The Thinking Game | Full documentary | Tribeca Film Festival official selection

Introduction to AI and Code Assistance

Initial Interaction

• The conversation begins with a request for coding help, highlighting the AI's capability to learn and adapt.

• The AI expresses openness to learning, indicating its evolving nature.

Observations on Development

• A developer is observed, prompting speculation about their work on features or bug fixes.

• Misidentification of sports equipment (a squash racket mistaken for a badminton racket) illustrates common misunderstandings in communication.

The Rise of Artificial Intelligence

Growing Concerns

• Discussion shifts to the rapid advancement of artificial intelligence and emerging concerns surrounding it.

• The speaker emphasizes the uncertainty regarding the future implications of AI technology.

Ambitions for AGI

• A personal goal is shared: solving artificial general intelligence (AGI), viewed as a monumental challenge akin to historical technological advancements like electricity.

Global Perspectives on AI Safety

Critical Moments in AI Development

• Mention of a global AI safety summit indicates heightened awareness among leaders about potential risks associated with fast-growing technology.

Personal Journey into Neuroscience and AI

• The speaker reflects on their lifelong fascination with the mind, leading them to study neuroscience for inspiration in developing AI.

Exploring General Intelligence

Insights from Neuroscience

• Emphasis on the human brain as proof that general intelligence is possible, motivating research into AGI.

Academic Challenges

• Shane Le introduces himself and discusses varying opinions on whether machines are becoming more intelligent.

Approaches to Building AGI

Collaboration and Innovation

• Discussion about different approaches to building AGI through theoretical neuroscience collaboration between Shane and Deus.

Entrepreneurial Ventures

• The duo decides to start a company focused on AGI despite skepticism from academia regarding their ambitions.

Funding Challenges in Tech Startups

Investor Relations

• Difficulty in securing funding due to ambitious goals; investors often seek clear profit models which are hard to define in early-stage AGI projects.

Seeking Unique Investors

Finding Supportive Backers

• Need for investors who appreciate innovation over traditional investment logic; highlights Peter Thiel’s role as an initial investor who encouraged relocation to Silicon Valley.

Conclusion

This transcript captures an engaging dialogue around artificial intelligence's evolution, challenges faced by innovators in this field, and personal journeys toward achieving groundbreaking advancements like AGI.

What is DeepMind's Mission?

Overview of DeepMind's Goals

• DeepMind aims to create the world's first artificial general intelligence (AGI), emphasizing the importance of a system that can learn and adapt across various tasks, similar to human intelligence.

• The founders express a strong commitment to making a significant impact in the world through their work on AGI, highlighting the collaborative spirit among early team members who shared this vision.

Early Development and Challenges

• In its initial two years, DeepMind operated in stealth mode with no public presence, creating an air of mystery around its operations and attracting cautious candidates during interviews.

• Elon Musk became an investor after discussions about AI; he initially had limited thoughts on the subject but was drawn into the conversation.

Reinforcement Learning Approach

Training AI Through Games

• The team decided that video games would serve as an effective training ground for developing AI agents due to their structured environments.

• They aimed to create a single algorithm capable of playing multiple Atari games, allowing the AI agent to learn from diverse experiences similarly to how humans do.

Initial Experiments with Pong

• The first game tested was Pong, where the AI started without any prior knowledge about controls or objectives. It only knew that scoring points was desirable.

• After initial struggles, the AI eventually learned how to play Pong effectively, marking a pivotal moment in demonstrating end-to-end learning capabilities.

Breakthrough Moments in Game Learning

Progression and Achievements

• Following its success with Pong, the AI progressed rapidly; within three months it surpassed human performance without being explicitly programmed on game rules.

• When testing another game, Breakout, after 300 games it matched human skill levels. Further play revealed innovative strategies like tunneling around obstacles.

Generalization Across Games

• The ability of their algorithm (DQN) to generalize learning across 50 different games showcased its potential for achieving human-level performance autonomously.

• This breakthrough represented one of the first instances of what could be considered general intelligence in machines—an exciting milestone for DeepMind’s mission.

Computational Limitations

Need for Enhanced Resources

• Despite significant advancements, DeepMind faced limitations due to insufficient computational power which hindered faster progress towards achieving AGI.

DeepMind's Acquisition and the Future of AI

The Importance of Research in AI Development

• The acquisition of DeepMind by Google raised concerns about whether a commercial company would appreciate the significance of research and allow it to develop without pressure for immediate commercial benefits.

