AI winter is well on its way - <AUTHOR> https://blog.piekniewski.info/2018/05/28/ai-winter-is-well-on-its-way/ <comment><USER2> I was recently "playing" with some radiology data. I had no chance to identify diagnoses myself with untrained eyes, something that probably takes years for a decent radiologist to master. Just by using DenseNet-BC-100-12 I ended up with 83% ROC AUC after a few hours of training. In 4 out of 12 categories this classifier beat best human performing radiologists. Now the very same model with no other change than adjusting number of categories could be used in any image classification task, likely with state-of-art results. I was surprised when I applied it to another, completely unrelated dataset and got >92% accuracy right away.

If you think this is a symptom of AI winter, then you are probably wasting time on outdated/dysfunctional models or models that aren't suited for what you want to accomplish. Looking e.g. at Google Duplex (better voice synchronization than Vocaloid I use for making music), this pushed state-of- art to unbelievable levels in hard-to-address domains. I believe the whole SW industry will be living next 10 years from gradual addition of these concepts into production.

If you think Deep (Reinforcement) Learning is going to solve AGI, you are out of luck. If you however think it's useless and won't bring us anywhere, you are guaranteed to be wrong. Frankly, if you are daily working with Deep Learning, you are probably not seeing the big picture (i.e. how horrible methods used in real-life are and how you can easily get very economical 5% benefit of just plugging in Deep Learning somewhere in the pipeline; this might seem little but managers would kill for 5% of extra profit).

<comment><USER3> AI winters are a result of a massive disparity between the expectations of the general public and the reality of where the technology currently sits. Just like an asset bubble, the value of the industry as a whole pops as people collectively realize that AI, while not being worthless, is worth significantly less than they thought.

Understand that in pop-sci circles over the past several years the general public is being exposed to stories warning about the singularity by well respected people like Stephen Hawking and Elon Musk ([http://time.com/3614349/artificial-intelligence- singularity-...](http://time.com/3614349/artificial-intelligence-singularity- stephen-hawking-elon-musk/)). Autonomous vehicles are on the roads and Boston Dynamics is showing very real robot demonstrations. Deep learning is breaking records in what we thought was possible with machine learning. All of this progress has excited an irrational exuberance in the general public.

But people don't have a good concept of what these technologies can't do, mainly because researchers, business people, and journalists don't want to tell them--they want the money and attention. But eventually the general public wises up to the unfulfillment of expectations, and drives their attention elsewhere. Here we have the AI winter.

<comment><USER4> > _AI winters are a result of a massive disparity between the expectations of > the general public and the reality of where the technology currently sits._

I think they also happen when the best ideas in the field run into the brick wall of insufficiently developed computer technology. I remember writing code for a perceptron in the '90s on an 8 bit system, 64 k RAM - it's laughable.

But right now compute power and data storage seem plentiful, so rumors of the current wave's demise appear exaggerated.

<comment><USER2> I wish GPUs were 1000x faster... Then I could do some crazy magic with Deep Learning instead of waiting weeks for training to be finished...

<comment><USER5> That's more a matter of budget than anything else. If you problem is valuable enough spending the money in a short time-frame rather than waiting for weeks can be well worth the investment.

<comment><USER2> I cannot fit a cluster of GPUs into a phone where I could make magic happen real-time though :(

<comment><USER5> Hm. Offload the job to a remote cluster? Or is comms then the limiting factor?

<comment><USER2> It won't give us that snappy feeling; imagine learning things in milliseconds and immediately displaying them on your phone.

<comment><USER4> Jeez. That would be faster than protein-and-water-based systems, which up until now are still the faster learners.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER6> This is a deep, significant post (pardon pun etc).

The author is clearly informed and takes a strong, historical view of the situation. Looking at what the really smart people who brought us this innovation have said and done lately is a good start imo (just one datum of course, but there are others in this interesting survey).

 _Deepmind hasn 't shown anything breathtaking since their Alpha Go zero._

Another thing to consider about Alpha Go and Alpha Go Zero is the vast, vast amount of computing firepower that this application mobilized. While it was often repeated that ordinary Go program weren't making progress, this wasn't true - the best, amateur programs had gotten to about 2 Dan amateur using Makov Tree Search. Alpha Go added CNNs for it's weighting function and petabytes of power for it's process and got effectiveness up to best in the world, 9 Dan professional, (maybe 11 Dan amateur for pure comparison). [1]

Alpha Go Zero was supposedly even more powerful, learned without human intervention. BUT it cost petabytes and petabytes of flops, expensive enough that they released a total of ten or twenty Alpha Go Zero game to the world, labeled "A great gift".

The author convenniently reproduces the chart of power versus results. Look at it, consider it. Consider the chart in the context of Moore's Law retreating. The problems of Alpha Zero generalizes as described in the article.

The author could also have dived into the troubling question as of "AI as ordinary computer application" (what does testing, debugging, interface design, etc mean when the app is automatically generated in an ad-hoc fashion) or "explainability". But when you can paint a troubling picture without these gnawing problems appearing, you've done well.

[1] [https://en.wikipedia.org/wiki/Go_ranks_and_ratings](https://en.wikipedia.org/wiki/Go_ranks_and_ratings)

<comment><USER7> >Deepmind hasn't shown anything breathtaking since their Alpha Go zero

They went on to make AlphaZero, a generalised version that could learn chess, shogi or any similar game. The chess version beat a leading conventional chess program 28 wins, 0 losses, and 72 draws.

That seemed impressive to me.

Also they used loads of compute during the training but not so much during play.(5000 TPUs, 4TPUs).

Also it got better than humans in those games from scratch in about 4 hours whereas humans have had 2000 years to study them so you can forgive it some resource usage.

<comment><USER8> It's not like humanity really needs another chess playing program 20 years after IBM solved that problem (but now utilizing 1000x more compute power). I just find all these game playing contraptions really uninteresting. There are plenty real world problems to be solved of much higher practicality. Moravec's paradox in full glow.

<comment><USER9> I guess there are reasons why researchers build chess programs: it is easy to compare performance between algorithms. When you can solve chess, you can solve a whole class of decision-making problems. Consider it as the perfect lab.

<comment><USER10> What is that class of decision-making problems? It's nice to have a machine really good at playing chess, but it's not something I'd pay for. What decision-making problems are there, in the same class, that I'd pay for?

 _Consider it as the perfect lab._

Seems like a lab so simplified that I'm unconvinced of its general applicability. Perfect knowledge of the situation and a very limited set of valid moves at any one time.

<comment><USER11> > What decision-making problems are there, in the same class, that I'd pay > for?

an awful lot of graph and optimization problems. See for instance some examples in [https://en.wikipedia.org/wiki/A*_search_algorithm](https://en.wikipedia.org/wiki/A*_search_algorithm)

<comment><USER12> Perfect information problem solving is not interesting anymore.

Did they manage to extend it to games with hidden and imperfect information?

(Say, chess with fog of war also known as Dark Chess. Phantom Go. Pathfinding equivalent would be an incremental search.)

Edit: I see they are working on it, predictive state memory paper (MERLIN) is promising but not there yet.

<comment><USER13> Strongly disagree. There are a lot of approximation algorithms and heuristics in wide use - to the tune of trillions of dollars, in fact, when you consider transportation and logistics, things like asic place & route, etc. These are all intractable perfect info problems that are so widespread and commercially important that they amplify the effect of even modest improvements.

(You said problems, not games...)

<comment><USER12> Indeed, there are a few problems where even with perfect information you will be hard pressed to solve them. But that is only a question of computational power or the issue when the algorithm does not allow efficient approximation (not in APX space or co-APX).

The thing is, an algorithm that can work with fewer samples and robustly tolerating mistakes in datasets (also known as imperfect information) will be vastly cheaper and easier to operate. Less tedious sample data collection and labelling.

Working with lacking and erroneous information (without known error value) is necessarily a crucial step towards AGI; as is extracting structure from such data.

This is the difference between an engineering problem and research problem.

<comment><USER13> Perhaps a unifying way of saying this is: it's a research problem to figure out how to get ML techniques to the point they outperform existing heuristics on "hard" problems. Doing so will result in engineering improvements to the specific systems that need approximate solutions to those problems.

I completely agree about the importance of imperfect information problems. In practice, many techniques handle some label noise, but not optimally. Even MNIST is much easier to solve if you remove the one incorrectly-labeled training example. (one! Which is barely noise. Though as a reassuring example from the classification domain, JFT is noisy and still results in better real world performance than just training on imagenet.)

</comment></comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER14> A different take by Google's cofounder, Sergey Brin, in his most recent Founders' Letter to investors:

"The new spring in artificial intelligence is the most significant development in computing in my lifetime."

He listed many examples below the quote.

"understand images in Google Photos;

enable Waymo cars to recognize and distinguish objects safely;

significantly improve sound and camera quality in our hardware;

understand and produce speech for Google Home;

translate over 100 languages in Google Translate;

caption over a billion videos in 10 languages on YouTube;

improve the efficiency of our data centers;

help doctors diagnose diseases, such as diabetic retinopathy;

discover new planetary systems; ..."

