A.I. Expert Answers A.I. Questions From Twitter | Tech Support | WIRED

AI expert Gary Marcus addresses various questions on the impact and future of AI. He discusses the potential of AI in transforming college essays, the factors that brought AI to the mainstream, and the challenges of creating a successful AI company. Marcus also explains the technical aspects of building large language models and the limitations of current AI systems, such as their inability to understand the world causally. He touches on the risks of AI, including misinformation and the potential for AI to make poor decisions when faced with new, untrained scenarios. Marcus emphasizes the need for a paradigm shift in AI to achieve logical consistency and truthfulness, suggesting a neuro-symbolic approach that combines neural networks with symbolic reasoning. He also highlights the influence of hardware on AI's success and the importance of understanding the human brain's structure for advancing AI.

Takeaways

• 📚 ChatGPT and similar AI can write essays, but they tend to produce average quality work that may not earn top grades without further refinement by human input.
• 🚀 AI went mainstream in 2022 due to advancements in deep learning, increased data availability, and improvements in chatbot technology.
• 💡 To build a successful AI company, focus on a unique problem, study AI broadly, and understand why people would pay for your product.
• 🤖 The core of large language models is neural networks, which use self-supervised learning and transformer models to make predictions based on context.
• 🧸 Furby was not truly learning; it was pre-programmed to mimic language development, creating an illusion of learning.
• 🚗 True self-driving cars are still years away due to the complexity of handling outlier cases and the vast variety of real-world scenarios.
• ⚖️ The Turing Test is outdated and not a reliable measure of intelligence; a better test might involve comprehension and reasoning about information.
• 🧠 Human intelligence is multifaceted and flexible, whereas current machine intelligence is primarily focused on pattern recognition.
• 👶 Human babies and primates learn about the world's structure, while current AI learns correlations without a deeper understanding of causality.
• 🛡️ Preventing AI from going rogue involves careful development, avoiding sentience, and being cautious about integrating AI into critical systems.
• 🌟 The best-case scenario for AI includes revolutionizing fields like medicine, climate change solutions, elder care, and personalized tutoring.
• 🧵 The difference between AI, machine learning, and deep learning is that deep learning is a technique within machine learning, which is a subset of AI that also includes other techniques like search and planning.

Q & A

What is Gary Marcus' view on the potential impact of ChatGPT on college essays?

Gary Marcus believes that while ChatGPT can easily write essays, they tend to be of average quality rather than top-tier. He suggests that professors could use ChatGPT as a tool and then engage students in discussions to improve the essays, making the process more interactive and promoting critical thinking about writing.

Why did AI become more mainstream in 2022, according to Gary Marcus?

Marcus attributes the mainstream popularity of AI in 2022 to several factors, including improvements in chatbots that no longer say terrible things, advances in deep learning for applications like image enhancement, and the availability of more data to feed data-hungry AI models.

What advice does Gary Marcus give to someone looking to build a trillion-dollar AI company?

Marcus advises focusing on a unique problem that others are not addressing, such as learning with limited data. He also emphasizes the importance of understanding AI broadly, not just the current popular models, and considering why people would pay for the product or service.

How does Gary Marcus describe the process of building a large language model AI?

Marcus explains that large language models are based on neural networks with nodes that act like neurons, connected to an output. They use self-supervised learning to tune the connections between nodes to make accurate predictions. Additionally, transformer models include an 'attention' mechanism to focus on relevant parts of a sentence for better predictions.

What is Gary Marcus' opinion on the Turing Test as a measure of machine intelligence?

Marcus considers the Turing Test outdated and not a good measure of intelligence. He suggests that a better test would be a comprehension challenge where a system must explain a movie or a piece of text, demonstrating true understanding.

How does Gary Marcus differentiate between human intelligence and current machine intelligence?

Marcus points out that human intelligence is multifaceted, including visual, verbal, and mathematical intelligence, with a key aspect being flexibility in coping with new situations. In contrast, current machine intelligence is primarily focused on pattern recognition and lacks the breadth of human intelligence.

What are the major differences in learning styles between human babies, primates, and current AI, according to Gary Marcus?

Marcus notes that human babies and primates learn about the structure of the world and how objects and people interact, building a model of the world. In contrast, current AI systems store examples and look for patterns without developing a causal understanding of the world.

What potential risks does Gary Marcus foresee with AI being connected to critical systems like power grids?

Marcus is concerned about the misuse of large language models to control critical systems due to their limitations and the potential for making bad decisions when faced with situations different from their training data.

What are some of the best-case scenarios for AI that Gary Marcus envisions?

Marcus believes AI could revolutionize fields like biological science, medicine, climate change solutions, and elder care. He also sees potential in personalized tutoring systems that can understand and assist learners effectively.

