Andreessen-Horowitz craps on "AI" startups from a great height - <AUTHOR> https://scottlocklin.wordpress.com/2020/02/21/andreesen-horowitz-craps-on-ai-startups-from-a-great-height/ <comment><USER2> "Huge compute bills" usually come from training, or to be more precise, hyperparameter search that's required before you find a model that works well. You could also fail to find such a model, but that's another discussion.

So yeah, you could spend one or two FTE salaries' (or one deep learning PhD's) worth of cash on finding such models for your startup if you insist on helping Jeff Bezos to wipe his tears with crisp hundred dollar bills. That's if you know what you're doing of course. Literally unlimited amounts could be spent if you don't. Or you could do the same for a fraction of the cost by stuffing a rack in your office with consumer grade 2080ti's. Just don't call it a "datacenter" or NVIDIA will have a stroke. Is that too much money? Not in most typical cases, I'd think. If the competitive advantage of what you're doing with DL does not offset the cost of 2 meatspace FTEs, you're doing it wrong.

That, once again, assumes that you know what you're doing, and aren't doing deep learning for the sake of deep learning.

Also, if your startup is venture funded, AWS will give you $100K in credit, hoping that you waste it by misconfiguring your instances and not paying attention to their extremely opaque billing (which is what most of their startup customers proceed to doing pretty much straight away). If you do not make these mistakes, that $100K will last for some time, after which you could build out the aforementioned rack full of 2080ti's on prem.

<comment><USER3> I find it fun how the cost of the cloud is forcing people to consider what absolutely must run in the cloud (presumably for stability and compliance reasons) and what can be brought back on-prem.

We don't train ML models, but we are in a similar boat regarding cloud compute costs. Building our solutions for our clients is a compute-heavy task which is getting expensive in the cloud. We are considering options such as building commodity threadripper rigs, throwing them in various developers' (home) offices, installing a VPN client on each and then attaching as build agents to our AWS-hosted jenkins instance. In this configuration we could drop down to a t3a.micro for Jenkins and still see much faster builds. The reduction in iteration time over a month would easily pay for the new hardware. An obvious next step up from this is to do proper colocation, but I am of a mindset that if I have to start racking servers I am bringing 100% of our infrastructure out of the cloud.

<comment><USER4> If I worked from home and my employer asked me to install a server in my home, I would tell them to go fuck themselves.

It's noisy, it takes up space, and presumably I'm on call to fix it if it breaks.

You should pay them an extra 24x(PSU wattage)x(peak $/Wh in area) per day for the electricity too.

I'm alarmed that someone in your company felt this idea was appropriate enough to propose.

<comment><USER3> We would certainly compensate employees. I didn't feel it appropriate to disclose every last detail regarding the arrangement in a thread which is only tangentially-related to the OP.

This was my idea. I am a developer in my company. We are a flat structure. We have a lot of respect for each other. I am on a standup with the CEO every day. We all believe in our product and would happily participate in whatever activity brings it to market more quickly. We do not hire or retain the kind of talent that would flatly refuse to participate in experimental projects like this. At least not without some sort of initial conversation about why it's not a good fit for a particular individual.

I certainly see how someone might share your perspective. I used to work for a souless megacorp and I could have easily found myself telling my former employer to "go fuck themselves" if a proposal similar to this was imposed upon me.

<comment><USER5> Try the value data center providers like he.net.

A 42u rack and 1 Gbps connection is $400 per month.

Put cheap supermicro EPYC servers in rather than threadrippers (or build your own). High capacity RDIMMs are cheaper than UDIMMs.

This will give you a much more maintainable solution than workstations at employee's home via overlay VPN.

There is a maintenance cost for infrastructure that people tend to forget these days.

</comment></comment></comment></comment></comment><comment><USER6> > most people haven't figured out that ML oriented processes almost never > scale like a simpler application would. You will be confronted with the same > problem as using SAP; there is a ton of work done up front; all of it > custom. I'll go out on a limb and assert that most of the up front data > pipelining and organizational changes which allow for [ML to be used > operationally by an org] are probably more valuable than the actual machine > learning piece.

Strong agreement from me: I've never worked on deploying ML models, but have worked on deploying operations-research type automated decision systems that have somewhat similar data requirements. Most of the work is client org specific in terms of setting up the human & machine processes to define a data pipeline to provide input and consume output of the clever little black box. A lot of this is super idiosyncratic & non repeatable between different client deployments.

<comment><USER7> That's because, ML and operations-research problems can be simplified to set of optimization problems and the underlying math and statistics are all very similar if not identical in some cases.

And the input matters, a lot. So the differentiating factor isn't the models, it's the data and companies like Google figured it out a long time ago.

In short, find interesting problems, then the solutions -- not the other way around.

<comment><USER8> "The data" means more than pure computer science people want to admit. In any "advanced" application, that means annotators. Radiologists drawing circles around cancer, attorneys labeling contract clauses as unacceptable, drivers labeling stop signs, etc.

ML is a mining problem. Digitizers are the miners. Annotators are the refiners.

<comment><USER9> Basically, the system is massively ad-hoc and driven by this large scale annotation, training and testing.

The big question here is, _what happens when the world changes next year?_ You rebuild the application. I know there are companies that advertise doing continuous updating of deep learning models but it seems like calculating total costs and total benefits is going to be hard here.

<comment><USER8> Sometimes the mine makes money, sometimes it doesn't make sense to run the mine.

<comment><USER10> To extend the mining metaphor, and relate back to the original articles:

People and organizations are chasing what they believe, or are told to believe, is pay dirt.

Many unfamiliar investors have rushed in, possibly fearing missing out, and fund many of the prospectors, yet many of the prospectors and investors aren't really aware of the costs of running a mine, nor the practices required to run them efficiently.

It turns out that there's more aspects to the value creation process than dig/refine/polish (data/train/predict), especially when usefulness in application matters and there are finite resources available for digging.

Companies selling shovels are some of the primary beneficiaries of this, by selling shovels (i.e. renting compute) funded by the malinvestment.

Additional beneficiaries are the refiners (training experts) that are able to charge steep labor premiums, however organizations are starting to figure out that their refiners are expensive to keep idle and often operate the mines poorly in terms of throughput/cost-effectiveness/repeatability/application (see the various threads on "Data Engineers")

</comment></comment></comment></comment></comment></comment><comment><USER11> I just spent $50K on coloc hardware. I'm taking a $10K/mo Azure spend down to a $1K/mo hosting cost.

But the real kicker is that I get x5 the cores, x20 RAM, x10 storage, and a couple of GPUs. I'm running last-generation Infiniband (56gb/sec) and modern U.2 SSDs (say 500MB/sec per device).

I figure it is going to take me about $10K in labor to move and then $1K/mo to maintain and pay for services that are bundled in the cloud. And because I have all this dedicated hardware, I don't have to mess around with docker/k8s/etc.

