What is artificial intelligence and how is it used In 2021 .

When we talk about AI and our brain gathers images of Terminator machines demolishing the world. Thankfully, the current image is considerably more optimistic. So, let’s investigate how AI is enabling our planet and at last profiting humankind. In this blog on Artificial Intelligence applications, I’ll be examining how AI has influenced numerous fields like marketing, finance, banking, and so on.

If you’re new to AI make sure to check out this blog on what is AI.

What is Artificial Intelligence utilized For?

• AI In Marketing

• AI In Banking

• AI In Finance

• AI In Agriculture

• AI In HealthCare

• AI In Gaming

• AI In Space Exploration

• AI In Autonomous Vehicles

• AI In Chatbots

• AI In Artificial Creativity

1)  Artificial Intelligence Applications: Marketing

Marketing is a means to sugar coat your products to persuade more clients. We, humans, are pretty nice at sugar coating, but what if an algorithm or a bot is there exclusively for the objective of marketing a brand or a business? It would do a pretty incredible job!

In the early 2000s, if we surveyed an online store to discover a product without understanding its actual name, it would become a nightmare to discover the product. But now when we investigation an item on any e-commerce store, we get all feasible outcomes related to the item. It’s like these search engines are browsing our minds! In a matter of seconds, we get a list of all related items. An instance of this is discovering the perfect movies on Netflix.

2)  Artificial Intelligence Applications: Banking

AI in banking is thriving rapidly than you thought! Plenty of banks have already approved AI-based systems to give customer support, distinguish irregularities, and credit card extortions. An instance of this is HDFC Bank. HDFC Bank has developed an AI-based chatbot called EVA (Electronic Virtual Assistant), created by Bengaluru-based Senseforth AI Research.

Since its launch, Eva has addressed over 3 million customer inquiries, interacted with over half a million different users, and held over a million discussions. Eva can compile information from thousands of sources and give easy answers in less than 0.4 seconds.

3) Artificial Intelligence Applications: Finance

Ventures have been depending on computers and data scientists to discern future patterns in the market. Trading primarily banks on the proficiency to foresee the future accurately.

Machines are incredible at this because they can grind an enormous amount of data in a brief span. Machines can moreover discover to examine patterns in past data and foresee how these patterns might reiterate in the future. In the epoch of ultra-high-frequency trading, financial companies are turning to AI to enhance their stock trading performance and stimulate earnings.

Top 12 Artificial Intelligence Tools & Frameworks you require to comprehend in 2021 -

Artificial Intelligence has promoted the processing of a huge amount of data and its use in the industry. The volume of tools and frameworks accessible to data scientists and developers has improved with the expansion of AI and ML. This blog  on Artificial Intelligence Tools & Frameworks will list out some of these in the subsequent sequence:

Artificial Intelligence Tools & Frameworks for 2021-

List of AI Tools and Frameworks

• Scikit Learn

• TensorFlow

• Theano

• Caffe

• MxNet

• Keras

• PyTorch

•CNTK

•Auto ML

•OpenNN

•H20: Open Source AI Platform

• Google ML Kit

Artificial Intelligence Tools & Frameworks

The growth of neural networks is a lengthy procedure that needs a lot of thought behind the architecture and a whole multitude of nuances that certainly make up the system.

List of  AI Tools & Frameworks

From the beginning of mankind, we as a species have always been striving to make things to encourage us in day-to-day responsibilities. From stone tools to modern-day machinery, to tools for making the development of programs to help us in day-to-day life. Some of the greatest crucial tools and frameworks are:

1) Scikit Learn

Scikit-learn is one of the most well-known ML libraries. It underpins many supervised and unsupervised learning calculations. Precedents incorporate direct and planned relapses, choice trees, bunching, k-implies, etc. It expands on two crucial libraries of Python, NumPy, and SciPy.

It comprises a lot of calculations for regular AI and data mining assignments, comprising bunching, relapse, and order. Certainly, even undertakings like altering information, characteristic determination, and ensemble techniques can be enforced in a couple of lines. For a fledgling in ML, Scikit-learn is a more-than-adequate instrument to work with, until you start actualizing progressively problematic calculations.

2) Tensorflow

On the off chance that you are in the world of Artificial Intelligence, you have most likely found out about, attempted, or enforced some category of deep learning calculation. Is it accurate to say that they are crucial? Not always. Is it accurate to say that they are steady when done right? Truly!

The intriguing thing about Tensor flow is that when you formulate a program in Python, you can categorize and keep running on either your CPU or GPU. So you don’t require to assemble at the C++ or CUDA level to keep running on GPUs.

