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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 (2023)

: 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

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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:


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)

Learn more about the latest happenings in AI in the following video:

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.

Artificial Intelligence Creates Synthetic Data For Machine Learning

Introduction Artificial Intelligence Creates Synthetic Data for Machine Learning

Generative Adversarial Networks are one of the main tools that artificial intelligence is used to produce synthetic data (GANs). A generator plus a Bayesian classifier make up a GAN, a particular kind of neural network. The generator oversees producing fake data, while the discriminator determines if the data is real or fake. Together, the two networks are trained, with the generator attempting to produce data that the discriminator finds difficult to separate from actual data and the discriminator working to become more adept at recognizing artificial information.

Synthetic Data

Two sources exist for synthetic data −

Real World Data

Simulated Data

Although personally identifying information (PII) and personal health information (PHI) can be removed from real-world data, this does not completely protect privacy since the data records can still be matched to other sources that can be used to identify individuals. Like the COVID-19 example, the anonymized data must be mixed again in a way that keeps all the data set’s statistical characteristics for the machine learning algorithms to make accurate inferences and develop accurate rules.

In some cases, a lack of real-world data is a challenge for machine learning. Sometimes it would be impractical or too expensive to acquire data from the real world. Simulated data may occasionally be close enough to real-world instances for machine learning algorithms to recognise it. The self-driving car industry, for instance, blends real sensor data from moving vehicles with simulated data from driving simulations (even video games like Grand Theft Auto).

Unfortunately, adopting synthetic data comes with several difficulties that must be overcome. The requirement that the synthetic data be representational of the real-world data presents one of the key obstacles. The machine learning model may not function well if the synthetic data does not precisely match the real-world data. Another challenge is that the synthetic data must be sufficiently varied to account for every scenario that the model might face in the actual world.

Another challenge is that biassed models might be produced using fake data. Biased models are models that have learned to produce inaccurate predictions for certain groups of people. For example, a model that is trained on synthetic data that is biased towards a particular race or gender may produce inaccurate predictions for people who are not in that group. To avoid this, it is important to ensure that the synthetic data is diverse and representative of the real-world data.

Synthetic Data Applications

Software Testing that is automated for DevOps. Test data has always been necessary for software development, but today’s quick Agile development cycles of DevOps demand more test data than ever.

Robots and Automation in manufacturing. Synthetic data can speed up the training of AI systems in robotics and manufacturing applications because real-world data collecting can be sluggish and expensive, like automobile data collection.

Monetary services. Personal financial data is subject to strict confidentiality restrictions, just like healthcare data, and synthetic data provides developers and business users with access to larger datasets without invading privacy.

Consumer Behavior Simulations in Marketing. Since the GDPR and other restrictions apply to actual consumer online behavior, marketing AI can be trained more broadly and thoroughly using a synthetic dataset.

Clinical Medical Investigation. Since PHI is heavily regulated, artificial intelligence (AI) and machine learning are made viable in situations where datasets might otherwise be too limited to be helpful.

Facial Identification to avoid privacy violations and biases from underrepresented types of faces, synthetic facial data can be used instead of real-world pictures to train facial recognition.


In conclusion, AI is being used to create synthetic data that can be used to train machine learning models. Synthetic data can be used to augment limited real-world data sets, as well as to create data for tasks that are difficult or impossible to collect real-world data for. However, it is important to ensure that the synthetic data is representative of the real-world

A Fascinating Look At How Artificial Intelligence And Machine Learning Work At Apple

Respected journalist Steven Levy has scored another nice exclusive with a new write-up over at Backchannel, a Wired Media Group property, giving us a rare inside look at how artificial intelligence and machine learning work at Apple.

Welcome to The Temple of Machine Learning

Apple has “a lot” of engineers working on machine learning—some of which weren’t necessarily trained in the field before they joined Apple—and any results of their work are shared and readily available to other product teams within the company.

“We don’t have a single centralized organization that’s the Temple of Machine Learning in Apple,” says Apple’s boss of Software Engineering, Craig Federighi. “We try to keep it close to teams that need to apply it to deliver the right user experience.”

Of course some talent comes from acquisitions as Apple’s recently been buying 20 to 30 small companies a year. “We’re looking at people who have that talent but are really focused on delivering great experiences,” said Federighi.

Making Siri sound more like an actual person

Before we get to it, there are four aspects to Siri:

Speech recognition—turning speech to text

Natural language understanding—understanding what you’re saying

Execution—fulfilling a query or request

Response—talking back to you

Although Apple was still licensing much of its speech technology for Siri from a third party (likely Nuance), that’s “due for a change,” Levy writes, as Apple explores in-house speech capabilities for the digital personal assistant.

In fact, it’s deep learning that enables Siri to talk back to you more naturally while machine learning techniques smooth out recordings of individual words, making Siri responses sound more like an actual person than when it relied on Nuance.

All of that also helps with bringing Siri to your favorite apps in a way that doesn’t require you to use specific language to access third-party skills on iOS 10 and macOS Sierra.