It's not really a big data problem but it shows the ROI on owning your own hardware. If you need 100 servers for one day per month, the cloud is amazing. But I do a bunch of resampling, simple models, and interactive BI type stuff, so co-loc wins easily.

<comment><USER12> I'm sure your right for your case. But I'd add one caveat for those less experienced: if you own the hardware, you need to be prepared to go to the colo when something breaks. The various clouds are a much nicer experience when hardware fails. At the very least people should have enough spare capacity that a hardware failure means going sometime in the next couple of weeks, rather than getting up at 3 am and fixing things under pressure.

<comment><USER13> Or take the middle road and just get rent the hardware (aka, dedicated hosting). You pay more than colo but still way less than cloud, get the same level of hardware support as a cloud provider but the same performance as colo.

<comment><USER14> Yep, for example Hetzner offers bare metal servers as well as cloud instances at a very reasonable price. (Not affiliated in any way. Just a happy customer.)

</comment></comment></comment></comment><comment><USER15> The number of places where machine learning can be used effectively from both a cost perspective and a return perspective are small. They are usually tremendously large datasets at gigantic companies, and they probably have to build in house expertise because it's hard to package this up into a product and resell it for various industries, datasets, etc.

Certainly something like autonomous driving needs machine learning to function, but again, these are going to be owned by large corporations, and even when a startup is successful, it's really about the layered technology on-top of machine learning that makes it interesting.

It's kind of like what Kelsey Hightower said about Kubernetes. It's interesting and great, but what will really matter is what service you put on top of it, so much so that whether you use Kubernetes becomes irrelevant.

So I think companies that are focusing on a specific problem, providing that value added service, building it through machine learning, can be successful. While just broadly deploying machine learning as a platform in and of itself can be very challenging.

And I think the autonomous driving space is a great example of that. They are building a value added service in a particular vertical, with tremendous investment, progress, and potentially life changing tech down the road. But as a consumer it's really the autonomous driving that is interesting, not whether they are using AI/machine learning to get there.

<comment><USER16> How would you explain the rise (and success) of machine learning in science? A lab that uses some learning-based method will likely be limited to just one or two people (responsible for data acquisition, feature engineering, evaluation, etc.) and extremely finite data.

<comment><USER17> It's not clear there has been any deep impact actually, but there has been a lot of discussion (and grant proposals)

I've seen a lot of cross pollination of ML and AI techniques into various disciplines. A large percentage just didn't work at all, most of the rest were more "kind of interesting, but". Nothing earthshaking happened although pop sci press likes to talk about it a lot.

If you have more digital data than you used to, using modern free frameworks and toolkits to do basic (i.e. older, boring, but understood) ML stuff to understand it seems to have a reasonable return. Mostly I think this is because it becomes accessible to someone without much background in the area, and you can do reasonable things without having to put 6 months of reading and implementing together before starting.

</comment></comment></comment><comment><USER18> Having briefly worked for an AI company, I agree with the conclusion that AI companies are more like services businesses than software companies. I would add only one other thing: to me going forward there likely won't be "AI companies" \- AI exists to power applications. And in my experience, unless the output is truly differentiated, customers aren't willing to spend more for something "powered by AI" \- they just expect that software has evolved to provide the kind of insights that AI sometimes deliver.

<comment><USER6> For an example of a genuine software company vaguely in this ecosystem, consider companies that build the tools that some AI/ML/optimisation systems use as building blocks. Eg optimisation algorithms.

If you need to solve gnarly industrial scale mixed integer combinatorial optimisation problems in the guts of your ML / optimisation engine, the commercial MIP solvers (gurobi , CPLEX ) or non-MIP based alternative combinatorial optimisation systems (localsolver ) can often give more optimal results in exponentially less running time than free open source alternatives.

1% more optimal solutions might translate into 1% more net profit for the entire org if you've gone whole hog and are trying to systematically profit optimise the entire business, so depending on the scale of the org it might be an easy business case to invest a few million dollars to set this system in place.

Annual server licenses for this commerical MIP solver software was 0(100k) / yr per server & the companies that build these products bake a lot of clever tricks from academia into these products that you can exploit by paying the license fee. ( my knowledge of pricing is out of date by about 7 years ) .

<comment><USER19> I'm all for linear optimization and other optimization techniques. It's refreshing to see other people talk about Gurobi, CPLEX, etc... Having done research in the field of scheduling and now getting contacted by companies, it's demoralizing to see that everybody usually speaks about machine learning while many problems can be solved in a more precise way with other techniques.

</comment></comment></comment><comment><USER20> So, way back in the last millenium, I did my Master's thesis (way smaller deal than a Ph.D. thesis) on neural networks. Since then, I have looked in on it every few years. I think they're cool, I like using them, and writing multi- level backpropagation neural networks used to be one of the first things I'd do in a new language, just to get a feel for how it worked (until pytorch came along and I decided for the first time that using their library was easier than writing my own).

So, it's not like I dislike ML. But, saying an investment is an "AI" startup, ought to be like saying it's a python startup, or saying it's a postgres startup. That ought not to be something you tell people as a defining characteristic of what you do, not because it's a secret but rather because it's not that important to your odds of success. If you used a different language and database, you would probably have about the same odds of success, because it depends more on how well you understand the problem space, and how well you architect your software.

Linear models or other more traditional statistical models can often perform just as well as DL or any other neural network, for the same reason that when you look at a kaggle leaderboard, the difference between the leaders is usually not that big after a while. The limiting factor is in the data, and how well you have transformed/categorized that data, and all the different methods of ML that get thrown at it all end up with similar looking levels of accuracy.

There used to be a saying: "If you don't know how to do it, you don't know how to do it with a computer." AI boosters sometimes sound as if they are suggesting that this is no longer true. They're incorrect. ML is, absolutely, a technique that a good programmer should know about, and may sometimes wish to use, kind of like knowing how a state machine works. It makes no great deal of difference to how likely a business is to succeed.

<comment><USER21> Saying that you're going to "use AI" is more akin to saying "we're going to have a web application" back in 1998.

Back then a lot of startups didn't have websites, because they were making other products (hardware, boxed software, etc). If they had a website it was just a marketing page.

So saying that you were going to make a "web application" did in fact differentiate you, in that it showed your approach was very different from the boxed software folks, but it didn't tell you much beyond that.

<comment><USER22> "Web application" came later. In the nineties it was called a "cgi web page" by your webmaster.

<comment><USER23> In the nineties, there was a huge difference between 1995 and 1998. It wasn't all that apparent to some of us until later, but things moved really fast in that timeframe. The years leading up to 2000 were almost like the imagining of approaching an event horizon or asymptote.

<comment><USER24> What you're describing is so hard to convey to people. In 1994 the we were building raised-floor data centers with halon for suppressors and marveling at our 2GB behemoth UNIX boxes. And writing our own web application framework using CGI. In '99 we were renting a suite at a colo and putting our own hardware there, running ColdFusion web apps. In '04 we were renting half a rack at the same colo and trying not to write three tier Java servlet based apps with 1,000 line web.xml files. And then AWS happened.