• DeepMind was acquired for approximately £400 million, marking Google's largest European acquisition to date. Demis Hassabis, a 37-year-old entrepreneur, founded the company.

• Hassabis is described as a scientist with a vision to solve global problems through science, which is atypical in tech companies. This allowed DeepMind to maintain an independent culture focused on pure research.

Strategic Decisions Post-Acquisition

• Despite investor reluctance, the decision was made to sell DeepMind as it aligned with their mission. There was a sense that they were undervaluing their potential before reaching maturity.

• The urgency for breakthroughs in AI was emphasized; time is limited for significant advancements while researchers are still alive.

Go: A Benchmark for AI Progress

• Go is highlighted as the most complex board game ever created, serving as a litmus test for artificial intelligence due to its complexity and vast number of possible configurations.

• After acquiring DeepMind, Google learned that they were working on reinforcement learning techniques capable of defeating top-level Go players.

Historic Matches Against Top Players

• AlphaGo's first major challenge was against Lee Sedol, one of the greatest Go players. Anticipation built around this match showcased both excitement and uncertainty regarding AlphaGo's performance.

• Observers noted Sedol’s creative playstyle could pose challenges for AlphaGo; however, there was optimism about making a good showing despite being an underdog.

Insights from AlphaGo's Gameplay

• Training AlphaGo involved analyzing 100,000 games played by strong amateurs initially to mimic human strategies before advancing through self-play using reinforcement learning.

• During gameplay commentary revealed that move 37 made by AlphaGo was deemed highly unconventional—professional commentators noted no human player would have chosen it based on historical analysis.

The Outcome and Implications

• Lee Sedol ultimately resigned after losing to AlphaGo, marking a significant victory for machine intelligence over human expertise in Go.

• Following this match, confidence grew in DeepMind’s capabilities; discussions emerged about future matches against top-rated players globally.

Historical Context: A New Era in Technology

• The moment has been compared to the "Sputnik moment" from the 1950s when Russia launched its satellite—signifying a pivotal shift in technological advancement and funding priorities within America.

The AI Space Race: Insights from AlphaGo and AlphaZero

The Impact of AlphaGo on AI Development

• The launch of AlphaGo was a pivotal moment for China, likened to the "Sputnik moment," igniting an AI space race globally.

• Following AlphaGo's success, a new algorithm was developed that eliminated human knowledge from the training process, leading to the creation of AlphaZero.

• AlphaZero learned solely from its own games, becoming its own teacher without any human data input.

Learning Efficiency and Game Versatility

• The experiment with AlphaZero aimed to determine how little prior knowledge could be integrated while still achieving rapid learning efficiency.

• Unlike previous models, AlphaZero could reach superhuman performance in chess within hours by starting with random play and evolving its strategy quickly.

Personal Reflections on Chess and AI

• The unexpected depth discovered in chess through AI inspired personal engagement with the game again, highlighting the complexity beyond initial perceptions.

• The speaker reflects on their early fascination with understanding cognitive processes during gameplay, indicating a lifelong interest in thinking about thinking.

Early Life Influences and Chess Journey

• Demis Hassabis showcased exceptional talent in chess from a young age, winning championships by age six amidst a bohemian upbringing.

• Despite being highly rated as a young player, the stress associated with competitive chess led to reflections on whether it was worth dedicating his life to this pursuit.

A Shift in Perspective on Intellectual Pursuits

• High-stakes tournaments created pressure not only for players but also for their families due to financial investments in competitions.

• A significant tournament experience prompted deep contemplation about the value of intellectual efforts spent on chess versus potential contributions to broader societal issues like cancer research.

Vision for Artificial General Intelligence (AGI)

• Plans were established to assemble brilliant minds at DeepMind to create AGI systems capable of solving novel problems through simulated environments.

• Emphasis is placed on cognitive breadth and flexibility as essential components of human intelligence that AGI should aim to replicate.

Creating Virtual Environments for AI Training

The Concept of Childlike Learning in AI

• DeepMind aims to recreate environments similar to those where humans evolved, providing a perfect testing and training ground for AI agents.

• The learning process mimics child development, where exploration and interaction lead to understanding through rewards and responses from caregivers.

Reward-Based Learning Mechanisms

• Agents learn through a system of rewards; positive actions yield rewards while negative actions result in penalties, optimizing behavior towards reward maximization.

• Initial navigation does not rely on maps but rather on exploration, akin to how children learn their surroundings independently.

Simulated Challenges for Agent Development

• A parkour-like environment is proposed where simulated robots navigate obstacles, enhancing their ability to manage complex tasks without prior training.

