IBM is not doing "cognitive computing" with Watson (2016) - <AUTHOR> http://www.rogerschank.com/fraudulent-claims-made-by-IBM-about-Watson-and-AI <comment><USER2> The author's particular gripe is that the Watson advertisements showing someone sitting down and talking to "Watson." They bother me as well (and did so when I was working at IBM in the Watson group) because they portray a capability that nothing in IBM can provide. Nobody can provide it (again to the author's point) because dialog systems (those which interact with a user through conversational speech) don't exist out side specific, tightly constrained, decision trees (like voice mail or customer support prompts).

If SpaceX were to advertise like that, they would have famous people sitting in their living room, _on mars_ , and talking about what they liked about the Martian way of life. In that case I believe that most people would understand that SpaceX wasn't already hosting people on Mars.

Unfortunately many, many people think that talking to your computer in actually already possible, they just haven't experienced it yet. Not sure how we fix that.

<comment><USER3> Kinda how Tesla advertises autopilot as a self driving car that's safer than human drivers?

> Full Self-Driving Hardware on All Cars

> All Tesla vehicles produced in our factory, including Model 3, have the > hardware needed for full self-driving capability at a safety level > substantially greater than that of a human driver.

[https://www.tesla.com/autopilot/](https://www.tesla.com/autopilot/)

<comment><USER4> But it's technically correct: the hardware _is_ present. The software isn't there yet, that's true. So is this a "lie by omission"? Reading the whole page, there's this quote: "It is not possible to know exactly when each element of the functionality described above will be available." What else needs to be said?

At some point, critical thinking is required to navigate the world. The customer should ask "and when will the software catch up?" And I'll concede that the overwhelming majority of the population doesn't think like that. Should Tesla update the site with statements to confirm the negative cases? e.g. "Our software can't yet do X, Y, Z, etc"

<comment><USER5> Since we have yet to build a system that is capable of "full self-driving ... at a safety level substantially greater than that of a human driver" I'm not even sure they can make the claim that their existing hardware supports this. They might have high hopes, but there's no way to know what combination of hardware and software will be necessary until the problem is actually solved.

</comment></comment></comment></comment><comment><USER6> The most ingenious trick that the IBM marketing department pulled was to get non-technical (and probably even technical people, judging by this thread) to think that Watson is some kind of singular thing. Like that it's a single big neural network with different APIs on it, or something. I honestly think that's what most people think Watson refers to.

Watson is like Google Cloud Platform. It's just a name for a platform with a bunch of technologies.

E.g. Watson Natural Language Understanding was previously AlchemyLanguage. It was just rebranded.

It's very clever though, I'll give them that. Use a human name so it has all the anthropomorphic connotations and let people think it's some kind of AI learning things.

<comment><USER7> I'm not even convinced Watson is a platform. My impression is that it's just a consulting division of the company that deploys teams to build solutions that are in some way related to AI, with each solution or implementation potentially being completely unique from the ground up. Perhaps someone from IBM can correct me though.

<comment><USER8> I'm currently sitting in a meeting about implementing the Watson Enterprise Search product in my company and that is more or less the impression I've gotten. They sell it as a platform that is easy to customize and then once you're in they bill you tons of hours to help you because the system is indecipherable and poorly documented.

<comment><USER9> And that, folks, is enterprise software services/consulting in a nutshell!!

</comment></comment></comment></comment><comment><USER10> I briefly worked with a Watson team on a cool idea to map a person's 'knowledge space' (or probable knowledge space given their background) against Watson's knowledge space and guide them to relevant learning materials and journal articles and the like.

The idea was to save people time so they aren't rehashing stuff they know down pat or jumping ahead into material they cannot understand but, instead, find that next step into what they almost know. The idea from there would be to let them specify where they want to go and guide them, step by step, exposure by exposure, to that summit.

In a few days, it turned into Just Another News Article Recommendation Engine based on interest and similar profiles with other clients. Yawn.

<comment><USER11> In a similar vein, I find a machine beating a grand master fairly boring. Show me a machine that can teach someone to become a master (or even a very good amateur) and we can talk.

That is, don't beat the game, beat the opponent, understand why, and then model an adaptation strategy for the opponent (teach).

<comment><USER12> > In a similar vein, I find a machine beating a grand master fairly boring.

I know a lot of people felt that way even before it happened. It feels like an accounting problem rather than intelligence. But, it's fun to remember when it was thought by some very smart people that chess required more accounting than was ever possible by a machine, so beating a grand master would have to be a demonstration of intelligence.

> don't beat the game, beat the opponent, understand why, and then model an > adaptation strategy for the opponent (teach).

Yeah that would be closer. My favorite Turing test, if you will, is whether the AI can tell you you're asking it the wrong question. If Watson got bored of beating grand masters at chess and started refusing to play, maybe then a case could be made it was reasoning.

<comment><USER13> > Watson got bored of beating grand masters at chess and started refusing to > play

That's when you reboot it, if that doesn't fix it good old persuasive maintenance techniques can also come into play

<comment><USER12> "I'm afraid I can't let you do that, Dave" \- HAL

</comment></comment></comment></comment></comment><comment><USER14> Watson is the IBM marketing department going mad about ways in which IBM can continue to remain relevant in a world that increasingly doesn't care about what hardware a particular computer program runs on.

If there is going to be a 'second AI winter' I fully expect Watson and other such efforts to be the cause.

<comment><USER2> IBM hasn't been about the hardware for a long time, instead it has been about the consulting services contract. And when we, as a startup, first engaged with the Watson folks it was clearly a sales funnel for their consulting services.

That said, IBM has a tremendous amount of research they have done in AI over the years. It is not that they don't have a lot of interesting technology they can throw at different business problems, it seems like they are having a hard time getting invited to the party if they don't track the same hype buzz that the current ML/AI craze has embraced.

<comment><USER14> The Watson stuff is so oversold it is almost comical.

And yes, sure IBM hasn't been about hardware for a long time, they've been a services company for decades now. But as far as AI/ML is concerned Google and Facebook are attracting the top talent these days, Apple and Microsoft much further down the line.

What would be nice is if they would take the opposite tack, rather than marketing the hell out of it quietly solve _lots_ of problems that are hard to solve in a traditional way. Every time I hear about Watson it is in the context of something where I ask myself "What's the point of being able to do that?". If all there is to hype is the hype itself then it is hollow.

</comment></comment></comment><comment><USER15> I used to work for IBM, but a few years ago.

One thing about Watson that I remember is this presentation by a _very_ senior guy. He had just come back from the US and was presenting what he learned there about Watson Healthcare (IIRC, that's what it was called), which I assumed was a division of the Watson team that was focused on cancer and stuff like that.

I'm paraphrasing, but during the presentation he said something like: "The project was not originally called Watson Healthcare, it was called X (I can't remember exactly), but potential customers were like 'No, no, leave X, we want Watson', so we had to change the name to Watson Healthcare for the sake of our customers. Watson Healthcare actually doesn't have anything to do with Watson."

I couldn't believe, at the time, how much respect I lost for IBM in about 20s. First of all, he thought we're idiots. You have to be brain dead in order to believe that he renamed X to Watson Healthcare in order to help customers. They just wanted to ride the hype train of the Watson brand and were lying to everybody about it.

<comment><USER16> his customers are CIO's they need help in selling things to the board, I guess that's what he's talking about.

<comment><USER17> A lot of CIOs are surprisingly non-technical themselves.