Siri’s brain transplant

On July 30, 2014, Siri had a brain transplant.

That’s when Apple began moving Siri’s voice recognition to a neural-net based system following initial complaints from users regarding erroneously interpreted commands. They next trained the neural net for Siri using lots of data and GPUs.

“We have the biggest and baddest GPU (graphics processing unit microprocessor) farm cranking all the time,” says Siri’s Alex Acero. “And we pump lots of data.” According to Acero, Siri began using machine learning to understand user intent in November 2014, and released a version with deeper learning a year later.

By applying machine learning techniques such as deep neural networks (DNN), convolutional neural networks, long short-term memory units, gated recurrent units and n-grams, Apple was able to infuse Siri with deep learning and cut her error rate by a factor of two in all the languages.

“That’s mostly due to deep learning and the way we have optimized it — not just the algorithm itself but in the context of the whole end-to-end product,” says Acero.

The iBrain is here and with iOS 10, it’s already in your phone thanks to machine learning, artificial intelligence, the power of Apple-designed silicon and Differential Privacy techniques.

The iBrain is here and with iOS 10, it’s already in your phone thanks to machine learning, artificial intelligence, the power of Apple-designed silicon and Differential Privacy techniques.

The size of Apple’s user base allowed Apple to quickly train Siri.

Steve Jobs said you’re going to go overnight from a pilot, an app, to a hundred million users without a beta program. All of a sudden you’re going to have users. They tell you how people say things that are relevant to your app. That was the first revolution. And then the neural networks came along.

But where else does machine learning help Apple?

Apple and machine learning

Apple uses deep learning to detect fraud on the Apple store, to extend battery life between charges on all your devices, and to help it identify the most useful feedback from thousands of reports from its beta testers. Machine learning helps Apple choose news stories for you. It determines whether Apple Watch users are exercising or simply perambulating.

It recognizes faces and locations in your photos. It figures out whether you would be better off leaving a weak Wi-Fi signal and switching to the cell network. It even knows what good filmmaking is, enabling Apple to quickly compile your snapshots and videos into a mini-movie at a touch of a button.

If you were to argue that Apple’s competitors do many similar things, you’d be right.

Clockwise, from upper left: Tom Gruber, Siri’s Advanced Development Head; Eddy Cue, Apple’s SVP of Internet Software and Services; and Alex Acero, Siri’s Senior Director.

Clockwise, from upper left: Tom Gruber, Siri’s Advanced Development Head; Eddy Cue, Apple’s SVP of Internet Software and Services; and Alex Acero, Siri’s Senior Director.

But, you must keep in mind that Apple’s way of providing these features also protects your privacy. In using the power of in-house designed A-series chips that power iOS devices and Differential Privacy techniques, Apple’s software is able to pull those things off directly on the device, without dumping significant user data on its servers to let the cloud do the heavy-lifting.

RELATED: A closer look at Differential Privacy on iOS 10 and macOS Sierra

In a rare revelation, Apple told the journalist that the dynamic cache which enables machine learning on the iPhone takes up about 200 megabytes of storage space.

The system deletes older data, but the actual size of the cache depends on how much personal data is used, stuff like information about app usage, interactions with other people, neural net processing, a speech modeler, natural language event modeling and data for the neural nets that power object recognition, face recognition and scene classification in the Photos app.

Here’s Apple’s marketing boss Phil Schiller:

Our devices are getting so much smarter at a quicker rate, especially with our Apple-designed A- series chips. The back ends are getting so much smarter, faster, and everything we do finds some reason to be connected. This enables more and more machine learning techniques, because there is so much stuff to learn, and it’s available to [us].

These techniques are also used to power some “new things we haven’t be able to do,” he added.

But it’s not just the silicon, says Federighi:

Apple isn’t just dipping its toes in Differential Privacy, it’s fully embracing it.

“It’s a technique that will ultimately be a very Apple way of doing things as it evolves inside Apple and in the ways we make products,” said Schiller.

Less visible AI-driven features

Artificial intelligence is also used to improve other aspects of the iOS experience.

Here are some of the examples of AI-driven features:

Identifying who a caller not in your Contacts, based on their emails

Populating the iOS app switcher with a shortlist of the apps you’re most likely to open next

Getting reminders from appointments based on emails

Surfacing a Maps location for the hotel you’ve reserved before you type it in

Remembering where you parked your car

Palm rejection for Apple Pencil

Recognizing ‘Hey Siri’ hot word in a power-friendly way

Improving QuickType keyboard suggestions

And, of course, lots more.

Software boss Craig Federighi listens to Siri’s speech guru Alex Acero discussing the voice recognition software at Apple headquarters. Oh, how I’d like to be a fly on that wall!

Software boss Craig Federighi listens to Siri’s speech guru Alex Acero discussing the voice recognition software at Apple headquarters. Oh, how I’d like to be a fly on that wall!

