Well first off: letters to investors are among the most biased pieces of writing in existence.

Second: I'm not saying connectionism did not succeed in many areas! I'm a connectionist by heart! I love connectionism! But that being said there is disconnect between the expectations and reality. And it is huge. And it is particularly visible in autonomous driving. And it is not limited to media or CEO's, but it made its way into top researchers. And that is a dangerous sign, which historically preceded a winter event...

The difference between the current AI renaissance and the past pre-winter AI ecosystems is the level of economic gain realized by the technology.

The late 80s-early 90s AI winter, for example, resulted from the limitations of expert systems which were useful but only in niche markets and their development and maintenance costs were quite high relative to alternatives.

The current AI systems do something that alternatives, like Mechanical Turks, can only accomplish with much greater costs and may not even have the scale necessary for global massive services like Google Photos or Youtube autocaptioning.

The spread of computing infrastructure and connectivity into the hands of billions of global population is a key contributing factor.

I would argue this is well discounted by level of investment made against the future. I don't think the winter depends on the amount that somebody makes today on AI, rather on how much people are expecting to make in the future. If these don't match, there will be a winter. My take is that there is a huge bet against the future. And if DL ends up bringing just as much profit as it does today, interest will die very, very quickly.

This is analogous to the way electricity took decades to realize productivity gains in the broad economy.

That said, the hype will dial down. I am just not sure the investment will decrease soon.

So I guess we're waiting for something similar to happen with AI and then get AI 2.0?

The current road infrastructure (markings, signs) has been designed for humans. Once it has been modernized to better aid the self-driving systems, we don't probably need "perfect" AI.

AI is overhyped and overfunded at the moment, which is not unusual for a hot technology (synthetic biology; dotcoms). Those things go in cycles, but the down cycles are seldom all out winters. During the slowdowns best technologies still get funding (less lavish, but enough to work on) and one-hit wonders die, both of which is good in the long run. My friends working in biology are doing mostly fine even though there are no longer "this is the century of synthetic biology" posters at every airport and in every toilet.

Those are actual features that are available today to anyone, that were made possible by AI. Do you think it would be possible to type "pictures of me at the beach with my dog" without AI in such as short time frame? Or to have cars that drive themselves without a driver? These are concrete benefits of machine learning, I don't understand how that's biased.

If there are 100 facts that indicate a coming AI winter, and Brin just talks up the 15 facts that indicate AI's unalloyed dominance, that's definitely biased.

Second, I'm not quite sure that's how it works. Like in mathematics, if your lemma is X, you can give a 100 examples of X being true, but I only need a single counter-example to break it.

In my opinion a single valid modern use-case of AI is enough to show that we're not in an AI winter. By definition an AI winter means that nothing substantial is coming out of AI for a long period of time, yet Brin listed that Google alone has had a dozen in the past few years.

You cannot ask a generic question, then attack the answer based on absence of evidence for a specific example.

>translate over 100 languages in Google Translate;

>caption over a billion videos in 10 languages on YouTube;

barely even work. Yeah, it's a difficult problem but it's not even close to being solved.

Google's captioning works well when people speak clearly and in English. Google translate works well when you translate well written straightforward text into English. It's impressive but it's got a long way to go to reach human grade transcription and translation.

I think when evaluating these things people underestimate how long the tail of these problems is. It's always those pesky diminishing returns. I think it's true for many AI problems today, for instance it looks like current self-driving car tech manages to handle, say, 95% of situations just fine. Thing is, in order to be actually usable you want something that critical to reach something like 99.999% success rate and bridging these last few percent might prove very difficult, maybe even impossible with current tech.

Every time I activate it I am in for a good laugh more than anything actually useful.

    Have now, ah! Philosophy,
    Law and medicine,
    And unfortunately also theology
    Thoroughly studied, with great effort.
    Here I am, I poor gate!
    And I'm as smart as before

It's unable to 'understand' that 'I poor gate' makes no sense at all.