[https://abc.xyz/investor/founders- letters/2017/index.html](https://abc.xyz/investor/founders- letters/2017/index.html)

An example from another continent:

"To build the database, the hospital said it spent nearly two years to study more than 100,000 of its digital medical records spanning 12 years. The hospital also trained the AI tool using data from over 300 million medical records (link in Chinese) dating back to the 1990s from other hospitals in China. The tool has an accuracy rate of over 90% for diagnoses for more than 200 diseases, it said."

[https://qz.com/1244410/faced-with-a-doctor-shortage-a- chines...](https://qz.com/1244410/faced-with-a-doctor-shortage-a-chinese- hospital-is-betting-big-on-artificial-intelligence-to-treat-patients/)

<comment><USER8> 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...

<comment><USER14> 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.

<comment><USER8> > 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.

<comment><USER14> 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.

<comment><USER15> 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.

<comment><USER16> 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.

</comment></comment></comment></comment></comment></comment></comment><comment><USER17> I'm a scientist from a field outside ML who knows that ML can contribute to science. But I'm also really sad to see false claims in papers. For example, a good scientist can read an ML paper, see claims of 99% accuracy, and then probe further to figure out what the claims really mean. I do that a lot, and I find that accuracy inflation and careless mismanagement of data mars most "sexy" ML papers. To me, that's what's going to lead to a new AI winter.

<comment><USER18> You hear Facebook _all the time_ saying how it "automatically blocks 99% of the _terrorist content_ " with AI to the public and governments.

Nobody thought to ask: "How do you _know_ all of that content is terrorist content? Does anyone check every video afterwards to ensure that all the blocked content was indeed terrorist content?" (assuming they even have an exact definition for it).

<comment><USER19> Also, how do they know how much terrorist content they aren't blocking (the 1%), since they by definition haven't found it yet?

</comment></comment></comment><comment><USER20> FYI This post is about deep learning. It could be the case that neural networks stop getting so much hype soon, but the biggest driver of the current "AI" (ugh I hate the term) boom is the fact that everything happens on computers now, and that isn't changing any time soon.

We log everything and are even starting to automate decisions. Statistics, machine learning, and econometrics are booming fields. To talk about two topics dear to my heart, we're getting way better at modeling uncertainty (bayesianism is cool now, and resampling-esque procedures aged really well with a few decades of cheaper compute) and we're better at not only talking about what causes what (causal inference), but what causes what when (heterogeneous treatment effect estimation, e.g. giving you aspirin right now does something different from giving me aspirin now). We're learning to learn those things super efficiently (contextual bandits and active learning). The current data science boom goes far far far far beyond deep learning, and most of the field is doing great. Maybe those bits will even get better faster if deep learning stops hogging the glory. More likely, we'll learn to combine these things in cool ways (as is happening now).

<comment><USER21> Bayesian can be seen as a subset of deep learning or hell a superset.

AI is a superset and Machine learning is a subset of AI and most funding is in deep learning. Once Deep Learning hit the limit I believe there will be an AI winter.

Maybe there will be hype around statistic (cross fingers) which will lead to Bayesian and such.

<comment><USER22> >Bayesian can be seen as a subset of deep learning or hell a superset.

 _eh-hem_

DIE, HERETIC!

 _eh-hem_

Ok, with that out of my system, no, Bayesian methods are definitely _not_ a subset of deep learning, in any way. Hierarchical Bayes could be labeled "deep Bayesian methods" if we're marketing jerks, but Bayesian methods mostly do not involve neural networks with >3 hidden layers. It's just a different paradigm of statistics.

<comment><USER21> My mentor was very very adamant about Bayesian network and hierarchical as being deep learning.

He sees the latent layer in the hierarchical model as the hidden layer and the Bayesian just have a strict restrictions/assumptions to the network where as the deep learning is more dumb and less assuming. A few of my professor thinks that PGM, probability graphical model is a super set of deep learning/neural network.

This is where my thinking come from.

IIRC, a paper have shown that gradient descent seems to exhibit MCMCs (blog with paper link inside that led to this conclusion of mine: [http://www.inference.vc/everything-that-works-works- because-...](http://www.inference.vc/everything-that-works-works-because-its- bayesian-2/)).

But I am not an expert in Neural Network nor know the topic well enough to say such a thing. Other than was deferring to opinions of some one that's better than myself. So I'll keep this in mind and hopefully one day have the time to do more research into this topic.

Thank you.

<comment><USER22> I think your link, and your mentor, are somewhat fundamentalist about their Bayesianism.

</comment></comment></comment></comment></comment><comment><USER23> Forget about self driving cars - the real killer application of deep learning is mass surveillance - there are big customer for that (advertising, policing, political technology - we better get used to the term) and its the only technique that can get the job done.

I sometimes think that there really was no AI winter as we got other technologies that implemented the ideas: SQL Databases can be seen as an application of many ideas in classical AI - for example its a declarative language for defining relations among tables; you can have rules in the form of SQL stored procedures; actually it was a big break (paradigm shift is the term) in how you deal with data - the database engine has to do some real behind the scenes optimization work in order to get a workable representation of the data definition (that is certainly bordering on classical AI in complexity).

these boring CRUD applications are light years ahead in how data was handled back in the beginning.

<comment><USER24> The BBC recently requested information about the use of facial recognition from UK police forces. Those that use facial recognition reported false positive rates of >95%. That led some to abandon the systems, others just use it as one form of pre-screening. Mass surveillance with facial recognition is nowhere near levels where it can be used unsupervised. And that's even before people actively try to deceive it.

For advertising, I'm also not sure if there's been a lot of progress. Maybe it's because I opted out too much but I have the feeling that ad targeting hasn't become more intelligent, rather the opposite. It's been a long time that I've been surprised at the accuracy of a model tracking me. Sure, targeted ads for political purposes can work very well but are nothing new and don't need any deep learning nor any other "new" technologies.

Where I really see progress is data visualisation. Often dismissed it can be surprisingly hard to get right and tools around that (esp for enterprise use) have developed a lot over recent years. And that's what companies need. No one's looking for a black-box algorithm to replace marketing, they just want to make some sense of their data and understand what's going on.

<comment><USER25> Aha, yeah I saw this in the news - pretty classic use of people horribly misunderstanding statistics and/or misrepresenting the facts. Let's say one person out of 60 million has sauron's ring. I use my DeepMagicNet to classify everyone, and get 100 positive results. Only one is the ringbearer, so I have a 99% error rate. Best abandon ship.

<comment><USER24> I do think of myself that I can read statistics. They didn't mention it as a false positive rate, that's what I interpreted. I don't have the article here but they said that the system gave out ~250 alerts, of which ~230 turned out to be incorrect. It didn't specify at all how big the database of potential suspects was. The number of scanned faces was ~50-100k each time (stadium). Nevertheless, the 230/250 is the issue here, simply because it destroys trust in the system. If the operator already knows that the chance of this being a false alarm is ~5%, will they really follow up all alerts?

</comment></comment></comment></comment><comment><USER26> > Deepmind hasn't shown anything breathtaking since their Alpha Go zero.

Didn't this _just_ happen? Maybe my timescales are off, but I've been thinking about AI and Go since the late 90s, and plenty of real work was happening before then.

Outside a handful of specialists, I'd expect another 8-10 years before the current state of the art is generally understood, much less effectively applied elsewhere.

<comment><USER27> Also why does every single result has to be breathtaking? Here's a quick example, at IO they announced that their work on Android improved battery life by up to 30%. That's pretty damn impressive.

<comment><USER8> > Also why does every single result has to be breathtaking?

If you build the hype like say Andrew Ng it better be. Also if you consume more money per month than all the CS departments of a mid sized country, it better be.

<comment><USER28> In terms of hype you may be right, but it doesn't mean that if something doesn't live up to the hype of Andrew Ng or Elon Musk it won't still be pretty good.

For instance: even if Elon Musk doesn't colonize Mars but instead just builds the BFR, that would still be amazing; even if BFR is never build but falcon 9 becomes fully reusable that would be great; even if falcon 9 won't be fully reusable, the fact that it cut the launching cost to space is still pretty good.

Even if we don't achieve any great breakthroughs with AGI, the fact that we started to use transfer learning to diagnose human disseases is pretty amazing; the fact that a japanese guy used tensorflow on a raspbery pi to categorize real cucumbers by shape is amazing.

All of this stuff won't go away; people will not say "hey, let's just forget about this deep learning thing and put it in some dusty shelf, it's useless for now". Maybe it will take 20 or 50 more years, maybe it's a slow thaw, but how could this be a winter?

<comment> rm_-rf_slash Honestly I think the raspberry pi indicates the (short-term) future of AI. Most of the "easy" problems have been solved (image classification, game playing), but the hard ones like NLP are orders of magnitude more complex and therefore elusive.

I'm happy to leave the hard problems for the PhDs and the big tech researchers. Go nuts, folks.

In the meantime, the applications for small-scale, pre-trained neural networks seem limitless. Manufacturing, agriculture, retail, pretty much any industry could make use of portable neural networks.

<comment><USER28> I feel exactly the same way. Just wait 2-3 years before someone launches an embedded TPU and the sky will be the limit.

</comment></comment></comment></comment></comment></comment><comment><USER29> Warning 23 year old CS grad angst ridden post:

I'm very sick of the AI hype train. I took a PR class for my last year of college, and they couldn't help but mention it. LG Smart TV ads mention it, Microsoft commercials, my 60 year old tech illiterate Dad. Do any end users really know what it's about? Probably not, nor should that matter, but it's very triggering to see something that was once a big part of CS turned into a marketable buzzword.

