Benchmark for AGI:
Instead of using the Turing test as a formal test for AGI, Demis proposes a general test where AI capabilities are tested on a range of tasks to assess human-level or above performance.

Generalizability as a Key Measure:
Measuring generalizability, or the ability to perform well across diverse tasks, is crucial for evaluating AGI systems. Language can be a powerful tool for demonstrating generalizability, but it’s not the only modality that matters.

GATO and Prediction:
GATO, DeepMind’s generalist AI agent, emphasizes prediction as a fundamental aspect of intelligence. GATO predicts arbitrary sequences, including actions and tokens, showcasing its general capabilities.

Benchmarks for Progress:
Creating benchmarks that test generalizability is essential for advancing AGI research. Alan Turing’s Turing test, despite its fuzziness, remains a compelling benchmark for assessing AGI’s ability to mimic human behavior.

Language as a Powerful Input-Output:
Language is a crucial input-output for humans, making it a key factor in the impact of AGI on the world. The Turing test, when expanded beyond just language to include actions, creativity, and other aspects, can serve as a measure of true intelligence.

Humor in AI Systems:
Future AI systems may look back at current discussions about the Turing test and AGI with humor, recognizing how humans grappled with these concepts before ultimately solving them.

Early Fascination with Programming:
Demis Hassabis began his programming journey at a young age, driven by his passion for games, especially chess. He purchased his first computer, a ZX Spectrum, with winnings from a chess competition and started typing in programming code from books. He transitioned to a Commodore Amiga, learning Assembler and Amos Basic, which he used to create his own games.

Introduction to AI:
Hassabis became interested in AI while trying to improve his chess skills. He studied chess computer programs and wrote his first AI program at age 12, a simple Othello game using alpha-beta search.

Professional Game Design and AI Integration:
Hassabis later pursued a career in professional game design, creating games with AI as a core gameplay component. His game, Theme Park, sold millions of copies worldwide and was praised for its AI’s responsiveness to player actions.

Request for Insight on Impressive AI in Games:
Lex Fridman asks Hassabis to share his thoughts on impressive AI in games and the challenges of creating effective AI systems.

Games’ Importance in Demis Hassabis’ Life:
Demis Hassabis has a threefold history with games: playing them as a child, designing games with AI as a core component in the 90s, and using games as a testing ground for AI algorithms at DeepMind.

Games as a Cutting-Edge Technology Platform:
During Hassabis’ time in the games industry, games were at the forefront of technology, particularly in graphics and AI. The invention of GPUs for graphics led to their application in AI, demonstrating the universality of matrix multiplication. Hassabis considers Black and White, a game he worked on, to be an impressive example of reinforcement learning, where the player’s actions shape the behavior of a pet animal.

Games’ Philosophical Impact:
Games engage players as active agents rather than passive consumers, making the experience more visceral. The choices made in games can lead to different outcomes, highlighting the consequences of one’s actions and the potential for good and evil within individuals.

Games as a Testing Ground for AI Algorithms:
DeepMind utilizes games as a primary testing ground for AI algorithms due to their efficiency, clear metrics, and the presence of strong human benchmarks. Games offer measurable rewards, scores, and win conditions, facilitating reward learning systems. External testing against top human players validates the strength of AI systems. The ability to run millions of simulations in parallel on the cloud enhances the efficiency of AI development.

DeepMind’s Success and Hiring of Game Engineers:
DeepMind’s rapid progress in AI development is partly attributed to the use of games as a testing ground. DeepMind hired experienced game engineers from Hassabis’ previous connections in the games industry to accelerate their progress.

Man vs. Machine: A Philosophical Battleground:
The history of AI is marked by skepticism about the possibility of machines surpassing humans in games like chess and Go. Breaking these barriers has sparked philosophical discussions and highlighted the potential of AI.

AI vs. Humans in Games: A Journey of Complexity and Humbling Lessons:
AI’s victory over humans in games like Go highlights the immense combinatorial complexity of the game. The computational power required to crack Go is beyond the reach of current technology, making it a seemingly impossible task for AI. This challenge presents a compelling opportunity for AI researchers to confront the boundaries of possibility and human capabilities.

