Artificial intelligence has quite often been in the news for doing what human beings can do, but for this rapidly changing business scenarios it has become much more than being just a fascination. Nowadays, AI can be used solve various array of problems which arise from our day to day transaction. Machine learning and other AI techniques have made it quite possible to analyse our transactional data and derive a viable solution from it.

Relationship between AI, Machine Learning and Data science

• AI (Artificial intelligence) is a subfield of computer science, that was created in the 1960s, and it was (is) concerned with solving tasks that are easy for humans, but hard for computers. In particular, a so-called Strong AI would be a system that can do anything a human can (perhaps without purely physical things). This is fairly generic, and includes all kinds of tasks, such as planning, moving around in the world, recognizing objects and sounds, speaking, translating, performing social or business transactions, creative work (making art or poetry), etc.
• NLP (Natural language processing) is simply the part of AI that has to do with language (usually written).
• Machine learning is concerned with one aspect of this: given some AI problem that can be described in discrete terms (e.g. out of a particular set of actions, which one is the right one), and given a lot of information about the world, figure out what is the “correct” action, without having the programmer program it in. Typically some outside process is needed to judge whether the action was correct or not. In mathematical terms, it’s a function: you feed in some input, and you want it to produce the right output, so the whole problem is simply to build a model of this mathematical function in some automatic way. To draw a distinction with AI, if I can write a very clever program that has human-like behaviour, it can be AI, but unless its parameters are automatically learned from data, it’s not machine learning
• Data science is combination of all this algorithms and subsequent implementation of these algorithms. Data science can lead to some significant progress in coming few years.

Financial application

Due the introduction of Digital markets all our transactional data is accessible by the banks or financial institutions. AI and machine learning algorithms can significantly improve the efficiency with detecting frauds which otherwise would have been a time consuming and irrelevant. With the help of different algorithms this process is catalysed and fraud is detected in time. AI can also be used for noticing trends in businesses.

Police investigation

AI algorithms can be used in police investigation for generating leads based on data available. Running a match through a database is nothing but AI algorithm going one by one through each and every database entry. Recently, an app called “Hotels-50K” used AI algorithm to match the photos of victims kidnapped to the hotel rooms nearby to minimize the search area.

Farming

With the help of proper IoT devices, data science can a real and practical boon for farming in many parts of the world. Moisture, temperature, salinity of soil, etc. are different factors which affect in products of farming. With the help of data science and IoT devices connected to it changes in these variables can be properly predicted before time and efficiency of farming can be increased substantially. Start-up from pune has been seamlessly in the pursuit of this concept. You can check them out at senseitout.com

Medical purposes

In medical applications, AI and data science can be used for digital consultation, doing repetitive tasks such as analysing x-rays, CT scans. Remote healthcare monitoring is recent application of this tech.