Shweta Shetty


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AI/ML

Some articles on AI/ML

Explore a featured selection of my writing work below.
V2 AI •

Five AI Disruptions Insurance Leaders Cannot Ignore | V2 AI

Adopting AI while maintaining the insurance sector's rigorous compliance standards is hard enough. But long-standing brands must do so while operating within legacy systems and tightly controlled operational frameworks.

Some leading insurance organisations are cracking the AI code. A V2 AI client identified an opportunity in their quotes and claims processing workflows. The client’s existing process required coordination across several teams to securely analyse customer documentation and prepar...
TRG Datacenters •

NVIDIA GPUs H100 vs. A100 - Architecture, Performance, and Cost Comparison | TRG Datacenters

NVIDIA GPUs H100 and A100 GPUs represent the cutting edge of AI acceleration. Initially designed for tasks like rendering images and videos, they have become indispensable for AI and ML due to their Tensor Cores that speed up matrix operations fundamental to neural networks.

Both the H100 and A100 offer impressive improvements over non-GPU computing methods. However, the H100 is a significant upgrade on the A100, and has several new features. This article compares their performance, cost, and a...
Nebius •

Spark vs. Hadoop in data engineering

Hadoop and Spark are two open-source data processing technologies frequently found in data pipelines. Both are used to transform massive data sets for analytics. Spark is an advancement to Hadoop’s processing layer but still uses Hadoop for data storage. It is a leading technology in big data ML use cases and works alongside Hadoop in massive ML/AI training pipelines.
Spark and Hadoop allow ML engineers to process data faster at scale. You can do model training and SQL transformations within a s...
Nebius •

Entropy in machine learning — applications, examples, alternatives

Entropy is a machine learning term borrowed from thermodynamics that measures randomness or disorder in any system. Why measure disorder? Consider a real-life system like your office desk. The number of ways you can organize the items on your desk is limited, but the number of ways to mess it up is unlimited! Mathematics uses entropy to measure this chaos — or, more specifically, the probability of chaos.
Claude E. Shannon introduced the concept of entropy to data science in his famous 1948 pape...
Nexla •

LLM Hallucination—Types, Causes, and Solution

The development of large language models (LLMs) and generative AI solutions, notably demonstrated by ChatGPT’s impressive abilities, is changing how we use AI systems. IBM research found that nearly 50% of CEOs report adopting Generative AI in their companies.

However, this progress is delayed because of a phenomenon known as LLM hallucination. The term describes when LLMs produce text that is incorrect, makes no sense, or is unrelated to reality. A Telus survey highlighted this concern, reveal
Nexla •

Data Drift in LLMs—Causes, Challenges, and Strategies

Data drift, also known as concept drift or data shift, refers to the phenomenon where the distribution of input data used to train a machine learning model changes over time, leading to degradation in the model’s performance on new data. While this has been an issue plaguing machine learning models for decades, the explosion of popularity of LLMs means that the adverse effects of data drift have a far more significant impact on software end users. It is more important than ever to understand wha
Amazon Web Services, Inc. •

What is Generative AI? - Generative Artificial Intelligence Explained - AWS

Traditional machine learning models were discriminative or focused on classifying data points. They attempted to determine the relationship between known and unknown factors. For example, they look at images—known data like pixel arrangement, line, color, and shape—and map them to words—the unknown factor. Mathematically, the models worked by identifying equations that could numerically map unknown and known factors as x and y variables.

Generative models take this one step further. Instead of
Amazon Web Services, Inc. •

What is Overfitting? - Overfitting in Machine Learning Explained - AWS

You can prevent overfitting by diversifying and scaling your training data set or using some other data science strategies, like those given below.

Early stopping

Early stopping pauses the training phase before the machine learning model learns the noise in the data. However, getting the timing right is important; else the model will still not give accurate results.

