Machine Learning Algorithms
topic on 6 shows · 15 statements across 13 episodes
the Y Combinator Startup Podcast 
the Knowledge Project 
the MAD Podcast 
the a16z Podcast 
TBPN 
20VC
15 statements about Machine Learning Algorithms, every show
Hotz: Current machine learning models are 1,000x less data efficient than humans
“Like current machine learning algorithms, like a thousand X less data efficient than humans. So yeah, you need a thousand X more data, right? If a human can learn something in one example, or 10 examples, the computer is going to need a thousand or 10,000.”
Kolter: Machine learning algorithms fail to extract maximum data value
“What that means is our current algorithms, we are not yet maximally extracting the information from data we have. And there are way more deductions and inferences and other processes that we can apply to our current data to provide more value.”
Ghodsi: Early Big Tech achieved AI breakthroughs using 1970s algorithms with massive data
“What they were doing is they were taking those algorithms from the seventies that do not work, but they were applying orders of magnitude, more data to it. So a lot of data on modern hardware, and they were getting superhuman results.”
Saez-Gil: Remote sensing and AI allow precise forest carbon estimation
“Those data points of the forest, and you can be incredibly precise today at estimating carbon storage and carbon capture by forest.”
Chen: Neuromorphic computing chips may shortcut AI coach development
“Now, it's certainly possible that we can get to the smart coach on our current trajectory, which is more data fed to better machine learning algorithms, but this might be a shortcut. On the hardware side, this is called a neuromorphic chip, and it basically ex…”
Pedro Domingos: Every major ML algorithm can theoretically learn any function
“On a theoretical level, every one of these major machine learning algorithms has a theorem that says if you give it enough data, it can learn any function.”
Frank Chen: Autonomous driving algorithms calculate safe paths, not ethical tradeoffs
“If you look at the current crop of machine learning algorithms that drive autonomy, they're not making high level decisions like, let's calculate the life expectancy of the people that I'm about to wipe out. They're not doing that. They're looking at, there's …”
Jensen Harris: Machine learning algorithms will rapidly become commodities
“I think all of the algorithmic stuff in machine learning is going to be commodity. Like, there are, like, 20 places in the world where they're inventing new algorithms, and that's, you know, educational institutions and huge companies, and that's really import…”
Fei-Fei Li: Many Industry Applications Rely on Non-Deep Machine Learning
“In fact, many, many industry applications today still use some of the most powerful machine learning algorithms that are not deep.”
Chen: Humans fail at optimization beyond four dimensions while AI excels
“And after about three or four dimensions, your mind just kind of gives up. Your brain isn't programmed or optimized for that type of mathematical optimization, but machine learning algorithms love lots of data and are able to do this in a way that human brains…”
Bloom: ML model detected supernova in 11 hours, driving Nature publications
“So one of the great things is our, ah, machine learning algorithm and framework wound up finding a new supernova that was in a very nearby galaxy. And because it was found about 11 hours after explosion, which were days earlier than had ever been found for tha…”
Domingos: Some Hedge Funds Are Completely Run by Machine Learning
“So for example, these days there are hedge funds that are completely run by machine learning algorithms for the most part, you know, hedge fund will use machine learning as one of its inputs. But there are some where the machine learning algorithms, they look …”
Domingos: Major ML Algorithms Are Mathematically Proven Universal Function Approximators
“And there are several major such algorithms today that have mathematical proofs that if you give them enough data, they can learn any function.”
Domingos: Machine Learning Algorithms Typically Outperform Human Doctors in Medical Diagnosis
“Machines are remarkably better than human doctors at doing all types of medical diagnosis, not just from x-rays, but from, you know, symptoms, right? You have a patient, you have their symptoms, what is the diagnosis? And even very simple machine learning algo…”
Rao: Uber drivers generate the R&D data used to automate their jobs
“When an Uber driver picks a passenger from point A and drops him off at point B, that passenger gets a ride and the driver gets paid some money. But the data that's generated that goes and feeds machine learning algorithms that, you know, improve everything fr…”