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Overview

Check out 10 of the most awesome Artificial Intelligence (AI) startups in India

We focus on a diverse field of AI startups, including startups from Healthcare, Logistics, FinTech, among others

Introduction

“AI is the new electricity.” – Andrew Ng

When Andrew Ng speaks, you drop everything and pay attention. That’s what I (and thousands of others) did when Andrew Ng compared our age of AI to the discovery of electricity.

We are truly living in the age of artificial intelligence. Companies are spending billions of Dollars just to stay relevant in today’s ever-changing environment. If you’re not AI-compatible, the consensus is that you will soon be an also-ran in the industry.

This got me thinking – could I bring out the AI startups in India truly bringing about a global revolution? It was an interesting quest. I put down a framework or criteria to filter the top AI startups in India (which we will see soon).

So, this article highlights the top Indian AI startups, the top initiators, that are using AI to build a better future for us. Ready to dive in and look at the framework I used to create this list?

If you’re entirely new to artificial intelligence, here are a few articles to get you started:

Framework to shortlist the startups

This list has been curated based on certain parameters which act as indicators for the success of startups. The parameters on which startups have been evaluated are:

The Sector / Market: The industry in which the startup plays and the opportunities that it can leverage in that market

Investment: Whether the startup has raised funds, the amount and quality of funding received

Of course, the primary criteria was that the startup should be in India!

Our Pick of the Best AI Startups in India:

Conversational AI Startups

Haptik.ai

Avaamo

Healthcare Startups

Niramai

Doxper

Logistics Startups

LogiNext

Locus.sh

Fintech Startups

Rubique

LendingKart

Other Awesome AI Startups

CropIn

Niki.ai

Conversational AI Startups

Chatbots are ubiquitous these days. From businesses to the research lab, they have become an integral part of an organization’s strategy. Learning how to create a chatbot from scratch is a much-vaunted skill in data science.

Haptik.ai is the world’s largest conversational AI platform. I’m a huge Haptik fan. The company was launched back in 2013 as a one-stop app for all everyday tasks and is now undoubtedly a market leader in its field.

Haptik provides 16 different channels of texts and voices for the users to build, deploy and manage a conversational application. Haptik only focuses on use cases to enable end consumer engagement on the back of real conversational data of a billion+ messages.

With the help of AI, Haptik helps millions in their day to day tasks including me. It’s quick response and query solving capability is quite remarkable. It’s indeed a personal assistant.

Avaamo is another conversational startup that is currently serving six industries: Insurance, Financial Services, Healthcare, Telecommunications, Retail.

Founded in 2014, Avaamo has created a name for itself in around 40 countries now. Pretty impressive!

Avaamo specializes in conversational interfaces to solve specific, high impact problems in the enterprise. It uses AI to make conversational computing for the enterprise a reality.

Unlike the first generation conversational AI where a user could only pass commands like play music or open camera, Avaamo brings in second generation conversational AI experience that executes rich multi-turn conversations capable of handling queries in customer service, generating quotes in insurance, or answering claims inquiries in healthcare.

Healthcare Startups

This AI startup will resonate with a lot of you. Healthcare is one domain where AI needs to make its mark. Progress has been slow due to various reasons but things have been looking promising in the last couple of years.

NIRAMAI stands for “Non-Invasive Risk Assessment with Machine Intelligence”. In Sanskrit, Niramai means being free from illness. It is a novel breast cancer screening solution.

Breast cancer is the leading cause of cancer death today in women.

According to WHO, one in every 12 women have the risk of a breast abnormality. Indian women have only about 50% chance of survival.

Niramai provides a cost-efficient way to detect breast cancer and is a better alternative to the existing method of mammography which requires a high capital cost. The major drawback of mammography is that it can be used only for women above 45 years of age because it cannot identify tumors effectively for younger women.

The core of the NIRAMAI solution is Thermalytix, a computer-aided diagnostic engine that is powered by Artificial Intelligence. Go through this link to know more about how Niramai uses AI for the early detection of cancer.

Doxper

Doxper is another Indian AI startup working in the healthcare sector.