I get triggered when I can't even skim through the news without hearing Elon Musk and Steven Hawking ignorantly claim AI could potentially takeover humanity. People believe them because of their credentials, when professors who actually teach AI will say otherwise. I'll admit, I've never taken any course in the subject myself. An instructor I've had who teaches the course argues it doesn't even exist, it's merely a sequence of given instructions, much like any other computer program. But hey, people love conspiracies, so let their imagination run wild.

AI is today what Big Data was about 4 years ago. I do not look highly on any programmer that jumps bandwagons, especially for marketability. Not only is it impure in intention, it's foolish when their are 1000 idiots just like them over-saturating the market. Stick with what you love, even if it's esoteric. Then you won't have to worry about your career value.

<comment><USER30> AI taking over is the biggest threat facing humanity, but I don't think Hawking ever claimed it was imminent; it's likely 1000+ years away.

It should be obvious why a superior intelligence is something dangerous.

<comment><USER31> "it's likely 1000+ years away."

That seems like a pretty high number when you consider the exponential rate of technological advancement.

1000 years in the future is probably going to be completely unrecognizable to us given the current rate of change in society/tech.

<comment><USER32> True exponentials exist very rarely. What we are used to call exponential is often at most quadratic and even then the trend usually exists only for some limited time window.

I wouldn't dare to make prediction about mankind 1000 years from now based on a relatively short time window of technological growth. There are many huge obstacles in the way.

<comment><USER31> Even the present would be mostly unrecognizable to someone who lived 1000 years ago.

1000 years from now will certainly be even stranger to us.

</comment></comment></comment></comment></comment><comment> oh-kumudo Author's reasons:

1.Hype dies down (which is really good! Meaning the chance of burst, is actually lower!)

2.Doesn't scale is false claim. DL methods have scaled MUCH better than any other ML algorithms in recent history (scale SVM is no small task). Scaling for DL methods are much either as comparing to other traditional ML algorithms, where it can be naturally distributed and aggregated.

3\. Partially true. But self-driving is a sophisticated area by itself, DL is part of it, it can't really put full claim on its potential future success or ultimate downfall.

4\. Gary Marcus isn't an established figure in DL research.

AI winter will ultimately come. But it is because people will become more informed about DL's strengths and limits, thus becoming smarter to tell what is BS what is not. AGI is likely not going to happen just with DL, but that is no way meaning it is a winter. DL has revolutionized the paradigm of Machine Learning itself, the shift has now complete, it will stay for a very very long time, and the successor is likely to build upon it not subvert it completely as well.

<comment><USER8> Author here: I'm using deep learning daily so I have a bit of an idea on what I'm talking about.

1) Not my point. Hype is doing very well. But narrative begins to crack, actually indicative of a burst... 2) DL does not scale very well. It does scale better than other ML algorithm because those did not scale at all. If you want to know what scales very well, look at CFD (computational fluid dynamics). DL in nowhere near that ease in scaling. 3) self driving is the poster child of current "AI-revolution". And it is where by far most money is allocated. So if that falls, rest of DL does not matter. 4) Not that this matters, does it?

<comment><USER33> The scaling argument in the article doesn't make any sense. There are rhetorical queries like "does this model with 1000x as many parameters work 1000x as well?" but what it means to scale or perform are not clearly or consistently defined - let alone defined in a way that would make your point about the utility of the advances.

OpenAI's graph shows new architectures being used with more parameters because people are innovating on architecture and scale at the same time. Arguing that old methods "failed to scale" is like arguing that processor development was a failure because Intel had to develop a 486 instead of making a 386 work with more transistors (or more _something_ ).

And what does CFD have to do with anything, except maybe an odd attempt to argue from authority? Can you formalize from CFD a notion of "scaling well" well that anyone else agrees is useful for measuring AI research?

<comment><USER34> CFD was merely used as an example of something that does scale well. I'm not sure it was the best example, since CFD isn't very common. But basically you have a volume mesh and each cell iterates on the Navier-Stokes equation. So if you have N processor cores, you break the mesh in N pieces, each of which get processed in parallel. Doubling the number of cores allows you process double the amount in the same time, minus communication loses (each section of the mesh needs to communicate the results on its boundary to its neighbors).

I don't fully understand the graph, but it looks like his point is that Alpha Go Zero uses 1e5 times as many resources than AlexNet, but does not produce anywhere near 10,000 times better results. We saw that with CFDt 1e5 more cores resulted in 1e5 better results (= scales). The assertion is that DL's results are much less than 1e5 better, hence it does not scale.

Basically the argument is:

1\. CFD produces N times better results given N times more resources [this is implied, requires a knowledge of CFD]. That is, f(a _x) = a_ f(x). Or, f(a _x) = 1_ a * f(x).

2\. Empirically, we see that DL has used 1e5 more resources, but is not producing 1e5 times better results. [No quantitative analysis of how much better the results are is given]

3\. Since DL has f(a * x) = b * a * f(x), where b < 1, DL does not scale. [Presumably b << 1 but the article did not give any specific results]

This isn't a very rigorous argument and the article left out half the argument, but it is suggestive.

<comment><USER8> Thanks for that, that is essentially my point. Agree it is not very rigorous, but it gets the idea across. By scalable we'd typically think "you throw more gpu's at it and it works better by some measure". Deep learning does that only in extremely specific domains, e.g. games and self play as in alpha go. For majority of other applications it is architecture bound or data bound. You can't throw more layers, more basic DL primitives and expect better results. You need more data, and more phd students to tweak the architecture. That is not scalable.

<comment><USER33> More compute -> more precision is just one field's definition of scalable... Saying that DNNs can't get better just by adding GPUs is like complaining that an apple isn't very orange.

To generalize notions of scaling, you need to look at the economics of consumed resources and generated utility, and you haven't begun to make the argument that data acquisition and PhD student time hasn't created ROI, or that ROI on those activities hasn't grown over time.

Data acquisition and labeling is getting cheaper all the time for many applications. Plus, new architectures give ways to do transfer learning or encode domain bias that let you specialize a model with less new data. There is substantial progress and already good returns on these types of scalability which (unlike returns on more GPUs) influence ML economics.

<comment><USER8> OK, the definition of scalable is crucial here and it causes lots of trouble (this is also response to several other posts so forgive me if I don't address your points exactly).

Let me try once again: an algorithm is scalable if it can process bigger instances by adding more compute power.

E.g. I take a small perceptron and train it on pentium 100, and then take a perceptron with 10x parameters on Core I7 and get better output by some monotonic function of increase in instance size (it is typically a sub linear function but it is OK as long as it is not logarithmic).

DL does not have that property. It requires modifying the algorithm, modifying the task at hand and so on. And it is not that it requires some tiny tweaking. It requires quite a bit of tweaking. I mean if you need a scientific paper to make a bigger instance of your algorithm this algorithm is not scalable.

What many people here are talking about is whether an instance of the algorithm can be created (by a great human effort) in a very specific domain to saturate a given large compute resource. And yes, in that sense deep learning can show some success in very limited domains. Domains where there happens to be a boatload of data, particularly labeled data.

But you see there is a subtle difference here, similar in some sense to difference between Amdahl's law and Gustafson's law (though not literal).

The way many people (including investors) understand deep learning is that: you build a model A, show it a bunch of pictures and it understands something out of them. Then you buy 10x more GPU's, build model B that is 10x bigger, show it those same pictures and it understands 10x more from them. Look I, and many people here understand this is totally naive. But believe me, I talked to many people with big $ that have exactly that level of understanding.

<comment><USER33> I appreciate the engagement in making this argument more concrete. I understand that you are talking about returns on compute power.

However, your last paragraph about how investors view deep learning does not describe anyone in the community of academics, practitioners and investors that I know. People understand that the limiting inputs to improved performance are data, followed closely by PhD labor. Compute power is relevant mainly because it shortens the feedback loop on that PhD labor, making it more efficient.

Folks investing in AI believe the returns are worth it due to the potential to scale deployment, not (primarily) training. They may be wrong, but this is a straw man definition of scalability that doesn't contribute to that thesis.

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER35> I'm not sure how the hype wagon started but I for one am glad it's about to pop.

I am working (founded) a startup and while we have AI on the roadmap for about a years time, it isn't something that's central to our product. (We already use some ML techniques but I'd not confidently boast its the same thing as AI).

Cue an informal lunch with a VC guy who takes a look, says we're cool and tells us just to plaster the word AI in more places - he was sure we could raise a stupendous sum of cash doing that.

As an AI enthusiast I was bothered by this. We have everyone and their mother hyping AI into areas it's not even relevant in, let alone effective at.

A toning down would be healthy. We could then focus on developing the right technology slowly and without all the lofty expectations to live up to.

<comment><USER36> > ...just to plaster the word AI in more places...

I bet that's how the new "AI-assisted" Intellisense in Visual Studio got greenlit:

[https://blogs.msdn.microsoft.com/visualstudio/2018/05/07/int...](https://blogs.msdn.microsoft.com/visualstudio/2018/05/07/introducing- visual-studio-intellicode/)

If an AI-infested text editor isn't a sure sign that the bubble is going to pop soon then I don't know ;)

<comment><USER37> I think part of this ridiculousness may just be that the common lexicon doesn't have enough words for AI. "AI-assisted Intellisense" at the moment seems to boil down to a marginally novel way of ordering autocomplete suggestions, and like...

