The DeepSeek R1 LLM was developed and released by the Chinese AI lab DeepSeek on January 20, 2025. In just a few days since its launch, this model has impressed researchers with its powerful capabilities in chemistry, coding, and mathematics.
Building on the success of DeepSeek V-3, a Mixture-of-Experts (MoE) language model with 671 billion parameters, DeepSeek R1 adopts a similar MoE architecture. This state-of-the-art model is designed to approach problems step-by-step, mimicking human reasoning and providing advanced analytical capabilities.
AI researchers worldwide have praised DeepSeek R1 for its exceptional performance. The model has achieved remarkable results in benchmarks such as MATH-500 (Pass@1) and GPQA Diamond (Pass@1), securing a 96.3 percentile rank compared to human participants. Its ability to rival leading models, such as OpenAI o1-mini, GPT-4o, and Claude 3.5 Sonnet, has stunned and thrilled the tech community.
Currently, DeepSeek R1 comprises two versions, DeepSeek-R1-Zero and DeepSeek-R1, along with six compact distilled models. The former model version is thoroughly trained through reinforcement learning (RL) and did not undergo supervised fine-tuning. This approach has allowed DeepSeek-R1-Zero to develop robust reasoning capabilities and provide superior output for various domains.
Another standout feature of DeepSeek R1 is its cost-effectiveness. While it is not fully open-source, the model’s “open-weight” release under the MIT license allows researchers to study, modify, and build upon it easily. The R1 token pricing is substantially lower than OpenAI’s o1, positioning it as a more promising tool for advanced AI access and research.
DeepSeek, a Chinese AI company, released DeepSeek-R1, an open-source reasoning model, stating that this model has surpassed OpenAI’s o1 model on key performance benchmarks. Earlier, DeepSeek, the Hangzhou-based company had unveiled the DeepSeek V3 model and claimed that it outperformed Meta’s Llama 3.1 and OpenAI’s GPT-4o.
Designed for advanced problem-solving and analytical functions, DeepSeek-R1 consists of two core versions: DeepSeek-R1-Zero and DeepSeek-R1. The DeepSeek-R1-Zero is trained through the reinforcement learning (RL) method without any supervised fine-tuning. On the other hand, DeepSeek-R1 is built on DeepSeek-R1-Zero with a cold-start phase, efficiently curated data, and multi-stage RL.
According to the technical report released by DeepSeek, DeepSeek-R1 has performed well on several important benchmarks. It scored 79.8 percent (Pass@1) on the American Invitational Mathematics Examination (AIME) 2024, slightly surpassing OpenAI’s o1. DeepSeek-R1 also achieved an accuracy of 93 percent on the MATH-500 test.
Demonstrating its coding capabilities, DeepSeek secured a 2029 Elo rating on the Codeforces and performed better than 96.3 percent of human participants. It scored 90.8 percent and 71.5 percent on the general knowledge benchmarks MMLU and GPQA Diamond, respectively. To test writing and question-answering capabilities, DeepSeek-R1 was tested on the AlpacaEval 2.0 benchmark and achieved an 87.6 win rate.
Such high-performance caliber makes DeepSeek-R1 suitable for solving complex mathematical problems and code generation in software development. Its ability to generate responses in a stepwise manner, like human reasoning, makes DeepSeek-R1 useful for research, attracting the attention of the scientific community.
Launched under the open-source MIT license, DeepSeek-R1 can be freely used by enterprises for commercial purposes. However, they will have to spend an additional amount on customization and fine-tuning. In addition, companies outside China may be skeptical about using DeepSeek-R1 due to AI regulatory challenges and geopolitical reasons.
Have you ever wondered how your smartphone can recognize your face or how virtual assistants like Siri and Alexa understand your commands? The answer lies in deep learning, a powerful subset of artificial intelligence that functions as the human brain.
Deep learning is the core of many advanced technologies that you use daily. Large language models (LLMs) such as ChatGPT and Bing Chat, as well as image generators such as DALL-E, rely on deep learning to produce realistic responses.
In this article, you will explore various deep learning applications used across various domains.
What Is Deep Learning?
Deep learning is the specialized subfield of machine learning that utilizes a layered structure of algorithms called an artificial neural network (ANN) to learn from data. These neural networks mimic the way the human brain works, with numerous interconnected layers of nodes (or neurons) that process and analyze information.
In deep learning, “deep” indicates the number of layers present in a neural network that enable the model to learn complex representations of patterns in the data. For instance, in image recognition, initial layers could be as simple as finding edges, with subsequent layers capable of identifying more complex structures like shapes or specific objects. Such hierarchical learning in deep learning models makes it easy to derive information and predict diverse applications accurately.
How Is Deep Learning Different from Machine Learning?
Machine learning and deep learning are subsets of artificial intelligence, often used interchangeably, but they are not the same. The table below highlights the comparison of both across different parameters:
Aspect
Machine Learning
Deep Learning
Data Requirements
Can work with smaller datasets.
Requires huge amounts of data to train effectively.
Feature Extraction
Requires manual feature selection and engineering.
Automatically learns features from data.
Training Time
Shorter training time.
Longer training time.
Model Complexity
Simpler models.
Complex neural networks.
Computational Needs
Can run on CPUs.
Requires specialized hardware like GPUs.
Use Cases
Suitable for structured data tasks (e.g., classification, regression).
Best for unstructured data tasks (e.g., image recognition, natural language processing).
Why Is Deep Learning Important?
The global deep-learning market size is projected to reach $93.34 billion by 2028. So, you might be wondering what’s fueling such rapid growth. Let’s look into the substantial advantages you can derive by adopting this technology.
Automatic Feature Extraction: Deep learning models automatically learn relevant features from raw data without manual feature engineering. This adaptability allows them to work with different types of data and problems.
Enhanced Accuracy: With access to more data, deep learning models perform effectively. Its multi-layered neural networks can capture intricate patterns and relationships in data. This leads to improved accuracy in specific tasks like image classification and natural language processing.
Handling Unstructured Data: Unlike traditional machine learning methods, deep learning is particularly adept at processing unstructured data, which is a significant portion of the information generated today. This makes deep learning models drive technologies like facial recognition and voice assistants.
Improved Personalization: Deep learning models power personalized experiences in consumer applications such as streaming platforms, online shopping, and social media. By analyzing user behavior, they enable you to offer tailored suggestions, resulting in higher user engagement and satisfaction.
How Deep Learning Works?
