Exploring the AI Universe: How Technologies Collaborate Like a Solar System.
In the AI Universe, much like in our Solar System, different technologies collaborate to drive advancements that transform our everyday lives. This constantly expanding universe encompasses everything from planets and stars to light and time. Similarly, AI encompasses all the technologies that allow machines to mimic human intelligence, with interconnected phenomena and elements.
Next, I’ll be navigating and exploring through each of these elements to see how they play a crucial role in the functioning of AI.
Planets and Large Language Models (LLMs).
Earth represents large language models (LLMs), the most advanced and versatile in the realm of natural language processing (NLP) today. These models are like massive encyclopedias, trained on millions of texts, capable of performing multiple language-related tasks, such as answering questions and generating coherent content.
Additionally, they are constantly evolving.
Generative Models.
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Mars corresponds to generative models for images, video, and audio, like those used in AI applications such as Midjourney, Runway, ElevenLabs, o Suno.
These models turn text into images, videos, music, or voices from simple instructions -prompts- similar to how a writer develops an idea in a book. However, for these models to function efficiently, they rely on a more advanced power source: neural networks residing on planet Jupiter.
Deep Learning and Neural Networks.
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Jupiter, the largest planet in our solar system, represents deep learning (DL), based on neural networks, a set of algorithms inspired by how the human brain works. These networks, made up of layers of artificial ‘neurons’, process data, learn to recognize patterns, and improve with large volumes of data (Big Data). Neural networks work alongside advanced algorithms like transformers and convolutional neural networks (CNNs), allowing increasingly complex tasks.
Without deep learning, other planets like generative models or LLM systems couldn’t function accurately and efficiently.
The Atmosphere and NLP.
NLP is a set of techniques and models that allow machines to understand and generate human language in text or voice, facilitating natural interaction between humans and machines.
NLP acts like an atmosphere that envelops the various planets, enabling techniques such as automatic translation, sentiment analysis, text comprehension, and conversational interaction. In essence, NLP ‘filters’ or ‘translates’ natural language for other technologies to use, allowing other planets to receive text instructions and convert them into images, videos, music, or voices.
But without the Sun (the core of fundamental algorithms), the planets wouldn’t function properly, and information exchange between technologies wouldn’t occur.
The Moons and Applications and Tools.
Orbiting around these planets are moons, which represent specific applications like chatbots, translators, image generators, –Midjourney, NewArc.ai,聽Magnific,聽Runway,聽Luma, Leonardo AI,聽Basedlabs, Suno,聽ElevenLabs,聽 etc.-
These applications use the models and technologies of the planet they orbit to offer specific functions to users.
The Sun, Stars, and Fundamental Algorithms.
At the center of the solar system is the Sun, which symbolizes fundamental algorithms, such as neural networks and transformers. These provide the energy -processing and DL capacity- necessary for models (LLMs, Generative Models) and applications like ChatGPT, Midjourney, Runway, ElevenLabs, Suno and any other.., to function.
AI technologies work together in perfect coordination, like a solar system where each element has a key role. From understanding and processing language to creating images, videos, music, and voices, each ‘planet’ contributes to the generation of new media and the continuous evolution of AI.
Understanding these dynamics not only helps us better understand the past of the AI Universe, but this knowledge has the potential to refine AI models and guide future ‘astronomical’ research to new, better, and greater stages.
For instance, there is currently debate about the sustainability of LLMs. A study by MIT reveals that as models grow larger and more sophisticated, their training costs and maintenance complexity increase, leading to discussions about new advanced or vertical language models, smaller but better focused.
Does this mean generative AI has no future? Not at all.
Researchers are already exploring new pathways, seeking more efficient and sustainable ways to train AI models. The next frontier in AI is near, and it will be fascinating to see how far we can go.
聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 聽 15 Octubre ’24聽 I聽 Felipe