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Shubho Sengupta

Showing 1–13 of 13 transcripts.

  1. RAISE Summit40 min

    Architecting the Agentic Enterprise | Traversal, Kong, Pigment & Twelve Labs | RAISE 2026

    Shubho Sengupta, Anish Agarwal, Carl Mattsson, Eleonore Crespo, Soyoung Lee, Cathy Gao

    Moderated by Kathy Gao of Sapphire Ventures, this panel unites five founders to address the critical transition of AI agents from experimental proof-of-concepts to reliable, autonomous enterprise production in 2026. The discussion identifies key obstacles such as non-deterministic failure modes, security friction, and accountability gaps, while proposing strategies like deterministic output layers, structured human-in-the-loop oversight, and outcome-based pricing to ensure value delivery. By analyzing data showing that most current AI initiatives fail to impact EBITDA, the session outlines a roadmap for shifting investment from edge use cases to core business operations through proprietary data infrastructure and stable governance models.

  2. Lex Fridman1h 12m

    Foundations and Challenges of Deep Learning (Yoshua Bengio)

    Yoshua Bengio, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Shubho Sengupta

    Yoshua Bengio outlines five essential ingredients for human-level machine learning, emphasizing that deep neural networks overcome the curse of dimensionality through parallel and sequential composition to efficiently represent complex functions. He contrasts current high-dimensional optimization landscapes, which are dominated by saddle points rather than local minima, against historical theories while highlighting unsupervised learning as a critical mechanism for developing generalizable world models. The presentation concludes by addressing future challenges in training long-term dependencies and integrating neuroscience-inspired alternatives to backpropagation, alongside administrative notes regarding an upcoming textbook by Bengio, Ian Goodfellow, and Aaron Courville.

  3. Lex Fridman1h 25m

    Foundations of Unsupervised Deep Learning (Ruslan Salakhutdinov, CMU)

    Ruslan Salakhutdinov, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    This presentation details the evolution of unsupervised learning from sparse coding and autoencoders to complex probabilistic frameworks like Restricted Boltzmann Machines, Variational Autoencoders, and Generative Adversarial Networks. It highlights how these non-probabilistic and probabilistic models overcome the scarcity of labeled data by learning hierarchical representations, with GANs notably producing sharper images than VAEs by avoiding explicit density estimation. The discussion further illustrates practical applications ranging from multimodal image-text modeling and semantic vector arithmetic to one-shot learning capabilities.

  4. Lex Fridman57 min

    Torch Tutorial (Alex Wiltschko, Twitter)

    Alex Wiltschko, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Quoc Le, Yoshua Bengio, Shubho Sengupta

    This presentation details the practical implementation and theoretical foundations of the Torch deep learning framework using the Lua language, developed in collaboration with experts from Facebook, Google, and Twitter. The speaker explains how Torch leverages LuaJIT for high-performance embedded deployment while utilizing its dynamic Autograd system to support flexible control flow and custom gradients without the overhead of static computation graphs. Case studies from Twitter demonstrate the framework's transition from a research tool for cutting-edge models like GANs to a production environment for serving media, highlighting its efficiency in both training via reverse-mode differentiation and inference through lightweight C++ integration.

  5. Lex Fridman1h 21m

    Sequence to Sequence Deep Learning (Quoc Le, Google)

    Quoc Le, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Yoshua Bengio, Shubho Sengupta

    This presentation details the evolution of sequence-to-sequence learning for automating email responses, transitioning from bag-of-words models to Recurrent Neural Networks and advanced attention mechanisms. Key technical advancements include the encoder-decoder architecture with beam search decoding, personalized user embeddings, and gated units like LSTMs to manage long-term dependencies and vocabulary limitations. The discussion concludes by highlighting real-world applications in machine translation and conversational AI, alongside future research directions in unsupervised learning and global sequence optimization.

  6. Lex Fridman1h 29m

    Deep Learning for Natural Language Processing (Richard Socher, Salesforce)

    Richard Socher, Hugo Larochelle, Andrej Karpathy, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    This presentation outlines the evolution of Natural Language Processing from hierarchical linguistic analysis to deep learning architectures like Word2Vec, GRUs, and Dynamic Memory Networks that handle sequence modeling and visual question answering. Key researchers discussed how continuous vector representations and gated units address ambiguity and long-range dependencies, achieving state-of-the-art performance on benchmarks such as Facebook's bAbI dataset and reducing language modeling perplexity to 70. Despite these advances, the discussion highlights persistent challenges regarding unified joint models, data scarcity in specialized domains, and the need for improved robustness against adversarial inputs and false premises.

  7. Lex Fridman1h 27m

    Deep Reinforcement Learning (John Schulman, OpenAI)

    John Schulman, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    This technical presentation delineates Deep Reinforcement Learning as a sequential decision-making framework that employs neural networks to maximize cumulative rewards through policy gradients and Q-function learning. Key figures in the field, such as those at DeepMind, have leveraged these methods to master complex environments including Atari games, Go, and robotic locomotion by addressing challenges like reward sparsity and non-stationary state dynamics. The discussion further contrasts algorithmic trade-offs between sample efficiency and robustness while outlining future directions like hierarchical structures and model-based approaches to enhance real-world deployment.