Deep learning works by using a neural network composed of layers. These interconnected layers work together, each serving a different role in processing and transforming the input data to produce output. Let’s understand each of these layers in detail:
Input Layer
The input layer is the primary layer that serves as the entry point for raw data into the network. This layer does not perform any computations; it simply passes the data to the next layer for processing.
Hidden Layers
These layers are the core of the network where the actual data processing takes place. Each hidden layer comprises multiple neurons, and each neuron computes a weighted sum and then applies an activation function (like ReLU or sigmoid) to introduce non-linearity. This non-linearity facilitates the network to learn complex patterns beyond simple linear relationships. The more hidden layers the network has, the deeper it becomes to capture abstract features in the data.
Output Layer
This is the final layer of the deep learning models that generate the prediction or classification result. The number of neurons in this layer depends on the task. For example, if you have a binary classification problem, the output layer will have just one neuron. Whereas for a multi-class classification, the number of neurons will match the number of possible classes.
Types of Deep Learning Models
Let’s take a closer look at some of the most commonly used deep learning models:
Feedforward Neural Networks (FNNs): These are the simplest type of artificial neural networks. In FNNs, information moves in only one direction—from input nodes, through hidden nodes, and finally to output nodes without traveling backward. They are used for tasks like classification and regression.
Convolutional Neural Networks (CNNs): CNNs are particularly effective for image processing tasks. They use convolutional layers to automatically detect features in images, such as edges and textures. CNNs are ideal for applications like image recognition, object detection, and video analysis.
Recurrent Neural Networks (RNNs): RNNs are widely used for tasks such as speech recognition and NLP. They can retain information from previous steps in a sequence, which makes them particularly good at understanding the context of sentences or phrases.
Generative Adversarial Networks (GANs): GANs primarily consist of two neural networks—a generator and a discriminator that work against each other. The generator creates fake data while the discriminator evaluates its authenticity. This setup is effective for generating realistic images and videos.
Autoencoders: These models are used for unsupervised learning tasks, like dimensionality reduction and feature learning. An autoencoder comprises an encoder that compresses the input into a lower-dimensional representation and a decoder that reconstructs the original input from this representation.
Examples of Deep Learning Applications
Deep learning applications are making an impact across many different industries. Let’s explore a few of them:
Autonomous Vehicles
Driverless vehicles depend greatly on advanced learning, particularly Convolutional Neural Networks (CNNs). These networks assist the vehicle in examining visuals from cameras in order to identify entities such as walkers, other automobiles, and road signs. Corporations such as Tesla utilize CNNs to drive their automated vehicle platforms.
Speech Recognition
Deep learning has significantly advanced speech recognition technologies. By utilizing recurrent neural networks (RNNs), the systems can understand and transcribe spoken language with high accuracy. Applications include virtual assistants like Siri and Alexa, which rely on deep learning to interpret user commands and provide relevant responses. This technology has made human-computer interaction more intuitive and accessible.
Fraud Detection
Financial institutions use deep learning models to detect fraudulent transactions. These models analyze patterns in data, such as transaction history or user behavior, to spot irregularities that might indicate fraud. By using a combination of neural networks, these systems identify suspicious activity in real-time, helping prevent unauthorized transactions.
Healthcare Diagnostics
Deep learning is revolutionizing healthcare diagnostics by improving the accuracy of disease detection through medical imaging. Algorithms trained on extensive datasets can analyze images from MRIs and X-rays to identify abnormalities that may be indicative of conditions like neurological disorders.
Predictive Analytics
Predictive analytics enhances the accuracy and efficiency of demand forecasting. Deep learning models can analyze huge volumes of historical information to forecast predictions on trends and consumer behavior. This helps in optimizing inventory, marketing strategies, and resource allocation.
Challenges of Using Deep Learning Models
While deep learning offers multiple benefits, it also comes with certain challenges. Let’s take a look at a few of them:
Data Requirements
Deep learning models often require massive amounts of data to perform effectively. Without diverse datasets, these models struggle to generalize and often produce biased or inaccurate results. Collecting, cleaning, and labeling such large datasets is time-consuming and resource-intensive.
Computational Resources
Training deep learning models requires significant computational power, especially for complex architectures like deep neural networks. High-performance GPUs or TPUs are often necessary, making the process expensive and less accessible to smaller organizations or individuals.
Overfitting
Deep learning models might be prone to overfitting, especially when trained on small or noisy (that contain large amounts of irrelevant information) datasets. They try to fit the training data entirely and fail to generalize and perform well in the case of unseen data scenarios. Techniques such as regularization and dropout can help mitigate this issue, but they add complexity to the model design.
Final Thoughts
This article offered comprehensive insights into the benefits of deep learning, how it works, and its diverse applications. As a powerful branch of artificial intelligence, deep learning offers significant advantages for businesses across various industries. While it demands substantial computational resources, the benefits far outweigh these challenges.
Its ability to process vast amounts of unstructured data facilitates organizations in uncovering patterns and making data-driven decisions more effectively. Through the development of innovative solutions, deep learning continues to drive advancements in areas such as healthcare, finance, and technology, driving future growth and progress.
FAQs
How can overfitting be reduced in deep learning models?
Overfitting takes place when a model performs exceptionally well on the training data but poorly on new data. This can be reduced by using more training data, simplifying the model, and applying techniques like dropout, regularization, and data augmentation.
What are the advantages of deep learning over traditional machine learning?
Deep learning can automatically identify and extract features from raw data, minimizing the need for manual feature engineering. It is effective for tasks like image and speech recognition, where traditional methods often face challenges.
What is the purpose of the loss function in deep learning?
A loss function measures how well a model’s predictions match the true outcomes. It provides a quantitative metric for the accuracy of the model’s predictions, which can be used to minimize errors during training.
Generative AI models are trained on large datasets and use this data to generate outputs. However, training these models on finite and limited information isn’t enough to keep the model up-to-date, especially when answering domain-specific questions.
That’s where Retrieval-augmented generation (RAG) comes in. RAG enables these models to search for relevant information outside training data, ensuring they are better equipped to generate more accurate answers.
This article explores the benefits of RAG and how it improves the accuracy and relevance of the outputs generated by LLMs. Let’s get started!
What is Retrieval-Augmented Generation?
Retrieval-augmented generation (RAG) is an AI framework designed to enhance your applications by improving the accuracy and relevance of LLM-generated outputs. By integrating RAG, you can enable your LLM to retrieve relevant data from external sources such as databases, documents, or web content.
With access to up-to-date information, your model can generate contextually correct and reliable answers. Whether you’re building a customer support chatbot or research assistant, RAG ensures your AI delivers precise, timely, and relevant output.