<comment><USER25> CGI to ColdFusion to Java servlets. Sounds enterprise-y.

<comment><USER24> It was all very start-uppy. What were you using to build your commercial web applications in 1996 if not CGI? Mod_perl did not even exist until 1995, and FastCGI didn't exist IIRC until after Netscape released their enterprise server.

<comment><USER25> Huh. I agree with CGI, but CF certainly had alternatives.

<comment><USER24> The boring low-risk unsexy thing that works is often underrated. I didn't choose CF — at the time I argued that it was a tool for scrubs, but the VPE said, "Hey, I know it, and I know it can do the job." We launched that CF- based web site on time and sold the company for $350MM fewer than six months later. Only then did we incrementally port it to Java.

</comment></comment></comment></comment></comment></comment></comment></comment></comment><comment><USER26> I agree with the author's opinion about

> I'll go out on a limb and assert that most of the up front data pipelining > and organizational changes which allow for it are probably more valuable > than the actual machine learning piece.

Especially at non-tech companies with outdated internal technology. I've consulted at one of these and the biggest wins from the project (I left before the whole thing finished unfortunately) were overall improvements to the internal data pipeline, such as standardization and consolidation of similar or identical data from different business units.

<comment><USER27> I was a consultant at one of the giant outsourcers and nod my head vigorously at this comment. The least sexy projects were MDM (master data management) but they were absolutely essential to the success of any other fancy analytics/BI/ML project.

<comment><USER28> Interestingly I too worked on MDM systems about ten years ago, when I was at IBM Research. Ironically, one of my first ideas for applying machine learning was in de-duplication of data in an MDM server. However the technology was a bit too primitive back in 2010 and the project was a hard sell so it was abandoned.

</comment></comment></comment><comment><USER29> No need to look at AZ for this. If you're building "AI" I wish you a speedy road to being acquired by a company that can put it to use. You've become a high priced recruiting firm.

If you're solving a real problem and use ML in service of solving that problem, then you've got a great moat....happy trusting customers.

It's not complicated

<comment><USER30> Sssh! Valuations are a function of projected market size and opacity of the problem. Clarity like this collapses the uncertainty and destroys value. If you pour enough capital into rooms full of PhD's something's gotta hit.

My way of saying, you're very, very right.

</comment></comment><comment><USER31> I wrote an article I published a week ago about how AI is the biggest misnomer in tech history [https://medium.com/@seibelj/the-artificial-intelligence- scam...](https://medium.com/@seibelj/the-artificial-intelligence-scam-is- imploding-34b156c3537e)

I wrote it to be tongue-in-cheek in a ranting style, but essentially "AI" businesses and the technology underpinning it are not the silver bullet the media and marketing hype has made it out to be. The linked article about a16z shows how AI is the same story everywhere - enormous capital to get the data and engineers to automate, but even the "good" AI still gets it wrong much of the time, necessitating endless edge-cases, human intervention, and eventually it's a giant ball of poorly-understand and impossible to maintain pipelines that don't even provide a better result than a few humans with a spreadsheet.

<comment><USER32> Coming from a fellow masshole: that's a great rant.

There was this meme in the 70s about "self driving cars" following magnetic strips in the road in restricted highways. I remember at the time, being, like 8 and thinking "sure seems like an overly complicated train."

<comment><USER31> Thanks man! Lifelong masshole here.

Your post was much better than mine, but I appreciate the comment.

</comment></comment></comment><comment><USER33> >That's right; that's why a lone wolf like me, or a small team can do as good or better a job than some firm with 100x the head count and 100m in VC backing.

goes on to say

>I agree, but the hockey stick required for VC backing, and _the army of Ph.D.s required to make it work_ doesn't really mix well with those limited domains, which have a limited market.

Choose one?

Also assumes running your own data center to be easy. Some people don't want to be up 24x7 monitoring their data center or to buy hardware to accommodate the rare 10 minute peaks in usage.

<comment><USER34> >rare 10 minute peaks

But is that really the use case here? I haven't worked in ML. But I'm not seeing where you are going to need to handle a 10 minute spike that requires a whole datacenter.

A month's worth of a quad gpu instance on AWS could pay for a server with similar capacity in a few months of usage.

And hardware is pretty resilient these days. Especially if you co-locate it in a datacenter that handles all the internet and power up time for you. And when something does go wrong, they offer "magic hands" service to go swap out hardware for you. Colocation is surprisingly cheap. As is leasing 'managed' equipment.

</comment></comment><comment><USER35> I found it fun to read this after reading this other post that made the rounds today about AI automating most programming work and making program optimization irrelevant: [https://bartoszmilewski.com/2020/02/24/math-is-your- insuranc...](https://bartoszmilewski.com/2020/02/24/math-is-your-insurance- policy/)

</comment><comment><USER36> A thread about the original article, from a few days ago: [https://news.ycombinator.com/item?id=22352750](https://news.ycombinator.com/item?id=22352750)

</comment><comment><USER37> I predict a great future for startups that sell pickaxes, err, tools for AI.

AI is like the new gold rush. And just like back then, it's not the gold diggers that will get rich.

"Most people in AI forget that the hardest part of building a new AI solution or product is not the AI or algorithms — it's the data collection and labeling."

[https://medium.com/startup-grind/fueling-the-ai-gold- rush-7a...](https://medium.com/startup-grind/fueling-the-ai-gold- rush-7ae438505bc2)

(from 2017)

<comment><USER38> Is it the new gold rush though. I work in a large organisation that has a lot of data and inefficient processes, and we haven't bought anything.

It hasn't been for a lack of trying. We've had everyone from IBM and Microsoft to small local AI startup try to sell us their magic, but no one has come up with anything meaningful to do with our data that our analysis department isn't already doing without ML/AI. I guess we could replace some of our analysis department with ML/AI, but working with data is only part of what they do, explaining the data and helping our leadership make sound decisions is their primary function, and it's kind of hard for ML/AI to do that (trust me).

What we have learned though, is that even though we have a truck load of data, we can't actually use it unless we have someone on deck who actually understands it. IBM had a run at it, and they couldn't get their algorithms to understand anything, not even when we tried to help them. I mean, they did come up with some basic models that their machine spotted/learned by itself by trawling through our data, but nothing we didn't already have. Because even though we have a lot of data, the quality of it is absolute shite. Which is anecdotal, but it's terrible because it was generated by thousand of human employees over 40 years, and even though I'm guessing, I doubt we're unique in that aspect.

We'll continue to do various proof of concepts and listen to what suppliers have to say, but I fully expect most of it to go the way Blockchain did which is where we never actually find a use for it.

With a gold rush, you kind of need the nuggets of gold to sell, and I'm just not seeing that with ML/AI. At least no yet.

