9 ms·

AI is not coming for you

120 points by ntang 8y ago

tyingq 8y ago
It mentions the blockchain hype mania as well. I happen to currently be the clearinghouse for lots of people's silly blockchain ideas right now. I mostly have no issue with the ones that are basic "chain of hashed blocks" git style ideas, intended to show some simple sequence of integrity, while still requiring trust. I don't get many of those. But there are so many that try to shoehorn the full trustless model onto an idea that obviously still has a central authority. Argh. Is there a good resource I can hand to non-technical people that explains why "full blockchain" only makes sense for a handful of use cases?
- ghaff 8y ago
  Bruce Schneier has a talk which is probably online and I think has written a blog on how there’s always some of trust in the system needed. Kevin Werbach has written a book on blockchain and probably has shorter pieces.
- yoshuaw 8y ago
  Bitcoin for laypeople is pretty good. https://gist.github.com/mafintosh/bd9e6d350ebf02441c9707c5f799d05b https://gist.github.com/mafintosh/bd9e6d350ebf02441c9707c5f7...
- rorykoehler 8y ago
  From where I am standing it seems as if any resource to explain anything to non-technical people will fall on deaf ears. The reason for this is money. People aren't looking for the best technical solution for their problem. They don't even have a problem. They are looking for the best way to get their hands on money quickly. If people will hand over cash they will do it. This goes for AI too. Many investors are stupid (naively optimistic if I'm being kind) and people are out to get their money.
  - tyingq 8y ago
    Ahh, yes, the motivation is somewhat similar here. It's internal projects at a big company. The people proposing wouldn't get money per se, but they want the reputational thing of doing something cool and newsworthy. I'm the first person they have to get past with their (usually) bad idea.
  - gk1 8y ago
    Repeating my other comment in this thread: > This is a tired trope, that “business people” are brainless and gullible. Almost as tired as the “VCs are so dumb they’ll throw money at anything-AI” idea repeated in the article. > Maybe, just maybe, the people running multi-billion-dollar companies, and multi-billion-dollar investment funds, are not stupid? > It would be much more interesting and productive to discuss what they see in AI and why they feel so much urgency, rather than dismissing them as fools falling for magic. VCs only need one big winner out of every ten investments. You are looking at their nine failures and calling them stupid and naive.
    - barbecue_sauce 8y ago
      Experienced VCs are not the only business people who give out money.
      - gk1 8y ago
        My problem is with the generalization. The article and some comments in this thread paint all VCs and business people as dummies falling for shiny objects. I don’t think anybody would argue with an article that said some percentage of any population makes stupid decisions for stupid reasons. But then again, nobody would read that article, either.
    - tyingq 8y ago
      I agree that saying all VC's are dumb, all the time, is silly. On the other hand, things like Theranos and Ubeam do prove that some investments that some VC's make are spectacularly dumb. One out of every ten still requires basic due diligence, or at least questioning business plans that require setting physics or other basic knowns aside.
- codeulike 8y ago
  http://doyouneedablockchain.com/ http://doyouneedablockchain.com/
- techbubble 8y ago
  My Open Source project focuses on educating mainstream users about blockchain and cryptocurrency with simple videos and content https://www.trycrypto.com https://www.trycrypto.com
- boronine 8y ago
  You may want to pick one of the following flow charts: https://medium.com/@sbmeunier/when-do-you-need-blockchain-decision-models-a5c40e7c9ba1 https://medium.com/@sbmeunier/when-do-you-need-blockchain-de...
  - tyingq 8y ago
    The "Birch-Brown-Parulava" and Lewis models look very promising. I may try to combine them. Thanks!
w8rbt 8y ago
So is Machine Learning. It can be dangerous too: https://www.usenix.org/conference/usenixsecurity18/presentation/mickens https://www.usenix.org/conference/usenixsecurity18/presentat... James Mickens is awesome BTW.
vnorilo 8y ago
I liked the article. It mentions the fallacy of Objectivity by Indirection: "I didn't say that - the system (I designed) said it!"
- maxxxxx 8y ago
  I think that’s the future. AI will give people something to hide behind. Imagine a US health insurance that declines you because of “AI”. It’s already bad enough the way it is but this will bring it to a whole new different level.
- djsumdog 8y ago
  I mean, but we've been doing that with mathematical models for years. I mean, just look at the controversy over books like The Bell Curve or Freakconomics.
charlysl 8y ago
Maybe it's just me, but it seems to me that the author doesn't have a clue of what he is writing about. For instance, when he mentions the Amazon hiring AI embarrassment, it seem to me like he mixes the very different concepts of bias in machine learning with the everyday concept of bias as prejudice. If the sample data is representative of the population, then it is unbiased in machine learning terms, even if the population as a whole is prejudiced, or biased, which will be duly reflected in the learned model, which shouldn't surprise anyone with at least a rudimentary knowledge of machine learning.
- piokoch 8y ago
  I have the same impression. If AI system figured out that the risk of the car being stolen when someone lives in a certain place is bigger and adjusted fee to be higher, then it means AI worked correctly. One might claim (I presume author does and this is perfectly ok) that this is a bad thing that there are "bad neighborhoods" and we should fight it, but what does it have to do with proper calculation of insurance fee? Should we expect that AI will apply some "social justice" concepts while calculating insurance price. I am not sure what author proposes.
  - charlysl 8y ago
    Or maybe I am wrong and the author is right. After all, he claims that the problem is not AI but the humans, and maybe we shouldn't blame systems trained on human data, which reflects all human's prejudices. Then again, if AI's aim is to perform activities until now restricted to humans, just like humans, how could it not be prejudiced? And who decides how AI should make decisions that are politically loaded?
  - sgt101 8y ago
    Remember that the prediction is from the rear view window - on data that has been gathered in the past, and that now you are fixing risk in the future.
  - skybrian 8y ago
    I'm not impressed with the article, but the idea here is that some forms of bias are okay, others are not, and it's up to the people working on the system to build it properly. There are laws about these things and you can't expect some algorithm to know the law automatically and avoid prohibited discrimination without going through the effort to build it in. It's kind of odd to think this would happen automatically. There is some kind of wishful thinking going on that technology can't have bad effects if you don't have bad intentions.
jerkstate 8y ago
> And it turns out that racially discriminatory lending and coverage can be quite profitable – particularly when enabled with technological precision. this is a troubling statement, is this just the author speaking or is there data to back it up?
- currymj 8y ago
