ARTIFICIAL INTELLIGENCE

PART I

Records show that- AI has famously beaten humans in other high-profile competitions. In 1997, IBM’s Deep Blue AI machine beat chess grandmaster Gary Kasparov, and in 2016, Google DeepMind’s AlphaGo beat Go world champion Lee Sedol.

But not this time around.  Human effort regained its top position. Filling the air with a new spirit. This time, AI failed killing the human coding star.

On Wednesday (7/16/25), a programmer from Poland, Przemysław Dębiak, beat an OpenAI tool at the AtCoder World Tour Finals 2025 Heuristic Contest in Tokyo, which is sponsored by the company. Dębiak, who competed under the name “Psyho,”

In February 2025, Sam Altman said that by the end of this year, OpenAI could surpass humans in coding competitions. AI is already writing big chunks of code at Microsoft, Google, and Meta, the companies’ CEOs have all said in recent months.

Machine learning is a type of AI that allows computer programs to learn when exposed to new data without being programmed. The official idea & definition of AI was first coined by Jay McCarthy in 1955 at the Dartmouth conference. McCarthy proposal is under quotehere.

“Every aspect of learning or any other feature of intelligence can be made to stimulate it. An attempt will be made to find out how to make machines, use language from abstractions and concepts, solve kinds of problems now reserved for humans and inspire thoughts.” In essence, AI is a machine with the ability to solve problems that are usually done by us humans with our moral intelligence. Therefore, AI is about making intelligence universally accessible and making everyone massively more productive.

OpenAI is an AI research and Development Company. This is an organization that creates and promotes friendly AI,. AI code can be kept private, OpenAI approach is where the code is made publicly available, often as open-source software. This enables others to inspect the code to understand how the AI was trained and operates and potentially modify and improve upon it. OpenAI utilizes advanced machine learning techniques, such as deep neural network to …

A neural network is a type of software that gets built by being trained over and over with lots of data. Once it’s been trained, it can recognize faces, predict chess moves, or recommend a Netflix movie for a customer. Also, known as a ‘model’ a neural network is often made up of many different layers and nodes that process information in a vaguely similar way to our brain’s neurons. The more the model is trained, the better those nodes get at predicting or recognizing things.  

The OpenAI organization Musk founded with Sam Altman. In 2015, Musk and Altman co-founded OpenAI together but now run competing AI companies. Founded as a nonprofit OpenAI later turned to seek profits. it took OpenAI seven years (from its founding in 2015) to release the first version of its chatbot, ChatGPT. So, What ChatGPT can do? ChatGPT, is able to converse about the concept of…, can talk about religion and else.  Musk parted ways with OpenAI in 2018. In 2023, Musk founded a rival AI start-up, xAI and launched a chatbot called Grok to compete with ChatGPT. xAI is the company behind Grok, which runs a chatbot on Musk’s X platform.  Musk’s xAI is running on 100,000 of Nvidia’s more advanced H100s chips at a computing cluster in Tennessee. These chips usually retail for $30,000 each.

END of – PART 1

From Google:

Neural Networks: An Overview

Neural networks, also known as artificial neural networks (ANNs), are computational models inspired by the structure and functioning of the human brain. They consist of interconnected nodes (neurons) organized into layers: an input layer, one or more hidden layers, and an output layer. These networks are a cornerstone of machine learning and deep learning, enabling tasks like pattern recognition, decision-making, and data classification.

Each neuron processes input data by applying weights, biases, and an activation function. If the output exceeds a threshold, the neuron activates and passes data to the next layer. This process is known as forward propagation. Neural networks learn by adjusting weights and biases through backpropagation, which minimizes errors using optimization techniques like gradient descent.