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Comment by felippee | Hacker News Reader

Hi, author here:

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...

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I agree that self-driving had/have been overhyped over the previous few years. The problem is harder than many people realize.

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.

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

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.

Because there is a dearth of experts and a lack of deep technical knowledge among many business people, there are still a great many companies that have not yet started investing in deep learning or AI despite potential profits based on current technology. Non-tech sectors of the economy are probably underinvesting at the moment.

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.

While I agree there is underinvestment in non-tech sectors, I don't see why that would change and they will use deep learning. There are lots of profitable things in non-tech sectors that can be done with linear regression but not done.

There are lots of things in the non-tech sector that can be automated with simple vanilla software but isn't. To use AI instead, you need to have 1) sophisticated devs in place, 2) a management that gets the value added, 3) lots of data in a usable format, 4) willingness to invest & experiment. Lots of non-tech businesses are lacking one if not all of these.

This. And at the end of the day, deep learning is just a more sophisticated version of linear regression. (To listen to some people talking, you'd think if a machine just curve-fits enough data-points, it'll suddenly wake up and become self-aware or something? The delusion is just unbelievable!)

Just like it did for ecommerce? Expectations we're wildly inflated, there was a bust, the market re adjusted and value was created

Only after the dot-com bust killed off all the weaklings ... there was a brief "cold-snap" after 2001 ... then Web 2.0 happened.

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

> I agree that self-driving had/have been overhyped over the previous few years. The problem is harder than many people realize.

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.

But current signs designed for humans work well. They're machine readable (traffic sign detection is available from basically all manufacturers) and can (usually) be understood without prior knowledge and don't need much change over decades. I think there are few examples where messages were designed for computers but are easy to understand independent of the system, manufacturer. ASCII encoded text files are the only thing that come to mind.

Hi, why in your analysis you spoke only about the companies that are not doing so well in self driving leaving out waymo success story? They are already have been hauling passengers without a safety pilot since last October. I guess without the minimum problem otherwise we would have heard plenty in the news like it happened for Tesla and Uber accidents. Is it not too convenient to leave out the facts that contradict your hypothesis?

Is this happening inside google campus or in a city?

In Arizona, on the streets: https://arstechnica.com/cars/2018/02/robotaxi-permit-gets-ar...

Where are the Waymo cars running? Everywhere? Are they still veering into buses to avoid sandbags?

Phoenix, and they plan to start a commercial service by the end of the year in several US cities.

Making cars that drive safely no current, busy roads is a very difficult task. It is not surprising that the current systems do not do that (yet). It is surprising to me how well they still do. The fact though that my phone understands my voice and my handwriting and does on the fly translation of menus and simple requests is a sign of a major progress, too.

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.

How can something be biased when it's listing facts?

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.

How can something be biased when it's listing facts?

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.

First, what are said 100 facts? The article looks at fairly mundane metrics such as number of tweets or self-driving accidents...

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.

> First, what are said 100 facts?

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

Can't speak for the other items, but these

>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.

YouTube captioning in English works surprisingly well, the improvement over the last few years is huge. It still chokes on proper nouns but in general it mostly works.

Mh no it does not. It is just a source of hilarity apart from a few very specific cases (political speeches mostly, because of their slow pace, good english and prononciation I guess).

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

I think it's a bit like self-driving cars in the sense that it's good enough to be impressive but not good enough to be actually usable everywhere. Of course self-driving is worse because people seldom die of bad captions.

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.

What's important to remember, I think, is that we should not compare YouTube auto captions to human made captions, because auto captions were not created as a substitute for human made captions - if it wasn't for auto captioning, all these videos wouldn't get any captions at all. They may never be perfect, but they're not designed to be, they're creating new value on their own. And IMO they crossed the threshold of being usable, at least for English.

It works for general purpose videos. Transcripts of any kind appear to stop working whenever there's domain knowledge involved. That doesn't matter for most youtube videos but is crucial if you want to have a multi purpose translator/encoder.

A. Cooper had a nice example of this kind: a dancing bear. Sure, the fact that bear dances is very amusing, but let it not distract us from the fact that it dances very very badly.

Google Translate is way, way better than it used to be (at least German > English which I suppose is probably an easier task than many languages).

Google Translate of Goethe:

    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

word for word not completely bad, but then it breaks when we have to translate 'Tor'. Google Translate is clueless because it is unable to derive that here the 'fool' is meant.

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

Google Translate is the 'poor gate'.

On the other hand Deepl gives translations for news articles that are of such quality that it allows me to read international news as if it were local. Definitely useful.

DeepL is better, but it basically has the same problems. I understand both German and English - and I can easily detect where DeepL also shifts the meaning of sentences, sometimes even to mean the opposite. DeepL like Google Translate has no concept for 'is the meaning preserved?'.

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.

Not sure if I agree. If you have some knowledge of the language is still mostly easier to translate the words you don't understand. Easy texts work, anything more complicated (e.g. science articles) not really.

" letters to investors are among the most biased pieces of writing in existence. "

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.

Of course Google (or any other company) aren't going to blatently lie in a letter to investors (that kind of thing gets you sued) but it's pretty easy to spin words to sound more impressive than they may actually be.

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...

... I don't think I understand that video even with my own ears. YouTube captioning has actually significantly improved from it's previous hilarious state

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