  there’s decades of history about the practice of redlining in the US (good term to Google), as well as more recent court cases related to subprime lending.
  - securingsincity 8y ago
    More recently facebook was charged by HUD for allowing people to buy ads and filter out by race, sex, another protected statuses under the fair housing act https://cdn.theatlantic.com/assets/media/files/hud_v_facebook.pdf https://cdn.theatlantic.com/assets/media/files/hud_v_faceboo...
- overcode 8y ago
  Not sure what data you expect to see. People generally don't publish their discriminatory lending and insurance policies. Or do you think that the statement that it's profitable might be incorrect?
carnagii 8y ago
I see a lot of similarities between "AI", as understood by business people, and the pursuit of alchemy and the philosophers stone. It seems like a hustle to separate rich dupes from their money by promising them the keys to infinite wealth, immortality, mars colonys, etc. In that respect it is mostly harmless but it can be quite dangerous to the people who take it seriously.
- gk1 8y ago
  This is a tired trope, that “business people” are brainless and gullible. Almost as tired as the “VCs are so dumb they’ll throw money at anything-AI” idea repeated in the article. Maybe, just maybe, the people running multi-billion-dollar companies, and multi-billion-dollar investment funds, are not stupid? It would be much more interesting and productive to discuss what they see in AI and why they feel so much urgency, rather than dismissing them as fools falling for magic.
  - deleted 8y ago
    [deleted]
  - alkonaut 8y ago
    I can only speak for managers I have met: what they “see” is a promise of cost cutting and an opportunity to tell higher managers that they are ahead of the hype. But never, ever, have I seen one of these people say anything that suggests they have even a vague idea of what AI or ML is or what it can realistically achieve. And I’m not sure they are interested. Because as you say - they aren’t stupid - I just think they are part of a game of BS I don’t understand, involving higher managers, investors etc. I don’t think it’s so much about about producing anything using AI, it’s AI for the sake of saying you are using it. So perhaps not all fools but somewhere between con artist, willfully ignorant and fool. (Note: this is all from “traditional” industry, I.e the manager at the hammer factory proudly launching initiatives to “use more AI” in the factory. Not tech industry. Not plausible or concrete use of AI)
    - gk1 8y ago
      You are repeating the trope, just substituting “stupid” with other deriding terms: BS, con artist, willfully ignorant, fool... Does it really have to be those things just because you don’t understand it? Is it possible those people running multi-billion-dollar organizations just know something you don’t, or have a perspective that you don’t?
jaabe 8y ago
I think ML has potential that Blockchain doesn’t, but not in all the areas that are being hyped. I work in the public sector of Denmark, and we’re targeted by a lot of the hype. Which is worrisome, because it might actually lead to stupid projects if it becomes a political focus. So far it hasn’t though, and blockchain never did, so who knows. The thing about ML is that all the BI is worse than what we are already doing. Because it’s hyped we’ve naturally done proofs of concepts, with universities and with big tech, and no one has been capable of providing ML based analytics or BI that is even remotely close to what we already do. Because the simple truth is that we have been working with data for four decades. We have full time analysts who do nothing else, and they are simply lightyears ahead of anything ML we’ve seen, and, they can actually explain their results to our politicians and decision makers. Where ML does work, and the fact that it does separates it from blockchain, is for recognition. We have a lot of data, often in poor quality, and ML can troll through it faster and with higher quality than our human workers. We had to go through every casefile in a specific area, and identify which ones were missing a specific form. A casefile can be 500 A4 pages long, sometimes scanned in really terrible quality, and we had 500.000 of them. It took 12 people 6 months to do so, simultaneously we did a ML poc. It took 3 months to train the algorithm, but only one employee, and once it was done being trained, it took around five hours with a lot of Azure iron to troll through the data. ML had better results than our human effort and it was obviously way cheaper. So ML and AI may be a lot of hype, but it’s also more than that.
- djsumdog 8y ago
  I think what the post was going for is that things are often mislabeled as AI when really they're much simpler algorithms.
- novalis78 8y ago
  Just came back from the IOHKSummit in Miami with Stephen Wolfram and Caitlin Long as guest speakers. An incredible array of scientists (cryptography, theoretical computer science, mathematics, functional programming languages) presented their research and results of peer reviewed papers over the past few years. It was definitely the most substantial blockchain conference I have had a chance to attend in years. Fascinating talks on the ability to use standardized financial contracts (ARCTUS) via functional languages and gathered as library for developers in smart contracts blew my mind. The supply chain pro/con by Wyoming’s ranchers and their beef tracking USDA approved new standard showed the other end of the spectrum in terms of real world applications that already have an impact on industry. While there is a lot of hype and mania any time money is involved there is also an incredible universe of academic and tangible private progress being made using these distributed immutable / trustless (to higher degrees at least) ledgers.
jatsign 8y ago
Got a recruiting email about a company working in Augmented Reality, remote. Normally I'd ignore recruiting emails, but the idea of sitting at home wearing a hololens sounded too cool to not at least LISTEN to the pitch. Yah, they're not doing AR. The guy explained it was a marketing gimmick for VCs (though he made it sound nicer than that).
currymj 8y ago
this article is of course reasonable, but what’s depressing is we genuinely have made really incredible strides in machine learning. like enough that it shouldn’t be possible to overhype it. of course people manage though.
legitster 8y ago
Do we all remember "algorithms"? Like, when every company had an "algorithm" as part of the pitch? Some of them even had the ability to self learn and adjust to new data. At the end of the day, AIs and MLs are just new labels for slightly more advanced "algorithms". I think everyone would be a bit better off if we tempered our expectations around the technology to "better algorithms".
- ska 8y ago
  Do we all remember when it was called “AI” in the 70’s? To some degree this stuff is just cyclical.
bsenftner 8y ago
Current AI is a marketing term for trained algorithms performing analysis using machine derived statistical models (that's the learning part). The marketing term was created to help companies raise funding, and then taken over by journalists who are tired of eating dirt and write about any possible fear for the clicks revenue. That's where this really took off, in the public media where any idiot can write anything and somewhere an entire sub-culture believes them.
carlmcqueen 8y ago
