GPT 4 and its advancements over GPT 3

The field of natural language processing has witnessed remarkable advancements over the years, with the development of cutting-edge language models such as GPT-3 and the recent release of GPT-4. These models have revolutionized the way we interact with language and have opened up new possibilities for applications in various domains, including chatbots, virtual assistants, and automated content creation.

What is GPT?

GPT is a natural language processing (NLP) model developed by OpenAI that utilizes the transformer model. Transformer is a type of Deep Learning model, best known for its ability to process sequential data, such as text, by attending to different parts of the input sequence and using this information to generate context-aware representations of the text.

What makes transformers special is that they can understand the meaning of the text, instead of just recognizing patterns in the words. They can do this by “attending” to different parts of the text and figuring out which parts are most important to understanding the meaning of the whole.

For example, imagine you’re reading a book and come across the sentence “The cat sat on the mat.” A transformer would be able to understand that this sentence is about a cat and a mat and that the cat is sitting on the mat. It would also be able to use this understanding to generate new sentences that are related to the original one.

GPT is pre-trained on a large dataset, which consists of:

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Scholarship Test for PG Certificate in Data Science, AI/ML from IIT Roorkee. Earn Rs 75,000 Discount in One Hour.

We all know what’s ruling technology right now.

Yes, it is Artificial Intelligence, Machine Learning, Data Science, and Data Engineering. 

Therefore, now is the time to propel your Data Science career. Look no further because you can enroll for a PG Certificate Course in Data Science from IIT Roorkee. To make enrolment easy for you, here’s a Free Scholarship Test you can take and earn discounts up to Rs.75,000!

The Scholarship Test is a great opportunity for you to earn discounts. There are 50 questions that you have to attempt in one hour.
Each question you answer correctly earns you a discount of Rs 1000, and you can earn a maximum discount of Rs 75,000! (50/50 rewards you with an additional 25000 scholarship)

This Scholarship Test for the Data Science course is a great way to challenge yourself in basic aptitude and basic programming questions and to earn a massive discount on the course fees. 

The PG Certificate course from IIT Roorkee covers all that you need to know in technology right now. You will learn the architecture of ChatGPT, Stable Diffusion, Machine Learning, Artificial Intelligence, Data Science, Data Engineering and more! The course will be delivered by Professors from IIT Roorkee and industry experts and follows a blended mode of learning. Learners will also get 365 days of access to cloud labs for hands-on practice in a gamified learning environment. 

Data Scientists, Data Engineers, Data Architects are some of the highly sought after professionals today. With businesses and life-changing innovations being data driven in every domain, the demand for expertise in Deep Learning, Machine Learning is on the rise. This PG Certificate Course gives you the skills and knowledge required for a propelling career in Data Science. 

So what are you waiting for? Seats to the PG Certificate Course in Data Science from IIT Roorkee are limited. Take the Scholarship Test, earn discounts, and enroll now.

Link to the Scholarship Test is here.

Details about the PG Certificate Course in AI, Machine Learning, and Data Science are here.

Impact of AI on various industries in 2024

Artificial intelligence (AI) is having a profound impact on many different industries and is transforming the way businesses and organizations operate and serve their customers. With the help of AI, organizations are able to automate complex processes, make better predictions and decisions, and provide more personalized and efficient services to their customers.

One of the key areas where AI is making a big difference is in the field of healthcare. AI algorithms are being used to analyze medical data, such as images, records, and biomarkers, and to make more accurate predictions about the likelihood of diseases and the effectiveness of treatments. This can help healthcare providers to diagnose and treat patients more effectively, and improve the overall quality of care.

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I’m from the telecom industry, should I learn Data Science and AI?

In the telecom industry, the use of AI and data science is becoming increasingly important for companies that want to stay competitive and deliver the best possible services to their customers.

Only by leveraging the power of AI and data science, telecom companies can gain valuable insights into their operations and make data-driven decisions that can help them improve efficiency, reduce costs, and develop new products and services.

One key area where AI and data science can help telecom companies is in network optimization. By analyzing vast amounts of data from network sensors and other sources, AI algorithms can identify patterns and anomalies that can indicate where the network is underperforming or prone to failure. This can help telecom companies take proactive steps to improve network reliability and reduce downtime, leading to a better overall customer experience.

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I’m from the manufacturing industry, should I learn Data Science and AI?

In today’s competitive manufacturing landscape, companies that want to stay ahead of the curve are turning to AI and data science to improve efficiency and drive innovation. By harnessing the power of AI and data science, manufacturing companies can gain valuable insights into their operations and make data-driven decisions that can help them improve productivity, reduce costs, and develop new products and services.

One key area where AI and data science can help manufacturing companies is in the realm of predictive maintenance. By analyzing vast amounts of data from sensors and other sources, AI algorithms can identify patterns and anomalies that can indicate when equipment is likely to fail. This can help companies schedule maintenance and repairs at the optimal time, reducing downtime and improving overall equipment reliability.

