Big Breakthroughs in Computer Science in 2023

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[Greg Bernhardt]

If you don't have ten min to watch here is an AI-generated summary from the transcript:

1. AI Limitations and Reasoning: The video begins by discussing the limitations of artificial neural networks, especially in reasoning by analogy, which is a natural process for human brains. It highlights the challenge AI faces in scaling up its statistical abilities to learn new concepts, an approach known as statistical AI.
2. Hyperdimensional Computing: A new approach called hyperdimensional computing, which combines the power of statistical AI with symbolic AI (logic-based programming using symbols for concepts and rules), is introduced. This method uses vectors to represent information in a complex, multi-dimensional way, enabling the encoding of information without adding more nodes.
3. IBM Research Breakthrough: In March 2023, IBM Research in Zurich achieved a breakthrough by combining statistical and symbolic methods to solve the Ravens progressive matrix, a puzzle requiring abstract reasoning. This approach significantly accelerated the inference times for abstract reasoning tasks.
4. Quantum Computing and Shor’s Algorithm: The transcript then shifts to quantum computing, discussing mathematician Peter Shor's algorithm developed in the 1990s, which threatened online cryptography by enabling quantum computers to break large numbers into prime factors.
5. Oded Regev’s Algorithm: In August 2023, mathematician Oded Regev published a paper improving Shor’s algorithm. His approach involves transforming the periodic function from one dimension to multiple dimensions, allowing for faster and more efficient integer factoring.
6. Emergent Behaviors in AI: The video concludes by discussing emergent behaviors in AI, particularly in large language models (LLMs). Emergence, where systems exhibit behaviors not found in their individual units, is seen as a key development. The introduction of transformers in 2017 allowed LLMs to process text more effectively, leading to capabilities like zero-shot or few-shot learning. However, the unpredictability of emergent behaviors, both beneficial and potentially harmful, poses challenges and raises questions about the future development of AI technologies.