“Machine learning is enabling us to say yes to some things that in past years we would have said no to,” says marketing honcho Phil Schiller. “It’s becoming embedded in the process of deciding the products we’re going to do next,” says Schiller, adding:

The typical customer is going to experience deep learning on a day-to-day level that [exemplifies] what you love about an Apple product. The most exciting [instances] are so subtle that you don’t even think about it until the the third time you see it, and then you stop and say, “How is this happening?”

Thanks to the article, I also learned that some of the AI code that recognizes hand-scrawled Chinese characters into text on iOS or power the new letter-by-letter Apple Watch input on watchOS 3 actually draws from Apple’s work on handwriting recognition for its failed Newton message pad in the early 1990s.

The piece provides additional insights into how Apple approaches artificial intelligence and machine learning work versus its competitors. It’s a fascinating article and you’ll wholeheartedly recommended to give it a read.

Source: Backchannel

10 Artificial Intelligence (Ai) Startups In India You Should Know


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


“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


Healthcare Startups



Logistics Startups


Fintech Startups



Other Awesome AI Startups


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. 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 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 2023, the company provides facilities like route planning and optimizing, real-time fleet tracking, insights and analytics, and automated shipment sorting and rider allocation. 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:’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! 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.


Top 10 Courses And Certifications In Artificial Intelligence

A fundamental establishment in the standards and practices around artificial intelligence (AI), automation and cognitive systems is something which is probably going to turn out to be progressively important, paying little heed to your field of business, skill or profession. There are so many courses and certifications for individuals who need to jump straight into coding their own artificial neural networks, and naturally, accept a specific degree of technical ability. Others are valuable for the individuals who need to figure out how this innovation can be applied by anybody, paying little mind to prior technical expertise, to tackling real-world issues. Let’s look at some of the best AI courses and certifications which can help in improving your knowledge and skills in the field of artificial intelligence.  

If learning Machine Learning is at the forefront of your thoughts, at that point there is no looking further. Made by Andrew Ng, Professor at Stanford University, more than 1,680,000 students and experts worldwide have joined up with this program, who have evaluated it profoundly. This course gives a prologue to the core ideas of this field, for example, supervised learning, unsupervised learning, support vector machines, kernel, and neural networks. Draw from various case studies and applications and get hands-on to apply theoretical ideas to practice. Before the end of the classes, you will have the certainty to apply your insight into real-world situations.  

Artificial intelligence in Finance is an online course created by CFTE and Ngee Ann Polytechnic for experts to comprehend the utilizations of Artificial Intelligence and Machine Learning in financial services. The course pursues a comparable configuration to CFTE’s Fintech Foundation Course.  

This course is made for people who are keen to learn about techniques and strategies of artificial intelligence to take care of business issues. After the essential themes are understood you will go over how AI is affecting various industries just as the different tools that are engaged with the operations for creating efficient solutions. By the end of the program, you will have various methodologies added that can be utilized to improve the performance of your company.  

It has two three-month programs that enable you to ace the abilities important to turn into an effective Machine Learning Engineer. It’s unquestionably one of the more career-centered programs and like the Stanford course, covers the core ML principles and furthermore plunges deeper into the domain of predictive modelling. It’s beginner-focused however, anticipate an enthusiastic test. A one-to-one technical mentor is accessible. Likewise reasonable for those on a financial budget, as access is charged on a month to month basis, making it conceivably less expensive if you can finish the course quicker.  

If you need to kick off a profession in AI, at that point this specialization will enable you to accomplish that. Through this variety of 5 courses, you will learn the fundamental points of Deep Learning, see how to construct neural networks, and lead fruitful ML projects. Alongside this, there are chances to work on case studies from different real-world businesses. The practical assignments will enable you to rehearse the concepts in Python and in Tensorflow. Furthermore, there are discussions from top pioneers in the field that will give you inspiration and help you to comprehend the situations in this profession.  

Join up this certification to pick up mastery in one of the fastest developing areas of computer science through a progression of lectures and assignments. The classes will assist you in getting a strong comprehension of the core principles of artificial intelligence. With an equivalent accentuation on practical and theory, these exercises will instruct you to manage real-world issues and think of appropriate AI solutions. With this certification in your pack, it is sheltered to state that you will have a high ground at job interviews and other opportunities.  

MIT partners with e-learning stage GetSmarter to address the developing interest among business experts to get a comprehension of what precisely artificial intelligence is and how it will affect business. This online AI course is for all intents and purposes centered and follows a comparative pattern to the MIT Fintech Certificate in which students were first given a prologue to the subject and then given a capstone project to apply their comprehension.  

Offered by IBM, this introductory course will help you learn the basics of artificial intelligence. With this course, you will realize what AI is and how it is utilized in the software or application development industry. During the course, you will be presented to different issues and worries that encompass artificial intelligence like morals and bias, and jobs. Subsequent to finishing the course, you will likewise exhibit AI in real life with a smaller than usual project that is intended to test your insight into AI. In addition, in the wake of completing the project, you will likewise get your certificate of completion from Udacity.  

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