A lot of the discussions of how AI is coming for your jobs follows the same reasoning of how automation is/was, even before it was called machine learning. The data science team at a major bank I used to work for historically had a team that sourced and cleaned the data, then a team who would explore and build the models and then a team who would learn and run the models going forward. In my opinion the final team was the most important and the most tragic for being condensed, they noticed when the models needed to return to the model builders to be adjusted when missing the mark. The "lost jobs" in this case is that when I was on the team we had to learn the entire bank database structure and source our own data, build the models and then automate them in such ways to "catch" when they were missing the mark. The team will be further shrunk as the software tools provided have better auto-sql for sourcing the data, automated model building functions, and automated visualizations thus removing even that as a special skills.
- moneil971 8y ago
  Like automation, the ideal is that workers who are no longer doing the jobs “AI” (aka automated systems) can now do move on to doing higher level or more interesting work. But I agree that there’s often far too much trust that a system can now run itself with little to no checks or humans to tune the system.
bunderbunder 8y ago
> No one has any idea what “artificial intelligence” even means. I'll tell you exactly what it means: It means that the software's own vendor is so uncertain of the actual value of what the product does that they've decided to promote how it does what it does - in other words, implementation details - instead.
- commandlinefan 8y ago
  > promote how it does And nobody who works there actually knows that, either.
- linuxftw 8y ago
  Succinctly put. Sadly, there's a whole new crop of students being sold this hype. Eventually the money is going to dry up for this line of work IMO, and their going to find themselves behind the curve.
cromwellian 8y ago
The claims in this article that corporations don’t do pure research and every project is targeted at revenue generation is false. I’ve been in R&D at both Google and IBM TJ Watson on projects that were never even tangentially revenue generating. A company with 80,000 employees can easily afford to let hundreds or even thousands of employees operate on non-product focused research. In fact on orientation day at IBM we were explicitly told that nothing we do will ever be a product, that there’s a firewall between TJ Watson and the rest of IBM and that any products will be rewritten by product teams. It seems like the author is speculating how things work Not from experience.
- deleted 8y ago
  [deleted]
sonnyblarney 8y ago
"Global, monopolistic platforms like Google, Facebook and Amazon do not pursue “AI” as a science project. Nor do hospitals, insurance companies, banks, airlines or governments. They do so for specific, strategic purposes, which in corporate settings are aimed at generating new revenue. " Well, many companies will throw some money willy nilly at AI 'because' HBR said to, or consultants are pushing it, or they 'know it's coming' and need to start somewhere. It might be rational to do some experimenting, but many companies don't have a clue really, they're throwing money at it. FB and G obviously have a clue, but even they have so much money and talent they can afford to experiment whereupon the ROI might be way, way off.
- kthejoker2 8y ago
  I sell AI and ML service-based solutions to large enterprises and if anything they're highly skeptical of AI and ML adding value and are always comparing its ROI to the alternative of basic data management, mastering, out-of-the-box analytics tools. I'd say they're a lot savvier than anyone in this thread is giving them credit for.
leesec 8y ago
I don't know what this author is talking about and I'm not sure he does either. I think his premise is that AI is bad and vague. And its bad because it's bias. But he just picks a few examples with no counter argument and spitballs from there. AI in my opinion is technology that outperforms humans, often done with learning algorithms instead of explicit rule based algorithms. Except the AI I have been following is wildly successful in a variety of topics and probably is coming for you. The author is right it could be biased but people are working on these problems and the author is acting as if no progress will ever be made in this relatively young field.
biophysboy 8y ago
I don't know if I agree with the author's argument that AI is bad only because people are bad. I'm aware that garbage in yields garbage out. But I also find myself squirming at the idea of accomplishing company goals by identifying nonlinear patterns in data with networks. Especially if those goals are modifying user behavior, a la watchtime. Am I being a paranoid biophysicist? This isn't my field. If I use AI, am I not, by definition, ignoring responsibility? At least with a regular computer program, I understand every step of the algorithm, and can judge if its doing the right thing. But with neural networks I'm just feeding data into a black box and hoping the ends justify the means. I don't actually know what patterns it is finding and utilizing.
ackbar03 8y ago
I think the term ai has been completely abused. In fact I try to avoid using the word ai if I can help it and use deep learning instead because that's really the whole crux of this new tech. I think most people, definitely author of the piece, miss the whole core impact of ai. Ai is basically a new way to process and calculate data, a new type of algorithm. We can now identify objects in pictures using computer vision with deep learning. This was NOT POSSIBLE with the tech before. A computer can now beat a team of human ai players in dota. This was definitely NOT POSSIBLE before. At the end of the day this is the impact and its not a small impact. It opens a lot of possibilities to what can now be done. To me it's far more impactful blockchain will ever be. To some degree I'm actually quite happy with all the misleading hype because the people who understand and are capable of using the technology are quietly making real differences behind all the smoke and cloud
- ska 8y ago
  While there have been some good incremental improvements, I think you are overstating the place of deep learning vis a vis everything in machine learning that came before it. The same thing happened to SVMs in the early 2000s, but with much less industry noise. NB I’m not trying to compare the impact of the two, just noting it was also over stated. After all, deep learning is fundamentally a continuation of much older techniques. And we absolutely could identify objects in pictures, we’ve been doing that for 40 years now - deep learning techniques have given a very nice jump in accuracy for some tasks but they didn’t come out of nowhere. I agree the whole labelling “AI” is problematic. That is also a many decades old problem though...
  - kthejoker2 8y ago
    AI developments are more like battery storage improvements. Just 10% improvements year over year, in hardware, in architecture, in algorithms, in data capturing and labeling ... they've just really started to add up. But 10% growth YOY compounds very quickly.
    - ska 8y ago
      10 percent would compound quickly if we were seeing that - I suspect most of the recent acceleration has far more to do with data availability than any technical improvements though.
olivermarks 8y ago
'“AI” is not something anyone needs to be worried about. A world mediated by unaccountable corporate software platforms is.' Perfect
musicale 8y ago
