7 ms·

What should we learn from past AI forecasts?

72 points by ghosh 10y ago

zxcvvcxz 10y ago
No! Be quiet and let me raise money via overfitting my datasets and making short demo vids! More seriously though, even if there's a lot of undeserved hype (I don't think so, I just think non-technical people are making up foolish expectations), all this work and investment into technical development of statistical machine learning algorithms and software is going to pay off. Much in the same way that overinvestment in internet infrastructure during the dot-com bubble paid off.
- fapjacks 10y ago
  Yeah, exactly. I am actually solving problems with ML these days.
- studentrob 10y ago
  > undeserved hype (I don't think so, I just think non-technical people are making up foolish expectations), That's what undeserved hype is and where it comes from.. > Much in the same way that overinvestment in internet infrastructure during the dot-com bubble paid off. Sure. The longer the hype can be tempered with articles like this, the more reasonable the investments, the bigger the pay off.
krylon 10y ago
One of my favorite comic artists put it more appropriately than I could - in the form of a flow chart: http://www.smbc-comics.com/index.php?id=4122 http://www.smbc-comics.com/index.php?id=4122 TL;DR - once strong AI arrives, we're as good as dead. ;-) Can't argue with a flow chart!
2sk21 10y ago
I remember reading the famous book by McCorduck and Feigenbaum about the Japanese fifth generation project in the early 1980s. That book had a strong influence on my career and I recall that I resolved to become an AI expert. As it happened, it turned out that the Japanese fifth generation project achieved pretty much nothing.
- mcguire 10y ago
  I think it was Bruce Porter in an AI class at UT Austin who described the fifth generation project as "we need to do machine translation and planning and...so therefore we will build a Prolog machine."
  - DonaldFisk 10y ago
    Some of the decisions made were considered strange even at the time: the choice of Prolog instead of Lisp seemed to be largely down to it not being American. The main hardware they wanted to develop was dataflow, for which Prolog isn't a particularly good match.
- studentrob 10y ago
  Well, some projects fail entirely, and their people go on to do other great things based on what they learned. Some people from Thinking Machines Corporation (founded 1983) [1] went on to build a cool parallelizable programming paradigm that even those without programming experience could utilize with relative ease. It's called Ab Initio (founded 1995) and is largely used as a Data Warehousing / ETL (Extract, Transform, Load) tool for getting data from databases, running a lot of the same operations on it, then sending it to another database. > Thinking Machines alumni ("thunkos") helped create several parallel computing software start-ups, including Ab Initio Software, independent to this day; and Applied Parallel Technologies, which was later renamed Torrent Systems and acquired by Ascential Software, which was in turn acquired by IBM. [1] Ab Initio has a GUI front end for code that looks like a data flow diagram. The "graphs" are converted to k-shell scripts that grab the data from various sources. I guess the k-shell implementation made it easy to parallelize. I always thought this was cool because this company had found a way to allow inexperienced programmers, and even some other STEM graduates with no programming experience, to write software that operated on data in parallel, which is generally considered to be a hard thing to do, even today in some cases, let alone in early 2000s. They're very secretive, so many people don't know them, but I believe they are very successful. In the late 1990s and early 2000s, they had clients like AOL and double click, which is basically where all the advertising revenue was going. All that processing and recalculating was handled by this software. I know because my first job out of college was to use this software on Fannie Mae's loans database. That system was SUPER complicated but working with Ab Initio on it made it a cake walk compared to using, say, Java, which a previous contractor had tried and failed to do. [1] https://en.wikipedia.org/wiki/Thinking_Machines_Corporation https://en.wikipedia.org/wiki/Thinking_Machines_Corporation [2] https://en.wikipedia.org/wiki/Ab_Initio_Software https://en.wikipedia.org/wiki/Ab_Initio_Software
Animats 10y ago
The difference this time is that 1) AI is profitable, and 2) everything is on a much larger scale. As I've mentioned before, I went through Stanford CS in 1985, just as it was becoming clear that Expert Systems were not going to yield Strong AI Real Soon Now. Back then, AI research was about 20 people at Stanford, and similar sized departments at MIT and CMU, with a few other small academic groups and some people at SRI. There were a few small AI startups including Teknowledge and Denning Robotics; few if any survived. Everybody was operating on a very small scale, and nobody was shipping a useful product. Most of this was funded by the US DoD. Now, machine learning, etc., is huge. There are hundreds of companies and hundreds of thousands of people in the field. Billions are being spent. Companies are shipping products and making money. This makes the field self-sustaining and keeps it moving forward. With all those people thinking, there's forward progress. Also, we now have enough compute power to get something done. Kurtzweil claims a human brain needs about 20 petaFLOPS. That's about one aisle in a data center today.
- ktRolster 10y ago
  just as it was becoming clear that Expert Systems were not going to yield Strong AI Real Soon Now It's fairly clear that deep learning isn't going to yield Strong AI Real Soon Now. If it's profitable, it will be sustainable anyway.
