Why is AI so useless for business? - <AUTHOR> https://mebassett.info/ai-useless-for-business <comment><USER3> AI gets such a bad rap. People only think of unsolved or sort-of-solved problems as AI, and don't give AI any credit for the problems it has solved, I guess because by definition those problems seem easy.

Think how much Microsoft Office and competitors have amplified business productivity over the last 20 years (yeah yeah, make your jokes too). Word and Powerpoint and Excel are full of AI whether it's spellcheck or auto-fill, drawing algorithms like "fill with color", etc. So many things that were AI research papers of the 70s, 80s, or 90s. And those innovations continue today.

Logistics companies rely on huge amounts of optimization and problem-solving. Finding routes for drivers and deliveries, planning schedules, optimizing store layouts, etc. -- that's AI.

Employees use AI tools to improve their lives and productivity whether it's a rideshare or maps app to get to work, speech-to-text, asking Siri for answers, translating web pages, etc. All of this comes out of research in AI or related fields.

How many office jobs _don 't_ require someone to use a search engine to find and access information related to a query? Information retrieval is one million percent AI.

Robotics and automated manufacturing has been huge for a long time -- robotics is closely connected AI and related problems like control theory.

The best applications of AI have almost always been to support and enhance human decisionmaking, not replace it.

<comment><USER4> The AI effect: AI gets a bad rap because once something exists and is practical it's no longer called AI.

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

<comment><USER5> This reminds me of the Tim Minchin song Storm. "Do you know what they call alternative medicine that has been proven to work?...Medicine."

I've never thought of it that way before but alternative medicine and AI have a lot in common.

<comment><USER6> What're some examples of things that were called "alternative medicine" but that were later proven to work and regarded as "medicine"?

Edit: Since people are bringing up ancient examples and kind of missing the point of the question: I'm not looking for examples from the Roman Empire here. Let's stick with the past < 50 years. Maybe something your parents might actually remember being dismissed as "alternative medicine" (whatever that meant at the time) but which now is clearly just accepted "medicine". Basically, try to find something that's in the spirit of the question. The goal is obviously to find things that modern medicine actually previously dismissed and later accepted, not to find a loophole in the question.

<comment><USER7> The Australian doctor who proved ulcers and stomach cancer could be caused by bacterial infection was dismissed until he inoculated himself and cured it with antibiotics.

[https://www.discovermagazine.com/health/the-doctor-who- drank...](https://www.discovermagazine.com/health/the-doctor-who-drank- infectious-broth-gave-himself-an-ulcer-and-solved-a-medical-mystery)

<comment><USER6> That is incredible and terrifying. Thanks for sharing!

<comment><USER5> I'm personally very grateful to that doctor. I used to suffer from quite painful stomach ulcers in my teens. I was scheduled for an endoscopy but the doctor I was referred to had obviously heard of this research. One week of antibiotics later and I have never suffered from it again.

</comment></comment></comment></comment></comment></comment></comment><comment><USER8> I'm going to go against the flow of most comment here and say that it's not always business misunderstanding AI. Bad labeled data and unclear goals/expectations sure, but the latter one should be identifiable by a good ML/Data scientist, if you have any insight to what you can actually deliver.

But most ML/Data Science people have no proper understanding of AI/ML, and when just traditional "coding" can solve the problem rather than throwing fancy statistical models and buzzword.

I'm not in business nor AI/ML, but in Robotics. And as a person in Robotics it's always the same experience working with AI/ML engineers: They first say they require large amount of data, then give great promises. (but never specific metrics, except maybe a percentage of success) Then they deliver a module that fails outside the _perfect_ scope of deployment(works only in the lab at 1pm). This is ofc never specified in the delivery. Also crucially, it does not give a good indication of failure. The amount of ad-hoc you need to add _after_ the thing is delivered is just staggering.

On top of this reality, most ML/Data science peoples response to this entire process is to point and blame the data, or the "well you guys is expecting too much from this!" when they had ample time to outline the scope, limitation and requirement _before_ they even started collecting the data.

<comment><USER9> > have any insight to what you can actually deliver

Maybe that's the problem. In lots of AI/ML problems _you just CAN 'T know ahead of time what can be deliver_ you need to spend the time and resources to do it and _then_ see how well it works...

The problem imo is on the business side, most businesses _don 't know how to transform unpredictable progress into profit_ (even if average on a large timeframe that progress might be HUGE).

So ML/DS people need to overpromise in order to get anything approved, otherwise they'd just have to sit around and do nothing, and overall everyone would be worse too, bc that real but unreliable progress would never happen.

<comment><USER10> I would love to learn how unpredictability can be leveraged as a positive. Any tips?

<comment><USER11> How does insurance work?

<comment><USER10> Is this not exactly the opposite? They try to bet that they can predict the occurrence of something better than what you can.

</comment></comment></comment></comment></comment><comment><USER12> I've been working in the "real world business processes that companies are trying to AI-ify" realm for quite a while now. Pharma, cyber security, oil and gas production, etc.

This article doesn't mention a really, really straightforward factor for why AI hasn't invaded these domains despite billions of dollars being dumped into them.

An automated process only has to be wrong _once_ to compel human operators to double or triple check every other result it gives. This immediately destroys the upside as now you're 1) doing the process manually anyway and 2) fighting the automated system in order to do so.

99% isn't good enough for truly critical applications, especially when you don't know for sure that it's actually 99%; there's no way to detect which 1% might be wrong; there's no real path to 100%; and critically: there's no one to hold responsible for getting it wrong.

<comment><USER13> Huh, N26, a major online bank in Europe is famous for some of its customers getting their accounts blocked every time they tweak their ML model and yet is doing great financially dispite the shitstorm it generates each time.

It's not like Google blocking your email or YouTube account, we're taking about your friggin bank account here.

I don't know how they're still in business and growing with such a process in place.

<comment><USER14> Not working directly in the field, but i believe ML for fraud detection is very common nowadays..

<comment> saiya-jin Yeah but usually the bank just buys package solution with some customization for accessing the data & reporting, so they don't have to keep up the army of very narrowly specialized folks catching up with ever changing laws etc.

</comment></comment></comment></comment><comment><USER15> In my opinion, most of the issues leading about AI "failing" in traditional organizations are due to the following:

(1) Inflated expectations from higher/middle management which trickle down the organization. AI is seen as a high-profile case which has to lead to success (and a larger budget next year for my dept.)

(2) Data quality issues. The data itself has issues, but the key issue is lack of metadata and dispersed sources. Lack of historical labels (or them being stuck in Excel or on paper) is part of this as well. Big data without any labels is mostly useless, contrary to expectations

(3) Most AI or ML projects are not about ML. In fact, they're mostly about automation or rethinking an internal or customer-facing process. In many cases, such projects could be solved much better without a predictive component at all, or by simply sourcing a 1 cent per call API. AI is somehow seen as necessary, however, without which our CX can never be improved. ("We need a chatbot" vs. "No, you just need to think about your process flow")

(4) Deployment issues and no clean ways to measure ROI leads to projects being in development indefinitely without someone daring to stop them early. This is also related to orgs starting 30 projects in parallel (2m lead times with one to two data scientists for each), which end up all doing kind of the same preprocessing and all lead to kind of the same propensity model. No one dares to invest in long-term deeply-impacting projects as "we want to go for the low hanging fruit first"

<comment><USER16> I pretty much agree with all of your points, but I also think there may be a more fundamental issue at play here. ML doesn't actually "understand" things - it can do very sophisticated and accurate pattern matching without actually "knowing" the logic of the patterns it's matching.

This in turn means that it may fail catastrophically when faced with adversarial examples or with examples that are drawn from a different distribution than that of the training set.

<comment><USER17> How is that different from a typical human given a routine boring role? Specifically in regards to adversarial input, humans are often the weakest link in terms of process security.

<comment><USER16> I wasn't thinking that there are no roles that can be automated, rather that there are some that can't be.

> Specifically in regards to adversarial input, humans are often the weakest > link in terms of process security

I have yet to encounter a (healthy) person who looks at a photo of static and mistakes it for a cat.

<comment><USER18> How about a photo of a dress where some people say it's blue and some say it's gold?

<comment><USER16> Optical illusions indeed highlight limitations in human perception. However, the dress illusion seems to me far less of a problem than mistaking noise for an object.

More relevant however is that we humans can understand that we're faced with an optical illusion and we can make adjustments accordingly. We have formed the concept of an "optical illusion" and we just place "The dress" in that category. A machine needs to be specifically trained on adversarial examples in order to be able to predict them. Once you come up with a different class of adversarial examples it will continue to fail to detect them. There is no understanding there, just more and more refined pattern matching.

Does a machine that can match any pattern actually "understand"? I would say no. But these are already philosophical considerations :D

<comment><USER18> > More relevant however is that we humans can understand that we're faced with > an optical illusion and we can make adjustments accordingly.

Broadly speaking, yes. At the same time that's not what happened in 2015. It produced so much polarizing content with people deeply entrenched in their believes. They might have recognized it as an optical illusion, but they refused to make adjustments.