"No one has any idea what “artificial intelligence” even means." Usually AI refers to solving real-world problems that humans (or smart animals) are good at that aren't easily solved using traditional algorithms such as sorting/searching, graph algorithms, numerical methods, combinatorial optimization, etc.. Sometimes it also refers to a computer opponent in a game, or the algorithms and strategies used by such an opponent. There is something to the statement though in that AI seems to be a bit of a moving target: once we have a good algorithm (and input data) to solve a problem (e.g. winning at checkers) conclusively, then it may no longer qualify as "AI" - even if it did for years up to that point!
RosanaAnaDana 8y ago
I want to strongly disagree with the conclusion based on 1-off personal anectdota. Last year my working group within our company secured a major contract; largest for our company for the year. We expected we would have to hire 20+ entry level positions to execute on the contract. While the process was fundamentally based on ML, we knew there would be a large quantity of human labor as well to execute on time. My colleagues and I, instead of going on a hiring spree, asked the overlords for 3 months of overhead time to develop 'intelligent automation' to reduce the number of new / temporary hires. We were able to make substantial enough gains to not have to bring on any new hires and complete the work, ahead of schedule and way under budget with our existing crew. It was adjusting our thinking about how we do our work and incorporating ML at multiple levels in our system to intelligently guide our process that allowed us to eliminate 60% of the new positions that previously would have been generated by this work. 100% ML is coming for you.
- linuxftw 8y ago
  Must not have been 20 entry level tech workers, just 20 unskilled workers. 20 entry level tech workers would be worth 2 senior workers, no management team would ever allow this.
natch 8y ago
>But we don’t need to “regulate AI.” As we’ve seen, “artificial intelligence” is mostly a constructed catch-all term for lots of different types of technology The author forgets here that AI is not static. It’s a moving target. The examples of AI that exist today may be easy to brush off, but let’s not assume that extends to examples of AI in the future. In the meantime it is super annoying that simple techniques get marketed as “AI”.
douglaswlance 8y ago
So long as any form of automation has existed, people have been claiming that the sky is falling and that automation will put people out of work. And in some respects it does, but in the grand scheme of things, quality of life improves and people find work. In the most simple terms it works like this: 1. Workers create Automated systems. 2. Automated systems create wealth. 3. Wealth creates workers. And the cycle repeats, ad infinitum. So long as there are people with money, there will be jobs to be done. Perhaps in the future, AI employers will hire human workers that are trained by AI educators to do human tasks. There may come a time where AI/Robotics are better than humans at every task, and when that day comes, humans will compete on price, and when they're priced out of the market, they'll either merge with the machines or simply be left behind. But that time is many hundreds of years away, and I'm not convinced that machines would even want to stay on Earth. Space is much more conducive to a machine society.
- noego 8y ago
  Well said. Job creation is not the hard part. Eliminating jobs is. Every time a job becomes automated, the workers eventually find other industries/professions to work in, boost aggregate productivity in those other sectors, and thus end up creating more wealth for society as a whole. https://outlookzen.com/2019/01/05/prosperity-comes-from-eliminating-jobs-not-saving-them/ https://outlookzen.com/2019/01/05/prosperity-comes-from-elim...
  - canjobear 8y ago
    “Eventually” is the key word. Lives can be ruined during that eventually, and some don’t make it.
    - douglaswlance 8y ago
      The same AI that puts people out of work, will be used to make complex tasks simple enough for unskilled workers, as well as training those unskilled workers to be more skilled.
- chillacy 8y ago
hprotagonist 8y ago
>Translation software was once considered by serious people to be “AI” – until it became easy. Excuse me? Translation is possibly impossible for interesting work, but certainly nothing we're doing now is even trying[0]. What we're doing right now is the equivalent of "translating" road signs and dinner menus. [0] https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s...
iandanforth 8y ago
The author is uninformed and wrong. The hype wave propagates faster than the utility wave, but if you believe there was no lightning just because you haven't heard the thunder yet you will be sorely mistaken. Function approximation for classification tasks is slowly but surely "eating the world." Planning and sequential decision making based on RL are being hybridized with classical control methods for demonstrated utility. Reasoning, rapid learning, and useful adaptation are being attacked in hundreds of research papers a month. Remember that technologies don't arrive they go through a phase transition of "That's not real X" to "That's obvious and boring." with almost nothing in the middle. Whether you want to call all the above "AI" is irrelevant. Tasks which were recently assumed to require humans have been shown to be tractable with other methods.
jammygit 8y ago
Companies try to replace people with machines every time they think its even half possible. A recent example with Suncor: "As Suncor Energy prepares to shed 400 jobs to prepare for the implementation of driverless ore-hauling trucks, the union representing workers at the company is publicly condemning the decision." https://globalnews.ca/news/4000125/suncor-union-outcry-automation-oilsands-jobs/ https://globalnews.ca/news/4000125/suncor-union-outcry-autom...
mindgam3 8y ago
Good piece. In some ways the hype is even worse than OP suggests. It’s not even that the current trend of “machine learning” (ie data processing) is at best a tiny subset of what anyone would consider to be actual intelligence (ability to act rationally in a wide range of situations, including novel ones, ie common sense). It’s that the very idea of “artificial intelligence” is impossible. Terry Winograd, one of the earliest AI researchers in the 60s/70s wrote a great book about this called Computers and Cognition in which he lays out a coherent theory, grounded in philosophy and biology, for why the pursuit of machines that think like humans is doomed to fail. The crux of the argument is that the essence of intelligence/common sense has to do with something other than symbolic representation, ie the kind of thinking one does while playing chess or using language. It has to do with “being-in-the-world”, which sounds weird but is a really useful concept from Heidegger. Winograd gives the example of a man using a hammer to illustrate this. A man using a hammer has no mental model of a hammer while he is pounding in a nail. The hammer simply becomes an extension of his arm. The only time a representation or mental construct of “hammer” becomes relevant is if there is a breakdown, like if the hammer slips. But otherwise, the intelligent act of hammering occurs without any “hammer” objects in the mind of the hammerer. I’m vastly oversimplifying here, but the book (first published 1985) is quite well argued and frankly persuasive. Highly recommended to all those who seek to separate AI fact from fiction in these buzzy times.