  - PeterisP 10y ago
    Yup, the major difference between deep learning and e.g. expert systems is not about the near-future enabling of General AI but that expert systems didn't really work for practical narrow, niche AI/ML tasks in a commercially usable quality but deep learning does so in many areas.
- YeGoblynQueenne 10y ago
  >> Also, we now have enough compute power to get something done. Kurtzweil claims a human brain needs about 20 petaFLOPS. That's about one aisle in a data center today. It's a bit late over here so what I want to say may not come out as I mean it, but, basically, who cares how much computing power we have? Most people are not trying to simulate the human brain anymore. And even if Kurzwail's maths were good (I doubt it - the whole idea of measuring anything about brains in bytes sounds just silly) it would just mean that all the computing power in the world is no good if you don't know how to connect the dots. And we don't. Lots of computing power is good for machine learning, obviously, but strong AI is never going to happen like that, by brute-forcing crude approximations from huge data sets while pushing our hardware to its limits. That's a dumb way to make progress. Basic computer science says if you want to go anywhere with a problem, you need to solve it in linear time at the very worse. Look at what we have instead: machine learning algorithms' complexities start at O(n²) and go up. We just don't have good algorithms, we don't have good solutions, we don't have good anything. We have "more powerful computers" today. So what. Today we solve the stuff that's possible to solve, the low-hanging fruit of problems. Tomorrow, the next problem we try to tackle is ten times bigger, a hundred times harder, we need machines a thousand times more powerful... and it takes us another fifty years to make progress. So, no, it doesn't matter how powerful our computers are. We can't cheat our way around the hard problems. Because they're just that: hard. We're just gonna have to be a lot smarter than what we are right now.
lawpoop 10y ago
"That we don't know what the hell we're talking about."
argonaut 10y ago
What I find interesting is that Hacker News manages to hold the same mentality that there's a startup bubble, this time it's not different, while at the same time holding the mentality that AI is going to take over, this time it's different (in aggregate, I'm not saying everyone holds this view, this seems to be the plurality view). Many of the same arguments used to argue how AI is different this time around are the exact same arguments you can use to justify that it's not a startup bubble this time. My guess is something about the fantastical sci-fi aspect of AI captures the deep-seated imagination and wishful-thinking of many engineers, whereas the rush of money, business-people, and focus on sales/growth aspect of startups draws out deep-seated disdain from many engineers.
- mrqwerty 10y ago
  Or perhaps this time it is different. I think most think human level AI is not just around the corner. But automating a lot of the mental processes that constitute work? Possibly not very far away at all. That's where the hype is coming from. And for me I think there is a very good chance the hype is justified...
- ddt_Osprey 10y ago
  I might take a guess that, when sensational amounts of money are flying to and fro, there's a degree of truth to the fact that humans trading imaginary numbers or paper tokens or what-have-you, is a centuries old game that consistently harkens back to simple gambling. Taking calculated risks, and swindling fools with asymetric information is just stupid human tricks in redux. Machines that get up do things for us with minimal hand-holding really would be something unprecedented, new and different. And truly disruptive to the economy, moreso than the boiler room poker games of investment and High Finance, which are disruptive, if only beneficial to a self-serving few. Whether the reality of such a thing mirrors our imaginations, if it ever happens, things might get really, really weird thereafter, and good luck at guessing how. We'll be confronted by deceptive situations, while circumstances change around us. And within that mix of confusion, add to that the already comedic/tragic stupid human tricks that continue to beguile so many.
- tim333 10y ago
  The reason HN probably thinks that with the startup bubble, this time it's not different, while with AI, this time it's different is because that's what the facts indicate and HN tends to be well informed. The bubble is same old and it's debateable whether it even is a proper bubble or just some frothy valuations. Such periods of overvaluation have happened many times and will come and go. The AI thing is quite different. If you accept the human brain is effectively a biological computer and that normal computers get more powerful every year then inevitably there's a point when they get more powerful (in terms of processing power and memory) than the biological ones. This is happening over the next twenty years roughly. No one said that is happening before. It's a one off inevitable event that will happen once and only once in human history, of a significance comparable to the evolution of multicellular life forms say. It should be interesting to see.
aburan28 10y ago
Our bar for achieving true Artificial Intelligence is set way to high in my opinion. This talk from CCC 2015 is very relevant https://www.youtube.com/watch?v=DATQKB7656E https://www.youtube.com/watch?v=DATQKB7656E
- studentrob 10y ago
  lol, yes it is. Can't believe you're being downvoted on HN. Just shows how hyped it is. People can't handle disagreement. This video looks good to me from the intro. I'm going to check it out. Thanks!