</comment></comment><comment><USER39> An many times all these AI computations go into solving mundane problems like "What's the likelihood of this Ad to perform well".

AI is so shiny that makes people want to jump as fast as they can into that boat but a reasonable objective analysis shows that a huge and not insignificant amount of software problems can still be solved without relying on the "AI black box".

</comment><comment><USER40> You all know a GTX 1070 with 8GB on a gaming laptop with 32GB is still doing wonders and covering 90%+ business cases when coupled with smart & batch techniques the likes of you learn from fast.ai or under direct pytorch implementation, right??

</comment><comment><USER41> > Training a single AI model can cost hundreds of thousands of dollars (or > more) in compute resources

Why don't they buy their own hardware for this part? The training process doesn't need to be auto-scalable or failure-resistant or distributed across the world. The value proposition of cloud hosting doesn't seem to make sense here. Surely at this price the answer isn't just "it's more convenient"?

<comment><USER42> because you are trading speed for cash.

Say you have $8M in funding, and you need to train a model to do x

You can either:

a) gain access to a system that scale ondemand and allows instant, actionable results.

b) hire a infrastructure person, someone to write a K8s deployment system. Another person to come in a throw that all away. Another person to negotiate and buy the hardware, and another to install it.

Option b is can be the cheapest in the long term, but it carries the most risk of failing before you've even trained a single model. It also costs time, and if speed to market is your thing, then you're shit out of luck.

<comment><USER41> Why in the world do you need a Kubernetes deployment system to run a single, manual, one-time (or a handful of times), high-compute job?

<comment><USER43> Because when all you have is a hammer, everything looks like a nail.

We have become so DevOps and cloud dependent that everyone has forgotten how to just run big systems cheaply and efficiently.

</comment></comment></comment></comment><comment><USER27> > Better user interfaces are sorely underappreciated.

This is why I'm much more excited by AR and VR than AI. Human brains are fucking amazing at certain kinds of data processing and inference and pretty mediocre at others. We should be focusing more on creating interfaces and data visualizations that unlock that superpower for wider applications.

</comment><comment><USER44> I'm not terribly convinced of point 4.

> Machine learning will be most productive inside large organizations that > have data and process inefficiencies.

I strongly believe ML is at worst dangerous and at best pointless here. Data and Process inefficiencies => garbage in, garbage out. ML is NOT a silver bullet in large organisations that have these issues*, I've seen managers try to adopt ML to solve issues, but the results are almost always suspect and/or marginally better than simple if else rules but require a multiple people or teams to get all the data and models right.

</comment><comment><USER45> " Embrace services. There are huge opportunities to meet the market where it stands. That may mean offering a full-stack translation service rather than translation software or running a taxi service rather than selling self- driving cars. Building hybrid businesses is harder than pure software, but this approach can provide deep insight into customer needs and yield fast- growing, market-defining companies. Services can also be a great tool to kickstart a company's go-to-market engine – see this post for more on this – especially when selling complex and/or brand new technology. The key is pursue one strategy in a committed way, rather than supporting both software and services customers."

Exactly wrong and contradicts most of the thesis of the article - that AI often fails to achieve acceptable models because of the individuality, finickiness, edge cases, and human involvement needed to process customer data sets.

The key to profitability is for AI to be a component in a proprietary software package, where the VENDOR studies, determines, and limits the data sets and PRESCRIBES this to the customer, choosing applications many customers agree upon. Edge cases and cat-guacamole situations are detected and ejected, and the AI forms a smaller, but critical efficiency enhancing component of a larger system.

<comment><USER46> The thesis of the article is that this is going to be called consultancy.

Single-focus disruptors bad. Generic consultancy good - with ML secret sauce, possibly helped by hired specialist human insight.

Companies that can make this work will kill it. Companies that can't will be killed.

It's going to be IBM, Oracle, SAP, etc all over again. Within 10 years there will be a dominant monopolistic player in the ML space. It will be selling corporate ML-as-a-service, doing all of that hard data wrangling and model building etc and setting it up for clients as a packaged service using its own economies of scale and "top sales talent" (it says here).

That's where the big big big big money will be. Not in individual specialist "We ML'd your pizza order/pet food/music choices/bicycle route to work" startups.

Amazon, Google, MS, and maybe the twitching remnants of IBM will be fighting it out in this space. But it's possible they'll get their lunch money stolen by a hungry startup, perhaps in collaboration with someone like McKinsey, or an investment bank, or a quant house with ambitions.

5-10 years after that customisable industrial-grade ML will start trickling down to the personal level. But it will probably have been superseded by primitive AGI by then, which makes prediction difficult - especially about that future.

<comment><USER47> The big consulting firms have been building in-house ML libraries for common business problems for 3+ years. They don't need to acquire the data startups because as the article points out, these models are commoditized pretty quickly (especially when you have access to the transactional data of many large multinational companies). There is no secret sauce to ML that makes you any more likely to succeed with it than Accenture -- and they have a much deeper pipeline than you do. ML is a mature capability at all of the enterprise-tier consultancies, and they bundle it with their $100M system deployments. The mid-market consultancies are working on it. There is very little money to squeeze out of this market.

We're also a long way off from AGI. Nobody really even has a roadmap to what an AGI would look like. Heck, DNN/ML techniques have been widely-known since the early 90s; they just became practical with access to cloud-scale hardware, so the current situation has been 25+ years in the making.

</comment></comment></comment><comment><USER48> Now a days DL models are becoming commodities very fast. By the time you train NN to solve a particular problem, a new efficient model is out somewhere and is available public. So you need to go through the process entirely or else you risk losing business. Unless your NN is so unique like you are handcrafting your own in which case you take lot of time to arrive at a best model and you need more PhDs.

<comment><USER49> Props to the ML community for being so open.

<comment><USER50> Open does not mean patent-free.

</comment></comment></comment><comment><USER51> That is a great write up and very accurate description of both the costs and human intervention based on my experience with "AI" tools.

</comment><comment><USER52> AI on the algo side is only half the story -- it has to sit in a domain specific framework to be most effective

I see a lot of 'bolt-on' tech emerging -- it looks mostly snake oil -- there is no obvious way to be competitive against teams that baked it in to the bare metal design

Also most commercial use-cases I've seen need effective ML more than anything else

</comment><comment><USER53> > In the old days of on-premise software, delivering a product meant stamping > out and shipping physical media – the cost of running the software, whether > on servers or desktops, was borne by the buyer. Today, with the dominance of > SaaS, that cost has been pushed back to the vendor. Most software companies > pay big AWS or Azure bills every month – the more demanding the software, > the higher the bill.

This irrational sheep mentality amuses me. Yes, tehre are some very specific cases where AWS & ca. is clearly a better choice, but for the most cases I saw the TCO with hosting it on premises or renting servers is much lower, sometimes by an order of magnitude (in some cases even more). But people insist on doing it because others do it. We'll soon have an entire generation of engineers completely hooked on AWS & co. and not even realizing other solutions are possible, not to mention lower TCO.

