Matthew Griffin, described as “The Adviser behind the Advisers” and a “Young Kurzweil,” is the founder and CEO of the 311 Institute, a global futures and deep futures consultancy working between the dates of 2020 to 2070, and is an award winning futurist, and author of “Codex of the Future.” Regularly featured in the global media, including AP, BBC, CNBC, Discovery, RT, and Viacom, Matthew’s ability to identify, track, and explain the impacts of hundreds of revolutionary emerging technologies on global culture, industry and society, is unparalleled. Recognised for the past six years as one of the world’s foremost futurists, innovation and strategy experts Matthew is an international speaker who helps governments, investors, multi-nationals and regulators around the world envision, build and lead an inclusive, sustainable future. A rare talent Matthew’s recent work includes mentoring Lunar XPrize teams, re-envisioning global education and training with the G20, and helping the world’s largest organisations envision and ideate the future of their products and services, industries, and countries. Matthew's clients include three Prime Ministers and several governments, including the G7, Accenture, Bain & Co, BCG, BOA, Blackrock, Bentley, Credit Suisse, Dell EMC, Dentons, Deloitte, Du Pont, E&Y, GEMS, HPE, Huawei, JPMorgan Chase, KPMG, McKinsey, PWC, Qualcomm, SAP, Samsung, Sopra Steria, UBS, and many more.
WHY THIS MATTERS IN BRIEF
As babies humans often “just learn” things, it’s a gift, but now AI’s are catching us up and starting to learn in the same way we do and it’ll change AI forever.
Given the glut of artificial intelligence (AI) breakthroughs in the past year, from AI’s building more AI’s, AI’s that can create and code their own programs, and AI’s that now learn as fast as humans do – and those are just for starters – it’s sometimes tough to pinpoint events that are especially pertinent. And this is one of those times.
Earlier this week, for example, I covered OpenAI’s “breakthrough” where their AI “just evolved” into an unsupervised learning system, and now Baidu have announced their own breakthrough where an AI agent that was taught English by a virtual teacher ended up being able to use natural language it had never seen before – and that’s the key here – to navigate its way around a video game.
Okay, so far kind of so boring. However, this means that Baidu, as per their announcement, have managed to create a system that’s managed to achieve fabled “Zero shot learning” which is the ability of an AI to solve a learning task without ever having been trained on it. And so far that type of learning capability, even for the world’s best AI researchers and teams, from DeepMind to OpenAI, haven’t been able to crack.
The Baidu experiment took place in an 2D maze like environment called XWORLD, a game where you have to kill a dragon to win, and their AI agent had to navigate its way around using just the natural language commands issued to it by the virtual English teacher.
In the beginning, the agent didn’t know anything about the language – every word was equally meaningless, because, for example, if you’re in a maze and someone says “Mind the dragon” that’s meaningless unless you know what a dragon is, where it is and what context the word “mind” is being used in.
However, as the agent explored the environment, the virtual teacher gave positive and negative feedback, then to help the agent learn faster, the teacher asked it some simple questions about the environment while it was navigating its way round. During the experiment the agent needed to correctly answer the teachers questions, then, by rewarding correct actions and penalising incorrect ones the teacher managed to train it to understand never seen before natural language and sentences, and as easily as that it “just learned” grammar – something that humans “just do” when we’re babies.
Zero shot learning. Voila, and while it sounds easy apparently it isn’t… but what do I know.
Humans use this form of learning all the time, and we’re great at taking a set of experiences from one situation and applying them to a never seen before situation, but now it looks like we might not be the only ones capable of learning in such a way and that’s a breakthrough. Score another one for AI.