:

> We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments.

> Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subsequent experiments validated these proposals, confirming that the suggested drugs inhibit tumor viability at clinically relevant concentrations in multiple AML cell lines.

and,

> For this test, expert researchers instructed the AI co-scientist to explore a topic that had already been subject to novel discovery in their group, but had not yet been revealed in the public domain, namely, to explain how capsid-forming phage-inducible chromosomal islands (cf-PICIs) exist across multiple bacterial species. The AI co-scientist system independently proposed that cf-PICIs interact with diverse phage tails to expand their host range. This in silico discovery, which had been experimentally validated in the original novel laboratory experiments performed prior to use of the AI co-scientist system, are described in co-timed manuscripts (1, 2) with our collaborators at the Fleming Initiative and Imperial College London. This illustrates the value of the AI co-scientist system as an assistive technology, as it was able to leverage decades of research comprising all prior open access literature on this topic.

The model was able to come up with new scientific hypotheses that were tested to be correct in the lab, which is quite significant.

> So, I'm not a believer in the classic winner takes all approach here where one company turns into this trillion dollar behemoth and the rest of the industry pays the tax to that one company in perpetuity.

I agree with this sentiment. There are a lot of frontier model players that are very competent (OpenAI, Anthropic, Google, Amazon, DeepSeek, xAI) and I'm sure more will come onboard as we find ways to make models smaller and smaller.

The mental framework I try to use is that AI is this weird technology that is an enabler of a lot of downstream technology, with the best economic analogy being electricity. It'll change our society in very radical ways, but it's unclear who's going to make money off of it. In the electricity era Westinghouse and GE emerged as the behemoths because of their ability to manufacture massive turbines (which are the equivalent of today's NVIDIA and perhaps Google).