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Partly this is because we've given up trying to get computers to "understand" and have focused on making them useful with sophisticated software. That is, the w

Partly this is because we've given up trying to get computers to "understand" and have focused on making them useful with sophisticated software. That is, the work is no longer about artificial intelligence, but about trained, new-style, expert systems.

jsharf 6y ago
This is just redefining what counts as "real intelligence" to raise the bar. Once we have computers that can think like humans, there'll be people saying 'oh, it's not real "understanding", it's just generating speech that matches things it's heard in the past with some added style', not realizing that the same also applies to human writers. As long as the bar keeps rising and we've found business applications, there will be no AI winter.
- slowmovintarget 6y ago
  It isn't redefining anything. There's a classification in AI research called "strong AI" that includes Artificial General Intelligence and machine consciousness. Current uses of ML are instances of so-called "weak AI" which is focused on problem-solving and utility. No redefinition, and using proper distinctions takes nothing away from the accomplishments made in the field. We don't even have anything close to a scientific understanding of awareness or consciousness. Being able to create machines actually possessing awareness seems fairly far off. Being able to create machines that can simulate the qualities of a conscious being, doesn't seem so far off. I suspect when we get there the data will point to there being a qualitative difference between real consciousness and simulated. Commercial interests, and likely government bureaucracy will have a vested interest drowning out such ideas, though. The bar hasn't moved. We've just begun aiming at different targets. That we succeed in hitting the more modest ones only makes sense.
  - jrsala 6y ago
    This is a nitpick but regarding "strong AI" and "weak AI", I take issue with the use of those expressions to refer to actual software systems, or to sub-domains of AI research. Those expressions in fact refer to two hypotheses pertaining to the philosophy of AI and not to any concrete AI research field. Even within the weak AI hypothesis, a system that perfectly replicates the appearance of human consciousness is not conscious. See [0]. Therefore the strong vs. weak dichotomy is unrelated to progress towards emulating human intelligence and behavior. It is concerned with the question of whether a certain fundamental barrier can be broken, not unlike the speed of light. The meanings people ascribe to those terms nowadays are very diverse and distant from the original hypotheses. Sometimes language evolves usefully so that expression is easier and ideas can be conveyed more accurately, but I'm afraid when it comes to "strong" and "weak" AI, another, more damaging kind of semantic drift has taken place that has muddied and debased the original ideas. Those terms are victims of the hype surrounding AI. I suspect this is part of why the field has trouble being taken seriously. [0] https://ai.stackexchange.com/questions/74/what-is-the-difference-between-strong-ai-and-weak-ai https://ai.stackexchange.com/questions/74/what-is-the-differ...
- deelowe 6y ago
  AGI is not currently in scope for any solutions on the market. The current crop of AI systems are simply analytical engines.
  - zedshawmotherfu 6y ago
    AGI isn't something that is scoped. It must be painted.
quadcore 6y ago
I wonder if we could detect harassment today in a chat for example. With like GPT3 or something.
- qayxc 6y ago
  Highly unlikely. It's pretty much impossible to detect the difference between a friendly banter, a heated debate, trolling, and genuine harassment for most people given just a chat log. There's often much more information required (prior communication history of the involved parties, previous activities in other chats, etc.). Using automated systems like GPT-3 would simply lead to people switching to different languages (not in the literal sense, but using creative metaphors and inventing new slang). Pre-canned "AI" is unable to adapt and learn and I doubt that any form of AGI (if even possible without embodiment) that isn't capable of real-time online learning and adapting would be up to the task.
  - quadcore 6y ago
    Mmh I'm not convinced. There is pattern in aggressiveness. One of them being the harasser is talking about the victim or something that's strongly linked to the victim (like it's work, member of familly, etc).
- zedshawmotherfu 6y ago
  A double bind detector!
- ChefboyOG 6y ago
  We can, though the teams I know who are doing it are not using GPT-3. If you look at any platform that has live chat features (think Instagram Live or Twitch) and an ML team, then there is almost certainly someone working on toxicity filtering along with propaganda detection etc. It's a really really hard problem, particularly when you think about how chat platforms tend to develop their own short hand and slang.
bordercases 6y ago
You need to look at these techniques as being in continuity with how mathematical modeling has been generally performed by humans for a couple centuries now. To directly explain the mechanics of what should otherwise be simple or everyday phenomena (like with three-particle orbits or the flow of water in your bathtub) often requires many equations and parameters. Since physical systems tend to be not-so-heterogeneous in structure, we can use experimental insight and exploit symmetries to reduce the number of equations and parameters up front. But this amounts to finding ways to simplify because we don't understand the precise system in play, only an analogous one. For larger systems we have historically relied on equation solvers to do the reductions and find solutions. But there are systems for which the dimensionality cannot be easily reduced, like with language and vision. Then to be precisely predictive about these high-dimensional outcomes, we still need to compute beyond our ability to reduce the equations from the outset. This all converges on deep learning - since in the end, it's much of the same linear algebra used by solver packages, just recursively applied to enormous high-dimensional datasets. Maybe calling it intelligence gives more agency to the process than we can stand to attribute. But in many ways it's just an extension of our usual ways of mathematical modeling before computers, but moving into so many dimensions and datapoints that it produces outcomes similar to the behavior we use our entire brain to recreate instinctually (like image recognition and language use).
stingraycharles 6y ago
Isn’t artificial intelligence a very broad term anyway, of which expert systems, statistics, fuzzy logic and all the new wave neural network / deep learning are examples? You seem to be thinking about artificial general intelligence, which is much more difficult to achieve.
- pvarangot 6y ago
  AGI is even difficult to define, it's still up for debate if/what is part of what we call intelligence and if it's present on animals and if it can exist without a "subconscious" or "feelings".
  - zedshawmotherfu 6y ago
    The hard problem of consciousness, which everyone has a hard time defining?
  - skyde 6y ago
    I don't know if Something that have a survival instinct is AGI but to me this is enough to call Strong AI. Does the Caenorhabditis elegans worm have AGI ?
- slowmovintarget 6y ago
  Time was that the term AI meant what has now come to be referred to as AGI or machine consciousness. Marvin Minksy wasn't looking to make better facial recognition, or better factory robots. His research was about making a mind. Twenty years ago, that kind of work ran into a brick wall. But neural networks were still useful. Enter Machine Learning, and it borrowing the term "AI." But Artificial Intelligence, for many, always meant the dream of building R. Daneel Olivaw, or R2D2.
bopbeepboop 6y ago
I believe Shaowei Lin and some others are working on the problem of “machine understanding” by connecting type theories and traditional machine learning.