</comment></comment></comment><comment><USER18> The way IBM talks about it is completely bs. However, this round of AI is definitely better than the last one. Specifically, whats different this time around is that previously, expert based systems and many machine learning techniques require that you specifically hand code things like:

1\. Parsing and providing the input dataset into 'features'

2\. Hand coding the logic and rules for many different cases (Expert systems)

Now, it has become easier to train a model such as a neural net where you can provide much 'rawer' data; similarly you just provide it a 'goal' in the form of a loss function which it tries to optimize over the dataset.

By 'true' AI, I think most people mean 'how a human learns' \- which is actually a very biased thing, since we humans have goals of things like the need to survive, etc. I do believe it would be possible to encode these into goals, although doing that properly and more generically seems a little bit in the future.

<comment><USER19> One of the neat parts of expert systems was that you could get surprising and bizarre interactions between rules, leading to entertainingly nonsensical answers.

One of the neat parts of neural networks is that you have no idea what rules it's using, but it still manages to produce answers that are sometimes not entertainingly nonsensical.

<comment><USER14> The bigger problem is that they produce that are most of the time making perfect sense and then every now and then they spectacularly fail on input that is indistinguishable from inputs that gave correct answers.

<comment><USER20> I think this also happens in the human brain quite a bit. There's a lot of times where you see something out of the corner of your eye that isn't there, or you duck because you think something was coming towards you, or you wave because you think you recognize someone.

I bet at a lower level, various systems in the mind fail constantly but we have enough redundant error correction to filter it out.

</comment></comment></comment></comment><comment><USER21> Having had some level of access to inside IBM the whole cognitive initiative has just been this bizarre self-feeding marketing sales escalation where the real engineering has to 'bring the cognitive' in the most Dilbert pointy- haired boss sort of way.

</comment><comment><USER22> Dan Klein shared in a graduate NLP class at Berkeley a few years back a AAAI article on Watson back in 2010 (when it actually was a distinct technology stack and not just marketing nonsense). At that time IBM was focused on question answering in Jeopardy. It was pretty clearly incremental rather than novel— Dan used the example to show that 1) ensemble techniques can be effective if done properly and 2) hyper parameters matter, a lot 3) there's human intelligence and then there's Ken Jennings intelligence: looking at precision and percent answered, he's in his own separate league. It made me think a lot about individual differences in terms of declarative knowledge.

[https://www.aaai.org/Magazine/Watson/watson.php](https://www.aaai.org/Magazine/Watson/watson.php)

<comment><USER23> It was also unclear to me when they did the contest as to whether Watson only had access to the analog audio and/or image of the questions asked. So did they have to parse the question the same as Ken.

Also, it was clearly optimized for a specific use case. If the questions were reworded with more clues that were puns or needed inference, I think Ken would have done about the same but Watson would have faired much more poorly.

<comment><USER24> They had the text of the question (the sentence), and they had to parse that and then the resulting question was then sent through a text to speech engine obviously, but there was no speech to text.

[https://www.ibm.com/blogs/research/2011/01/how-watson- sees-h...](https://www.ibm.com/blogs/research/2011/01/how-watson-sees-hears- and-speaks-to-play-jeopardy/)

> At exactly the moment that the clue is revealed on the game board, a text is > sent electronically to Watson[...]

</comment></comment></comment><comment><USER25> "Machine learning" was a pretty good buzz word, but "Artificial Intelligence" is even better. And in a way, ML is part of AI so it isn't really lying.

IBM tries to sell into c-suites of companies that are less technically-adept than the average HN reader. Their marketing seems to be pretty effective, at least in getting proof of concept projects signed with big names.

Watson is simply IBM's ML product, but they call it AI and wrap it in marketing for all the reasons every AI startup does the same thing.

<comment><USER26> I disagree that companies implementing "AI" are less technically-adept than the average HN reader. This sort of comment has happened on other similar discussions.

It is untrue. They are very technically adept, with teams of people who are also aware of their problem spaces, and technology.

I understand there are some cases of lack of technical teams making these sort of decisions, but it isn't the norm, and even smaller companies often have incredibly technical teams.

I don't understand where this idea comes from. I've done consulting, and implementation of these type of projects for most of my life. My experience says it's false. What is the feeling that this is true?

Is it from people who aren't a part of the process theorizing that some unknown force must not be as intelligent as they are? Is it from looking at the decision making in general (why did they buy an ERP?), and making correlations?

I'm honestly unsure how there's this widespread idea that there aren't brilliant people everywhere doing the same work they are. Yes there are problems, and challenges all over the place, but I find I am amazed all across the country, and world at the level of expertise in companies.

<comment><USER27> > _I disagree that companies implementing "AI" are less technically-adept than > the average HN reader._

He didn't say that. He said that the c-suite of those companies was less adept than our fellow readers. This is almost certainly true. I'm not sure that it matters.

</comment></comment></comment><comment><USER28> Yep, matches my experience. We invited IBM to our company to pitch Watson, there was very little that was impressive about it. "Watson" is mostly just a coding services integration team, who will assign a team to add some basic NLP to your web services. Someone with a free weekend and a book on TensorFlow or NLTK can replicate most of what the IBM sales engineers pitch for Watson.

</comment><comment><USER29> Oh wow, Roger Schank [1]! Haven't heard that name in a while - he was quite famous in the early days of AI. I wonder if he has figured out a good way to marry ML to his theory of Conceptual Dependency (CD) [2] - because that would could be ground-breaking for hard NLP problems.

Interestingly I started reading the article without paying much attention to who the author is. A few lines in I began to wonder if this is going to be unproductive rant, and if the author has heard of things like CD etc ... It became funny right about then because that's also when I happened to glance at the URL and saw Schanks name.

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

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

<comment><USER30> In the 1980s I spent too much time trying to use Conceptual Dependency in a few small R&D projects. Looked promising, but I had little success with it.

<comment><USER29> I think one of the challenges with using CD in the real world is the "unclean" input that need to be mapped to the various primitives and structures of CD, or stuff in the same vein that came after. Without a way to do that automatically, in a scalable fashion and with minimal human assistance, its utility is limited. Which is why I feel that if we had a way to leverage the current breed of ML techniques to automatically (or even semi-automatically) define this mapping, it would be a big step forward.

<comment><USER30> Interesting idea!

</comment></comment></comment></comment><comment><USER31> I'd like to make a plug for my company ([http://www.cyc.com](http://www.cyc.com)) whose "AI" is not machine-learning based, does actual cognition and generalized symbolic reasoning, and lived through the AI winter of the 80s. We've gotten some contracts as a direct result of companies being disenchanted with Watson's capabilities.

<comment><USER30> I would like to ask you a question: over the years I experimented quite a bit with OpenCyc that you stopped distributing last year. Is ResearchCyc reasonable to experiment with on a small server of powerful laptop? Is an OWL version available?

<comment><USER32> OWL is not available, but ResearchCyc will definitely run on a laptop

</comment></comment></comment><comment><USER33> OT from the headline, but I take issue with the author's claim that Bob Dylan's work doesn't relate to the theme "love fades". Dylan has had a vast career beyond his protest song days, and I'd argue that one of his best albums, "Blood on the Tracks" would be accurately summed up as "love fades".

<comment><USER34> I agree; OP is one of those articles, that start to make a reasonable point only to frame it with a highly debatable specific example.

<comment><USER35> Dylan didn't win the Nobel for his songs about faded love, and he won't be remembered for them especially.

Schank is 72 years old. He came to know Dylan when when his protest lyrics gave voice to 20-somethings like Schank in the 60's. Like the rest of us, Schank likely remembers Dylan less for his retrospective years thereafter.

<comment><USER36> What you've called Dylan's "retrospective" years include an earth-shattering comeback with three top-charting albums in a row starting with 1997's "Time Out Of Mind" (which, incidentally, has quite a few 'love fades' lyrics). Dylan himself has singled out "Time Out Of Mind" as the only one of his own records that he himself goes back and listens to.