</comment><comment><USER54> The A16Z piece makes all these points quite clearly. This editorial is trying to put a finer point on a sharp knife.

</comment><comment><USER55> There are many problems which are simply impossible to do with traditional optimisation or human analysis, that ML can do really well at. But I get the sense that this is not the type of problem that these "AI" startups referred to are addressing. Instead its like 'here is a problem I can charge for, with some ML magic it will be easy'. This is classic snake oil.

Being able to sift/classify/analyse data with ML really can be a 'moat', an extreme competitive advantage. But using "AI" doesn't automatically get you there.

Separately, AWS is an expensive luxury, which is worth it if for some reason you can't manage your own computers.

I really annoys me when analysts like this guy mangle together things which are obvious and then comes up with an unsupported conclusion, like "second AI winter is coming man".

</comment><comment><USER56> Agree mostly but he only talk about some AI start-ups that have a 1 to 1 model or at best a 1 to few. There is some AI startups like ours which have a 1 to many model. We use Computer Vision to collect data from video streams and sell data and transformed data through our API. The output of our models is the same for everyone.

Cost wise though it's clearly being not knowledgeable about how it works or at least think all AI startups have huge training set. For many companies owning your hardware for training is a very easy step to rationalise cost.

It feels like an article written about all AI companies but actually (very) true only for some AI companies.

</comment><comment><USER57> I wonder how much of the formidable amount of computing resources required for deep learning can be attributed to wasteful and inefficient programming practices. A lot of the ML libraries that I see are written in Python with very little attention paid to aspects such as memory usage, cache coherency, concurrency, etc.

If we focused on writing more efficient software instead of demanding bigger and faster machines with more and more GPUs, would the cost of ML become more practical? More importantly, as the author pointed out, would smaller companies have a better chance at making advancements in the field?

</comment><comment><USER58> On the other hand some of the startup is doing absolutely fraud on the name of AI.I went to a self checkout store (AIFI.io). I did not touch anything but they charge me $35.10. According to the receipt I took 17 packs of snacks :) These guys are doing fraud on the name of AI. They have no technology no software just put up some camera and open a store so that they can defraud the investor. Anyone can try if intersted [https://www.aifi.io/loop-case- study](https://www.aifi.io/loop-case-study)

</comment><comment><USER59> Here's what cloud gives you that is very costly to implement internally, cost accountability. Analysts running the same queries over and over would peg internal hardware all the time. When we went to the cloud, we made a budget for each division, problem solved. Same with DS. Give them a blank check, they'll spend it, manage to a budget, they'll do it.

</comment><comment><USER60> "(my personal bete-noir; the term "AI" when they mean "machine learning")"

This is so right. Using a term "artificial intelligence" for machine learning is like using "artificial horses" to describe cars. It is even worse, since we cannot even define what "natural intelligence" actually is. Stop talking about "artificial intelligence".

<comment><USER61> Or "artificial swans" that "appear even more lifelike".

[https://www.louwmanmuseum.nl/ontdekken/ontdek-de- collectie/b...](https://www.louwmanmuseum.nl/ontdekken/ontdek-de- collectie/brooke-25-30-hp-swan-car-1910)

>The bodywork represents a swan gliding through water. The rear is decorated with a lotus flower design finished in gold leaf, an ancient symbol for divine wisdom. Apart from the normal lights, there are electric bulbs in the swan's eyes that glow eerily in the dark. The car has an exhaust-driven, eight-tone Gabriel horn that can be operated by means of a keyboard at the back of the car. A ship's telegraph was used to issue commands to the driver. Brushes were fitted to sweep off the elephant dung collected by the tyres. The swan's beak is linked to the engine's cooling system and opens wide to allow the driver to spray steam to clear a passage in the streets. Whitewash could be dumped onto the road through a valve at the back of the car to make the swan appear even more lifelike.

>The car caused panic and chaos in the streets on its first outing and the police had to intervene.

</comment></comment><comment><USER62> I interviewed at some AI companies a year or two back. They all had teams of people dedicated to support each client: to clean their data, train their models, integrate the domain-specific requirements, customize UIs, etc. They sold themselves as the next AI-powered mega-unicorns, but they were more like boutique consultancies with no obvious path to scale up.

<comment><USER63> "Boutique Consultancy" is quite recapitulative for most AI companies for now. But this may be the only way to empower their clients. One of these startups will find the path to scale up eventually.

</comment></comment><comment><USER10> Related to the topic of marginal benefits of AI models versus their costs:

Green AI (Roy Schwartz, Jesse Dodge, Noah A. Smith, Oren Etzioni - 2019)

[https://arxiv.org/abs/1907.10597](https://arxiv.org/abs/1907.10597)

</comment><comment><USER64> I sometimes contribute to methodology projects in neuroscience ("AI" for scientists). The most tiring part of it is explaining essentially these things over and over. Very interesting to see the sentiment vindicated in Startupistan.

</comment><comment><USER65> Nice article. The flip side of the coin is that all these "problems" are potential moats for a well tuned ML company to use to defend market share.

</comment><comment><USER66> I view AI as the application of ML and ML as the implement (tool). Therefor, tooling efficiency is a competitive advantage of good ML projects.

</comment><comment><USER67> > "AI coming for your jobs" meme; AI actually stands for "Alien (or) > Immigrant" in this context.

Finally a correct use of "AI".

</comment><comment><USER68> Well, duh. Unless you invent AGI you're always going to be fitting new models for new clients. The best case scenario is getting bought by a client and becoming their full-time ML tailor.

For a pure ML company to IPO they'd have to both solve intelligence and manufacture their own hardware. FOMO screwed a lot of investors who would've been better off buying Google stock.

</comment><comment><USER69> Generally the use of the phrase from a great height implies the height is one of morality, intellect, or valor (each of these decreasing in usage), I'm not exactly sure what the great height Andreessen-Horowitz craps from is composed of - maybe money?

I think they may just be crapping on them from a reasonable vantage point.

<comment><USER42> The height is not really about morals. Its more about the blast radius of the shit.

<comment><USER70> Or like "nuked from orbit"

</comment></comment></comment><comment><USER71> I guess I won't mention Kubeflow here.....

</comment><comment><USER72> This is a terrific article. Two thumbs up.

</comment><comment><USER73> Is the misspelling of "Andreessen-Horowitz" and use of "A19H" instead of "a16z" intentional?

<comment><USER32> I suck at spelling. If I was one of the cool kids I'd claim to be dyslexic.

<comment><USER74> hi OP. We built an open-source library called, BentoML([https://github.com/bentoml/bentoml](https://github.com/bentoml/bentoml)) to make model inferencing/serving a lot easier for Data scientists in various serving scenarios.