It uses the arrangement of multi-layered hubs that facilitate you to quickly set up, train, and send counterfeit neural systems with enormous datasets. This is the thing that facilitates Google to acknowledge questions in photographs or comprehend verbally communicated words in its voice-acknowledgment application.

3) Theano

Theano is wonderfully folded over Keras, an abnormal state neural systems library, that operates almost in parallel with the Theano library. Keras’ crucial favorable role is that it is a moderate Python library for deep discovering that can keep moving over Theano or TensorFlow.

It was built to make actualizing profound learning models as abrupt and reasonable as feasible for creative work. It keeps operating on Python 2.7 or 3.5 and can invariably execute on GPUs and CPUs.

What sets Theano segregated is that it influences the PC’s GPU. This facilitates it to make data escalated counts up to numerous times sharper than when kept running on the CPU alone. Theano’s speed makes it extremely productive for profound learning and additional computationally complex efforts.

4)  Caffe

 ‘Caffe’ is a profound learning structure made with articulation, speed, and measured quality as a top priority. It is built by the Berkeley Vision and Learning Center (BVLC) and by network donors. Google’s Deep Dream banks on Caffe Framework. This structure is a BSD-authorized C++ library with Python Interface.

 5) MxNet

It enables trading computation time for memory via ‘forgetful backdrop which can be extremely valuable for recurrent nets on very long sequences. Built with scalability in mind (fairly easy-to-use assistance for multi-GPU and multi-machine training).

Lots of cool details, like easily writing custom layers in high-level languages Unlike almost all different major frameworks, it is not rapidly regulated by a major business which is a prosperous situation for an open-source, community-developed framework. TVM support, which will further enhance deployment support, and enable running on a whole host of new device types

6) Keras

If you like the Python-way of doing stuff, Keras is for you. It is a high-level library for neural networks, using Tensor Flow or Theano as its backend.

The majority of logical dilemmas are more like:

• picking an architecture suitable for a problem,

• for image recognition problems – utilizing weights trained on ImageNet,

• configuring a network to optimize the results (a long, iterative process).

In all of these, Keras is a jewel. Also, it gives a conceptual structure that can be handily converted to different frameworks, if required (for compatibility, performance, or anything).

 7) PyTorch

 PyTorch is an AI system created by Facebook. Its code is accessible on GitHub and at present has more than 22k stars. It has been gaining huge power since 2017 and is in a relentless reception development.

8)  CNTK

CNTK facilitates users to competently comprehend and incorporate central model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It enforces stochastic gradient descent (SGD, error back propagation) learning with automatic differentiation and parallelization across numerous GPUs and servers. CNTK is available for anyone to experiment, under an open-source license.

9)  Auto ML

Out of all the tools and libraries listed above, Auto ML is probably one of the strong and fairly recent expansions to the arsenal of tools accessible at the disposal of a machine learning engineer.

As defined in the beginning, optimizations are crucial in machine learning tasks. While the advantages earned out of them are lucrative, prosperity in determining optimal hyper parameters is no simple task. This is particularly valid in the black box-like neural networks wherein inferring things that matter becomes extra and further tough as the depth of the network rises.

Thus we join a new world of meta, wherein software helps up create more efficient software. Auto ML is a library that is utilized by many Machine learning engineers to optimize their models. Apart from the obvious time saved, this can furthermore be incredibly helpful for someone who doesn’t have plenty of experience in the field of machine learning and thus needs the intuition or experience to make specific hyper parameter changes by themselves.

10) OpenNMS

Jumping from something that is entirely beginner-friendly to something meant for competent developers, Open NN gives an arsenal of progressive analytics. It features a tool, Neural Designer for refined analytics which gives graphs and tables to infer data entries.

11)  H20: Open Source AI Platform

H20 is an open-source deep learning platform. It is an artificial intelligence tool that is business-oriented and enables them to determine data and facilitates the user to bring insights. There are two open-source versions of it: one is standard H2O and the other is paid version-" Sparkling Water" which can be used for predictive modeling, risk and fraud analysis, insurance analytics, advertising technology, healthcare, and customer intelligence.

 12) Google ML Kit

Google ML Kit, Google’s machine learning beta SDK for mobile developers, is designed to facilitate developers to create personalized elements on Android and IOS phones.

The kit enables developers to embed machine learning technologies with app-based APIs running on the device or in the cloud. These comprise characteristics such as face and text recognition, barcode scanning, picture labeling, and more.

Developers are moreover able to create their own Tensor Flow Lite models in cases where the built-in APIs may not suit the use case.

Conclusion -

With this, we have come to the end of our blog on  Artificial Intelligence Tools & Frameworks blog. These were some of the tools that serve as a platform for data scientists and software engineers to understand real-life problems which will make the fundamental architecture competently great and more powerful.