Pruning

You might identify several features or parameters that impact the final prediction when you build a model. Feature
Amazon Web Services, Inc. •

What is RLHF? - Reinforcement Learning from Human Feedback Explained - AWS

The applications of artificial intelligence (AI) are broad-ranging, from self-driving cars to natural language processing (NLP), stock market predictors, and retail personalization services. No matter the given application, the goal of AI is ultimately to mimic human responses, behaviors, and decision-making. The ML model must encode human input as training data so that the AI mimics humans more closely when completing complex tasks.

RLHF is a specific technique that is used in training AI syst
Amazon Web Services, Inc. •

What are Transformers? - Transformers in Artificial Intelligence Explained - AWS

What are transformers in artificial intelligence? Transformers are a type of neural network architecture that transforms or changes an input sequence into an output sequence. They do this by learning context and tracking relationships between sequence components. For example, consider this input sequence: "What is the color of the sky?" The transformer model uses an internal mathematical representation that identifies the relevancy and relationship between the words color, sky, and blue. It uses
Daily MT •

Machine translation vs. CAT Tools—Collaboration or Competition?

As technology rapidly evolves, professionals across all fields are prompted to modernize their tools and learn new skills to boost productivity. The translation industry is no exception. Gone are the days when translators relied solely on dictionaries and typewriters; today’s experts use CAT tools and MT to streamline tasks. Both computer-assisted translation (CAT) and Machine Translation (MT) are revolutionary software technologies that enhance a translator’s efficiency.

However, the Machine T
Amazon Web Services, Inc. •

What is Conversational AI? - Conversational AI Chatbots Explained - AWS

Conversational AI technology brings several benefits to an organization's customer service teams.

Conversational AI chatbots can provide 24/7 support and immediate customer response—a service modern customers prefer and expect from all online systems. Instant response increases both customer satisfaction and the frequency of engagement with the brand.

Additionally, you can integrate past customer interaction data with conversational AI to create a personalized experience for your customers. Fo
Amazon Web Services, Inc. •

What is Artificial Intelligence? - Artificial Intelligence (AI) Explained - AWS

In Alan Turing’s seminal paper from 1950, "Computing Machinery and Intelligence," he considered whether machines could think. In this paper, Turing first coined the term artificial intelligence and presented it as a theoretical and philosophical concept. Between 1957 and 1974, developments in computing allowed computers to store more data and process faster. During this period, scientists further developed machine learning (ML) algorithms. The progress in the field led agencies like the Defense
Amazon Web Services, Inc. •

What is Boosting? - Boosting in Machine Learning Explained - AWS

The following are the three main types of boosting:

Adaptive Boosting (AdaBoost) was one of the earliest boosting models developed. It adapts and tries to self-correct in every iteration of the boosting process.

AdaBoost initially gives the same weight to each dataset. Then, it automatically adjusts the weights of the data points after every decision tree. It gives more weight to incorrectly classified items to correct them for the next round. It repeats the process until the residual error, o

Affinda | Invoice Information Extraction Using OCR and Deep Learning

Modern organizations are turning to AI to improve efficiency in all operational aspects. Digitization and cloud migration has helped everyone from HR to marketing to automate their processes. However, accounts teams are still struggling with manual, paper-based workloads. If invoice processing is slowing down your operations, it's time to make a change. This article explores invoice information extraction using OCR and deep learning technologies. Are these methods really as secure, efficient, an
Daily MT •

Machine Translation Post Edition: Strategy and Best Practices

Machine translation technology can translate millions of words in a single day. While translation is accurate and efficient, for some documents , it cannot fully replace a human translator. The machine may miss finer nuances like humor, emotion, or sarcasm. In addition, it may sometimes translate difficult words out of context or mistranslate a proper noun. For example, consider the sentence, “My dog, King, went to the market.” King is the dog’s name, and the French translation should be “Mon ch
Daily MT •

Neural networks in Machine Translation – Beginner’s Guide

If you are a social media user, you may have seen the translate option on Social Media. The Translate now link shows up if anyone writes in a language that is different from your usual settings. When you click on it, the foreign language comment, tweet or post is automatically translated into English.

This translation has been done by software. But how can a computer program understand and translate languages as a human does? The technology behind automatic translation services is called machin