A No-nonsense Approach to Deep Learning, LLM, Supervise Learning, Generative AI, and Everything in Between

With this post I will share a few resources freely available in the internet that I believe can serve as an entry point for understanding the world around AI in a no-nonsense manner. The domain is relatively vast and we will cover topics like Deal Learning, Large Language Models, Supervise Learning, Generative AI, and a few more keywords that are popular at the time of writing this post. Clearly we are in an era where the interest in Generative AI and Large Language Models (LLMs) is capturing attention from both academia and practitioners in various industrial sectors. However, I am still surprised to know that in many contexts both domains are used in a synonymous manner: they are not the same and you can refer to this article for some clarifications about LLM and Generative AI.

Pexels.com<\/a>","created_timestamp":"0","copyright":"","focal_length":"0","iso":"0","shutter_speed":"0","title":"bionic hand and human hand finger pointing","orientation":"0"}" data-image-title="Generative-AI-Supervise-Learning" data-image-description="

" data-image-caption="

A No-nonsense Approach to Deep Learning, LLM, Supervise Learning, Generative AI, and Everything in Between

In the realm of business and AI, Supervised Learning (yet another AI technique that is becoming a bit old fashioned nowadays) and Generative AI emerge as pivotal techniques offering transformative potential. They are effective especially when approached as development tools tailored to specific domains rather than mere products or services to be integrated into existing business frameworks. This perspective advocates for leveraging AI technology not only as a tool but as a toolbox containing customizable instruments for domain-specific innovation. By understanding the intricacies of these techniques, businesses can harness their capabilities more effectively, thereby maximizing their impact on society and fostering sustainable growth. In essence, it’s about not just using the tool but understanding and utilizing the toolbox itself for the betterment of society and business alike.

The video below presents the business and AI view according to the AI Fund perspective:

Moreover, I would personally (i.e. this is my opinion) advocate that the combination of both, the “old fashion” supervised learning and the popular generative AI (with LLM leading the pack), coupled with sound understanding of information enrichment techniques will probably offer the best cocktail for a successful venture capable to create value for society.

In the rest of this post I will try to expand on this point by first looking at what Large Language Models are and how they function. Next, I will share a pointer to a comprehensive (and free!) resource for familiarizing with deep learning tools and techniques.

Large Language Models: What They Are, How to Make Your Own, and How to Engineer an Application

Let’s start by looking at Large Language Models using the following two videos. LLM are text manipulation tools that are capable of both summarizing and creative writing (witting code can be considered as a creative endeavor). Thanks to recent progresses (that we can date with the launch of ChatGPT), the structure and the consistency of such generated text is increasing in accuracy and, consequently, in usefulness. The video below can serve as a good introduction to how Large Language Models work and their capability of guessing the next word:

However, there is a notable gap in research concerning the integration of such approaches into everyday industrial practices. For example (to name the one that I hinted at before), the potential fusion of structured knowledge graphs that are typical of databases-oriented information systems with AI-based semantic embedding, remains largely untapped. Furthermore, the exploration of multiagent aspects and memory-resilient LLMs holds promise for improving business processes, yet systematic empirical validation of their efficacy is lacking. The video below is an introduction to how to engineer Large Language Models in order to perform tangible tasks of value:

Promising directions of investigation include:

Exploring diverse applications of Large Language Models (LLMs) tailored to specific subtasks in composing a comprehensive global capability model.
Investigating optimal development configurations and orchestrating multiple-agent LLMs to enhance solution effectiveness.
Assessing the potential of memory-based agents in facilitating the synthesis of various capabilities.
Establishing best practices for presenting semantically enriched data to LLMs in a meaningful manner.
Integrating embeddings and implicit semantics with explicit knowledge from knowledge graphs to enrich the understanding and inference capabilities of LLM.

While numerous methods exist for grasping the utility of a tool, I contend that learning its construction can accelerate mastery and unlock its full potential. This video provides an insightful overview on (re)implementing a transformer architecture, as detailed in the seminal paper “Attention is all you need,” which underpins the success of ChatGPT.

This concludes the conversation on Large Language Models that are a part of the “Generative AI” family. What follows is an introduction to the old fashioned deep learning that, as I mentioned at the beginning of this post, will still probably cover an important role in the next years

A Few Notes on Deep Learning

Perhaps now that we know the details of the transformer architecture (and everything else related to LLM), we can zoom out from Generative AI (and LLM) and take a look at a larger context taking into account other aspects of AI. Deep Learning and Generative AI are intertwined fields within artificial intelligence, each serving distinct yet complementary purposes. Deep Learning, a subset of machine learning, employs neural networks with multiple layers to learn representations from data, excelling in tasks like classification, regression, and pattern recognition. Generative AI, on the other hand, focuses on creating new data samples that resemble those in the training data, utilizing techniques such as generative adversarial networks (GANs) and variational autoencoders (VAEs). The relationship between Deep Learning and Generative AI is evident in how Deep Learning techniques, like convolutional and recurrent neural networks, form the foundation for building generative models. For instance, GANs employ adversarial training between a generator and discriminator network, while VAEs use encoder-decoder architectures, both rooted in Deep Learning principles. Together, Deep Learning and Generative AI enable the development of sophisticated models capable of learning from data, generating new insights, and advancing artificial intelligence across various domains.

The video below presents the book “Understanding Deep Learning”. It has been published in December 2023 by MIT Press and is presenting itself as a comprehensive guide for learning modern machine learning.

However, as mentioned in the video, the field is currently growing at the rate of 4000 papers a month. Therefore, is almost impossible to be able to cover all the relevant aspects. However, the book is free and you download it at the following link:

https://udlbook.github.io/udlbook/

A Final Note on AI, LLM, Generative AI, Supervised Learning and Everything in Between

In conclusion, the surge of interest in Generative AI and Large Language Models (LLMs) across academic and industrial spheres underscores their potential to revolutionize various sectors. Embracing Supervised Learning and Generative AI as developmental tools tailored to specific domains, rather than mere commodities, holds promise for driving transformative innovation in business and beyond. By comprehending the intricacies of these techniques, businesses can harness their capabilities effectively, thereby maximizing societal impact and fostering sustainable growth. The exploration of diverse applications, optimal configurations, memory-based agents, semantic data presentation, and knowledge integration mark promising directions for future research. While understanding the construction of tools accelerates mastery, the broader context of Deep Learning and Generative AI highlights their intertwined roles in advancing artificial intelligence.

A No-#Nonsense Approach to #deeplearning , #LLM (#LLMs), Supervise Learning, #GenerativeAI, and Everything in Between
Click To Tweet

Magazine

A No-nonsense Approach to Deep Learning, LLM, Supervise Learning, Generative AI, and Everything in Between

Large Language Models: What They Are, How to Make Your Own, and How to Engineer an Application

A Few Notes on Deep Learning

A Final Note on AI, LLM, Generative AI, Supervised Learning and Everything in Between

About the author

Author's Latest Articles

THE %(site_name)s COMMUNITY