</comment></comment></comment></comment><comment><USER37> Dear engineers, merit is useless when you're trying to sell something. Authority is king, and people remember emotion and hyperbole. Marketing and sales is almost always about representing authority regardless of merit. The only thing that matters after a Watson sale is if Watson can help solve the problems the customers have.

<comment><USER38> True, it's well known that engineers don't often have appreciation for the emotionality of marketing. But there's a limit here. We can't just have no- holds-barred hyperbole and outright lying to the point of unfairly deceiving customers.

Obviously it takes a legal professional to judge where that line falls, but for something as specialized as this it's hard for laypeople to appreciate the distinction between stretching the truth with enthusiastic self-promotion and full-on false advertising. It's interesting to think about.

<comment><USER37> Well, it might be argued that with the exception of this author (who apparently coined the terms they're using) neither the IBM marketers, nor any of the potential consumers understand what these words mean. It is pure abstraction to most people. If the marketers were pressed, I'd guess that they'd just define these terms differently than the people making claims of fraud.

I'm imagining the glossy eyes of a judge or jury trying to grasp the nuance and just throwing in the towel.

</comment></comment></comment><comment><USER39> Anyway none of the company that pretend doing AI are actually doing AI. AI nowadays is pure branding bullshit.

<comment><USER40> I heard someone say that A.I. is just what we call technology that doesn't work yet. Once it works, we give it a specific name, like "natural speech recognition".

<comment><USER41> However, if a robot from scifi were to walk out of the lab, like Data or Ava from Ex Machina, or we had access to HAL or Samantha from Her, we wouldn't just give it a specific technical name. We would consider those to be genuine AIs, in that they exhibit human-level cognitive abilities in the generalized sense.

It's true that in Her, Samantha was just an OS at the start, kind of like how the holographic doctor was just a hologram at the beginning of Voyager, but as both stories progress, it becomes clear they are more than that. By the end of Her, Samntha and the other OSes have clearly surpassed human intelligence.

Those are fictional examples, but they illustrate what we would consider to be genuine artificial intelligence and not just NLP or ML. The reason people always downplay current AI is because it's always limited and narrow, and not on the level of human general intelligence, like fictional AIs are.

</comment></comment></comment><comment><USER42> "This was about promoting expert systems. Where are they now?"

In 2017, Intuit, Inc., owners of Quicken, posted revenue of about $5 billion.

Not too shabby.

<comment><USER43> As someone who is currently working on successful commercial product with a strong a expert system component, I agree with the sentiment. The funny thing about this project is that the product owners nor marketers and not even the coders ever use the term "expert system". It just doesn't sell any licenses nor garner any attention.

My view is that expert systems are a ubiquitous part of many products to the degree it's hard to even recognize them as such. They're not the main focus of anyone's marketing budget, because that makes about as much sense as promoting your "revolutionary axle technology" to sell a car.

<comment><USER44> A bit off topic, but I've been wondering about what expert systems are a lot lately, and I hope you don't mind me asking a few things about them.

I started studying machine learning long after statistical models was the absolute standard, and all I really know about expert system, are passing phrases in textbooks about how the world has moved away from it.

How does one go about building one? MNIST character recognition is often called the "Hello world of machine learning" ... what is the "Hello world of expert systems?"

Is there a modern term for Expert Systems?

</comment></comment></comment><comment><USER45> It's a pretty open secret in the community that what IBM pitches that Watson can do, versus what it (or any state of the art system) really does is pretty much bunk. This author calls it fraud, but a more charitable interpretation would be extreme marketing. We've seen a lot of failures with Watson, particularly in the medical space - MD Anderson's Cancer work for example ([https://www.forbes.com/sites/matthewherper/2017/02/19/md- and...](https://www.forbes.com/sites/matthewherper/2017/02/19/md-anderson- benches-ibm-watson-in-setback-for-artificial-intelligence-in- medicine/#2533023e3774)) where MD Anderson payed around $40 million (on a original contract deal somewhere near $4 million) and eventually abandoned it.

I do think Watson may be a fake it until you make it thing - in particular, they still have access to a incredible amount of data, and data determines destiny on a lot of AI.

<comment><USER46> but their message is for outside the community, so I don't think they deserve any charitable interpretation, they lost this privilege a long time ago. I have to say I'm biased, I wish I can see them disappear soon, but I think I have the right motivations.

</comment></comment><comment><USER47> Had IBM sales people present on Watson as a security solution recently. The stench of BS was so bad I nearly had to leave the room. It wouldn't bother me if they kept things generic, but they deliberately sprinkle the presentations with specific terms referencing hyped technology (deep learning, etc), with the clear objective of deceiving the audience into thinking they are using those technologies when they clearly aren't. It was unethical IMHO.

<comment><USER48> Did your company still buy it?

<comment><USER47> No, we aren't quite the target market yet, but I can see the ground is being prepared for when we might be.

</comment></comment></comment><comment><USER49> Chomsky calls it a sophisticated form of computational behaviorism. Just like the research program of behaviorism died out, this will eventually too. There are other respectable criticisms of AI, like Hubert Dreyfus' 'What computers can't do'.

Neither Chomsky nor Dreyfus claimed that machine learning and/or AI won't solve any problems, but rather that the kind of problems these solve are not relevant in terms of aspiring to be humans.

<comment><USER50> Chomsky's "Where AI went wrong" is often ignored by the mainstream AI community or dismissed. Peter Norvig's retort was poorly constructed, and showed that he didn't comprehend Chomsky's argument.

Machine Learning and AI are stuck in a rut and apparently these so called ML/AI experts know better. The Deep Learning (along with ML) craze is a hindrance to a true scientific theory of intelligence.

</comment></comment><comment><USER51> I'm no expert in the field of AI or Machine Learning, so I have a question for the experts here? Has there been any theoretical breakthrough in the AI in the recent years? I knew we had neural networks and different types of classifiers, etc. for more than a few of decades now, so apart from better marketing, has there been a significant breakthrough that explains this sudden surge in the interest in AI?

<comment><USER52> There have been a number of breakthroughs in specific fields recently. In fact it seems like there is a paper every week that pushes the state of the art forward in some branch of AI/ML. I think the big one that triggered the current excitement was the success of convolutional networks in image recognition tasks. You can read about that one (in 2012) here: [https://www.technologyreview.com/s/530561/the- revolutionary-...](https://www.technologyreview.com/s/530561/the- revolutionary-technique-that-quietly-changed-machine-vision-forever/)

EDIT: This paper refers to the algorithm as SuperVision which was the team name, but it is more commonly called AlexNet. Here is another article discussing it:

[https://qz.com/1034972/the-data-that-changed-the- direction-o...](https://qz.com/1034972/the-data-that-changed-the-direction-of- ai-research-and-possibly-the-world/)

<comment><USER51> Thank you. The very answer I was looking for.

</comment></comment></comment><comment><USER53> I saw a demo of Watson a couple years ago at a trade show and was not super impressed. Looked like a glorified Markov chain to me.

</comment><comment><USER54> Everything IBM says about Watson should be taken with a few tons of salt.

I don't know how much I'm allowed to say, but they couldn't even get it to work acceptably internally for some of the basic datamining and natural language processing that are among the things so highly touted in some of their TV advertising. This is with a gargantuan dataset compiled from years of relevant interactions to train on in the particular area of interest.

<comment><USER55> Re: _Everything IBM says about Watson should be taken with a few tons of salt._

How is this different from nearly all _other_ "AI" companies? IBM is not the only one guilty of hype. Neural nets can do specific things well, such as mirror a specific training set well, but still have major gaps in terms of what many call "common sense". It does smell like AI Bubble 2.0 brewing out there.

