@sambasivanganesan8595
It is a joy to listen to a true expert who speaks in non technical English
@lnebres
I now want to read everything Ananthaswamy has ever written.
@DarshanAS
Kudos for displaying the relevant papers on screen, when they were being talked about!
@liviubeschieri
Wow, amazing interview. He's got such clear and candidly reasonable views, plus this beautiful breath of knowledge. Highly appreciated this one too! There's two reflections that came to mind as I was watching. 1. Reflecting on the idea of defining intelligence, seems to me that a general definition could be simply "the ability to reach goals with the resources that you have" which can apply all across the board from the "goals/needs" a cat might have, to the ones a dog would, or a human, and then AI agent, robot etc. 2. And when it comes to sense of self, or absence of it, spiritual traditions have been striving to facilitate that for millenia, calling it Liberation from the suffering narrative self, who feels separate from Life, and is engaged in this endless seeking for perceived security, and then calls that happiness. And liberation in this case would mean absence of this layer of inner seeking, and just being lived "as if by the system" or flow of life, through the natural configuration/personality that we already have built in. Ultimately, the self seems to be a layered cake of a) awareness, b) structures of perception (like Kant's categories) and c) subjective story of "self seeking something." And they can be slowly deconstructed and "seen through." Which leads to this liberation both on a mental level, as well as on an energetic/physical level. It's as if once the conceptual framework of the self gets loosened, so does the energetic contraction of sense of self gets loosened in the body and even in the physical brain, eventually leading to this absence of selfhood. And as they say in Nonduality... "just this great wholeness" remains. Pixels on the screen of aliveness, where before we were totally immersed in the movie, character and story itself.
@kwikwi2000
Ananthaswamy is making complex ideas sound interesting, will watch this again after readying the book.
@SalehElm
Thanks for a really nice interview with Anil. He is a wonderful writer and his work is quite enjoyable.
@abacha007
Wow, what an amazing interview — a huge ocean of knowledge flowing throughout! Deep thanks to Anil and the MLST team for this incredible insight.
@timdipayanmazumdar1089
Mr. Ananathaswamy greetings from Bangalore and rural Ontario. 1959 is not merely about perceptron convergence . Its the year the first real computational method for matrices called the Golub-Kahan theorem came into being, This becomes a practical way to work with Eigenvalues and Singular values of a Symmetric Matrix. Indeed the basis for hundreds of methods to come. ALL the Machine Learning comes hugely under Matrix computations esp. of the GEMM type. "CALCULATING THE SINGULAR VALUES AND PSEUDO-INVERSE OF A MATRIX" - G. GOLUB AND W. KAHAN
@Thomas_basiv
Thanks so much for still conducting the interview despite the schedule clash. Great stuff
@isatousarr7044
The Elegant Math Behind Machine Learning highlights the crucial mathematical principles that underpin modern machine learning algorithms. At the heart of machine learning lies a blend of probability theory, linear algebra, calculus, and optimization techniques, which collectively enable machines to learn patterns from data and make predictions or decisions. Linear algebra plays a pivotal role in managing and manipulating large datasets, especially when dealing with high-dimensional data in tasks like image recognition or natural language processing. Concepts like vectors, matrices, and eigenvalues are essential for understanding how algorithms transform and process data. Calculus is equally important, as it helps in optimizing models by minimizing loss functions through methods like gradient descent. This allows models to improve iteratively by adjusting parameters to reduce errors in their predictions. Probability and statistics are the foundation for many machine learning models, particularly in fields like Bayesian networks and Markov chains. These models help quantify uncertainty and make probabilistic predictions, essential for tasks where outcomes are not deterministic but subject to variability. Techniques like regularization and cross-validation are used to prevent overfitting and ensure that models generalize well to new data. The elegance of machine learning math lies not just in the individual components but in how these concepts come together to form powerful and efficient algorithms. From deep learning networks to decision trees, the underlying mathematics provides the structure that enables machines to "learn" and improve performance autonomously. As machine learning continues to evolve, a deeper understanding of the math behind it allows researchers and practitioners to refine models, improve efficiency, and tackle more complex problems in areas such as healthcare, finance, and autonomous systems.
@Star_Being
Such priceless insight being shared here by Anil Ananthaswamy ! Would love to get my hands on his book . Thank you for sharing
@NunTheLass
i love his down to earth explanation about emergence : if we had gradually increased the size of the models and datasets we would have slowly seen these new capabilities arise like something emerging out of the fog rather than being blown away by these unexpected new capabilities we were suddenly confronted with.
@AttiaKhaliqkamran
This talk is really interesting and motivational as well even for a non tech person like me, to deep dive into how machines actually work.
@nephronpie8961
Such clarity of thought and an ability to effectively communicate them. Thanks a lot for this podcast 😊
@justinduveen3815
Wonderfully enlightening show!! Thank you gentlemen!!
@DaneSouthard-m4d
1:48:32 Very solid point. It is noble to live properly
@amoghavarshamurthy
Talking about Terra incognita, i find this analogous to how we learn things. As kids / novices with no prior knowledge, we learn how to do stuff and then as teens / amateurs we make mistakes and our error rate goes up. Then in the 2nd phase of learning we learn to avoid these mistakes/ errors and finally we reach a stable state as adults / pros. Very interesting phenomena.
@sardesj
Amazing interview Guest and congratulations for the set of questions you asked. Kind of reading my mind ! Mr Anil Ananthswamy has clarified so many things for me in this field. I am eager to read his books!
@ARATHI2000
Fascinating video...really enjoyed it. So much so that I did post it in my linked profile. Thank you!
@MachineLearningStreetTalk