120 points by ntang 8y ago

It mentions the blockchain hype mania as well. I happen to currently be the clearinghouse for lots of people's silly blockchain ideas right now. I mostly have no issue with the ones that are basic "chain of hashed blocks" git style ideas, intended to show some simple sequence of integrity, while still requiring trust. I don't get many of those. But there are so many that try to shoehorn the full trustless model onto an idea that obviously still has a central authority. Argh. Is there a good resource I can hand to non-technical people that explains why "full blockchain" only makes sense for a handful of use cases?

ghaff 8y ago
Bruce Schneier has a talk which is probably online and I think has written a blog on how there’s always some of trust in the system needed. Kevin Werbach has written a book on blockchain and probably has shorter pieces.
yoshuaw 8y ago
Bitcoin for laypeople is pretty good. https://gist.github.com/mafintosh/bd9e6d350ebf02441c9707c5f799d05b https://gist.github.com/mafintosh/bd9e6d350ebf02441c9707c5f7...
rorykoehler 8y ago
From where I am standing it seems as if any resource to explain anything to non-technical people will fall on deaf ears. The reason for this is money. People aren't looking for the best technical solution for their problem. They don't even have a problem. They are looking for the best way to get their hands on money quickly. If people will hand over cash they will do it. This goes for AI too. Many investors are stupid (naively optimistic if I'm being kind) and people are out to get their money.
- tyingq 8y ago
  Ahh, yes, the motivation is somewhat similar here. It's internal projects at a big company. The people proposing wouldn't get money per se, but they want the reputational thing of doing something cool and newsworthy. I'm the first person they have to get past with their (usually) bad idea.
- gk1 8y ago
  Repeating my other comment in this thread: > This is a tired trope, that “business people” are brainless and gullible. Almost as tired as the “VCs are so dumb they’ll throw money at anything-AI” idea repeated in the article. > Maybe, just maybe, the people running multi-billion-dollar companies, and multi-billion-dollar investment funds, are not stupid? > It would be much more interesting and productive to discuss what they see in AI and why they feel so much urgency, rather than dismissing them as fools falling for magic. VCs only need one big winner out of every ten investments. You are looking at their nine failures and calling them stupid and naive.
  - barbecue_sauce 8y ago
    Experienced VCs are not the only business people who give out money.
    - gk1 8y ago
      My problem is with the generalization. The article and some comments in this thread paint all VCs and business people as dummies falling for shiny objects. I don’t think anybody would argue with an article that said some percentage of any population makes stupid decisions for stupid reasons. But then again, nobody would read that article, either.
  - tyingq 8y ago
    I agree that saying all VC's are dumb, all the time, is silly. On the other hand, things like Theranos and Ubeam do prove that some investments that some VC's make are spectacularly dumb. One out of every ten still requires basic due diligence, or at least questioning business plans that require setting physics or other basic knowns aside.
codeulike 8y ago
http://doyouneedablockchain.com/ http://doyouneedablockchain.com/
techbubble 8y ago
My Open Source project focuses on educating mainstream users about blockchain and cryptocurrency with simple videos and content https://www.trycrypto.com https://www.trycrypto.com
boronine 8y ago
You may want to pick one of the following flow charts: https://medium.com/@sbmeunier/when-do-you-need-blockchain-decision-models-a5c40e7c9ba1 https://medium.com/@sbmeunier/when-do-you-need-blockchain-de...
- tyingq 8y ago
  The "Birch-Brown-Parulava" and Lewis models look very promising. I may try to combine them. Thanks!

w8rbt 8y ago

So is Machine Learning. It can be dangerous too: https://www.usenix.org/conference/usenixsecurity18/presentation/mickens https://www.usenix.org/conference/usenixsecurity18/presentat... James Mickens is awesome BTW.

vnorilo 8y ago

I liked the article. It mentions the fallacy of Objectivity by Indirection: "I didn't say that - the system (I designed) said it!"

maxxxxx 8y ago
I think that’s the future. AI will give people something to hide behind. Imagine a US health insurance that declines you because of “AI”. It’s already bad enough the way it is but this will bring it to a whole new different level.
djsumdog 8y ago
I mean, but we've been doing that with mathematical models for years. I mean, just look at the controversy over books like The Bell Curve or Freakconomics.

charlysl 8y ago

Maybe it's just me, but it seems to me that the author doesn't have a clue of what he is writing about. For instance, when he mentions the Amazon hiring AI embarrassment, it seem to me like he mixes the very different concepts of bias in machine learning with the everyday concept of bias as prejudice. If the sample data is representative of the population, then it is unbiased in machine learning terms, even if the population as a whole is prejudiced, or biased, which will be duly reflected in the learned model, which shouldn't surprise anyone with at least a rudimentary knowledge of machine learning.

piokoch 8y ago
I have the same impression. If AI system figured out that the risk of the car being stolen when someone lives in a certain place is bigger and adjusted fee to be higher, then it means AI worked correctly. One might claim (I presume author does and this is perfectly ok) that this is a bad thing that there are "bad neighborhoods" and we should fight it, but what does it have to do with proper calculation of insurance fee? Should we expect that AI will apply some "social justice" concepts while calculating insurance price. I am not sure what author proposes.
- charlysl 8y ago
  Or maybe I am wrong and the author is right. After all, he claims that the problem is not AI but the humans, and maybe we shouldn't blame systems trained on human data, which reflects all human's prejudices. Then again, if AI's aim is to perform activities until now restricted to humans, just like humans, how could it not be prejudiced? And who decides how AI should make decisions that are politically loaded?
- sgt101 8y ago
  Remember that the prediction is from the rear view window - on data that has been gathered in the past, and that now you are fixing risk in the future.
- skybrian 8y ago
  I'm not impressed with the article, but the idea here is that some forms of bias are okay, others are not, and it's up to the people working on the system to build it properly. There are laws about these things and you can't expect some algorithm to know the law automatically and avoid prohibited discrimination without going through the effort to build it in. It's kind of odd to think this would happen automatically. There is some kind of wishful thinking going on that technology can't have bad effects if you don't have bad intentions.

