JangoSteve

As a software developer who spent nearly 10 years founding and building a genomic startup, this is a good start, but does have a lot of vast oversimplifications and a few inaccuracies. The people making this know what they're doing, so I'm sure these are known shortcomings they likely deemed necessary for a quick introduction. You'd need a further study at the end to start being able to do some real-world work.

Anecdotally, I've noticed an uptick in my eBay feeds recently of items being immediately relisted after having supposedly sold. This has always happened occasionally, but within the past couple weeks, I've noticed it happening for like 25% of my watched items. I'm wondering if bots are buying things for which the owners are then canceling when it comes time to pay.

I've also had to return a few items for which eBay's AI-generated description was wrong in ways that couldn't be verified in the product's images. I can only imagine the increase in canceled/returned orders from all the different AI features and bots.

This was a common approach called a "honeypot". As I recall, bots eventually overcame this approach by evaluating visibility of elements and only filling out visible elements. We then started ensuring the element was technically visible (i.e. not `display: none` or `visibility: hidden`) and instead absolutely positioning elements to be off screen. Then the bots started evaluating for that as well. They also got better at reading the text for each input.

Interestingly, the award was specifically for the impact of AlphaFold2 that won CASP 14 in 2020 using their EvoFormer architecture evolved from the Transformer, and not for AlphaFold that won CASP 13 in 2018 with a collection of ML models each separately trained, and which despite winning, performed at a much lower level than AlphaFold2 would perform two years later.

I've gotten so much push-back over the years when I've asserted that startups are too quick to describe their products as AI in industries where the target customer considers it undesirable. I think it's done for ego and to impress investors. Examples I've seen include products in clinical diagnosis and financial accounting. Some needs require utmost predictability, observability, and ultimately, understandability.

Of course, there are some industries and markets that desire the capabilities only AI can provide. But that's the point. Analysis should precede the message. We should market the benefits. I've seen a few people at least claim AI isn't a benefit, it's a feature. I'd argue it's not even a feature, it's an implementation detail, like using an object-oriented programming language or a relational database; it has advantages and disadvantages.

Focus on the needs of the customer and industry. Describe the benefits. For customers and investors alike, remove the AI veil of opacity, by describing simply what the AI is doing and how/why.

It's interesting to see a study that seems to corroborate my anecdotal experiences. It's a marketing study though, so it shouldn't be overly generalized until more studies reproduce the results. Studies about human behavior tend to be difficult to reproduce and can yield conflicting conclusions. I wouldn't be surprised to see another study with slightly different questions or methods come to the opposite conclusion, especially if they don't control for consumer segments, industries, or types of products.

It's a little worse than that. If you need to upgrade at least once per billing cycle, the system keeps charging you more and more (since an upgrade will always have a higher total cost than the previous upgrade) each cycle while accruing credits that will never start paying down unless/until you have a billing cycle without an upgrade. That's not a great design for most systems, but especially bad for a scalable image service intended to facilitate the flexibility of growth.

> And it turns out that a lot of human reasoning is statistically predictable enough in writing that you can actually obtain reasoning-like behavior just by having a good auto-complete model.

I would disagree with this on a technicality that changes the conclusion. It's not that human reasoning is statistically predictable (though it may be), it's that all of the writing that has ever described human reasoning on an unimaginable number of topics is statistically summarizable, and therefore having a good auto-complete model does a good job of describing human reasoning that has been previously described at least combinatorially across various sources.

We don't have direct access to anyone else's reasoning. We infer their reasoning by seeing/hearing it described, then we fill in the blanks with our own reasoning-to-description experiences. When we see a model that's great at mimicking descriptions of reasoning, it triggers the same inferences, and we conclude similar reasoning must be going on under the hood. It's like the ELIZA Effect on steroids.