It's not wrong? It's neat, it's potentially useful, and it's powered by something under the umbrella of "AI". The problem is that that umbrella is gigantic, and covers everything from the AI system providing routes for the AI system in a self-driving truck on AI-provided schedules for a hypothetical mostly-automated shipping business, to a script I whipped up in ten minutes to teach something 2+2 and literally nothing else.

So we get to the nonsense position where there isn't a better way to describe a minor improvement to what is essentially the ordering of a drop-down list except by comparison to the former example.

</comment></comment></comment><comment><USER38> AI winter is not on its way. We constantly get new breakthroughs and there's no end in the view. For example, in the last year a number of improvements in GANs were introduced. This is really huge, since GANs are able to learn a dataset structure without explicit labels, and this is a large bottleneck in applying ML more widely.

IMO, we are far away from AGI, but even current technologies applied widely will lead to many interesting things.

<comment><USER8> I sure agree there are many interesting things going on, there is no question about that. Also most of them are toy problems focused in some restricted domains, while a huge bag of equally interesting real world problems is sitting untouched. And let me tell you, all those VC's that put in probably way north of $10B are not looking forward to more NIPS papers or yet another style transfer algorithm.

<comment><USER38> >Also most of them are toy problems focused in some restricted domains, while a huge bag of equally interesting real world problems is sitting untouched.

It always starts with toy problems. Recognizing pictures from imagenet was also a toy problem back then.

</comment></comment></comment><comment><USER39> "Deepmind hasn't shown anything breathtaking since their Alpha Go zero"

... what about when the Google assistant near perfectly mimicked a human making a restaurant reservation .... the voice work was done at DeepMind.

All the problems in AI haven't been solved yet? Well no, of course not. Limitations exist and our solutions need to be evolved.

I think perhaps the biggest constraint is requiring huge amounts of training data so solve problem X. Humans simply don't need that, which must be some indication that what we're doing isn't quite right.

<comment><USER40> _what about when the Google assistant near perfectly mimicked a human making a restaurant reservation_

Any sufficiently advanced technology is indistinguishable from a rigged demo

</comment></comment><comment><USER41> Disclaimer: I am a lay technical person and don't know much about AI.

I find this article somewhat condescending. I look at all the current development as stepping stones to progress, not an overnight success that does everything flawlessly. I imagine the future might be some combination of different solutions, and what the author proposes may or may not play a part in it.

<comment> bra-ket it's not a stepping stone, if you look closely it's a dead end

<comment><USER42> I don't see how systematically accurate image classifiers and facial recognition systems built on deep learning is a 'dead end'. Products are products. If deep learning has led to actual profits in actual companies, it's not a dead end. As to whether this leads to AGI is a completely different question.

<comment><USER43> The point is that the profits may not be as grand as the current level of hype may indicate

Edit: additionally it could be a dead end because the hype tends to narrow the directions we explore with ML. If everyone is obsessing about DL, we could be infuriatingly ignoring other research directions right under our noses.

</comment></comment></comment></comment><comment><USER44> An AI "winter" is a long period in which [edit: funding is cut because...] researchers _are in disbelief_ about having a path to real intelligence. I think that is not the case at this time, because we have (or approaching) adequate tools to rationally dismiss that disbelief. The current AI "spring" has brought back the belief that connectionism may by the ultimate tool to explain the human brain. I mean you can't deny that DL models of vision look eerily like the early stages in visual processing in the brain (which is a very large part of it). Even if DL researchers lose their path in search for "true AI", the neuroscientists can keep probing the blueprint to find new clues to its intelligence. Even AI companies are starting to create plausible models that link to biology. So at this time, it's unlikely that progress will be abandoned any time soon.

E.g. [https://arxiv.org/abs/1610.00161](https://arxiv.org/abs/1610.00161) [https://arxiv.org/abs/1706.04698](https://arxiv.org/abs/1706.04698) [https://www.ncbi.nlm.nih.gov/pubmed/28095195](https://www.ncbi.nlm.nih.gov/pubmed/28095195)

<comment><USER17> No, AI winter was when the AI people oversold the tech, then failed to deliver, and lost their funding. This is well documented in histories of the field.

<comment><USER44> I think the scientific pessimism preceded the funding cuts:

[https://en.wikipedia.org/wiki/AI_winter](https://en.wikipedia.org/wiki/AI_winter)

</comment></comment></comment><comment><USER8> Author here: seriously I'm here at the front page for the second day in the a row!?

The sheer viral popularity of this post, which really was just a bunch of relatively loose thoughts indicates that there is something in the air regarding AI winter. Maybe people are really sick of all that hype pumping...

Just a note: I'm a bit overwhelmed so I can't address all the criticism. One thing I would like to state however, is that I'm actually a fan of connectionism. I think we are doing it naively though and instead of focusing on the right problem we inflate a hype bubble. There are applications where DL really shines and there is no question about that. But in case of autonomy and robotics we have not even defined the problems well enough, not to mention solving anything. But unfortunately, those are the areas where most best/expectations sit, therefore I'm worried about the winter.

<comment> s-shellfish Do you think there's a balance to be struck between connectionism and computationalism?

</comment></comment><comment><USER45> The argument is that self-driving won't work because Uber and Tesla had well- publicized crashes. But I don't see how this tells us anything about other, apparently more cautious companies like Waymo. There seem to be significant differences in technology.

More generally, machine learning is a broad area and there's no reason to believe that different applications of it will all succeed or all fail for similar reasons. It seems more likely there will be more winners along with many failed attempts.

<comment><USER46> _The argument is that self-driving won 't work because Uber and Tesla had well-publicized crashes. But I don't see how this tells us anything about other, apparently more cautious companies like Waymo. There seem to be significant differences in technology._

Yes. I've been saying this for a while. Waymo's approach is about 80% geometry, 20% AI. Profile the terrain, and only drive where it's flat. The AI part is for trying to identify other road users and guess what they will do. When in doubt, assume worst case and stay far away from them.

I was amazed that anyone would try self-driving without profiling the road. Everybody in the DARPA Grand Challenge had to do that, including us, because it was off-road driving and you were not guaranteed a flat road. The Google/Waymo people understood this. Some of the others just tried dumping the raw sensor data into a deep learning system and getting out a steering wheel angle. Not good.

<comment><USER47> A lot of companies fear the ship's leaving without them. They try to rush ahead without thinking and then crashes. That's what it feels like every time a car company or another tech company say they're going to build the next self-driving car. I don't know why but I always feel like waymo is already 10,000 miles ahead.

</comment></comment></comment><comment><USER48> Honestly, I think this is a good thing for both AI researchers as well as AI practitioners. One mans AI-winter is another mans stable platform.

While the number of world-shattering discoveries using DL may be on the decline (ImageNet, Playing Atari, Artistic Style Transfer, CycleGAN, DeepFakes, Pix2Pix etc), now both AI researchers and practitioners can work in relative peace to fix the problem of the last 10%, which is where Deep Learning has usually sucked. 90% accuracy is great for demos and papers, but not even close to useful in real life (as the Uber fiasco is showing).

As an AI practitioner, it was difficult to simply keep up with the latest game-changing paper (I have friends who call 2017 the Year of the GAN!), only to later discover new shortcomings of each. Of course, you may say, why bother keeping up? And the answer is simply that when we are investing time to build something that will be in use 5-10 years from now, we want to ensure the foundation is built upon the latest research, and the way most papers talk about their results makes you believe they are best suited for all use cases, which is rarely the case. But when the foundation itself keeps moving so fast, there is no stability to build upon at all.

That and what jarym said is perfectly true as well.

The revolution is done, now it's time to evolution of these core ideas for actual value generation , and I for one am glad about that.

</comment><comment><USER49> AI winter? Hardly. Current methods have only been applied to a very tiny fraction of problems that they can help solve. And, this trend will only accelerate until computing resources become too expensive.

As long as there is ROI, AI projects will continue to be financed, top thinkers around the world will be paid to do more research, and engineers will implement the most recent techniques into their products and services to stay competitive. This is a classic feedback system that results in exponential progress.

</comment><comment><USER7> This seems over negative. Just the opening argument, that companies were saying "that fully self driving car was very close" but "this narrative begins to crack"

Yet here they are self driving [https://www.youtube.com/watch?v=QqRMTWqhwzM&feature=youtu.be](https://www.youtube.com/watch?v=QqRMTWqhwzM&feature=youtu.be) and you should be able to hail one as a cab this year [https://www.theregister.co.uk/2018/05/09/self_driving_taxis_...](https://www.theregister.co.uk/2018/05/09/self_driving_taxis_waymo/)

<comment><USER29> You sure about that? [https://www.youtube.com/watch?v=8IqpUK5teGM](https://www.youtube.com/watch?v=8IqpUK5teGM)

Tesla self driving cars have crashed too. Arrogant people like Elon Musk are making a bad name for the hardworking AI developers who are actually trying to make self driving cars faulty proof.

<comment><USER7> That vid of the Uber crash - just because one company has a crap product doesn't mean they are all bad. Waymo is about the only one that seems about ready to go and I think partly because they don't just count on deep learning.

