CS123, Intro to AI
Topics | |
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Overview of AI | Neural networks and deep learning |
AI Problem Solving Revisited Machine Learning—Part 1 Applications of AI | Generative AI + Prompt engineering |
Machine Learning—Part 2 | Custom chatbot creation |
History of AI + Midterm | Social and ethical issues of AI Future of AI, Final |
The SingularityAI Industry and Thought Leaders Who Are AI OptimistsMoore's LawExponential Growth: Rice on a ChessboardWhat is the Computing Power of the Brain?Estimation Range for Brain’s Computing PowerContext with Modern AI HardwareConclusionReferences
Ray Kurzweil, a renowned futurist and director of engineering at Google, has made several predictions about what he calls "The Singularity". Here are some key points:
Law of Accelerating Returns: Kurzweil describes his law of accelerating returns which predicts an exponential increase in technologies like computers, genetics, nanotechnology, robotics, and artificial intelligence².
Timeline
Computers Surpass Humans: By 2029, a computer will pass the Turing test.
Humans Become Machines: By the early 2030s, technology will be able to copy human brains and put them onto electronic mechanisms.
The Singularity: Kurzweil predicts that the Singularity — the moment when technology becomes smarter than humans — will happen by 2045.
Machine intelligence will be infinitely more powerful than all human intelligence combined. Machines' intelligence and humans will merge.
Benefits of the Singularity
Accelerated Innovation Innovations like curing diseases, extending human lifespans, or developing sustainable energy solutions could happen faster and more effectively.
Economic Productivity
Automation of nearly all jobs could lead to unprecedented economic growth and efficiency.
Labor-intensive and dangerous tasks could be handled exclusively by machines, reducing risk and increasing productivity.
Improved Decision-Making Global issues like climate change, economic instability, and geopolitical conflicts might be addressed more effectively.
Personalized Education and Healthcare
AI could provide highly personalized learning experiences tailored to individual needs and abilities.
In healthcare, diagnosis, treatment, and even disease prevention could be revolutionized through precise and individualized care.
Enhanced Quality of Life
mMachines could take over mundane, repetitive tasks, allowing humans more time to pursue creativity, leisure, and personal fulfillment.
Universal basic income or similar economic systems might emerge to distribute wealth generated by superintelligent automation.
Scientific Discovery
Superintelligent machines could help uncover fundamental truths about the universe, such as understanding consciousness, exploring deep space, or unraveling quantum mysteries.
Simulations and predictions of complex systems could become far more accurate, aiding fields like astrophysics and molecular biology.
Potential for a Post-Scarcity Society
Automation could make essential goods and services abundant and affordable, reducing or eliminating poverty and inequality.
Renewable energy and resource optimization could address global scarcity.
Interfacing with Human Cognition
Neural enhancement through brain-machine interfaces could augment human intelligence, creativity, and emotional capacity.
Collaborative human-machine intelligence could tackle problems synergistically.
Elon Musk: CEO of Tesla, SpaceX, and Neuralink.
Musk is more cautious than Kurzweil, he has expressed concerns about advanced AI and the need for regulations to prevent its misuse. He acknowledges the transformative potential of AI and supports human-AI symbiosis through his Neuralink project.
Nick Bostrom: Philosopher and director of the Future of Humanity Institute at Oxford University.
As author of Superintelligence: Paths, Dangers, Strategies, Bostrom examines the risks and opportunities of superintelligent AI, advocating for ethical considerations in its development.
Sam Altman: CEO of OpenAI.
Altman has discussed the potential of AGI to fundamentally alter society, advocating for collaborative efforts to ensure positive outcomes.
Bill Gates: Co-founder of Microsoft, philanthropist.
Gates is optimistic about the future of AI, particularly in its potential to address global challenges like healthcare, education, and inequality. While acknowledging the possibility of the singularity with it's radical benefits, he is more focused on AI’s capacity to enhance human capabilities rather than surpass them.
Max Tegmark: Physicist and founder of the Future of Life Institute.
Tegmark explores AI futures, including scenarios where advanced AI reshapes human life. He discusses these ideas in his book Life 3.0.
Peter Diamandis: Founder of XPRIZE and co-author with Ray Kurzweil. As co-founder of Singularity University, Diamandis often speaks about the exponential growth of technology leading to transformative changes.
Moore's Law is an empirical observation named after Gordon Moore, the co-founder of Fairchild Semiconductor and Intel⁴. Here are some key points about it:
Observation: Moore's Law is the observation that the number of transistors in an integrated circuit (IC) doubles approximately every two years.
