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A related talk that Michael Jordan gave at a machine learning conference called Sys ML last month. https://youtu.be/4inIBmY8dQI
I'm not an AI researcher (although from my internet reading that's not a hard title to claim ;)) but I feel that ML/DL can't go much farther than they already have. The concept of "we just need more power" is an obvious fallacy to me.
I'm not a researcher either, so it's difficult for me to articulate exactly what problems I see, but (shameless self plug) I did write an article a little while ago attempting to: https://medium.com/@danShumway/modern-ai-techniques-arent-wo...
Earlier quoted context omitted.
Humans are proof that machine intelligence can be improved quite a bit. We are just complicated machines, no?
I'm specifically talking about the strategies used in Deep / Machine learning to approximate intelligence through probability.
I'm not an AI researcher (although from my internet reading that's not a hard title to claim ;)) but I feel that ML/DL can't go much farther than they already have. The concept of "we just need more power" is an obvious fallacy to me.
Why don't you think it will continue to progress?
At current trajectory, we're not headed towards a general intelligence. Progress has been made, but there are big gaps. Smart home devices are a great case in point. They are somewhat flexible in the voice commands they accept. Specific phrasing and pronunciation are not necessarily required. Their responses and speech, however, are all pre-programmed and templated by humans.
Edit: There is potential for more breakthroughs in the future, but I am not seeing them on the horizon at the moment.
The biggest challenge yet unconquered is getting your "average" business ($1-100m revenue, zero AI knowledge) using ML to help with literally anything they do . I'd wager less than 1% of businesses outside of SV even have a clue where to begin, or what to use it for, my employer included. "Do we hire some AI guys?" We'll need to crack the 1%-using-it mark for me to consider the revolution "begun"...
The biggest challenge yet unconquered is getting your "average" business ($1-100m revenue, zero AI knowledge) using ML to help with literally anything they do . I'd wager less than 1% of businesses outside of SV even have a clue where to begin, or what to use it for, my employer included. "Do we hire some AI guys?" We'll need to crack the 1%-using-it mark for me to consider the revolution "begun"...
Does the AI which can help with literally everything a business does exist? That sounds rather general purpose and extremely open-ended.
I'm not an AI researcher (although from my internet reading that's not a hard title to claim ;)) but I feel that ML/DL can't go much farther than they already have. The concept of "we just need more power" is an obvious fallacy to me.
I mean, all these "structures" and algorithms that people create in software, each a little different than the last, each with different performance/result trade offs. But nobody actually understands the fundamentals as to what is behind some of the very impressive results we are seeing (due to the technology allowing us to throw computing horsepower at it).
I’ve been finding it difficult to find concrete examples of where AI is useful. Suggestion engines? Alg optimization? What would be something my parents would recognize that is being helped by AI?
I’ve been finding it difficult to find concrete examples of where AI is useful. Suggestion engines? Alg optimization? What would be something my parents would recognize that is being helped by AI?