</comment></comment><comment><USER56> It's an unusually beautiful written article, well worth the read just for the prose. As for the main sentiment that we have a new AI winter, I'm not so sure. My lay person view is that we see quite a lot of commercial success with these systems so the current wave will be well funded for at least a decade.

<comment><USER57> What are the big successes? Speech recognition? Which still seems rather bad to me.

Language translation (which for the languages I'm interested in (Japanese) is still almost totally unusable?

Self driving cars? Which are not yet in production (and where the social issues are probably far harder than the technical ones, and likely have been since the 90s).

Is there some big application if ML that I'm missing that is a clear win?

<comment><USER42> Amazon's system for finding related products is eerily accurate. The more you buy, the better it gets at anticipating what you want to buy.

... but the larger wins they've had are behind the scenes, in the predictive modeling that helps them get product into warehouses in front of demand spikes (and into geographically-relevant warehouses to satisfy the spikes).

<comment><USER58> > Amazon's system for finding related products is eerily accurate.

Amazon's system for finding related products fucking sucks. After I buy an X, I see ads for more X for months, which is completely useless. Sometimes I'll see ads for the exact thing I bought... what?!

<comment><USER59> Yep, I see this all the time too. Buy a printer? Amazon thinks you want to buy a new printer every day for months.

Google too. I bought a Pixel 2 online, from Google, signed in to Google. Now I see ads on Google ad networks all day long to buy a Pixel 2. It has been 5 months of daily ads from Google to buy the phone I just bought. Ridiculous.

Ad networks are a clear financial win for AI - but they also show the ridiculousness and are clear windows into the failures so far.

<comment><USER60> This. For all the talk about how Facebook is mining data to hyper-target customers, it still seems like they're still trying to throw stuff at the wall to see if it sticks.

</comment></comment></comment></comment></comment></comment><comment><USER61> > People learn from conversation and Google can't have one. It can pretend to > have one using Siri but really those conversations tend to get tiresome when > you are past asking about where to eat.

At first blush he sounds like my technologically semi-literate grandma who would definitely conflate Siri and Google as being part of the same grand internet program. I had to read this twice to understand that in saying "it can pretend to have one using Siri", he meant that asking Siri a question sometimes redirects to a Google search, but wrote it in a way that personified Google as the actor with intent in that transaction. What an odd and paradigm- breaking way to look at that.

</comment><comment><USER62> I feel like they've moved on from their lies about Watson's capabilities to lies about their capabilities with blockchain technology.

</comment><comment><USER63> The problem is that even internally, there is this notion of "sprinkling a little Watson to do the hard jobs" and then 'poof': problem solved. Marketing reflects internally, too.

</comment><comment><USER64> Can an mod add the date to the title? This piece is from 2015.

</comment><comment><USER65> AI is definitely starting to enter the "through of disillusionment" in its hype cycle.

<comment><USER59> Is it really? I don't see that at all. Rather I see AI as finally being entrenched in normal, everyday products and services. AI is here to stay and the hype hasn't even started yet, at least not compared to what's coming.

Billions of people own devices that they can talk to, that can talk back, that can translate between more languages than humans can, that know facts about you that you didn't even know yourself, etc.

Basic AI has come to be an expectation of many consumer products now. And _real_ AI is coming faster than ever.

Fake audio and fake video, generated from computers/AI is here. Self-driving cars may be just a domain-specific expertise, but it is still AI by any traditional definition.

AI is not reaching any kind of trough of disillusionment that I can tell. We're still obviously just getting started with what can be done.

</AI hype post.>

<comment><USER66> While there are domains where AI is having successes, the current general expectation is far ahead of where the technology is. People think (and in part due to the marketing like IBM is putting out) that ML/AI can do things that aren't possible yet.

There may be a reset in expectation soon which will lead to become more pessimistic at claims being made and the marketing. Once we are through that, it will be easier to get people to understand what use cases align with the available technology and implementing ML/AI will become more productive. Aka the Trough of Disillusionment followed by the plateau of productivity.

</comment></comment></comment><comment><USER67> I would only refer to Episode 4-5 of Silicon Valley in Season 5.

This will be the mistake we will make when we introduce a technology we think it is smart enough to make decision for us and turns out all it does is read words faster than us and interpret them literally.

Even with the closing statement of "AI winter is coming soon." I can see Watson having a problem understanding that statement even with context.

</comment><comment><USER68> Something feels wrong about the assessment of the second block of text being written by a human.

I feel some conflict in my head that someone would talk about Bob Dylan as "overstepping" a claim about his prominence and then conceding that he does belong in a "Top 10 Bob Dylan Protest Songs list." Of course he belongs in a Bob Dylan list he is Bob Dylan.

Does that sound human?

<comment><USER69> I think the text is pulled from a "Top 10 Bob Dylan Protest Songs Lost" article.

</comment></comment><comment><USER70> Watson's marketing is obnoxious, but it's not just Watson. There is plenty of bullshit, ignorance, and pseudo-intellectualism to go around. Mind you, many of the technical fruits of AI itself, properly understood, are not bullshit (the name "AI" is misleading IMO; I wouldn't be able to tell you what distinguishes AI from non-AI because it seems largely a matter of convention rather than a substantive difference). The field offers plenty of useful techniques for mechanizing things people have had to do preiously. However, the very idea of a "thinking computer" is unjustifiable and superstitious. There's too much sloppy, superficial thinking.

The author of the article mentions concepts and indicates a distinction between them and word counting. Certainly, there is a difference between word counting and conceptualization, and it is patently obvious computers don't do the latter. But it's worse than that. Technically, computers aren't even counting words. They aren't even counting, nor do they have any concept of a word (we count words by first knowing what it is that we should be counting, i.e., words). What we call word counting when a computer does it is a process which produces, only incidentally, a final machine configuation that, if read by a human being, corresponds to a number. The algorithm is a proxy for actual counting. It is a process produced by thinking humans to produce an effect that can consistently be interpreted as the number of words (tokens) in a string. That's not thinking. There is zero semantic content and zero comprehension in that process, and no number of tortured metaphors or twisted definitions can change that. AI, as it becomes more sophisticated, is at best a composition of processes of the same essential nature. No degree of composition -- no matter how sophisticated or complex -- magically produces thought anymore than taking sums of ever more composed and expansive sequences of integers ever gets you the square root of two. It's not a mystery.

</comment><comment><USER71> It's not just sleazy advertising, the money IBM has taken from cancer research for snake oil is downright fraud in my book

</comment><comment><USER72> Advertising != Peer reviewed publication. Drinking Coke doesn't make you sexy and desirable, as suggested by their ads, just makes you gassy.

IBM is allowed some creative liberties in their mass-media advertising campaigns.

</comment><comment><USER73> IBM's customers probably understand that Watson (the jeopardy-playing bot) isn't really relevant, and that what they're buying isn't a pre-written software package so much as software consulting services. But there's still a serious problem, which is that a customer of Watson would reasonably believe that they're getting the team of engineers that solved Jeopardy. In reality there is no overlap whatsoever in personnel between Watson-the-PR-project and Watson-the-thing-you-hire.

<comment><USER74> Watson Services are very much a product you buy/subscribe to. Just like the Machine Learning services you get from Microsoft or Google. You can use them pretty easily with absolutely no consulting at all.

</comment></comment><comment><USER75> Start by asking "What is Watson for?"

Watson is for helping decisions at large corporate customers: The CEO has heard of Watson. He saw it on a screen in the VIP tent at the golf tournament, and thinks it's neat. The CIO feels safe with "Watson" in an RFP response from IBM because the CEO thinks it's neat. IBM is happy with this pettifoggery because it keeps the SOW vague and open to maximizing revenue from the project.

