Latest Interviews

MIT 6.S093: Introduction to Human-Centered Artificial Intelligence (AI)

The presentation argues that learning-based artificial intelligence will supersede optimization models but requires "machine teaching" and continuous human supervision to ensure safety, fairness, and explainability. Key strategies include active learning algorithms that minimize data requirements, reward engineering to align systems with societal values, and uncertainty signaling through ensemble disagreements to trigger human intervention in high-stakes domains. These human-AI collaborations aim to overcome persistent perception challenges in face and emotion recognition while scaling autonomous technologies to societal levels where safety and symbiosis are paramount.

Hiring Tips from Pebble Watch Founder Eric Migicovsky

Eric Migicovsky, Eric Michikovsky

Y Combinator partner Eric Michikovsky identifies flexibility, trust, and multidisciplinary skills as the three critical hiring criteria for early-stage startups. He argues that founders must prioritize candidates who approach work as a problem-solving mission with creative adaptability, rather than strict routine adherence. These qualities enable independent execution without micromanagement, ensuring the team can pivot effectively as the product roadmap evolves toward market fit.

How to Find a Technical Cofounder - Michael Seibel

Michael Seibel

To secure technical co-founders, the speaker recommends prioritizing direct inquiries to friends and current coworkers who actively code, converting interest into formal offers with specific equity and salary details rather than informal requests. If immediate networks are insufficient, the strategy involves joining a small startup for one to two years to build proximity to engineering teams or acquiring coding skills independently through online platforms. Additionally, college is highlighted as a high-yield environment for identifying future co-founders, as demonstrated by the successful recruitment of peers who were learning to code.

How to Get and Test Startup Ideas - Michael Seibel

Michael Seibel

This session challenges the notion that startup ideas must be perfect at inception, using Justin Kan's co-founding of Twitch to illustrate how prioritizing deep personal connection to a problem over the concept itself drives resilience. Kan advises founders to maintain "problem books" rather than idea logs, validate issues through direct community impact, and rigorously handpick early users to test minimum viable products. Ultimately, the discussion emphasizes that successful entrepreneurs must fall in love with the customer's pain point rather than their initial product, ensuring the team remains uniquely qualified to solve a specific, verified need.

Why is chicken so cheap? | The Economist

David Speller

Driven by the 1940s "Chicken of Tomorrow" competition, the global poultry industry now sustains a population of 23 billion birds through intensive genetic breeding that compresses broiler lifespans to 38 days. Consultant David Speller exemplifies this scale by managing millions of genetically uniform chickens with automated environmental controls, prioritizing rapid growth over natural maturity to meet high-volume, low-cost consumer demand. While organic and free-range systems offer significantly longer lifespans and better welfare conditions, their higher production costs and lower turnover rates keep them marginal compared to the dominant industrial model.

Evan Thomas: Author, "First: Sandra Day O’Connor"

Evan Thomas, Sandra Day O'Connor

Appointed by President Reagan as the Supreme Court's first female justice, Sandra Day O'Connor leveraged her background as a state legislator and political network builder to become the decisive swing vote in roughly 330 major cases. Her judicial philosophy emphasized strategic compromise and bridge-building, yielding landmark rulings that prohibited gender discrimination in state institutions, refined the "undue burden" standard for abortion rights, and upheld affirmative action. O'Connor transformed the Court's internal culture into a cohesive body through weekly lunches and served as a critical mentor to future female justices, proving that practical negotiation could achieve significant legal progress without rigid ideological activism.

How Pitching Investors is Different Than Pitching Customers - Michael Seibel

Michael Seibel

The event analyzes the critical divergence between investor and customer pitches, noting that investors seek scalable business potential while customers require immediate problem resolution. It details how founders must employ industry jargon to build credibility with clients during sales calls, whereas investor presentations demand plain language to clearly communicate monetization and market size to an uninformed audience. Y Combinator observations highlight that most new founders initially struggle to maintain these distinct narratives, requiring iterative practice to effectively address the different motivations of each stakeholder group.

Oliver Cameron (CEO, Voyage) - MIT Self-Driving Cars

Oliver Cameron, Lex

Oliver Cameron founded Voyage to deploy Level 4 autonomous vehicles within closed-loop retirement communities, leveraging exclusive licensing agreements to secure defensible market positions while addressing the mobility needs of seniors. Previously accelerating AV talent development through Udacity's program, Cameron applied rigorous engineering solutions like 128-channel LiDAR and deep learning perception networks to eliminate edge cases such as foliage occlusion and pedestrian clustering. The company's strategy prioritizes slow-speed safety and remote human intervention over competing in dense urban centers, aiming to capture a 47-million-person market by integrating dynamic risk assessment with Intact Insurance.

