Hriday Jain: “The internet already made knowledge free decades ago. During the software revolution and the internet boom, access to knowledge and skills skyrocketed. Tutorials, blogs, courses, and open-source code almost anything you wanted to learn was available to anyone, often for free. But there was a limitation. Internet knowledge is static and broadcasted. It’s written for everyone, which means it’s perfect for no one. You had to spend time finding the right content, translate it into your own context, fill in the gaps, and hope you were understanding it correctly. AI changes this completely. Knowledge is no longer one-to-many. It is shared on a 1:1 basis, at immense speed. You can reshape information until it fits how you think and what you are trying to do. You can counter-question anything instantly. You can ask for explanations in exactly the form you need and keep refining them until they click. This is what I like to call Personalized knowledge.”
WSJ: “Employees say AI isn’t saving them much time in their daily work so far, and many report feeling overwhelmed by how to incorporate it into their jobs. Companies, meanwhile, are spending vast amounts on artificial intelligence, betting that the technology’s power to speed everything from sales to back-office functions will usher in a new era of efficiency and profit growth. The gulf between senior executives’ and workers’ actual experience with generative AI is vast, according to a new survey from the AI consulting firm Section of 5,000 white-collar workers. Two-thirds of nonmanagement staffers said they saved less than two hours a week or no time at all with AI. More than 40% of executives, in contrast, said the technology saved them more than eight hours of work a week.”
Mint: “A glue player holds a team together, helps new recruits settle in and identifies the best way forward—without seeking the spotlight. Employers who formally recognize and reward such employees could count on team cohesion for superior results.”
FT: “Physical AI takes robots to a new level. They can now combine autonomy with hardware that moves objects in the physical world – the robot itself, instruments or materials – using sensors to perceive their surroundings. This marks the next step in the evolution of robots from deterministic machines, which perform the same precise task over and again, to those that can complete varied, complex tasks and which respond to changing circumstances. Robots can learn from seeing people perform tasks and even from watching videos of people doing a particular job. They perfect their actions through trial and error, either in the real world or, increasingly, in a simulated environment. So-called one-shot learning algorithms require only a single demonstration for a robot to learn a task. These however are newer and more difficult to design. They require extensive training and are not yet widely available.”