<comment><USER29> Like I said, Uber and Tesla have both been reported. If two companies of that status with their funding abilities are potentially dangerous, I wouldn't doubt any smaller company would.

</comment></comment></comment></comment><comment><USER50> I thought there would be more of a backlash / winter onset when people realize that Alexa is so annoying to deal with (and you basically have to learn a set of commands) because AI isn't that clever yet. Also, when people realize that autocorrect took a dive for making edits when Google started putting a neural net in charge. (No! Stop deleting random words and squishing spaces during edits).

In other words I figured it would be the annoyances at what "should be easy by now" that would get Joe CEO to start thinking "Hm. Maybe this isn't such a good investment." When measurements are made and reliable algorithmic results attract and keep more users than narrowly trained kind of finicky AIs.

I don't want there to be an AI winter, and it won't be as bad as before. There are a lot of applications for limited scope image recognition, and other tasks that we couldn't do before. Unfortunately,I do agree with the post that winter is on its way.

</comment><comment><USER51> The OP is obviously not keeping up with the field and has lot to learn about scientific approach. He basically uses the count of tweets from AndrewNg and crashes from risk-taking companies as indicator of "AI winter". He should have tried to look in to metrics such as number of papers, number of people getting in to field, number of dollars in VC money, number of commercial products using DL/RL etc. But you see, that's a lot of work and your conclusion might not align with whatever funky title you had in mind. Being an armchair opinion guy throwing link bait titles is much more easier.

<comment><USER8> I'll happily read your next post where you will include all of those. In fact amount of VC money spent in that field would only support my claim. And the number of papers is irrelevant. There were thousands of papers about Hopfield network in the 90's and where are all of them now? You see, all the things you point out is the surface. What really matters is that self driving cars crash and kill people, and no one has any idea how to fix it.

</comment></comment><comment><USER52> I think the most important question is what 'winter' really means in this context. The new concepts in AI tend to follow the hype cycle so the disillusionment will certainly come. One thing is the general public see the amazing things Tesla or Google do with deep learning and extrapolate this thinking we're on the brink of creating artificial general intelligence. The disappointment will be even bigger if DL fails to deliver its promises like self-driving cars.

Of course the situation now is different than 30 years ago because AI has proved to be effective in many areas so the research won't just stop. The way I understand this 'AI winter' is that deep learning might be the current local maximum of AI techniques and will soon reach the dead end where tweaking neural networks won't lead to any real progress.

</comment><comment><USER53> AI winter is not "on its way". There is AI hype and anti-AI hype, and then there is actual practice. This article is anti-AI hype, just as bad as its opposite. In practice there are tons of useful applications. We haven't even begun to apply ML and DL to all the problems laying around us, some of which are quite accessible and impactful.

The hype cycle will pass with time, when we learn to align our expectations with reality.

<comment><USER54> Give us a list then, because what I'm seeing is shoehorning "AI" into everything but not with significant results.

<comment><USER53> It's not my job to supplement the press or to do paper reading for you. If you're really interested in AI and don't just read the clickbait press, then open Arxiv and look there.

[http://www.arxiv-sanity.com/](http://www.arxiv-sanity.com/)

If, on the other hand, your opinion that AI is in a winter has been already decided without reading the latest scientific papers, then there's nothing I can say to you that will change your mind.

</comment></comment></comment><comment><USER55> I think that we will have AI Winter once we see the true limitations that face us having a level 5 fully autonomous self driving car. The other thing we will see happen is the deflation of the AdTech bubble. Once we see both of these events occurring that should start the AI Winter.

<comment><USER43> I agree. The AdTech sphere is keeping the current hype alive more than anything else. There's some obvious imbalances in AdTech that should lead it to a damning end soon enough.

</comment></comment><comment><USER56> AI and machine learning is a tool. Like any other tool it's perfect for some problems and doesn't work well for other. Pick the right tools for the problem that you are working on. Don't follow the hype and don't use AI/ML just for sake of using it.

</comment><comment><USER57> Judea Pearl sees a way out of the winter.

[https://www.theatlantic.com/technology/archive/2018/05/machi...](https://www.theatlantic.com/technology/archive/2018/05/machine- learning-is-stuck-on-asking-why/560675/)

<comment><USER58> I think a lot of GOFAI approaches ought to be revisited to see whether they benefit from the new perceptual and decision capabilities of Deep Learning systems. Alex Graves's papers are particularly good at this.

Things like this reinforcement learner for theorem proving are pretty exciting possibilities. [https://arxiv.org/pdf/1805.07563v1.pdf](https://arxiv.org/pdf/1805.07563v1.pdf)

</comment></comment><comment><USER59> There's a lot of good stuff coming from research in AI these days. Still, I think the author's right.

As with the onset of the previous AI winter a generation ago, the problem is this: Once a problem gets solved (be it OCR or Bayesian recommendation engines or speech recognition or autocomplete or whatever) it stops being AI and starts being software.

As for self-driving cars: I recently took a highway trip in my Tesla Model S. I love adaptive cruise control and steering assistance: they reduce driver workload greatly. But, even in the lab-like environment of summertime limited access highways, driverless cars are not close. Autosteer once misread the lane markings and started to steer the car into the side of a class 8 truck. For me to sit in the back seat and let the car do all the work, that kind of thing must happen never.

Courtesy is an issue. I like to exit truck blindspots very soon after I enter them, for example. Autosteer isn't yet capable of shifting slightly to the left or right so a driver ahead can see the car in a mirror. Maybe when everything is autonomous that won't be an issue. But how do we get there.

Construction zones are problems too: lane markings are confusing and sometimes just plain wrong, and the margin for error is much less. Maybe the Mobileye rig in my car can detect orange barrels, but it certainly doesn't detect orange temporary speed limit signs.

This author is right. AI is hype-prone. The fruits of AI generally function as they were designed, though, once people stop overselling them.

</comment><comment><USER60> While I basically agree, really it ought to be called "AI autumn is well on its way", since I'm not sure we're into actual winter (i.e. dramatic reduction in $$ available for research) quite yet. But, probably soon.

<comment><USER8> Author here, yeah, it is the autumn. But I guess not many people would recognize the meaning, winter on the other hand is not ambiguous...

<comment><USER60> True.

</comment></comment></comment><comment><USER61> "it is striking that the system spent long seconds trying to decide what exactly is sees in front (whether that be a pedestrian, bike, vehicle or whatever else) rather than making the only logical decision in these circumstances, which was to make sure not to hit it."

That is striking. It always sort of bothered me that AI is really a big conglomeration of many different concepts. What people are working on is deep learning _for machines_ , but we think that means "replicating human skill/behavior". It's not. Machines will be good at what they are good at, and humans good at what they're good at. It's an uphill battle if your expectation is for a machine that processes like a human, because the human brain does not process things like computer architectures do.

Now, if some aspiring scientist wanted to skip all that and _really_ try to replicate (in a machine) how the human brain does things, I think such a person would be starting from a very different perspective than even modern AI computing.

<comment><USER62> That's why Augmented Intelligence is a better term. It doesn't scare up visions of Skynet or Hal 9000 run amok. Nor does it promise utopian singularity right around the corner.

It just means better tools to increase human capacity. But it's not nearly as good at getting headlines in the media.

</comment></comment><comment><USER63> We call it deep learning, but it is deep pattern matching. Extremely useful, but don't expect it to result in thinking machines.

<comment><USER64> Are our brains magic? If they aren't then surely they must be doing something that we can reproduce. We've built so many things that we considered "thinking machines" in the recent past (realistic speech synthesis, image recognition and captioning, human-level translation, elaborate recommender systems, robust question answering) on "deep pattern recognition".

<comment><USER65> Brains are not magic, and will be reproduced eventually, but DNNs are a fundamentally weaker architecture and won't be enough. Neural nets can solve some problems that brains can solve easily and lots of other ML methods couldn't solve, which is great. But the space of problems that brains can solve and neural nets can't is still rich, and will remain so until better methods are developed.

</comment></comment></comment><comment><USER66> The discussion on radiology is extremely sloppy.

Andrew Ng claimed human level performance on one radiology task (pneumonia). This claim seems to hold up pretty well as far as I can tell. Then the person criticizing him on twitter posts results on a completely different set of tasks which are just baseline results in order to launch a competition. These results are already close to human level performance, and after the competition it's very possible they will exceed human level performance.

Yes it's true that doing well at only Pneumonia doesn't mean that the nets are ready to replace radiologists. However, it does mean that we now have reason to think that all of the other tasks can be conquered in a reasonably short time frame such that someone going into the field should at least consider how AI is going to shape the field going forward.

</comment><comment><USER67> Well, the breathless hype around deep learning (with and without reinforcement learning) is bound to subside sooner or later, and attendance to staid academic conferences like NIPS sooner or later will revert back to a smaller group of academics and intellectuals who are truly interested in the subject over the long term.[a] That much is certain.

But we're still in the early stages of a _gigantic wave of investment_ over the next decade or two, as organizations of all sizes find ways to use deep learning in a growing number of applications. Most small businesses, large corporations, nonprofits, and governments are not using deep learning for anything yet.

[a] [https://twitter.com/lxbrun/status/908712249379966977](https://twitter.com/lxbrun/status/908712249379966977)

</comment><comment><USER68> Well, now that the cat's out of the bag in regards to AI/ML, we can all get in on the ground floor of the next hype wave - quantum computing!

