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If this product develops successfully and gains adoption, it will be a huge deal. I've heard repeatedly that scheduling is one of the biggest problems that exec

by hooande 12y ago

If this product develops successfully and gains adoption, it will be a huge deal. I've heard repeatedly that scheduling is one of the biggest problems that executives have and it's one of the main reasons that they get personal assistants. It's good to have someone to get coffee or pick up laundry, but scheduling back and forth is a tedious task that takes up a lot of time. I've been thinking about an ai solution to this for awhile now, it's good to see one emerge and be used in the wild. The concept of a good ai based personal assistant is one of the Next Big Things. And I mean facebook size big thing. If the busiest and most important people in society start doing all of their scheduling through one software platform it won't be long before everyone else follows. And that opens the door to all kinds of other social and communication services. "Have your bot talk to my bot and we'll figure out a good time to meet up". Maintaining a list of your friends and contacts is important, but algorithmically maintaining your relationships and schedule is even moreso. Ton of value here waiting to be unlocked. As with all data based applications, the value increases exponentially with the amount of data being analyzed. The ideal personal assistant is one that will make the same decisions that you would make in the absence of instruction. If you tell a PA "book dinner reservations for my wife and I next thursday", a good assistant will know what kind of restaurant is appropriate, where you like to sit, whether you prefer dinner at 7:30 or 8:30, etc. You don't have to say, because they learn your preferences from the data they've collected from you and previous employers. Just like a computer would. There are a ton of small breakthroughs and conceptual leaps to be made between there and where we are now and the field is still wide open. x.ai seems like an encouraging step in that direction, one that I'll be watching closely.

soup10 12y ago
I think you could accurately say machine learning is a subfield of ai. I think of it as the mathematical toolbag with which many ai applications are built. (Before he edited his post OP had a comment about the confusion between the terms 'machine learning' and 'artificial intelligence')... Put another way I think machine learning specifically refers to the mathematical ideas used to teach things to a machine, and the ai to the resulting entity which often makes use machine learning techniques(usually in addition to specialized code specific to the application). I think some people feel that "general intelligence" would require a paradigm that somehow transcends math and algorithms which seem to only result in specialized a.i's. And that maybe it all needs to be tossed out and re-examined with a blank slate. On the other hand, i would suggest that specialized ai software will become more and more generalized until we can't tell the difference. I think it's all about having a sane perspective. In this day and age with machines and gadgets in every room, man-made artifacts teach us how to think, and we teach machines how to think in a recursive interdependent cycle. In that sense reaching the final goal post means progressively improving our machines to be active communicators and good listeners, to the point where they can learn things from each other. Eventually their interactions will become complex enough that their communications will be unpredictable but still productive on the whole. So we'll leave them on so they can talk. At which point you could call it a society of machines.... Anyway I guess the point is specificity of language seems to be important learning and thinking about AIs.
- fchollet 12y ago
  > I think you could accurately say machine learning is a subfield of ai. Yes, that is a widely accepted view of AI/ML. "AI" designates any method to give machines cognitive capabilities, while ML is a particular family of techniques (statistical learning) for giving machine such capabilities. Other branches of AI would include symbolic AI, expert systems, and other 70s techniques where processes were explicitly programmed rather than learned from data. Today, because these other branches have largely fallen out of favor over the past 40 years, ML has become almost synonymous with AI. The semantic distinction is still there, though.

by hooande 12y ago

soup10 12y ago

I think you could accurately say machine learning is a subfield of ai. I think of it as the mathematical toolbag with which many ai applications are built. (Before he edited his post OP had a comment about the confusion between the terms 'machine learning' and 'artificial intelligence')... Put another way I think machine learning specifically refers to the mathematical ideas used to teach things to a machine, and the ai to the resulting entity which often makes use machine learning techniques(usually in addition to specialized code specific to the application). I think some people feel that "general intelligence" would require a paradigm that somehow transcends math and algorithms which seem to only result in specialized a.i's. And that maybe it all needs to be tossed out and re-examined with a blank slate. On the other hand, i would suggest that specialized ai software will become more and more generalized until we can't tell the difference. I think it's all about having a sane perspective. In this day and age with machines and gadgets in every room, man-made artifacts teach us how to think, and we teach machines how to think in a recursive interdependent cycle. In that sense reaching the final goal post means progressively improving our machines to be active communicators and good listeners, to the point where they can learn things from each other. Eventually their interactions will become complex enough that their communications will be unpredictable but still productive on the whole. So we'll leave them on so they can talk. At which point you could call it a society of machines.... Anyway I guess the point is specificity of language seems to be important learning and thinking about AIs.

fchollet 12y ago
> I think you could accurately say machine learning is a subfield of ai. Yes, that is a widely accepted view of AI/ML. "AI" designates any method to give machines cognitive capabilities, while ML is a particular family of techniques (statistical learning) for giving machine such capabilities. Other branches of AI would include symbolic AI, expert systems, and other 70s techniques where processes were explicitly programmed rather than learned from data. Today, because these other branches have largely fallen out of favor over the past 40 years, ML has become almost synonymous with AI. The semantic distinction is still there, though.