Artificial Intelligence and new techniques that enhance its potential
It’s fascinating how, by imitating human qualities, the scientists and engineers developing Artificial Intelligence have been able to achieve results that, on average, resemble those produced by humans. It all started with the invention of neural networks and has since expanded with techniques like reinforcement learning, culminating in the two techniques we’ll discuss today: Chain of Thought and Contrastive Evaluation.
Chain of thought: Guiding AI to work step by step
It all began with the advent of the first Large Language Models (LLMs) and experiments conducted by researchers who found that using prompts like “think step by step” significantly improved results. This technique was dubbed Chain of Thought and has since become one of the most popular approaches to writing prompts. Today, the latest language models developed by OpenAI incorporate this step-by-step methodology into their design, prioritizing deliberate thinking over rushing to provide the fastest possible response.
Interestingly, humans operate in much the same way, as even popular wisdom reflects with sayings like “dress me slowly, I’m in a hurry” or “I’ll sleep on it.” Here, we see that AI functions like humans when given time to think. In the case of Generative AI, adding more elements to its thought process and, especially, cross-checking results with another AI helps avoid many errors, commonly referred to as hallucinations, resulting in more accurate outcomes.
Structured reasoning and error reduction
The step-by-step reasoning technique in AI isn’t just about slower processing but involves breaking down the problem. For instance, when posed with a complex question, an LLM can divide it into more manageable subproblems, solve each one, and then consolidate the answers into a final response. This technique reduces the likelihood of errors and increases accuracy, particularly in complex tasks requiring deep analysis.
Contrastive evaluation of results
Contrastive evaluation of results is another element gaining popularity in the development of Generative AI. This is especially evident when discussing Intelligent Agents, which don’t function as single entities but are composed of multiple LLMs collaborating as a team.
Just as teamwork is essential for organizations to achieve their goals, the same principle applies to Intelligent Agents. Within a single agent, various LLMs collaborate to accomplish a designated task. For instance, one LLM might determine what tasks need to be completed to achieve a goal, another might evaluate which of these tasks would yield the best results, and a third might execute them. When all of this is connected and runs iteratively, we get an agent that operates autonomously as long as its goal is clearly defined.
Artificial awareness
Using multiple AI models in parallel has paved the way for creating a kind of artificial awareness that recognizes and corrects its own limitations and errors. In this process, models review and evaluate the quality of their own responses before presenting them to the user. Over time, these techniques could lead to the development of AI that is not only accurate but also aware of its limitations and capable of adjusting its responses based on its own mistakes.
Critical AI, aware of its limits
If you think about it, this isn’t too different from how humans operate. When working alone, we often need to validate our ideas with others or use various techniques to verify our results. Even the market itself validates our work.
In practice, Intelligent Agents will help organizations automate complex planning and decision-making tasks, maximizing operational efficiency by cross-checking results internally. These systems reduce errors and enable companies to operate more nimbly and precisely.
If you’re interested in learning more about Artificial Intelligence and mastering these techniques, TecnoFor is here to guide you on your journey.