<comment><USER55> IMHO Quantum computing is as well hyped as Cold Fusion and shares some of its properties. Until "quantum supremacy" occurs or something that will show a real speedup we won't hear that much from it.

<comment><USER58> Cold Fusion was outright scientific misconduct. I'm not optimistic about QC working as intended, but I think the hope around it is honest.

</comment></comment></comment><comment><USER69> Stopped reading after the first half. The evidence for the idea that deep learning is failing is that Deep Mind haven't produced anything revolutionary since Alpha Go Zero which was published not even a year ago? And that preformance doesn't scale linearly with the number of parameters? And speculation about why Lecun made a certain career decision? Not very convincing.

<comment><USER70> You're forgetting that Andrew Ng is tweeting 30% less this year! Isn't that enough to convince even the staunchest critic?

</comment></comment><comment><USER71> Only tangentially related to the article, but it's always struck me as a little unethical that Demis Hassabis' name goes on every paper that's written by Deepmind. No-one produces that much research output.

</comment><comment><USER72> No, but wait! We're just on the verge of replacing doctors! ;-)

There's still a lot of space for the improvement of "curve-fitting" AI in the workplace. The potential of existing tech is far from being thoroughly exploited right now. I believe the next big improvements will come more from better integration in the workplace (or road system) than new scientific advances, so that might seem less sexy. But I also believe this will be a sufficient impetus to drive the field forward for the years to come.

</comment><comment><USER73> I would not call it the "AI winter". If you look at what people have called AI over time, the definition and the approaches have evolved (sometimes drastically) over time.

Instead of being stuck on the fact that deep learning and the current methods seem to have hit a limit I think I am actually excited about the fact that this opens the door for experimenting other approaches that may or may not build on top of what we call AI today.

<comment><USER74> Perhaps it'd be more correct to call it a "Strong AI Winter". We're no closer to "aware" machines. We've simply gotten very good at automating tasks that were once difficult to automate.

<comment><USER73> A friend that's more optimistic about Strong AI once said that the ML that goes on today will probably serve the purpose of driving the peripheral sense organs of a future AI. Although it stretches a bit what's possible today I could see that. I would call this a win if this ends up happening although I still belive we're hundreds of years away from Strong AI.

<comment><USER75> I'm inclined to agree with your friend.

This ability of DL to convert streams of raw noisy data into labeled objects seems like exactly what's needed to solve an intelligent agent's perceptual grounding problem, where an agent that's new to the world must bootstrap its perception systems, converting raw sensory input into meaningful objects with physical dynamics. Only then can the agent reason about objects and better understand them by physical interaction and exploration. This is one of the areas where symbolic AI failed hardest, but DL does best.

With some engineering, it's easy to imagine how active learning could use DL to ground robot senses - much like an infant human explores the world for the first year of life, adding new labels and understanding their dynamics as it goes.

I suspect the potential for DL's many uses will continue to grow and surprise us for at least another decade. If we've learned anything from the past decade of DL, it's that probabilistic AI is surprisingly capable.

</comment></comment></comment></comment><comment><USER76> This reminds me of a recent Twitter thread [1] from Zachary Lipton (new machine learning faculty at CMU) arguing that radiologists have a more complex job than we, as machine learning enthusiasts, think.

[1] [https://mobile.twitter.com/zacharylipton/status/999395902996...](https://mobile.twitter.com/zacharylipton/status/999395902996516865)

</comment><comment><USER77> I think all talk about computer intelligence and learning is bullshit. If I'm right, then AI is probably the most /dangerous/ field in computer science because it sounds just likely enough that it lures in great minds, just like a sitcom startup idea[0].

[0] [http://paulgraham.com/startupideas.html](http://paulgraham.com/startupideas.html)

</comment><comment><USER7> You could actually make a reasonable argument for the opposite of a winter, that we are heading into an unprecedented AI boom.

The article's main argument for a winter is that deep learning is becoming played out. But this misses the once in history event of computer hardware reaching approximate parity with and overtaking the computing power of the human brain. I remember writing about that for my uni entrance exam 35 years ago and have been following things a bit since and the time is roughly now. You can make a reasonable argument the the computational equivalent of the brain is about 100 TFLOPS which was hard to access or not available in the past but you can now rent a 180 TFLOP TPU from Google for $6.50/hr. While the current algorithms may be limited there are probably going to be loads of bright people trying new stuff on the newly powerful hardware, perhaps including the authors PVM and some of that will likely get interesting results.

</comment><comment><USER78> Deep learning maybe not the complete answer to gai, but it's moving down the right path. Computers though are still years/decades away from approaching human brain power and efficiency, so my take is that current ai hype is 10 years too early - a good time to get in.

<comment><USER8> > but it's moving down the right path

Time will tell. I think DL is amazing, but is no the right path towards solving problems such as autonomy. I think if you enter this field today, you should definitely take a look at other methods than DL. I actually spent a few years reading neuroscience. It was painful, and I certainly can't tell I learned how the brain works, but I'm pretty certain it has nothing to do with DL.

</comment></comment><comment><USER79> Great essay but this "Deep learning (does not) scale" I think is missing an important point.

There are many ways to think about scale.

If you think about a learned skill then that skill actually scales extremely well to other machines and thus to other industries that might benefit from the same skill.

The primary problem with technology is that society doesn't just implement it as fast as it gets developed so you will have these natural bottlenecks where society can't actually absorb the benefits fast enough.

In other words, Deep Learning scales as long as society can absorb it and apply it.

</comment><comment><USER80> Has anyone done something genuinely useful with ml/ai/whatever outside of advertising or stock trading? I am genuinely curious if it has really been applied to real commercial applications.

<comment><USER81> Improvements in search, translation, image recognition and categorization, voice recognition, and text to speech off the top of my head. I'm sure there are a lot more.

<comment><USER80> Yeah but those are all pretty terrible to the actual end consumer. They might be cool technologies but at the end of the day, I am a user that hates dealing with them. IVRs are terrible a experience. Image recognition is iffy at best. Text to speak is terrible. In 10 years maybe they will have it hashed out... just like 10 years ago, or 20 years ago

<comment><USER81> I'd have to disagree. While IVR sucks (most current implementations don't use ML by the way), image recognition and categorization is at or better than human levels in most cases now. Cutting edge TTS is now nearly indistinguishable from a human. Just check out some samples [1]. And while translation still sucks, ML based translation is still far better than previous approaches.

[1] [https://www.theverge.com/2018/3/27/17167200/google-ai- speech...](https://www.theverge.com/2018/3/27/17167200/google-ai-speech-tts- cloud-deepmind-wavenet)

</comment></comment></comment></comment><comment> d--b Sure the thing is overhyped, but the problem is that we cannot be sure about the next big thing. The advances are slow but then a giant step forwards happen all of a sudden.

Everyone dropped their jaws when they saw the first self driving car video or when alpha go started to win. This was totally unthinkable 10 years ago.

Some guy may come up with a computer model that incorporates together intentionality, some short term/long term memory, and some reasoning, who knows?

</comment><comment><USER82> AI is favorable for big companies to better scale their services. It seems that Facebook have also faced AI scaling drawbacks and they are developing there own AI hardware for it [https://www.theverge.com/2018/4/18/17254236/facebook- designi...](https://www.theverge.com/2018/4/18/17254236/facebook-designing- own-chips-ai-report)

</comment><comment><USER83> AI has a lot to offer to the industry right now I think where you don't need good worst case performance (ex., information retrieval, optimization, biology, etc.). The big problems in terms of application start appearing when you try and remove humans from the loop completely. That's not even close to possible yet but that doesn't mean the economic utility of even current AI is close to being maximized.

</comment><comment><USER84> I know this about the state of Deep Learning but I like to point out:

While autonomous driving systems aren't perfect, statistically they are much better at driving than humans. Tesla's autonomous system has had, what, 3 or 4 fatal incidents? Out of the thousands of cars on the road that's less than 0.001%.

There will always be a margin of error in systems engineered by man, just hopefully moving forward fewer and fewer fatal ones.

<comment><USER85> Depending upon your statistical sources, US traffic fatalities are around 1.25-1.50 per 100 million miles. [1] All forms of real-world autonomous driving across all manufacturers across the world are still somewhere below 200 million miles, conservatively estimated. [2] [3] Between the Tesla and Uber fatalities, by these rough back-of-the-envelope numbers, autonomous of various grades is still roughly 2X higher than human drivers. Maybe 1X if you squint at the numbers hard enough, but likely not orders of magnitude lower. I don't anticipate rapid legislative and insurance liability protections for autonomous systems until we see orders of magnitude differences on a per 100 million miles driven basis, and that will take time.

Waymo racks up about 10,000 miles per day across about 600 vehicles spread in about 25 cities. [4] Roughly 3.6 million miles per year if they stay level, but they're anticipated to rapidly add more vehicles to their fleet. In the US alone, about 3.22 trillion miles were driven in 2016. [5] Don't know what a statistically valid sample size is based upon that (I get nonsensical results below 2000 miles, so I'm doing something stupid), though. If Waymo puts two orders of magnitude more cars out there, they'll still "only" rack up about 365 million miles per year, and not all the miles on the same version of software.