Retrieval-Augmented Generation Architecture and Its Working
There is not one specific way to implement RAG within an LLM model. The core architecture depends on the particular use case, accessing specific external sources, and the model’s purpose. The following are the four basic foundational aspects that you can implement within your RAG architecture:
Data Preparation
The first component of the RAG architecture involves data collection, preprocessing, and chunking. Start by collecting data from internal sources such as databases, data lakes, documentation, or other reliable external sources. Once collected, clean and format the data and divide it into smaller chunks using methods like normalization or chunking. These chunks make it easier to embed the data in the model efficiently.
Indexing
Use a transformer model accessible through platforms like OpenAI and Hugging Face to transform the document chunks into dense vector representations called embeddings. These embeddings help to capture the semantic meaning of the text. Next, utilize a vector database to store the embeddings. These databases provide fast and efficient search capabilities.
Data Retrieval
When your LLM model processes a user query, it uses vector search to identify and extract information from the database. The vector search model matches the user’s input query with the stored embeddings, ensuring only the most contextually relevant data is retrieved.
LLM Inference
The final step of RAG architecture is to create a single accessible endpoint. Add components like prompt augmentation and query processing to enhance interaction. This endpoint serves as a connection between the LLM model and RAG, enabling the model to interact efficiently through a single point of contact.
What Are the Benefits of RAG?
Retrieval-augmented generation brings several benefits to your organization’s generative AI efforts. Some of these benefits include:
Access to fresh information: RAG helps the LLMs maintain context relevance by enabling them to connect directly to external sources. These sources include social media feeds, news sites, or other frequently updated information sources that provide the latest data.
Reduce Fabrication: Generative AI models sometimes ‘make up’ content when it doesn’t have enough context. RAG addresses this issue by allowing LLM to extract verified data from reliable sources before generating responses.
Control Over Data: The Retrieval-Augmented generation provides flexibility in specifying the sources the LLM can refer to. This ensures the model produces responses that align with industry-specific knowledge or authoritative databases, giving control over the output.
Improves Scope and Scalability: Instead of being limited to a static training set, RAG allows the LLM to retrieve information dynamically as needed. This enables the model to handle a wider variety of tasks, making it more versatile.
Difference between RAG and Semantic Search
Both RAG and Semantic Search approaches are used to improve the accuracy of the LLM but have slightly different frameworks. RAG uses semantic search as a part of its larger framework, while semantic search focuses on improving how to find relevant information.
Semantic search leverages natural language processing techniques to understand the context and meaning behind the words in a query. It helps to retrieve output that is more closely related to the intent of the question, even if some keywords differ. You can use semantic search in applications where only relevant document retrieval is needed, such as search engines, document indexing, or recommendation systems.
Example of Semantic Search
If you enter a query such as “What are the best apple varieties for baking pies?” a semantic search system first processes and interprets the meaning. Then, it will retrieve information about different varieties of apples suitable for baking.
RAG goes beyond semantic search. It first uses semantic search to retrieve relevant information from a database or document repository, then integrates this data into the LLMs prompt. This enables the LLM model to generate more accurate and contextually correct content.
Example of RAG
You can ask a chatbot powered by the RAG system, “What are the latest advancements in solar panel technology?”. Instead of relying on pre-trained data, the RAG will allow the chatbot to search across recent research articles, industry reports, or technical documents about solar panels. This extended search provides the chatbot LLM with additional data that can be used to generate a more accurate answer to your question.
What Are the Challenges of Retrieval-Augmented Generation?
RAG applications are being adopted widely in AI-driven customer service and support, content creation, and other fields. While RAG enhances the accuracy and relevance of responses, implementing and maintaining these applications comes with its own set of challenges.
Maintaining Data Quality and Relevance: As your data sources expand, ensuring data quality and relevance becomes harder. You will need to implement mechanisms to filter out unreliable or outdated information. Without this, conflicting or irrelevant data might slip through, leading to responses that are either incorrect or out of context.
Complex Integration: Integrating RAG with LLMs involves many steps, such as data preprocessing, embedding generation, and database management. Each step demands considerable resources to function, adding complexity to your system.
Information Overload: You should maintain a delicate balance when providing contextual information to LLM. Feeding too much data into the RAG model can overwhelm it, leading to prompt overload. The data overload makes it harder for the model to process the information accurately.
Cost of Infrastructure: Building and maintaining RAG systems can be costly. You need to manage infrastructure for storing, updating, and querying vector databases, along with the computational resources required to run the LLM. These costs can add up quickly if you are working on large-scale applications.
Retrieval-Augmented Generation Use Cases
The RAG framework significantly improves the capabilities of various natural language processing systems. Here are a few examples:
Content Summarization
The RAG framework contributes to generating concise and relevant summaries of long documents. It allows the summarization model to retrieve and attend to key pieces of text across the document, highlighting the most critical points in a condensed form.
For example, you can use RAG-powered tools like Gemini to process and summarize complex studies and technical reports. Gemini efficiently sifts through large amounts of text, identifies the core findings, and generates a clear and concise summary, saving time.
Information Retrieval
RAG models improve how information is found and used by making search results more accurate. Instead of just showing a list of web pages or documents, RAG combines the ability to search and retrieve information with the power to generate snippets.
For example, when you enter a search query, like ‘best ways to improve memory,’ a RAG-powered system doesn’t just show you a list of articles. It looks through a large pool of information, extracts the most relevant details, and then creates a short summary to answer your question directly.
Conversation AI Chatbots
RAG improves the responsiveness of conversational agents by enabling them to fetch relevant information from external sources in real-time. Instead of relying on static scripted responses, the interaction can feel more personalized and accurate.
For instance, you have probably interacted with a virtual assistant on an e-commerce platform while placing or canceling an order or when you wanted more details about the product. In this scenario, a RAG-powered virtual assistant instantly fetches up-to-date information about your recent orders, product specifications, or return policies. Using this information, the Chatbot generates and provides you with information relevant to your query, offering real-time assistance.
Conclusion
Retrieval-augmented generation represents a significant advancement in LLMs’ capabilities. It enables them to access and utilize external information sources. This integration allows your organization to improve the accuracy and relevance of AI-generated content while reducing misinformation or fabrication.
The benefits of RAG enhance the precision of responses and allow for dynamic and scalable applications across various fields, from healthcare to e-commerce. It is a pivotal step towards creating more intelligent and responsive AI systems that can adapt to the rapidly changing text landscape.
FAQs
Q. What Is the Difference Between the Generative Model and the Retrieval Model?
A retrieval-based model uses pre-written answers for the user queries, whereas the generative model answers user queries based on pre-training, natural language processing, and deep learning.