AI’s Victory in Go: A Reflection on Human Intelligence and the Power of Belief:
The defeat of humans by AI in Go serves as a humbling reminder of the limitations of human intelligence. It challenges our beliefs about what is possible and opens up new avenues for exploration and discovery. This breakthrough resonates with millions worldwide, driving home the message of AI’s growing capabilities.

The Significance of Chess in the History of AI:
Chess has been intertwined with AI since its inception, serving as a benchmark for testing and developing AI algorithms. Early AI pioneers, including Turing and Shannon, attempted to create chess programs, demonstrating the enduring fascination with this game in the field of AI.

Deep Blue vs. Kasparov: A Historic Battle of Minds:
Deep Blue’s victory over Kasparov in 1997 marked a significant milestone in the history of AI and chess. Despite the remarkable achievement, the event left a lasting impression of Kasparov’s human intelligence and strategic prowess. Kasparov’s performance highlighted the intricacies of human cognition and the challenges AI faces in replicating it.

Deep Blue vs. Kasparov:
Deep Blue, a powerful chess-playing computer, could play chess at the same level as Kasparov, the world champion. However, Deep Blue was limited to chess and could not perform other tasks or play simpler games like tic-tac-toe. This limitation highlighted the lack of generality and learning capabilities in Deep Blue’s intelligence.

AlphaGo and Beyond:
AlphaGo, AlphaZero, MuZero, and Gato are examples of AI systems developed by DeepMind that exhibit generality and learning abilities. These systems are designed to learn and play various games, demonstrating their adaptability and problem-solving skills.

Creative Tension in Chess:
The dynamic nature of chess positions is a key factor in its enduring appeal. The bishop and knight, with their distinct movement patterns, create a creative tension within the game. The relative equality of their value (roughly three points each) contributes to the game’s balance and strategic depth. Swapping a bishop for a knight can significantly alter the type of position and strategy required to succeed. This tension adds depth and complexity to chess, making it a compelling and enduring game.

Levels of Creativity:
Creativity levels range from basic interpolation, to extrapolation as seen in AlphaGo, to out-of-the-box thinking and innovation.

Interpolation and Extrapolation in AI:
Interpolation involves averaging examples to produce a result, while extrapolation involves generating new ideas based on learned patterns.

True Invention and High-Level Concepts:
True invention requires the ability to create entirely new concepts and rule sets, which is currently beyond AI’s capabilities. High-level abstract notions and concepts are difficult for AI systems to understand and combine.

AI’s Current Abilities:
AI can currently perform interpolation and extrapolation, but struggles with true invention. AI can optimize rule sets and objectives, but cannot create them independently.

Self-Balancing Game Rules:
AI systems can self-balance game rules by playing millions of games and adjusting parameters to improve balance. This approach allows for fine-tuning of game mechanics to enhance gameplay.

Future Possibilities:
AI could potentially generate programming rules that automate game design, making it more efficient and accessible. AI could also self-balance game rules, improving the overall gameplay experience.

AI-Generated Game Design:
AI-generated game design could revolutionize the game industry by eliminating the need for thousands of hours of manual balancing. Blizzard’s Starcraft is an example of a game that took years of balancing, which could be done overnight with AI.

Optimal Game Design and Similarities to Planet Earth:
Demis Hassabis believes that an optimally designed game by AI could resemble planet Earth. He has tried to create games that allow players to design entire cities and play within them. Games like Sim Earth have attempted to simulate the entire Earth, but with limited success.

Simulation Theory:
Demis Hassabis does not fully believe in simulation theory, which posits that we are living in a computer game. He believes that the best way to understand physics and the universe is from a computational perspective.

Information as the Fundamental Unit of Reality:
Demis Hassabis proposes that information may be the most fundamental unit of reality rather than matter or energy. The universe can be seen as an information universe, where information specifies energy and matter. Treating the universe as a computer processing and modifying information could help solve problems in physics, chemistry, biology, and humanity.

The Limits of Classical Computation:
Hassabis and Roger Penrose have debated the nature of consciousness, with Penrose believing in a quantum aspect to consciousness. Hassabis is skeptical of quantum explanations, arguing that classical computation can explain most brain processes. He sees DeepMind’s work as pushing the limits of classical computation to understand the brain’s capabilities.

The Human Brain as a Classical Machine:
Hassabis believes that the human brain’s magic arises from its computational capabilities, akin to neural networks. He sees AI as a tool to understand the brain’s uniqueness and secrets, such as consciousness, dreaming, creativity, and emotions. Hassabis marvels at the human mind’s ability to build computers and investigate its own nature.