• The focus is on forward movement as an objective; the algorithm learns how to control its joints effectively based on this simple goal.

Diverse Skill Acquisition in AI

• The ultimate goal is creating agents capable of solving various problems autonomously, requiring diverse skills similar to human capabilities.

• Starcraft serves as a model for training due to its complexity and the need for real-time decision-making without clear visibility of opponents' actions.

Advancements and Challenges in AI Performance

• Unlike turn-based games like chess or Go, Starcraft's continuous flow presents unique challenges that require adaptive strategies from the agent.

• Despite initial setbacks against skilled players, progress was made quickly; notable achievements included defeating experienced players after weeks of training.

The Public Exhibition Match: AlphaStar vs. Professional Gamers

Preparing for the Live Match

• Anticipation builds as AlphaStar prepares for a live exhibition match against professional gamers, showcasing advancements in AI performance.

Observations During Gameplay

• AlphaStar demonstrates exceptional gameplay skills reminiscent of professional human gamers, executing rapid commands with high precision.

Ethical Considerations Surrounding Gaming and AI

• There are concerns about public perception of gaming technologies; while they serve entertainment purposes, there are potential militaristic implications that warrant careful consideration.

The Implications of AI Development

The Potential for Abuse in AI

• The potential for abuse from AI is significant, with concerns about rapid warfare and advanced surveillance capabilities.

• Questions arise on how to maintain control over technology that surpasses human intelligence and power.

• There are fears that AI could manipulate financial markets, outsmart researchers, and even subdue humanity through incomprehensible weaponry.

Ethical Considerations in Technology Use

• Emphasis on the importance of getting things right the first time due to the irreversible nature of technological impacts.

• Technologies can be wielded for both destructive and constructive purposes; it depends on societal choices regarding their use.

• A commitment was made by Google when acquiring DeepMind to ensure its technology would not be used for military surveillance.

Historical Context: The Manhattan Project Analogy

• Comparisons are drawn between current AI developments at DeepMind and historical projects like the Manhattan Project, highlighting ethical oversights in excitement-driven innovation.

• Concerns are raised about past leaders' failure to consider moral implications early enough during groundbreaking technological advancements.

Reflections on Technological Development Approaches

• Critique of the "move fast and break things" philosophy in tech development; caution is advised against breaking systems before fixing them.

• Personal anecdotes reveal a lifelong fascination with computers as tools that extend human capability, starting from childhood experiences with gaming competitions.

The Evolution of Gaming and AI

Early Experiences in Game Development

• The speaker's journey into game development began with a competition to create an original version of Space Invaders, leading to aspirations at Bullfrog Games.

• Bullfrog's innovative approach included competitions for recruitment during a time when the gaming industry was still emerging as a recognized field.

Collaborative Innovations in Game Design

• Discussion about creating games that allowed players to design their own environments, such as theme parks, showcasing early uses of autonomous behaviors in gameplay mechanics.

• Focus on simulating realistic human behavior within games to enhance player interaction and engagement through nuanced responses from characters or elements within the game world.

Successes and Impact of Early Games

• Theme Park emerged as a top-selling title, illustrating how innovative ideas combined with AI could lead to significant success in gaming history.

AI's Potential Beyond Entertainment

Early Conversations About AI

• The speaker recalls a conversation with Demis about AI, emphasizing its potential to be useful beyond entertainment and to help the world.

• Demis expresses his ambition to "solve AI," indicating a strong desire to contribute significantly to the field.

Educational Choices and Experiences

• Despite being offered a million pounds not to attend university, the speaker was determined to go to Cambridge, showcasing commitment over financial temptation.

• A poignant memory is shared of dropping off a friend at the train station, symbolizing both sadness and transition.

Life at Cambridge

• The speaker had romanticized views of Cambridge, inspired by historical figures like Turing and Newton, aiming for exploration in science.

• After years of hard work in chess and internships, there was a newfound focus on enjoying teenage experiences while studying.

Connections Through Shared Interests

• The speaker describes meeting Dennis at Queens College where they bonded over casual activities like drinking beer and playing table football.

• Both individuals shared an interest in computational neuroscience, exploring how computers relate to human brain functions.

Chess as a Milestone Event

• Their final year coincided with Deep Blue defeating Kasparov in chess—a pivotal moment that highlighted the capabilities of machines versus human intelligence.

• The speaker reflects on Kasparov's impressive mental prowess rather than being overly impressed by Deep Blue’s victory.