[1] [https://en.wikipedia.org/wiki/Transportation_safety_in_the_U...](https://en.wikipedia.org/wiki/Transportation_safety_in_the_United_States)

[2] [https://www.theverge.com/2016/5/24/11761098/tesla- autopilot-...](https://www.theverge.com/2016/5/24/11761098/tesla-autopilot- self-driving-cars-100-million-miles)

[3] [https://www.theverge.com/2017/5/10/15609844/waymo-google- sel...](https://www.theverge.com/2017/5/10/15609844/waymo-google-self-driving- cars-3-million-miles)

[4] [https://medium.com/waymo/waymo-reaches-5-million-self- driven...](https://medium.com/waymo/waymo-reaches-5-million-self-driven- miles-61fba590fafe)

[5] [https://www.npr.org/sections/thetwo- way/2017/02/21/516512439...](https://www.npr.org/sections/thetwo- way/2017/02/21/516512439/record-number-of-miles-driven-in-u-s-last-year)

</comment></comment><comment><USER86> Woah, I was prepared to be all gung-ho for this post, given that I've suspected the winter was going to be here for quite a while now. But strangely, this post actually caused the opposite effect for me. The winter will probably come one day, but is all the evidence the poster can find? Andrew NG tweeting less and a statement that DNNs doesn't scale based on flimsy data is not at all convincing to me.

</comment><comment><USER87> Is this AI Winter 2.0? I was hopeful that logic programming would have developed more and spread to a larger audience at this point.

</comment><comment><USER88> As a beginner in deep learning space, I am a bit baffled about the case "You need a lot of computational power". Good models learn fast, so if potential model looks promising on local machine, one can do training on gcloud for 100$ on high end machines. Where am I wrong in this line of thinking?

<comment><USER89> No, you are absolutely right. And modern transfer learning improves this even more in many domains.

</comment></comment><comment><USER90> Thank god. We're definitely not ready and perhaps could never be ready for true general purpose ai.

</comment><comment><USER91> This is something I always wondered about AI and it promises. Sometimes, the last 1% is the hardest or can be even impossible. Self-driving cars, in particular, are a good case. We get to solve 99% of the use cases but achieving full autonomous vehicles might be just out of reach.

</comment><comment><USER92> But, they're getting more and more data every year, right? All those almost millions of teslas running around could provide enough video input for the training data

Besides "Good software takes 10 years", according to Joel Spolsky. As I see it, we're, what 5 year into ML.

<comment><USER54> 5 years ha! Wow the rebranding has worked well.

</comment></comment><comment><USER93> Another case in point. [http://www.latimes.com/local/lanow/la-me-ln-tesla- collision-...](http://www.latimes.com/local/lanow/la-me-ln-tesla- collision-20180529-story.html)

</comment><comment><USER94> > Nvidia car could not drive literally ten miles without a disengagement.

From the same source as the author cites, that's because their test runs are typically 5 miles and resuming manual control at the end of a test counts as a disengagement.

</comment><comment><USER95> Deep Learning was a noticeable improvement over previous neural models, sure. But deep learning is not the entire field of AI and ML. There has been more stuff going on like neural turing machines and differentiable neural computers.

</comment><comment><USER96> We are beginning to see some sweet differential embeddings of discrete things like stacks and context free grammars. This is where deep learning gets really fun because it is learning to program.

</comment><comment><USER97> [https://twitter.com/jvmancuso/status/1002387357776207872](https://twitter.com/jvmancuso/status/1002387357776207872)

</comment><comment><USER98> For me Google is attacking on 2 main fronts 1\. Quntam computing 2\. Machine Learning/AI

If they are able to combine the 2. A big if though the cost analysis will change for AI quite dramatically.

</comment><comment><USER99> Number of tweets as reliable data points? Very dubious. Simple explanation: They are busy working, so less time to tweet.

Maybe they're working on something so cool, that the AI winter may not even come. Sure, there's a lot of marketing-speak around AI at the moment.

But this wave of AI seems a lot stronger with better fundamentals than 20 years ago. At the very least, at least we have the hardware to actually RUN NN's cost effectively now as oppose to grinding your system to a halt back then.

Before AlphaGo, it wasn't even clear when a computer could beat a top professional in go, let alone crush humans in the game - low bound guesses were 50 years.

</comment><comment><USER100> [https://en.wikipedia.org/wiki/Lighthill_report](https://en.wikipedia.org/wiki/Lighthill_report) (1973)

</comment><comment><USER101> Low hanging fruits are scarce now. With 3 orders of magnitude difference in power (MW over few watts), clearly this is not the right way for reaching the tree top.

</comment><comment><USER102> /shrug people need time to research;

Anyway i also don't get what the issue is with the model from radiology. It is already that good?! This is impressive. One model is close to well trained experts.

Just today i had an small idea for a new product based on what google was showing with the capabilities to distinguis two people talking in parallel.

At the last Google IO i was impressed because in comparision to the previous years, ML created better and more impressive products.

I was listing for years at key nodes about big data and was never impressed. I hear now about ML and im getting impressed more and more.

<comment><USER43> Google IO is a developer's conference with an emphasis on marketing its own products and tools. We have to take the news from it with a grain of salt.

</comment></comment><comment><USER103> If only there were some technology that might enable us to discern patterns so that we could better predict fluctuations in demand for AI software.

</comment><comment><USER104> Truly, I agree.

I've long been interested in learning about AI and deep learning, but to this day haven't done much that truly excites me within the field. It feels more or less impossible to make anything significant without Google-scale databases and Google-scale computers. AI really does make it easier for the few to jump far ahead, leaving everyone behind.

I also agree that a lot the news around AI is just hype.

Honestly, I'm yet to see _anything_ practical come out of AI.

But hey, if something eventually does, I'm all for it.

<comment><USER105> I'm kinda curious now where those giant datasets will come from now that there's a big push for privacy with things like GDPR preventing some random researcher from just buying data off whatever data mining corp is most relevant to their AI's purpose

<comment><USER106> I'm kinda curious now which researchers have been buying PII from data mining corps.

<comment><USER107> Cambridge analytica comes to mind

</comment></comment></comment></comment><comment><USER108> Bet your house on it if it's "well on it's way"

<comment><USER109> Yeah this is a dumb article. Number of tweets by AndrewNg? Really? All those articles denying the reality of the revolution brought by AI have an emotional basis, but I don't understand what it is. Are they feeling threatened? Or is it an undergrad/early 20s thing, like a complete lack of understanding of the dynamics coupled with abnormally strong opinions?

</comment></comment><comment><USER110> This reinforces the need to benchmark any 'human expert equivalent' project against the wattage of the human brain.

</comment><comment><USER111> How much of this can we pin on IBM's overhype of Watson?

</comment><comment><USER112> Yawn. Contrarianism is easy and this article offers little. The real world application you're speaking of has a comically small amount of data (a few million miles?). You hear about a handful of accidents that still average to better than human performance and suddenly the sky is falling.

When machine learning stops successfully solving new problems daily, then maybe a thread like this will be warranted.

</comment><comment><USER113> without being an expert just by reading articles it seems to me that some people wish foe an AI winter. It makes them feel better somehow

</comment><comment><USER114> Oh, I thought "AI winter" would refer to a state of ruin after AI had come into existence and destroyed everything, analogous to nuclear winter.

<comment><USER115> AI Winter is a very well-known term in the industry referring to a general lack of funding of AI research, after the last time AI was overhyped.

<comment><USER114> I guess it could be used about anything that experiences a low level of interest, then.

</comment></comment></comment><comment><USER116> A real Winter is a lack of warmth. An AI winter is a lack of ______

</comment><comment><USER117> If we would stop calling this stuff "AI" it would make all our lives a lot easier, but people can't resist.

When computers first came on the scene a lot of people had a very poor conception of what it was the human mind did, computationally. So when computers turned out to be good at things that were challenging "intellectual" tasks for humans like chess and calculus many were duped into thinking that computers were somehow on a similar level to human brains and "AI" was just around the corner. The reality was that one of the most important tasks that the human brain performs: contextualization, categorization, and abstraction was taken for granted. We've since discovered that task to be enormously computationally difficult, and one of the key roadblocks towards "true AI" development.

Now, of course, we're at it again. We have the computational muscle to make inference engines that work nothing like the human brain good at tasks that are difficult to program explicitly (such as image and speech recognition) and we've built other tools that leverage huge data sets to produce answers that seem very human or intelligent (using bayesian methods, for example). We look at this tool and too many say "Is this AI?" No, it might be related to AI, but it's just a tool. Meanwhile, because of all the AI hype people overpromise on neural networks / "deep learning" projects and people get lazy about programming. Why bother sitting down for 15 minutes to figure out the right SQL queries and post processing when you can just throw your raw data at a neural network and call it the future?

One of the consistently terrible aspects of software development as a field is that it continues to look for shortcuts and continues to shirk the basic responsibilities of building anything (e.g. being mindful of industry best practices, understanding the dangers and risks of various technologies and systems and being diligent in mitigating them, etc.) Instead the field consistently and perversely ignores all of the hard-won lessons of its history. Consistently ignores and shirks its responsibilities (in terms of ethics, public safety, etc.) And consistently looks for the short cut and the silver bullet that will allow them to shirk even the small vestiges of responsibility they labor under currently. There's a great phrase on AI that goes: "machine learning is money laundering for bias", which points to just one facet among so many of what's wrong with "AI" as it's practiced today. We see "AI" used to sell snake oil. We see "AI" used to avoid responsibility for the ethical implications inherent in many software projects. We see "AI" integrated into life critical systems (like self-driving cars) without putting in the effort to ensure it's robust or protect against its failures, with the result being loss of life.

