AI Algorithms

AI Algorithms – How To Explain Them To The Masses

AI algorithms is the general term for artificial intelligence, also known as Deep Learning. The field of Artificial Intelligence has developed so much in the past two decades that it is now widely used and people have started to realize its potential. It started with a super computer science project like the Human brain project and has evolved into something much more powerful and useful. 

Machine learning method

One such machine learning method is the deep learning for artificial intelligence. It deals with convolutional networks (one of the four major types of artificial intelligence) and is widely used to train a system’s internal logic. It is currently being used for speech recognition, language understanding, image processing, time and sequence prediction, natural language processing and many other tasks. In short it deals with high level problems where the answer is almost always unpredictable. 

Why are we still waiting for deep learning? I believe that we have reached the peak of Machine Learning. At some point, no one will be able to stop the development of super computers. Will humans be able to compete with them? In many ways they already are, due to their massive storage and computing power and ability to process massive amounts of data, the Internet, for example, is mostly machine-learning based. 


This article will mainly focus on one particular machine learning method called Jaakko. If you are not familiar with this term, Jaakko is a special type of algorithm that solves optimization problems by manipulating random variables instead of a specified target function. If you are not an expert in machine learning, I recommend reading a great paper by Geoff Chamberlin called “Explainability Analysis for the Designer”. This is a review of the various difficulties in designing and implementing a good AI algorithm. 

Human decision-making

One of the reasons why human decision-making and social systems seem so frustrating is because they are too rigidly bounded by natural laws. The most obvious example is that humans cannot reason from the physical laws of the world. For instance, the weather is always unpredictable. If you ask me how long the rain will last today, I’ll have to say, “I don’t know, but you’ll see.” Similarly, the future of artificially intelligent artificial intelligence systems may not be predictable even if the future machine learning methods are. 

Deep learning is the foundation for all of the machine learning methods that are currently in use. It’s basically a branch of AI algorithm programming where the developers of the software system to create tools that can approximate any desired function efficiently, taking into consideration the limitations of natural intelligence. Deep learning experts like Karp and Norton have been working for years to solve the problems of explainability, memory, and learning. These three problems are the cornerstones of AI. 

Today, almost every major university has an AI department with a wide variety of interesting research projects. Computers now play a far greater role in society and businesses than ever before. Human employees in businesses are no longer merely data processors. Instead, humans are becoming creative creators of new products, services, and ideas. AI is starting to play an increasing role as the creator of future decisions.

If you’re a developer, it is important that you understand the challenges that you will face as you attempt to create artificial intelligence. One of the biggest challenges is explain ability, particularly as it applies to natural language processing tasks. As an artificial intelligence expert, I believe that a lot of the problems we face with decision-making and complex communication will be solved through better explainability in AI algorithms. In particular, I’m concerned that better tools for educating computers will improve the accuracy of those decisions.

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