As I’m a student of B.Tech CSE AI/ML, I do know the importance and requirement of the technology we are using today. Computer science and Machine Learning could be a hot topic within the tech industry. Perhaps quite our daily lives AI (AI) are impacting the business world more. AI is everywhere, from gaming stations to preserving multifaceted information at work. Computer Engineers and Scientists are working hard to communicate intelligent behavior within the machines making them think and reply to real-time circumstances. Tech titans like Google and Facebook have positioned huge gambles on computer science and Machine Learning and are already using it in their products. But this is often just the start, over the following few years; we might even see AI and ML functioning for the entire world solely.
Topics to be covered in this blog:
- What is Artificial Intelligence?
- What is Machine Learning?
- What is Deep Learning?
- AI/ML examples and use cases?
What is Artificial Intelligence?
Artificial intelligence is the capability of an automatic data processing system to mimic human cognitive functions like learning, understanding and problem-solving. Through AI, an ADP system uses maths and logic to simulate the reasoning that individuals use to be told from new information and make decisions. The aptitude of creating decisions by the automated machines and computers goes to form the way far difference within the technological world within the future. Specific practical applications of AI include modern web search engines, personal assistant programs that understand oral communication of humans, self-driving vehicles and recommendation engines, like those employed by Spotify, Netflix and Google by observing the information of the user and sorting it to urge the specified results. AI’s mission is to create computers smart enough to mimic the human mind behavior.
Machine learning and Deep learning (DL) are subparts of Artificial Intelligence.
What is Machine Learning?
AI and ML are much trending and also most confused terms nowadays. ML could be a subpart of computer science. ML could be a science of manipulative and applying procedures that are able to learn things from past cases. If some behavior occurred in past, then you will predict if or it can happen again in future. Means if there are not any past cases then there’s no forecast.
ML is applied to resolve tough issues like MasterCard fraud discovery enable self-driving cars and face recognition and appreciation. Machine Learning uses complex procedures that constantly repeat over large data sets, analyzing the patterns in data and facilitating machines to reply different situations that they need not been explicitly programmed. The machines learn from the history to provide reliable results for future. The Machine Learning algorithms use applied science and Statistics to predict rational products.
What is Deep Learning?
Deep learning may be a subset of machine learning that attempts to emulate human neural networks, eliminating the necessity for pre-processed data. Deep learning algorithms are able to consume, process and analyze vast quantities of unstructured data to be told with none human interference. As with other styles of machine learning, a deep learning algorithm can progress over time. Some practical applications of deep learning currently include developing computer image, face recognition and linguistic communication processing and image search.
AI/ML examples and use cases?
Let’s take a look at some practical use cases and examples where AI and ML is being used to transform industries today.
Healthcare
AI/ML is being used in healthcare uses to increase clinical proficiency, boost diagnosis quickness and accuracy, and improve patient conclusions.
Telecommunications
In the telecommunications industry, machine learning is gradually being used to gain insight into customer behavior, enhance customer experiences, and to optimize 5G network enactment, among other things.
In fact, according to our State of Enterprise Open Source report issued in early 2021, 66% of telco organizations expect to be using enterprise open source for AI/ML within the next two years, equaled to only 37% today.
Insurance
In the insurance industry, AI/ML is being used for a variety of submissions, including to automate claims processing, and to send use-based insurance services.
A majority of insurers believe that the innovation of their core systems is a key to differentiating their services in an extensive marketplace, and machine learning is part of those upgrading efforts.
Financial Services
Financial services are similarly using AI/ML to renovate and improve their offerings, including to personalize customer services, improve risk investigation, and to better detect fraud and money laundering.
As the quantity of data financial institutions have to deal with lasts to grow, the capabilities of machine learning are expected to make fraud detection models more vigorous, and to help optimize bank service processing.
Automotive
The automotive industry has seen an huge amount of change and disturbance in the past few years with the advent of electric and autonomous vehicles, prognostic maintenance models, and a wide array of other disorderly trends across the industry.
And of course AI/ML is a big part of this alteration. For example, it is a key part of BMW Group’s automated vehicle enterprises.
Energy
Energy providers around the world are also in the middle of an industry conversion, with new ways of generating, storing, delivering and using energy changing the competitive background. Additionally, global climate concerns, market drivers and technological progressions have also changed the landscape noticeably.
The energy sector is already using AI/ML to develop intellectual power plants, optimize consumption and costs, develop prognostic maintenance models, optimize field operations, safety and improve energy interchange.
Tushar
1st year student (B.Tech-AI/ML)
School of Engineering and Technology
Sushant University
