Happy Earth Day! This episode’s focus is on entrepreneurial climate action: what are businesses – particularly the AI sector – doing to combat the climate crisis and help reach net zero?  

Helping our hosts Mark and Simon answer these questions are Sims Witherspoon (current UCL student studying for MSC in climate change and Climate and Sustainability Lead at Google DeepMind), Buffy Price (COO and Co-Founder of Carbon Re, an AI startup which aims to accelerate decarbonisation), and Zoe Cokeliss Barsley (Director of Sustainability at Oxford University Press) and UCL alumna (MSc Conservation 2003)  

Carbon Re https://carbonre.com/ 
Oxford University Press Responsible Publishing report https://oup.foleon.com/responsible-publishing-report-2022-23/2022-23/ 

For more information and to access the transcript: https://www.ucl.ac.uk/climate-change/podcasts-videos/generation-one-climate-podcast  

Sign up to our climate newsletter here: https://r1.dotdigital-pages.com/p/6T06-7DL/subscribe-to-ucl-climate-hubs-newsletters  

Date of episode recording: 14th February, 23rd February and 1st March 2024 
Duration: 32.15 minutes 
Language of episode: English  
Presenter: Mark Maslin and Simon Chin-Yee  
Guests:  
Sims Witherspoon, Google DeepMind 
Buffy Price, Carbon Re 
Zoe Cokeliss Barsley, Oxford University Press 
Producer: Adam Batstone

Season 4 - AI and the business of climate action

🎙️ Welcome to "AI with Shaily," your trusted source for the latest and greatest in artificial intelligence breakthroughs! I'm Shailendra Kumar, and today we're exploring some thrilling advancements in reinforcement learning (RL) that are making AI not only smarter and faster but also more environmentally friendly. 🌱🤖

Imagine training a robot to perform tasks without guzzling massive amounts of computing power. That’s the goal of cutting-edge RL research. One standout innovation involves using randomized neural networks within a new actor-critic algorithm. While it sounds complex, think of it as injecting a bit of creative randomness into AI’s learning process. This clever twist speeds up training and dramatically cuts down on computational effort. As someone who's spent countless hours debugging AI models, I find this approach like discovering a shortcut through a maze—elegant, efficient, and even a bit fun! 🧩⚡

Another fascinating breakthrough comes from applying reinforcement learning after a language model has already been trained. This technique helps AI speak more concisely without sacrificing accuracy. Imagine your AI assistant delivering sharp, to-the-point responses, saving both time and energy. It’s like trimming the fluff from a story to reveal only the most important parts. I remember asking an AI chatbot to summarize a book once, and it spun a 10-minute epic saga! This new method could have saved me many coffee breaks. ☕✂️📚

Why does this matter beyond the tech? Reinforcement learning is no longer just a lab experiment—it’s driving real-world applications. From autonomous vehicles navigating traffic jams 🚗💨, to optimizing supply chains for faster deliveries with less waste 📦🌍, and even helping doctors make better healthcare decisions 🏥💡, RL is making a tangible impact. But here's a critical question: how do we balance our desire for smarter machines with the urgent need to protect the environment? RL is proving to be a powerful tool that pushes AI forward sustainably, reducing resource use without compromising performance. 🌿⚖️

For those of you experimenting with RL, here’s a bonus tip: try integrating randomized neural network layers into your models. This might be the secret sauce to slash your training costs while boosting efficiency. Sometimes, a little randomness leads to smarter, leaner AI. 🎲💡

To close, I’ll leave you with the wise words of Richard Feynman: “What I cannot create, I do not understand.” Reinforcement learning is helping us build AI that truly understands and adapts, all while respecting the limits of our computational resources. 🔧🤓

Stay connected with me on YouTube, Twitter, LinkedIn, and Medium for deeper insights and updates. Don’t forget to subscribe to AI with Shaily for your weekly dose of AI news. I’d love to hear your thoughts on the push for AI efficiency—drop a comment and let’s keep the conversation alive! 💬✨