> A machine needs to be specifically trained on adversarial examples in order > to be able to predict them. Once you come up with a different class of > adversarial examples it will continue to fail to detect them. There is no > understanding there, just more and more refined pattern matching.

Moving away from image recognition examples, isn't that exactly what happens with humans predicting whether an email is a phishing attempt? I remember reading here on Hacker News this week about phishing tests at GitLab. It had a lot of comments about tests and training employees to spot adversarial emails. Some companies are more successful than the others. It is a complicated problem; otherwise we would have solved it already. But it's the same principle because phishers come up with different ways of tricking people. And some people will fail to detect them.

<comment><USER16> I would say that there are indeed many examples of things that are hard to categorize for humans. Sometimes there isn't even a way to categorize things perfectly (there may be a fuzzy boundary between categories). It is for example really hard to train people to figure out if a certain stock is going to be profitable or not - there are many other such examples.

This doesn't mean that the kind of thinking that goes on in the human mind is the same as the pattern-matching that goes on in an ANN (for example). Think about how ppl learn to talk. It's not like we expose infants to the wikipedia corpus and then test them on it repeatedly until they learn. There are structures in the brain that have a capacity for language - not a specific language, but language as an abstract thing. These structures are not the same as a pre-trained model.

The truth is I don't know enough about cognitive science to properly express what I'm thinking, but I'm pretty sure it's not just pattern matching :D

</comment></comment></comment></comment></comment></comment></comment></comment><comment><USER19> I have been in this space in the financial sector for two years. I think this article is mostly spot on. There is one other piece, typically the places that can most benefit from innovation can get most of it just through automation and RPA as it is now called. Basically some guy filling a spreadsheet and copying it someone else, replace that with a bot.

But even that and other processes are difficult because a lot of these corporate enterprises have a bazillion different systems that don't talk to one another. Forget data science or ML, you really just need a unified data view. Typically the workflow is some use case comes, somebody, an analyst manually pulls data from some system via the GUI (because that is all they interact with). A model is built based on that data set and the project stops dead in its tracks from there on because it's impossible to get an API to query for that data from its source system. That is a technology and business process project and will rapidly blow up into a clustefuck.

The key competitive advantage of these so called "technology" companies is really this. The ability to expose any part of your data storage and pipeline to any other part of the organization as a API. Every piece of software is built with that concept in mind.

<comment><USER20> Microsoft recently developed a modular form of Microsoft Office which allows you to integrate a modifiable spreadsheet into things other than .xlsx files. Automation may be easier if disparate systems all used the same modules. This is especially true in the finance sector where everyone uses Excel.

<comment><USER21> Recently? That sounds like OLE [1], which has been around since the 1990s. Have they redone it?

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

<comment><USER20> Microsoft's Fluid Framework was announced at this year's Build conference. It's like OLE + Google Docs "on steroids." Imagine sending an email with an embedded spreadsheet to multiple recipients who then collaborate and edit in- line without opening the Excel file. Or PowerPoint, Word, etc... Everyone would see the edits in real time. The modules are drag and droppable. The whole framework is also open source, so Microsoft envisions developers making new applications out of the core concept.

[https://www.theverge.com/2020/5/19/21260005/microsoft- office...](https://www.theverge.com/2020/5/19/21260005/microsoft-office-fluid- web-document-features-build)

</comment></comment></comment></comment><comment><USER22> Because "Artificial Intelligence" is a label forever applied to the effort of replicating some human cognitive ability on machines. A well-known lament goes something like: "once it's possible, it's no longer AI".

Business is about exploiting what exists. This is why the buzzword is "innovation", not "invention". Incremental improvements, not qualitative jumps. So nothing will ever be really considered "Artificial Intelligence" once it is boring enough for business.

Scheduling algorithms are incredibly useful for business. There was a time when this was considered AI, but that was the time when they didn't work well enough to be useful.

</comment><comment><USER23> > why can't it read a PDF document and transform it into a machine-readable > format?

> why can't I get a computer to translate my colleague's financial spreadsheet > into the format my SAP software wants?

Because you probably expect it to be 100% or maybe 99.999% accurate, and we can't do that. Imagine "AI" translating someones financial spreadsheet into a different format and dropping a zero somewhere. Oops.. but your test set accuracy is 99.8984%. Still not good enough. Just getting 1 thing wrong breaks everything. This is fundamentally different from clicking on image search and ignoring the false positives.

<comment><USER24> Why exactly do you need an AI that reads a PDF ? Unless you're dealing with ancient data wouldn't it be easier to have whatever that's generating the PDF return machine readable data ?

This suggests to me that lot of office jobs will be lost just by modernizing systems and making them spit out JSON

<comment><USER25> PDFs do not solely consist of computer generated files. PDFs also come in the form of paper that have been hand written on or scans.

<comment><USER24> Those should be replaced by web apps or mobile apps. Its 2020.

</comment></comment></comment></comment><comment><USER26> Our immediate goal should be to set our sights lower; forget ML, instead improve and expand technologies like RPA ([https://en.wikipedia.org/wiki/Robotic_process_automation](https://en.wikipedia.org/wiki/Robotic_process_automation)), which is only "AI" in the narrowest sense.

Example: my wife is an admin in a school office, and a ludicrous amount of her and her colleagues' time is spent on replicating data entry between a multiplicity of different incompatible systems. The Rolls Royce / engineer's solution to this would be to provide APIs for all these disparate systems and have some orchestration propagating the data between them, except of course that's never going to be remotely practical; instead, dumbly spoofing the typing that the workers do into the existing UIs is a far more tractable approach. My (admittedly not 1st person based) experience of these things is that they currently still require significant technical input in the form of programming and "training", but this fruit has got to be hanging a lot lower than any ML-based approach.

<comment><USER27> RPA can be a dangerous band aid. It often uses screen scraping or similar brittle interfaces that are known to change. Or, it doesn't know about certain error conditions, etc.

Also, if it's been running for months before it breaks, the humans that used to do the work are gone, or have forgotten how to do it.

<comment><USER28> At my work we are leaning heavily on RPA to automate away drudgery and ultimately reduce expendature. However it has been immensely frustrating, prone to errors and garnered endless suspicion. The experience has been that bots written by the service desk staff doing the job function better and are much more under our teams governance, which the official "automation" teams within our org are painful to deal with due to their lack of availability and not having first hand knowledge of the things being automated. I think the RPA approach requires dedicated people developing and monitoring the bots who have an active part in the process being automated. The difference in approach determines the result.

<comment><USER27> I've experienced the same. Centralized RPA teams tend to, for example, do web scraping when they could easily use an existing REST API. Because they either don't know it exists, or don't have that skill set.

Similarly, seen things like using a email as a trigger, when the source application has configurable web hooks.

Feels like there's an RPA culture of sorts to assume the things being automated only have human based interfaces.

</comment></comment></comment></comment><comment><USER29> A better question would be why AI works great for some business (e.g. Netflix, AirBnB, Uber, Waze, Amazon) yet fails miserably for other (JC Penney, Sears). In my view, the older companies are trying to strap on AI on top of a traditional dataset, which never collected any useful signals. The new companies designed their entire business concepts around data, and collected what's needed from the get-go. Sears may have a 100 years worth of useless data. AirBnB has about 13, but so much more informative. Amazon applies A/B testing all the time - would anyone at Sears even know what it is?

A secondary issue with business data is that vast majority of the features are categorical, for example: vendor id, client id, shipper id, etc. These usually get hot-one encoded, and you end up with hundreds of features where there's no meaningful distance metric. Random Forest and XGB are about the only that produce somewhat rational models, but in reality, they are good because they approximate reverse engineering of business process.

And lastly, the hype far outweighs the possibilities, at least until the business are ready to re-engineer the processes, if it's not too late.

<comment><USER30> AI works for online businesses where you have millions going to a single interface and thus any testing is on a random selection of the population.

You couldn't do that as well with different stores simply because malls have different demographics (meaning any conclusions could be noise) and the costs of shuffling where things are located is high so you can't just test 50 different setups and see what works.

<comment><USER29> Sears has been around for about a 100 years, and they have invented the catalog sales model - they were the Amazon of that era. I don't blame them for not doing things like A/B testing a 100 years ago, but 10 or 20? They were asleep at the wheel. That's when Walmart took off like a space rocket. Essentially the same offering to the same demographic. Yet Sears collapsed.

<comment><USER31> There were other factors at play in the downfall of Sears than just failure to take advantage of their positions at the time. Sears, in a sense, became a victim of its own success. In the heyday of parasite capitalism, it became more profitable for a small group of bad actors to make Sears fail.

Xerox is a more apt example of the 'missed opportunity' narrative.

</comment></comment></comment></comment><comment><USER32> I like to focus on the need for a spec.