Q. What Is the Difference Between RAG and LLM?
LLMs are standalone Gen AI frameworks that respond to user queries using training data. RAG is a new framework that can be integrated with LLM. It enhances LLM’s ability to answer queries by accessing additional information in real-time.
Artificial Intelligence (AI) has changed how your business interacts with customers. At the forefront of this transformation are AI-powered chatbots. It provides a way to help you automate customer service, handle large-scale inquiries, and improve user experiences in various sectors.
With its simplicity and rich set of libraries, Python is one of the most powerful programming languages that enables you to build intelligent bots. Whether you’re a beginner or an experienced developer, this comprehensive guide details creating a functional AI chatbot using Python.
What Is an AI Chatbot?
An AI chatbot is an advanced software program that allows you to simulate human conversations through text or voice. By utilizing AI, the bot understands your questions and provides appropriate responses instantly. You can find AI-powered chatbots on e-commerce, customer service, banking, and healthcare websites, as well as on popular instant messaging apps. They help you by offering relevant information, answering common questions, and solving problems anytime, all without needing a human expert.
What makes AI chatbots effective is their ability to handle many conversations simultaneously. They learn from previous conversations, which enables them to improve their responses over time. Some chatbots can also customize their replies based on your preferences, making your experience even more efficient.
Why Do You Need AI Chatbots for Customer Service?
Continuous Availability: Chatbots help you respond instantly to customer inquiries 24/7. This continuous availability ensures that end-users can receive assistance at any time, leading to quicker resolutions and higher customer satisfaction.
Enhanced Scalability: Chatbots enable your business to manage various customer interactions simultaneously.
Cost-Efficiency: By reducing the need for additional staff, chatbots help you save on hiring and training expenses over time.
Gathering Valuable Data Insights: Chatbots allow you to collect essential information during customer interactions, such as preferences and common issues. Analyzing this data can help you recognize market trends and refine strategies.
How Does AI Chatbots Work?
AI chatbots combine natural language processing (NLP), machine learning (ML), and predefined rules provided by data professionals to understand and respond to your queries. Here are the steps to learn how AI chatbots operate:
Step 1: User Input Recognition
You can interact with the chatbot by typing a message or speaking through a voice interface. Once the chatbot recognizes your user input, it will prepare to process the input using NLP.
Step 2: Data Processing
In the processing step, chatbots use the following NLP techniques for better language understanding and further analysis:
Tokenization: This enables the chatbot to break down the input into individual words or characters called tokens.
Part-of-Speech Tagging: The chatbot can identify whether each word in a sentence is a noun, verb, or adjective.
Named Entity Recognition (NER): Allows the chatbot to detect and classify important entities like names, organizations, or locations.
After processing the input, the chatbot determines the intent or context behind your query. The chatbot uses NLP and ML to analyze the entities in your input. For example, consider a prompt like, “Can you tell me the latest iPhone?.”The chatbot finds key phrases like “latest” and “iPhone” from this prompt through NER. Then, it analyzes the emotional tone of the query by performing sentiment analysis and produces a relevant response.
Step 4: Generating Responses
Once the chatbot understands the intent and context of your input, it generates a response. This can be a pre-written reply, an answer based on information found in databases, or a dynamically created response by searching online resources. Finally, the chatbot replies to you, continuing the conversation.
Step 5: Learning and Improvement
In this step, the chatbot uses ML to learn from previous interactions and user preferences to improve the responses over time. By understanding past conversations, chatbots can figure out what you need, clarify any confusion, and recognize emotions like happiness or sarcasm. This helps the chatbot to handle follow-up questions smoothly and provide tailored answers.
Types of AI Chatbots
Each type of AI chatbot meets different needs and shows how AI can improve user interaction. Let’s look at the two types of AI chatbots:
Rule-Based Chatbots
Rule-based chatbots are simple AI systems that are trained on a set of predefined rules to produce results. They do not learn from past conversations but can use basic AI techniques like pattern matching. These techniques help the chatbots to recognize your query and respond accordingly.
Self-Learning Chatbots
These chatbots are more advanced because they can understand your intent on their own. They use techniques from ML, deep learning, and NLP. Self-learning chatbots are sub-divided into two:
Retrieval-Based Chatbots: These work similarly to rule-based chatbots using predefined input patterns and responses. However, rule-based chatbots depend on simple pattern-matching to respond. On the other hand, retrieval-based chatbots use advanced ML techniques or similarity measures to get the best-matching response from a database of possible responses. These chatbots also have self-learning capabilities to enhance their response selection over time.
Generative Chatbots: Generative chatbots produce responses based on your input using a seq2seq (sequence-to-sequence) neural network. The seq2seq network is a model built for tasks that contain input and output sequences of different lengths. It is particularly useful for NLP tasks like machine translation, text summarization, and conversational agents.
Build Your First AI Chatbot Using Python
You have gained a solid understanding of different types of AI chatbots. Let’s put theory into practice and get hands-on experience in developing each bot using Python!
Common Prerequisites:
Install the Python version 3.8 or above on your PC.
Tutorial on Creating a Simple Rule-Based Chatbot Using Python From Scratch
In this tutorial, you will learn how to create a GUI for a rule-based chatbot using the Python Tkinter module. This interface includes a text box for providing your input and a button to submit that input. Upon clicking the button, a function will process your intent and respond accordingly based on the defined rules.
Prerequisites:
The Tkinter module is included by default in Python 3. x versions. If you do not have the Tkinter module installed, you can do it by using the following pip command:
pip install tk
Steps:
Open Notepad from your PC or use any Python IDE like IDLE, PyCharm, or Spyder.
Write the following script in your code editor:
from tkinter import *
root = Tk()
root.title("AI Chatbot")
def send_query():
send_query = "You -> "+e.get()
txt.insert(END, "\n"+send_query)
user_name = e.get().lower()
if(user_name == "hello"):
txt.insert(END, "\n" + "Bot -> Hi")
elif(user_name == "hi" or user_name == "hai" or user_name == "hiiii"):
txt.insert(END, "\n" + "Bot -> Hello")
elif(e.get() == "How are you doing?"):
txt.insert(END, "\n" + "Bot -> I’m fine and what about you")
elif(user_name == "fine" or user_name == "I am great" or user_name == "I am doing good"):
txt.insert(END, "\n" + "Bot -> Amazing! how can I help you.")
else:
txt.insert(END, "\n" + "Bot -> Sorry! I did not get you")
e.delete(0, END)
txt = Text(root)
txt.grid(row=0, column=0, columnspan=2)
e = Entry(root, width=100)
e.grid(row=1, column=0)
send_query = Button(root, text="Send", command=send_query).grid(row=1, column=1)
root.mainloop()
Save the file as demo.py in your desired directory.
