Learn About the Different Types of Machine Learning Systems
Machine Learning (ML) is now a widely explored subject among Artificial Intelligence Experts and Data Scientists. 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 branches of Artificial Intelligence (AI) which help 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 back-end. The concept of Machine Learning first came into picture in 1959 by Arthur Samuel (pioneer in AI and computer gaming).
Types of Machine Learning Systems
Edward Feigenbaum, Father of Experts systems, is the person who should be credited for introducing Expert systems as initial application of AI by implementing machine learning systems. It worked strictly on RBML (Rule based machine learning) which worked on strong principle of identifying and evolving rules by storing and manipulating.
Effortlessness in maintenance uplifted as a major advantage in knowledge based expert system, since expert systems eventually claimed to be developing systems themselves. Rapid prototyping is again an important factor, since expert systems with help of the 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.
BI & Analytics
In today’s world Business Intelligence (BI) and AI are shaking hands together to utilize machine learning techniques in order to grasp and foresee smart data capabilities. In order to understand better AI and Machine learning is helping Business Intelligence to emerge and evolve from self-service to now much “smart” analytical tools.
Machine Learning add-ons to BI tools are leveraging them to explore more with predictive analytics rule. 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 visualisations.
DOMO’s Mr Roboto is working on the principle of ML, AI and predicative analytics and is 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 are hugely benefitted with much easier data available and low tool maintenance.
Formed by artificial neurons or nodes, Neural Network (NN) is also popularly known as Artificial Neural Network (ANN). The reason these terms are matching to 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 the machine learning system that replicates the functioning of brain. Moving through a lot of research and development, a much usage of ANN could be seen in medical field, gaming, image recognition and speech recognition. One prime usage not 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 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 Hidden state (much similar to neurons), and even learns from every algorithmic approach.
Started as a passionate project to imitate human brain functioning, now is taken very seriously when it comes to business data predications, self-learning capabilities and its probabilistic prediction power. With major clients such as Facebook and Google investing heavilyon ANN algorithmic research and development, some major advancement and utilization could be predicted in the near future.
The initial phase for Machine Learning systems have been severely challenging. ML systems served much time with data scientists and researchers, until recent years. With 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 a entirely differently 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 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.