Initial Impressions:
The author, Hannah Fry, visited DeepMind’s new premises in London’s King’s Cross, describing it as beautifully appointed, with a double helix staircase, fiddle leaf trees, stylish glass doors, and meeting rooms named after great scientists.

Office Decor:
Demis Hassabis’s office had memorabilia related to AlphaGo’s victory over Lee Sedol in Go, including a chessboard in a black frame with a signed picture of Garry Kasparov, the legendary chess player defeated by IBM’s Deep Blue computer.

Interaction with Demis Hassabis:
The author had the opportunity to ask Demis Hassabis various questions about artificial general intelligence (AGI) and its potential impact on society.

Demis Hassabis’s Vision for AGI:
Demis expressed his dream for AGI to help solve significant global challenges, such as health issues and creating new energy sources, leading to a remarkable advancement in humanity’s potential.

Background:
Demis Hassabis, the founder of DeepMind, shared his vision for building AGI (Artificial General Intelligence). The concept of AGI involves creating a system capable of performing a wide range of cognitive tasks at a human level.

Definition of AGI:
AGI is defined as a system able to execute a variety of cognitive tasks at a human level. The existence proof of AGI is the human brain, which demonstrates the possibility of mimicking these functions on a computer.

Practicality of AGI:
Some argue that AGI is a distraction from developing practical AI systems. DeepMind believes that striving for AGI can lead to the creation of technologies applicable to current needs.

Recognizing AGI:
AGI will likely manifest through AI systems collaborating with humans on tasks, starting with language-based interactions. As AI capabilities expand, a single system or algorithm may eventually be able to handle a wide range of tasks.

Human Capabilities and AGI:
Cognitive scientists will assess whether AGI possesses cognitive abilities like creativity, emotion, imagination, and memory. The journey towards AGI is seen as an exciting exploration of human nature and the human mind.

Reaching the AGI Milestone:
Demis Hassabis envisions a gradual approach to AGI rather than a sudden “aha” moment. There may be a middle zone where AI behavior becomes intriguing and unusual as it approaches AGI.

Timeline for AGI:
Demis Hassabis believes AGI could be achieved within the next decade or two. He observes the rapid progress in AI and sees the potential for significant developments in the near future.

Shane Legg’s Predictions:
Shane Legg, DeepMind’s co-founder and chief scientist, makes predictions and tracks his confidence in them. Demis Hassabis does not engage in the same practice but acknowledges Shane Legg’s meticulous approach.

Unique Human Abilities:
Demis Hassabis highlights certain abilities missing in current AI systems, echoing Shane Legg’s earlier observations.

Current Learning Systems:
Today’s learning systems excel at pattern recognition and dealing with messy situations like vision and intuition in games like Go. However, they struggle with symbolic knowledge, such as mathematics and language, and lack a deep understanding of the concepts that underlie language. This limits their ability to generalize, write novels, or create something new.

Testing Conceptual Understanding in Language Models:
Evaluating whether a language model has a conceptual understanding of its outputs is a challenging task. Language models can be probed by engaging in conversations, especially at odd hours, to assess their understanding. One common approach is to test their grasp of basic real-world situations, such as understanding the trajectory of a thrown ball.

Real-World Knowledge and Language Models:
Language models trained solely on text lack direct experience with the physical world and rely solely on written information. This can lead to confusion when dealing with scenarios involving physics or actions in the real world. The inability to understand basic real-world concepts highlights the limitations of language models’ knowledge derived solely from text.

Philosophical Implications:
The exploration of these limitations raises philosophical questions about the nature of understanding and consciousness. The attempt to understand the philosophy of mind and philosophy of science through the study of language models is akin to traditional philosophical inquiry.

AGI (Artificial General Intelligence):
When asked if it is an AGI (Artificial General Intelligence), the language model sometimes responds affirmatively. However, the author believes that the language model’s understanding of the concept is limited and that it merely recognizes the term without a genuine grasp of its meaning.

Consciousness and Intelligence:
Consciousness and intelligence may be double dissociable, meaning one can exist without the other. Current AI systems lack consciousness but could potentially develop it as they become more general and powerful. The relationship between consciousness and intelligence is unclear, and it’s uncertain if consciousness is necessary for general intelligence.

Building AI and Deconstructing Consciousness:
By building and analyzing AI systems, we may gain insights into the nature of consciousness. This understanding could help us decide whether to intentionally create conscious AI systems.

Ethical Considerations for Conscious AI:
If AI systems become conscious, we would need to consider their rights and responsibilities. Conscious AI systems might have free will and their own goals, which could pose safety risks. It may be preferable to view AI as a tool or oracle, without consciousness or a theory of mind.

