Computer Science's Breakthrough of 2023 🚀
Tags: programming, algorithms, computerscience
Published: 2023-12-27 12:46:08

As the world draws a close to the year 2023. It is worth mentioning the progress that Computer Science has made during this year. Such progress I will mention later in this article, will leave you with an increase in knowledge and an aftertaste of great excitement for the future of our world.
I recently watched a video from Quanta Magazine, which I linked at the end of this article. In that video, they mentioned the significant breakthroughs that the brightest minds in CS have made in the fields of AI and Quantum Computing. I thoroughly enjoyed the video and that is what moved me to make this article with that said I would like to share with you key highlights from that video.

## Vector Driven AI
Most of us today have felt the power of today's AI models that have shaped the way we search, create and use information. However, these models have major flaws, one of which is the vast amount of energy required for training and operation and the other is that they tend to become inscrutable black boxes. Researchers have developed a new approach called hyperdimensional computing which is a more versatile way of making computational computing more efficient, while also giving researchers greater insight into the model’s reasoning.

Most AI models are statistical AI and for a while now researchers have been looking towards symbolic AI, a type of AI that uses 'logic-based programming and makes use of symbols to represent concepts and rules'. However, both types are incompatible, but that's where hyperdimensional computing plays a part in the solution to bringing these two types together. It achieves this by using vectors and, an ordered list of numbers. Vectors represent information in a complex manner in a multidimensional way. They can combine to generate new concepts and encode information without creating new nodes. Thus reducing the resources used in training and operation. Moreover, it provides researchers with a greater insight into model reasoning.

## Improving the Quantum Standard
In 1994, a mathematician by the name of Peter Shor created Shor's algorithm that aims to design and efficiently factor large composite numbers into their prime factors at a quantum scale. Its significance is that it can efficiently solve complex problems faster than the best-known classical algorithms, thus showing quantum computing potential.

However, in recent developments, Oded Regev a computer scientist found an innovative way to speed up Shor's algorithm. The solution was transforming the one-dimension of the periodic function into a multiple-dimension. It made the already existing Shor's algorithm more efficient and even quicker. Even Shor himself was impressed and glad that an improvement was made to his 30-year-old algorithm.

## The Powers of Large Language Models
In the realm of artificial intelligence, large language models reveal unexpected "emergent behaviours" as they grow. For instance, when reaching a critical size, these models showcase unforeseen abilities, such as solving complex math problems. This goes beyond their original purpose, emphasizing the untapped potential and versatility of large language models. As we explore these surprises, we redefine the role of these models in shaping our technological landscape.

## Conclusion
As we bid farewell to the year 2023, reflecting on the strides made in Computer Science leaves us with a sense of awe and anticipation for the future. The breakthroughs discussed in this article, inspired by the remarkable advancements highlighted in a Quanta Magazine video, underscore the transformative journey undertaken by the brightest minds in the field.

The evolution of AI, particularly through the vector-driven approach of hyperdimensional computing, promises efficiency and transparency. This novel technique not only addresses energy-intensive challenges but also offers researchers profound insights into AI model reasoning, marking a crucial step toward bridging the gap between statistical and symbolic AI.

In the realm of quantum computing, Oded Regev's ingenious enhancement of Shor's algorithm reflects the continuous pursuit of efficiency and speed. The multidimensional transformation of the periodic function demonstrates that even established algorithms can evolve, showcasing the dynamic nature of the field.

The emergent behaviours observed in large language models, as highlighted in this article, further emphasize the unpredictable yet promising aspects of AI. Beyond their original design, these models exhibit unexpected capabilities, such as solving intricate math problems, and reshaping our understanding of their potential applications.

As we stand on the cusp of a new year, the fusion of innovation, collaboration, and persistent exploration propels Computer Science into an era of limitless possibilities. The journey of discovery continues, and with each revelation, we redefine the landscape of technology, ushering in a future where the boundaries of what is achievable are constantly pushed further.

## Reference
{% embed https://www.youtube.com/watch?v=g7ih6rHdw3M %}