Have you ever seen hospital records? Keeping the records of patients is a hectic task – it is truly a formidable function. Doxper helps in simplifying the way healthcare data is recorded.

Doxper helps doctors, hospitals, and patients as well. It improves the doctor-patient interaction. Doxper aims to provide easy storage & retrieval, automated patient follow-ups, and auto data transcription.

When information is written using Doxper, it gets stored in the cloud and is automatically shared with the patient immediately. This helps in keeping the records safe and makes communicating with the patient really simple.

Hospitals can use it to immediately digitize Casualty/Emergency, OPD, and ICU. This can also be used in existing HIS systems through intuitive APIs.

This is one remarkable product helping the healthcare sector to digitize the records of their patients.

Logistics Startup

AI has left no stone untouched. It has found its niche in almost every sector, even the previously gigantic and manual logistics field. There are quite a few AI-powered logistics startups springing up and LogiNext is definitely among the leaders right now.

LogiNext helps in managing field services. It tracks and optimizes field agent movements in real time on a single map interface.

LogiNext helps organizations plan and manage the dispatch schedule, delivery routes and capacity in the most cost-optimized way.

It empowers its users by providing them the power to track (real-time) the shipment every single minute. It provides insights and visualizations based on predictive and big data analytics. Logistics analytics helps the user to accurately predict the future with algorithm-enabled location intelligence and optimize the logistics and field service management operations.

It provides every single detail right from pickup to delivery. It also provides complete field service management analytics.

The logistics and field workforce management solutions are fully automated, effective, secure and can be seamlessly integrated with multiple platforms providing complete logistics automation.

Another logistics startup in the list is Locus. Started back in 2024, the company provides facilities like route planning and optimizing, real-time fleet tracking, insights and analytics, and automated shipment sorting and rider allocation.

Locus.sh offers a number of features, including:

Smart Geocoding: It converts the fuzziest addresses into precise geographical coordinates. This increases order delivery in fewer vehicles and reduces fuel costs

Dispatch Overview: This feature allows you to visualize your plans on a screen in the post-planning session via three different metrics – geography, time & vehicle. This helps in visualizing the cluster forms and ensures a minimum overlap of routes

Predictive alerts: It warns you in any kind of breach. This feature helps in keeping track of on-ground resources. You get instant alerts in case of any deviation from the planned route, undesignated halts, exceeding break times, etc.

Live Tracking: Locus.sh’s system allows to share the dispatch details of the order with the customers. Current latitude and longitude, contact details of the driver, etc. are provided which result in improved customer engagement

Enroute Analytics: This feature helps in comparing planned versus executed delivery routes. This also helps in analysing driver’s riding behaviour

Fintech Startups

AI in finance just intuitively makes sense. Finance is all about number crunching (well, almost!) and machines are well equipped at this point to work with numbers. It is a perfect match. So it’s no surprise that the FinTech sector has seen a massive surge in AI applications.

Rubique is one such FinTech startup that is contributing to making finance simple by using AI.Rubique wants to revolutionize the finance industry by introducing predictability. This helps its users to find the best match to his/her credit requirements with the help of an AI-based recommendation engine and Rubique’s financial matchmaking platform.

Rubique’s multi-sided lending platform provides features like e-KYC, bank statement analysis, credit bureau check, credit memo generation & MCA integration along with real-time application tracking to make it a paperless experience.

Rubique is doing a great job of providing top-notch solutions to the entire lending spectrum.

LendingKart is another brilliant startup tackling the financial sector by providing loans to small businesses. Spread over 1300+ cities, LendingKart is on its way to becoming one of the leading FinTech companies in the world. Here’s their official statement:

LENDINGKART Group aims to make working capital finance available at the fingertips of entrepreneurs, so that they can focus on business instead of worrying about the gaps in their cash-flows.

LendingKart has developed technology tools based on big data analysis which facilitates lenders to evaluate borrower’s creditworthiness. They don’t focus on past records of the vendor. The main aim of LendingKart is to make capital funds available to help entrepreneurs focus on their business instead of worrying about cash-flow flaws.

Other Awesome AI Startups

The agriculture sector is synonymous with Indian values. It is an integral part of this country and what we are all about. So what can AI do to accelerate progress in this field? CropIn, a Bengaluru-based startup, provides a glimpse into the future of agriculture.

