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Machine Learning Systems: Which one is the best and why?


Machine Learning (ML) is now a widely explored subject among Artificial Intelligence Experts and Data Scientists. It has also led to a number of various Machine Learning Systems. With recent advancement in machine learning, many reports suggest, Data Discovery is the next in-line project for machine learning usage. While machine learning makes it way up to medical applications, weather forecast, social media, virtual assistants, smart IOTs (Echo, Google Home), etc., it has opened doors for vast utilization in AI development.


Curious about Machine Learning?


Machine learning is a branch of Artificial Intelligence (AI) which helps computers utilize pattern recognition, computational learning theories and statistical data analysis, in order to learn and improve themselves. In simpler terms, machine learning study data feeds and uses that study to predict and improve its working without any much programming done at the backend. The concept of Machine Learning first came into the picture in 1959 by Arthur Samuel (pioneer in AI and computer gaming).


Types of Machine Learning Systems:


Expert Systems


Edward Feigenbaum, Father of Experts systems, is the person who should be credited for introducing Expert systems as the initial application of AI by implementing machine learning systems. It worked strictly on RBML (Rule based machine learning) which worked on the strong principle of identifying and evolving rules by storing and manipulating.


Effortlessness in

maintenance uplifted as a major advantage in knowledge based expert systems, since expert systems eventually

claimed to be developing systems themselves. Rapid prototyping is again an

important factor, since expert systems with the help

of machine learning were developing prototypes in days rather than months,

moreover with lesser rule inputs.


Hearsay, MYCIN, CADUCEUS, Dendral, SMPH.PAL and Mistral became some of the early models of Expert systems involving machine learning approaches of association rule meaning, artificial immune and learning classifier systems.


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BI & Analytics


In today’s world,

Business Intelligence (BI) and AI are shaking hands together to utilize machine

learning systems and techniques

in order to grasp and foresee smart data capabilities. In order to understand

it better, AI and Machine learning is helping Business Intelligence to emerge

and evolve from self-service to many “smart”

analytical tools.


Machine Learning

add-ons to BI tools are leveraging them to explore more with predictive

analytics rules. This

certainly is very beneficial to non-programming business users, since they need

not to invest a lot of time in understanding data reports or visualizations.


DOMO’s Mr Roboto is

working on the principle of ML, AI and predictive analytics and is an AI

based Business Dashboard. ApptusAvvande by Microsoft & Accenture and

MindSphere by Siemens are also some of the common artificially intelligent BI

tools that are serving smart business data analysis, data storage and data

predictions. While BI is utilizing full potential of AI, business users have

hugely benefited

with much easier data available and low tool maintenance.


Neural Networks


Formed by artificial neurons or nodes, Neural Network (NN) is also popularly known as Artificial Neural Network (ANN). The reason these terms match biological terminology is because of the simple fact that the concept of ANN, started by Walter Pitts and Warren McCullough (in 1943), was to create machine learning systems that replicate the functioning of the brain. Moving through a lot of research and development, a lot of usage of ANN could be seen in the medical field, gaming, image recognition and speech recognition. One prime usage not to be missed here is Social Network Filtering using ANN and algorithmic approach.


ANN came into

picture after the basic algorithmic approaches were not able to yield  the desired result with non-linear data

relationships among variables. With much enhanced machine learning capabilities

ANN provided predictions on linear complex functions. Artificial Neural Network

basically operates from a Hidden state (much similar to neurons), and even

learns from every algorithmic approach.


Started as a passionate project to imitate human brain functioning, now it is taken very seriously when it comes to business data predictions, self-learning capabilities and its probabilistic prediction power. With major clients such as Facebook and Google investing heavily on ANN algorithmic research and development, some major advancement and utilization could be predicted in the near future.


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Final Take: –


The initial phase for Machine Learning systems has been severely challenging. ML systems served much time with data scientists and researchers, until recent years. With the increasing business race, the demand of much faster predictive tools increased as well. Although treated as a subset of Artificial Intelligence, Machine Learning is taken as an entirely different and vast domain by many ML innovators. Although ML possesses self-learning capabilities, still it feeds on the rules and data entered which eventually could be manipulated. A manipulated data itself in the first place would either teach ML something wrong. Therefore there is a lot of developmental scope to create such ML systems which do not produce prejudiced results, even after false inputs. But as of now, with enough investments and working applications, Machine Learning Systems are shaping the whole world into a ‘smarter’ globe.



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