The hardest part about programming is that you have to say what you want to happen clearly and precisely. You can't just say "I want a text editor", you need to say all sorts of specific things about how the cursor moves through the text and how you decide what text is displayed on the screen when there is too much to show all at once and how line-wraps work and whether you acknowledge the existence of "fonts", and what happens when you click randomly on every pixel of the display.

The program usually shouldn't _be_ the spec, but you can't write the program without actually specifying everything that can possibly happen over the course of that program executing.

One of the things that makes AI/ML so hard is that we don't want to write a spec, most of the time. If we could write a precise spec that a computer could understand, we've typically already written the program we want. There are some cases where we can, like games or math, but most of the time, what we want to do is provide our AI/ML with a bunch of data and say "you figure out what I mean". "Label the pictures of dogs", "identify the high-risk loan applicants", and so forth.

Our AI/ML is actually solving two problems: first, it has to come up with a spec on its own, and then, it has to create a solution to it.

And here is where things get rough: we generally don't know what spec our AI/ML came up with. Did we train a model to identify dogs, or to identify dog collars? Does this model find high-risk loan applicants, or people of certain ethnic backgrounds?

The problem with many real-world and business applications for AI/ML is that the spec is really, really important.

<comment><USER33> One of the core features of a spec is semantics.

We're just getting "42" and are stumped, because we didn't describe the meaning of the question nor the answer in a formal way.

To apply meaning we need "us" or the real world. And we need to decide its form.

</comment></comment><comment><USER34> > why can't it read a PDF document and transform it into a machine-readable > format?

It can definitely do that, but you might not like the cost/benefit analysis, depending on how many such documents you want to process. The costs are coming down steadily though as the tech improves. If you need to do millions of such documents, yeah a model will probably be worth it. But if you need to do a few hundred you probably should just do them manually.

The thing is, reality has surprisingly high resolution. When you give out a task like this to a person, they will likely come back to you for clarification about how you want to deal with some of the examples. Your initial requirements will be underspecified, or incorrect, in some details. When you are dealing with a person, these minor adjustments are pretty inconsequential, and so you don't really notice it happening. The worker might also have enough context to guess what you want and not ask, and just tell you the summary when they deliver the work.

If you're training a model, you need to work through all these annoying details about what you want, just as you would when you're creating any other sort of program. This adds some overhead, and places a lower bound on how many examples you'll need to have annotated -- you'll always need enough examples of annotation to actually specify your requirements, including various corner- cases. You need enough contact with the data to realise which of your initial expectations about the task were wrong.

So there will always be a lower scaling limit, where the automation isn't worthwhile for some small volume of work. The threshold is getting lower but there will always be a trade-off.

<comment><USER35> > The thing is, reality has surprisingly high resolution. When you give out a > task like this to a person, they will likely come back to you for > clarification about how you want to deal with some of the examples.

Why isn't this generally solved, though?

<comment><USER34> What could that even look like? In the extreme case this is like asking, "Why do I have to write programs, can't the computer just do what I tell it?". Writing the program _is_ telling it what you want. In supervised machine learning, annotating the data is, instead.

If you tell me, "Go to ebay and get me a list of the prices of washing machines", that sounds simple, but then I'm faced with some washer/dryer combo or some hand-crank contraption. These are things you didn't think of. I can either ask you, or take a guess and hand you something that needs to be cleaned up later.

If I'm instead training a model, I need to encounter these tricky examples in order to ask you for a policy on them. If I'm collecting an unbiased sample of the training data, this could take a very long time. If there's some sampling strategy maybe it's faster, but there's still a minimum number of examples we need to think about, no matter what.

</comment></comment></comment><comment><USER36> In fact, the author could actually dig further and look at the potential losses an "AI-fied" solution could bring forth.

1\. Unexplainable algorithms that cannot demonstrate fairness and biased algorithms - causing firms to be dragged to court for discrimination - <USER2> AI was used for decision making which impacted lives/careers (lending, credit, recruitment, medical procedure suggestion, financial modeling etc - just to name a few)

2\. Biased algorithms resulting in small tainted outputs that could later snowball into a larger loss that get built over slow leaks over time. (Few AI based cloud app/infra monitoring systems ending up deciding the wrong scaleout factor/sizing - based on past history but not considering real situational context/need - resulting in a net loss over a larger time)

3\. Some AIfied solution just outright denying users the level of control that's really warranted. ("full automatic , no manual" mode). This mostly happens where the buyer never uses it firsthand but buys based on brochure/ppt walkthroughs, and real users are disconnected from the decision making ivory towers. The risk ibeing these systems getting into the way, instead of aiding productivity, they end up being another JIRA - a hassle one could really do without.

</comment><comment><USER37> I have been an AI practitioner since the 1980s, sort of a fan! That said, I like this article on several levels most particularly for calling out possible AI products for business.

I lived through the first AI winter. As effective as deep learning can be, problems like model drift, lack of explainability, and getting government regulators to sign off on financial, medical, etc. models are very real problems.

Two years ago I was at the US Go Open and during a social break I was talking to a lawyer for the Justice Department and he was telling me how concerned they were about the legal problems of black box models.

</comment><comment><USER38> > We've taught computers to beat the most advanced players in the most complex > games. We've taught them to drive cars and create photo-realistic videos and > images of people.

No, we haven't. I mean, we've made progress in those areas, but there's still a long way to go.

The best AI in Starcraft, AlphaStar, still can't beat the strongest players without relying on simply out-clicking them.

Driverless cars are still in the testing and development phase, none of them are smart enough yet for widespread deployment.

</comment><comment><USER39> You might just as well ask why that miraculous cure for baldness is so useless. You let some used-car salesman talk you into believing that it actually works, but it doesn't — no matter how much you want it to.

</comment><comment><USER40> In my opinion, it's because business operations isn't that complicated and people don't know what AI is.

By "not that complicated", I mean a decent CRM system to track information about the organization is approaching peak operational efficiency for most businesses. Most inefficiency I see after that is people/political problems.

By "people don't know what AI is", I mean that business owners are unable to describe their business problem as a supervised learning problem. If you can formulate your business problem as a supervised learning problem, then you can probably solve it with AI (which, yes, is really just a marketing term for supervised ML).

But most business problems are really "order taking" or "production/delivery" or "moving things through a funnel" problems and thus AI isn't the solution, CRM or CRUD apps are the solution.

<comment><USER41> "AI is a 'brain' that you plop down in the organization and it does 'unsupervised learning' to automate your business and delight your clients with optimized outcomes." \- As explained to me

I now see Google sales dragging these people out to 'AI meetings', which are about Dialog Flow. (Meeting pitch email gets around, business manager reply all: 'WTF is this?') Later they summarized the meeting, as they understood it: "You just need to drag and drop the CRM on the 'Brain' and then you can book haircut appointments and open a bank account. We were like um, we develop and support software, they were like - just drag and drop you knowledge management API on it too." Uh... what KM system?

The complicated part is that in small and midmarket business, their knowledge is tribal and processes are folklore. There are processes that are followed though they don't exist, and business rules violated as they aren't aware.

Meanwhile, no one in sales puts useful information in a CRM system if they can avoid it, the least effort principle on administrivia is important if you are ever going to hit your ever growing number.

Finally, the management must still be spooging for SPOGs, I sill see marketing for them as some sort of nirvana. Meanwhile, they have been collecting data randomly about everything, all over the place, excel documents, FTP servers, and of course databases. Here, no one in IS/IT will admit: The servers were here when they started their job and they have no idea what is on them or if they are even needed anymore. They certainly aren't mentioning that to the incoming CIO, who gets focused on improving efficiency as per his executive mandate. So he/she gets to work with cloud migrations (more hilarity ensues)

I need to stop now. It's a rat hole...

</comment></comment><comment><USER16> One thing I haven't seen explicitly mentioned is the (probably) intrinsic limitations of AI/ML in classifying/predicting human behavior. For a number of years I have worked on fraud prevention tasks where the goal was to take in some information about a payment and decide whether it was fraudulent or not.

Even though what we were doing was primitive and could have benefited from a lot more ML, I suspect that even then the best that you could have gotten would have been some sort of anomaly detection system that can catch a good share of the kind of fraud that you have seen in the past, but will never be very good at detecting an intrinsic change in fraudster behavior.

On top of this, especially when dealing with humans, you often are expected to be able to explain why a certain decision was made. Setting payments aside, think of predictive policing or sentencing decisions. In those cases ML is essentially guaranteed to build in all sorts of biases regarding somebody's race, gender, place of living etc.

</comment><comment><USER41> I forget the name of that silly robot in the picture, Ginger or something. Designed to take food orders and possibly deliver them to tables, they get dragged out to bank branches and need to be supervised by employees the whole time. It is bad enough that they could only provide the most trivial of information (worse than web search), IT also struggled to keep the things connected to WiFi. Multi-billion dollar corps 'doing AI'.

</comment><comment><USER42> The article is just vaguely complaining about undefined problems which makes it very difficult to defend or argue against. Are we talking about ML? Optimization problems? Operational research in general? Automation? And which tasks in which businesses? There's tonnes of useful stuff in each of those categories and vaguely saying that it's all useless doesn't get you anywhere.