CropIn is a smart farming app that provides future-ready farming solutions to the entire sector. It is an intuitive, intelligent and self-evolving system. The company provides real-time solutions to predict trends, archive patterns and to make a blueprint for future businesses.CropIn has the capability of live reporting, and it also provides geographical analysis, interpretation, and insight. Data gathering through a smartphone app ensures efficient operations, lower costs and better visibility for your field agents at all times.

CropIn’s vision is to maximize per acre value and the mission is to make every farm traceable.

Real-time insights help to take planned and responsive business decisions. The predictability of quantity & quality of yield combined with a reduced cost of operations results in higher productivity for businesses.

Imagine an AI helping you perform online transactions end-to-end. Sounds too futurustic to be true. But here’s the good news – it’s already here!

Niki.ai is a virtual agent that does end-to-end online transactions for you in various domains. Everything from postpaid recharges to hotel bookings, Niki covers the length and breadth of it.

Niki uses AI to book and make payments after taking a few inputs from the users. This reduces the end-to-end interaction time of the customer and service provider and helps in providing fast and responsive services.

Booking movie tickets, household bills, bus booking, event booking, etc. – you name and it and Niki does it.

End Notes

In this article, we saw some of the most amazing AI startups in India that are changing the way we live. This is in no way an exhaustive list. There are a LOT of AI startups springing up right now that bring their own unique brand to the table.

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What You Should Know For A Career In Artificial Intelligence

Across the realms of consumers and businesses, artificial intelligence (AI) has been wielding great influence and reshaping society as it were. Hailed as a key component of the 4th Industrial Revolution (along with the Internet of Things (IoT), robotics, quantum computing, and other technologies), the rise of AI could well be considered as a seminal stage in the development of humanity.

What is Artificial Intelligence?

Artificial Intelligence (AI), broadly speaking, is the ability of machines to replicate the abilities of human intelligence in tasks such that efficiency is boosted and errors go down. With concepts of basic engineering, mathematics, computer science, linguistics, and psychology, among others, AI bases itself on the fundamentals of reasoning, learning, and problem-solving. A career in artificial intelligence is certainly an attractive prospect.

Related: – Is Artificial Intelligence Replacing Animators?

Where can AI be applied?

AI can be categorized in multiple ways, of which the most common is as below:

Weak/Narrow AI: trained to do specific tasks or functions

Strong AI: capable of making its own decisions when presented with actionable data

AI has great potential in terms of applicability in a variety of sectors. Some examples are given below:

Autonomous driving: Tesla, Google and others have been working for a while on autonomous driving technology, where AI is a basic building block. Ongoing work could soon see wider instances of fully-autonomous driving technology across the world.

Predictive maintenance: proactively scheduling maintenance and hence minimizing costs

Smart cities: better safety, reduced crime, higher energy efficiency

The future uses of AI are even more exciting. Possible applications are many:

Personalized, dynamic pricing: online and offline stores map prices as per consumer behavior

Faster designing of products: aided by quicker sifting through large amounts of customer data through machine learning and deep learning

Related: – The Illusion of CreArtificial Intelligence

How good is the potential for an AI career?

As a technology, AI is still in its initial stages, and there is a lot more that the technology could do with further development. The market is expanding fast and a lot of opportunities keep coming up for a career in artificial intelligence. A survey by job site Indeed suggested that in the UK alone, the number of jobs available in AI has gone up by 485% during 2014-2024 i.e. demand outstrips supply by a factor of 2:1! Across the world, job postings in this field have more than doubled.

Automation and AI were feared as possible death knells for “traditional” jobs, but what has transpired is that AI has in effect created many more jobs than it has taken away i.e. the net effect has been one of new job opportunities coming up. Salaries in the AI domain are skyrocketing too – well-skilled AI professionals in the US can comfortably earn six figures and possibly even higher packages.

How does one begin a career in AI?