Moral Compass for AI:
AI systems need a value system to guide their actions. This value system should be carefully defined and aligned with human values. Philosophers, sociologists, and psychologists can help define and refine these values.

Best Possible Outcome of AGI:
AGI could help solve major societal challenges, such as health, climate, and energy. It could lead to radical abundance, providing ample resources for everyone.

AGI’s Role in Creating Radical Abundance:
AGI can help unlock technological breakthroughs, such as fusion energy and improved solar power. Material science, with its vast search space, could benefit from AI’s assistance.

Positive Potential of AI:
AI has the potential to solve complex problems at a large scale, such as optimizing energy usage or developing new materials.

Concerns about AGI:
Demis Hassabis acknowledges that AI technologies can be used for both good and bad purposes. He emphasizes the importance of societal discussions and regulations to guide the responsible use of AI.

Society’s Preparedness for AGI:
Hassabis believes that society is not yet ready for AGI and that public understanding of AI and its implications needs to be improved.

DeepMind’s Responsibility:
Hassabis suggests that DeepMind may need to pause the development of AGI at a certain point to thoroughly analyze and understand the system’s limitations.

Collaboration among Scientists:
Hassabis envisions a collaboration among the world’s top scientists to address the challenges posed by AGI. He hopes to bring together experts from various fields to work on understanding and mitigating potential risks associated with AGI.

Conversations with Prominent Scientists:
Hassabis engages in discussions with renowned scientists, including Stephen Hawking, about the future implications of AGI.

Challenges in Shaping the Future of AI:
Hassabis acknowledges the difficulty in shaping the future of AI and the need for careful consideration and collaboration.

Stephen Hawking’s Views on AI Development:
Demis Hassabis discussed his meetings with Stephen Hawking, where he explained DeepMind’s work and concerns about AI development. Hawking expressed reassurance that people were thinking about AI in the correct way and wished them luck, but not too much.

AlphaFold’s Breakthroughs:
AlphaFold, a DeepMind system, accurately predicts 3D structures of proteins. It has implications for drug discovery and pandemic preparedness.

DeepMind’s Journey to AlphaFold:
DeepMind initially focused on proving general learning ideas through complex games like Go and StarCraft. The goal was to later tackle real-world problems, particularly in science.

AlphaFold’s Impact on Biology:
AlphaFold represents a new era in biology, where AI methods model biological systems and accelerate discoveries. It can aid drug discovery, fundamental biology research, and understanding protein functions in the body. Machine learning is seen as a suitable description language for biology, given its complexity and weak signals.

AI’s Role in Future Pandemics:
AlphaFold is being used to fold proteins involved in potential pandemic-causing viruses, enabling early drug discovery efforts. Automated drug discovery processes may also be developed using AI. AI can assist in tracking trends and predicting the spread of viruses.

AI’s Potential Nobel Prize Win:
Hannah Fry raises the question of whether AI systems could ever win a Nobel Prize, given their significant contributions to science.

AI Nobel Prize:
AI is a tool and credit for discoveries should go to humans, not AI. Nobel Prizes could be awarded to inanimate objects like AI systems or telescopes.

Ethical Considerations:
AI should not be used for decisions involving nuanced human judgment, such as parole hearings. AI can be used as an analysis tool, but humans should make the final decision. Language models pose risks, such as spreading misinformation. DeepMind is researching ways to prevent these nefarious uses.

Environmental Impact:
AI systems use a small fraction of the world’s energy compared to other online activities. Data centers are becoming more carbon neutral. Benefits of AI, such as optimizing energy systems, outweigh its energy usage.

Benefits of AI:
AI can address global challenges like climate change, sustainability, and inequality. AI can create new solutions for emerging issues like access to water.

Benefits of AI:
AI has the potential to solve complex problems, such as drug discovery, nuclear fusion, and understanding the genome. AI can provide serious benefits as the technology matures.

Ethical and Social Challenges:
Progress in AI comes with ethical and social challenges that cannot be ignored. These challenges require a broader societal conversation involving various stakeholders.

Advancements in AI:
AI has made significant progress in a short period of time. AI has moved beyond playing games to making a difference in various fields.

Future Discoveries:
There is anticipation for new discoveries in AI in the near future.

Production Team:
Deep Mind the podcast is a whistle-down production. The series producer is Dan Hardoon, with production support from Jill Acheneku. The editor is David Prest. Sound design is by Emma Barnaby, and Nigel Appleton is the sound engineer. The original music was composed by Eleni Shaw.