<comment><USER43> Seems pretty exclusively focused on ml

</comment></comment><comment><USER44> I'm at a hospital where someone at long last got authorized to try ML on the clinical database.

The ethical committee required that prior to using any data, you have to make a static copy in another database. Their argument is

1\. They don't want excel files flying around (which will happen regardless) and

2\. To perform any analysis, you "obviously" have to have "structured data", which "obviously" means that you have to extract a csv from the base system (MongoDB) and put that into a RDBMS (redcap).

Go figure...

<comment><USER45> Why is the ethical committee allowed to make technical architectural decisions if they aren't in the technical team? I would resign.

<comment><USER46> Why are the ethics committees allowed to make any decisions?

When I was on one I would by default let everything through unless I came across something where I thought I could help improve the experimental design. My colleagues on the other hand would go through them like little emperors and cause grief for the applicant just because they could.

</comment></comment></comment><comment><USER47> I disagree with the headline of the article, but I agree with the conclusion of the article.

AI is in the process of having a huge effect in business software, it's just not the type of business software most of us think of when we think of business software.

Many people think of horizontal business software like MS word, excel, quickbooks, salesforce, etc. Products like this will be hard to automate significantly with AI, since every company is using these products slightly differently. The products are intentionally designed to have as wide a TAM as possible, and so they are general purpose enough to do anything business related.

There is another very large group of business software that people don't as readily know about, and that is vertical-specific business software. These products are not designed to have a wide TAM, but instead to be tailored to specific industries, and provide a ton of value as a result. These vertical products are a perfect fit for automation with AI. The author says "Each business process is a chance for automation", and in these products, these business processes (and all their inputs and outputs) are structured and represented in software.

I am building AI systems at a vertical software company right now and am a big believer in the future of AI in these products. If you have ML expertise and are interested in working on such systems, feel free to email me your resume.

</comment><comment><USER48> Most AI guys I know are just interested in playing around in pilot projects and learning stuff while they train to get a job at Google. It'll only be a few years before managers figure out there will be very little delivered.

</comment><comment><USER49> The problem goes much deeper than researchers not having enough PDFs. If you look at where machine learning is successful it's usually processing spatially related data. The pixels in a photograph have a spacial component where points near each other are more related than points far apart. Same goes for audio, and even text. Words in a paragraph that are close to each other are usually more related than words far apart.

A spreadsheet has very little or no spacial component for a neural network to learn, and the location of an important number in a pdf probably has little to do with the number's significance. Without a spacial component to do pattern recognition a lot of the recent advancements in machine learning like transformers or convolution get thrown out the window.

There are some machine learning problems that can be solved with more or better data, but I don't think PDF to JSON is one of them.

<comment><USER50> IMO machine learning has nothing to do with learning. It might be able to find out some patterns in the data set which can be very useful to recognize something like license plates etc. But unfortunately finding out patterns does not mean understanding them which is the fundamental of learning. Then the result is quite obvious and simple: you don't know what you don't know and you cannot transform something you don't understand.

</comment></comment><comment><USER30> AI is terrible at dealing with unexpected events. Games AI is good at are relatively deterministic, i.e. all possible outcomes are known. Replicating art and images is the same way.

If you could script new combat units in a video game on the fly or tweak the rules slightly, the human would slaughter the AI when an equally skilled human opponent would not lose so easily or even necessarily lose at all. You can see this in games like Galactic Civilizations where you can build your own units and unusual combos confuse the heck out of the computer opponent.

Same with cars. The entire approach is currently based around exposing the AI to every possible outcome. I remember a seminar on AI Safety where the vehicle AI had a problem with plastic bags in the air and it would swerve to avoid them. No human would have an issue with that.

I worked in innovation for a bank looking at automating all these kinds of things and even spent a few days doing the jobs (and this was eye-opening) and I was a developer, so not a manager looking at a job spec, but someone who would have actually done the work. 90% of the job could be automated, but 10% was dealing with wacky exceptions, many of which they had never specifically seen before. We had someone who had the job of taking PDFs and extracting tables of income and expenses. They were generally standardized PDFs, so that seems like something good to automate right?

Well, no. As tons of the financial advisors had added custom rows which the person doing the input had to interpret into another column. It was quite eye- opening that while the jobs were menial data entry, it was nowhere near as mundane as one might imagine as the guy was still making a judgment call on whether to classify "farm income" under a person's investment income category or whether to classify it as regular income for the purpose of investment advice.

I have a friend currently on a robotic process automation internship with another bank. Same issue. When the RPA dev people actually go and do these jobs, they realize that is frequently deviates from the approved job spec with the people in them making small but significant judgment calls.

It is not a lack of knowledge about what AI can do in either of those cases. It is not a lack of data as both banks have armies of people doing it and millions of clients. It is that for AI to do the job, all manner of other things would need to be standardized and reformed and if that were done, why use AI to solve the problem in the first place as a lot of it would simply be computational.

<comment><USER51> >You can see this in games like Galactic Civilizations where you can build your own units and unusual combos confuse the heck out of the computer opponent.

AIs will do the same to humans when trained against other machines, instead of being trained on human match data. Since the AIs will try out things most humans would think are illegal, thus not use them in regular matches. Like when Chamley-Watson first struck an opponent with his foil from behind his back.

</comment></comment><comment><USER52> The essay makes a very good point about the availability of documents/data, but from what I have seen working on ERP projects and business processes I don't think attacking this problem top-down from how businesses works is a good idea: a lot of documents produced can be mostly useless artifacts of human interactions, and most businesses have highly inefficient processes. Would you train an AI on current HR recruiting practices for example? At its worst you have businesses with so much sales power (a purely human process) that their internal processes can be an absolute mess and still go fine. What an AI trained on these data coud output? Meeting recaps that are never read from meetings with already questionable value in the first place? Random reports and strategies meant to praise egos?

A huge part of the business world is service over service over human interactions that are far removed from the core value production and are side effect of these very interactions. Sure at their core business must produce something, but apart from industrial or software processes, the enterprise world is mostly a giant social game, with success linked to the execution of sales, marketing and sometimes lobbying, so not much to AI-ify from that angle.

Edit: just to be clear on the tone it's not meant to be bitter, in fact after a hard time learning this world it can even be fun.

</comment><comment><USER53> AI is useful for business, and it's used in business. Hiring people to do menial mental tasks that would be particularly easy to automate is cheap. Hiring programmers and AI developers to automate those tasks is expensive. I know people who lacked a CS degree and could barely program who transitioned to work as a programmer by getting hired to do a menial task and writing mundane old-fashioned code to automate away their job because that was the only way they could find to transition from menial tasks to a highly skill occupation.

You don't even need AI to automate a lot of these tasks. Good old fashioned programming can automate anything truly menial better than AI can, but if you're going to solve a real problem through code there are only two ways to do it: 1) write the code yourself, or 2) spend millions of dollars hiring other people to do it.

Same is true for AI. In contrast, you can very often hire people to solve the same tasks for minimum wage, or if its a sufficiently digital task, even less than that, through a service like mechanical chimp.

AI isn't used to automate away menial tasks because the economics of it doesn't make sense. None of the problems raised by that article are difficult to overcome, it's just expensive to hire people who solve them well.

This has nothing to do with technology and everything to do with the current organization of society and its economy.

</comment><comment><USER54> A rather lazy / uninformed article by someone who grossly underestimates the complexity of what would it would take to completely automate business processes of a company.

The title question is equivalent to asking: a robot cannot build a car, it's so useless for manufacturing?

Robots can build cars but we need to arrange them in an "alien dreadnought".

Businesses are not prepared to pay the price of full automation, what they expect is to put some open source AI run by a fresh graduate and fire all office clerks the next day.

</comment><comment><USER55> When I think about companies I've encountered over the past few years, it seems to me like the AI problem has been two-fold: 1) They didn't need AI. 2) They would have been better off listening to the human experts they already employed.

That is to say - when you look at business case studies of the kinds of problems that businesses perceive they are going to solve, it's things like supply-chain "We figured out when it's going to snow, we should have snow shovels in stock!" Well, of course, and there are a whole lot of humans in your company that already know this, but they aren't being heard.

A lot of the places where AI has worked out, like spell checkers, various in- app automations - as the article and people in this thread indicate, are exactly the kind of problems more companies should probably instead focus their energy. For example, I think about various gyrations I've watched people do in order to format their data the way they needed for presentations. Not AI in the theoretical sense, but definitely time consuming tasks that exist in every business that would save gobs of time and money if they could be automated away. But, so long as their isn't clear profit motive good luck getting your project green-lighted.

</comment><comment><USER56> The obvious answer to me is that the hardware is only available to handful of players and the libraries aren't mature yet. PyTorch has been around for about 4 years; that isn't enough time for a lot of people to have gotten comfortable with it.

The people who have access to people with software and hardware have found a lot of uses for the tech - I assume AI basically is Google Image Search.

</comment><comment><USER57> It's sneaking in, just not announcing itself.

