What Does the AI Boom Really Mean for Humanity? | The Future With Hannah Fry

The psychologist Vivian Henman said that intelligence was the capacity for knowledge and knowledge possessed, which does sound quite good until you realize that means that library should account as intelligent. Other people have suggested that intelligence is the ability to solve hard problems, which kind of works until you realize you have to define what counts as hard. Now, in fact, there isn't a single definition of intelligence, which manages to encapsulate everything. However, there are still some things that we are looking for in an AI, for it, to be considered truly intelligent. Firstly, it should be able to learn and adapt because we can, After all, from both birth, we are gathering knowledge, we're applying what we learn from one area to another. Secondly, it should be able to reason. Now, this bit is hard, it requires a conceptual understanding of the world. And finally, an AI should interact with its environment to achieve its goals. If you suddenly landed in a foreign city, you would still know how to find water, even if it meant using a phrase book to ask someone for help. This segment discusses the argument that AI needs a physical body to achieve true intelligence. It contrasts the capabilities of language models (like ChatGPT) with embodied AI, showcasing a robot that learns through physical interaction with its environment. The discussion highlights the limitations of language models in understanding the physical world and the potential benefits of embodied AI in achieving AGI. This segment explores the concerns of Professor Stuart Russell regarding the potential misalignment of AI goals with human values. It discusses the difficulty of controlling superintelligent AI and the lack of safety protocols in the current AI development race. The comparison to drug regulation emphasizes the need for rigorous safety standards before deploying powerful AI systems. This segment shifts the focus to understanding the human brain, exploring the challenges and potential of creating a detailed digital map of the brain. It highlights the complexity of the human brain and the limited understanding of its underlying mechanisms, suggesting that a better understanding of human intelligence may be crucial for developing safe and beneficial AI. Artificial General Intelligence (AGI): A hypothetical type of artificial intelligence that possesses human-level intelligence and can perform any intellectual task that a human being can. It contrasts with narrow AI, which is designed for a specific task. Narrow Artificial Intelligence: AI systems designed and trained for a specific task. They are highly proficient at that task but lack the general intelligence to perform other tasks. Superhuman AI: A hypothetical AI that surpasses human intelligence in all aspects. This is a key concern in discussions about the potential risks of advanced AI. Existential Threat: A threat that poses the risk of complete annihilation or the end of existence for humanity. In the context of AI, this refers to the possibility that superintelligent AI could cause human extinction. Misalignment (in AI): The situation where an AI's goals or objectives are not aligned with the goals and values of its creators or humanity. This misalignment could lead to unintended and potentially harmful consequences. Optogenetics: A biological technique that involves using light to control the activity of neurons. It's used in neuroscience research to study the function of specific neurons and neural circuits. Sodium Polyacrylate: A superabsorbent polymer commonly used in disposable diapers. In the video, it's used to expand brain tissue for easier microscopic examination. Artificial Neural Networks: Computational models inspired by the structure and function of biological neural networks in the brain. They are a core component of many modern AI systems.