jerkstate 8y ago

> And it turns out that racially discriminatory lending and coverage can be quite profitable – particularly when enabled with technological precision. this is a troubling statement, is this just the author speaking or is there data to back it up?

currymj 8y ago
there’s decades of history about the practice of redlining in the US (good term to Google), as well as more recent court cases related to subprime lending.
- securingsincity 8y ago
  More recently facebook was charged by HUD for allowing people to buy ads and filter out by race, sex, another protected statuses under the fair housing act https://cdn.theatlantic.com/assets/media/files/hud_v_facebook.pdf https://cdn.theatlantic.com/assets/media/files/hud_v_faceboo...
overcode 8y ago
Not sure what data you expect to see. People generally don't publish their discriminatory lending and insurance policies. Or do you think that the statement that it's profitable might be incorrect?

carnagii 8y ago

I see a lot of similarities between "AI", as understood by business people, and the pursuit of alchemy and the philosophers stone. It seems like a hustle to separate rich dupes from their money by promising them the keys to infinite wealth, immortality, mars colonys, etc. In that respect it is mostly harmless but it can be quite dangerous to the people who take it seriously.

gk1 8y ago
This is a tired trope, that “business people” are brainless and gullible. Almost as tired as the “VCs are so dumb they’ll throw money at anything-AI” idea repeated in the article. Maybe, just maybe, the people running multi-billion-dollar companies, and multi-billion-dollar investment funds, are not stupid? It would be much more interesting and productive to discuss what they see in AI and why they feel so much urgency, rather than dismissing them as fools falling for magic.
- deleted 8y ago
  [deleted]
- alkonaut 8y ago
  I can only speak for managers I have met: what they “see” is a promise of cost cutting and an opportunity to tell higher managers that they are ahead of the hype. But never, ever, have I seen one of these people say anything that suggests they have even a vague idea of what AI or ML is or what it can realistically achieve. And I’m not sure they are interested. Because as you say - they aren’t stupid - I just think they are part of a game of BS I don’t understand, involving higher managers, investors etc. I don’t think it’s so much about about producing anything using AI, it’s AI for the sake of saying you are using it. So perhaps not all fools but somewhere between con artist, willfully ignorant and fool. (Note: this is all from “traditional” industry, I.e the manager at the hammer factory proudly launching initiatives to “use more AI” in the factory. Not tech industry. Not plausible or concrete use of AI)
  - gk1 8y ago
    You are repeating the trope, just substituting “stupid” with other deriding terms: BS, con artist, willfully ignorant, fool... Does it really have to be those things just because you don’t understand it? Is it possible those people running multi-billion-dollar organizations just know something you don’t, or have a perspective that you don’t?

jaabe 8y ago

I think ML has potential that Blockchain doesn’t, but not in all the areas that are being hyped. I work in the public sector of Denmark, and we’re targeted by a lot of the hype. Which is worrisome, because it might actually lead to stupid projects if it becomes a political focus. So far it hasn’t though, and blockchain never did, so who knows. The thing about ML is that all the BI is worse than what we are already doing. Because it’s hyped we’ve naturally done proofs of concepts, with universities and with big tech, and no one has been capable of providing ML based analytics or BI that is even remotely close to what we already do. Because the simple truth is that we have been working with data for four decades. We have full time analysts who do nothing else, and they are simply lightyears ahead of anything ML we’ve seen, and, they can actually explain their results to our politicians and decision makers. Where ML does work, and the fact that it does separates it from blockchain, is for recognition. We have a lot of data, often in poor quality, and ML can troll through it faster and with higher quality than our human workers. We had to go through every casefile in a specific area, and identify which ones were missing a specific form. A casefile can be 500 A4 pages long, sometimes scanned in really terrible quality, and we had 500.000 of them. It took 12 people 6 months to do so, simultaneously we did a ML poc. It took 3 months to train the algorithm, but only one employee, and once it was done being trained, it took around five hours with a lot of Azure iron to troll through the data. ML had better results than our human effort and it was obviously way cheaper. So ML and AI may be a lot of hype, but it’s also more than that.

djsumdog 8y ago
I think what the post was going for is that things are often mislabeled as AI when really they're much simpler algorithms.
novalis78 8y ago
Just came back from the IOHKSummit in Miami with Stephen Wolfram and Caitlin Long as guest speakers. An incredible array of scientists (cryptography, theoretical computer science, mathematics, functional programming languages) presented their research and results of peer reviewed papers over the past few years. It was definitely the most substantial blockchain conference I have had a chance to attend in years. Fascinating talks on the ability to use standardized financial contracts (ARCTUS) via functional languages and gathered as library for developers in smart contracts blew my mind. The supply chain pro/con by Wyoming’s ranchers and their beef tracking USDA approved new standard showed the other end of the spectrum in terms of real world applications that already have an impact on industry. While there is a lot of hype and mania any time money is involved there is also an incredible universe of academic and tangible private progress being made using these distributed immutable / trustless (to higher degrees at least) ledgers.

jatsign 8y ago

Got a recruiting email about a company working in Augmented Reality, remote. Normally I'd ignore recruiting emails, but the idea of sitting at home wearing a hololens sounded too cool to not at least LISTEN to the pitch. Yah, they're not doing AR. The guy explained it was a marketing gimmick for VCs (though he made it sound nicer than that).

currymj 8y ago

this article is of course reasonable, but what’s depressing is we genuinely have made really incredible strides in machine learning. like enough that it shouldn’t be possible to overhype it. of course people manage though.

legitster 8y ago

Do we all remember "algorithms"? Like, when every company had an "algorithm" as part of the pitch? Some of them even had the ability to self learn and adjust to new data. At the end of the day, AIs and MLs are just new labels for slightly more advanced "algorithms". I think everyone would be a bit better off if we tempered our expectations around the technology to "better algorithms".

ska 8y ago
Do we all remember when it was called “AI” in the 70’s? To some degree this stuff is just cyclical.

bsenftner 8y ago

Current AI is a marketing term for trained algorithms performing analysis using machine derived statistical models (that's the learning part). The marketing term was created to help companies raise funding, and then taken over by journalists who are tired of eating dirt and write about any possible fear for the clicks revenue. That's where this really took off, in the public media where any idiot can write anything and somewhere an entire sub-culture believes them.