Love to hear your thoughts on our library

<comment><USER75> I was really hoping that you where about to offer an ML framework to improve spelling.

</comment></comment></comment></comment><comment><USER76> All of this might be true currently, but that's because this current first generation "AI" (technically should just be called ML) is mostly bullshit. To clarify, I don't mean anyone is lying or selling snake oil - what I mean by bullshit is that the vast majority of these services are cooked up by software developers without any background in mathematics, selling adtechy services in domains like product recommendation and sentiment analysis. They are single discipline applications accessable to devs without science backgrounds and do not rely on substantial expertise from other fields. That makes them narrow in technical scope and easy to rip off (hence no moat, lots of competition, and human reliance and lack of actual software).

The next generation of Machine Learning is just emerging, and looks nothing like this. Funds are being raised, patents are being filed, and everything is in early stage development, so you probably haven't heard much yet - but these ML startups are going after real problems in industry: cross disciplinary applications leveraging the power of heuristic learning to make cross disciplinary designs and decisions currently still limited to the human domain.

I'm talking about the kind of heuristics which currently exist only as human intuition expressed most compactly as concept graphs and, especially, mathematical relationships - e.g. component design with stress and materials constraints, geologic model building, treatment recommendation from a corpus of patient data, etc. ML solutions for problems like these cannot be developed without an intimate understanding of the problem domain. This is a generalist's game. I predict that the most successful ML engineers of the next decade will be those with hard STEM backgrounds, MS and PhD level, who have transitioned to ML. [Un]Fortunately for us, the current buzzwordy types of ML services give the rest of us a bad name, but looking at _these_ upcoming applications the answers to the article tl;dr look different:

>Deep learning costs a lot in compute, for marginal payoffs

The payoffs here are far greater. Designs are in the pipeline which augment industry roles - accelerate design by replacing finite methods with vastly quicker ML for unprecedented iteration. Produce meaningful suggestions during the development of 3D designs. Fetch related technical documents in real time by scanning the progressive design as the engineer works, parsing and probabilistically suggesting alternative paths to research progression. Think Bonzi Buddy on steroids...this is a place for recurring software licenses, not SaaS.

>Machine learning startups generally have no moat or meaningful special sauce

For solving specific, technical problems, neural network design requires a certain degree of intuition with respect to the flow of information through the network, which both optimizes and limits the kind of patterns that a given net can learn. Thus designing NN for hard-industry applications is predicated upon an intimate understanding of domain knowledge, and these highly specialized neural nets become patentable secret sauces. That's half of the most - the other comes from competition for the software developers with first-hand experience in these fields, or a general enough math heavy background to capture the relationships that are being distilled into nets.

>Machine learning startups are mostly services businesses, not software businesses

Again only true because most current applications are NLP adtechy bullshit. Imagine coding in an IDE powered by an AI (multiple interacting neural nets) which guides the structure of your code at a high level and flags bugs as you write. This, at a more practical level, is the type of software that will eventually change every technical discipline, and you can sell licenses!

>Machine learning will be most productive inside large organizations that have data and process inefficiencies

This next generation goes far past simply optimizing production lines or counting missed pennies or extracting a couple extra percent of value from analytics data. This style of applied ML operates at a deeper level of design which will change everything.

<comment><USER32> >The next generation of Machine Learning is just emerging, and looks nothing like this. Funds are being raised, patents are being filed, and everything is in early stage development, so you probably haven't heard much yet ...

Citations needed. Large claims: presumably you can name one example of this, and hopefully it's not a company you work at.

I've seen projects on literally all the things you mention: materials science, medical stuff, geology/prospecting -none of them worked well enough to build a stand alone business around them. I do know the oil companies are using DL ideas with some small successes, but this only makes sense for them, as they've been working on inverse problems for decades. None of them buy canned software/services: it's all done in house. Probably always will be, same as their other imaging efforts.

<comment><USER76> >Citations needed. Large claims: presumably you can name one example of this, and hopefully it's not a company you work at.

Unfortunately this is all emerging just now and yes, I do work at such a company, but I'm old enough to not be naively excited by some hot fad. There's something profound just starting to happen but everyone is keeping the tech rather secret because it isn't developed/differentiated enough yet to keep a competitor from running off with an idea, yet. Disclosure is probably 1-3 years out of estimate.

>I do know the oil companies are using DL...as their other imaging efforts.

You're correct, and I happen to have experience in this domain - except there are a handful of up and commers courting funds from global majors like Shell and BP, and seismic inversion is near the end of the list of novel applications. Peteoleum is ground zero for a potential revolution right now, if we can come up with something before the U.S. administration clamps down on fossil fuels.

But we're talking complex algorithms which consist of multiple interacting neural networks. We are rapidly moving toward rudimentary reasoning systems which represent conceptual information encoded in vectors. I'm jaded enough that I wouldn't say we're developing AGI, but if the progressing ideas I'm familiar with and Workin on personally pan out, they will be massive baby steps towards something like AGI.

The space is evolving at least as rapidly as the academic side, which I think is an unprecedented pace of development for a novel field of study. I can't help but feel like these are the first steps towards some kind of singularity. There's no question that we are on to something civilization changing with neural networks, what remains to be seen is whether compute scaling will keep up with the needs of this next generation ML. Even if research stopped today, the modern ML zoo has exploded with architectures with fruitful applications across domains. The future is here!

<comment><USER32> I mean, the fact that you just wall of texted me with "trust me" doesn't inspire a lot of confidence. You could at least point me at an impressive paper or something!

I read all the NIPS stuff every other year; don't see anything game changing in there!</comment></comment></comment></comment>


Mercury – Banking built for startups - <AUTHOR> https://mercury.co/# <comment><USER2> I am co-founder, CEO of Mercury.

Excited to see this on HN. Happy to answer any questions.

We set out wanting to:

a) deliver an amazing online banking experience for startups b) help entrepreneurs understand their revenues/costs better and succeed c) deliver great financial services.

We have a long way to go but are excited to unveil what we have achieved so far.

Mercury will provide your startup a FDIC-insured checking account, debit card, savings account (earning 1-2%). All online, with an application that is finished on average in under 10 minutes and approved usually within a few hours.

We focused pretty heavily on the UI. Hopefully you'll appreciate little details, like our extremely flexible transaction date picker.

We have a lot more to come on the product side, including better analytics to help you understand your revenue/costs and APIs to build on top of Mercury, as well as new financial services to help startups succeed.

We'd love to hear any and all feedback you have. We'll be looking at comments here all day, or you can email us directly at immad AT mercury.co, max AT mercury.co or jason AT mercury.co.

<comment><USER3> Immad, congratulations on the launch!

1) Do you support international wire transfers? If so, any advantages in terms of fees and exchange rates over a traditional bank such as Wells Fargo?

2) Do you require hardware keys or does 2 factor auth suffice?

<comment><USER4> I'm not apart of Mercury, but per their pricing page [0] sending domestic and international wires are $5 and $35 respectively while receiving from both is free.