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Jobs that will emerge/stay relevant in the near future.

As AI and other technologies continue to advance, it is likely that many jobs that are currently considered essential will become obsolete, while new job opportunities will emerge in areas related to AI and other emerging technologies.

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I’m from the banking industry, should I learn Data Science and AI

If you work in the banking industry, learning about data science, machine learning, and AI could be a valuable investment in your career. These fields are rapidly growing and are expected to play an increasingly important role in the banking industry in the coming years.

Here are a few reasons why learning about data science, machine learning, and AI could be beneficial for individuals in the banking industry:

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How to Crack Machine Learning Interviews with Top Interview Questions(2024)

Machine Learning is the most rapidly growing domain in the software industry. More and more sectors are using concepts of Machine Learning to enhance their businesses. It is now not an add-on but has become a necessity for businesses to use ML algorithms for optimizing their businesses and to offer a personalised user experience.

This demand for Machine Learning in the industry has directly increased the demand for Machine Learning Engineers, the ones who unload this magic in reality. According to a survey conducted by LinkedIn, Machine Learning Engineer is the most emerging job role in the current industry with nearly 10 times growth.

But, even this high demand doesn’t make getting a job in ML any easier. ML interviews are tough regardless of your seniority level. But as said, with the right knowledge and preparation, interviews become a lot easier to crack.

In this blog, I will walk you through the interview process for an ML job role and will pass on some tips and tactics on how to crack one. We will also discuss the skills required in accordance with each round of the process.

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Natural Languages and AI

During one of the keynote speeches in India, an elderly person asked a question: why don’t we use Sanskrit for coding in AI. Though this question might look very strange to researchers at first it has some deep background to it.

Long back when people were trying to build language translators, the main idea was to have an intermediate language to and from which we could translate to any language. If we build direct translation from a language A to B, there will be too many permutations. Imagine, we have 10 languages, and we will have to build 90 (10*9) such translators. But to come up with an intermediate language, we would just need to encode for every 10 languages and 10 decoders to convert the intermediate language to each language. Therefore, there will be only 20 models in total.

So, it was obvious that there is definitely a need for an intermediate language. The question was what should be the intermediate language. Some scientists proposed that we should have Sanskrit as the intermediate language because it had good definitive grammar. Some scientists thought a programming language that can dynamically be loaded should be better and they designed a programming language such as Lisp. Soon enough, they all realized that both natural languages and programming languages such as Lisp would not suffice for multiple reasons: First, there may not be enough words to represent each emotion in different languages. Second, all of this will have to be coded manually.

The approach that became successful was the one in which we represent the intermediate language as a list of numbers along with a bunch of numbers that represent the context. Also, instead of manually coding the meaning of each word, the idea that worked out was representing a word or a sentence with a bunch of numbers. This approach is fairly successful. This idea of representing words as a list of numbers has brought a revolution in natural language understanding. There is humongous research that is happening in this domain today. Please check GPT-3Dall-E, and Imagen.

If you subtract woman from Queen and add Man, what should be the result? It should be King, right? This can be easily demonstrated using word embedding.

Queen — woman + man = King

Similarly, Emperor — man + woman = Empress

Yes, this works. Each of these words is represented by a list of numbers. So, we are truly able to represent the meaning of words with a bunch of numbers. If you think about it, we learned the meaning of each word in our mother tongue without using a dictionary. Instead, we figured the meaning out using the context.

In our mind, we have sort of a representation of the word which is definitely not in the form of some other natural language. Based on the same principles, the algorithms also figure out the meaning of the words in terms of a bunch of numbers. It is very interesting to understand how these algorithms work. They work on similar principles to humans. They go through the large corpus of data such as Wikipedia or news archives and figure out the numbers with which each word can be represented. The problem is optimization: come up with those numbers to represent each word such that the distance between the words existing in a similar context is very small as compared to the distance between the words existing in different contexts.

The word Cow is closer to Buffalo as compared to Cup because Cow and buffalo usually exist in similar contexts in sentences.

So, in summary, it is very unreasonable to pursue that we should still be considering a natural language to represent the meaning of a word or sentence.

I hope this makes sense to you. Please post your opinions in the comments.

Classification metrics and their Use Cases


In this blog, we will discuss about commonly used classification metrics. We will be covering Accuracy Score, Confusion Matrix, Precision, Recall, F-Score, ROC-AUC and will then learn how to extend them to the multi-class classification. We will also discuss in which scenarios, which metric will be most suitable to use.

First let’s understand some important terms used throughout the blog-

True Positive (TP): When you predict an observation belongs to a class and it actually does belong to that class.

True Negative (TN): When you predict an observation does not belong to a class and it actually does not belong to that class.

False Positive (FP): When you predict an observation belongs to a class and it actually does not belong to that class.

False Negative(FN): When you predict an observation does not belong to a class and it actually does belong to that class.

All classification metrics work on these four terms. Let’s start understanding classification metrics-

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