It's not about AI.

</comment><comment><USER76> I always feel that this is one step away from that famous quote, "The greatest trick the Devil ever pulled was convincing the world he didn't exist," attributed to various ([https://quoteinvestigator.com/2018/03/20/devil/](https://quoteinvestigator.com/2018/03/20/devil/)). In this case, the greatest trick is convincing that it does exist... and maybe is the harder one.

</comment><comment><USER77> I don't have gripes with this marketing approach and I read it more as "expect to be surprised by what is possible" rather than harp on what isn't .. at least not yet. For a comparison, it is like apple branding their display tech as "retina display" to communicate the intention (you can't tell pixels apart), possibilities (you can now use any font) and quality rather than any claims about mimicking the eye.

</comment><comment><USER78> "These guys are a fraud. Come look a thing my online Academy. Call for the price."

Roger Schank used to be a serious researcher. Also tied to consulting firms like Accenture.

</comment><comment><USER79> I think the biggest concern is that the general public outside of this industry think that AI and machine learning is Hey Google not understanding them and their bank working out you've run out of money – which they already know. When AI _actually_ arrives, they'll be bored, ignore it, and then, well, I guess it'll know and take over the world. We're all doomed.

</comment><comment><USER80> >A point of view helps too. What is Watson's view on ISIS for example?

>Dumb question? Actual thinking entities have a point of view about ISIS. Dogs don't but Watson isn't as smart as a dog either. (The dog knows how to get my attention for example.)

Ouch. But at the same time, for something that didn't grab his attention he sure had a lot of words to say about it.

</comment><comment><USER81> >...counting words, which is what data analytics and machine learning are really all about

The piece is welcome anti-hype, but, what? How can a true expert in the field say something like this? Or, maybe I should tell my colleague who is working on ML for diagnostic radiology to think of voxels as, uh, words?

</comment><comment><USER27> > _I started a company called Cognitive Systems in 1981._

Ahh. So just from the article, his gripe is of the "begs the question" sort — he's not pleased with the evolution of idiom. Since he was doing "real AI" back then, who are these frauds to claim they are doing AI?

His point may or may not be valid, but his specific argument is quite weak. He notes that even _a person_ , an actual intelligence, wouldn't know what Dylan was singing about without context. He goes on to presume that Watson doesn't have context, but who's to say? Watson could certainly read all the articles about Dylan that he so helpfully cites, and come to "understand" the songs. And maybe Watson has.

If you follow the links to his academy, you divine a bit more of the motivation.

His software development course provides "a unique automated mentor, employing natural-language processing technology derived from our decades of artificial intelligence research". He is desperate to stay away from calling it actual AI, yet can't resist implying that it is. This is probably most irksome to him.

My advice: sometimes you have to join 'em.

<comment><USER82> He specifically addresses that he _wasn't_ doing "real AI" then, and given they're doing fundamentally the same or similar things, IBM aren't now either.

<comment><USER27> Yes, that is my point (sorry that I mis-stated it a bit). He is offended by the claims of these charlatans when he knows the technology hasn't advanced to the point of being AI. How dare they claim otherwise.

Also, he knows that they know it's a lie. Unforgivable.

But he's fixated on the literal (once-true) meaning of "AI". It doesn't mean that anymore, not to the lay person and not to the average technologist either.

Like "begs the question", one has to just get over it.

<comment><USER83> Not to the lay person? Really? Do you have any evidence to back it up? Hint: HN is not full of lay persons.

</comment></comment></comment></comment><comment><USER84> I remember seeing "Watson" mentioned in the news like five years ago, but besides a couple HN threads, I've not seen it mentioned since then. Am I missing something (besides TV, which I don't watch)?

</comment><comment><USER85> Here's a great video that covers "AI winters" for those who are curious: [https://vimeo.com/170189199](https://vimeo.com/170189199)

</comment><comment><USER86> Isn't it more or less common knowledge now that Watson is all marketing buzz? The Watson that IBM is selling CIOs on is a very different thing from what was seen on Jeopardy.

</comment><comment><USER87> We won't know how to build machines that understand until we know what understanding actually _is_ at a biological level. I'm not convinced that we do.

<comment><USER88> I am not even convinced that understanding happens at a biological level. I think there is still a lot that can be done to model aspects of human reasoning in software to produce useful results, especially if it is married with machine learning, but I don't think we will get there by looking at biology.

</comment></comment><comment><USER89> Discussed at the time: [https://news.ycombinator.com/item?id=11751267](https://news.ycombinator.com/item?id=11751267).

</comment><comment><USER90> "People learn from conversation and Google can't have one. It can pretend to have one using Siri"... Google doesn't use Siri.

</comment><comment><USER91> "Search is all well and good when we are counting words, which is what data analytics and machine learning are really all about."

There are machine learning models which go far beyond counting words, for example see [https://arxiv.org/abs/1502.01710](https://arxiv.org/abs/1502.01710)

<comment><USER44> Parent links to Yann LeCunns's "Text understanding from Scratch" paper, from 2015, where the authors uses a conv-net, originally build for image recognition to do text categorisation.

The NN techniques falls squarely in the "counting words"-bracket, although this one is actually counting characters.

It is a great paper, with great results, but none of those models therein have an opinion on ISIS, an ability to converse or anything the author of TFA calls cognition.

</comment></comment><comment><USER92> I do agree that Watson seems oversold, but the evidence in this article (Watson's shallow opinion of Bob Dylan, compared to the author's opinion of Bob Dylan) seems kind of weak. I was hoping for some insider information on the implementation of Watson, but unfortunately there is none.

</comment><comment><USER93> The day AI understands my dirty jokes, it's the day I call it cognitive.

</comment><comment><USER94> It's just advertising.

<comment><USER95> False advertising. Is that not worth calling out?

</comment></comment><comment><USER96> (2016)

<comment><USER42> "©2018 Roger Schank" at the bottom? Unless that's just an auto-generated copyright output that updates to the current year.

<comment><USER96> that's just auto generated... I saved the bookmark in 2016... could be even earlier. I don't see any dates. Wayback machine has its first saved copy in May 2016 which matches my bookmark.

[http://web.archive.org/web/20160523070729/http://www.rogersc...](http://web.archive.org/web/20160523070729/http://www.rogerschank.com/fraudulent- claims-made-by-IBM-about-Watson-and-AI)

</comment></comment></comment><comment><USER97> Methinks voice will in fact win.

</comment><comment><USER98> Normally my comments are very sceptic, but this article is just spot on.

The problem is not only context or subtext, it is even worse from an entirely predictable standpoint:

Consider a large corpus of text (books, articles, ...).

1) Concepts that are WELL UNDERSTOOD BY HUMANS will not be explained by humans when they reference them: the word or concept "pet" (as a verb) will show up in many sentences refering to the petting of cats, dogs, horses,... and ML will correctly predict the conjunction of "pet" with any of these words in the sentence. It will even be able to train ML to confabulate realistic sentences in the sense of echolalia. Consider then the sentence:

"The lady pets the cat"

The computer will recognize that the presence of cat is not surprising (bingo!). The computer will have no idea that this probably involves one or more cycles of the lady's hand _gently_ pressing down on the fur belonging to the cat, then while still preessing down, moving the hand in the 'natural direction of the hairs' (NOT the other way around) probably from closer to the head towards the tail, and probably lifting the hand before repeating the cycle so as to massage the cat or perhaps so as to remind the cat of its time as a kitten being licked by the mother cat.

No book or conversation in the corpus will give this detailed description exactly because humans expect each other to understand this.

