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> human-machine cooperation is probably where the big money will be. As it has been for as long as machines have existed, really. This reminds me of Douglas En

> human-machine cooperation is probably where the big money will be. As it has been for as long as machines have existed, really. This reminds me of Douglas Engelbart and his vision for computers. I'll cite the section of his wikipedia page that paraphrases an interview with him from 2002[0][1]. > [Douglas Engelbart] reasoned that because the complexity of the world's problems was increasing, and that any effort to improve the world would require the coordination of groups of people, the most effective way to solve problems was to augment human intelligence and develop ways of building collective intelligence. He believed that the computer, which was at the time thought of only as a tool for automation, would be an essential tool for future knowledge workers to solve such problems. He was right of course, and his work lead to "The Mother of All Demos"[1]. Machine learning is the next step in using computers as thought enhancement tools. What we still need to figure out is an appropriate interface that is not as "black-boxy" as "we trained a neural net, and now we can put X in and get Y out". EDIT: Now that I read that quoted section of wikipedia again, it's funny to note that computers were "only seen as tools of automation", and how modern fears of AI are also about automation. Automation of thinking. [0] https://en.wikipedia.org/wiki/Douglas_Engelbart https://en.wikipedia.org/wiki/Douglas_Engelbart [1] https://www.youtube.com/watch?v=VeSgaJt27PM https://www.youtube.com/watch?v=VeSgaJt27PM [2] https://www.youtube.com/watch?v=yJDv-zdhzMY https://www.youtube.com/watch?v=yJDv-zdhzMY

seanmcdirmid 9y ago
Ironically enough, Engelbart was often derided by his colleagues at the time who thought hard AI was just around the corner and so all of this intelligence augmentation stuff would be obsolete soon enough. Today we are closer than ever (always just 20 years away!), but still IA rather than AI is very much the way to go.
- ethbro 9y ago
  (if you check an old comment) Do you have any sources for this? I find "sentiment at the time" especially hard to find, historically speaking. And I'd be fascinated to read something about this.
leggomylibro 9y ago
It's funny that you bring that up - it does seem like the concept of 'extended cognition' is one of the biggest benefits that we've collectively realized from computers (and other relatively nonvolatile communication mediums like books.) This is a computer-oriented analogy, but most fields have their own tables and charts and maths that are tedious to keep on the tip of your mind. Still, for example, I don't need to remember the details of every API that I use; I can just remember that there is a 'do X' call available, and refer to the documentation when and if I need to actually use it. In the same vein, I can quickly get a feel for whether an idea is possible by stringing together a bunch of abstract mental models. "Can I do X?" becomes, "are there good tools available for doing A, B, C, and D?", and that information is only a quick search away. Actually using those tools involves an enormous amount of detail, but it's detail that I can ignore when putting an idea together. And in most cases, that 'detail' is a library or part that already abstracts a broad range of deeper complexities into something that I don't have to think about. The question becomes something like: how do we expose people to enough information that they are aware of how much they can learn if they need to, without drowning them in trivia that they will never be interested in?
- cinquemb 9y ago
  Your example also is related to my experience briefly working in P&G chemicals R & D lab; the ChemE's around me routinely used google to look up reaction kinetics of different compounds (as well as other similar queries) rather than rely on their memory of such. I was attending a local university at the time (mostly for calculus and mathematical modeling using mathematica, and french), but I'd say this experience is largely one that started my questioning the value attending university in general (I dropped out an ivy about two years later, for this reason among others). I suspect that the concept of 'extended cognition' as it is realized with the use of computers and how people use it day to day to get work done is in conflict with how we all are mostly taught via rote memorization, and then application of information; therefore it should naturally follow that those who are heavily invested/exposed in 'non extended' cognition services have relatively more to lose, as well as any currently realistic answer to this: >The question becomes something like: how do we expose people to enough information that they are aware of how much they can learn if they need to, without drowning them in trivia that they will never be interested in? will bring cognitive dissonance to those who need the answer most (those with heavy exposure to relatively 'non extended' cognition services).
aalleavitch 9y ago
This is a fantastic point, and I think a lot of AI development that goes in the direction of trying to replace human beings is essentially absurd. We already have humans, why would we want something that can do what humans can already do? Rather, we want something that extends the capabilities of humans into areas where they aren't proficient. For instance, why do we put all this effort into natural language processing when humans are already totally optimized for it? What we need is a solution to scaling, not a solution to NLP itself. EDIT: To expand; one way to do something like Siri would be to have a system that routed requests to human operators. The human operator would give the correct answer to the request, and then the system would use that as training data. If the system was reasonably confident it already knew the answer to the request from previous training data, it would answer right away, but if it was below a certain confidence it would route to a human. This seems like the smartest way to leverage machine learning in these kinds of scenarios, and I'd be surprised if someone hasn't already tried it or something similar in the past.