AI is just the latest excuse by software developers to avoid responsibility and rigor while cashing checks in the meantime. At some point this is going to become obvious and there is going to be a backlash. Responsible developers should be out in front driving for accountability and responsibility now instead of waiting until a hostile public forces it to happen.

</comment><comment><USER118> I've always understood the claim that deep learning scales to be a claim about deployment and use of trained models, not about training. The whole point is that you can invest (substantial) resources upfront to train a sufficiently good model, but then the results of that initial investment can be used with very small marginal costs.

OP's argument on this front seems disingenous to me.

His focus on Uber and Tesla (while not even mentioning Waymo) is also a truly strange omission. Uber's practices and culture have historically been so toxic that their failures here are truly irrelevant, and Tesla isn't even in the business of making actual self driving cars.

I'm the first to argue that right now AI is overhyped, but this is just sensationalist garbage from the other end of the spectrum.

<comment><USER8> Hi, it appears that "sensationalist garbage" triggered quite a bit of a discussion. This is typically indicative that the topic is "sensitive". Perhaps because many people feel the winter coming as well. Maybe, maybe not, time will tell.

And FYI, Tesla is in the business of making self driving car. If you read the article, you might learn that Tesla is actually the first company to sell that option to customers. You can go to their website right now and check that out.

Uber, like it or not is one of the big players of this game. I agree they may have somewhat toxic culture, but I guarantee you there are plenty of really smart people there who know exactly the state of the art. And their failure is therefore indicative of that state of the art.

I also omitted Cruise automation and a bunch of other companies, perhaps because they have more responsible backup drivers that so far avoided fatal crashes. But I analyze the California DMV disengagement reports in another post if you care to look. And by no means any of these cars is safe for deployment yet.

<comment><USER118> > Hi, it appears that "sensationalist garbage" triggered quite a bit of a > discussion.

Yes. Sensationalist.

> I also omitted Cruise automation and a bunch of other companies, perhaps > because they have more responsible backup drivers that so far avoided fatal > crashes.

So your explicit reason for omitting Waymo, as I understand it, is that it didn't support your argument?

<comment><USER8> > Yes. Sensationalist.

Yes, perhaps. But I'm entitled to my opinion just as you are entitled to yours. And time will tell who was right.

> So your explicit reason for omitting Waymo, as I understand it, is that it > didn't support your argument?

You see, when you make any argument, you always omit the infinite number of things that don't support it and focus on the few things that do. The fact that something does not support my argument, does not mean it contradicts it.

You might also note that this is not a scientific paper, but an opinion. Yes, nothing more than an opinion. May I be wrong? Sure. And yet this opinion appears to shared by quite a few people, and makes a bunch of other people feel insecure. Perhaps there is something to it? We will see.

But in the worst case it will make some people think a bit and make an argument either for or against it. I may learn today a good argument against it, that will make me think about it more and perhaps I will change my opinion, or I'll be able to defend it.

So far you have not provided such an argument, but I wholeheartedly encourage you to do so.

<comment><USER119> This is a list of your phrases in this comment that I find, in my opinion, condescending.

> And time will tell who was right.

> You see, when you make any argument

> You might also note that this is not a scientific paper, but an opinion. > Yes, nothing more than an opinion.

> And yet this opinion appears to shared by quite a few people, and makes a > bunch of other people feel insecure. Perhaps there is something to it? We > will see.

> So far you have not provided such an argument

I immediately identified this same tone in your paper. In your argumentation, you quite agressively hinted hat people which don't share your views are not very intelligent. You also have a tendency to present your sayings as prophetic, which appeared multiple times both in the paper and in this comment.

These observations put me in alarm towards your arguments, which I found mostly weak, sometimes used in bad faith. I flagged as such the Twitter argument, analysing the frequency of A. Ng's tweets, and denouncing its "outrageous claims", with an example where the AI score is overall only 0.025 less accurate than a practician.

I also thought that you used a different (your own) definition of scaling than most, and used it to make an argument, which was therefore unconvincing (but parent said that already).

Overall, to me, this was not a very pleasant read, and I dislike the fact that you attack the hype on machine learning by enjoying the polarization that comes with anti-hype articles such as yours. I also don't think that making people feel insecure is such a great indicator that what you're saying is relevant or prophetic.

I hope this helps you prophecies [https://www.physics.ohio- state.edu/~kagan/AS1138/Lectures/Go...](https://www.physics.ohio- state.edu/~kagan/AS1138/Lectures/GottIII.html) ;)

</comment></comment></comment></comment></comment><comment><USER120> One of the more silly articles on HN in a while. Waymo has cars as I type this driving around Arizona without safety drivers.

People were freaked out by the Google demo of Duplex a couple of weeks ago as it was just too human sounding.

Can give so many other example. One is foundational. The voice used with Google Duplex is using a DNN at 16k cycles a second in real-time and able to offer at a competitive price.

That was done by creating the TPU 3.0 silicon. The old way of piecing together was NOT compute intensive and therefore doing it using a DNN requires proprietary hardware to be able to offer at a competitive price to the old way.

But what else can be done when you can do a 16k cycles through a DNN in real- time? Things have barely even got started and they are flying right now. All you have to do is open your eyes.

DNN - Deep Neural Network.

</comment><comment><USER121> It's the same story again like exaggerating the influence of IoT 5 years ago. The whole thing is exaggerated to raise money from investors and attract customers instead of actually buidling superior product

<comment><USER122> It's 99% marketing and places like HN and reddit eat it up and try to hype it up even more. When you confront these characters about the basis on which they claim AI will solve whatever problem or evolve to whichever point, they only reply "it'll only keep getting better (given time, data, resources, brains, etc)"

It's a buzzword people brainlessly use to fetishize technological progress without understanding the inherent limitations of the technology or the actual practicality and real-life results outside of crafted demos or specific problem domains (for example Alpha Go beating a grandmaster has almost no bearing on a problem like speech cognition).

It's turned me off a lot from reading about advances in the field because I know like a lot of science releases that most of it is empty air that won't really have bearing on the actual software I use (I've watched the past two Google's I/O where pretty much every presentation mentions AI, but the Android experience still remains relatively stale).

</comment></comment><comment><USER123> Deep Recession '18

</comment><comment><USER124> Winter is coming.

</comment><comment><USER125> What a relief.

</comment><comment><USER126> The inconvenient but amazing truth about deep learning is that, unlike neural networks, the brain does not learn complex patterns. It can see new complex patterns and objects instantly without learning them. Besides, there are not enough neurons in the brain to learn every pattern we encounter in life. Not even close.

The brain does not model the world. It learns to see it.

<comment><USER127> This post is very uninformed.

"It can see new complex patterns and objects instantly without learning them."

Except, it doesn't. It is clearly false. When animals grow up in an environment without certain patterns, they will be unable to see these patterns (or complex combinations of these) at a later stage. We see complex patterns as combinations of patterns we have seen before and semantically encode them as such. This is very similar to how neural networks work at the last fully connected layers.

"Besides, there are not enough neurons in the brain to learn every pattern we encounter in life."

There is a lot of self-similarity in our environment. Compression algorithms (and NN auto-encoders) are able to leverage this self-similarity to encode information in a very small number of data-points / neurons.

"The brain does not model the world. It learns to see it."

Except, it doesn't. Your brain continually makes abstractions of the world. When you 'see' the world you see a (lossy) compressed version of it, compressed towards utility. Similar to how MP3 compression works: the information gain of higher frequencies is low, so your brain can safely filter these out.

<comment><USER128> We learn to see patterns, but we see through physical and cultural action patterns that are simply present, not learned.

It's like a river flowing... yes, the water molecules each "discover" the their path, but the path of the river is a property of the landscape. It is not learned.</comment></comment></comment>


Wastebook 2013 - <AUTHOR> http://www.amazon.com/Wastebook-United-States-Government-Senate/dp/1494746336/ref=cm_cr_pr_product_top

<comment><USER2> Thanks for this link, looks like lot of money is wasted for trivial causes.</comment>


COVIDSafe is noncompliant with privacy policy, ignored infosec and didn't fix - <AUTHOR> https://youtu.be/r9nF9-KYx2o <comment><AUTHOR> You can find specifics and the research at [https://twitter.com/GeoffreyHuntley/status/12581744339796254...](https://twitter.com/GeoffreyHuntley/status/1258174433979625474?s=19) for more information. Let's continue the discussion here however.

</comment><comment><USER2> Does anyone have an article link instead of video/twitter on what happened/background? I'm not Australian so don't have the context on what this app is/how hard the government is pushing it/who made it etc, but would like to read up on it. Thanks.

<comment><USER3> Here is a technical document on the security vulnerabilities being reported:

[https://docs.google.com/document/d/1u5a5ersKBH6eG362atALrzuX...](https://docs.google.com/document/d/1u5a5ersKBH6eG362atALrzuXo3zuZ70qrGomWVEC27U/edit#)</comment></comment>


Ask HN: What is your opinion on SEO? - <AUTHOR> <comment><USER2> I think it is important to understand a little, especially content marketing if you hope to get your project in front of eyeballs.</comment>