Related: – AI Technology skills highest in Demand in 2023

Some of the key skills for AI specialists include:

Bayesian networking (including neural nets)

Computer science (gain coding experience with popular programming languages)

Cognitive science theory

Engineering

Physics

Robotics

Algebra, calculus, logic and algorithms, probability, statistics

If you are already a software engineer, you could take a definitive step towards a career in AI with some specialized or focused AI courses, many of which are available freely on the internet as well as from recognized institutions and delivered both online as well as through classroom training. If you do not have the time or other resources for long-duration courses, you could choose an AI certification that you could do part- or full-time.

What are the possible career paths?

The possible career paths differ as per the experience level. These include:

Freshers: these would do well to get skilled in mathematics as well as courses in machine learning, programming skills like C++, general business knowledge, and hands-on training.

Programmers: these could start coding by moving to algorithms.

Data analysts and data scientists: these need to strengthen their programming skills by knowing how to prepare data, be proficient at visualizing and building models, and have good communication skills and business knowledge.

Top 10 Artificial Intelligence Research Centers In India To Work With

Know about the top ten artificial intelligence research centers in India to work with Top 10 artificial intelligence research centres in India Robert Bosch Centre for Data Science and AI at IIT Madras

Robert Bosch Centre for Data Science and AI at IIT Madras is set to work on multiple projects by leveraging data science and artificial intelligence with the largest network analytics, NLP, deep learning, and many more. This artificial intelligence research centre has the vision to become a globally known centre for AI research as well as data science research with cutting across disciplines to create a significant impact on India.  

NV AI Centre at IIT Hyderabad

NVIDIA has established the first-ever NV AI Centre at IIT Hyderabad to boost AI research on artificial intelligence and commercial applications. This is one of the top artificial intelligence research centres in India where IIT Hyderabad has procured three NVIDIA DGX-1TM systems and two NVIDIA DGX-2TM systems. This AI research is focused on accelerating work on multiple areas of AI.  

Intel AI Research Centre at IIT Hyderabad AI Innovation Hub at Accenture AI Research Lab at Wipro with IISc SCAI by Microsoft Research India

The Centre for Societal impact through Cloud and Artificial Intelligence (SCAI) is launched by Microsoft Research India for creating and validating technologies to have a large-scale impact on India. The AI research centre is dedicated to providing access to researchers and expertise from Microsoft Research and other groups in the company. It is focused on providing financial grants and complete access to top-notch Microsoft researchers. It is known for working with Navana Tech to build text-free and voice-assisted technology.  

Philips Innovation Centre in Bengaluru

Philips Innovation Centre in Bengaluru is focused on taking India’s AI products to the global tech market. This AI research centre helps to transform ideas on artificial intelligence and machine learning into viable and tangible products to improve the economy of India efficiently and effectively. There are more than 2,500 researchers, doctors, engineers, data scientists, and software developers who are focused on healthcare transformation services. This AI lab has created a global innovation hub and a health tech platform to boost productivity and yield more revenue.  

DAIR at IIT Delhi

Data Analytics and Intelligence Research at IIT Delhi is a well-known AI research group focused on combining as well as integrating multiple fields of data science and artificial intelligence to build intelligent software systems.  The AI research centre helps to build applications of different national and international importance with solutions to fundamental scientific questions. IIT Delhi has started offering specialized courses on artificial intelligence at different levels.  

CFILT Lab at IIT, Bombay

The Center for Indian Language Technology (CFILT) is one of the top AI labs in India that was set up with a generous grant from the Department of IT, Government of India in 2000 at IIT Bombay. There are around 30 research members in CFILT for PhD, Master’s, Bachelors and many more with multiple stresses on semantics to research in lexical resources, shallow parsing, machine translation, cognitive NLP, and many more with the integration of artificial intelligence and NLP.  

CAIR at DRDO, Ministry of Defence, Government of India

Top 10 Ingenious Drone Startups In India 2023

The rising demand for drones in the market has driven some of the great innovators to come up with a wide array of innovative drone solutions. A number of startups can be seen flourishing in this arena. Here are the top 10 ingenious drone AUS, the first Indian company to develop professional drone solutions for enterprises, is a start-up that originated from IIT Kanpur in the year 2013 and presently headquartered in Bangalore, the technology hub of the country. In the last 5 years of its existence, drones manufactured at AUS are constantly setting world-class technology benchmarks because of the continuous efforts on Drone Intelligence, Hardware optimization & Design innovation. Having served key players across various verticals both for the private and public sectors, AUS is already the undisputed leader of commercial drones in India. AUS is backed by some of the most reputed firms and individuals such as GrowX, 500 Startups, StartupXseed, 3one4 capital, Valpro, Mr. Ashok Atluri (Zen technologies) and Mr. Sanjay Jesrani (GoNorth ventures).  

