Simple definition of Data Science, Machine Learning and Deep Learning.

Machine Learning, or “Machine Learning,” is like teaching a computer to learn from experience.

Imagine teaching a child to differentiate between apples and bananas by showing them various examples of each. Over time, the child will learn to identify them on their own.

Similarly, in machine learning, we feed the computer many examples, and over time, it can make predictions about other similar objects based on those examples without us explicitly telling it. It’s like giving the computer the ability to learn and improve with experience.

Data Science: It’s the field of study that combines skills in programming, statistics, and business knowledge to extract insights and knowledge from data. Think of it as being a data detective: you look for clues, identify patterns, and make discoveries that can help companies make more informed decisions.

Machine Learning: It’s the technique that allows computers to learn from data. Instead of programming specific rules to perform a task, you provide the computer with examples, and it “learns” from them. It’s like teaching a child to differentiate objects: after seeing enough examples, it can identify them on its own.

Deep Learning: It’s a technique within Machine Learning that uses neural networks with many layers (hence the “deep” in its name) to analyze various types of data. It’s like your computer having a virtual brain that can recognize complex patterns after being trained on massive amounts of data. It’s especially useful for tasks like image recognition and natural language processing.

What is the reason for the recent exponential acceleration of new AI?

The exponential acceleration in the world of artificial intelligence, specifically in machine learning, is the result of the confluence of several factors that have converged in recent years. These factors have allowed tasks that were once considered extremely complex and laborious to be performed in significantly shorter times.

Here we break down the reasons behind this acceleration:

More data.

We live in the era of big data. Every day, exabytes of data are generated due to the widespread use of mobile devices, social networks, sensors, IoT devices, among others. This data deluge has provided the necessary “fuel” to train more accurate and robust ML models.

New AI Methodology.

– Machine Learning: The evolution of algorithms and techniques has enabled machines to learn from data more efficiently.

– Neural Networks and Deep Learning: Inspired by the functioning of the human brain, these networks have proven to be extremely effective in tasks such as image recognition and natural language processing.

– Reinforcement Learning: An approach where software agents learn to make decisions by receiving rewards or penalties based on the actions they take.

– Generative Adversarial Networks (GANs): These networks can generate data that is nearly indistinguishable from real data.

– Transformers: An architecture that has revolutionized natural language processing, with models like BERT and GPT.

– Diffusion Models, Reinforcement Learning from Human Feedback (RLHF), LLMs: These are advanced techniques and models that have emerged in recent years, further expanding AI capabilities.

Faster Computers.

– GPU: Graphics processing units are not only essential for gaming but have also proven to be extremely useful for training ML models, especially in deep learning, due to their ability to handle parallel matrix operations.

In summary, the combination of increased data availability, methodological advancements, and improvements in computing power has propelled the exponential acceleration in the field of artificial intelligence.

Simple definition of LLM and its influence on the new AI.

Imagine a computer program that has “read” so many books, articles, and web pages that it can talk about nearly any topic. It not only remembers what it has “read,” but it can also combine and use that information to answer questions, help you write, or even chat with you. That’s a Large Language Model (LLM). It’s like a super language expert that has absorbed a wealth of information from the world.

Thanks to LLMs, artificial intelligence has taken a huge leap. Instead of just following instructions, these models can interact, adapt, and provide more human-like and personalized solutions.

They have transformed how we work with AI, allowing us to collaborate with machines in language and communication tasks in a much more natural and effective way. It’s like going from having a simple calculator to having an intelligent companion that understands and assists in your tasks.