2) Concepts that are POORLY UNDERSTOOD BY HUMANS will be vigorously (but often erroneously) explained by humans communicating to each other what they think is going on: endless texts about religion, sexuality, perpetuum mobiles, economy, ...

How do we even expect the computer to produce a sane result, even if it correctly guesses the context?

That said, I do believe relatively helpful natural language processors to be possible, but they will have to be vigorously trained by multiple human curators individually analyzing a sentence and trying to find (probably true) statements about what a sentence implies:

Starting again with:

"The lady pets the cat"

One curator might mention hands touching fur while moving.

Another curator might add that one can also conclude the hand probably presses down on the fur.

Yet another notes the sentence also implies the lady is still alive, for else she would not be able to pet.

The first curator now adds that the cat as well is probably alive, for else the lady would probably not want to pet the cat, since massaging is useless to a dead cat.

The third curator now mentions that the sentence implies one or more cycles of an individual pet stroke.

Etc...

As you can see this quickly becomes an expensive operation.

Now one might train an adversarial neural network to look at a sentence (or sentence with context) and a list of probable conclusions, to predict if the list of valid conclusions is complete or incomplete. And then only send the incomplete ones to humans?

</comment><comment><USER99> What is the point of changing the headline of an article like this? The headline was an accurate summary of the contents ("THE FRAUDULENT CLAIMS MADE BY IBM ABOUT WATSON AND AI"). Maybe you agree, maybe you don't. But the current headline is just wrong.

From the article:

> I will say it clearly: Watson is a fraud. I am not saying that it can't > crunch words, and there may well be value in that to some people. But the > ads are fraudulent.

That's what this is about. Not "Claims made by IBM about Watson and AI."

<comment><USER100> The HN submission guidelines include the following rule:

"Please use the original title, unless it is misleading or linkbait."

The original headline is a bit sensational/clickbaity. That's a judgment call in this case, IMO, but I suspect that's why the title was changed.

<comment><USER101> I wish click bait was more well defined. In this case, I feel the original title summarizes the entire claim made in the article. Using the title "Claims made by..." sounds like it'll be an article summarizing the claims made by AI and Watson, rather than a push back on those claims.

<comment><USER102> I'll go ahead and define clickbait using a simplified and possibly anthropomorphized version of information theory.

First:

\- The more improbable a message is, the more information it contains, assuming the message is true

\- If the message is untrue it contains no information

\- If a message is already known by everyone it contains no information

Not Clickbait:

If a message is surprising (seemingly unlikely or previously unknown), and true, then it contains high information, and will be very likely to be clicked. This is not just a good thing, it's the most optimal thing!

Clickbait:

If a message is surprising and untrue, it will also very likely be clicked if the user cannot easily determine that the message is untrue. The user may then be disappointed when they discover the message actually contained no information because it was false. Incidentally, false messages will always have a high probability of appearing to be high information messages, because they will often appear to have the least likelihood.

Incidentally, this is (in my opinion) the theoretical problem of fake news. It will always appear to be high information to those unable to determine if it's true or false. In other words, it will appear to be of the highest value, when really it has no value (or even negative value if you look at the system level rather than just information level).

</comment></comment></comment></comment><comment><USER103> It's not new! IBM does what it's best at which is advertising.

</comment><comment><USER104> Machine Learning in a lot of ways is legitimate, but its applications to a lot of NLP / chat-bot technologies is far from cognitive computing.

</comment><comment><USER105> "Recently they ran an ad featuring Bob Dylan which made laugh, or would have, if had made not me so angry." Wait, is Roger Schank an AI trying to convince humans that AIs are impotent and harmless? Pretty sneaky, Schank-bot.

</comment><comment><USER106> I worked on data systems that fed weather data into Watson.

IBM's technology, and IBM's marketing are very different beasts. The marketing is somewhat trivial, but the reality of ML and the Watson data systems is incredible. They are pumping a huge amount of data into it and they have data scientists doing incredible things with the data already. It is the largest growing segment of the company (and maybe the only growing segment of the company.)

The marketing of AI, and the realities of ML are always going to be disconnected. Sure, you will likely just get an email that says routine maintenance has been ordered on your elevator, not a box in the corner that tells you in a sexy, clear, non-robotic voice. As for Bob Dylan's songs... Christ. AI winter isn't coming unless the term AI gets squashed and people start calling it ML. This article is pretty FUD.

<comment><USER107> The problem is that all of this AI and ML is being used to target Ads better. You don't need AI and ML to do this, it is a solution looking for a problem.

Your example about routine elevator maintenance does't need AI at all. Whoever logs the elevator maintenance can just press a button to send a notification message. Hell, it can be wired up such that when you submit a notification notice, it automatically sends an email. They probably have condo management software that does this already.

<comment><USER108> Speaking for OP - I think the idea was that ML would determine that your elevator was in need of maintenance and place the order without human intervention.

Although the word "routine" belies that, but so.

<comment><USER107> Exactly. We already know when the elevator needs to be maintained. If it isn't being maintained, it's not because people don't know, it's because they are intentionally NOT doing the maintenance, maybe because it costs $120/hr minimum 6 hours.

</comment></comment></comment></comment><comment><USER109> Question: how can the author know what Watson really is doing unless the author worked on Watson?

If you're going to try and explain the above to me, don't do it by explaining what Watson "really" is doing (unless you worked on Watson yourself). Explain exactly how you can tell the difference from the outside.

As far as I can tell all the author did was point out that Watson made a mistake (saying that Dylan's songs is about love fading) and this is not enough, since humans make mistakes too.

<comment><USER29> I am not sure if the author did this (he certainly doesn't mention it), but a lot of what constituted Watson at the time of Jeopardy was published as a set of 17 papers called "This is Watson" in the IBM Journal Of Research and Development.

Other than that too, I think you can make a reasonable assessment of any NLP system at this level of abstraction by looking at what is cutting edge in NLP today (by following conference proceedings etc).</comment></comment>


Obama: NYC's Kelly 'Very Well Qualified' To Lead DHS - <AUTHOR> http://www.newsmax.com/newswidget/kelly-obama-homeland-security/2013/07/16/id/515450

<comment><AUTHOR> All questions about Kelly's policies and track record in NYC aside, what's interesting about this move on Obama's part is the utter vapidness of it. There's no reason to presume that the skills of leading a large (even one of the world's largest) police forces is in any way transferable to the task of running a sprawling federal agency like the DHS (if in fact it can be "run.") It's a completely alien environment, in fact.

But Kelly is being pumped for this job solely on the basis of stature -- and nothing else. If anything it's just another sign of how useless and zombie- like the DHS has become in the short time since it was created. Which pretty much everyone -- including, it would seem, Obama himself -- already knows on some level, though very few are willing to state openly.

Brings to mind that Leonard Cohen song:

      Everybody knows that the boat is leaking
      Everybody knows that the captain lied...</comment>


Quantum Entanglement Can Be A Measure Of Free Will, Say Physicists - <AUTHOR> http://www.technologyreview.com/blog/arxiv/25665/?ref=rss

<comment><USER2> From the paper, they appear to be using a rather extreme definition of free will: that free will means a radically unconstrained-by-the-material-world decision-marker, who lives somewhere outside the world (perhaps in some sort of Cartesian "mind" or Christian "soul"), making completely free decisions on some ethereal plane, and then swooping into the material world to enact those decisions now and then. Hence their "bits of free will" metric, which appears to measure just how ethereal and outside-of-physics's-control each bit is. But that's hardly an uncontroversial definition of free will, and probably actually a strongly minority view among modern philosophers (it was dominant in the middle ages, but not anymore). For alternative views, see, say, Daniel Dennett, who argues that free will isn't even incompatible with determinism--- the "free" in "free will" doesn't mean "free of physics", but a description of a certain kind of behavior _within_ physics.