72 points by ghosh 10y ago

No! Be quiet and let me raise money via overfitting my datasets and making short demo vids! More seriously though, even if there's a lot of undeserved hype (I don't think so, I just think non-technical people are making up foolish expectations), all this work and investment into technical development of statistical machine learning algorithms and software is going to pay off. Much in the same way that overinvestment in internet infrastructure during the dot-com bubble paid off.

fapjacks 10y ago
Yeah, exactly. I am actually solving problems with ML these days.
studentrob 10y ago
> undeserved hype (I don't think so, I just think non-technical people are making up foolish expectations), That's what undeserved hype is and where it comes from.. > Much in the same way that overinvestment in internet infrastructure during the dot-com bubble paid off. Sure. The longer the hype can be tempered with articles like this, the more reasonable the investments, the bigger the pay off.

krylon 10y ago

One of my favorite comic artists put it more appropriately than I could - in the form of a flow chart: http://www.smbc-comics.com/index.php?id=4122 http://www.smbc-comics.com/index.php?id=4122 TL;DR - once strong AI arrives, we're as good as dead. ;-) Can't argue with a flow chart!

2sk21 10y ago

I remember reading the famous book by McCorduck and Feigenbaum about the Japanese fifth generation project in the early 1980s. That book had a strong influence on my career and I recall that I resolved to become an AI expert. As it happened, it turned out that the Japanese fifth generation project achieved pretty much nothing.

mcguire 10y ago
I think it was Bruce Porter in an AI class at UT Austin who described the fifth generation project as "we need to do machine translation and planning and...so therefore we will build a Prolog machine."
- DonaldFisk 10y ago
  Some of the decisions made were considered strange even at the time: the choice of Prolog instead of Lisp seemed to be largely down to it not being American. The main hardware they wanted to develop was dataflow, for which Prolog isn't a particularly good match.
studentrob 10y ago
Well, some projects fail entirely, and their people go on to do other great things based on what they learned. Some people from Thinking Machines Corporation (founded 1983) [1] went on to build a cool parallelizable programming paradigm that even those without programming experience could utilize with relative ease. It's called Ab Initio (founded 1995) and is largely used as a Data Warehousing / ETL (Extract, Transform, Load) tool for getting data from databases, running a lot of the same operations on it, then sending it to another database. > Thinking Machines alumni ("thunkos") helped create several parallel computing software start-ups, including Ab Initio Software, independent to this day; and Applied Parallel Technologies, which was later renamed Torrent Systems and acquired by Ascential Software, which was in turn acquired by IBM. [1] Ab Initio has a GUI front end for code that looks like a data flow diagram. The "graphs" are converted to k-shell scripts that grab the data from various sources. I guess the k-shell implementation made it easy to parallelize. I always thought this was cool because this company had found a way to allow inexperienced programmers, and even some other STEM graduates with no programming experience, to write software that operated on data in parallel, which is generally considered to be a hard thing to do, even today in some cases, let alone in early 2000s. They're very secretive, so many people don't know them, but I believe they are very successful. In the late 1990s and early 2000s, they had clients like AOL and double click, which is basically where all the advertising revenue was going. All that processing and recalculating was handled by this software. I know because my first job out of college was to use this software on Fannie Mae's loans database. That system was SUPER complicated but working with Ab Initio on it made it a cake walk compared to using, say, Java, which a previous contractor had tried and failed to do. [1] https://en.wikipedia.org/wiki/Thinking_Machines_Corporation https://en.wikipedia.org/wiki/Thinking_Machines_Corporation [2] https://en.wikipedia.org/wiki/Ab_Initio_Software https://en.wikipedia.org/wiki/Ab_Initio_Software

Animats 10y ago

The difference this time is that 1) AI is profitable, and 2) everything is on a much larger scale. As I've mentioned before, I went through Stanford CS in 1985, just as it was becoming clear that Expert Systems were not going to yield Strong AI Real Soon Now. Back then, AI research was about 20 people at Stanford, and similar sized departments at MIT and CMU, with a few other small academic groups and some people at SRI. There were a few small AI startups including Teknowledge and Denning Robotics; few if any survived. Everybody was operating on a very small scale, and nobody was shipping a useful product. Most of this was funded by the US DoD. Now, machine learning, etc., is huge. There are hundreds of companies and hundreds of thousands of people in the field. Billions are being spent. Companies are shipping products and making money. This makes the field self-sustaining and keeps it moving forward. With all those people thinking, there's forward progress. Also, we now have enough compute power to get something done. Kurtzweil claims a human brain needs about 20 petaFLOPS. That's about one aisle in a data center today.