I used to work for a really well-known medical dictation/transcription, documentation, and coding (in the medical billing sense) company.

They're using ML models all over for speech to text, document analysis, etc.

It enables some very real efficiency gains but it's not positioned the same as something like IBM's Watson and it's somewhat ridiculous AI claims.

<comment><USER58> I'm surprised to hear this organisation is successfully doing ML speech to text. Is it running 100% of volume in production? Or is it more of a pilot type thing? I know of a French multinational bank that just tried for 2 years to get a ML speech transcription up and running, for transcribing conversations with customers, but due to unreliable results, recently put the project on ice. Their experience was much along the lines of everything discussed in this comment thread.

<comment><USER57> I think the expectation of "drop some AI in" and do XYZ job is off in the same way that "we're going to drop a humanoid robot in" and do XYZ job is off.

Where I've seen it used well is as a piece in a larger system of automation. In the healthcare case, it's doing a first pass at transcribing an audio dictation so that a transcriptionist can then start with a 90%+ accurate document.

This is tough, their role shifts some (more editor/correctionist than true transcriber) and not everyone makes that transition well, but the end result is 2x+ efficiency gains.

</comment></comment></comment><comment><USER59> Reminds me of the more general paradox: [https://en.wikipedia.org/wiki/Productivity_paradox](https://en.wikipedia.org/wiki/Productivity_paradox)

As one of the other comments points out, technology can only change productivity when you change the _process_ , sometimes radically. And that may mean restructuring the business.

</comment><comment><USER60> Because humans aren't very good at doing what they think they do, and people who have little idea how to do it are going to make a good choice. So, it's a good choice to have something that works on a large scale, even if it is just an AI but that doesn't require that you use it for a lot of other things, you will probably end up with lots of other things just to make the machine go into something less complicated.

If the AI is so good that it can actually be useful for anything, people will probably stop giving it advice on how to do what they want from a simple and simple and simple task; this is the key difference between the AI and humans at work.

In fact, when I asked about what people really want from a simple task and it wasn't really a good one, I was very disappointed because it was so complex. That is why I've come to believe it's really not that useful for anything other than a little bit more automation.

</comment><comment><USER61> > No group of researchers can train a "document-understanding" model simply > because they don't have access to the relevant documents or appropriate > training labels for them

This is because you could rename deep learning as "over-parameterized statistics". And statistics is just about building some model of the data. That is the only thing "training" a model is for: discovering/optimizing a statistical distribution (a distribution is a generalization of a function). This means the entirety of deep learning is simply building highly complicated statistical models of a bunch of data.

It is unlikely that this is equivalent to general intelligence found in biology.

We could probably solve the AI problem if the _entirety_ of all of research was not directed at deep learning. And it would also likely be far more valuable to any organization or individual.

But, that is just the 2 cents of some random HN commenter. So, I will keep dreaming.

</comment><comment><USER62> AI has plenty of useful applications for business. Fraud detection, forecasting, enterprise resource planning, logistics - all of these were considered AI at one point.

The specific problem the author cites - digitizing data in PDFs - shouldn't even require AI. It can be solved better by just inputting data in digital form to begin with (like with a web form), and passing it around in a standardized machine-readable data format. But the commercial real estate industry is pretty backwards, and can continue to be pretty backwards because its core competency is _ownership of the real estate_ , and digitization labor is round-off error compared to the profits generated by it. It'll take a major recession (and this current coronapocalypse may qualify) to create selection pressures to weed out inefficient firms, and until that happens there's no incentive for them to upgrade their processes.

</comment><comment><USER63> With AI, the production of a result is not the same as an answer/solution.

Instead of a know-it-all, AI comes across as a guess-it-all (narrowing all results by the criteria programed) and as comments stated, AI is good at producing 99% maybes.

Looking past the hype, AI has thus far just added to the cacophony of noise among so called experts. (AI is not an expert)

</comment><comment><USER64> Mostly because business isn't really that hard, and we're still struggling to even define the rules it operates by.

AI alqays looks cool, but isn't very useful in practice. The past ten years feels like everything is keynote-driven development; get something nifty looking that demos well and try to shoehorn it into a business case.

</comment><comment><USER65> The hardest things to automate are always the things that are so easy for us they don't even register consciously

<comment><USER66> While true for some things, it's not for others.

In our application, one key operation is that the user is required to classify a line item based on the text description. There's a huge code list of possible classifications, and the user has to pick one that is the most correct.

This is definitely a task that registers consciously. And, while most of the time it's fairly easy for trained users, there are often cases which require extra thought or research.

For example, a T-shirt of mostly cotton vs a T-shirt of mostly synthetic fibers should be classified differently. How would you know based on the description "Small Womens V neck Short"?

<comment><USER45> It's impossible to know the fabric there. So humans would just guess and often be wrong. That system needs an "unknown material" code.

<comment><USER66> Right, so the user would have to do further research like contacting their customer and ask.

Of course, an AI/ML system that could reliably classify a majority, and reliably classify the rest as "unknown", would be interesting. Not sure how close we are to that though.

</comment></comment></comment></comment><comment><USER67> Because at this stage of the game, "artificial intelligence" is still an oxymoron .

It's really just a database developed through trial and error (aka "training") that we "hope" contains enough differentiating data points to produce a reasonable, weighted "best guess".

</comment><comment><USER68> I forget who first made this argument, but it basically was a response to critics of philosophy. Critics would challenge the field by asking if philosophy has made any real contributions to human knowledge. Has it actually discovered anything that's both non-obvious and conclusively true?

And apologists would that philosophy has made huge, unambiguous contributions. Only once this happens, those fields tend to be no longer considered "philosophy". Astronomy, physics, economics, and logic were all sub-domains of philosophy originally. Once they were formalized, with rigorous, specialized methods they moved into their own standalone fields. But it was philosophers who laid the foundation. Consequently when we think of "philosophy", there's a lot of selection bias, because it's basically the subset of open unsolved problems that remain.

I think there'a a close analogy here with what we think of as generalized "business problems". There are many specialized sub-fields like finance, logistics, marketing, industrial psychology, and accounting. All of those things used to be thought of as a generic part of business. But eventually domain-specific methods and technologies led to the point where specialized practitioners unambiguously out-performed generalist C-suite executives.

Think of techniques like Markowitz portfolio optimization, or five factory personality testing, or applying Benford's law to profile for accounting fraud. Those are all examples where something like AI/ML solved what at the time was a generic business problem. But afterwards it was now just considered a success of the respective sub-field that those techniques helped create.

The point I'm making is that formal rules-based processes (I won't use AI/ML here because it's so ambiguous, especially in a historical context) have had a long history of success in business. We just don't recognize it because we're begging the question. What we think of as "generalized business issues" is mainly those open problems that haven't yet succumbed to specialized formal techniques.

</comment><comment><USER60> The author suggests that AI is _something_.

That is, if you think you have a better idea of what you want to do that isn't the right question. I would say that if you actually get better than an AI based version of a product then it's actually a useful tool and it can be improved to be used.

For example, if you say this is AI because a particular feature that is useful does not exist then you are actually talking about AI because someone is using it, just that it's useful.

But that statement only has a part of what you mean. That is, if your idea isn't useless in the sense that it is useful in a specific way, you are simply asking how you did it.

So to answer the question is: Why is AI useless for business?

</comment><comment><USER69> Many real world business processes assume a certain knowledge of the world and the relationships between entities, and not just a limited set of data points about the task at hand. Such kind of knowledge is not yet incorporated into today's ML systems.

</comment><comment><USER70> Being in a human-minimum seems to be part of it. AI and software _could_ do far more than they do, but the problem is that everything around it assumes human-evolved systems, which destroys the potential for software. So if you look at just what AI can be wedged into the cracks, you'll conclude it's largely useless, but then if you can replace whole systems, you get much larger gains: [https://www.overcomingbias.com/2019/12/automation-as- coloniz...](https://www.overcomingbias.com/2019/12/automation-as-colonization- wave.html)

</comment><comment><USER71> People try to apply AI to high-risk problems that smart people can't solve. When AI is applied to lower risk probelms that are usually easy for people to solve, we seem to get great results (i.e. recommendation engines).

<comment><USER72> Never in my life I encountered a good recommendation engine, let alone a great one.

<comment><USER73> I seem to emit some kind of anti-AI field - voice recognition works about 40% of the time so is basically useless, recommendation algorithms seem to recommend stuff I have already watched or seem completely random and as for the mechanisms that select adverts - Youtube seems to be taking the approach of showing me the same adverts again and again and again (and yes, I do click thumbs down on them) until I passionately hate the products being advertised to me (because I watch videos about cars does not mean I want to watch the _same_ BMW 8-series advert for a few months).

<comment><USER58> The recommendation algorithms seem to be just the most simplistic type of pigeon holing. Think Netflix, just because I watched a European political thriller last week, it is assumed I now want to watch this genre for eternity.