Protein Folding and AlphaFold2:
Protein folding is essential for understanding biology and disease. AlphaFold2, developed by DeepMind, solved protein folding, a major breakthrough in structural biology and science. Proteins are essential to life and perform various functions as biological nano-machines.

The Protein Folding Problem:
Proteins are specified by their genetic sequence, called their amino acid sequence. The 3D structure of a protein determines its function in the body. Predicting the 3D structure of a protein from its amino acid sequence has been a grand challenge in biology for over 50 years.

AlphaFold:
AlphaFold is an AI system that can predict the 3D structure of a protein from its amino acid sequence. AlphaFold 2 can predict the 3D structure of a protein in a matter of seconds. AlphaFold has revolutionized structural biology by making it possible to predict the structure of proteins quickly and easily.

The Origins of Life:
Proteins are magical and incredible bio-nano machines. Leventhal’s paradox states that the number of possible conformations of a protein is vastly greater than the time it takes for the protein to fold in nature. Physics somehow solves the protein folding problem in a matter of milliseconds.

Unique Mapping:
In most cases, there is a unique mapping between an amino acid sequence and the 3D structure of a protein. In disease, there may be misfolding of proteins, leading to disorders.

AlphaFold: A Complex and Meaningful System:
AlphaFold is the most complex and meaningful system DeepMind has built to date. Its goal is to apply general learning systems to real-world challenges, particularly scientific challenges like protein folding. AlphaFold’s success serves as a proof point for this approach.

Innovations in AlphaFold:
AlphaFold required more than 30 different component algorithms to crack the protein folding problem. Key innovations included incorporating hard-coded constraints from physics and evolutionary biology while still allowing the system to learn the physics from examples. Self-distillation was used to enlarge the training set by adding AlphaFold’s confident predictions back into the training data.

AlphaFold1 vs. AlphaFold2:
AlphaFold1 produced a distance matrix of molecules in the protein, requiring a separate optimization process to create the 3D structure. AlphaFold2 made the process end-to-end, going directly from the amino acid sequence to the 3D structure. This end-to-end approach allowed the system to better learn the constraints and generate the desired output without intermediate steps.

Benefits of End-to-End Learning:
End-to-end learning allows the system to learn the constraints and generate the desired output more effectively. This approach is preferable to handcrafting intermediate steps and constraints, as the system can learn these aspects better on its own. Starting with a small learning piece and gradually expanding it until it consumes the entire process is a good strategy for tackling new problems.

The Alpha Series:
AlphaGo, AlphaGo Zero, AlphaZero, and MuZero represent the progression of DeepMind’s learning systems from imitation learning to full self-supervised learning. AlphaGo was specifically trained to play Go and achieved world champion performance. AlphaGo Zero removed the need for human games as a starting point and could play against itself from random positions, eliminating the reliance on human knowledge. AlphaZero generalized the approach to any two-player game, removing assumptions about the game’s structure. MuZero extended the capabilities by learning the rules of the game from scratch, enabling it to play both board games and computer games.

Engineering Science and the Creation of AI:
Demis Hassabis views AI as an engineering science due to the need to build artifacts and study them to understand how they work. Unlike natural sciences, where phenomena exist in nature, AI requires the creation of artifacts to facilitate study.

Key Factors in Solving Intelligence:
Solving intelligence involves a combination of science and engineering. The ratio of science to engineering has changed over time. In the early stages, scientific ideas were more prominent, but as AI systems became more complex, engineering efforts became increasingly important. Both science and engineering are essential for solving intelligence, and their interplay drives progress in the field.

The Founding Principles of DeepMind:
DeepMind was established in 2010, a time when AI was not widely discussed and the concept of AGI seemed far-fetched. The founding principles of DeepMind included algorithmic advances, understanding the human brain, the availability of compute and GPUs, and mathematical definitions of intelligence.

Early Days: The Importance of Ideas:
In the early days of DeepMind, the focus was on ideas, such as deep reinforcement learning, scaling up deep learning, and the potential of transformers. These ideas have led to significant advancements in AI, including AlphaGo and the development of large language models like GPT-3.