  8. Lex Fridman1h 20m

    Nuts and Bolts of Applying Deep Learning (Andrew Ng)

    Andrew Ng, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta, lexfridman, Peter, Andre, Shubo, Sammy

    Baidu structures its 1,000-person AI organization around unified data warehouses and integrated ML-HPC teams to drive deep learning performance that scales linearly with data volume rather than traditional algorithms. The presentation outlines critical diagnostic frameworks for bias and variance, emphasizing human-level error as a benchmark for defining theoretical limits and guiding the shift toward end-to-end learning in data-rich perception tasks. Finally, the discussion establishes practical heuristics for product automation and career development, advocating for synthetic data engineering and the rigorous "dirty work" of replicating research papers to master the field.

  9. Lex Fridman1h 2m

    TensorFlow Tutorial (Sherry Moore, Google Brain)

    Sherry Moore, Hugo Larochelle, Andrej Karpathy, Richard Socher, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta, lexfridman, Zach, Pichin Lo

    Google Brain's Sherry Moore presented a tutorial on transitioning from research to production using the TensorFlow framework, highlighting its open-source architecture that supports diverse applications like image recognition, voice processing, and deep learning. The session detailed core concepts such as data flow graphs, placeholders, and session execution while guiding attendees through hands-on labs for linear regression and MNIST digit classification. Moore also outlined the platform's extensive portability across mobile and cloud devices and invited community contributions to further develop the library's modular design.

  10. Lex Fridman1h 25m

    Deep Learning for Computer Vision (Andrej Karpathy, OpenAI)

    Andrej Karpathy, Hugo Larochelle, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta, lexfridman

    This presentation traces the evolution of convolutional neural networks from 1960s neuroscience foundations to the 2012 AlexNet breakthrough, highlighting how deep learning displaced traditional feature extraction by achieving near-human accuracy on the ImageNet dataset. Key architectural innovations, such as residual skip connections in ResNets and efficient Inception modules, enabled the training of deeper, wider models that serve as generic feature extractors for diverse tasks ranging from object detection to medical imaging. The discussion concludes with practical deployment strategies emphasizing the use of pre-trained models and GPU-accelerated infrastructure to overcome computational bottlenecks in both cloud and edge environments.

  11. Lex Fridman1h 3m

    Theano Tutorial (Pascal Lamblin, MILA)

    Pascal Lamblin, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    This presentation details Theano, an eight-year-old symbolic expression compiler that enables high-performance deep learning by automatically differentiating mathematical graphs and compiling them into optimized C++ or CUDA code. The session demonstrates the framework's core capabilities, including graph manipulation for neural network backpropagation, GPU acceleration via shared variables, and sequence modeling through the `scan` operator, while showcasing practical implementations of logistic regression, convolutional networks, and LSTMs on datasets like MNIST. Addressing deployment challenges inherent in its tight Python integration, the discussion concludes by highlighting Docker containers as the standard solution for distributing models and outlines a roadmap for enhanced 3D convolution and cuDNN support.

  12. Lex Fridman1h 32m

    Deep Learning for Speech Recognition (Adam Coates, Baidu)

    Adam Coates, Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    Deep learning has revolutionized speech recognition by replacing traditional, error-prone pipeline architectures with end-to-end neural networks that map raw audio directly to text, achieving character error rates below 6% in Mandarin. This shift utilizes techniques such as Connectionist Temporal Classification and advanced data augmentation to overcome historical limitations in accuracy and scalability, enabling systems to match human transcriber performance while significantly increasing user productivity. As researchers address computational bottlenecks through optimized training strategies like dynamic batching, these models are transitioning from experimental benchmarks to production-ready tools for consumer applications ranging from real-time captioning to hands-free vehicle control.

  13. Lex Fridman1h 1m

    Foundations of Deep Learning (Hugo Larochelle, Twitter)

    Hugo Larochelle, Andrej Karpathy, Richard Socher, Sherry Moore, Ruslan Salakhutdinov, Andrew Ng, John Schulman, Pascal Lamblin, Adam Coates, Alex Wiltschko, Quoc Le, Yoshua Bengio, Shubho Sengupta

    This lecture by Hugo Lavachel delivers a comprehensive technical analysis of feedforward neural networks, detailing the mathematical foundations of backpropagation, universal approximation, and modern optimization algorithms like Adam and stochastic gradient descent. The presentation critically evaluates architectural choices, contrasting activation functions such as ReLU against sigmoid and tanh while explaining how dropout and batch normalization mitigate overfitting and stabilize training in deep architectures. By bridging theoretical constraints with practical debugging strategies, the course equips practitioners with the necessary tools to implement, tune, and validate deep learning models effectively.