Open the command prompt and go to the folder where you save the Python file using cd.
Type Python demo.py in the Python interpreter and press Enter.
Once you execute the file, you can communicate with the chatbot by running the application from the Tkinter interface.
Sample Output:
Tutorial on Creating a Rule-Based Chatbot Using Python NLTK Library
NLTK (Natural Language Toolkit) is a powerful library in Python that helps you work with NLP tasks while building a chatbot. It provides tools for text preprocessing, such as tokenization, stemming, tagging, parsing, and semantic analysis. In this tutorial, you will explore advanced rule-based AI chatbots using the NLTK library:
Prerequisites:
Install the NLTK library using the pip command:
pip install nltk
Steps:
Create a new Notepad file as demo2.py and write the following code:
import nltk
from nltk.chat.util import Chat, reflections
dialogues = [
[
r"my name is (.*)",
["Hello %1, How are you?",]
],
[
r"hi|hey|hello",
["Hello", "Hey",]
],
[
r"what is your name ?",
["I am a bot created by Analytics Drift. You can call me Soozy!",]
],
[
r"how are you ?",
["I'm doing good, How about you?",]
],
[
r"sorry (.*)",
["It's alright","Its ok, never mind",]
],
[
r"I am great",
["Glad to hear that, How can I assist you?",]
],
[
r"i'm (.*) doing good",
["Great to hear that","How can I help you?:)",]
],
[
r"(.*) age?",
["I'm a chatbot, bro. \nI do not have age.",]
],
[
r"what (.*) want ?",
["Provide me an offer I cannot refuse",]
],
[
r"(.*) created?",
["XYZ created me using Python's NLTK library ","It’s a top secret ;)",]
],
[
r"(.*) (location|city) ?",
['Odisha, Bhubaneswar',]
],
[
r"how is the weather in (.*)?",
["Weather in %1 is awesome as always","It’s too hot in %1","It’s too cold in %1","I do not know much about %1"]
],
[
r"i work in (.*)?",
["%1 is a great company; I have heard that they are in huge loss these days.",]
],
[
r"(.*)raining in (.*)",
["There is no rain since last week in %2","Oh, it's raining too much in %2"]
],
[
r"how (.*) health(.*)",
["I'm a chatbot, so I'm always healthy ",]
],
[
r"(.*) (sports|game) ?",
["I'm a huge fan of cricket",]
],
[
r"who (.*) sportsperson ?",
["Dhoni","Jadeja","AB de Villiars"]
],
[
r"who (.*) (moviestar|actor)?",
["Tom Cruise"]
],
[
r"I am looking for online tutorials and courses to learn data science. Can you suggest some?",
["Analytics Drift has several articles offering clear, step-by-step guides with code examples for quick, practical learning in data science and AI."]
],
[
r"quit",
["Goodbye, see you soon.","It was nice talking to you. Bye."]
],
]
def chatbot():
print("Hi! I am a chatbot built by Analytics Drift for your service")
chatbot = Chat(dialogues, reflections)
chatbot.converse()
if __name__ == "__main__":
chatbot()
Open your command prompt and go to the folder in which you save the file.
Run the code using the following command:
Python demo2.py
You can now chat with your AI chatbot.
Sample Output:
In the above program, the nltk.chat module utilizes various regex patterns, which enables the chatbot to identify user intents and generate appropriate answers. To get started, you must import the Chat class and reflections, a dictionary that maps the basic inputs to corresponding outputs. For example, if the input is “I am,” then the output is “You are.” However, this dictionary has limited reflections; you can create your own dictionary with more replies.
Tutorial on Creating Self-Learning Chatbots Using Python Libraries and Anaconda
This tutorial offers a step-by-step guide to help you understand how to create a self-learning Python AI chatbot. You must utilize Anaconda and various Python libraries, such as NLTK, Keras, tensorflow, sklearn, numpy, and JSON, to build your bot.
Initially, you must Import the necessary libraries for lemmatization, preprocessing, and model development using the following script:
import json
import numpy as np
import random
import nltk
from nltk.stem import WordNetLemmatizer
from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, Dropout
from tensorflow.keras.optimizers import SGD
Load the following data file (“intents.json) into your Python script. This file includes tags, patterns, and responses for your chatbots to interpret your input and respond.
Sample JSON file:
{
"intents": [
{
"tag": "greeting",
"patterns": [
"Hi",
"Hello",
"How are you?",
"Is anyone there?",
"Good day"
],
"responses": [
"Hello! How can I help you today?",
"Hi there! What can I do for you?",
"Greetings! How can I assist you?"
]
},
{
"tag": "goodbye",
"patterns": [
"Bye",
"See you later",
"Goodbye",
"I am leaving",
"Take care"
],
"responses": [
"Goodbye! Have a great day!",
"See you later! Take care!",
"Bye! Come back soon!"
]
},
{
"tag": "thanks",
"patterns": [
"Thanks",
"Thank you",
"That's helpful",
"Thanks for your help",
"Appreciate it"
],
"responses": [
"You're welcome!",
"Glad to help!",
"Anytime! Let me know if you need anything else."
]
},
{
"tag": "noanswer",
"patterns": [],
"responses": [
"Sorry, I didn't understand that.",
"Can you please rephrase?",
"I’m not sure I understand. Could you clarify?"
]
},
{
"tag": "options",
"patterns": [
"What can you do?",
"Help me",
"What are your capabilities?",
"Tell me about yourself"
],
"responses": [
"I can assist you with various inquiries! Just ask me anything.",
"I'm here to help you with information and answer your questions."
]
}
]
}
Once you create the above JSON file in the Jupyter Notebook, you can run the following Python Script to load them:
with open('intents.json') as file:
data = json.load(file)
The next step involves preprocessing the JSON data by tokenizing and lemmatizing text patterns from intents.
lemmatizer = WordNetLemmatizer()
corpus = []
labels = []
responses = []
for intent in data['intents']:
for pattern in intent['patterns']:
word_list = nltk.word_tokenize(pattern)
word_list = [lemmatizer.lemmatize(w.lower()) for w in word_list]
corpus.append(word_list)
labels.append(intent['tag'])
label_encoder = LabelEncoder()
labels_encoded = label_encoder.fit_transform(labels)
all_words = sorted(set(word for words in corpus for word in words))
This processing helps you to generate a corpus of processed word lists, encoding labels, and a sorted list of unique words. These outputs will be used to train your chatbot model.
