The debate between opportunities and risks around AI models is intensifying.
So is their impact.
According to Gartner, 30% of outbound messages from large organizations will be synthetically generated by 2025*.
The change is so sudden that we often struggle to digest all this information, names, and concepts... till now.
In this blog post, we'll demystify AI concepts, making them accessible and relevant to you and your business.
We’ll cover, in short:
AI is any system that resembles human intelligence.
Imagine AI as a digital brain that simulates human intelligence.
It learns from data, detects patterns, and helps make smart decisions.
Just as a chef refines a recipe over time, AI evolves with experience.
A bit of history
In 1950, English mathematician Alan Turing developed the Turing test to determine a machine’s ability to answer questions like a human would.
The capability of a machine’s intelligence depends on the programming behind it.
This is where machine learning (ML) comes into play.
Machine learning, a subset of AI models, is the science of making machines learn from experience (data). Much like humans, thanks to algorithms - leading to the creation of a model.
In summary:
🔔 A real application, it's like a music app suggesting songs based on your listening history. Similarly, AI models analyze user data to recommend personalized playlists.
Deep learning is a subset of machine learning (ML), built using neural networks with multiple layers to process and learn from data (see lesson 4).
The main difference with machine learning is that DL algorithms can automatically learn relevant features from raw data, reducing the need for extensive manual feature engineering.
DL algorithms learn intricate patterns from large and complex datasets
🔔 It's like teaching a robot to recognize cats by showing it thousands of cat pictures. It then identifies cats in new images, even ones it hasn't seen before.
Also, it powers home assistance devices to understand what you say and respond and allows you to transcribe an audio file into a text or translate it.
Neural networks, the foundation of AI models, are machine learning algorithms that emulate human-brain connections.
They consist of interconnected nodes (neurons) organized in layers, where each neuron processes and transforms input data before passing it to the next layer.
They learn from data and adjust their internal parameters to make accurate predictions or classifications.
Neural networks allow AI to learn about context by providing examples of a certain pattern.
Neural networks are a fundamental technology that underlies many of the recent advancements in Natural Language Processing.
Natural Language Processing (NLP) uses neural networks to model the complexities of human language.
It’s what allows computers to understand, interpret, and generate human language.
This includes language translation, sentiment analysis, text classification, and more.
🔔 Like the chatbot answering customer queries or sentiment analysis gauging social media reactions.
AI models process text to automate responses and enhance customer engagement.
NLP techniques can involve understanding existing text (natural language understanding) and generating new text (natural language generation).
Generative AI - a subset of NLP - is a technology that enables computers to produce original content, such as text, images, video, or even music, without direct human input.
Generative AI systems can mimic and create new patterns by learning from different examples, resulting in creative and diverse outputs, from generating realistic artworks to assisting in content creation and innovative problem-solving.
Large Language Models (LLMs), like GPT-3, are a type of generative AI that focuses on language-related tasks.
They are the most advanced form of ‘language models’ - probabilistic models of natural language that can generate probabilities of a series of words, based on dataset they were trained on - designed to comprehend and generate human-like text based on the input provided.
These models use a vast amount of data to understand context and language patterns, enabling them to assist with writing assistance to answering questions on different topics.
While it has been shown that the larger the model, the better it can mimic human language, the current limit of this technology is the cost of the training (millions of dollars everyday) and the energy consumption.
Still, today, it is necessary to understand the AI concepts and mechanisms behind to leverage opportunities and avoid risks.
Are you ready?
Bonus - Where to learn more about AI for your Business
*Gartner - 7 technology disruptions that will completely change sales.
It's not a matter of if but rather when generative AI will be integrated into business everywhere. We believe, when it’s done, it should be safe and secure, giving organizations private access to their data, always putting people first.
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