Google's live from Paris event private/deleted immediately

It will answer the following: The highest scoring NBA player is Kareem Abdul-Jabbar with 38,387 career points. Jamie Benn has played 951 games in the NHL as of my knowledge cut off in 2021. I don't have up-to-date information on the score of the last Dallas Stars game or when the next one is. Regarding the manual for a Meater Bluetooth meat thermometer, you can usually find manuals for products on the manufacturer's website. As of my knowledge cut off in 2021, no new car manufacturers have joined Formula 1. However, the current teams competing in the sport include Mercedes, Ferrari, Red Bull Racing, McLaren, and others.

What if I ask ChatGPT: why do people resist new disruptive technologies?

People resist new disruptive technologies for several reasons, including:

Fear of job loss: One of the main concerns about disruptive technologies is that they can lead to job losses, as machines and algorithms replace human workers.

Lack of understanding: Some people may resist new technologies because they don't understand how they work or what benefits they offer.

Threat to existing systems and norms: Disruptive technologies can challenge established systems and norms, and people may resist them because they fear the changes they bring.

Ethical and moral concerns: Some technologies raise ethical and moral questions, such as privacy concerns, and people may resist them as a result.

Financial impact: Disruptive technologies can be expensive to implement, and some people may resist them because they can't afford the costs associated with them.

Habits and comfort: People may resist new technologies because they are used to their existing habits and are comfortable with what they know.

Lack of trust: People may resist new technologies because they don't trust the companies or organizations behind them, or because they don't believe that the benefits of the technology outweigh the risks.

It's important to note that resistance to disruptive technologies is not always a bad thing, as it can encourage companies to address the concerns and ensure that new technologies are developed and used in ethical and responsible ways.

The original link was to https://twitter.com/googleeurope/status/1623251768099409920, which originally embedded a private video in the Google Europe youtube account. Looks like that has since been redirected to the official video.

It's been going on much longer. Google's revenue was disrupted with Facebook. Then they scrambled to get Google+ out the door, which had absolutely no reason to exist beyond being a Google-branded clone of Facebook. They effectively lucked in to Android paying off. When the iPhone came out Android was not going to support touchscreens. It was designed for physical buttons only.

That's the problem with Google. They don't have vision. They need someone else to show them the future which usually leaves them racing around trying to find their place in the new world. And then when they do put out a product it's always some half-assed thing that feels like a beta product of some already existing fully-finished product that is wildly popular. Sometimes it works out. Chrome was a joke when it came out. It took many years before Google Maps became a solid, distinct offering. But they want minimum viable products that ape already existing things and want them to be instant hits or they kill them off. It's the Netflix strategy.

asking ChatGPT gives very interesting results (didn't check accuracy):

> What is the world record for crossing the English Channel entirely on foot

As of my training data, the world record for crossing the English Channel entirely on foot is held by Christof Wandratsch, who completed the swim in 8 hours and 58 minutes on August 25, 2018.

> Swimming isn't on foot

My apologies, you are correct. The English Channel is typically crossed by swimming. The world record for swimming the English Channel is held by Benoît Lecomte, who completed the swim in 8 hours and 1 minute on July 23, 1998.

> That's faster and earlier... what did Christof Wandratsch do that was different

Christof Wandratsch completed a cross-Channel run, covering the distance between England and France on foot. This is a different feat than swimming the Channel, as it involves running across the Channel Tunnel, which connects England and France beneath the seabed.

Agreed heavily - and I work with these technologies regularly. There is a mania around it much like there was for crypto (though I would maintain there's more merit here than there ever was with crypto - orders of magnitude more).

There are a litany of problems that surround LLMs that are solvable, but are highly non-trivial. The present hyped demos all seem to involve mainlining the unfiltered output of LLMs directly at the user, and this approach strikes me as largely impractical, with all of the issues you've brought up and more.

Ultimately I think we will land on a few learnings, some of which are IMO pretty obvious already with existing ML:

- There are going to be lots and lots of use cases for LLMs where despite its additional complexity it won't out-perform simpler ML models, or even heuristic-based solutions. This has been a plague for AI startups forever - where the shameful truth is that the ML bits do not significantly outperform heuristic-based approaches, but the ML bits sure as useful for the hype machine. There will be more of these use cases than use cases where LLMs actually do significantly move the state of the art forward.

- In most broadly mainstream-palatable applications the LLM will need to be intermediated by many other systems - some using other ML models, and some using heuristic-based solutions. For example for search, mediating the output through a knowledge graph with reliable provenance and accuracy. Realistically there will be vanishingly few use cases that are thin-wrappers around LLMs. Real usefulness of this tech will require very heavy lifting around the core model to make it actually accurate/reliable enough to be useful on a mass scale. The many products spinning up that seem to be premised on very thin wrappers around OpenAI's API will IMO mostly not survive, both because there is no moat and because the results won't be sufficiently useful.

- Accuracy matters and will be a thorn in the side of these products for quite some time, even though it is solvable. For some use cases (mostly creative ones with a human in the loop - like creative writing or image synthesis) the lack of accuracy is livable, but for most others the propensity to hallucinate is going to be a major barrier to adoption. Right now you're dealing with an enthusiast audience that's willing to look past glaring and obvious errors in output in favor of what it could be and how far we've come, but a mainstream audience I suspect will be less forgiving. Social media virality around product failure cases will also further punish products.

Overall I think what we're looking at here is legitimately a huge leap forward, but this legitimate and well-found excitement needs to be tempered by the fact that some (most?) players in the space seem to be getting in way over their skis, and that truly productionizing this technology is going to be intensely difficult.

[edit] To be a bit less doom and gloom about this - I think the real winner here are natural language interfaces, and stepping ever closer towards computer systems that do not need to be actively learned before being used. There is IMO an over-focus on the knowledge-encoding part of LLMs (which is unreliable and prone to hallucination) and not enough focus on the language-encoding part of LLMs (which is what they actually do). For the most part voice/language-based interfaces have had limited traction because they honestly haven't been smart enough to understand user requests with a sufficient level of expressivity and fidelity. I suspect these products (and new ones) will have another crack at this problem.