It is a clip from a dialog with Jeremy Howard on the Synthetic Intelligence podcast. You’ll be able to watch the total dialog …
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I had a hunch when studying machine learning that deep learning could be impractical and a waste of time especially for beginners.
Its inconcteivable how someone can be this good at ai, purely inconceivable
I am revisiting this video after reading Decision Transformers for sequence modelling of Offline RL …..which needs a dataset to learn…why would I train a model again if I have already cloned n number of agents to generate that dataset
Is it possibly more beneficial to try to push DL by way of a startup? In this case all you need is to become profitable then any work you’re doing can advance as far as the market will need it
Papers are so inflationary and broadly without formal fundation
ID claim this "research" is actually try and error engineering, not scientific at all.
As a former PhD student, I can validate that this is true.
I don't want this video to be discouraging people which are just getting started with deep learning ( especially PhD students )
Interesting ! As a newcomer in data science, i wanted to focus my study into deep learning things. But for the first time i just heard this "active learning" term. I think i should looking into it,
Is there any guides on active learning
I’m having a hard time finding examples in pytorch
AI won't be anything. It will fall down. I'm sorry to disappoint you, but the future is much more tragic than these weird and dystopical technological future, when there are screens everywhere, human like robots that are unrecognizable to us, and extreme realistic second world in VR. No, it won't happen.
Maybe the scientific world takes its cure from the whole human world, where things are produced not for practicality but as an exploration of the human consciousness?
Imagine if we could collate all human knowledge into a coherent dataset that would allow us to have a better perspective of what we do not know, which is more than we do.
love Jeremy
Dr. Fridman can you please do an update on transfer learning in 2022?
I think the major problem is current publishing methods. Journals publish only "new" and "interesting" research and researchers get evaluated by the amount of papers the can get published. Veritasium made a great video about the issue with the title "Is Most Published Research Wrong?" – it nicely explains why things cannot get better unless we can introduce better measurement of researcher quality.
“Whoever, in the pursuit of science, seeks after immediate practical utility, may generally rest assured that he will seek in vain.”
– Hermann von Helmholtz (one of the greatest scientists this planet has ever witnessed)
So many people commenting have completely misrepresented his position to the point that I don't think they're even listening to the words coming out of his mouth.
He's not saying basic science is pointless. He's criticizing the fact that the vast majority of the machine learning community is hyper-fixated on a very narrow set of problems. As a result, the majority of that research is "useless" as the field is bloated with inconsequential optimizations by people simply looking to get citations.
In fact, he's advocating for MORE basic science in under-explored fields that could still make huge breakthroughs and radically improve the state of machine learning.
It's frustrating that people don't even respond to the video but rather to the imaginary argument they constructed in their minds when they saw the video title.
hey need a bit of help i am a graduated chemical engineer and now thinking to study further in machine learning and data science but i almost know nothing of machine learning and i have to submit a research proposal in machine learning or data science can someone help me to chose a topic or how to find a topi thanks
Well talking about the improvements, the innovations in any field also originate from active research on something which was already created. I condemn things that are done just because you want to publish your paper, you did corrections here and there and then created a paper out of it, but these small corrections are the basis of something useful we are using these days.
Talking about a simple technology of face detection. If only a single paper on face detection is published and all people would have started creating another innovation do you think that it would have ever been implemented in our mobile phones?
"Research" means you are doing something on a thing that is already created, this is the basis of modern technological advancements.
People thought number theory was useless until computers and stats became the oil of tech today.
Every effort is a step and journey of a thousand miles begins with a simple step.
The title didn't age well…
Proof that Scientists are fucking wrong sometimes lol.
He isnt someone who is actively involved in research , niether has he ever produced any paper worth any serious attention. He is just a good practitioner of technology. Completely incompetent on commenting of research.
The idea that education and academic research should be transformed to respond to the fleeing economicinterests of markets instead of being a quest for truth and knowledge and deep insights that may only create value in the long term is yet another sign of our decaying socio-economic system.
Who cares about spacetime geometry? Design a cheaper vapor engine that I can sell at a profit, Einstein!
I think there is something important that is missed here: Neural networks were 'useless stuff' for 20 years but some researchers kept on doing academic research on them. Academics continue to explore many directions and most of them end up being useless, but the whole point is that we don't know what will end up having an impact in the real world. That is why its important to keep an ecosystem that explores basic science with a long-term horizon, while companies focus on short term immediate gains.
Well, that turned-out to be totally wrong.
To say "research is a waste of time" is kind of silly at best. I assume that you realize that there is a significant difference between theoretical research, analytical research, applied research, correlational research, action research and other types and branches of research. You sound a lot like Noam Chomsky, who said exactly that for decades about deep learning, asserting it is a waste of time and money, and instead, people need to just apply basic machine learning in AI and stop wasting time and money trying of AI deep learning research.
Scientist calls methodology of science a waste of time, other scientist endorses and publishes it.