Its products include the 1MW Geospatial Intel Suite, 1MW Backduck, and 1MW xFleetY. 1 Martian Way’s brand family includes the Indian Drone Racing League (IDRL) and Roboland. The company also licenses its technology to third party vendors and companies.  

CRON Systems is a leading provider of high-performance, sensing products and perception software that brings vision to the UAVs, automobiles, mapping, security, and general surveillance and other industries to enhance situational awareness. CRON Systems is backed by top-tier strategic partners and investors, including YourNest Venture Capital, Techstars Adelaide, and Cisco Launchpad.  

AerialPhoto (Event digital Technologies) seeks to acquire and create the preeminent aerial imagery there is to capture. To provide its assistance at a national scale, offering its clients services they cannot refuse. To work incessantly and give its customers the most coercive aerial filming experience there is. AerialPhoto’s vision is to set the highest standards possible in every facet of the Aerial Industry. To be a one-stop production house offering diversity in its business acumen. The company offers a broad range of services from Aerial Filming & Cinematography to Real Estate Filming to Construction Site Filming to Mapping & Surveying 360 Degree Virtual Tours. It also has a broad range of equipment by which it can cater to different requirements of clients and customers.  

Founded by a team of Indian Institute of Technology, Madras graduates, and Professors, Detect Technologies incubated in 2024 at the IIT Madras Research Park with the aim to assist process industries in driving digital transformation for asset integrity solutions. Detect technologies reside at the intersection of patented hardware and intelligent algorithms and create value for its industry partners by enabling the development of real predictive capability. An intelligent drone for reliable inspection of assets- is the first of its kind truly automated drone. As the assets differ in sizes and locations, Noctua can be programmed to cater to specific requirements of each equipment such as columns, vessels, reactors, etc. This flight path is repeatable with extreme precision making it a truly repeatable periodic inspection. Noctua is the only drone in the world, which can do a programmed flight indoors owing to its unique positioning system. The calibration of the state of art thermal camera installed on Noctua is maintained consistently by intelligent software.  

Rchobbytech Solutions Private Limited is a company started by a team of technocrats a couple of years ago and is prominently making its way to the top and leading the markets. The base of the company is in Kolkata (West Bengal, India), from where it is eminently functioning and successful carrying out business dealings throughout the nation. The company is backed by a team of experts who is highly experienced in their respective domains and are also well versed in the objectives of the organization. The company Rchobbytech Solutions Private Limited, has been greatly functioning and is excelling by offering outclassing products and services. The company manufactures Agricultural Drones, Pesticide Spraying Drones, Crop Monitoring Drones, Surveillance Drones, Drones Camera, and Photography Drones. Being a service provider, the company has gained expertise in offering Drone Manufacturing, Aerial Photography, Surveillance Drone, and Digital Photography services to the customers.  

Aero360 is a Drone Solutions company offering end-to-end drone solutions for Enterprises. Its drone solutions include: Turnkey Services: 360*solutions including data acquisition, processing and analysis globally through the Pilot Partner Network & Centralised Data Processing for your projects Drone Tool Kits: Industry-specific drone toolkits & accessories to enable in-house drone programs for your enterprise Data Processing & Analysis: Process & Analyse raw data from your UAV/Drone for deriving actionable intelligence Training & Consulting: Industry relevant UAV training and expert consultancy services for your project requirements Drone Repair Services: Extensive local support for repairs & maintenance through company’s network of drone repair centers for your Drones Aero360’s solutions provide high-resolution aerial images & videos to enable better scoping, assessment, planning, surveying, design, inspection & maintenance decisions.  