Following the previous step, you can create a training dataset for your chatbot. The training data should then be converted into numerical format.
x_train = []
y_train = []
for words in corpus:
bag = [0] * len(all_words)
for w in words:
if w in all_words:
bag[all_words.index(w)] = 1
x_train.append(bag)
x_train = np.array(X_train)
y_train = np.array(labels_encoded)
The x_train list holds the feature vectors or bag of words for each input, while y_train stores the encoded labels corresponding to your input.
The next step involves building and training a chatbot using the Keras sequential model. The sequential model allows you to build a neural network layer by layer, with each layer having exactly one input tensor and one output tensor.
Here, you need to initialize the sequential model and add the required number of layers, as shown in the following code:
To predict responses according to your input, you must implement a function as follows:
def chatbot_reply(text):
input_words = nltk.word_tokenize(text)
input_words = [lemmatizer.lemmatize(w.lower()) for w in input_words]
bag = [0] * len(all_words)
for w in input_words:
if w in all_words:
bag[all_words.index(w)] = 1
prediction = model.predict(np.array([bag]))[0]
tag_index = np.argmax(prediction)
tag = label_encoder.inverse_transform([tag_index])[0]
for intent in data['intents']:
if intent['tag'] == tag:
return random.choice(intent['responses'])
return "Sorry, I did not understand that.”
Collecting the inputs and associated responses, you can make a self-learned chatbot from past interactions and feedback.
You can begin interaction with your chatbot using the following Python code:
from tensorflow.keras.models import load_model
model = load_model('chatbot_model.h5')
def chat():
print("Chatbot: Hello! I am your virtual assistant. Type 'quit' to exit.")
while True:
user_input = input("You: ")
if user_input.lower() == 'quit':
print("Chatbot: Goodbye! Have a great day!")
break
response = chatbot_response(user_input)
print(f"Chatbot: {response}")
chat()
Tutorial on Developing a Self-Learning Chatbot Using Chatterbot Library
The Python Chatterbot library is an open-source machine learning library that allows you to create conversational AI chatbots. It uses NLP to enable bots to engage in dialogue, learn from previous messages, and improve over time. In this tutorial, you will explore how to build a self-learning chatbot using this library:
Prerequisites:
Ensure you have installed Python version 3.8 or below on your PC.
Import required libraries to develop and train your chatbot.
from chatterbot import ChatBot
from chatterbot.trainers import ChatterBotCorpusTrainer, ListTrainer
Create your chatbot instance with a unique name and storage adapter. The storage adapter is a component that allows you to manage how the chatbot’s data is stored and accessed.
Alternatively, you can utilize ListTrainer for custom model training.
custom_conversations = [
"Hello",
"Hi there!",
"How are you?",
"I'm doing great, thanks!",
"What's your name?",
"I am a self-learning chatbot.",
"What can you do?",
"I can chat with you and learn from our conversations.",
]
Once the custom conversation list is created, you can train the chatbot with it.
Define a function to communicate with your chatbot:
def chat():
print("Chat with the bot! (Type 'exit' to end the conversation)")
while True:
user_input = input("You: ")
if user_input.lower() == 'exit':
print("Goodbye!")
break
bot_response = chatbot.get_response(user_input)
print(f"Bot: {bot_response}")
chatbot.learn_response(user_input, bot_response)
You can begin chatting with your AI bot now.
if __name__ == "__main__":
chat()
You can also embed your chatbot into a web application created using Django or Flask.
Best Practices for Creating AI Chatbots Using Python
Use NLP techniques such as NER and intent classification, along with ML models trained on large datasets, to enhance understanding of varied inputs.
Handle complex contexts using dialogue management and session tracking tools available in a flexible conversation AI software, Rasa.
Train the chatbot to manage unfamiliar or out-of-scope queries by directing your customers to human experts or suggesting alternate questions.
Implement personalization by using your client’s name and tailoring responses based on preferences and past interactions.
Plan for scalability and performance monitoring of AI chatbots over time with cloud services and robust deployment practices.
Use Cases of AI Chatbot
E-commerce: AI chatbots assist you in finding products, making purchases, and providing personalized recommendations based on your browsing history.
Travel Booking: AI chatbots assist travelers in planning trips, booking flights and hotels, and providing travel recommendations.
Healthcare: Chatbots can help your patients by providing information about symptoms, scheduling appointments, and reminding them about medication or follow-ups.
Personal Finance: You can manage your finances by seeking budget advice, tracking expenses, and gaining insights into spending habits.
Final Thoughts
Building an AI chatbot using Python is an effective way to modernize your business and enhance the user experience. By leveraging the powerful Python libraries, you can create a responsive and intelligent chatbot. These chatbots will be capable of handling a large number of inputs, providing continuous support, and engaging you in meaningful conversations.
While rule-based chatbots serve their primary purpose, self-learning chatbots offer even more significant benefits by adapting and improving based on past conversations and feedback. This capability enables them to understand the user intents, tailor responses better, and create more personalized customer service.
FAQs
Which libraries are commonly used to build chatbots in Python?
Popular libraries include Chatterbot, NLTK, spaCy, Rasa, and TensorFlow.
Do I need to know machine learning to build a chatbot?
Basic chatbots can be created using rule-based systems and do not need to know machine learning. However, understanding machine learning can enhance your chatbot’s capabilities.
In a series of interviews conducted by the Wall Street Journal on January 21st, 2025, Anthropic CEO Dario Amodei announced that the company will launch new AI models.
Future releases will combine web access and “two-way” voice chat functionality with the existing Claude chatbot.
According to Amodei, this AI system will be referred to as a “Virtual Collaborator.” It will run on a PC, write and compile code, execute workflows, and interact with users through Slack and Google Docs.
The new AI model is said to have an enhanced memory system, which will help Claude remember about users and past conversations.
Amodei stated, “The surge in demand we’ve seen over the last year, and particularly in the last three months, has overwhelmed our ability to provide the needed compute.”
Competing with global counterparts, like OpenAI, Anthropic anticipates that the new models will support leading the AI market.
For innovations and new products, Anthropic has reportedly raised around $1 billion from Google, which equates to the tech giant’s total stake in Anthropic at $3 billion. This includes the past year’s investment of $2 billion.
Anthropic is also in talks to raise another $2 billion from investors like LightSpeed at a valuation of $60 billion.
