Introduction Evolutionary Neural Architecture Search (NAS) can be an enigma, even for those well-versed in machine learning and AI fields. Taking inspiration from the Darwinian model of evolution, evolutionary NAS represents a novel approach to optimize neural networks. This post aims to demystify evolutionary NAS, discuss its model mutations, delve into use cases, identify drawbacks, and provide alternatives. The Basics of Evolutionary NAS Just as biological species adapt and evolve over time, neural architectures can also ‘evolve’ to optimize their efficiency and effectiveness. Evolutionary NAS utilizes the principles of evolution—mutation, recombination, and selection—to automatically search for the best neural architecture […]
Fanning the Flames of AI: A Call for Mindful Utilization of Language Learning Models (LLMs)
In the pulsating heart of the digital era, we stand on the cusp of Artificial Intelligence (AI) advancements that appear almost magical in their potential. Large language models (LLMs) like GPT-4 take center stage, embodying our boldest strides into the AI frontier. But as with any frontier, amidst the opportunity and wonder, shadows of uncertainty and fear stir. Some view LLMs as the magician’s wand of the tech universe, casting spells of human-like text generation, language translation, and simulated conversation. Yet, lurking in the dark corners of this magic are specters of potential misuse – hackers, job insecurity, and fears […]
Mitigating Catastrophic Forgetting in Neural Networks: Do Machine Brains Need Sleep?
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 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 […]