How Evolution Shaped Our Mental Power

Our inheritance as hunter-gatherers is something we carry with us.Your DNA is their DNA. The world we live in is far removed from our prehistory, but genetics hasn’t caught up.It’s funny to think that we built a civilisation that we’re not optimised for. Our bodies assume that we’ll walk most of the day, hunt and maybe know a few hundred people.Then again, civilisation has its benefits. Most people would call it a fair trade.This distance between who we are and what our bodies evolved to do is worth keeping in mind. It explains some of the more powerful ways to shape your unconscious mind.

For example, we have excellent memories for locations. Yes, even if you’re the sort of person who gets lost easily. You can easily imagine the floorplan of your home, office and favourite restaurant.And you might not remember the address of your childhood home, but I bet you could mentally walk through its halls right now.Before GPS, before cars, even before farming, humans had to navigate using what they knew. They had to know which tribes lived where, how far it takes to reach the lake and where the best vegetables grow.You have an instinct for locations that is more powerful than you might appreciate.And your unconscious mind doesn’t just store this as raw geographical data. Maybe one shore of the lake represents relief, comfort and safety. The other shore belongs to enemies, so anger comes to mind when you think of it.There’s the grassland where you triumphed over a large deer – your first hunt – that fills you with a sense of power.A tall, rocky hill that lets you see for kilometres in every direction. It’s a great place for scouting and for thinking about the future.

Nothing in your mind exists in isolation. Something as fundamental as landscapes attract many associations. One place becomes a symbol for so many things.This is how your mind works. The more you pay attention to something, the more ideas, concepts and memories link to it.Your mind always tracks where you are and where you’ve been.Even when you don’t think about it.Even when you’re lost.And even when you’re asleep.This feature keeps you grounded in a complex world. Once you know this, you can use it. However you access your unconscious, use landscapes that mean something to you.

What Are the Challenges of Machine Learning in Big Data Analytics?

Machine Learning is a branch of computer science, a field of Artificial Intelligence. It is a data analysis method that further helps in automating the analytical model building. Alternatively, as the word indicates, it provides the machines (computer systems) with the capability to learn from the data, without external help to make decisions with minimum human interference. With the evolution of new technologies, machine learning has changed a lot over the past few years.

Let us Discuss what Big Data is?

Big data means too much information and analytics means analysis of a large amount of data to filter the information. A human can’t do this task efficiently within a time limit. So here is the point where machine learning for big data analytics comes into play. Let us take an example, suppose that you are an owner of the company and need to collect a large amount of information, which is very difficult on its own. Then you start to find a clue that will help you in your business or make decisions faster. Here you realize that you’re dealing with immense information. Your analytics need a little help to make search successful. In machine learning process, more the data you provide to the system, more the system can learn from it, and returning all the information you were searching and hence make your search successful. That is why it works so well with big data analytics. Without big data, it cannot work to its optimum level because of the fact that with less data, the system has few examples to learn from. So we can say that big data has a major role in machine learning.

Instead of various advantages of machine learning in analytics of there are various challenges also. Let us discuss them one by one:

  • Learning from Massive Data: With the advancement of technology, amount of data we process is increasing day by day. In Nov 2017, it was found that Google processes approx. 25PB per day, with time, companies will cross these petabytes of data. The major attribute of data is Volume. So it is a great challenge to process such huge amount of information. To overcome this challenge, Distributed frameworks with parallel computing should be preferred.
  • Learning of Different Data Types: There is a large amount of variety in data nowadays. Variety is also a major attribute of big data. Structured, unstructured and semi-structured are three different types of data that further results in the generation of heterogeneous, non-linear and high-dimensional data. Learning from such a great dataset is a challenge and further results in an increase in complexity of data. To overcome this challenge, Data Integration should be used.
  • Learning of Streamed data of high speed: There are various tasks that include completion of work in a certain period of time. Velocity is also one of the major attributes of big data. If the task is not completed in a specified period of time, the results of processing may become less valuable or even worthless too. For this, you can take the example of stock market prediction, earthquake prediction etc. So it is very necessary and challenging task to process the big data in time. To overcome this challenge, online learning approach should be used.
  • Learning of Ambiguous and Incomplete Data: Previously, the machine learning algorithms were provided more accurate data relatively. So the results were also accurate at that time. But nowadays, there is an ambiguity in the data because the data is generated from different sources which are uncertain and incomplete too. So, it is a big challenge for machine learning in big data analytics. Example of uncertain data is the data which is generated in wireless networks due to noise, shadowing, fading etc. To overcome this challenge, Distribution based approach should be used.
  • Learning of Low-Value Density Data: The main purpose of machine learning for big data analytics is to extract the useful information from a large amount of data for commercial benefits. Value is one of the major attributes of data. To find the significant value from large volumes of data having a low-value density is very challenging. So it is a big challenge for machine learning in big data analytics. To overcome this challenge, Data Mining technologies and knowledge discovery in databases should be used.