</comment></comment></comment></comment><comment><USER74> Machine learning requires a large high-quality dataset, which a lot of companies simply don't have. Building one takes a lot of time and money. The gains don't outweigh the costs in many cases.

Another problem is that machine learning models are never a 100% correct and not easily interpretable, so they cannot be used for some critical processes. Good luck with explaining a customer why his account blocked due to a false positive made be the AI.

I think there is still a lot of potential for boring symbolic AI, in a lot of domains you can get results quickly, reliably and if the AI is wrong it's easy to debug.

<comment><USER63> By the time you read about it, it`s old news.

They have been flooding many of the outlets with counterfeit emotional capital (social/MSM) and counterfeit intellectual capital (psuedo- science/education/politics) for quite some time now.

As long as the "I" or the "L" in those fancy acronyms know the difference, the quality of the data will not be in dispute.

Even without AI/ML, it`s been obvious how bad data integrity is in the most basic sense of the meaning.

</comment></comment><comment><USER75> Here is a prediction: This article will not age well. Asking why "AI is so useless for business" in 2020 is like asking why can't I easily order clothes from the internet in 1994. The question is simply 5-10 years too early. The AI startup rush started maybe 2 years ago. Pytorch (the Netscape of ml) has only been released 3 years ago! It's simply too early to make any judgment.

Let's wait 5 years and see. I predict that all the business processes he mentioned will be automated (maybe with mechanical Turk oversight). In 10 years, most of the menial desk jobs will not exist.

</comment><comment><USER60> The best thing about technology is that it seems to be getting more and more sophisticated in the industry. It seems like there is some sort of big disruptive force working at this point, but the big innovation seems to be the technology that is being used to create and manage the most values.. For example for a video game I imagine if a team of people working on this game could create games using some AI (maybe with 3D games) to generate the most value and then get some of the benefits of those games out there and then get real value out of it.

</comment><comment><USER76> Huh? Most major companies use a staggering amount of AI that makes them a butt load of money. -- Oh, the title was practically unrelated to the content of the article and was just to generate clicks, I see. Well, at least the article raises a good point about AI being used to solve menial tasks that let people focus on the larger creative aspects of their work as an assistive tool. That said, seeing ML push the boundaries of what "menial" (sliding goal post) tasks it solves is both massively cool and massively value generating.

</comment><comment><USER77> This article is really focused on the question "why is AI so bad at data extraction from PDFs when it can beat humans at Go?" and it does answer its own question toward the end. AI is very good at inverting simulations (chess, Go) because you can generate an infinite corpus of perfectly labeled data. It is bad at inverting document creation because there is no exhaustive MS Word simulator. Soon people will realize that applied AI is really an exercise in simulation design.

</comment><comment><USER78> The article fails to address the bigger issue: if enough data is provided and the information becomes clear, what then is the resultant?

Without a genera A.I. (which we are a long way from) how can the process work be done and what will it mean if it can be done? We'd like to think that we'd be more productive... we'd create new and better "things"... but if history is any guide, we'd simply focus on straightforward profit generating bullshit...

</comment><comment><USER79> At lot of this has to do with a move away from solid statistical workflows within an already trend prone field. Computer Science departments have only widened the cultural gap between their work an that of the Statisticians (you know the ones you need around to explain what your model is doing). Hiring for ML/AI sounds better than hiring a bunch of Statisticians which cannot be expected to deliver product.

</comment><comment><USER80> There is an implicit, but, in my opinion, wrong assumption here that AI should be able to do tasks like extract data from PDFs or convert Excel spreadsheets into some format. Nothing about these tasks requires intelligence - a fixed process solves the problem. Asking AI extract data from a PDF is akin to asking it to develop a process from vague inputs - a far cry from even the most advanced AI systems today.

</comment><comment><USER81> Because the so-called "AI" is only good for solving classification problems. Classification problems are great for art, but useless for business.

Business needs to solve the prediction (i.e., regression) problem, which is a completely different kettle of fish.

P.S. Of course by "AI" I mean the 2020 definition of "multilayered neural network".

</comment><comment><USER82> This is clickbait (1) to promote his startup, Proda.

ML is used in business workflows all the time - to date, I have built several solutions that are being used for 53 clients, internal and external.

Here is what makes B2B ML hard: People have to trust it.

This isn't some movie-recommendation engine, which spams you with more bank heist movies after you watch one. B2C ML systems can get it wrong, and customers are generally forgiving, because it's a low stakes game. B2B applications are generally higher-stakes, because they impact business workflows, and if someone has decided to automate it, it's probably a high- volume, critical workflow. It has to be extremely accurate, and demonstrably better than the equivalent human system.

The problem has to be well-defined enough that an ML system can act with high- accuracy, but not well-defined enough that a rule-system could replace it. Don't use ML if a rule-system will do a better job. (For those scenarios, you can still put an ML anomaly-detection system to make sure the rule-system is still valid, and to guard against data input changes.) As just mentioned, the problem also has to be important enough and high-volume enough to warrant an ML solution. The percentage of problems that fulfill these criteria is not very large.

Now to actual ML development and deployment - the model is the tip of the iceberg. The rest of the iceberg is data acquisition, feature selection, data/feature versioning, automated training, CI/CD, model performance monitoring, et cetera. If ML is being developed inside a software development organization, this isn't a problem, most people will understand this. If it is being developed within an embedded BI team inside a business unit - they will generally not have support/runway needed to build the full system. The ML model might make it to production, but it will probably run naked, be brittle, and hard to retrain. A dramatic failure with business impact is just a matter of time.

There are a lot of low-code, no-code ML solutions that have been developed, or are being developed, and some of the supporting infrastructure as well, but, at the risk of sounding parochial/protectionist, you need a rock-solid, end- to-end, integrated, data management system that is fully understood by whomever needs to pick up the phone at 2AM. It's the interfaces that are hard, and chaining together a bunch of third-party black-box systems just means more interfaces and behavior you don't control. Choose and use these systems wisely.

So yeah, B2B ML is hard. But it's generally not due to lack of data, and transfer learning is generally not necessary. Understanding business processes is important, I agree, but that's comparatively easy. It's what consultants have been doing for decades. The hard part is choosing a problem where ML can add value, and then executing on it with enough integrity that people will actually trust it.

(1) Ok, clickbait might be harsh. But it is self-promotion, and the article itself is a collection of generic banalities. I feel it falls on the wrong side of the line.

</comment><comment><USER83> That's easy: the business people don't understand "AI" and the "AI" people don't understand the business.

Well the business people actually do understand AI, but their understanding of it is that it is a marketing tool they can use to sell to customers and/or investors. And in terms of doing that, it AI works very well.

</comment><comment><USER60> The reason seems fairly obvious to me. It's the same thing as how we do things and do them in a good manner - and that's very different from other things in the industry. For example, why would a doctor call you if he says you are not doing a diagnosis, but he isn't doing a doctor: you have to be a software engineer. A doctor could go to you and ask a question about the symptoms you are experiencing and find out if the doctor is doing a diagnosis. He has to understand that the doctors use the AI to do everything he can to avoid that problem. His job seems to be to get insurance and medication coverage.

You might think about this a few times - if you are able to make an AI out of the box, maybe you have all of the necessary knowledge to get it out as well. Just as a computer can make a database out of a document. A programmer, however, could do all the necessary knowledge to get that system working. But most software engineer is like a carpenter - no amount of math or programming will change that. They probably also have all of the necessary knowledge necessary to make a car, so how about a car that can take a picture, and a car, that can run the calculations of the wheels

</comment><comment><USER84> This reminds me of Data Robot laying off people "beacause of covid" right after finishing a 300 million dollar round. This AI companies have lied about their valuation for awhile and it's catching up to them.

Ironically enough, after data robot did a layoff they also completed a large acquisition and hired more executives.

</comment><comment><USER85> AI has become specially hard to define nowadays that we see companies using advanced ML techniques for solving issues that were perfectly solvable through linear regression modeling, just because that's the path for that sweet investor money.

</comment><comment><USER86> Because the very concept of "business" is supposed to be boring and not very intelligent (which doesn't mean it doesn't require knowledge in its field, it's just not... alive).

<comment><USER87> What?

</comment></comment><comment><USER88> When I did business intellegence programming for a law firm we used statistics and six sigma to figure things out. It is all about crunching numbers on spreadsheets or linear algebra.

</comment><comment><USER89> As per the required SEC disclaimer, past performance is not indicative of future results. AI is great at spotting patterns that conform to past performance. Not so much when things change.

</comment><comment><USER90> Well, regular intelligence isn't but so useful in business either. There are so many factors in business success, intelligence is just one, and usually not a very big one.

</comment><comment><USER91> I am an AI researcher but and I would love to investigate useful systems for business, but I have no idea about the business processes that this article mentions.

</comment><comment><USER92> Just ask a business person to get you a training set and it will be a while before you hear from them.

</comment><comment><USER93> > Why Is Artificial Intelligence So Useless for Business?

Because it doesn't make money? A big enough revelation?