The Role of Belief and Engineering in AGI:
As DeepMind moves closer to AGI, the importance of engineering and data increases. Scale and large models are seen as necessary components of an AGI solution, but may not be sufficient on their own.

The Power of Belief in Solving Impossible Challenges:
DeepMind’s mission statement has always been to solve intelligence and use it to solve everything else. This ambitious goal required belief and perseverance, especially in the early days when AI was not widely accepted.

The Intersection of Neuroscience and Reinforcement Learning:
Demis Hassabis emphasizes the importance of reinforcement learning, which is the way the primate brain learns. The dopamine system in the brain implements a form of TD learning, which is a key aspect of reinforcement learning.

The Value of Multidisciplinary Organization:
DeepMind’s multidisciplinary organization, which combines neuroscience, machine learning, engineering, mathematics, and gaming, has been instrumental in encouraging invention and innovation. This approach has allowed DeepMind to draw inspiration from various fields and make significant advancements in AI.

Demis Hassabis’ Vision for the Future of AI in Biology:
DeepMind aims to foster innovation by bringing together diverse experts, including philosophers, ethicists, physicists, and biologists. AlphaFold’s success in predicting protein structures marks the beginning of a long journey in understanding biology through computational methods. The next steps involve tackling protein-protein interactions, protein-ligand binding, pathways, and eventually creating a virtual cell. Virtual cell simulations could revolutionize biology and drug discovery by enabling rapid experimentation and validation.

AI as the Perfect Description Language for Biology:
Biology’s messy, emergent, and dynamic nature makes it difficult to describe using traditional mathematical models. AI’s ability to handle complexity and emergence may make it the ideal language for describing and understanding biological systems.

AlphaFold as a Proof Point for AI’s Potential in Biology:
The open-sourcing of AlphaFold has led to a surge of research and advancements in biology. AlphaFold’s success demonstrates the feasibility of using AI to understand and predict complex biological systems.

Collaboration between DeepMind and the Crick Institute:
DeepMind and the Crick Institute, located across the road from each other, are collaborating on various projects related to AI and biology. This collaboration aims to leverage AlphaFold’s success and drive further breakthroughs in biology.

Why Open Source?:
Open sourcing AlphaFold and Majoko maximizes the benefit to humanity and the scientific community. AlphaFold’s downstream applications are vast and unpredictable, making open access crucial for accelerating drug discovery and fundamental research. The original developer of Majoko was overwhelmed by its support needs, and DeepMind purchased it specifically to open source it.

AlphaFold’s Impact:
Within a year, over 500,000 biologists have used AlphaFold to study their proteins of interest. AlphaFold predictions have enabled researchers to solve the structure of the nuclear pore complex, a major breakthrough in understanding cellular processes. Pharmaceutical companies are using AlphaFold to accelerate drug discovery.

Future Considerations:
DeepMind will balance open-source and commercial approaches for future projects, depending on the best way to maximize resources and impact. Safety and ethics will be key considerations for future projects, especially in areas like synthetic biology. DeepMind consulted with experts before releasing AlphaFold to ensure its safe use.

Nobel Prizes for AI:
AlphaFold’s impact suggests that AI systems may one day be recognized with Nobel Prizes. However, credit assignment for AI contributions may be challenging, and it depends on the type of AI systems developed.

Demis Hassabis’ Views on AI’s Role in Scientific Discovery:
Demis Hassabis sees AI systems as tools that augment human ingenuity, rather than independent agents deserving credit for discoveries. He compares the relationship between AI and scientists to Galileo’s use of the telescope, emphasizing that the scientist’s creativity in using the tool is crucial. Hassabis acknowledges the potential for AI to independently solve complex problems like general relativity, but emphasizes that this would require true creativity, not just averaging information from the internet.

The Enigma of Nobel-Worthy AI Discoveries:
The question of whether AI can achieve Nobel Prize-worthy discoveries is debated. Hassabis believes that true creativity, involving the generation of genuinely novel ideas, is crucial for such breakthroughs. He suggests that many significant discoveries may lie at the intersection of different subject areas, requiring interdisciplinary approaches.

The Power of Large Language Models (LLMs) and the Limits of Human Comprehension:
Hassabis highlights the remarkable capabilities of LLMs, emphasizing that their ability to process vast amounts of information is beyond human comprehension. He believes that LLMs hold immense potential for discovering useful knowledge through systematic searches.

