211–220 of 403 posts
Previously, when "The computer says no" it was possible that an engineer somewhere might know why the computer said no. With AI, nobody knows why the computer said no.
We have also been watching these machine learning models for 6 months: - increase the volatility in virtually every financial market they touched - be exploited by adversarial learning networks to amplify funded propaganda as news - use poorly contrived sentiment analysis to generate incomprehensibly meaningless news headlines These non-linear "function approximators" have absolutely unpredictable and insane non-line…
Intelligence is the amalgamation of many smaller problems working together and building on top of each other. * Facial recognition/detection * Facial synthesis (deepfakes) * Speech synthesis, including mimickry * Speech recognition * Natural language processing * Gait/walking algorithms * Motion planning * etc. Complexity arises from simple units working together in parallel. We're working on the smaller, specialized…
Aren't your criteria for "intelligence" human-centric, implying that there is no other form of "intelligence"?
Aren't your criteria of the "black box" type, given that AFAIK no human can really completely explain how he recognizes faces/does NLP/walks/...?
Earlier quoted context omitted.
The underlying derivatives are linear (like all derivatives) but neural networks' ability to approximate arbitrary non linear functions is one of their biggest strengths.
Yes, so I'm left wondering, when making the association of the math to the badness, how do you decide if the linearity or the non-linearity is the salient part?
Some function being linear means it's easier to guess. If a real world phenomenon is tied to a linear function, then it's easy for AI to guess/approximate.
Earlier quoted context omitted.
Ok, consider me intrigued. What is that we are going to see/experience once you folks had some time? Genuine question! Can you give us a basic idea of the things that you are already sure by now will see the light of day?
Mathematical modeling that is 3-6 orders of magnitude faster, we are already talking deployment. Same for ML powered solutions to data management - I don't want to say enough to identify anything. My team has been working on a rudimentary humanlike reasoning engine based loosely on what AlphaGo proved: that machines can learn heuristics identical, equal to, or better than those of humans. And for perspective, AlphaGo…
How seriously can we take that phrase? Please mathematically model the Covid-19 pandemic's next 6 months. The epidemiology community dropped the ball and you can hardly do worse. Take the "Covid Challenge!"
mtgp1000 says>"These next few decades are going going to see a phenomenal acceleration that's already starting ...assuming we don't hit some unforseen[sic] limit or block." -
Nobody can predict next year's economy and certainly nothing beyond 5 years! You're predicting for the "next few decades"?
mtgp1000 says*>"But even with what we've discovered now, we have more than enough in the way of new tools to refine across industry and society for decades."
Spoken like a true non-English marketeer!
The cycle keeps repeating. A new advancement in computing power, networking, or algorithms means there's a new batch of low-hanging fruit for AI to pick, so we pick it. Investors say "What about the high-hanging fruit?" and we say "No problem. We just need a slightly longer ladder."
Two years later everybody finally realizes the high-hanging fruit is on the moon.
Earlier quoted context omitted.
Where are stop signs hexagons? Am I being a pedantic numpty or am I illustrating a point about the many ways errors creep in, regardless of the natural- or artificial-ness of the intelligence?
You're being a pedantic. Human beings are tremendously better at driving than machines despite sometimes saying hexagonal rather than octagonal. Humans and current AIs both make mistakes but humans manage a kind of robustness, ability to deal gracefully with unexpected situations, that current AIs don't seem to be progressing towards.
Human drivers: 1 death per 88 million miles traveled (in the US) [1]
Tesla Autopilot: 5 deaths per 3 billion miles [2]
[1] https://www.iihs.org/topics/fatality-statistics/detail/state...
[2] https://electrek.co/2020/04/22/tesla-autopilot-data-3-billio... and https://en.wikipedia.org/wiki/List_of_self-driving_car_fatal...
Here we are in 1989 again. The cycle keeps repeating. A new advancement in computing power, networking, or algorithms means there's a new batch of low-hanging fruit for AI to pick, so we pick it. Investors say "What about the high-hanging fruit?" and we say "No problem. We just need a slightly longer ladder." Two years later everybody finally realizes the high-hanging fruit is on the moon.
We have also been watching these machine learning models for 6 months: - increase the volatility in virtually every financial market they touched - be exploited by adversarial learning networks to amplify funded propaganda as news - use poorly contrived sentiment analysis to generate incomprehensibly meaningless news headlines These non-linear "function approximators" have absolutely unpredictable and insane non-line…
"AI" is a very vague term. What you described aren't entirely "machine learning", but a combination of existing linguistic techniques and machine (deep) learning. People confuse what AI can do, and what is AI all the time. It also doesn't help when there are so many inexperienced data scientist making promises that they can't achieve. In your example, I'd argue that a human is not necessarily a better driver than a m…
Until we can characterize the behavior of these systems, they must not be put in control of life-critical processes like driving.
I'm not sure how anyone who's watched the exponential growth of a brand new domain can pick a point today to and say that things aren't as good as we expected. What may have happened was that some eager CEOs have overpromised on timelines and resources. But the revolution is coming, ML is already starting to change society. We're building the tech. Right now. The author does not even realise the immeasurable potentia…
Yeah, there's also shitty sentencing recommendations[1], new-age phrenology[2], and high-tech redlining[3].
I think your entire field needs to take a year off and take some ethics and philosophy courses before going any further. Otherwise we're all going to end up much worse off.
[1] https://www.nytimes.com/2017/10/26/opinion/algorithm-compas-...
[2] https://www.faception.com/
[3] https://www.fastcompany.com/90269688/high-tech-redlining-ai-...