Historical Trend: Rather than a law of physics, it is an empirical relationship linked to gains from experience in production.
Impact: This trend has led to rapid improvements in computing speed, efficiency, and cost, as well as related technologies like artificial intelligence.
Origins: In 1965, Moore posited a doubling every year in the number of components per integrated circuit, and projected this rate of growth would continue for at least another decade. In 1975, he revised the forecast to doubling every two years.
End of Moore's Law: According to MIT Professor Charles Leiserson, Moore’s Law, which predicted the doubling of transistors every two years, has been over since 2016.
Moore's Law has been used in the semiconductor industry to guide long-term planning and to set targets for research and development, thus functioning to some extent as a self-fulfilling prophecy. Despite its end, the number of transisters in a CPU chip continues to increase. One of AMD's latest (as of mid-2024), the Ryzen 9000, has over 8 billion transistors.
The story of rice grains on a chessboard is a famous illustration of exponential growth, often referred to as the "rice (or wheat) and chessboard problem". Here's how the story goes:
A wise ruler wanted to reward a servant for an act of extraordinary bravery. The servant requested the following: "Master, I ask you for just one thing. Take your chessboard and place on the first square one grain of rice. On the second day, place on the second square 2 grains for me to take home. On the third day, cover the third square with four grains for me to take. Each day double the number of grains you give me until you have placed rice on every square of the chessboard. Then my reward will be complete."
The ruler, thinking this was a small price to pay, agreed. However, the power of exponential growth soon became apparent. By the time the ruler reached the 64th square, he owed the servant over 18 quintillion (18 X 1018) grains of rice! That's approximately 350 billion tons of rice!
This story illustrates the concept of exponential growth, showing how small quantities can quickly become extraordinarily large when they're repeatedly doubled. It's particularly relevant to concepts like Moore's Law.
Estimating the computing power of the human brain in terms of Tera Operations per Second (TOPs) or 1 x 1012 operations per second.
Neural Activity Basis:
The brain has roughly 86 billion neurons, each with an average of 1,000 to 10,000 synaptic connections.
Neurons communicate through electrical and chemical signals, firing at rates ranging from 0.1 to 200 Hz.
Researchers estimate the brain performs about 1015 to 1017 operations per second (1 to 100 peta operations per second, or PetaOps).
Energy Efficiency:
The human brain uses about 20 watts of power, making it incredibly energy-efficient compared to current computers.
Equivalent digital systems today require far more energy to approach even a fraction of the brain’s capability.
Translation to TOPs:
To express this in terms of tera operations per second, the brain’s performance is estimated to be between 1,000 TOPs and 100,000 TOPs.
Challenges in Precise Estimation
Different Architectures:
Biological neurons and digital processors operate fundamentally differently. The brain excels at massively parallel processing, pattern recognition, and adaptability, while computers are opt imized for linear and high-speed calculations.
Analog vs. Digital: The brain processes information in an analog-like, probabilistic manner, while computers are digital and deterministic. Defining “operation” in the context of the brain isn’t straightforward. Neural spikes, synaptic events, or high-level cognitive processes may all be considered operations, but they differ vastly in complexity.
Current state-of-the-art AI systems, like NVIDIA’s A100 GPUs, can perform up to 1,000 TOPs for specialized tasks like deep learning, but they are still far behind the brain in terms of generality and efficiency.
High-performance systems such as the Fugaku supercomputer reach exaflops (1018 operations per second) but require immense energy and are task-specific.
While the human brain’s estimated computing power is often cited as 1,000–100,000 TOPs, its qualitative capabilities, adaptability, and energy efficiency far surpass those of current artificial systems. These differences highlight the significant challenges in replicating human cognition through AI.
Ray Kurzweil's Most Exciting Predictions About the Future of Humanity—Futurism. The Singularity Is Near—Wikipedia.
Transhumanist author predicts artificial super-intelligence—Techspot.
Wheat and chessboard problem—Wikipedia.
What is Moore's Law? - Our World in Data.
The Death of Moore’s Law: What it means and what might fill the gap. Moore's law—Wikipedia, 2024.
AMD's Zen 5 chips pack in 8.315 billion transistors per compute die, a 28% increase in density—Tom's Hardware, 2024.
Intro to AI lecture notes by Brian Bird, written in , are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Note: GPT-4 and GPT-4o were used to draft parts of these notes.