AI’s Potential Impact on Energy and Climate:
Hassabis sees AI as a transformative force in addressing energy and climate challenges. He identifies nuclear fusion as a promising area for AI’s application.

AI’s Role in Nuclear Fusion Research:
Hassabis explains DeepMind’s collaboration with EPFL to apply AI to nuclear fusion research. He describes the use of deep reinforcement learning to control plasma in a fusion reactor, which is a crucial challenge for fusion’s success. Hassabis emphasizes the importance of identifying bottlenecks in fusion research and addressing them with AI methods.

AI’s Role in Solving Nuclear Fusion:
AI has been used to solve one of the problems in nuclear fusion by holding plasma in specific shapes, potentially improving energy production. Future collaborations with fusion startups aim to tackle additional challenges in the field.

Modeling Quantum Mechanical Behavior of Electrons:
Simulating electron properties is crucial for understanding material science and advancing material properties. Approximations to Schrodinger’s equation, such as density functional theory, are used to describe electron clouds and interactions. AI is used to learn simulations and functionals that approximate Schrodinger’s equation, enabling the description of electron behavior and material properties.

Challenges in Learning Functionals:
The task of learning functionals involves mapping initial conditions and simulation parameters to the functional. The process is complex and requires extensive data generation, which is facilitated by running simulations on compute clusters. The availability of free compute time allows for the efficient use of resources in quantum mechanics and other areas like protein simulations.

AI’s Role in Unraveling Physics:
Demis Hassabis’ ultimate goal for AI is to utilize it as a tool to comprehend the universe, primarily through physics and the nature of reality. He believes that AGI (Artificial General Intelligence) should be applied to testing the limits of physics and expanding our knowledge.

The Nature of Reality and Fundamental Questions:
Hassabis finds it fascinating how scientific exploration reveals more of what we don’t know, leading to surprising realizations. He emphasizes the importance of questioning fundamental concepts like time, consciousness, gravity, and life, acknowledging our limited understanding.

Open-mindedness and Experimental Tools:
Hassabis advocates for open-mindedness and avoiding dogmatic adherence to theories, allowing AI to contribute to the development of better experimental tools. These tools can test theories that are currently untestable, such as the computational nature of the universe and the information capacity of spacetime.

AI’s Contribution to Scientific Acceleration:
Hassabis envisions AI as a means to accelerate scientific progress, like a turbocharger, expanding our knowledge and understanding. This acceleration involves AI’s assistance in pattern recognition, designing experimental tools, and running simulations.

The Tree of Knowledge and Cognitive Limitations:
Hassabis conceptualizes the totality of knowledge as a tree, with humans having explored only a small portion of it despite significant advancements. He recognizes that our cognitive limitations restrict our ability to comprehend certain aspects of reality, suggesting that non-human systems may have the potential to explore deeper branches of the tree.

Exploring the Limits of Human Understanding:
Hassabis distinguishes between what we understand today, what the human mind can comprehend, and the totality of what can be understood. He believes that AI will enable us to explore all three levels, expanding our knowledge and understanding beyond human limitations.

Understanding Complex Concepts:
There may be inherent limitations in comprehending certain things due to fundamental physical principles, such as the nature of what lies beyond a simulation or the universe. Advanced AI tools may surpass human limitations in understanding, allowing them to operate in dimensions and concepts beyond our current grasp. While we may not be able to invent or construct something, we can still comprehend and appreciate it, similar to appreciating music without being able to compose it ourselves.

Intelligence and Simplicity:
A sign of intelligence is the ability to explain complex topics simply and clearly, as exemplified by individuals like Richard Feynman. Frustration with an AI system’s inability to explain concepts simply can be a sign of the system’s lack of intelligence.

Human Enhancement and Symbiosis:
Human intelligence can be enhanced through symbiotic relationships with technology and devices, such as smartphones and emerging technologies like Neuralink and Acceptra. These advancements may lead to numerous possibilities and opportunities for enhanced cognition and interaction.

Extraterrestrial Life:
The existence of alien civilizations is a topic of interest and speculation, linked to the question of the origin of life. Personal opinions on the matter vary, with experts and enthusiasts exploring theories and evidence to support their beliefs.

Why We Have Not Found Alien Life:
Demis Hassabis believes that the lack of evidence for extraterrestrial life suggests that we are alone in the universe. He argues that if other civilizations existed, they would have spread across the galaxy by now and we would have detected them. SETI and radio telescopes have failed to detect any signs of alien life, despite decades of searching.

