Have you heard the news from the international Dota 2 championship held in Canada in August last year, the AI-enhanced robotic gamers lost to human professionals in a tough competitive set of events? This shows how human mind and skills are still on the top and their strategic thinking and compilation is much beyond the coding system of machine learning or artificial intelligence. The complex procedure of machine learning cannot be dealt with understanding of just coding system only but has a lot more layers to it which needs to be understood in terms of becoming a successful machine learning developer.
Unlike any simple game like chess, fast-paced and multiplayer games like Dota sometimes become too complex for computers and robots. We must understand that complexity is one part of the human brain that works on the basis of common sense and works on the basis of how others are planning and responding to it accordingly. Deep communication skills and cognitive ability of the human mind is still lacking somewhere in the basic functioning of machine learning and its automation. Still the advancement of the technology has empowered machine learning in various sectors but it is falling behind in the strategic reasoning which is required to understand someone’s goal and move further to then formulate the response. In this article we will try to explore how machine learning cannot be just mere coding and human mind functioning will always prioritize in the function of technology and its application.
Mind as a simulator
During the growth phase of children they learn and understand everything, they observe and grasp to understand the basic principle of society and how it works. Their thinking is different, perceptive toward problem-solving, emotions and desires are also unique and distinct. But the point here is that they learn through observation and perception. Their brain began to process information and stimulus from the experience of past memory and other’s experiences; this is the way they respond to the environment and surroundings. But in case of machine learning the multiple layers of neural nets allow the extraction of the set pattern and learn from the already present large database. But like a human mind, it can work on the pre-programmed internal informational model that would allow working in a simpler manner. For example, if we train a robot, machine learning can stimulate the robot to turn left and right to choose its path and determine which direction to go in order to avoid the collision. This is the internal model which acts at the time of decision making but as a human mind, it can’t take the action on the basis of how others around will behave.
In fact, developers are working hard to develop a prototype robot that could achieve the understanding based on the anticipated behavior of humans. So the similar robot will be able to navigate without any collision in the corridor and could complete the journey halfway without the requirement for simulation. This allows the application theory of mind which could stimulate the companion at the time of need. This could determine the appropriate required response time with a more trustworthy result.
Better results from Machine Learning
One must be master in developing programs and applying algorithms to build more data science-oriented machine learning models, but the point to notice here is that one must enable it to have the intelligence and to have applicable creativity in the real-life use also. In order to execute the principles of machine learning it is foremost required that one must learn to train the mind first. Though it sounds a little absurd this is the truth, if we can train the mindset then machine learning is far beyond just coding, in fact, it is a matter of discipline. It has to be more creative, innovative and suspicious in things, moreover, it should be able to pre-define the human logic.
Now let us just understand a few aspects of functioning and application that allows the workings of the mind in the better sense while developing a machine learning module.
1. Focus more on the problem framing and its solution
In any of the machine learning object framing the issues detection is the crucial step. A perfect framing of the problem and finding its solution can make the outcome more agreeable and advance. You can find help through Google machine learning and find out the appropriate section to have the problem assessment in a better and deeper aspect of individual mindset.
2. Minimal or no coding policy
Try to figure it out whether you can complete the project with writing a single code. Though it may seem a bit challenging but not impossible. First always scrutinizes the issue and then understand the objective of the requirement. The whole process will let you have the development of the multiple methodologies that may fulfill the requirement with zero or minimal coding. The whole idea lies behind the breaking of the mindset of temptation toward code writing.
3. Try something new
With constant changes and amendments in technology, if you are still using the old one then the solutions our machine learning object will offer could be outdated too. Use of latest frameworks for change in the objective achievement is the key to deal for overcoming the limitation of the knowledge and abilities as they may be limited to one frame of mind.
4. Train constantly
You might be an expert in a technology that was “in” 10 years ago but now you must go with the swifter updated new addition in machine learning developmental techniques and must learn them. Stop hanging around to same old school thinking and dump them off to have a better approach toward the result formation. So you can observe and deal with the technological shift in a better way and learning efficiently without much conflict.
Machine Learning is much more than just coding
As a common notion goes that to understand machine learning one must be able to process lots of codes or software programming which does not hold true. Machine learning is primarily about creating models that can work on the basis of information extraction from a given set of data; this requires a fair bit of logic algorithm and reasoning so that you can move forward in the right direction of choosing the right module. The difference is similar to theoretical physics and applied physics. Coding and machine learning are not synonymous, coding is just a part of machine learning through the functioning of later is much larger. More and more initiatives are being made so that machine learning can work without the requirement of coding. In fact, many business giants like Google itself is working on products that allow the use of machine learning without programming. In other words, we can say that machine learning is much more beyond coding and the goal is to come up with more creative models which involve basic human intelligence superior to just programming.
As a technology trend currently focus is shifting on implementation of high performing attitude of the projects using a common algorithm for developing the more advanced machine learning framework. Another point that clearly explains the limitation of coding in machine learning is the statistical interference as you can study the long files of data but the ultimate decision and conclusion don’t simply require coding.
Machine learning projects do not only require coding but may require you to look into the dynamic dimension like the visualization of the data, tuning of the model, using an algorithm which is suitable to that particular model. There is not much extensive use of coding as in the traditional programming because you are leaving much on the machine to process, perceive and learn on its own.
While there is a certain amount of coding involved in machine learning but the level and the amount is much less compared to conventional traditional systemic programming. The simplest form of the code involves human-understandable input and machine-readable output; this can be converted into a more complex form in terms of presentation as well as requirement. So all in while it is part of the procedure and pipeline foundation of machine learning but does not hold exclusive to it.