Drago Anguelov (Waymo) - MIT Self-Driving Cars

Drago Anguelov, Kieran Strobel

Waymo commemorates a decade of autonomous driving and over 10 million public road miles by advancing its core AI architecture of perception, prediction, and planning to address complex edge cases through a hybrid machine learning and rule-based system. The company fuels this development with an "ML Factory" that utilizes active learning, automated neural architecture search, and a massive simulation environment capable of generating 7 billion virtual miles daily to validate safety across diverse scenarios. Future efforts focus on scaling a single adaptable model to new cities without retraining, relying on rigorous testing protocols and self-improving algorithms to gradually achieve widespread commercial deployment.

Self-Driving Cars: State of the Art (2019)

While autonomous vehicle technology has progressed through billion-mile testing milestones by companies like Waymo and Tesla, the industry remains divided between competing vision and LiDAR sensor approaches to solve complex urban safety challenges. Despite significant reductions in fatalities compared to human driving, public deployment in 2018 was largely restricted to experimental geofenced zones with safety drivers due to the high barrier of 10,000 vehicles required for societal impact. Experts warn that achieving true Level 4 or 5 autonomy necessitates overcoming not only engineering hurdles in sensor fusion and perception but also the critical sociological task of building human trust in machines that must handle unpredictable interactions without fallbacks.

MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL)

This presentation analyzes the architectural foundations of deep reinforcement learning, contrasting its trial-and-error paradigm with supervised learning while detailing critical algorithmic categories such as model-based, model-free, and actor-critic methods. It highlights pivotal breakthroughs like Deep Q-Networks and AlphaZero that leverage neural networks to achieve superhuman performance in complex decision-making tasks, while cautioning against the misalignment risks inherent in reward function design. The discussion further explores the transition from simulation to real-world deployment in robotics and autonomous driving, emphasizing the necessity of mathematical rigor and iterative implementation for effective research and development.

Deep Learning State of the Art (2019)

This 2019 lecture analyzes the transition from deep learning's initial breakthroughs to a new era of theoretical development, highlighting the 2018 NLP revolution led by the BERT model and architectural shifts toward Transformers. It details critical advancements in applied domains, including Tesla's neural network-driven Autopilot, NVIDIA's synthetic data strategies, and AlphaZero's success in complex games through self-play. The presentation concludes by evaluating the democratization of training efficiency via tools like FastAI and notes the impending need for fundamental optimization theories beyond standard backpropagation.

Deep Learning Basics: Introduction and Overview

The MIT course "Deep Learning for Self-Driving Cars" leverages the `deeplearning.mit.edu` platform and Google Colaboratory to guide students through fundamental architectures like CNNs and GANs while utilizing TensorFlow and PyTorch frameworks. It contextualizes the field's evolution from 1940s perceptrons to modern AlphaGo and BERT, emphasizing that current success relies on the synergy of massive datasets, specialized hardware like TPUs, and open-source tooling. Despite these advancements, the curriculum critically examines limitations in general intelligence and robustness, urging the integration of human oversight to navigate ethical challenges and transition the technology from the peak of inflated expectations to practical productivity.

a16z Podcast | The Genetics Of Drug Delivery

Vijay Pande, Russ Altman

Stanford professor Russ Altman leads a data science laboratory that integrates molecular, cellular, and population-level data to optimize drug response and identify previously unknown side effects. By analyzing diverse sources ranging from electronic medical records to web search logs, Altman's team successfully uncovered critical interactions, such as a glucose spike caused by combining paroxetine and pravastatin, and predicted new therapeutic uses for existing cancer drugs. This multi-scale approach, supported by funding from the National Institutes of Health and industry partners like Pfizer and Genentech, aims to replace fragmented clinical trial models with continuous, data-driven surveillance for safer and more effective pharmaceutical development.

Extending Human Lifespan

Kristen Fortney, Tanya Cushman

This presentation argues that targeting aging as a root cause of chronic disease offers a superior strategy to treating individual conditions, with projections indicating such interventions could extend average human lifespans to 113 years. Citing breakthroughs in rapamycin, senolytics, and metformin trials alongside genomic insights from exceptional longevity cases, the discussion highlights a shift from skepticism to clinical application where three distinct therapeutic classes are currently advancing toward adding a decade or more of healthy life.