0: [https://mercury.co/pricing](https://mercury.co/pricing)

</comment></comment></comment><comment><USER5> I'm a co-founder and CTO at Mercury. We have some amazing frontend UI (the onboarding flow especially) if you'd like to check it out, and our backend is 100% Haskell. AMA

<comment><USER6> How did you sell Haskell?

Once Haskell was decided on, how did you approach development?

<comment><USER5> We were a team of 3, with the CEO and myself technical, so ultimately it came down to just co-founder trust. Immad was somewhat skeptical, but after interviewing some amazing candidates who wanted to work for us to use Haskell, he's really liked it.

Development was pretty simple, I just created a yesod-postgres template and we got started with authentication. Are you asking about something specific?

<comment><USER6> Thanks for your reply. My question wasn't specific, more of a general approach, like how to find people and how to get started with development, etc.

So a small team with technically capable people with I guess little outside interference, that makes sense. I guess I was hoping for a good approach on how to make Haskell/ML/<other sane but not mainstream language> palatable to decision people who aren't all that technically inclined.

<comment><USER5> Hopefully Mercury can be a success story you can cite :)

<comment><USER6> I hope so. A backend using Haskell sure would be a selling point for me. I shudder to think of the software that is currently used by many financial firms to handle backend interactions.

Best of luck!

</comment></comment></comment></comment></comment></comment><comment><USER7> Do you have or can you add real check and ACH withdrawal security? I want my bank to decline all externally initiated withdrawals unless explicitly authorized. In other words, when I write a check, I want to tell my bank, and if someone tries to cash a check drawn on my account, I want it to be rejected. The same should go for ACH debits.

This might be harder, but it would be even better if unrecognized debits we're out of hold for a day or two so I could approve or reject them.

<comment><USER2> ACH - a basic account can't have this protection. We have an idea for an account that is for sending only that is always 0-balanced. That's not live yet, but perhaps it would satisfy your request.

Check - you can send a check to a specific person from the Mercury dashboard but you can't print a blank check against the account. That would be rejected. So standard check fraud is not possible.

<comment><USER8> re: ACH protections, it would be really useful to have the bank manage for me an account # per ACH contact. Then I could set limits on each contact (e.g. this contact is only allowed to deposit, this contact can only withdraw up to $X per week, etc.) I could also enable/disable accounts on a per-contact basis (e.g. this contact is now barred from receiving any money).

Most importantly, if party A leaks my ACH info, this shouldn't help any other party.

</comment></comment></comment><comment><USER9> Can't figure out why the tea gimmick draws me in so much, ha. Also slightly confusing--does the $250k criteria include checking+savings? Or strictly checking, as stated.

But otherwise, what do you see as differentiation from something like Azlo?

</comment><comment><USER10> You can't beat free. SVB and others get you with free for x months but once their fees and "analysis fees" kick in it really gets quite expensive for a small bootstrapped business even with little activity on your account.

It's also incredible how far behind all banks are with simple things like being able to search your transactions over a decent amount of time.

Good luck and I genuinely hope the free and open to all model proves to be sustainable.

<comment><USER2> The dreaded "analysis fees"!

We operate at reasonable unit economics. Free should be sustainable.

</comment></comment><comment><USER11> Do you have any plans to handle deposits in currencies other than USD? We're US-based but receive a fair amount of payments from customers in EUR and we also have obligations in EUR so we're looking for a way to avoid going from EUR to USD on deposit and back to EUR on payment.

<comment><USER2> That would be a cool feature. We don't have immediate plans to do this.

</comment></comment><comment><USER8> So far, I'm intrigued. But I'm also conservative when it comes to money. None of these questions are specific to Mercury, they are general to any of the fintech startups that have been popping up lately. So:

\- do people really use new banks like this?

\- how worried is it reasonable to be that bugs in the platform will cause interruption of business/money loss for customers? (I'd be particularly worried that I wouldn't immediately catch an error in the bank's favor.)

\- in general, is there ever a compelling reason to not wait a few years for the bank to smooth out its operations before getting on board?

All that said, I love to see innovation in the space and it looks like a refreshing start!

</comment><comment><USER12> Your focus on API-driven banking really intrigued us, however I wasn't surprised to see that you won't bank money services businesses. Kind of a shame, because we could really use an API to help with cash management.

<comment><USER2> We don't do MSBs on the website, but we can support them in theory. Just need extra diligence docs.

Send me an email and we can try to make it happen.

</comment></comment><comment><USER13> Congrats on the launch. We're with SVB but tired of their crappy ancient tech stack and want a bank with a decent API.

Mobile apps with check deposits and xero/intuit integration would be high priority features before a move.

<comment><USER14> I've never used startup banking before, curious what kinda thing people look to do with banking API.

<comment><USER13> We are a marketing software company with a previous ad network business. Lots of bills, invoices, credits generated by our system that use APIs to send payments and record statements in accounting software but bank reconciliation is completely manual. Automating even some of it would be helpful.

<comment><USER2> Our previous business was an Ad Network. Which is partly what inspired Mercury

</comment></comment></comment></comment><comment><USER15> Looks good but without hijacking the thread does a service like this exist in Europe? If not is it due to different financial regulations (maybe easier to get started in the US?) or something else?

<comment><USER16> Which features in particular do you need? If you are interested in the APIs, you might be pleased to know that starting September 14th, 2019, any bank in the EU must provide public APIs by law under the PSD2 EU directive for Open Banking. Most banks are already compliant (although their APIs are not exceptional).

</comment></comment><comment><USER17> Good onboarding. Confirmation of acceptance arrived within 30 minutes of completing my application. It was great to be able to save my application progress: I started on one device, moved to another, and logged out and back in a few times while finding docs and photos between bouts of work-work. Looking forward to discovering where Mercury saves me time and hassle.

</comment><comment><USER18> Looks very cool. What is the preferred way to deposit checks? I see it mentioned on the FAQ as "add new funds" flow, but I'm thinking more like payment for invoices from old-school customers.

Do you support an RDC scanner, or would it be recommended to use a traditional bank and then electronically transfer it in from them, or wait for a mobile app?

<comment><USER2> If you have a ton of checks (>10/month) we are probably not the best solution at the moment.

We are going to have web based check deposit going live next week and mobile in the near term.

</comment></comment><comment><USER19> Sadly it seems to be an US only or US centric type of service and I understand that it is as it should be since baking accounts and borders are complicated matter.

Here in Brazil here have Nubank which offers lots of modern features like APIs and so but they do not offer services for business so we have to rely on old crusty bureaucratic evil banks.

<comment><USER2> Sorry! I hear Nubank is great though.

</comment></comment><comment><USER20> What the story on fraud? I was surprised to discover that business bank accounts aren't governed by the same protections that consumer accounts are (Reg E).