</comment><comment><USER9> Unpopular opinion: AI will start being useful to business when it will start being used to re-organize and re-architect core business processes... _Not as part of existing business processes, the "augmentation" will never offer too much!_

It will be when AI systems will decide _who to hire_ and _who to fire_ and _who to promote and demote_ , or what other companies to acquire or to merge with - profit will be increased, and almost everyone will _hate it!_

It will be when huge fusioned megacorps AI systems will gain monopolies and replace free markets with centralized planning systems that will actually outperform markets ("socialist planning" can't work because it can't work with _humans_ ...bringing in "other" types of intelligences will change the game, and nobody will call it "socialism" bc it will _not even try to benefit the people_ this time around - and there will be markets still, just likely HFT- style ones that will block direct human actors from playing in them) _...and most will hate it and likely wage war against the societies that will embrace it this way!_

You'll see AI stops being useless to business, don't worry ...but it will come with many consequences and side effects, our society as it is _can 't_ handle it!

<comment><USER94> Silly conclusion. AI will also be able to answer the question "How can I change to further my career?". An answer that human overseers have a terrible time with.

And if you do what the AI suggests, it will work and you will prosper.

</comment></comment><comment><USER95> New technologies are overestimated in the short term and underestimated in the long term.

</comment><comment><USER96> Because AI is not really AI as was meant in the 60's. The capabilities of computers, algorithms and human researchers were vastly overestimated back then.

But nowadays we find that the AI buzzword sells really well so we decided to lower and lower the bar until almost any algorithm qualifies as AI (also, any machine qualifies as a robot).

</comment><comment><USER97> Not trying to just put in a baseless plug, but most of what you say can be refuted if you try out our product. Go here: [https://nanonets.com/ocr- api/](https://nanonets.com/ocr-api/)

</comment><comment><USER98> The entire premise up front is false and probably a primary culprit. Expecting ML to do things it can't yet by extrapolating from what it can do today (after reading current capabilities through a filter of marketing hype):

>Today's work in artificial intelligence is amazing. We've taught computers to beat the most advanced players in the most complex games. We've taught them to drive cars and create photo-realistic videos and images of people. They can re-create works of fine-art and emulate the best writers.

Today's work in ML _is_ amazing.

> We've taught computers to beat the most advanced players in the most complex > games.

Not true. You can spend a zillion dollars on self play to get an AI superhuman at games simple enough that you can simulate at many many times real life speed, but we're just now learning to do games like poker, which intuitively seems less intellectual than Go or Chess, but so does starcraft and that came after those other games. In ML, placing tasks in order of achievable to currently impossible can be really unintuitive for lay people.

> We've taught them to drive cars and create photo-realistic videos and images > of people.

No again. We're getting there with cars, but it turns out that it's really really hard. Harder than playing superhuman chess! But people who play chess better than computers can drive cars better than computers. Weird, right? Again, in ML, placing tasks in order of achievable to currently impossible can be really unintuitive for lay people.

We _can_ make photorealistic pictures of people, but we're sorta limited (it's complicated) to faces at high resolutions and just really really really recently getting them without weird artifacts. But the face is the most complex part of the body, right? So the rest should be easy!

> They can re-create works of fine-art and emulate the best writers.

This is soooo much of a nope, and you know what I'm going to say anways.

This xkcd is always relevant, even if the bar has moved. Maybe it's even harder because the bar is moving quickly. [https://xkcd.com/1425/](https://xkcd.com/1425/)

> In CS, it can be hard to explain the difference between the easy and the > virtually impossible.

In ML, we're really good at some tasks, so it seems like we should be good at adjacent tasks, but that's not how it works.</comment>


The first Bitcoin Classic Block has been mined - <AUTHOR> https://blockchain.info/block/000000000000000006e35d6675fb0fec767a5f3b346261a5160f6e2a8d258070 <comment><AUTHOR> If you did not follow the current drama in Bitcoinland, here is the discussion on Reddit:

[https://www.reddit.com/r/btc/comments/45phiu/the_first_class...](https://www.reddit.com/r/btc/comments/45phiu/the_first_classic_block_has_been_found_on/)</comment>


Show HN: Online photo proofing directly from your dropbox folders - <AUTHOR> https://piczland.com/ <comment><AUTHOR> Now on Product Hunt: [https://www.producthunt.com/posts/picz](https://www.producthunt.com/posts/picz)</comment>


For Jessica - UPDATE - <AUTHOR> http://jenniferlawler.com/wordpress/?p=757 This is an update for the highly upvoted story a month ago:<p>http://news.ycombinator.com/item?id=1539833 <comment><AUTHOR> This is an update for the highly upvoted story a month ago:

<http://news.ycombinator.com/item?id=1539833></comment>


Ask HN: What motivates you? - <AUTHOR> <comment><USER2> Seems the question of motivation is popular on "Ask HN" threads as it's been asked several times:

\- [https://news.ycombinator.com/item?id=13196460](https://news.ycombinator.com/item?id=13196460)

\- [https://news.ycombinator.com/item?id=7736695](https://news.ycombinator.com/item?id=7736695)

\- [https://news.ycombinator.com/item?id=7660442](https://news.ycombinator.com/item?id=7660442)

\- [https://news.ycombinator.com/item?id=2059591](https://news.ycombinator.com/item?id=2059591)

\- [https://news.ycombinator.com/item?id=2585939](https://news.ycombinator.com/item?id=2585939)

</comment><comment><USER2> The opportunity to create something, exercise my mind, work with smart and nice people.

Sufficient income and vacation to live well, take care of family, and explore the world.

</comment><comment><USER3> Solving immediate problems. This could be a function to convert units or making dinner

</comment><comment><USER4> Escapism

</comment><comment><USER5> Mania.

</comment><comment><USER6> one day it all ends

</comment><comment><USER7> Knowledge</comment>


GPU Map Reduce - <AUTHOR> http://www.cse.ust.hk/gpuqp/Mars.html

<comment><USER2> It may be putting two hot topics togeher (MapReduce and GPGPU), but they sure don't make it easy. One of the strong sells of the MapReduce feature was how it reduced the amount of code something as complex as PageRank was written in and made most programs fairly straitforward.

There is a lot of challenges of programming CUDA (disclosure: this is what my ongoing MS thesis work is on), but their code examples show they add no abstraction layer and because of the generality they force on their kernels (all must have the same signature) will be near impossible to reach full GPU performance.

tl;dr Unlike MapReduce, this does not make programming GPUs easier or accessing their hardware potential easier.

</comment><comment><USER3> Wow, totally read the title as GNU Map Reduce. This is an Nvidia CUDA implementation of Map Reduce, hence the GPU, not a project by the FSF for GNU or anything.

</comment><comment><USER4> the text in section 4.2 is kind of greyed (hatched) out (and unreadable). very odd. i'm using okular on opensuse / kde - is it odd an acrobat reader too?

<comment><USER5> Looks fine here (Acrobat/Photoshop, Windows 7)

<http://img85.imageshack.us/img85/6707/marspact087.png>

<comment><USER4> thanks! (that clearly doesn't have the issue i am seeing - will play around some more ad maybe raise a bug report somewhere).</comment></comment></comment>


AT&T's new data plans will ruin the future of mobile - <AUTHOR> http://www.knowyourcell.com/features/500300/atts_new_data_plans_will_ruin_the_future_of_mobile.html

<comment><USER2> The article points out a definite possibility. However, I prefer to be optimistic. Usage fees could give carriers a large motivation to make their networks faster and add capacity. As carriers increase capacity and speed, they will likely compete with each other to drive down prices per unit of usage. Right now, most carriers are offering 5GB for $30 and AT&T just made it 2GB for $25. That said, I can't see that pricing sticking around for long. As the four carriers push into 4G in the coming year, there will be intense competition to offer users data services. As capacity increases, carriers will take into consideration that increase as well as the cost of building that increase and figure out what they can offer more cheaply.

Usage fees aren't bad when they're modest. Most of us pay for heat and electricity that way. For those of us who have a server, we get an allocation of bandwidth and overages are usually very reasonable - or with Amazon, you pay for what you use. Now, telecoms haven't always had the nicest pricing structures in the past, but I remember when long-distance service became very competitive and the price of minutes sunk. I'm hoping that the combination of 4G and two new entrants to the wireless world (Cricket and MetroPCS) will make data look more like long distance service.

Usage fees aren't terrible. Carriers do have costs to increase capacity and by charging those who are using that capacity, they have an incentive to build a good network and don't end up making light users subsidize heavy users. We must be vigilant that usage fees aren't onerous or out of line with costs, but I'm hoping that the combination of competition and increased capacity will keep prices down.

<comment><USER3> "They will likely compete with each other to drive down prices per unit of usage"

I would say this is very unlikely. Look at call minutes, look at SMS messages.

Both of these have plenty of scope for carriers to cut costs and compete while still being a profitable item. Do the carriers do this? No, not at all. They all keep their prices artificially high and roughly equivalent because it is in their joint interests not to compete too much.