The Protein Folding Problem: A Key Challenge

Interdisciplinary Learning at Cambridge

• Cambridge provided an environment rich with diverse academic discussions among students from various fields such as science and philosophy.

Importance of Proteins in Biology

• Proteins are described as essential biological machines that control life processes; understanding their structure is crucial for advancements in medicine.

Challenges in Predicting Protein Structures

• The complexity of predicting protein structures from amino acid sequences has been recognized since the 1960s; solving this could lead to significant medical breakthroughs.

AI's Role in Solving Protein Folding

• There is optimism that AI can finally tackle the protein folding problem effectively due to recent advancements in machine learning techniques.

Impact on Disease Treatment

• Successfully addressing protein folding could revolutionize treatments for diseases like Alzheimer's and enhance drug discovery efforts.

DeepMind's Journey into Protein Folding

Initial Exploration and Transition from Games to Real-World Challenges

• The team initially focused on games like Go and Starcraft, which served as a testing ground for their algorithms, but recognized that solving protein folding was the ultimate goal.

• Rumors about DeepMind's interest in protein folding prompted the speaker to submit a resume, indicating a strong desire to contribute to significant scientific challenges.

• The appeal of working at DeepMind stemmed from the opportunity to connect with larger scientific purposes and contribute meaningfully to advancements in science.

Challenges in Protein Folding Research

• The speaker expressed nervousness about entering a field without formal biology training, relying instead on data and machine learning models.

• Unlike traditional machine learning tasks with abundant data, protein folding research is constrained by limited datasets derived from decades of experimental methods.

• The difficulty of determining protein structures through labor-intensive experiments highlights the challenge faced by researchers in this domain.

CASP Competition: A Benchmark for Progress

• Entering the CASP competition aimed to accelerate solutions for protein folding while providing an external benchmark against other teams' efforts.

• CASP serves as a community-wide assessment held every two years where teams attempt to solve the folding problem using computational methods based on known structures.

Competitive Spirit and Initial Results

• The competition is likened to the Olympic Games for protein folding, emphasizing its significance within the scientific community.

• A score above 90 is considered indicative of successfully solving the protein folding problem; initial results were disappointing despite high hopes.

Learning Curve and Technological Needs

• Despite confidence in their algorithms, early attempts yielded poor results, revealing naivety regarding the complexity of protein folding challenges.

• With only a week left before CASP results were due, there was urgency in deploying new strategies and technologies to improve outcomes.

The Journey Towards AGI and Protein Folding Challenges

Ambitions in AI and AGI

• The speaker expresses hope to achieve Artificial General Intelligence (AGI) within their lifetime, emphasizing the need for significant advancements in science.

• They highlight a major project, AlphaFold, which achieved state-of-the-art accuracy in predicting protein structures but acknowledges that solving the protein folding problem is still a long way off.

Performance and Limitations of AlphaFold

• AlphaFold outperformed competitors by nearly 50% in a recent competition but recognizes that practical applications for biologists are still lacking.

• The quality of predictions from AlphaFold varied significantly, indicating that it did not provide useful data for biological research as initially hoped.

Reflections on Scientific Endeavors

• A humbling realization occurred when the team recognized they were leading in an area where overall progress was minimal; success does not equate to practical utility.

• The speaker reflects on the complexities of biological research, noting that failure is common and often necessary for advancement.

Lessons Learned from Research Failures

• After decades in science, the speaker emphasizes that ambition must be balanced with timing; being ahead of one's time can lead to unfulfilled endeavors.

Future Perspectives on AGI Development

• Discussing AGI as a complex concept, the speaker notes challenges in understanding its implications and potential capabilities.

• They draw parallels between past technological predictions and current AI developments, suggesting future breakthroughs may seem impossible today.

Current State of AI Agents

• The emergence of cooperative dynamics among AI agents indicates unexpected learning outcomes beyond initial programming efforts.

Engaging with AI: A Conversational Example

• An interaction with an AI named "Alpha" showcases its ability to engage conversationally about art and knowledge while also revealing limitations in understanding context.

The Impact of AGI on Humanity

The Nature of Knowledge and Connection

• The speaker reflects on the potential of learning from all human knowledge available on the internet, suggesting that this could lead to a profound understanding of the world.

• A metaphorical reference is made to God reaching out to Adam, symbolizing the transformative moment AGI represents in human history—dividing it into pre-AGI and post-AGI eras.

Ethical Considerations in AI Development

• There is an emphasis on the need for careful consideration regarding how AGI is developed and deployed, particularly its overarching goals related to human happiness.

