It was in the late December 2020 when one evening, I was casually scrolling through my Twitter timeline that I caught a tweet from a famous YouTuber that I followed and I paused. He had tweeted about how it was a pain to go through the huge number of comments that each of this videos received and how too often, so many good comments -- to which he would've really loved to reply to -- get lost in the sheer volume. Being a data science practitioner, I was intrigued by the idea of efficiently handling such a huge inflow of comments on videos. Upon thinking about it for a few hours, I was ready to believe that it really was a genuine problem. It was then that the idea of doing a project based on that particular use case was born.

Counting and identifying characteristics of crowds can provide organizations with a lot of valuable insights. Yet challenges like image distortion, density, and different camera angles can make analyzing images accurately very challenging. Ganes Kesari, Co-founder and Head of Analytics at Gramener, joins the Intel on AI podcast to discuss how Gramener has created a crowd counting solution that can overcome those challenges and produce a very rapid and accurate analysis of images. He talks about how Gramener has utilized this solution for several AI for good projects including a joint effort with Microsoft* to count Antarctic penguin colonies. Ganes explains how their solution used convolutional neural networks (CNNs) using density-based estimations to deliver a more accurate penguin count than traditional manual counting methods.

The almighty dollar is a powerful factor in the current wave of artificial intelligence (AI) adoption. And why shouldn't it be? For millennia, companies have relied on technological progress to grow sales, cut costs, and improve customer satisfaction. But if we take a wider view, we see there is tremendous potential for AI to benefit society as a whole. Unfortunately, these "AI for good" projects often face big obstacles to success.

AI is the only thing that can let us see the whole world at once. Not recording it, but seeing it – creating a global real-time database of the world," says Stuart Russell, UC-Berkeley, lead of the AI for Good breakthrough team on AI and satellite imagery. The 2nd AI for Good Global Summit connected AI innovators with public and private-sector decision-makers. Four breakthrough teams – looking at satellite imagery, healthcare, smart cities, and trust in AI – set out to propose AI strategies and supporting projects to advance sustainable development. Teams were guided in this endeavour by an expert audience representing government, industry, academia and civil society.