This document discusses business intelligence, artificial intelligence, algorithms, and how machines are developing abilities that surpass humans in some areas. It notes that business intelligence has evolved from rule-based systems to incorporating machine learning powered by large data and improved algorithms. Artificial intelligence and machine learning are distinguished, with machine learning using statistical methods to improve system performance over time based on available data. The document outlines different types of machine learning algorithms and notes machines' advantages over humans in processing large data volumes quickly. While machines currently match or exceed humans in some tasks like image recognition, each machine learning application remains unique to its use case.

1. PRIYANKA SHAHI | JUNE 2017

2. AGENDA
• BUSINESS INTELLIGENCE
• ARTIFICIAL INTELIGENCE
• ALGORITHM
• MACHINE OVER HUMAN BEINGS
• CONCLUSION

3. BUSINESS INTELLIGENCE
• Business Intelligence came in the tech world several years ago, a technology-driven process which offers
useful information to top level executives, managers & other end users in order to help them in making
better business decisions.
• Data warehousing is the technology which integrates data from operations made in the company.
• It deducts the time it took to access data, but even after reaching its prime area, Business Intelligence
systems could do more than generate data and reports in a traditional organized way.
• This rules-based technology was not really serving intelligence at all.

4. CONT.
• With the advancement of artificial intelligence and mainly machine learning, real business intelligence is
actually on its way to the better enterprise.
• This self-learning software will work on servers, build into bots, run decision-making systems.
• Growing data processing power, availability of big data and improvement in algorithms are uniting to
make business intelligence better.
• There are factors like quality of the data, human programming, cultural resistance which could be
hurdle in machine learning progress and its unification into business.

5. ARTIFICIAL INTELLIGENCE
• Artificial Intelligence is the art and science of making computers perform actions which needs
intelligence when done by humans.
• It is imprecisely used interchangeably with machine learning.
• The term ‘Machine leaning’ is particular subgroup of artificial intelligence which make use of statistical
methods so that performance of a system improves over time.
• Programmers can write code in order to develop a program that more or less acts like a human but It
cannot be called machine learning unless the system has actually started learning how to behave based
on available data.

6. ALGORITHM
• There are types of machine learning such as supervised learning meaning algorithm is trained using
examples.
• These algorithm wherein input and correct data are known, unsupervised learning meaning the
algorithm must learn patterns in the data on its own.
• Reinforced learning wherein the algorithm is pleased and fined for the actions it takes depending on
trial and error.
• Each case has the machine which can learn from data – structures & unstructured in the future which
absorbs new behaviors and function with time.

7. MACHINE OVER HUMAN BEINGS
• The biggest advantage machines have over human beings is that they are able to manage huge amount of
data, take complete advantage of fast processing power which will automatically provide 24x7 services.
• Over the past few years, error rate in machine learning image recognition has fallen down to zero, nearly to
performance levels of human.
• Even after this, machine learning is different. Each and every illustration of machine learning is unique
depending on the area searched in.
• It may take less time for a computer to analyze the meaning of furrowed brow than to study to analyze text.

8. CONT.
• There is consumer adoption of machine learning technologies such as Amazon’s Echo and Apple’s Siri.
• Its an essential part in fraud recognition, investigation, image & voice recognition, etc.
• It’s an important component in fraud detection and surveillance, image and voice recognition, and
product recommendations.
• Therefore, Machine learning approaches are of great interest if used smartly in your organization.

9. CONCLUSION
• Machine learning community is open to everyone and hence people can research and share their ideas with
other individuals.
• Machine learning approaches applied in systematic reviews of complex research fields such as quality
improvement may assist in the title and abstract inclusion screening process.
• Machine learning approaches are of particular interest considering steadily increasing search outputs and
accessibility of the existing evidence is a particular challenge of the research field quality improvement.
• Increased reviewer agreement appeared to be associated with improved predictive performance.

10. THANK YOU!