When it comes to learning, our brains exhibit a unique trait: the ability to accumulate knowledge over time without forgetting the old lessons while learning new ones. This, however, is a big challenge for the digital brains of our era – the artificial neural networks, which face a predicament known as ‘Catastrophic Forgetting’. What is Catastrophic Forgetting? Catastrophic forgetting or catastrophic interference is a phenomenon in the field of artificial intelligence (AI) and machine learning (ML), where a model that has been trained on one task tends to perform poorly on that task after it has been trained on a […]
Neurosymbolic AI: An Unexpected Blend with Promising Potential
Imagine combining two powerful and contrasting AI technologies as one might pair pizza and pineapple. A blend that has sparked both love and disagreement. This is the idea behind Neurosymbolic AI, a novel field that unites the rigid logic of symbolic AI and the adaptive learning prowess of neural networks. To simplify, consider neural networks as quick decision-makers thriving on patterns and massive data but with a struggle to articulate their decisions. Conversely, symbolic AI is akin to an academic whiz that excels in logic, rules, and reasoning but finds it difficult to differentiate an image of a cat from […]
RNN, Vanishing Gradients, and LSTM: A Photo Fiasco Turned Into a Masterpiece
RNNs: The Overzealous Photographer Imagine a Recurrent Neural Network (RNN) as that friend who insists on documenting every single moment of a trip with photos. Every. Single. One. From the half-eaten sandwich at the roadside diner to the blurry squirrel spotted at a distance, nothing escapes the RNN’s camera. It processes and remembers every moment of the journey, just as an RNN processes sequences of data. Vanishing Gradients: When Memory Fails You Now, after days of intense photo-snapping, our overzealous photographer friend tries to recall the events of the first day. But alas! The details are as blurry as that […]
Unpacking the Power of TinyML: The Science Behind the Small
In the realm of Artificial Intelligence (AI), the trend is often towards bigger and better – larger datasets, more powerful processors, and complex models. But what if we could achieve equally meaningful insights with a fraction of the power and size? Welcome to the world of Tiny Machine Learning, or TinyML for short. Let’s dive a little deeper into this fascinating field. What is TinyML? At its core, TinyML is all about deploying machine learning models on resource-constrained, low-power devices like microcontrollers. These are essentially tiny computers embedded in everyday items, from toasters and thermostats to cars and pacemakers. The […]
The Architect and the Mason: Building a Career in AI and Cybersecurity
Being a career mentoring volunteer also has an exceptional charm to it. It makes you come across diverse people and even more diverse queries that not only give you a peek into different thought process prevailing around, but also propels you to brainstorm on the same to find out the truly satisfying answer. Recently, I found myself pondering a question posed by one of my mentees. They asked, “Which skillset should I build first, AI or cybersecurity, to thrive in the intersection of the two?” This question sparked an analogy in my mind, likening the process to building a house. […]
Generative AI: Breeding Innovation, Not Job Destruction
Today, I found myself in a room, listening to a discussion that was buzzing with words like ‘Deep learning’, ‘Neural networks’, and ‘Generative models’. Amidst the whirlwind of tech jargon, a statement by Dr. Christian Essling stood out: “AI will not replace doctors, but doctors using AI will replace doctors not using AI.” This sentence, much like a well-crafted tweet, was succinct yet profound. It was as if someone had handed me the Rosetta Stone to decipher the future of work in the age of AI. With the advent of Generative AI, a subfield of AI where machines learn to […]
Understanding different Reinforcement Learning Models using a simple example
In previous blogposts, we saw how supervised and unsupervised learnings have their own types and how they are different from one another. To understand the difference, we had taken a small and simple example and also identified if and how certain model types could be used interchangeably in specific scenarios. In this blogpost, we will see the different types of reinforcement learning and use the same strategy as before, to understand the different types of reinforcement learning and their alternate use in particular cases. Reinforcement Learning: A Brief Overview Reinforcement Learning (RL) is a subfield of machine learning and artificial […]
Decoding AI Deception: Poisoning Attack
Hi! Welcome to my series of blogposts, “Decoding AI Deception” wherein we will take a closer look into each kind of adversarial AI attack. This post covers the details of poisoning attack comprising common types of poisoning attacks, their applicable cases, vulnerabilitiesof models that are exploited by these attacks, and remedial measures. Poisoning Attack and its Types As we all know from previous post that poisoning attack is the form of adversarial AI attack that is used to corrupt data intended for either training or retraining of a model. It has few common forms which are as follows: – Applicable […]
Understanding different Unsupervised learning models using a single example
As a part of previous blogpost and in continuation with similar lines, this blogpost will try to clarify the difference and purpose of each kind of Unsupervised learning model using a common example across all these models. Apart from defining each model type, this post will highlight if any models could be used interchangeably for certain scenarios. Types of Unsupervised Learning Models Understanding Models using an Example Let’s consider the example of customer segmentation in a retail store. The store wants to group its customers based on their purchasing behavior and preferences, in order to better target their marketing campaigns […]
Understanding different Supervised learning models using a single example
Often we get confused between different types of Supervised learning models available. This is majorly due to lack of understanding of the goal and applicability of each kind of model. In this blogpost, I will try to clarify the difference and purpose of each kind of Supervised learning model using a common example across all these models. Apart from defining each model type, I will also mention if any models could be used interchangeably for certain scenarios. Types of Supervised Learning Models Understanding Models using an Example Let’s use the example of predicting whether a person has diabetes based on […]