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 […]