Dr. Paritosh Basu

AI can still amaze you, but remember that beneath every fluent answer there is software doing a great deal of mathematics. Words become numbers, and specialized processors keep performing the calculations that make the response appear effortless to you. Microblog @antgrasso Ask an AI model a question. Your words are converted into numerical representations. During inference, the model performs vast numbers of matrix and tensor operations to estimate what should come next. The fluency appears on the screen. The computation stays underneath. A general-purpose CPU can run AI workloads, but large-scale AI benefits from processors designed for parallel computation. Graphics processing units, or GPUs, and tensor processing units, or TPUs, can execute many of these mathematical operations at the same time. That difference helps explain why specialized compute has become such an important part of AI infrastructure. None of this makes AI less impressive. Quite the opposite. Understanding what happens underneath helps us appreciate the engineering without imagining magic where there is computation. AI can still amaze us. Awareness simply lets us admire it with our eyes open. #AI #AIComputing

A text carries a thought; it is not the thought itself. Until software can identify who supplied the thinking, AI detectors can't prove authorship. Calling a text 100% AI-written from patterns alone is no proof at all. It is confidence dressed as certainty. Microblog @antgrasso Take this post. I am Italian, and I think more naturally in Italian. I develop the idea and connect the concepts in my own language, then I explain to AI what I want to say. I do not want my thinking to come out in schoolbook English. I want to express it in the professional English that fits who I am and the work I do. So I talk to AI. I challenge words, change sentences, reject what does not sound like me, and keep working until the text says what I intended to say. Then someone takes the finished post, puts it into an AI detector, and the software reports: “100% AI-written.” Really? The detector sees the final language. It does not see who had the idea or who directed the reasoning that produced it. And this is where the problem gets worse for non-native English speakers. Their writing patterns can already be misclassified by AI detectors, even when the text was written by a human. So if I think, connect, decide, and then use AI to express that thought better in English, does the machine suddenly own the authorship? Ma fatemi il piacere. Use AI detection as a signal if you need one. Do not use it as proof of who produced the thinking behind the text. A detector can inspect words, but it cannot inspect authorship. #AIDetection #ResponsibleAI

Still expecting the CIO to say “mea culpa” when resilience fails? You may have once. Today, digital resilience is a board responsibility because disruption reaches operations and business continuity, far beyond the data center and IT function alone. Microblog by @antgrasso Why has the responsibility moved? Because a digital failure rarely stays inside IT. If a cloud outage stops online orders, restoring the infrastructure is a technology problem. Deciding which business services must return first is an executive leadership decision. If a cyberattack forces a factory to slow production, the CIO can coordinate recovery of the systems. But who decides how long operations can tolerate the disruption? Who decides what customers should be told and which business risks are acceptable? Executive leadership needs to make those decisions. Digital resilience now depends on knowing which operations the company cannot afford to lose and making sure technology recovery supports them. The CIO remains central, but cannot carry that responsibility alone. And resilience starts before the incident. Investment choices and continuity plans need agreement while everything is still working. Accountability must already be clear. When disruption arrives, the CIO can restore technology. Executive leadership must already know how the business will continue.

Data platform costs drift when ownership is unclear and consumption lacks accountability. Tagging and dashboards work only with clear decision rights, turning spend into action and reinvestment. @kearney Link bit.ly/4cwcWsR via @antgrasso