It might be the case that neural networks could theoretically, eventually reproduce the same kind of thinking we experience. But I think it's highly unlikely it'd be a single neural network trained on language, especially given the myriad studies showing the logic and reasoning capabilities of humans that are distinct from language. It'd probably be a large number of separate models trained on different domains that come together. At that point though, there are several domains that would be much more efficiently represented with something other than a neural network model, such as the modeling of physics and mathematics with equations (just because we're able to learn them with neurons in our brains doesn't mean that's the most efficient way to learn or remember them).

While a "sufficiently huge autocomplete model" is impressive and can do many things related to language, I think it's inaccurate to claim they develop reasoning capabilities. I think of transformer-based neural networks as giant compression algorithms. They're super lossy compression algorithms with super high compression ratios, which allows them to take in more information than any other models we've developed. They work well, because they have the unique ability to determine the least relevant information to lose. The auto-complete part is then using the compressed information in the form of the trained model to decompress prompts with astounding capability. We do similar things in our brains, but again, it's not entirely tied to language; that's just one of many tools we use.

I did something similar to this a while back with a one-liner aliased in my Bash includes, called gitsum (short for git summary).

    alias gitsum='git log --pretty=format:"* %s" --author `git config user.email`' #myself

It gives my git commit messages as a Markdown bullet-point list. It only works per-branch unlike the linked gist, but one cool thing about it is that you can tack on additional git flags, such as --since. For example:

    gitsum --since 1.day
    gitsum --since 1.week
    gitsum --since 8.hours

I usually pipe this into my clipboard (on Mac) to easily paste it into the time logging or reporting system:

    gitsum --since 1.day | pbcopy

I think my favorite part of this prompt is that it starts with, "Please..."

With this new class of products based on crafting prompts that best exploit a GPT's algorithm and training data, are we going to start seeing pull requests that tweak individual parts or words of the prompt. I'm also curious how the test suite for projects like this would look for specific facts or phrases to be contained in the responses for specific inputs.

A lot of good feedback here. One additional thought I'd provide is that I wouldn't rule someone out based on a "Contact us for price" button. I'd click the button, let them know what you need, and then let them rule themselves out based on their responses. Sometimes they may give you a price directly in their response (or at least a ballpark), sometimes they may insist on a call. Take the call, but draw the line where you're comfortable (e.g. give me a ballpark after 30 minutes or I'm out - you can be more tactful in your wording).

Think about it from the company's perspective. Let's imagine as a company, you do an experiment where you have one landing page that shows the pricing, and another landing page that says "contact us". Let's also imagine that your product is enough of an enterprise solution that no one ever buys it without talking to someone at some point during the evaluation process (even if the pricing is up front, it requires enough of an investment that the customer wants to be absolutely certain it will satisfy their needs both now and as they grow). Finally, let's imagine that you have 3 full-time sales people to handle the incoming communications at whatever step of the evaluation process.

Now, if the outcome of this experiment is that the "contact us for pricing" results in 1/5th the incoming contacts, but those then convert twice the rate, you might choose the up-front pricing (2x conversion rate but on 20% of the leads means only 40% the sales compared to up-front pricing).

However, what if the 5x incoming contacts is too many for your sales team of 3 to respond, causing the up-front pricing to result in fewer sales with more work? Then you might choose the "contact us for pricing".

But what if it's so many more incoming contacts at a consistent enough conversion rate that you can justify adding a 4th and 5th sales person to realize those sales? Then you might choose the up-front pricing.

But what if the up-front pricing pigeon-holes you into your beachhead market and makes it more difficult to expand vertically or horizontally, which could lead to a trail-off in the incoming contacts and sales? Then you might choose the "contact us for pricing".

The point is, the one that makes the most sense for a company depends on a lot of variables, most of which would be opaque to an outside observer. Being able to tell the difference between a company that has "contact us for pricing" because it made sense for them, compared to one trying to exploit price discrimination (as described by some of the more cynical takes) is next to impossible without talking with them. Even in that scenario though, if they end up giving you a better product for a better price, then being willing to reach out to them could end up being a competitive advantage for your company over another which disqualified them on that basis.