What happens if a criminal commits ACH fraud and is successful? Are Mercury's customers protected by some sort of guarantee, other than FDIC which only seems to cover bank insolvency?

<comment><USER2> ACH fraud protection is available to Mercury customers. I believe you have 90 days to reverse an ACH.

Customers do have better protection than businesses but in reality most banks extend the same rules to both.

</comment></comment><comment><USER21> The onboarding flow looks nice but unfortunately the document uploading doesn't work at all for me, for PDFs or JPEGs. I'm using the latest Firefox on the latest macOS.

<comment><USER2> Hi,

Do you mind sending me your email, and the picture that failed. Sorry about that.

<comment><USER21> I emailed you at the email address in your profile. Thanks!

</comment></comment></comment><comment><USER22> Really good to see more banks who are open to international customers. Your website is also super sleek. Definitely going to switch from SVB to you guys if things work out!

</comment><comment><USER23> This is AWESOME! A free business bank account, with great interest rates. I've been looking for this for months! The great design is just icing on the cake.

</comment><comment><USER24> I'd love if you integrated with the Stripe api to project future deposits!

<comment><USER2> That's part of the plan!

</comment></comment><comment><USER25> What exactly is the business model for this venture? Vendor lock-in?

<comment><USER2> Repost from another comment:

Banks make money on deposits and thats their primary source of revenue. We have a rev-share with the bank on deposits revenue. Most of the time there is no good reasons for there to be any fees in our opinion.

</comment></comment><comment><USER26> What are the partner rewards mentioned for Tea Room?

<comment> j-zhang We're starting with up to $100k in AWS credit, $1,000 off a 409a and 20% off Pilot bookkeeping for 6 months. More to come. Email me at jason at mercury.co if you want to know more.

<comment><USER23> $1,000 off a 409a? How much would the 409a cost after the discount?

Asking because our 409a cost us about $1,200...

<comment> j-zhang To clarify, it's through a specific partner (fast409a) which usually charges $2,000. Sorry, should have specified in the original post.

</comment></comment></comment></comment><comment><USER27> Awesome to see other Haskell startups out there!

</comment><comment><AUTHOR> Are you guys work with Stripe, PayPal directly?

<comment><USER28> Aside: we've removed "Show HN" from the title because it's for stuff you've built.

<comment><USER29> FYI I still see it in the title

<comment><USER28> Indeed. Thanks!

</comment></comment></comment></comment><comment><USER30> Do you integrate with Quickbooks Online?

<comment><USER2> We have Quickbooks compatible exports.

Looking into a deeper integration

</comment></comment><comment><USER31> Well, SynapseFi is backend!</comment>


Investigating Causal Effects of Mental Events in Cognitive Neuroscience - <AUTHOR> http://philsci-archive.pitt.edu/14507/ <comment><USER2> Related articles worth reading:

[0] [http://edwardfeser.blogspot.com/2011/01/against- neurobabble....](http://edwardfeser.blogspot.com/2011/01/against- neurobabble.html?m=1)

[1] [http://edwardfeser.blogspot.com/2012/03/reading-rosenberg- pa...](http://edwardfeser.blogspot.com/2012/03/reading-rosenberg-part- viii.html?m=1)

[2] [http://edwardfeser.blogspot.com/2017/01/revisiting-ross- on-i...](http://edwardfeser.blogspot.com/2017/01/revisiting-ross-on- immateriality-of.html?m=1)

</comment><comment><USER3> This is something which has always irked me about the "black box" arguments regarding artificial neural nets. Sure, it's hard to interpret exactly "why" an ANN gives the result it gives (other than "it did the maths and that was the result") but people always gloss over the fact that we really don't know much at all about how the human brain generates its results. We just accept individual introspection as ground truth when, as any psychologist will tell you, it's anything but reliable.

<comment><USER4> > We just accept individual introspection as ground truth

Isn't a fundamental of scientific method to not accept introspection, but to require publicly observable, reproducible evidence?

<comment><USER3> Yeah, but if a driverless car does something stupid, people want an exact diagnosis ("sensor X failed", "the vision algorithm misidentified Ms. Whatsit as a paper bag due to a bug on line 714", etc.) whereas a human driver can say "I thought they were going to brake" and we just accept that that's why the person did it.

<comment><USER5> The difference is that neural nets are technologicl artifacts that we design and manufacture, unlike our own minds. We generally make sure that when we design and create such things, we know how to control them.

For example, a car breaking system that randomly failed to stop the car under conditions that were impossible to understand would be unacceptable.

When a component of a safety-critical system fails, we want to know why it fails. Not because we're curious, but because we want to avoid failure in the future. If the component is a black box that can't be reasoned about, then we can't know whether it will fail again in the future.

Also: "Ms Whatsit"? In view of the Uber accident, that is extremely insensitive.</comment></comment></comment></comment>


Universal Declaration of Human Rights - <AUTHOR> http://www.un.org/Overview/rights.html

<comment><USER2> Very fitting that this link is posted by someone who shares the same nick as Richard Stallman.</comment>


MySpace, Auditude, And MTV Have Just Figured Out How To Monetize Online Video  - <AUTHOR> http://www.techcrunch.com/2008/11/02/myspace-auditude-and-mtv-have-just-figured-out-how-to-monetize-online-video/ <comment><USER2> Should read "May Have Just Figured Out." Why do people have to write such over-the-top headlines?

Let's give the technology a few weeks and then judge its efficacy. Techcrunch is the same site that claimed Hulu would crash and burn.

Oh, don't forget to read the last linked story:

[http://www.reuters.com/article/wtMostRead/idUSWEN87182007061...](http://www.reuters.com/article/wtMostRead/idUSWEN871820070612)

Apparently, Google tried something very similar _last June_.

</comment><comment><USER3> I installed the BlockSite addon for Firefox (<https://addons.mozilla.org/en- US/firefox/addon/3145>) specifically to disable techcrunch.com links on this site.

It's works WONDERFULLY! I see a sensational headline, and even if I give into impulse and click on it, it's not an active link, and I'm reminded about headline pandering without having wasted the time to read the blog post.

</comment><comment><USER4> It seems to me that this allows Myspace to identify owned content but does very little in terms of monetizing that content. How often -- really -- have you watched something on Youtube and said "Geez, I'd like to buy that!"

All this appears to do is to provide some ammo to the copyright holders that the technology exists to deal with takedown notices efficiently.

</comment><comment><USER5> the same technology could be used to point to free full versions of the clips.. wooohoooo... if only...

</comment><comment><USER6> On the other hand, users might be pissed off that the videos they upload are being changed.</comment>


Ask HN: VR Apps not related to gaming and movies - <AUTHOR> Are there any VR apps for Oculus/Vive that are not related to gaming and movies? Just curious, as I was thinking of getting Oculus or Vive but have no interest in gaming or movies. What else could I use VR for? <comment><USER2> porn?

<comment><AUTHOR> Isn't porn a type of movie?</comment></comment>