On Tuesday, January 21, 2025, OpenAI CEO Sam Altman, SoftBank chief Masayoshi Son, and Oracle co-founder Larry Ellison issued a joint statement outlining a new project. The joint venture, also known as the Stargate Project, aims to develop AI data centers across the US.
This initiative has multiple tech partners joining in, including Microsoft, Arm, and Nvidia. Previous investors of OpenAI, Middle East AI Fund MGX, and SoftBank are collaborating to create investment strategies for effectively executing the Stargate Project.
The project is initially supposed to start in Texas and then expand to other states. The partner companies have agreed to invest $500 billion over the next five years.
In the press conference conducted at the White House, US President Donald Trump spoke about the investment plans to expand infrastructure. All the tech giants were invited to attend this conference.
The data centers could store AI-enabled chips developed by OpenAI, which is said to be building a team of chip designers and electronics engineers. To achieve this, OpenAI is continuously working with semiconductor companies like TSMC and Broadcom. The designed chips are expected to enter the market by 2026.
Earlier in December 2024, SoftBank pledged to invest $100 billion in the US over the next few years. SoftBank’s consistent interest in investing in companies like OpenAI and other startups and projects has fostered a close relationship with the current government.
Previously, OpenAI has negotiated with Oracle to lease a data center in Abilene, Texas. This data center is anticipated to reach a gigawatt of electricity by mid-2026. Estimated to cost around $3.4 billion, the Abilene data center would be the first Stargate site. The project would then scale up to 20 data centers by 2029.
According to Larry Ellison, “Each building is half a million square feet,” and “there are 10 buildings currently being built.”
AI, or artificial intelligence, is rapidly becoming an integral part of everyday life. From personal assistants like Siri to advanced algorithms that recommend movies or music on platforms like Netflix and Spotify, AI significantly impacts your daily interactions with technology.
However, the widespread adoption of AI has also raised potential concerns like privacy, bias, and accountability. To address these challenges, it is essential to confirm that AI systems are designed and implemented ethically. This is where AI ethics become important, guiding the responsible use of AI solutions.
In this blog, you’ll explore the significance of AI ethics and the steps involved in developing ethical AI systems.
What Is AI Ethics?
AI ethics refers to the principles that govern the use of artificial intelligence technologies. The primary focus is ensuring that AI systems reflect societal values and prioritize the well-being of individuals. By addressing ethical concerns, AI ethics promotes privacy, fairness, and accountability in AI applications.
Several prominent international organizations have established AI ethics frameworks. For instance, UNESCO released the Recommendation on the Ethics of Artificial Intelligence. This global standard highlights key principles like transparency, fairness, and the need for human oversight of AI systems. Similarly, the OECD AI Principles encourage the use of AI that is innovative and trustworthy while upholding human rights and democratic values.
Why Does AI Ethics Matter?
Ethical AI not only helps mitigate risks but also offers key benefits that can enhance your organization’s reputation and operational efficiency.
Increased Customer Loyalty
Ethical AI promotes trust by ensuring fairness and transparency in AI solutions. When users feel confident that your AI solutions are designed with their best interests in mind, they are likely more inclined to remain loyal to your brand. This fosters a positive experience that contributes to long-lasting customer relationships.
Encourages Inclusive Innovation
Incorporating varied perspectives, such as gender, culture, and demographics, in AI development helps you create solutions that address the varying needs of a broader audience. This inclusivity can lead to innovative solutions that resonate with diverse user groups.
Mitigates Legal and Financial Risks
Adhering to artificial intelligence regulations can help your organization avoid potential legal complications. Many regions have established data protection regulations like the California Consumer Privacy Act (CCPA) and the EU’s General Data Protection Regulation (GDPR). By complying with such data protection laws, you can ensure the ethical handling of data, reducing the risk of legal challenges and costly fines.
Facilitates Better Decision-Making
Ethical AI supports data-driven decision-making while ensuring that these insights are derived from fair and unbiased algorithms. This leads to more reliable and informed decisions, promoting trust and efficiency within your organization.
Key Pillars of AI Ethics
From fairness and safety to transparency and accountability, let’s look into the key pillars that AI ethics stand on.
Fairness
Fairness in AI ensures that the technology does not perpetuate bias or discrimination against individuals or groups. It is vital to design AI systems that treat all users equitably, regardless of factors like race, gender, or socio-economic status. To attain fairness, you must actively seek to identify and mitigate any biases that may arise in the data or algorithms.
Safety
Safety focuses on building AI systems that operate without harming individuals or the environment. It ensures AI behaves as intended, even in unpredictable scenarios. To maintain safety, you should rigorously test applications under diverse conditions and implement fail-safes for unexpected situations.
Human Intervention as Required
This emphasizes the importance of maintaining human oversight in AI operations, especially in critical decision-making processes. While AI can automate and augment many tasks, it is vital that you retain the ability to intervene when necessary. In cases where ethical, legal, or safety issues arise, human judgment should override AI decisions.
Ensuring AI Is Sustainable and Beneficial
You should develop AI solutions that promote long-term sustainability and offer benefits to society as a whole. It is important to consider the environmental impact of AI systems and ensure that applications contribute positively to social, economic, and environmental goals.
Lawfulness and Compliance
AI systems must operate within the bounds of legal and regulatory frameworks. Compliance with data protection regulations and industry-specific standards ensures lawful and ethical AI operations. Staying updated with evolving regulations helps ensure that AI systems respect human rights, privacy, and ethical standards, preventing misuse.
Transparency
Transparency is crucial to building trust in AI systems. You must enhance transparency by making your AI systems understandable to users. Provide clear documentation detailing how algorithms work, including the data sources used and the decision-making processes. This also facilitates accountability, enabling mistakes or biases to be traced and addressed more easily.
Reliability, Robustness, and Security
AI models must be reliable and robust so that they can function consistently and accurately over time, even in unpredictable environments. You should design AI systems with strong safeguard mechanisms to prevent tampering, data breaches, or failures, especially in critical applications like finance, healthcare, and national security.
Accountability
Accountability in AI ensures that systems are designed, deployed, and monitored with clear responsibility for their actions and outcomes. If an AI model causes harm or unintended consequences, there should be a process to trace the root cause. To achieve this accountability, you must have governance frameworks, thorough documentation, and regular monitoring.
Data Privacy
Data privacy is fundamental in AI development. AI systems often rely on large datasets, which may include sensitive personal information. This makes it critical to safeguard individual privacy by securely handling, processing, and storing data in compliance with privacy laws, such as GDPR. You should implement encryption, anonymization, and other robust security measures that prevent unauthorized access or misuse.