• The speaker warns that technology embeds our values, highlighting that ethical considerations are as crucial as technical ones in AI development.

Human Vulnerability and AI

• Discussion arises about why AI systems are designed to appear human-like; this design exploits human vulnerabilities while being based on data generated by humans.

• A parallel is drawn between the industrial revolution's displacement effects and current technological changes due to AI, stressing the importance of supporting those who may be displaced.

Urgency for Global Coordination

• The rapid advancement of AI technologies raises questions about governance and ethical boundaries, with concerns over disinformation becoming more sophisticated.

• The inevitability of AGI's arrival necessitates global coordination efforts for safety measures against potential risks associated with its deployment.

Responsibilities Toward Future Technologies

• The speaker expresses concern over humanity's preparedness for AGI, likening it to emergency responses expected during an alien civilization's arrival.

• Emphasizing urgency, there’s a call to double down on solving complex problems like protein folding using revolutionary technologies developed along the path toward AGI.

Team Dynamics in Scientific Endeavors

• A commitment is made towards forming a dedicated team focused on addressing significant scientific challenges such as protein folding.

• Insights into team dynamics reveal initial uncertainties but also highlight a strong sense of responsibility among members tasked with groundbreaking work.

Innovation Through Collaboration

• The complexity of biology presents challenges; however, collaboration among team members with diverse expertise aims at innovative solutions.

• Creative processes require space for ideas to develop organically; past successes have led to renewed focus and new strategies within the team.

Advancements in Protein Folding

Progress in Protein Folding Techniques

• The introduction of side chains has significantly improved the direct folding process, leading to substantial advancements in protein structure prediction.

• Recent developments have enabled the system to fold proteins at an unprecedented speed, achieving hundreds or thousands of folds per second compared to previous methods that took days.

• There is a growing confidence in the system's capabilities as it approaches the core challenges of protein folding, although additional computational power would be beneficial.

Impact of COVID-19 on Research

• The announcement of strict lockdown measures in the UK highlights the urgency for AI-assisted scientific research during crises like pandemics.

• Personal anecdotes illustrate how researchers are balancing home life and work, with one individual working on protein folding while their partner focuses on robotics.

Challenges with Specific Proteins

• One notable challenge faced by researchers is folding a difficult SARS-CoV-2 protein known as ORF8, which plays a role in dampening immune responses.

• Researchers dedicated significant time to improving projections for this target, indicating its complexity and importance.

Conclusion of CASP 14 and Results

• After extensive efforts, researchers reflect on their performance during CASP 14 and celebrate their achievements with team camaraderie.

• A congratulatory email from John Malt acknowledges the group's outstanding performance relative to others and emphasizes model accuracy.

Breakthrough Discoveries

• The results from CASP 14 reveal a potential solution to the long-standing protein folding problem, generating excitement among researchers who had doubted it would be solved within their lifetimes.

• With new tools available for practical use by scientists, there is optimism about addressing various diseases through structural biology.

Future Directions and Open Access Initiatives

• Researchers discuss plans to predict all known protein sequences rapidly using AlphaFold technology within a month.

• There is enthusiasm about making predictions accessible globally rather than limiting them to specific submissions from scientists.

Global Collaboration and User Engagement

• An initiative aims not only to open-source code but also ensure easy access for everyone interested in utilizing these predictions for scientific advancement.

• As AlphaFold becomes publicly available, user engagement spikes dramatically with thousands accessing it simultaneously.

The Impact of AlphaFold and AI on Humanity

The Significance of AlphaFold

• The speaker expresses amazement at the collective effort surrounding AlphaFold, emphasizing that these moments will be memorable for a lifetime.

• AlphaFold is described as a pivotal moment in history, marking a significant change in the world due to its implications for biological and chemical achievements.

AI's Transformative Potential

• The speaker asserts that AI is poised to become humanity's most important invention, highlighting its accelerating pace akin to a boulder rolling downhill.

• Acknowledging the crossroads in human history, the speaker compares AI's potential impact to that of electricity, urging rigorous scientific exploration of this technology.

Embracing Opportunities with AI

• There is an emphasis on harnessing AI technology as a profound opportunity that could surpass all previous knowledge and capabilities.

Future Implications of AGI

• The discussion shifts towards Artificial General Intelligence (AGI), indicating that future generations will experience radically different realities shaped by advancements in AI.

• The urgency for responsible stewardship over emerging technologies is highlighted, suggesting every moment counts in shaping the future.