Google Translate is the 'poor gate'.

You may think that you can now read German news, but in fact you would not know if the sentence meaning has been preserved in the English translation. The words itself might look as if the sentence makes sense - but the meaning is actually shifted - slight differences, but also possibly the complete opposite.

The translation also does not give you any indication where this might be and where the translation is based on weak training material or where there is some inference needed for a successful translation.

Maybe true but they are words that are about things which are either true or not true. Has nothing to do where the words were shared. Saying they are on an investment letter so not relevant seems very short sighted.

But just looking at the last 12 months it is folly to say we are moving to a AI winter. Things are just flying.

Look at self driving cars without safety drivers or look at something like Google Duplex but there are so many other examples.

Using the list provided, one example

"caption over a billion videos in 10 languages on YouTube;" - This doesn't say how accurate the captions acutally are. In my experience youtube captioning even of english dialect isn't exactly great. For one example try turning on the captions on this https://www.youtube.com/watch?v=bQJrBSXSs6o

so it's true I'm sure to say they've captioned the videos AI based techniques, but that doesn't mean they're a perfected option.

Also (purely anecodtally) Google translate also isn't exactly perfect yet either...

This is notorious with current technology: you can demonstrate anything. A few years ago Tesla demonstrated a driverless car. And what? Nothing. Absolutely nothing.

I'm willing to believe stuff I can test myself at home. If it works there, it likely actually works (though possibly needs more testing). But demo booths and youtube - never.

The BICEP2 fiasco is a good example why.

Edit: also, a blog post with more examples and a link to the related publication: https://ai.googleblog.com/2018/04/looking-to-listen-audio-vi...

The original google report was discussed here a few weeks ago:

https://news.ycombinator.com/item?id=16813766

This is one of the areas I’m most enthusiastic about but … it’s still nowhere near the performance of untrained humans. Google has poured tons of resources into Photos and yet if I type “cat” into the search box I have to scroll past multiple pages of results to find the first picture which isn’t of my dog.

That raises an interesting question: Google has no way to report failures. Does anyone know why they aren’t collecting that training data?

I’ve assumed that the reason is the same as why none of the voice assistants has an error reporting UI or even acknowledgement of low confidence levels: the marketing image is “the future is now” and this would detract from it.

what is this 'understand'?

For most things, that people dream of and do marketing about need another leap forward, which we haven’t seen yet (it’ll come for sure)

Also, while a lot of these can be seen as "improvements", in many cases, that improvement put it past the threshold of actually being usable or useful. Self-driving cars for example need to be at least a certain level before they can be deployed, and we would've never reached that without machine learning.

Utterly useless. And I don't think it is improving.

As for the users, sure the translation may not be perfect, but I'm sure if you were deaf had no other way of watching a video, you would be just fine with the current quality of the transcription.

At least in English, they are now good enough that I can read without listening to the audio and understand almost everything said. (There are still a few mistakes here and there but they often don’t matter.)

I tried to help a couple channels to subtitle and the starting point was just sooo far from the finished product. I would guess I left 10% intact of the auto-translation. Maybe it would have been 5% five years ago; when things are this bad 100% improvement is hard to notice.

It is super cool how easy it is to edit and improve the subtitles for any channel that allows it.

If by 'almost everything', you mean stuff that a non native English speaker could have understood anyway, then yes.

The vast majority of English learners are not able to caption most Youtube videos as well as the current AI can.

You underestimate the amount of time required to learn another language and the expertise of a native speaker. (Have you tried learning another language to the level you can watch TV in it?)

Almost all native speakers are basically grandmasters of their mother tongue. The training time for a 15-year-old native speaker could be approx. 10 hours * 365 days * 15 years = 54,750 hours, more than the time many professional painists spent on practice.

A weak speaker may use a cognate, idiom borrowed from their native tongue or a similar wrong word more often. The translation app produces completely illegible word salad instead.