7 Key Steps to Develop Ethical AI Systems
Implementing ethical AI systems requires a systematic approach. Here are the seven essential steps to ensure ethical AI development and deployment:
1. Establish an Ethical AI Framework
The first step in implementing ethical AI is to create a structured framework. Begin by defining a set of ethical principles that align with your organization’s values. These should address core aspects such as transparency, fairness, accountability, and privacy. However, to ensure a broad perspective, you should involve various stakeholders, like customers, employees, and industry experts.
2. Prioritize Data Diversity and Fairness
AI models’ performance relies highly on the training data. A lack of diversity in the data can cause the model to generate biased results. To address this, you should use diverse datasets that accurately represent all user groups. This will enable the model to generalize across different scenarios and provide fair results.
3. Safeguard Data Privacy
AI often relies on large datasets, some of which may include personal information. As a safe measure, you can anonymize sensitive data and limit data collection to only what is strictly necessary. You must also employ techniques such as differential privacy and encryption to protect data. This safeguards user data from unauthorized access and ensures its use complies with privacy regulations like GDPR or CCPA.
4. Ensure Transparency and Explainability in AI Models
Make your AI system’s decision-making processes understandable to users. To achieve this, use explainable AI (XAI) techniques, such as LIME (Local Interpretable Model-Agnostic Explanations), which explains the prediction of classifiers by the ML algorithm. For example, if your AI system recommends financial loans, provide users with a clear explanation of why they were approved or denied.
5. Perform Ethical Risk Assessments
Assess potential ethical risks, such as bias, misuse, or harm, before deploying your AI systems. To conduct a thorough analysis, you can utilize frameworks like the AI Risk Management Framework developed by NIST. It offers a structured approach to managing the risks associated with AI systems. You can also leverage tools, such as IBM AI Fairness 360 or Microsoft Fairlearn, to detect and mitigate biases in your AI models.
6. Incorporate Ethical AI Governance
AI governance involves setting up structures and processes to oversee ethical AI development and deployment. You should establish an AI ethics committee or board within your organization to evaluate AI projects against ethical standards throughout their lifecycle. This helps you effectively address potential biases and ethical challenges.
7. Continuous Monitoring and Feedback Loops
After deployment, you need to collect user feedback and monitor the AI system for unexpected behaviors. Use performance metrics that align with your ethical principles, such as fairness scores or privacy compliance checks. For example, if your AI system starts showing biased outcomes in hiring decisions, you should have mechanisms in place to identify and correct this quickly.
Case Studies: Top Companies’ Initiatives and Approach to Ethical AI
Let’s explore the initiatives taken by leading organizations to ensure their AI technologies align with ethical principles and societal values.
Google’s AI Principles
Google was one of the first major companies to publish AI Principles, guiding its teams on the responsible development and use of AI. These principles ensure the ethical development of AI technologies, especially in terms of fairness, transparency, and accountability. Besides, Google explicitly states areas where they will not deploy AI, such as in technologies that could cause harm or violate human rights.
Microsoft’s AI Ethics
Microsoft’s approach to responsible AI is guided by six key principles—inclusiveness, reliability and safety, transparency, privacy and security, fairness, and accountability. It also established the AETHER (AI and Ethics in Engineering and Research) Committee to oversee the integration of these principles into the AI systems.
Wrapping Up
Ethical AI is essential to foster trust, fairness, and the responsible use of technology in society. The key pillars of AI ethics include fairness, safety, transparency, accountability, and data privacy, among others.
By adhering to principles of accountability and transparency of AI systems, you can avoid risks while enhancing your organization’s reputation. AI ethics also brings several benefits, including increased customer loyalty and facilitating better decision-making.
FAQs
How many AI ethics are there?
There are 11 clusters of principles identified from the review of 84 ethics guidelines. These include transparency, responsibility, privacy, trust, freedom and autonomy, sustainability, beneficence, dignity, justice and fairness, solidarity, and non-maleficence.
What are the ethical issues with AI?
Some of the ethical issues with AI include discrimination, bias, unjustified actions, informational privacy, opacity, autonomy, and automation bias, among others.
On January 22, 2025, Databricks announced that Meta had joined as a strategic investor in a $10 billion funding round. The company intends to use the money raised through fundraisers for expansion and product development.
Qatar Investment Authority, Temasek, and Macquarie Capital are other investors who have contributed to the Series J funding round. Databricks has also acquired a credit facility of $5.25 billion from JP Morgan Chase, Barclays, Citi, Goldman Sachs, and Morgan Stanley.
Founded in 2013, Databricks is a San Francisco-based data analytics and artificial intelligence company. It is already a part of Meta’s Llama, a collection of LLMs developed by Meta.
Ali Ghodsi, Databricks’s CEO and Co-founder, said, “Thousands of customers are using Llama on Databricks, and we have been working closely with Meta on how to best serve those enterprise customers with Llama. It naturally made sense for both parties to deepen that partnership through this investment.”
Last year, Databricks released its own open-source LLM called DBRX. It initially performed better than Meta’s Llama and some other models but was soon surpassed by them in efficiency. Ghodsi added that it is reasonable for them to ally with Meta, which has plenty of money to spend on model training. Databricks can utilize its money in other ways.
There has been a surge in investment in AI startups due to the increasing adoption of AI after the success of OpenAI’s ChatGPT.
On January 21, 2025, Aravind Srinivas, CEO of Perplexity AI and ex-OpenAI engineer, took over the social media platform X. In his post, Srinivas expressed that India could be the country that provides cost-effective solutions in the domain of artificial intelligence. He cited ISRO—Indian Space Research Organization—as an example, which has provided the world with affordable space exploration.
The following day, January 22, Srinivas announced a personal investment of $1 million and 5 hours per week to support individuals who aim to revolutionize AI in India.
He further emphasized, “Consider this as a commitment that cannot be backtracked. The dedicated team has to be cracked and obsessed like the DeepSeek team and has to open source the models with MIT license.”
In his post, Arvind Srinivas highlighted the remarkable achievement of DeepSeek, which released its model r1, outperforming OpenAI on LLM benchmarks. The r1 model offers 1 million token output at a staggering price of just $2.19. For the same output, OpenAI’s o1 model costs $60.
The existing Indian tech industry is utilizing pre-built open-source LLMs to develop applications. Disagreeing with this perspective, Aravind strives to foster fundamental LLM training in India. His vision aims to promote AI model training, which could further be released as open-source solutions.
Many professionals appreciate the tech giant’s enthusiastic view on AI model training. The post gained over a million views within two days. This shows that many individuals are intrigued by this initiative to push the boundaries of artificial intelligence in India.