How does Gary Marcus respond to the question of whether AI could ever surpass the human mind?

Marcus acknowledges the complexity and efficiency of the human brain, which current AI cannot match. However, he leaves open the possibility that future advancements in AI might bring it closer to human capabilities.

What is Gary Marcus' perspective on the distinction and relationship between AI, machine learning, and deep learning?

Marcus illustrates that deep learning is a technique within machine learning, which itself is a part of the broader field of AI. He suggests that while deep learning has been a recent focus, the field is starting to explore a wider range of techniques due to its limitations.

In Gary Marcus' view, what is the 'wall' that deep learning is hitting?

Marcus refers to the 'wall' as the ongoing issues with truthfulness and reliability in deep learning models. Despite improvements, these problems persist and represent a significant challenge for the field.

Outlines

00:00

📚 AI's Impact on Education and Mainstream Adoption

Gary Marcus, an AI expert, discusses the potential of AI tools like ChatGPT to transform essay writing in colleges. He suggests that while these tools can produce adequate essays, they are unlikely to replace the essay entirely. Instead, they can be used as a starting point for students to refine and improve their writing. Marcus also addresses the question of why AI went mainstream in 2022, attributing it to advancements in deep learning, the availability of more data, and the development of chatbots that are more sophisticated in their responses. He stresses the importance of studying AI broadly and understanding the historical context to build a successful AI company.

05:03

🚗 Challenges in Self-Driving Cars and AI's Future

Marcus is asked about the progress towards truly self-driving cars. He explains that while demonstrations have shown promise, these systems are currently only effective in specific locations with predefined routes. He highlights the challenge of outlier cases that the AI has not been trained on, such as navigating an airport environment. Marcus also discusses the Turing Test, suggesting it is outdated and a poor measure of intelligence. He proposes a comprehension challenge as a better test, where a system must explain and answer questions about a movie or a piece of text. Marcus further explores the concept of intelligence, comparing human intelligence to machine intelligence, and noting that while machines excel in pattern recognition, human intelligence is broader and more flexible.

10:04

🤖 AI's Potential and Risks

Gary Marcus outlines the best-case scenario for AI, which includes revolutionizing science, medicine, and technology, as well as addressing complex issues like Alzheimer's and climate change. He also mentions the potential for AI in elder care and personalized tutoring. Marcus addresses the question of what makes human intelligence superior to AI, noting that human babies and primates learn about the structure of the world, while current AI systems simply store examples and look for patterns. He also discusses the risks of AI going rogue and the importance of not creating sentient AI. Marcus differentiates between AI, machine learning, and deep learning, emphasizing that deep learning is just one technique within machine learning, which is a part of the broader field of AI.

15:07

🧠 The Limitations and Future of Deep Learning

Marcus talks about the limitations of deep learning, particularly its issues with truthfulness and reliability, which he refers to as 'hitting a wall.' He acknowledges that deep learning models are improving but asserts that the core problems persist. Discussing the future impact of AI, Marcus is cautious about predicting specific changes within a decade due to the rapid pace of technological advancement. He raises concerns about AI's potential to generate misinformation and its impact on trust in society. Marcus also addresses the ethical considerations of AI creating art and the need for legal clarity on the matter. He concludes by emphasizing the need for a paradigm shift in AI to achieve greater logical consistency and factual accuracy, suggesting a neuro-symbolic AI approach that combines neural networks with symbolic reasoning.

💡 The Role of Hardware in AI's Success

In the final paragraph, Marcus discusses the influence of hardware on the success of AI, referencing a paper by Sara Hooker that suggests the current state of AI is largely a result of the hardware being used. He contrasts a simple computer chip with the complex requirements of powering a large language model. Marcus speculates that future advancements may require different chip architectures or a shift in approach entirely, prompted by the limitations of current models. He also touches on the physical attributes of the human brain that are missing from modern deep learning architectures, noting the complexity and structured nature of the brain versus the simplicity of current neural networks. Marcus concludes by stating that solving neuroscience may require advanced AI, as the human brain's complexity may exceed our current understanding and computational capabilities.

Keywords

ChatGPT

ChatGPT is an AI language model that can generate human-like text based on given prompts. In the video, it is discussed in the context of its potential to revolutionize the way college essays are written, though the quality of essays produced by ChatGPT is compared to 'C essays, not A essays.' It is suggested that while it can aid in the writing process, it should be used as a tool for enhancing the essay rather than replacing the need for critical thinking and original content creation.

Deep Learning

Deep learning is a subset of machine learning that involves neural networks with many layers to analyze various factors of data. The video mentions deep learning as a significant field that has seen advancements, contributing to the rise of AI's mainstream presence. It is used to describe how AI systems like chatbots and image enhancement tools function, by learning from vast amounts of data.