ideaForge is India’s largest manufacturer of drones for defense, homeland security, and industrial applications. Founded in 2007 by IIT-Bombay alumni, the organization has a consistent market share of over 90% in the Security & Surveillance segment. The organization is a licensed manufacturer of UAVs approved by the Ministry of Defence (MoD). ideaForge is the first organization to indigenously develop and manufacture Vertical Take-off and Landing (VTOL) UAVs in India. It is the pioneer in the UAV industry in India and has multiple IPs to its credit, including the then world’s lightest autopilot in 2009. Currently, the organization has deployed over 700 systems and has trained over 1300 pilots in services including the Indian Army, Navy, Air-Force, all CAPFs (CRPF, BSF, NSG, etc), State Police Forces, Indian Railways, TAFE, NTPC, DRDO, L&T and many more. A vertically integrated organization with in-house R&D, design, software, manufacturing, services, and training operations, ideaForge delivers world-class end-to-end solutions for an array drone of requirements in defense, homeland security, and enterprise sectors. The company is continually innovating and experimenting to transform its aerial platforms, to offer greater performance, higher reliability, and autonomy.  

Indrones marks its inception from the year 2013 with participation in SAE Aero Design 2013 Competition held in Dallas, Forth Worth. Having achieved great success in the competition, the company started its journey professionally in the UAV Industry. The team at Indrones is quite early to explore the potentials of UAVs in the civilian sectors in India and worked on several big and small projects while working with several Government Departments. With all the experience to back the company, Indrones officially started off in June 2024 and since then it has been developing technologies to implement solutions for various applications using UAVs.

Artificial Intelligence (Ai) And Deep Learning

The horizon of what repetitive tasks a computer can replace continues to expand due to artificial intelligence (AI) and the sub-field of deep learning (DL). 

Artificial intelligence gives a device some form of human-like intelligence.

Researchers continue to develop self-teaching algorithms that enable deep learning AI applications like chatbots.

To understand deep learning better, we need to understand it as part of the AI evolution:

See more: Artificial Intelligence Market

Partly to eliminate human-based shortcomings in machine learning, researchers continue to try to create smarter ML algorithms. They design neural networks within ML that can learn on their own from raw, uncategorized data. Neural networks — the key to deep learning — incorporate algorithms based on mathematical formulas that add up weighted variables to generate a decision.  

One example of a neural network algorithm is all of the possible variables a self-driving car considers when making the decision if it should proceed forward: is something in the way, is it dangerous to the car, is it dangerous to the passenger, etc. The weighting prioritizes the importance of the variables, such as placing passenger safety over car safety.  

Deep learning extends ML algorithms to multiple layers of neural networks to make a decision tree of many layers of linked variables and related decisions. In the self-driving car example, moving forward would then lead to decisions regarding speed, the need to navigate obstacles, navigating to the destination, etc. Yet, those subsequent decisions may create feedback that forces the AI to reconsider earlier decisions and change them. Deep learning seeks to mimic the human brain in how we can learn by being taught and through multiple layers of near-simultaneous decision making.

Deep learning promises to uncover information and patterns hidden from the human brain from within the sea of computer data. 

AI with deep learning surrounds us. Apple’s Siri and Amazon’s Alexa try to interpret our speech and act as our personal assistants. Amazon and Netflix use AI to predict the next product, movie, or TV show we may want to enjoy. Many of the websites we visit for banking, health care, and e-commerce use AI chatbots to handle the initial stages of customer service.

Deep learning algorithms have been applied to:

Customer service: Conversational AI incorporates natural language processing (NLP), call-center style decision trees, and other resources to provide the first level of customer service as chatbots and voicemail decision trees.

Conversational AI incorporates, call-center style decision trees, and other resources to provide the first level of customer service as chatbots and voicemail decision trees.

Cybersecurity: AI analyzes log files, network information, and more to detect, report, and remediate malware and human attacks on IT systems.

Financial services: Predictive analytics trade stocks, approve loans, flag potential fraud, and manage portfolios.

Health care: Image-recognition AI reviews medical imaging to aid in medical analysis

Law enforcement: 

Track payments and other financial transactions for signs of fraud, money laundering, and other crimes

Extract patterns from voice, video, email and other evidence

Analyze large amounts of data quickly

See more: Artificial Intelligence: Current and Future Trends

We do not currently have AI capable of thinking at the human level, but technologists continue to push the envelope of what AI can do. Algorithms for self-driving cars and medical diagnosis continue to be developed and refined.