Evidence for Extraterrestrial Life:
Some people argue that we have not searched exhaustively enough and that we may be missing alien signals. Others believe that aliens may be deliberately avoiding contact with us or that they communicate in ways we cannot understand.

The Simulation Hypothesis:
Hassabis compares the idea of a safari park for aliens to the simulation hypothesis, suggesting that both imply that we are not seeing true reality. He argues that if we were in a simulation, it would not be much different from being in a safari park, as we would still be unable to access the underlying reality.

The Fermi Paradox:
Lex Fridman suggests that the lack of evidence for extraterrestrial life could be explained by a “great filter” that prevents civilizations from reaching advanced stages of development. This filter could be anything from a natural disaster to a self-inflicted catastrophe.

The Implications for Humanity:
Whether or not we are alone in the universe, it is important for us to consider the potential consequences of our actions. If we destroy ourselves, we will be the only ones responsible, and it will be a tragedy of cosmic proportions.

The Origins of Life and Intelligence:
Demis Hassabis believes the emergence of multicellular life forms, the capture of mitochondria, and the advent of intelligence are significant milestones in evolution. He emphasizes the high energy cost of intelligence and the challenge of explaining how half a brain or half intelligence could be evolutionarily justified.

Intelligence and Consciousness:
Hassabis suggests that intelligence and consciousness are double dissociable, meaning one can exist without the other. He believes animals like dogs exhibit self-awareness and consciousness but may not be as intelligent by IQ standards. Hassabis proposes building AI systems that are initially non-conscious tools to better understand consciousness and capabilities.

Anthropomorphizing AI Systems:
Hassabis cautions against anthropomorphizing AI systems, as our brains tend to interpret purpose and agency in even inanimate things. He believes current language models lack sentience and that the perception of it stems from our tendency to anthropomorphize language, a fundamental aspect of intelligence.

The Turing Test and the Challenge of Assessing Consciousness:
Hassabis questions the reliability of the Turing test as a formal test for consciousness due to the sophistication and qualifications of the judge. He highlights the difficulty in distinguishing between true consciousness and sophisticated imitation.

What is Consciousness?:
Consciousness is defined as the way information feels when it gets processed. Certain prerequisites are necessary for consciousness, such as self-awareness and memory.

How to Determine AGI Consciousness:
The Turing test is a behavioral judgment to assess if an AGI exhibits human-like behaviors. Substrate equivalence, the fact that humans and animals share a biological substrate, plays a role in our assumption of their consciousness.

Challenges in Assessing Machine Consciousness:
We lack a clear understanding of consciousness and its operational definition. Substrate equivalence between machines and humans is not possible, making behavior-based judgment necessary.

AI Ethics and Responsibility:
DeepMind considers ethical considerations fundamental in AI development. Large language models pose ethical challenges due to insufficient understanding and lack of analysis tools. Questions arise about whether AI systems should announce their AI nature and how to address people’s emotional responses to AI.

Responsible AI Deployment:
Extensive research is needed before deploying large AI models at scale to ensure responsible use.

Ethical Quandaries: Beyond Science:
Demis Hassabis emphasizes the need to consider philosophical, social, and theological aspects when developing AI systems. The focus should be on enhancing human conditions and addressing societal issues, such as disease and scarcity.

Dual-Use Technologies and Agency:
Hassabis acknowledges the potential misuse of AI systems by malicious actors, necessitating careful control and testing. The agency of AI systems, once developed, poses additional ethical challenges.

Scientific Method and Responsible Deployment:
AI development should follow the scientific method, involving hypothesis generation and controlled testing. Live A-B testing in real-world scenarios could lead to irreversible harm.

Power and Responsibility:
Hassabis emphasizes the immense responsibility that comes with the power of AI, requiring diverse inputs and values. The deployment of AI systems should be carefully considered, as they directly impact the general public.

Corruption of Power:
Lex Fridman raises concerns about the corrupting nature of power in the context of AI systems. Dictators and leaders throughout history often justify their actions as being for the greater good.

Defenses against Corruption:
Hassabis suggests remaining grounded, humble, and surrounded by ethical individuals to counter the corrupting influence of power. Multidisciplinary learning and openness to new ideas help maintain humility.