carlmcqueen 8y ago

A lot of the discussions of how AI is coming for your jobs follows the same reasoning of how automation is/was, even before it was called machine learning. The data science team at a major bank I used to work for historically had a team that sourced and cleaned the data, then a team who would explore and build the models and then a team who would learn and run the models going forward. In my opinion the final team was the most important and the most tragic for being condensed, they noticed when the models needed to return to the model builders to be adjusted when missing the mark. The "lost jobs" in this case is that when I was on the team we had to learn the entire bank database structure and source our own data, build the models and then automate them in such ways to "catch" when they were missing the mark. The team will be further shrunk as the software tools provided have better auto-sql for sourcing the data, automated model building functions, and automated visualizations thus removing even that as a special skills.

moneil971 8y ago
Like automation, the ideal is that workers who are no longer doing the jobs “AI” (aka automated systems) can now do move on to doing higher level or more interesting work. But I agree that there’s often far too much trust that a system can now run itself with little to no checks or humans to tune the system.

bunderbunder 8y ago

> No one has any idea what “artificial intelligence” even means. I'll tell you exactly what it means: It means that the software's own vendor is so uncertain of the actual value of what the product does that they've decided to promote how it does what it does - in other words, implementation details - instead.

commandlinefan 8y ago
> promote how it does And nobody who works there actually knows that, either.
linuxftw 8y ago
Succinctly put. Sadly, there's a whole new crop of students being sold this hype. Eventually the money is going to dry up for this line of work IMO, and their going to find themselves behind the curve.

cromwellian 8y ago

The claims in this article that corporations don’t do pure research and every project is targeted at revenue generation is false. I’ve been in R&D at both Google and IBM TJ Watson on projects that were never even tangentially revenue generating. A company with 80,000 employees can easily afford to let hundreds or even thousands of employees operate on non-product focused research. In fact on orientation day at IBM we were explicitly told that nothing we do will ever be a product, that there’s a firewall between TJ Watson and the rest of IBM and that any products will be rewritten by product teams. It seems like the author is speculating how things work Not from experience.

deleted 8y ago
[deleted]

sonnyblarney 8y ago

"Global, monopolistic platforms like Google, Facebook and Amazon do not pursue “AI” as a science project. Nor do hospitals, insurance companies, banks, airlines or governments. They do so for specific, strategic purposes, which in corporate settings are aimed at generating new revenue. " Well, many companies will throw some money willy nilly at AI 'because' HBR said to, or consultants are pushing it, or they 'know it's coming' and need to start somewhere. It might be rational to do some experimenting, but many companies don't have a clue really, they're throwing money at it. FB and G obviously have a clue, but even they have so much money and talent they can afford to experiment whereupon the ROI might be way, way off.

kthejoker2 8y ago
I sell AI and ML service-based solutions to large enterprises and if anything they're highly skeptical of AI and ML adding value and are always comparing its ROI to the alternative of basic data management, mastering, out-of-the-box analytics tools. I'd say they're a lot savvier than anyone in this thread is giving them credit for.

leesec 8y ago

I don't know what this author is talking about and I'm not sure he does either. I think his premise is that AI is bad and vague. And its bad because it's bias. But he just picks a few examples with no counter argument and spitballs from there. AI in my opinion is technology that outperforms humans, often done with learning algorithms instead of explicit rule based algorithms. Except the AI I have been following is wildly successful in a variety of topics and probably is coming for you. The author is right it could be biased but people are working on these problems and the author is acting as if no progress will ever be made in this relatively young field.

biophysboy 8y ago

I don't know if I agree with the author's argument that AI is bad only because people are bad. I'm aware that garbage in yields garbage out. But I also find myself squirming at the idea of accomplishing company goals by identifying nonlinear patterns in data with networks. Especially if those goals are modifying user behavior, a la watchtime. Am I being a paranoid biophysicist? This isn't my field. If I use AI, am I not, by definition, ignoring responsibility? At least with a regular computer program, I understand every step of the algorithm, and can judge if its doing the right thing. But with neural networks I'm just feeding data into a black box and hoping the ends justify the means. I don't actually know what patterns it is finding and utilizing.

ackbar03 8y ago

I think the term ai has been completely abused. In fact I try to avoid using the word ai if I can help it and use deep learning instead because that's really the whole crux of this new tech. I think most people, definitely author of the piece, miss the whole core impact of ai. Ai is basically a new way to process and calculate data, a new type of algorithm. We can now identify objects in pictures using computer vision with deep learning. This was NOT POSSIBLE with the tech before. A computer can now beat a team of human ai players in dota. This was definitely NOT POSSIBLE before. At the end of the day this is the impact and its not a small impact. It opens a lot of possibilities to what can now be done. To me it's far more impactful blockchain will ever be. To some degree I'm actually quite happy with all the misleading hype because the people who understand and are capable of using the technology are quietly making real differences behind all the smoke and cloud

ska 8y ago
While there have been some good incremental improvements, I think you are overstating the place of deep learning vis a vis everything in machine learning that came before it. The same thing happened to SVMs in the early 2000s, but with much less industry noise. NB I’m not trying to compare the impact of the two, just noting it was also over stated. After all, deep learning is fundamentally a continuation of much older techniques. And we absolutely could identify objects in pictures, we’ve been doing that for 40 years now - deep learning techniques have given a very nice jump in accuracy for some tasks but they didn’t come out of nowhere. I agree the whole labelling “AI” is problematic. That is also a many decades old problem though...
- kthejoker2 8y ago
  AI developments are more like battery storage improvements. Just 10% improvements year over year, in hardware, in architecture, in algorithms, in data capturing and labeling ... they've just really started to add up. But 10% growth YOY compounds very quickly.
  - ska 8y ago
    10 percent would compound quickly if we were seeing that - I suspect most of the recent acceleration has far more to do with data availability than any technical improvements though.

olivermarks 8y ago

'“AI” is not something anyone needs to be worried about. A world mediated by unaccountable corporate software platforms is.' Perfect

musicale 8y ago

"No one has any idea what “artificial intelligence” even means." Usually AI refers to solving real-world problems that humans (or smart animals) are good at that aren't easily solved using traditional algorithms such as sorting/searching, graph algorithms, numerical methods, combinatorial optimization, etc.. Sometimes it also refers to a computer opponent in a game, or the algorithms and strategies used by such an opponent. There is something to the statement though in that AI seems to be a bit of a moving target: once we have a good algorithm (and input data) to solve a problem (e.g. winning at checkers) conclusively, then it may no longer qualify as "AI" - even if it did for years up to that point!