So far, AI’s main challenges stem from unpredictability and bad training data: 

Biased AI judge (2024)

: To the great dismay of those trying to promote AI as unbiased, an AI algorithm designed to estimate recidivism, a key factor in sentencing, produced biased sentencing recommendations. Unfortunately, the AI learned from historical data which has racial and economic biases baked into the data; therefore, it continued to incorporate similar biases.

AI consists of three general categories: artificial narrow intelligence (ANI) focuses on the completion of a specific task, such as playing chess or painting a car on an assembly line; artificial general intelligence (AGI) strives to reach a human’s level of intelligence; and artificial super intelligence (ASI) attempts to surpass humans. Neither of these last two categories exists, so all functional AI remains categorized as ANI.

Deep learning continues to improve and deliver some results, but it cannot currently reach the higher sophistication levels needed to escape the artificial narrow intelligence category. As developers continue to add layers to the algorithms, AI will continue to assist with increasingly complex tasks and expand its utility. Even if human-like and superhuman intelligence through AI may be eluding us, deep learning continues to illustrate the increasing power of AI.

See more: Top Performing Artificial Intelligence Companies

Pandas Ai: Data Analysis With Artificial Intelligence

If you’re a Python programmer, chances are you’ve used the Pandas library for all your data manipulation and analysis needs. Well, guess what? It just got a turbo boost and is now diving headfirst into the world of AI! That’s right, hold on tight as we introduce you to the latest addition: Pandas AI.

PandasAI is an innovative Python library that integrates generative artificial intelligence capabilities with Pandas. This extension takes data analysis to the next level and provides a comprehensive solution for automating common tasks, generating synthetic datasets, and conducting unit tests. It allows you to use a natural language interface to scale key aspects of data analysis.

Data scientists can improve their workflow with Pandas AI and save endless hours thanks to its ability to reveal insights and patterns more quickly and efficiently. In this article, we’ll explore what Pandas AI is and how you can use it to supercharge your analytics.

Let’s get into it!

Pandas AI is a Python library that integrates generative AI capabilities, specifically OpenAI‘s technology, into your pandas dataframes.

It is designed to be used with the Pandas library and is not a replacement for it. The integration of AI within Pandas enhances the efficiency and effectiveness of data analysis tasks.

To get started with Pandas AI, you can install the package using the following code:

pip install pandasai

The command will install the Pandas AI package into your operating system.

After installing the library, you will need an API to interact with a large language model on the backend.

We will be using OpenAI model in the demonstration. To get an API key from OpenAI, follow the steps given below:

Go to “View API keys” on left side of your Personal Account Settings

Select Create new Secret key

After getting your API keys, you need to import the necessary libraries into your project notebook.

You can import the necessary libraries with the code given below:

import pandas as pd from pandasai import PandasAI

After importing the libraries, you must load a dataset into your notebook. The code below demonstrates this step:

dataframe = pd.read_csv("data.csv")

The next step you need to take is to initiate an LLM model with your API key.

from pandasai.llm.openai import OpenAI llm = OpenAI(api_token="YOUR_API_TOKEN")

Next, you can ask questions regarding your dataset with your Python notebook.

pandas_ai = PandasAI(llm) pandas_ai(dataframe, prompt='What is the average livingArea?')

This integration allows you to explore and analyze your dataset without writing any exploratory data analysis code.

Pandas AI offers several benefits when working with your pandas dataframes:

Generative AI: It adds an extra layer of AI capabilities to your data analysis process, enabling you to generate new insights from existing data.

Conversational Interface: Pandas AI makes dataframes more conversational, allowing users to interact with data in a more intuitive and natural manner.

Documentation: In-depth documentation is provided for users who want to understand how to effectively utilize the library’s features within their projects.

Using Pandas AI can significantly improve your efficiency and productivity, as it is machine learning model and makes data easier to work with and interpret. This can lead to informed decision-making and faster results.

