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….
Tag: ThinkingofAI
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…
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…
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…
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…
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…
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…
Key Research Work on AI against Traditional Cybersecurity Measures
With the intelligence accompanied, AI has tapped enormous strength to stealthily bypass traditional cybersecurity measures. This blogpost enlists some key research work available in public domain that bring out insightful results on how AI in its adversarial form can be used to fool or bypass traditional cybersecurity measures. Such research…
Comparative Assessment of Critical Adversarial AI Attacks
Often we come across various adversarial AI attacks. Over the time, there have been numerous attacks surfacing with extensive use of one or more AI model(s) together in any application. In this blog post, a one stop platform summarizing the critical adversarial AI attacks is provided. The comparative assessment of…
Comparative Assessment of Critical AI Models
This blog post is a one stop platform for summary of different AI models that are in predominant use. The comparative assessment of these models is based on various parameters such as – Definition, Process, Main Learning Approach, Pros, Cons, and Applications. The idea is to summarize these models and…