The Master Algorithm | Pedro Domingos | Talks at Google
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- เผยแพร่เมื่อ 30 ก.ค. 2024
- Machine learning is the automation of discovery, and it is responsible for making our smartphones work, helping Netflix suggest movies for us to watch, and getting presidents elected. But there is a push to use machine learning to do even more-to cure cancer and AIDS and possibly solve every problem humanity has. Domingos is at the very forefront of the search for the Master Algorithm, a universal learner capable of deriving all knowledge-past, present and future-from data. In this book, he lifts the veil on the usually secretive machine learning industry and details the quest for the Master Algorithm, along with the revolutionary implications such a discovery will have on our society.
Pedro Domingos is a Professor of Computer Science and Engineering at the University of Washington, and he is the cofounder of the International Machine Learning Society.
books.google.com/books/about/...
This Authors at Google talk was hosted by Boris Debic.
eBook
play.google.com/store/books/d... - วิทยาศาสตร์และเทคโนโลยี
2022 now. It is so exciting to see - "The Master" Algorithm is coming real, atleast in NLP fully and slowly in other domains.
start 2:00
five tribes 4:11
the single master algo 42:00
the five tribes should be 42:30
This guy speaks and explains things very clearly. I've never taken Computer Science past an intro course, but I feel like I learned a lot by watching this video because he explained the main concepts so well without using too much jargon.
Prof Domingos, thank you for the brilliant presentation.
Thanks for this great talk, I think this is one of the best introductions into the topic you can get. A perfect overview and definitely a strong appetizer that makes me learn more about it.
Portuguese Kevin Spacey.
I didn't dare writing that, but that's exactly my first thought ;)
+lonwulf0 He speaks like Portuguese Kevin Spacey as well
That's nowhere near as bad as my initial thought... Portuguese Pauly Shore....
LMFAO
haha exactly !
The most exiting presentation about learning algorithms I ever heard! Thank you!
Big thank you for your great work...!
Absolutely great, the way that he compresses de information is astonishing, almost not even a second of this video without good information.
Impressed with Pedro's communication skills, well structured + great execution. Thank you!
Great presentation Pedro!Keep up the great work
At 14:18 he says 'Yoshua Bengio'. It is also written in the slides
Thank you sir.
This was a really great talk that explains a lot of the terms people throw around in A.I. with the expectation that everyone already understands them. Obviously, Pedro really gets this material. Bravo!
56:11 - how do we prevent 360ª recommenders from being self-fulfilling?
This is at the absolute heart of the debate.
See recent talks from Jaron Lanier (father of virtual reality), or look at Rita Riley's "Raw Data is an Oxymoron"...
Like any technology, art or religion, machine learning works from data sets entirely created and mediated by humans.
I’ve read Master Algorithm, its my favourite AI book so far. I read Human Compatible, AI is Good For You, and How Does ChatGPT work by Stephen Wolfram. But I find Master Algorithm the most informative.
Absolutely fantastic. I always thought there was more to (machine) learning than neural networks. Thanks!
This has really opened my eyes further than just deep learning.
this is why is suscribed! love these videos!
56:20 - Great Question: "What's to stop a recommender system from being self-fulfilling?" When does a recommender system stop suggesting things, and start telling you what you want. Don't think Domingos answered this.
Michael Sander that’s a philosophical question. Unless your talking about regulation which he said the individual should maintain control of his own data.
which will be done with blockchain technology for secure decentralized personal data. Accessible any time anywhere only to you.
There has to be another method to error handling besides back-propagation. Finding what's responsible for an error is great. However, at the end of the day how do you tie those weighted adjustments into what is favorable vs unfavorable without constantly adding parameters?
+Sirloyn have like a cost to alter the weight and a budget upto which you can alter. Once you exhaust this budget you say the system is the best it can get. Then have the outputs from this system as an input for a following similar system with a new budget and so on. it might be better than tweaking the same system to exhaustion.
In the talk, Pedro gives gives 4 other algorithms that address this issue.
Great video, thank you. I'm really enjoying the book too.
the reinforcement learning tribe by sutton, silver is missing!!
Great video. Gives a great mental model for growing an understanding in ML.
48:12 Great idea.
Funny, Pedro says that 'computers don't understand natural language yet', which was true in 2016. Now with ChatGPT things are quite different. So adding 'induction' to LLMs could be a big jump forward.
Great talk, the only problem is the camera should focus more time on the slide for important concept instead of bouncing back and forth between the speaker.
Thank you, Great Presentation.
Muitos Parabéns, boa apresentação
My inner nerd is overjoyed to have the privilege of drinking this talk in. Talk about an endlessly fascinating subject!
major idea missing from Domingos presentation on cancer: Ph... all cancers flourish in an acidic environment, but that would not feed the pharmaceutical models that reason out the drug cures. The REPRESENTATION IS LINEAR. and non-self referring!
Best google talk ever
An algorithmic mind can guess an outcome humanely.
+Jacky Yu Great resource. Thank you!
Further resources:
Twitter: twitter.com/pmddomingos and
university page: homes.cs.washington.edu/~pedrod/
Coursera Machine Learning lecture: www.coursera.org/course/machlearning
I love this guy
Great video. Knowledge extraction by mechanism isn't new, and if, in principle it's resonance that is passed on in oral traditions that have been developed by mnemonic devices like clay tablets and libraries into general cultures, then machine learning is another step in the same progression. It's inclusivity that is at risk.