seanmcdirmid 9y ago

Ironically enough, Engelbart was often derided by his colleagues at the time who thought hard AI was just around the corner and so all of this intelligence augmentation stuff would be obsolete soon enough. Today we are closer than ever (always just 20 years away!), but still IA rather than AI is very much the way to go.

ethbro 9y ago
(if you check an old comment) Do you have any sources for this? I find "sentiment at the time" especially hard to find, historically speaking. And I'd be fascinated to read something about this.

leggomylibro 9y ago

It's funny that you bring that up - it does seem like the concept of 'extended cognition' is one of the biggest benefits that we've collectively realized from computers (and other relatively nonvolatile communication mediums like books.) This is a computer-oriented analogy, but most fields have their own tables and charts and maths that are tedious to keep on the tip of your mind. Still, for example, I don't need to remember the details of every API that I use; I can just remember that there is a 'do X' call available, and refer to the documentation when and if I need to actually use it. In the same vein, I can quickly get a feel for whether an idea is possible by stringing together a bunch of abstract mental models. "Can I do X?" becomes, "are there good tools available for doing A, B, C, and D?", and that information is only a quick search away. Actually using those tools involves an enormous amount of detail, but it's detail that I can ignore when putting an idea together. And in most cases, that 'detail' is a library or part that already abstracts a broad range of deeper complexities into something that I don't have to think about. The question becomes something like: how do we expose people to enough information that they are aware of how much they can learn if they need to, without drowning them in trivia that they will never be interested in?

cinquemb 9y ago
Your example also is related to my experience briefly working in P&G chemicals R & D lab; the ChemE's around me routinely used google to look up reaction kinetics of different compounds (as well as other similar queries) rather than rely on their memory of such. I was attending a local university at the time (mostly for calculus and mathematical modeling using mathematica, and french), but I'd say this experience is largely one that started my questioning the value attending university in general (I dropped out an ivy about two years later, for this reason among others). I suspect that the concept of 'extended cognition' as it is realized with the use of computers and how people use it day to day to get work done is in conflict with how we all are mostly taught via rote memorization, and then application of information; therefore it should naturally follow that those who are heavily invested/exposed in 'non extended' cognition services have relatively more to lose, as well as any currently realistic answer to this: >The question becomes something like: how do we expose people to enough information that they are aware of how much they can learn if they need to, without drowning them in trivia that they will never be interested in? will bring cognitive dissonance to those who need the answer most (those with heavy exposure to relatively 'non extended' cognition services).

aalleavitch 9y ago

This is a fantastic point, and I think a lot of AI development that goes in the direction of trying to replace human beings is essentially absurd. We already have humans, why would we want something that can do what humans can already do? Rather, we want something that extends the capabilities of humans into areas where they aren't proficient. For instance, why do we put all this effort into natural language processing when humans are already totally optimized for it? What we need is a solution to scaling, not a solution to NLP itself. EDIT: To expand; one way to do something like Siri would be to have a system that routed requests to human operators. The human operator would give the correct answer to the request, and then the system would use that as training data. If the system was reasonably confident it already knew the answer to the request from previous training data, it would answer right away, but if it was below a certain confidence it would route to a human. This seems like the smartest way to leverage machine learning in these kinds of scenarios, and I'd be surprised if someone hasn't already tried it or something similar in the past.