Data-Hungry AI

The term 'data-hungry AI' refers to AI systems that require large volumes of data to function effectively. In the context of the video, it is mentioned that the current popularity of certain AI applications is due to the availability of extensive data sets that these systems can learn from, which has allowed AI to advance and become more integrated into various aspects of life.

Self-Supervised Learning

Self-supervised learning is a technique in machine learning where the model learns to predict properties of the input data without explicit labeling. The video discusses this in relation to large language models, where the neural network is trained to make predictions based on the input data, with connections between 'neurons' being adjusted over time to improve accuracy.

Transformer Models

Transformer models are a type of neural network architecture that improve upon the basic neural network by incorporating an 'attention mechanism.' This mechanism allows the model to focus on different parts of the input data, enabling it to make more informed predictions. The video explains that these models are more complex than simple neural networks and are central to the functioning of advanced AI systems.

Furby

Furby is a toy that was marketed as having the ability to learn and develop language. The video clarifies that Furby's learning was an illusion, as it was pre-programmed with specific responses for certain days. It serves as an example of how AI can mimic learning without truly understanding or adapting to its environment.

Self-Driving Cars

Self-driving cars, also known as autonomous vehicles, are a topic of discussion in the video as an example of a technology that has potential but is not yet fully realized. The video mentions that while demonstrations exist, they are limited to specific routes and locations, and the challenge of outlier cases, such as unusual scenarios that the car has not been trained to handle, remains a significant obstacle.

Turing Test

The Turing Test is a measure of a machine's ability to exhibit intelligent behavior that is indistinguishable from that of a human. The video argues that the Turing Test is outdated and proposes a 'comprehension challenge' as a more accurate measure of intelligence. The Turing Test is criticized for being easily fooled and not being a true indicator of machine intelligence.

Human Intelligence

Human intelligence is a broad concept that encompasses various aspects such as visual, verbal, and mathematical intelligence. The video emphasizes flexibility and the ability to adapt to new situations as key components of human intelligence. It contrasts this with machine intelligence, which is primarily focused on pattern recognition and lacks the breadth and depth of human intelligence.

Neuro-Symbolic AI

Neuro-symbolic AI is a proposed paradigm shift in AI development that combines neural networks with symbolic reasoning. The video suggests that current AI systems struggle with truth and logical consistency because they are based on plausibility rather than factual knowledge. Neuro-symbolic AI aims to bridge this gap by integrating neural networks with a system that can reason over facts.

Hardware Lottery

The term 'Hardware Lottery' refers to the idea that the success of AI is heavily influenced by the hardware available at the time. The video discusses how current AI capabilities are largely a result of the hardware used, such as GPUs, and suggests that future advancements in AI may require new types of hardware or a shift in approach to achieve artificial general intelligence.

Highlights

ChatGPT can write essays but they are usually of average quality, not top-tier, and can be used as a starting point for students to improve upon.

AI went mainstream in 2022 due to advances in deep learning, increased data availability, and improved chatbot capabilities.

Building a successful AI company involves focusing on a unique problem and understanding AI beyond just large language models.

Large language models are built on neural networks and use self-supervised learning to predict outputs based on inputs.

Furby was not truly learning; it was pre-programmed to simulate learning and language development.

Fully self-driving cars are still years away due to the complexity of handling outlier cases.

The Turing Test is outdated and a poor measure of intelligence; a better test might involve comprehension challenges.

Human intelligence is multifaceted and flexible, while current machine intelligence is primarily about pattern recognition.

AI systems currently lack a causal understanding of the world, unlike human babies and primates.

AI should not be made sentient to avoid potential risks of them wanting autonomy.

AI has the potential to revolutionize various fields, including medicine, climate change solutions, and elder care.

The human brain's complexity and energy efficiency far surpass current AI capabilities.

AI, machine learning, and deep learning are interconnected fields, with deep learning being a subset of machine learning, which is a subset of AI.

Deep learning is facing challenges with truthfulness and reliability, which may represent a 'wall' in its development.

AI's impact on the future of work could lead to changes in fields like commercial art and cashier roles.

Generative AI and algorithmic art raise questions about originality and whether the use of training databases constitutes stealing.

Large language models can be a threat to democracy by enabling the mass generation of misinformation.

Despite their complexity, large language models work by predicting sequences of words based on probabilities derived from vast datasets.

AI's success is heavily influenced by hardware advancements, and the current reliance on GPUs may not be the path to artificial general intelligence.

Modern deep learning architectures lack the intricate physical attributes of the human brain, which could be key to more advanced AI.