Thank you for a great presentation. Where can I find out more about the robot scientist who discovered a cure for malaria?
www.scientificamerican.com/article/robot-scientist-discovers-potential-malaria-drug/
Shouldnt Writing be the fourth source of knowledge and computers the fifths?
writing is just a source of preseving the flow of knowledge but not the knowledge itself
12:51 i think your totally right... too much research is actually wrong and driven by grants we should invest in more robot scientists
Is the slides available for download somewhere??
Found it on the comment below by Jack Yu
Fantastic!
am glad i watched this
categorize object in one lens.
Cool presentation. Can the "Master Algorithm" then create its own coordinate system(s) for areas of focus?
My burning hope is AI on behalf of the consumer, or the person. Helping people fight back so to speak...
Buongiorno Google.felicidades
for Marifi & Yarman hodjas from metu...
@ 2:26 I disagree that computers are a source of knowledge. They are repositories and manipulators of data, which isn't the same thing at all. Computers can help us organize, navigate and transform data in our pursuit of knowledge and we can use it to record and disseminate our knowledge and receive the knowledge of other humans via computers but computers by themselves can't know anything, can't experience emotions or make value judgements relative to anything. At best computers can predict how humans generally, and possibly individual humans would feel about certain things because we've told them how we feel about similar things. So-called computer knowledge is just another form of cultural knowledge.
Scientists love to inflate the importance of their own field, so of course data scientists like to inflate data into knowledge, but it's important to understand the distinctions between real intelligence and artificial simulations of intelligence. Mere predictions generated by artificial intelligence isn't knowledge, it's still just derived data.
Artificial intelligence as implemented in computers and robots has no independent way to experience emotions and make value judgements. They can only know of such things through what their human programmers and users tell them. They have no independent basis upon which to take initiative and do something to further their own or their master's self interest that they haven't been told to do. They can be told by humans or by other computers to do a given task at certain times in the future, or at certain time intervals or when they observe certain events have occurred and they will attempt to do it, but they can't decide on their own that it would be a good idea to take over the world and add that task to their schedule or the schedule of another computer. Why? Because they literally don't know the difference between a good task and a bad one unless a human gets involved to make such a value judgement about the task.
actually it means computer algorithms will take data which will process into information and then finally into knowledge.... and they will do with an unprecendented power
Isn't our knowledge based on experience or exposure with the ultimate goal of successfully predicting outcomes?
Pls share the slides
50:13 ting
Computers have knowledge which is order of magnitude larger than DNA.... I laughed my ... out when I heard that!
why ?
it's seems computer as second mind
#2ndmind
good talk.
You know? Someone count how many times he says it...
+Simon W. Hall 113!
+Simon W. Hall It's a shame because he is very interesting. But that is so distracting I couldn't take it. Many brilliant scientists should have their lectures critiqued by professionals. The ability to communicate your ideas is important. This shortcoming is not uncommon.
@@brianjanson3498 your ability to filter out irrelevant parts of speech is also suboptimal. I have had no problem getting all his points
Unable to see the screen, too much focus on the presenter.unable to comprehend anything.
pause
Computer Science José Mourinho
He looks like Mourinho and Abramovich at the same time
"Jose Mourinho" of computer science. At least he definitely sounds like😂😂I sink
It also implies that the mortals are human
... that *some* mortals are human.
Can someone explain 19:00 - 20:36 to me???????????????????????????????????????????????
Holy crap so many things i can't understand
Mash sonirholtoi lects bolj bayrllaa.
His first classification is not true. I hope his book does not inspire algorithm that will dominate future AI.
ok
brrilliant
hahaha, an orwellohuxleyan Master teaching TheBigEvil (google) how to do it, so funny :-)
FALLACY OF AMBIGUITY
As philosopher John Searle argued, syntax is not semantics (understanding). Computing machines are capable of syntactical operations but not understanding.
Wikipedia: en.wikipedia.org/wiki/Knowledge
Knowledge is a familiarity, awareness, or understanding of someone or something, such as facts, information, descriptions, or skills, which is acquired through experience or education by perceiving, discovering, or learning.
Knowledge can refer to a theoretical or practical understanding of a subject. It can be implicit (as with practical skill or expertise) or explicit (as with the theoretical understanding of a subject); it can be more or less formal or systematic.[1] In philosophy, the study of knowledge is called epistemology; the philosopher Plato famously defined knowledge as "justified true belief", though this definition is now thought by some analytic philosophers[citation needed] to be problematic because of the Gettier problems while others defend the platonic definition. However, several definitions of knowledge and theories to explain it exist.
Knowledge acquisition involves complex cognitive processes: perception, communication, and reasoning;[3] while knowledge is also said to be related to the capacity of acknowledgment in human beings.
bro that is why humans are building capable tech to solve real world problems, i mean if the cure for cancer is by a computer of human doest matter coz what matters is the solution itself!
So its always wrong lol cause the info u first put in are wrong lol your math is wrong
A lot of the time it's best if computer scientists just stick in their lane... smh
Nice talk. Bad video production.