jsharf 6y ago

This is just redefining what counts as "real intelligence" to raise the bar. Once we have computers that can think like humans, there'll be people saying 'oh, it's not real "understanding", it's just generating speech that matches things it's heard in the past with some added style', not realizing that the same also applies to human writers. As long as the bar keeps rising and we've found business applications, there will be no AI winter.

slowmovintarget 6y ago
It isn't redefining anything. There's a classification in AI research called "strong AI" that includes Artificial General Intelligence and machine consciousness. Current uses of ML are instances of so-called "weak AI" which is focused on problem-solving and utility. No redefinition, and using proper distinctions takes nothing away from the accomplishments made in the field. We don't even have anything close to a scientific understanding of awareness or consciousness. Being able to create machines actually possessing awareness seems fairly far off. Being able to create machines that can simulate the qualities of a conscious being, doesn't seem so far off. I suspect when we get there the data will point to there being a qualitative difference between real consciousness and simulated. Commercial interests, and likely government bureaucracy will have a vested interest drowning out such ideas, though. The bar hasn't moved. We've just begun aiming at different targets. That we succeed in hitting the more modest ones only makes sense.
- jrsala 6y ago
  This is a nitpick but regarding "strong AI" and "weak AI", I take issue with the use of those expressions to refer to actual software systems, or to sub-domains of AI research. Those expressions in fact refer to two hypotheses pertaining to the philosophy of AI and not to any concrete AI research field. Even within the weak AI hypothesis, a system that perfectly replicates the appearance of human consciousness is not conscious. See [0]. Therefore the strong vs. weak dichotomy is unrelated to progress towards emulating human intelligence and behavior. It is concerned with the question of whether a certain fundamental barrier can be broken, not unlike the speed of light. The meanings people ascribe to those terms nowadays are very diverse and distant from the original hypotheses. Sometimes language evolves usefully so that expression is easier and ideas can be conveyed more accurately, but I'm afraid when it comes to "strong" and "weak" AI, another, more damaging kind of semantic drift has taken place that has muddied and debased the original ideas. Those terms are victims of the hype surrounding AI. I suspect this is part of why the field has trouble being taken seriously. [0] https://ai.stackexchange.com/questions/74/what-is-the-difference-between-strong-ai-and-weak-ai https://ai.stackexchange.com/questions/74/what-is-the-differ...
deelowe 6y ago
AGI is not currently in scope for any solutions on the market. The current crop of AI systems are simply analytical engines.
- zedshawmotherfu 6y ago
  AGI isn't something that is scoped. It must be painted.