If you look outside the US, there are plenty of telco's who's data prices are way higher and kept their for exactly this reason.

<comment><USER4> Both voice minutes and SMS tranches are for a mostly static demand though. I suspect that most users use about the same amount of voice minutes and SMS messages as they did last year (at least if they are in a similar cell/landline situation from year to year).

However, data demand isn't static, and it's going up. So I wouldn't expect to see data prices stay as stagnant as voice minute and SMS prices have, especially since that's the lucrative growth area cell networks have now.

<comment><USER3> According to CTIA, text message usage in the US grew 50% between 2008 and 2009, and so far in 2010 the increase is even greater. Can you guess what's happened to prices?

</comment></comment></comment></comment><comment><USER5> America had just about caught up to the rest of the world for a second there, but this puts us back where we're comfortable, five years behind everybody else.

A couple months ago, I was in Colombia, where for $20/month you can get unlimited wireless broadband, pre-paid, without a contract. Colombia, fer cryin' out loud, where they still have insurgents attacking the army and pirates operating near major cities, but somehow they manage to figure out how to do mobile broadband better than the USA.

Needless to say, Europe and Asia left us in the dust years ago. It's incredible that I still can't walk into a store in LA and buy a USB stick with a month's worth of unlimited bandwidth on it.

<comment><USER6> I was talking to my sister and her husband the other day. They recently moved to Singapore and have 12gb/month data plans. I don't know what they pay but as they're both casual users I assume they didn't get anything outrageous. Made me very jealous as I pay CAD$20/month for 1gb with Rogers here in Canada. I guarantee they're not paying $240/month for that plan.

I checked the SingTel site[1] and I'm guessing that they each have the $39 or $56 plans, $30 and $45 in Canadian dollars. That's their entire plan including voice, data, and everything else. I wish I didn't know how good it is in other countries. Fucking useless CRTC doesn't do anything about Canadian telecoms colluding and price fixing. The telecoms here have no interest in competing with each other.

[1] [http://info.singtel.com/personal/communication/mobile/mobile...](http://info.singtel.com/personal/communication/mobile/mobile- phones/iphone)

<comment><USER7> As a fellow Canadian I agree with the sentiment and would love to see lower prices.

But to be fair(er) to the telco's, it is a little easier to provide decent cellular coverage across Singapore than it is across even the densely populated swaths of Canada.

As a rural Canadian I'm not sure I could push much more than 2GB through my home internet connection a month if used it 24/7.

<comment><USER6> That's a common line from the telcos but in reality very little of Canada is actually populated and a huge percentage of our people live within 100-200k of the US border. The most difficult area to cover is BC because of all the trees & mountains. If you look at coverage maps they seem to have little trouble covering even the most rural parts of Alberta. Say Rogers has 1,000,000 3G customers (very modest). How many years does it take to pay off the cost of building the network? 2? 5? 10? At $50-80/month per customer they pull in at least $600,000,000/year. I don't care what their operational costs are they can cover it. That's assuming they have only 1,000,00 customers paying $50/month, while in reality they probably have closer to 5 or 10 million customers paying them upwards of $80/month.

I just agreed to pay Rogers $30 for 10MB of roaming data in the US while I'm at WWDC. I'm obviously going to go over that and will be charged $3/MB all the way up beyond the 10MB.

That is just greed. They take advantage of people who either have the company paying the bills or don't really know how much data they'll use. There's no way I'll use my phone normally while I'm away, but I should be able to. This has nothing to do with Canadian infrastructure and everything to do with being taken advantage of because we are locked into contracts, and even if we weren't Canadian telcos do not compete with each other. They all have outrageous pricing last I checked. Maybe things have changed with providers besides Rogers during the last couple years but I doubt it. They have no reason to compete with each other if they can fix prices and all make a killing. Prices are too similar across the board for me to think otherwise.

</comment></comment></comment></comment><comment><USER8> Sounds like the price of this resource is just finally getting in line with its actual cost. Bandwidth has never been free, and just hoping and praying that your customers don't use too much is stupid. When there was one real smart phone, and it could only use edge, with no custom apps and not many other services set up to use it, you could get away with "unlimited". Not now.

I mean, this is capitalism, right? How is one company going to charge too much more than the cost of the bandwidth without another company undercutting them? I don't pay monthly for unlimited breakfast cereal, or gasoline, and yet somehow capitalism has managed to keep those prices in check. I have no idea why this guy thinks that bandwidth is so different from every other good or service.

<comment><USER9> You can't compare breakfast cereal or gasoline to a cellular connection.

The cellular industry, due to its high barriers to entry (stemming from the enormous initial infrastructure costs), tends towards a natural monopoly. In this case, it hasn't quite reached a monopoly - instead, we have 4 main companies (AT&T, Verizon, Sprint, and T-Mobile), also known as an oligopoly. I know there are smaller carriers, but since they don't provide coverage everywhere, their product is not the same as the one provided by the 4 major carriers.

The problem is that when there is a disparity between the value of a product to the buyer and the cost of production to a seller, if there is natural competition (cereal, gasoline, etc. are relatively close to natural competition), the actual price will tend towards the cost for the sellers, since companies will keep undercutting each other until the price drops to the lowest they can accept without losing money on each sale. OTOH, if there is a natural monopoly (or something close to it), the actual price will tend towards the value for the buyers, since the lack of competition allows the price to rise as high as the buyer will tolerate.

In the case of bandwidth, the marginal cost for the telecom companies for each byte transferred is miniscule compared to the value it provides to us. If we had an industry with natural competition (like they do in other countries where the government owns the pipes and the telecom companies simply lease it out), then we would only be paying that miniscule cost (which essentially rounds off to a flat monthly fee for unlimited access). But since we have an industry that tends towards a natural monopoly, the companies will milk us for every penny we're willing to give them.

<comment><USER8> An oligopoly is fine as long as there's no collusion. If there is in the cell business, then yes, we have a huge problem. But if there isn't, then whatever ends up happening will be fair to all parties. I don't really think the marginal costs for cell bandwidth will approach zero for a long time now, but, if it does, then we'll see unlimited plans pop up again just like they have for wired connections.

<comment><USER9> You completely missed the point of my post. The issue is that when you have a monopoly or near-monopoly (oligopoly), the prices are primarily determined not by the cost for the sellers, but by the value for the buyers.

Therefore, unless the amount of value that each byte of cellular data transfer provides decreases significantly for a significant portion of the telecom companies' customers, the prices won't decrease, regardless of what changes occur in the costs to the companies.

That is, of course, unless the government steps in as it has in other countries and artificially fosters competition, so that the cost to the companies plays a more significant role in determining the prices.

</comment></comment></comment></comment><comment><USER10> Or, AT&T's new data plans will empower the future of mobile by making mobile app developers use bandwidth intelligently rather than wastefully. If bandwidth is free, there's no reason to focus on preserving it. The result is a tragedy of the commons where apps use more bandwidth than they have to, slowing the network for everyone and making some apps impossible to run in crowded (SXSW) or remote environments.

Maybe a challenge to use bandwidth wisely will spur innovation.

<comment><USER11> Oh yes, now everything mobile will look like Craigslist. Gee, what a step forward for everyone!

<comment><USER12> CSS doesn't take much space. That gives you colors, fonts, and layout.

</comment></comment></comment><comment><USER13> _How else will a service like Hulu be able to reach mobile devices? It would eat through caps like a hippy at a Phish concert._

Wow. With all the pun threads on the internet nowadays... that's really one of the best ones I've seen in a long time..

</comment><comment><USER14> In the uk where there are 5 competing GSM networks, you can get 10GB for £15 ($22) as a microsim for the iPad with no contract. You can do even better if you're willing to sign up for a long period.

AT&T's deal is more than 5 times the price of that. Is there a good explanation for this other than lack of competition?

<comment><USER15> Geography & population density play some role. The landmass of the US is 70x larger than the UK, which is about the size of Alabama, but the US has only 5x more population. I don't think that totally explains the 5x price difference but it's worth considering.

</comment></comment><comment><USER16> In short, the net neutrality debate just went mobile.

</comment><comment><USER17> _Except_ when moving, ubiquitous wifi would be better than 3G anyway. So instead of whining, let's blanket the nation with open wifi access points. This doesn't require any new lobbying or help from the mobile oligopoly.</comment>


Development of human limb muscles – links between ontogeny and evolution - <AUTHOR> https://dev.biologists.org/content/146/20/dev180349 <comment><AUTHOR> _Our study confirms the transient presence of several atavistic muscles – present in our ancestors but normally absent from the adult human – during normal embryonic human development, and reveals the existence of others not previously described in human embryos._ (linked article)

 _Strikingly, some of these muscles, such as the dorsometacarpales shown in the picture, disappeared from our adult ancestors more than 250 million years ago, during the transition from synapsid reptiles to mammals._ ([https://phys.org/news/2019-10-million-year-old- evolutionary-...](https://phys.org/news/2019-10-million-year-old-evolutionary- remnants-muscles-human.html))</comment>