RosanaAnaDana 8y ago

I want to strongly disagree with the conclusion based on 1-off personal anectdota. Last year my working group within our company secured a major contract; largest for our company for the year. We expected we would have to hire 20+ entry level positions to execute on the contract. While the process was fundamentally based on ML, we knew there would be a large quantity of human labor as well to execute on time. My colleagues and I, instead of going on a hiring spree, asked the overlords for 3 months of overhead time to develop 'intelligent automation' to reduce the number of new / temporary hires. We were able to make substantial enough gains to not have to bring on any new hires and complete the work, ahead of schedule and way under budget with our existing crew. It was adjusting our thinking about how we do our work and incorporating ML at multiple levels in our system to intelligently guide our process that allowed us to eliminate 60% of the new positions that previously would have been generated by this work. 100% ML is coming for you.

linuxftw 8y ago
Must not have been 20 entry level tech workers, just 20 unskilled workers. 20 entry level tech workers would be worth 2 senior workers, no management team would ever allow this.

natch 8y ago

>But we don’t need to “regulate AI.” As we’ve seen, “artificial intelligence” is mostly a constructed catch-all term for lots of different types of technology The author forgets here that AI is not static. It’s a moving target. The examples of AI that exist today may be easy to brush off, but let’s not assume that extends to examples of AI in the future. In the meantime it is super annoying that simple techniques get marketed as “AI”.

douglaswlance 8y ago

So long as any form of automation has existed, people have been claiming that the sky is falling and that automation will put people out of work. And in some respects it does, but in the grand scheme of things, quality of life improves and people find work. In the most simple terms it works like this: 1. Workers create Automated systems. 2. Automated systems create wealth. 3. Wealth creates workers. And the cycle repeats, ad infinitum. So long as there are people with money, there will be jobs to be done. Perhaps in the future, AI employers will hire human workers that are trained by AI educators to do human tasks. There may come a time where AI/Robotics are better than humans at every task, and when that day comes, humans will compete on price, and when they're priced out of the market, they'll either merge with the machines or simply be left behind. But that time is many hundreds of years away, and I'm not convinced that machines would even want to stay on Earth. Space is much more conducive to a machine society.

noego 8y ago
Well said. Job creation is not the hard part. Eliminating jobs is. Every time a job becomes automated, the workers eventually find other industries/professions to work in, boost aggregate productivity in those other sectors, and thus end up creating more wealth for society as a whole. https://outlookzen.com/2019/01/05/prosperity-comes-from-eliminating-jobs-not-saving-them/ https://outlookzen.com/2019/01/05/prosperity-comes-from-elim...
- canjobear 8y ago
  “Eventually” is the key word. Lives can be ruined during that eventually, and some don’t make it.
  - douglaswlance 8y ago
    The same AI that puts people out of work, will be used to make complex tasks simple enough for unskilled workers, as well as training those unskilled workers to be more skilled.
chillacy 8y ago

hprotagonist 8y ago

>Translation software was once considered by serious people to be “AI” – until it became easy. Excuse me? Translation is possibly impossible for interesting work, but certainly nothing we're doing now is even trying[0]. What we're doing right now is the equivalent of "translating" road signs and dinner menus. [0] https://www.theatlantic.com/technology/archive/2018/01/the-shallowness-of-google-translate/551570/ https://www.theatlantic.com/technology/archive/2018/01/the-s...

iandanforth 8y ago

The author is uninformed and wrong. The hype wave propagates faster than the utility wave, but if you believe there was no lightning just because you haven't heard the thunder yet you will be sorely mistaken. Function approximation for classification tasks is slowly but surely "eating the world." Planning and sequential decision making based on RL are being hybridized with classical control methods for demonstrated utility. Reasoning, rapid learning, and useful adaptation are being attacked in hundreds of research papers a month. Remember that technologies don't arrive they go through a phase transition of "That's not real X" to "That's obvious and boring." with almost nothing in the middle. Whether you want to call all the above "AI" is irrelevant. Tasks which were recently assumed to require humans have been shown to be tractable with other methods.

jammygit 8y ago

Companies try to replace people with machines every time they think its even half possible. A recent example with Suncor: "As Suncor Energy prepares to shed 400 jobs to prepare for the implementation of driverless ore-hauling trucks, the union representing workers at the company is publicly condemning the decision." https://globalnews.ca/news/4000125/suncor-union-outcry-automation-oilsands-jobs/ https://globalnews.ca/news/4000125/suncor-union-outcry-autom...

mindgam3 8y ago

Good piece. In some ways the hype is even worse than OP suggests. It’s not even that the current trend of “machine learning” (ie data processing) is at best a tiny subset of what anyone would consider to be actual intelligence (ability to act rationally in a wide range of situations, including novel ones, ie common sense). It’s that the very idea of “artificial intelligence” is impossible. Terry Winograd, one of the earliest AI researchers in the 60s/70s wrote a great book about this called Computers and Cognition in which he lays out a coherent theory, grounded in philosophy and biology, for why the pursuit of machines that think like humans is doomed to fail. The crux of the argument is that the essence of intelligence/common sense has to do with something other than symbolic representation, ie the kind of thinking one does while playing chess or using language. It has to do with “being-in-the-world”, which sounds weird but is a really useful concept from Heidegger. Winograd gives the example of a man using a hammer to illustrate this. A man using a hammer has no mental model of a hammer while he is pounding in a nail. The hammer simply becomes an extension of his arm. The only time a representation or mental construct of “hammer” becomes relevant is if there is a breakdown, like if the hammer slips. But otherwise, the intelligent act of hammering occurs without any “hammer” objects in the mind of the hammerer. I’m vastly oversimplifying here, but the book (first published 1985) is quite well argued and frankly persuasive. Highly recommended to all those who seek to separate AI fact from fiction in these buzzy times.