Thanks for tuning in! Remember, smarter AI doesn’t have to mean bigger energy bills. I’m Shailendra Kumar, and this was AI with Shaily. See you next time! 👋🤖🌟

Reinforcement Learning Revolution: Smarter AI with Less Energy

Welcome to *AI with Shaily*! 🎙️ Hosted by Shailendra Kumar, also known as Shaily, this engaging podcast dives deep into the latest advancements in artificial intelligence. In this episode, Shaily explores the transformative rise of edge computing in data science, projected to make a major impact by 2026. 🚀

Shaily begins by reminiscing about the early days of AI, when data had to be sent to distant cloud servers, causing frustrating delays and making real-time responses seem like a distant dream. Fast forward to today, and there's a groundbreaking shift: AI workloads are moving from centralized cloud systems to the "edge" — meaning the very devices where data originates. These devices, often equipped with powerful AI chips like NVIDIA Jetson, enable lightning-fast decision-making, reduce bandwidth costs, and open up new innovation possibilities across industries such as smart manufacturing and retail. ⚡📱🏭

But the edge isn't just about small devices. Edge data centers and micro data centers are now appearing everywhere — from hospitals to retail outlets to smart cities — acting like mini supercomputers right in local neighborhoods. When combined with hybrid and distributed architectures that blend cloud, edge, and on-premises resources, IT professionals are orchestrating a complex yet dynamic system that optimizes workflows efficiently. 🏥🏬🏙️💻

One of the most exciting benefits Shaily highlights is the power of real-time analytics enabled by edge computing. Industries like healthcare and autonomous vehicles gain immediate insights from IoT sensors, allowing for split-second, potentially lifesaving decisions. Plus, edge AI supports sustainable innovations such as smarter irrigation in agriculture, which conserves water and boosts crop yields through localized, energy-efficient AI models. 🌱🚗💧

For data scientists like Shaily — and listeners who are data enthusiasts — this shift is a game-changer. Instead of transferring massive raw datasets over networks, data processing happens closer to the source, speeding up analysis, reducing resource consumption, and enhancing responsiveness. Edge computing isn’t just a technical upgrade; it’s reshaping how data science is practiced by combining agility with efficiency. 📊⚙️

Shaily poses an important question for reflection: As data becomes king, how do we balance the incredible power of edge computing with the privacy and security challenges that come with decentralizing so much information? This is an ongoing conversation that’s crucial to follow. 🔐🤔

Before signing off, Shaily offers a bonus tip for AI practitioners: start exploring TinyML frameworks and edge-compatible hardware early. Hands-on experience with edge AI tools will prepare you for the widespread hybrid architectures that will dominate the near future. 🛠️🤖

Quoting Alan Turing, Shaily reminds us, “We can only see a short distance ahead, but we can see plenty there that needs to be done.” Edge computing opens a wide vista for data science innovation. 🌟

You can find Shailendra Kumar on YouTube, Twitter, LinkedIn, and Medium by searching *AI with Shaily*. Don’t forget to subscribe for the latest AI news, share your thoughts, and join the conversation about the exciting and sometimes challenging trends in edge computing. Until next time, stay curious and inspired! ✨👨‍💻📡

Edge Computing Revolution: How AI at the Edge is Transforming Data Science by 2026

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

UCL (University College London) is consistently ranked among the world's top ten universities (QS World University Rankings 2010 - 2021). Our excellence extends across all academic disciplines, from one of the world's foremost centres for research and teaching in the biological sciences to world-renowned centres for architecture (UCL Bartlett), education (UCL IOE) and fine art (UCL Slade School).

UCL is London's leading multidisciplinary university, with more than 13,000 staff and 43,000 students from 150 different countries. Founded in 1826 in the heart of London, UCL was the first university in England to welcome students of any religion and the first to welcome women on equal terms with men.

UCL Minds brings together UCL’s knowledge, insights, and ideas through events, activities, and digital content open to everyone. Find out what’s on UCL Minds: www.ucl.ac.uk/minds

New tracks tagged #Sustainable AI