quadcore 6y ago

I wonder if we could detect harassment today in a chat for example. With like GPT3 or something.

qayxc 6y ago
Highly unlikely. It's pretty much impossible to detect the difference between a friendly banter, a heated debate, trolling, and genuine harassment for most people given just a chat log. There's often much more information required (prior communication history of the involved parties, previous activities in other chats, etc.). Using automated systems like GPT-3 would simply lead to people switching to different languages (not in the literal sense, but using creative metaphors and inventing new slang). Pre-canned "AI" is unable to adapt and learn and I doubt that any form of AGI (if even possible without embodiment) that isn't capable of real-time online learning and adapting would be up to the task.
- quadcore 6y ago
  Mmh I'm not convinced. There is pattern in aggressiveness. One of them being the harasser is talking about the victim or something that's strongly linked to the victim (like it's work, member of familly, etc).
zedshawmotherfu 6y ago
A double bind detector!
ChefboyOG 6y ago
We can, though the teams I know who are doing it are not using GPT-3. If you look at any platform that has live chat features (think Instagram Live or Twitch) and an ML team, then there is almost certainly someone working on toxicity filtering along with propaganda detection etc. It's a really really hard problem, particularly when you think about how chat platforms tend to develop their own short hand and slang.

bordercases 6y ago

You need to look at these techniques as being in continuity with how mathematical modeling has been generally performed by humans for a couple centuries now. To directly explain the mechanics of what should otherwise be simple or everyday phenomena (like with three-particle orbits or the flow of water in your bathtub) often requires many equations and parameters. Since physical systems tend to be not-so-heterogeneous in structure, we can use experimental insight and exploit symmetries to reduce the number of equations and parameters up front. But this amounts to finding ways to simplify because we don't understand the precise system in play, only an analogous one. For larger systems we have historically relied on equation solvers to do the reductions and find solutions. But there are systems for which the dimensionality cannot be easily reduced, like with language and vision. Then to be precisely predictive about these high-dimensional outcomes, we still need to compute beyond our ability to reduce the equations from the outset. This all converges on deep learning - since in the end, it's much of the same linear algebra used by solver packages, just recursively applied to enormous high-dimensional datasets. Maybe calling it intelligence gives more agency to the process than we can stand to attribute. But in many ways it's just an extension of our usual ways of mathematical modeling before computers, but moving into so many dimensions and datapoints that it produces outcomes similar to the behavior we use our entire brain to recreate instinctually (like image recognition and language use).

stingraycharles 6y ago

Isn’t artificial intelligence a very broad term anyway, of which expert systems, statistics, fuzzy logic and all the new wave neural network / deep learning are examples? You seem to be thinking about artificial general intelligence, which is much more difficult to achieve.

pvarangot 6y ago
AGI is even difficult to define, it's still up for debate if/what is part of what we call intelligence and if it's present on animals and if it can exist without a "subconscious" or "feelings".
- zedshawmotherfu 6y ago
  The hard problem of consciousness, which everyone has a hard time defining?
- skyde 6y ago
  I don't know if Something that have a survival instinct is AGI but to me this is enough to call Strong AI. Does the Caenorhabditis elegans worm have AGI ?
slowmovintarget 6y ago
Time was that the term AI meant what has now come to be referred to as AGI or machine consciousness. Marvin Minksy wasn't looking to make better facial recognition, or better factory robots. His research was about making a mind. Twenty years ago, that kind of work ran into a brick wall. But neural networks were still useful. Enter Machine Learning, and it borrowing the term "AI." But Artificial Intelligence, for many, always meant the dream of building R. Daneel Olivaw, or R2D2.

bopbeepboop 6y ago

I believe Shaowei Lin and some others are working on the problem of “machine understanding” by connecting type theories and traditional machine learning.