Linear discriminant analysis (LDA), a traditional classification tool, suffers from limitations such as sensitivity to noise and computational challenges when dealing with non-invertible within-class scatter matrices. Traditional stepwise LDA frameworks, which iteratively select the most informative features, often exacerbate these issues by relying heavily on Wilks' $\Lambda$, potentially causing premature stopping of the selection process. This paper introduces a novel forward discriminant analysis framework that integrates Pillai's trace with Uncorrelated Linear Discriminant Analysis (ULDA) to address these challenges, and offers a unified and stand-alone classifier. Through simulations and real-world datasets, the new framework demonstrates effective control of Type I error rates and improved classification accuracy, particularly in cases involving perfect group separations. The results highlight the potential of this approach as a robust alternative to the traditional stepwise LDA framework.

Ashish Ranjan Jha received his Bachelors degree in Electrical Engineering from IIT Roorkee (India), Masters degree in Computer Science from EPFL (Switzerland) and an MBA degree from Quantic School of Business (Washington). He has received distinction in all 3 of his degrees. He has worked for large technology companies like Oracle, Sony as well as the more recent tech unicorns such as Revolut, mostly focussed around Artificial Intelligence. He currently works as a Machine Learning Engineer. Ashish has several years of working experience and specialisation in the field of Machine Learning, and Python is his go-to tool.

If the above gif looks familiar it's probably because it looks eerily similar to this: This, of course, is how the T-800 Terminator sees and recognizes objects in the world upon arrival from the future in Terminator 2: Judgement Day. Similar to the movie, researchers at MIT's Computer Science and Artificial Intelligence Laboratory have created an object recognition system that can accurately identify objects using a normal RGB camera (no threatening blood-red color filter required). This system could help future robots interact with objects more efficiently while they navigate our complex world. "Ideally we want robots to be cleaning our dishes at some point in the future. We want recognition systems where it does in fact see the objects that the robot should care about and manipulate them," says Sudeep Pillai, lead author of the study.

THIRUVANANTHAPURAM: It seems machines replacing humans is not science fiction anymore. Artificial Intelligence (AI) is the buzzword now in the IT field and many companies have started imparting training for employees in AI. Anoj Pillai, chief architect of UST Global's first, spoke at length about how AI was changing the world, at the company's developer's conference. Observing that AI would replace 80% of IT helpdesk, he said, "Labour-centric services will be wiped off eventually. For example, a person who attends a customer's call could be replaced by a machine. It is not going to change the employee. Only his profile will change," he said.

In this article, we describe the hardware and software challenges involved in realizing Claytronics, a form of programmable matter made out of very large numbers-potentially millions-of submillimeter sized spherical robots. The goal of the claytronics project is to create ensembles of cooperating submillimeter  robots, which work together to form dynamic 3D physical objects. For example, claytronics might be used in telepresense to mimic, with high-fidelity and in 3-dimensional solid form, the look, feel, and motion of the person at the other end of the telephone call. To achieve this long-range vision we are investigating hardware mechanisms for constructing submillimeter robots, which can be manufactured en masse using photolithography. We also propose the creation of a new media type, which we call pario. The idea behind pario is to render arbitrary moving, physical 3-dimensional objects that you can see, touch, and even hold in your hands. In parallel with our hardware effort, we are developing novel distributed programming languages and algorithms to control the ensembles, LDP and Meld. Pario may fundamentally change how we communicate with others and interact with the world around us. Our research results to date suggest that there is a viable path to implementing both the hardware and software necessary for claytronics, which is a form of programmable matter that can be used to implement pario. While we have made significant progress, there is still much research ahead in order to turn this vision into reality.