<comment><USER3> If you're interested in this sort of thing Vaughan Pratt has an excellent paper explaining all this mindy/body dualism using Chu spaces[1]. When I was reading linear logic I found it a pleasant diversion

[1] <http://boole.stanford.edu/pub/ratmech.pdf>

</comment></comment><comment><USER4> This reminds of of a very cool concept that Orson Scott Card made up in his "Ender's Game" series: philotic physics –

"A philote is the basic building block of matter, the true indivisible particle that is not made up of smaller ones. Philotes take up no space and are essential to the theory of philotic energy. Each atom has a philote of its own, each molecule likewise, and ultimately each human has an aiùa, an intelligent philote." Philotes have the desire/will to connect and join in an organized pattern and form matter. ([http://en.wikipedia.org/wiki/Concepts_in_the_Enders_Game_ser...](http://en.wikipedia.org/wiki/Concepts_in_the_Enders_Game_series#Outside))

<comment><USER5> These kinds of things always remind me of the central concept in Pullman's His Dark Materials - that dark matter is original sin.

</comment></comment><comment> saint-loup +10 points on crackpot index.</comment>


JavaScriptCore engine that powers Safari as a WASM package - <AUTHOR> https://wapm.io/package/jsc <comment><USER2> would be nice if we can run browsers inside wasm, then work on a new spec meta-wasm to run meta-native apps in that meta-web; it's wasm's all the way down</comment>


New iOS App Lets You Record What You Heard Five Minutes Ago - <AUTHOR> http://techcrunch.com/2013/06/23/new-ios-app-lets-you-record-what-you-heard-five-minutes-ago/

<comment><USER2> If the point of this is to keep running all the time, wouldn't it kill battery?

<comment><USER3> If the application registers itself as a VOIP application, it can run continuously in the background and have access to the microphone. It is likely that this app is using that to get the audio, so it would kill the battery.

</comment></comment><comment><USER4> How good is the mic pickup when the phone is in your pocket?

</comment><comment><USER5> Wouldn't using this app be illegal, at least in California?

</comment><comment><USER6> Is this like some sort of PRISM joke app?

<comment><USER7> Nope - it's the audio equivalent of bicycle commuters wearing GoPros or the Russian dashboard camera phenomenon. There are things that happen rarely, but who's consequences are serious. At some stage it becomes worth recording _everything_ to ensure that you've got a record of those rare-but-important events.

Apart from the possible legal consequences of using something like this, I can see it being a useful idea.

<comment><USER6> It was more of an ironical statement, however the legality you brought up is definitely interesting. At least in California it's not legal to record conversations without the entire party's consent (except in rare circumstances). A number of other states have the same laws but I'm not sure if it's completely nation wide.

Then of course there is the massive battery drain

<comment><USER7> Where I am (NSW, Australia), the current "best legal advice" about recording your interactions with police is that it's legal to record conversations (even secretly), but it's not legal to play those recordings to anyone else. So you can't bring up your recording of a conversation with a police officer as evidence in court. What you _can_ do though, is if an officer lies about the contents or existence of a conversation, you can tell the court you've got a recording that proves a police officer has told the court something that's not true - and the court can then (legally) ask to hear your recording.

(Disclaimer: I am not a lawyer - nor do I play one on youtube. If this matters to you, seek competent legal advice before acting on this information…)</comment></comment></comment></comment>


Zed Shaw - How To Backpedal And Boost Like A Pro - <AUTHOR> http://oppugn.us/posts/1272078502.html

<comment><AUTHOR> "Dude is a Class-A jerk who's attacked people in the past, and I have yet to run into too many people who think the guy is that great."

Man you can make me laugh out even at 3AM in the morning!</comment>


It's so hot at Death Valley National Park that cars are breaking down - <AUTHOR> https://news.yahoo.com/hot-death-valley-national-park-203440163.html <comment><USER2> Despite the high temperatures Death valley is extremely dry. This makes the temperatures a lot easier to deal with, but hydration is a lot more important. It's actually kinda nice in the summer if you have an umbrella and some cold drinks. I would rather sit in Death valley at 130F and 5% humidity (Heat index of 97F) than in Florida right now at 90F and 75% relative humidity (Heat Index of 109F)

</comment><comment><USER3> Nominative determinism?

<comment><USER4> great question</comment></comment>


Ask HN: Review my Startup - <AUTHOR>

Hello guys. A friend and I built a small web app last week and we would love to get some feedback. Are we providing enough value for the users? Is there enough information on the homepage for people to sign up with Facebook? Here's the page, thank you for your feedback!
www.doitbefore.com<p>Regards from Germany, Jan <comment><USER2> Clickable: <http://www.doitbefore.com>

I don't do Facebook logins on 3rd party sites, but the landing page looks nice.

</comment><comment><USER3> Just because you created a web app, doesn't mean you have a startup.

<comment><AUTHOR> hey. I just wanted to follow format conventions. "Ask HN: Review my Startup" is a thread I often see here, so I tried it out. Of course I know that a simple web app is no startup at all, that's the reason why I called it "web app" and not "startup" in the topic description.

</comment></comment><comment><USER4> So you built an app that's only viable 17 days ?

<comment><USER5> no they built a "startup" that is only viable for 17 days.

<comment><USER6> is it really import if it's "startup" or just a website? ...

</comment></comment></comment><comment><USER7> 17 days is a bit shortlived lol.

But, I feel it can be expanded to encompass anything you wish to do with friends where you can publicly list goals, add photos from each accomplishment etc etc with friends and family.

I don't like when apps use Facebook as a prerequisite, but I would find a way to utilize Facebook's reach without requiring it to participate. Use it as a tool - not as the foundation.

It's a concept, needs some refining - but there's promise.

<comment><AUTHOR> thank you for your feedback! "Use it as a tool - not as the foundation" is something we will strongly think about when building our next app.

</comment></comment><comment><USER8> First of all, congratulations for shipping, that's half the battle :) . I like the logo, background very much.

Like masnick, I don't really use Facebook logins unless I absolutely must (AND it's blocked at work).

I'd remove the "test test test" goal :) . Maybe you can pre-populate from the best-known bucket lists?

</comment><comment><USER9> I don't really see the value in your site - personally wouldn't use it.

But if you guys are stuck on the idea, I'd take out the big block of text that reads: "What if on December 21st 2012 the world really came to an end?"

and replace it with something a bit more to the point about what you guys are doing.

</comment><comment><USER10> Your product isn't clearly defined and the very last two sentences have a hint at what the product might be. What are you actually offering?

Also, a lot of people are fed up with this end-of-the-world thing and might be somewhat turned off that you are using it as a theme for your product description.

</comment><comment><USER11> Congratulations on going live! I think maybe a short explanation on how you add value/your USP might help. There are several goal achievement related websites out there.

Also, you probably want to think about how you are going to change the punch line post Dec 2012 :)

</comment><comment><USER12> Wow, really unnecessarily harsh people in here. Maybe someone should start a community for people who are interested in start-ups and tech projects so these people aren't burdened by posts like this.. oh wait.

</comment><comment><USER13> "Are we providing enough value for the users?"

Enough value for what?

</comment><comment><USER14> Nice looking landing page but I don't have facebook so the site is completely useless to me.

</comment><comment><USER15> "make piece with someone"

</comment><comment><USER16> change the Font Style... :/

</comment><comment><USER17> It uses Facebook? I don't. Bummer.

