Stop Machine Learning and Start Machine Studying

Machine Learning at Present

In the field of AI (Artificial Intelligence) Machine Learning is the key concept used for achieving intelligent systems. In the machine learning, a learning model is developed and it is trained using a sample data set to create the intelligence. The performance of the system is dependent on the elegance of the design and the amount and relatedness of the data set. The model is designed to capture the existing knowledge specialized into the domain which facilitates fast learning and improved accuracy. Specialized knowledge is required to design good learning modals and in today, the focus is completely given on designing better modals to come up with better AI systems. So there are more and more papers written on new modals expecting with better AI performance. But unfortunately, still there are less significant Machine Learning systems designed by small startups other than by a data-rich technology giants like Google or Microsoft.

Limitation of Data Driven Learning

The real advantage with in machine learning for technology giants is the amount of available data. Google have almost all the information the general public knows in their data centers. Facebook has data about us more than we know about ourselves (due to forgetting). An ordinary organization or even a university cannot afford that much of data at all. On the other hand it takes a lot of computational resources (CPU power and time) to train a better machine learning system due to the following reasons.

1. Scale of data used for training is large
2. Learning rate slows down in most machine learning systems with amount of learning

It seems an ordinary technology company cannot afford the capabilities of AI to a giant company with such a higher volume of resources. But if we go back to humans, where we are imitating the intelligence to our mechanical systems, we see something different. We learn a lot of stuff by our own even in absence of such a high volume of data or with higher energy consumption.

How do Humans Learn?

When it comes to humans, we have a learning system of neural networks similar to Artificial Neural Networks (ANN). But it has a difference. We learn the reliable information sources (first source is mother then father, relatives, teachers, friends, books, Internet and etc.) first and then get the wisdom directly from these sources. That is also a recursive process. We first identify who we can trust and believe in them. Then we change our believes according to their inputs if the new believes are not largely contradicting with our existing belief system. In that process we gather other reliable sources and get wisdom directly from them as well in the same process. For example we starts to believe mother and then we believe that father is also reliable to believe and starts to believe what the father says. Another example is that we believe school teachers and read their recommended books and believe what the book says about reality. We start to evaluate the validity of a knowledge by evaluating the knowledge itself or the source of knowledge, only when that piece of knowledge is not contradicting with the existing knowledge. For example when a child reads the benefits of capitalism, who was living in a socialistic society, will try to evaluate the reliability of the new knowledge of capitalism versus the existing knowledge of socialism.

In this way humans gather the wisdom gathered by other people for a long term process of learning and studying, by simply believing on the information source. In reality we purely learn a very little by ourselves compared to the amount we learn by studying other information sources. That made it possible us to know about very risky and time consuming experiences like death and aging.

How Machines Can Learn?

Similar to the way we learn by first learning on the reliable information sources, machines can be modeled to identify reliable information sources by conventional machine learning. Then machine itself can refer the information from the source and start to change the behavior according to the information. That is a process of converting the information obtained from the reliable source into meta information of the learning modal. This process can be recursively executed and the system can learn a lot of knowledge within a very little amount of learning. That is pure studying. But how the machines can study like humans?

Read Like Humans

The main source of knowledge of humankind is already stored in form of natural language in books and in online content. Machines can first study what humans have learned up to now in the history by reading the text contents in natural languages.

Source: http://rtechnews.com/tech-science/new-software-makes-use-of-machine-studying-to-personalize-emails-3479

Role of NLP

But the problem is that machines are not capable of reading human languages to learn from books. That is the situation when Natural Language Processing (NLP) comes in to play. Machines can use the existing NLP modals to extract information from as logical information into the system. The remaining work is how the logical information gathered can be converted into the meta information of learning modal and run the system in a controlled scope of logical learning and decision making. Existing modals to evaluate source credibility of information can be re-used to identify the reliable knowledge sources and natural language translation technologies can be further used to enhance the scope of knowledge available to learn throughout the world.