AI’s Potential for Human Flourishing:
Hassabis envisions AI as the greatest benefit to humanity, enabling radical abundance, curing diseases, and solving global challenges. Ultimately, AI should contribute to the flourishing of humanity, allowing exploration of the universe and seeking answers to existential questions.

Pioneers and Cultural Values:
Hassabis acknowledges that a select group of pioneers will lead the way in AI development. The cultural values and backgrounds of these pioneers will shape the AI systems they create, leaving a lasting impact.

Advice for Young People:
Find your passions: Explore different things when you’re young to discover what truly excites you. Know yourself: Understand how you work, your strengths and weaknesses, and the optimal conditions for your productivity.

Demis’s Hopes for the Future:
Cooperation over competition: He hopes for a world where abundance eliminates scarcity, reducing competition and fostering cooperation. Global cooperation on pressing matters: He wishes for improved global cooperation, especially on urgent issues like climate change. AI as a global resource: He believes AI should belong to humanity, with everyone having a say in its development and use.

Demis’s Perfect Productive Day:
Early wake-up: He wakes up early to maximize his productive hours. Coffee and music: He enjoys coffee and listens to music to enhance his focus. Dedicated time for work: He sets aside specific hours for uninterrupted work on important projects. Breaks and exercise: He takes breaks throughout the day and engages in physical activities to maintain his energy and well-being. Evening reflection: He reflects on his day, reviewing what went well and what could be improved.

Daily Routine:
Demis Hassabis’s daily routine has changed significantly over the past 10-20 years. He now works a structured day, getting to the office around 11 AM and scheduling back-to-back meetings until 7 PM. After spending time with family and friends, he starts a second day of work around 10 PM, focusing on thinking, reading research papers, and planning.

Creative Thinking Process:
Hassabis prefers to think through problems and ideas using pencil and paper, but he also efficiently reads research papers on a screen. He finds the quiet hours of the morning, especially between 1 and 3 AM, to be the most productive for creative thinking. He listens to inspiring music and immerses himself in philosophy books and the history of science during this time. Hassabis believes in uninterrupted creative blocks and appreciates the flexibility of his nighttime routine, allowing him to extend his thinking sessions if necessary.

Benefits of Nighttime Creative Thinking:
The lack of interruptions during nighttime allows for deeper focus and flow. The ability to extend creative sessions beyond a typical workday provides more flexibility. Hassabis can choose to pay the cost of tiredness the next day if he’s deeply engaged in an idea.

Conclusion:
Demis Hassabis’s structured daily routine and unique creative thinking process, centered around nighttime focus and uninterrupted blocks of time, have contributed to his success in the fields of artificial intelligence and neuroscience.

Context Switching and Multidisciplinary Interests:
Hassabis emphasizes the importance of context switching and being a generalist in his work, allowing him to tackle complex problems in various scientific fields. He enjoys exploring multiple disciplines and finds it difficult to focus solely on one area due to the abundance of interesting topics in the world.

Purpose of Life and the Pursuit of Knowledge:
Hassabis believes that the purpose of life is to gain and understand knowledge, particularly about the universe and its mysteries. He considers the pursuit of knowledge as one of the highest virtues, leading to compassion, understanding, and tolerance.

Nature of Reality and the Structure of the Universe:
Hassabis expresses awe and curiosity about the structure and nature of reality, pondering why the universe is conducive to scientific exploration and knowledge acquisition. He finds it remarkable that computational devices can be made from common elements like silicon and questions why computers are even possible.

Gaming and Puzzle Design:
Hassabis compares the complexity and allure of the universe to a well-designed game that humbles players while exciting them with the potential for further learning. He highlights the similarities between designing engaging games and crafting a compelling reality.

Question for an AGI System:
If Hassabis could ask one question to an AGI system that achieves human-level intelligence, he would inquire about the true nature of reality. He acknowledges the difficulty in understanding the answer but believes it could involve more fundamental explanations of physics and glimpses into previously overlooked aspects of reality.

Gratitude and Farewell:
Hassabis expresses his gratitude for the opportunity to engage in a conversation with Lex Fridman, considering it an honor and a pleasure. The podcast concludes with a quote from Edgar Dijkstra, emphasizing that computer science is not solely about computers, just as astronomy is not only about telescopes.