Good luck on the app though.</comment>


Ask HN: Hacker News high latency since some days - <AUTHOR> Since some days Hacker News is super slow to load on my laptop.
Some screenshots of the load times:
https://ibb.co/x8g2Gv9
https://ibb.co/rFXwRRC
I'm using Firefox on Windows 10, but I could reproduce it using Microsoft Edge. I live in Germany.
Is it just me? <comment><USER2> I am also seeing this, although seems to be across many domains including my own on dedicated servers. I was going to look at DNS hmmm no wondering if its just load on wider backbone.

</comment><comment><USER3> [Some mid-points or whatever has been problematical, afaik,] and afaik they are trying to route around it. It's not just you, dude(tte,) github and co also took the dive, and lots of other things. Things should be good('er) now though? Same timezone my geiger, good morning.

Stay safe in these times. Fiel gluck. #kaffeiprost

<comment><AUTHOR> It's still painfully slow. I didn't experience this on github, only HN.

Stay safe!

<comment><USER3> You too, man! I bet it'll pick up soon. It's only temporary.</comment></comment></comment>


Ask HN: Why PEP8 favors using spaces over tabs for code indentation? - <AUTHOR>

This is something that I, a novice python wannabe,has been struggling with. I love to use tabs in my editors to indent code. Much easier to remember than using 4 spaces (as per PEP8 standard). Can some python expert clarify this ? <comment><USER3> The reason is that when sharing code use of tabs can lead to confusion, subtle, and not so subtle errors, especially in Python, where consistent indentation is most important.

For example, one person has tabs set to the size of 8 chars and writes a lengthy script. A second person comes along with tabs set to 5 chars and makes changes. The indentation could well be wrong because they see the same file differently. Sometimes you get lucky and you get errors immediately, but other bugs can lurk... like the second statement under an if block for example. Should it be included in the if or not? Bug.

Therefore, tabs aren't worth the trouble they can potentially cause. Never mix them with spaces either.

Also, using spaces is not difficult in a modern editor. You don't have to actually type four spaces each level. You set tabs as 4 spaces in the prefs and then every time you hit the tab key you get 4 spaces instead, and when you hit the backspace it removes 4 spaces (geany does this).

Whatever you use, I suggest turning on the display of whitespace in your editor. It will then show tabs as "--->" and spaces as a dot. Change the color in the highlighting preferences to be very subtle so it does not overwhelm the code. It will help you keep every file you work on consistent and such errors a thing of the past.

<comment><USER4> In your example, if both developers are using TABs instead of spaces, why would it matter that one sees it as 5 spaces and the other as 8? It's still just a single TAB character either way.

Plus, each developer has the freedom to choose how many spaces a TAB is - <USER2> like 8 when I'm using my big monitor, but I prefer 4 on my laptop. With TABs, I can see it both ways depending on the machine.

On the off chance that I'm ever actually editing in a smaller window, I can even set it to 2 characters instead, but when I open that file again on another machine, I can see it as 4 or 8 characters.

<comment><USER3> Yes, there's an error there, conflating two ideas. I think tabs-only can still cause a problem, say when lining up comments on the end of a line because there might be a different amount needed per line depending on its length. Still, you can't be sure what other people will have, so for interop spaces it is.

It might not be the case for everyone, but my laptop screen is 1920x1080 and has more room for text than I can use. In a three way merge your strategy could help me perhaps.

</comment></comment></comment><comment><USER5> Using tabs over spaces is a catastrophic misunderstanding of modern text editors. Spaces are the ONLY one-size-fits-all solution for a multi-developer environment. Text editors can be configured to SHOW a tab character visually as any number of spaces you want - without changing the code. If someone likes 3 spaces, they can see a tab character as 3 spaces; if someone else likes 8 spaces, they can show each tab as 8 spaces. Treating a tab as a "tab stop" is the mindset of a typewriter user.

<comment><AUTHOR> Not sure if I followed what you said. What I mean is that isnt hitting the spacebar 4 times more difficult and annoying than hitting the tab key once ? Yes you can configure tab key for any number of spaces but still the point being about hitting spacebar multiple times.

<comment><USER6> Modern editors generally don't require you to hit space four times. There's a "convert tabs to spaces" option so you can hit the tab key, and most of them do auto-indenting anyway.

</comment></comment></comment><comment><USER7> I chose spaces over tabs long before I started writing Python. The issues with tabs are:

(1) Some editors will autoconvert them to spaces. Every time you switch editors, switch IDE's, edit your code on a different computer, or let someone else work on it, you have to remember to make sure your tabs are still there.

(2) With tabs, your code will look different for different people. I often line up parts of adjacent lines that are symmetric, and different tab widths will interfere with this. Ditto ASCII art in comments.

(3) It's hard to use tabs consistently because tabs and spaces are visually identical in most editors. (This is less of a problem in Python than other languages, because inconsistent tabs/spaces in your indentation are often caught quickly because they cause syntax errors.)

(4) My mathematical mind admires systems that are designed around simple and elegant ideas. A character display embodies the following two such ideas:

(a) Each location on the screen contains exactly one character.

(b) There is a one-to-one mapping between the pixel contents of an 8x8 box (or however big your font is, assuming all sane people use monospace fonts for coding) and an ASCII character. (Don't get me started on Unicode. I don't understand it, don't use it, and it haven't had it do anything for me but cause bizarre problems [1].)

Tabs break both of these properties. This causes cognitive dissonance and offends my sense of aesthetics.

[1] <http://news.ycombinator.com/item?id=4369323>

</comment><comment><USER8> Most editors automatically convert TABs to _x_ number of spaces automatically, so you don't have to think about it.

For example, see _set expandtab_ in vim: <http://vim.wikia.com/wiki/Converting_tabs_to_spaces>

</comment><comment><USER4> I'm no Python expert, but the only reason I can think of is a combination of developer inertia and tradition.

In most IDEs, a TAB can be displayed as any number of equivalent spaces (8 being the common indent size), so if I switch back and forth between my big monitor (8) and my laptop screen (4), I can view the code with an indent relative to the width of the screen.

If that code uses spaces, I'm usually stuck seeing an indent of 4 on the big screen monitor - something I find irritating because it makes it harder to visually scan and parse code quickly.

So, even in Python, I always use TABs whenever I have the choice. It's just more flexible.</comment>


Why So Secretive? The Trans-Pacific Partnership as Global Corporate Coup - <AUTHOR> http://andrewgavinmarshall.com/2012/11/21/why-so-secretive-the-trans-pacific-partnership-as-global-corporate-coup/ <comment><USER2> You'd be better off here:

[http://en.wikipedia.org/wiki/Trans- Pacific_Strategic_Economi...](http://en.wikipedia.org/wiki/Trans- Pacific_Strategic_Economic_Partnership#Controversy)

than this article. Also, it's pure, distilled politics; what would it be doing here on HN? There's a whole Internet full of sites where this stuff is germane. Flagged.

<comment><USER3> My guess is that he posted it as it may affect the IT-industry and startups therein severely, something HN is covering.

<comment><USER2> That's been said about virtually every political story posted here.

</comment></comment></comment><comment><USER4> So what can we do about this?</comment>


Debug Rust+Mynewt Firmware for PineTime on Raspberry Pi - <AUTHOR> https://firstname.lastname@example.com/debug-rust-mynewt-firmware-for-pinetime-on-raspberry-pi-4b9ac2d093a9 <comment><AUTHOR> Using only a Raspberry Pi, we may debug the firmware on PineTime Smart Watch: Step into the flashed program line by line, set a breakpoint to pause execution at a line, inspect variables at runtime, … Just like the Embedded Pros!

It's a great way to learn how real IoT products are created… At a fraction of the cost of professional embedded tools and workstations!

Here are the steps to configure a Raspberry Pi (1, 2, 3 or 4, with 1 GB RAM or more) for debugging the Rust+Mynewt Firmware for PineTime…</comment>