In this section, you’ll find some examples and use cases of using PandasAI in your projects. This will allow you to understand better when and how to use this tool.

You can ask PandasAI to find all the rows in a dataframe where the value of a column is greater than a certain value.

For instance, you could find all properties with a livingArea greater than a certain size with the following prompt:

pandas_ai(dataframe, prompt='Which properties have a livingArea greater than 2000?')

You can ask PandasAI to generate charts based on your data set.

For example, you could create a histogram showing the distribution of livingArea with the following command prompt:

pandas_ai(dataframe, prompt='Plot the histogram of properties showing the distribution of livingArea')

When generating charts, you can try different prompts and see if all give you the same output. Then choose the one that better fits your needs.

If you have data spread across multiple dataframes, you can use PandasAI as a manipulation tool by passing them all into PandasAI and asking questions that span across them.

Assuming you had another dataframe df2 with additional information about the properties:

pandas_ai([dataframe, df2], prompt='What is the average livingArea of waterfront properties?')

PandasAI provides a number of shortcuts to make common data processing tasks easier.

For example, you could impute missing values in your dataframe with the following prompt:

pandas_ai.impute_missing_values(dataframe)

If you want to enforce privacy, you can instantiate PandasAI with enforce_privacy = True so it won’t send the head (but just column names) to the LLM. This will make sure that your data is safe even if you are using a LLM.

You can use the following prompt:

pandas_ai = PandasAI(llm, enforce_privacy=True)

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PandasAI is an incredibly powerful tool that can simplify many data analysis tasks, but it’s not always the right tool for the job.

We’ve listed a few situations where you might not want to use PandasAI:

If you’re working with sensitive data, you may not want to use PandasAI, because it sends data to OpenAI’s servers.

Even though the library tries to anonymize the data frame by randomizing it, and it offers an option to enforce privacy by not sending the head of the dataframe to the servers, there could still be potential privacy concerns?.

PandasAI is not ideal for large dataframes. Because the tool sends a version of your dataframe to the cloud for processing, it could be slow and resource-intensive for large datasets.

For simple data manipulations and queries, using PandasAI might be overkill. Regular Pandas operations might be faster and more efficient.

For example, if you just want to calculate the mean of a column, using df[‘column’].mean() in Pandas is much more straightforward and faster than setting up a language model and making a request to an external server.

If you aim to learn data analysis and Python programming, relying on PandasAI might not be the best approach.

While it simplifies many tasks, it also abstracts away the underlying operations, which could impede your understanding of how things work under the hood.

OpenAI’s API is not free, and using it extensively could lead to high costs. If you’re working on a project with a tight budget, you might want to stick to traditional data analysis methods.

PandasAI stands as an important breakthrough in data analysis. It bridges the gap between natural language processing and traditional data science methodologies.

By integrating PandasAI into your workflow, you can simplify complex data tasks and embrace a more intuitive way of interacting with data. Furthermore, it significantly reduces the time spent on data analysis, allowing you to focus on deriving insights and making informed decisions.

However, remember that every tool has its place. PandasAI shines in many areas, but traditional data analysis methods still hold their own in specific use cases. The key is to understand when to utilize each tool for maximum efficiency.

PandasAI is a Python library that leverages the OpenAI Codex model to enable you to interact with your data using natural language. It simplifies complex data tasks, allowing you to ask questions, create plots, and manipulate dataframes using plain English commands.

PandasAI offers a more intuitive way of interacting with your data. Instead of writing lengthy code, you can simply ask questions or give commands in plain English. This can save significant time and effort, especially when working with more complex queries or multiple dataframes.

While PandasAI makes efforts to anonymize data, it does send a version of your dataframe to OpenAI’s servers. If you’re working with highly sensitive data, this might be a consideration. However, there’s an option to enforce privacy by not sending the dataframe’s head to the servers.

PandasAI might not be ideal for large dataframes, as it could be slow and resource-intensive. Also, for very simple queries or data manipulations, traditional Pandas operations might be more efficient.

Yes, OpenAI’s API, which PandasAI uses, is not free. Extensive use of the API could lead to costs, so it’s important to consider this when deciding whether to use PandasAI in your project.

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