Here’s How Biocomputing Works And Matters For AI | Bloomberg Primer

Summary

In this video, we explore the groundbreaking intersection of biology and technology, where living human brain cells on a silicon chip play the classic game of Pong. Researchers at Cortical Labs and FinalSpark are pioneering biocomputing, seeking to blend biology with artificial intelligence for more efficient and adaptive systems. The potential of this fusion extends beyond gaming, promising breakthroughs in AI and drug development. However, challenges remain, from scaling the technology to ethical concerns around the future of bio-engineered intelligence. As companies race to advance biocomputing, the implications for both science and society are profound.

Takeaways

• 😀 Human brain cells have been used to play the iconic game Pong, demonstrating the potential of biocomputing.
• 😀 Researchers at Cortical Labs grew a layer of 800,000 neurons on a silicon chip, creating DishBrain, a prototype for biocomputing.
• 😀 Neurons can learn quickly with far less training data compared to traditional machine learning algorithms.
• 😀 AI systems like DishBrain could potentially reduce energy consumption and computational data needs compared to silicon-based computing systems.
• 😀 The development of AI chips and silicon-based processors faces limitations as transistors approach their physical limits, as highlighted by Moore's Law.
• 😀 By 2034, data centers will require enormous energy resources, which may make them unsustainable without alternative computing technologies.
• 😀 DishBrain’s Pong-playing neurons show the possibility of combining biology and computing, leading to the concept of 'wetware'.
• 😀 Companies like Cortical Labs and FinalSpark are exploring biocomputing to improve AI's efficiency by leveraging human brain cells.
• 😀 Brain organoids, like those used by FinalSpark, are being explored as platforms for studying neurodegenerative diseases and advancing drug development.
• 😀 The ethical questions around biocomputing include concerns over whether brain organoids could become self-aware and what rights they would have if they did.

Q & A

What is the significance of teaching neurons to play Pong?

Teaching neurons to play Pong demonstrates how living brain cells, when connected to technology, can perform tasks similar to artificial intelligence. It showcases the potential for merging biology with computing, which could offer more efficient solutions compared to traditional AI systems.

Why are researchers exploring biocomputing and using human brain cells in experiments?

Researchers are exploring biocomputing because biological systems, such as human brain cells, have the potential to learn and process information more efficiently than current silicon-based computers. These systems could require less data and energy, offering a more sustainable and powerful alternative to conventional AI models.

What is the free energy principle, and how does it relate to DishBrain?

The free energy principle suggests that living systems, including the DishBrain neurons, are constantly trying to minimize surprise and predict their environment. In the case of DishBrain, this theory explains why the neurons began to improve their ability to play Pong after receiving feedback, as they sought to predict and understand the game’s patterns.

How do biological systems like the brain compare to traditional computers in terms of energy efficiency?

Biological systems, such as the human brain, are vastly more energy-efficient than traditional computers. While a supercomputer may require up to 40 megawatts of power, the human brain only consumes about 20 watts of power, making it far more efficient for certain tasks, like pattern recognition.

What are the key challenges in merging biology with computing, as mentioned in the script?

Key challenges include the environmental and logistical issues of keeping biological materials alive, the difficulty in scaling the technology for mass production, and ensuring the stability of biocomputing systems. Furthermore, the complexity of maintaining a hybrid system that combines living cells with artificial hardware adds to these challenges.

What are the potential applications of biocomputing in the pharmaceutical industry?

Biocomputing, particularly with brain organoids, holds great potential for accelerating drug development and improving disease modeling. It can be used to simulate human neurological conditions, such as Alzheimer's or Parkinson's, and test how drugs affect these diseases more effectively than traditional animal models.

What ethical concerns arise from using living brain cells for biocomputing?

Ethical concerns include the potential for brain organoids to become self-aware, which could raise questions about their treatment, rights, and whether it is acceptable to terminate them. Additionally, the use of stem cells and the implications of creating biological intelligence for non-medical purposes present complex moral dilemmas.

How does the concept of 'wetware' fit into the future of computing?

Wetware refers to the integration of biological systems, such as brain cells, with electronic hardware to create hybrid computing systems. It suggests a future where biology and artificial intelligence converge, offering potentially more flexible, efficient, and intelligent systems compared to current silicon-based computing.

What role does the company FinalSpark play in the biocomputing field?

FinalSpark is a deep-tech startup focused on developing brain organoids and offering them via a cloud computing platform called Neuroplatform. They allow researchers to observe and interact with real-time data from their brain organoids, offering a new way to study neuroscience and apply these findings in areas like robotics and drug testing.

What is the long-term vision for biocomputing, according to the script?

The long-term vision for biocomputing is to create a world where both biological and artificial components work together in computing systems. While the technology is still in its early stages, it could eventually lead to more efficient, adaptable, and sustainable AI systems, as well as groundbreaking advancements in biomedical fields.