How mathematics will drive the third wave of artificial intelligence

From driving a car to using a smartphone or even brewing a cup of coffee on a coffee machine with a digital timer or logging into your bank account online, we all encounter it in countless different places today algorithm.

Due to the ubiquitous nature of algorithms, they have captured the imagination of industry, academia, R&D, and just about any sector you can think of. Perhaps the technology most likely to benefit from cutting-edge algorithms, which in turn will have ripple effects on most other industries today, is artificial intelligence. A key component of the development of powerful algorithms is mathematics - graphs are the cornerstone of Shor's algorithm or Schrödinger's equations. Boolean algebra laid the foundation for today's "information age." Likewise, Ada Lovelace's algorithm, as early as 1842, is widely regarded as the first computer program.

As you can imagine, even the smallest AI (artificial intelligence) system requires basic instructions to run. Simply put, algorithms are step-by-step instructions that help computers complete calculations. They are like instruction manuals that let the machine know exactly what to do and when to do it. Therefore, in basic machine learning, algorithms are the first structural step in building artificial intelligence. Practice makes perfect – so through continued interaction with AI, we can hopefully improve its efficiency.

As a technology, artificial intelligence has endless potential if used properly. As mentioned earlier, AI is helping to enhance the healthcare, education, communications, energy and public safety sectors and even solve critical challenges such as global warming.

Today, unfortunately, we are in the early stages of artificial intelligence, mainly using simple algorithms, such as:

Classification algorithm

Use a specific way to classify a The type of algorithm used to classify group data.

Regression algorithm

A type of algorithm that predicts future outcomes based on a set of input data. A good example of this is the computer program used by modern meteorologists to predict the weather.

Clustering algorithm

This algorithm uses the entire data set to find similarities or differences between specific points. point. Identifying fraudulent transactions in accounting documents is an example of this kind of artificial intelligence work , what is needed is a revolutionary breakthrough that - because of the very nature of artificial intelligence - will originate in the field of mathematics.

We need new mathematics-based tools that capture the context and structure of the underlying data, rather than focusing solely on correlations. These tools are mainly based on topology and geometry. Today’s AI experts must focus on developing effective algorithms based on these structures that enable them to achieve transformative results.

All of this requires new thinking and new ways of learning, forcing those who use technology to step out of their comfort zones. As a critical step forward, today's digital-first society requires significantly improved training of future mathematicians and data crunchers. Although artificial intelligence is attracting more mathematicians than ever before, sophisticated mathematical tools that can simulate the way the brain works and enable us to better build the logic capabilities built into future artificial intelligence are critical.

We must accelerate the development of artificial intelligence technology, create powerful synergies, push the frontiers of knowledge, and benefit science, society, and the global economy.

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