Introduction
Even with such advancements AI fails to actually mimic human answers to a given prompt. One of the main challenges is the part to train AI systems to learn and think in a way that is similar to humans. Most AI’s relay on large data and some algorithms which fails while mimicking humans. These do not succeed in providing innovative answers like humans.
Another huge problem of these machines is their habit to hallucinate. To solve these Microsoft has bought a new type of training method called algorithm of thoughts which aims to solve these problems by using by combining human cognition with algorithmic logic.
Traditional large language models
Large language models (LLMs) are AI systems that can generate natural language texts based on a given input.
LLMs take in large amounts of text data and learn the relationships between words and phrases. They do this by assigning each word a numerical representation, called a word vector. The word vectors are then fed into the neural network to predict next phrases using the previous one.
But in this circumstances the LLM hallucinates at times. It is limited to the query’s data provided to it by the trainer. Hence it is not able to give its own innovative human like answer.
LLMs often struggle with complex reasoning tasks that require logical inference, common sense, and domain knowledge.
AOT algorithm of thought(working)
The very purpose of this model is to enhance the reasoning abilities of large language models by combining human cognition with algorithmic logic. It is inspired by how humans think. It also uses arithmetic logic extensively to enhance its performance.
It adds something called a “thought layer” to the LLM which represents the concepts, facts etc. The thought layer uses ‘thought embedding’ in order to translate words into thoughts.
This help in thought process of the model and most importantly generate new and unique thoughts and interconnections.
This can also help expand the domain of the LLM.
Problem solving capabilities of algorithm of thought
This method is a completely new approach compared to conventional algorithms.
Microsoft’s extensive investments in the field of AI place the company to in a position to seamlessly integrate the Algorithm of Thoughts into use in various other systems and services, potentially including the highly anticipated GPT-4.
Microsoft’s approach signifies a convergence of human and algorithmic attributes, giving rise to a hybrid model that surpasses the limitations of existing AI training techniques. It draws inspiration from both human intuition and machine-driven exhaustive exploration. AoT trys to improve the reasoning powers of generative AI models. Algorithm of Thoughts emerges as a pioneering method that could redefine the world of AI reasoning, putting it in an era of more intuitive and sophisticated problem-solving capabilities. I have learnt a lot on this topic from cryptonews
Conclusion
AI can help us improve our productivity, efficiency, quality, safety, and innovation. AI can also help us solve some of the most pressing challenges facing humanity, such as climate change, disease, and inequality. AI is expected to disrupt the customer service industry and many other consultancy services.
As these models grow in capacity and data, we will get to a point where hallucinations and errors in it will no longer be obvious to us unless they don't expose their mistake. They will not only be able to mimic but also be able to give better well rounded answers than humans. They will be able to take a lot of logical decisions. You can learn find courses on AI in the online course directory Pacificmultiverse.
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