ktRolster 10y ago
just as it was becoming clear that Expert Systems were not going to yield Strong AI Real Soon Now It's fairly clear that deep learning isn't going to yield Strong AI Real Soon Now. If it's profitable, it will be sustainable anyway.
- PeterisP 10y ago
  Yup, the major difference between deep learning and e.g. expert systems is not about the near-future enabling of General AI but that expert systems didn't really work for practical narrow, niche AI/ML tasks in a commercially usable quality but deep learning does so in many areas.
YeGoblynQueenne 10y ago
>> Also, we now have enough compute power to get something done. Kurtzweil claims a human brain needs about 20 petaFLOPS. That's about one aisle in a data center today. It's a bit late over here so what I want to say may not come out as I mean it, but, basically, who cares how much computing power we have? Most people are not trying to simulate the human brain anymore. And even if Kurzwail's maths were good (I doubt it - the whole idea of measuring anything about brains in bytes sounds just silly) it would just mean that all the computing power in the world is no good if you don't know how to connect the dots. And we don't. Lots of computing power is good for machine learning, obviously, but strong AI is never going to happen like that, by brute-forcing crude approximations from huge data sets while pushing our hardware to its limits. That's a dumb way to make progress. Basic computer science says if you want to go anywhere with a problem, you need to solve it in linear time at the very worse. Look at what we have instead: machine learning algorithms' complexities start at O(n²) and go up. We just don't have good algorithms, we don't have good solutions, we don't have good anything. We have "more powerful computers" today. So what. Today we solve the stuff that's possible to solve, the low-hanging fruit of problems. Tomorrow, the next problem we try to tackle is ten times bigger, a hundred times harder, we need machines a thousand times more powerful... and it takes us another fifty years to make progress. So, no, it doesn't matter how powerful our computers are. We can't cheat our way around the hard problems. Because they're just that: hard. We're just gonna have to be a lot smarter than what we are right now.

lawpoop 10y ago

"That we don't know what the hell we're talking about."

argonaut 10y ago

What I find interesting is that Hacker News manages to hold the same mentality that there's a startup bubble, this time it's not different, while at the same time holding the mentality that AI is going to take over, this time it's different (in aggregate, I'm not saying everyone holds this view, this seems to be the plurality view). Many of the same arguments used to argue how AI is different this time around are the exact same arguments you can use to justify that it's not a startup bubble this time. My guess is something about the fantastical sci-fi aspect of AI captures the deep-seated imagination and wishful-thinking of many engineers, whereas the rush of money, business-people, and focus on sales/growth aspect of startups draws out deep-seated disdain from many engineers.

mrqwerty 10y ago
Or perhaps this time it is different. I think most think human level AI is not just around the corner. But automating a lot of the mental processes that constitute work? Possibly not very far away at all. That's where the hype is coming from. And for me I think there is a very good chance the hype is justified...
ddt_Osprey 10y ago
I might take a guess that, when sensational amounts of money are flying to and fro, there's a degree of truth to the fact that humans trading imaginary numbers or paper tokens or what-have-you, is a centuries old game that consistently harkens back to simple gambling. Taking calculated risks, and swindling fools with asymetric information is just stupid human tricks in redux. Machines that get up do things for us with minimal hand-holding really would be something unprecedented, new and different. And truly disruptive to the economy, moreso than the boiler room poker games of investment and High Finance, which are disruptive, if only beneficial to a self-serving few. Whether the reality of such a thing mirrors our imaginations, if it ever happens, things might get really, really weird thereafter, and good luck at guessing how. We'll be confronted by deceptive situations, while circumstances change around us. And within that mix of confusion, add to that the already comedic/tragic stupid human tricks that continue to beguile so many.
tim333 10y ago
The reason HN probably thinks that with the startup bubble, this time it's not different, while with AI, this time it's different is because that's what the facts indicate and HN tends to be well informed. The bubble is same old and it's debateable whether it even is a proper bubble or just some frothy valuations. Such periods of overvaluation have happened many times and will come and go. The AI thing is quite different. If you accept the human brain is effectively a biological computer and that normal computers get more powerful every year then inevitably there's a point when they get more powerful (in terms of processing power and memory) than the biological ones. This is happening over the next twenty years roughly. No one said that is happening before. It's a one off inevitable event that will happen once and only once in human history, of a significance comparable to the evolution of multicellular life forms say. It should be interesting to see.

aburan28 10y ago

Our bar for achieving true Artificial Intelligence is set way to high in my opinion. This talk from CCC 2015 is very relevant https://www.youtube.com/watch?v=DATQKB7656E https://www.youtube.com/watch?v=DATQKB7656E

studentrob 10y ago
lol, yes it is. Can't believe you're being downvoted on HN. Just shows how hyped it is. People can't handle disagreement. This video looks good to me from the intro. I'm going to check it out. Thanks!