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Pavandeep Kalra is currently a Director of Data Science at Microsoft’s Artificial Intelligence and Research group, where he leads a team creating innovative AI solutions on the cloud with various customers and partners. His talk, entitled ‘Amplifying Human Ingenuity with AI’ was immediately ambitious. Opening the presentation with a slide demonstrating AI acceleration in relation to 4 billion years of geological time on Earth (the Cambrian acceleration), it’s clear that the implications of AI are truly historic – and Kalra believes it’s founded upon the three factors of cloud technology, intelligence, and big data. “We think we are seeing an AI acceleration in the making right now. We think that there is gonna be a real rate of change in the diversity of compute paradigms that we actually have.”
He illustrated this effectively using a number of unique cases, from image interfaces for autistic children to AI-powered hurricane damage inspection and leopard conservation.
“What this means is that this acceleration is starting to happen for everyone. IT will change everything – the way we live and work. So how should we go about creating these AI systems? Traditionally, there are two systems – systems of records – or systems of engagement, such as web channels, mobile, or chat. Systems of intelligence integrates these two systems.”
Kalra argued that intelligence systems have three primary functions from which businesses can benefit: understanding (the interpretation of meaning in text, voice, and image data); reasoning (learning and forming conclusions from data); and natural UI (engaging with people in natural ways). Microsoft’s own AI capabilities have been designed from the ground up to provide these functionalities, with their key focus on cognitive understanding; building custom AI reasoning models from data and real-time AI at edge cognitive applications; and conversational AI & bots. The backing of cloud technology, Kalra argued, is absolutely vital to the success of this approach – think Cortana.
Beyond infrastructure and technical functions, business leaders need to establish a clear strategy for AI uses. Kalra shared his team’s tips in this area, saying that “a lot of people, when they look at data and AI, think they can definitely do something useful with it. Don’t stop there – start from the business side of things. Establish a business performance metric to measure whatever object you’re trying to go after.”
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