'60 Leaders' is an initiative that brings together diverse views from global thought leaders on a series of topics – from innovation and artificial intelligence to social media and democracy. It is created and distributed on principles of open collaboration and knowledge sharing. Created by many, offered to all.
ABOUT 60 LEADERS
'60 Leaders on Artificial Intelligence' brings together unique insights on the topic of Artificial Intelligence - from the latest technical advances to ethical concerns and risks for humanity. The book is organized into 17 chapters - each addressing one question through multiple answers reflecting a variety of backgrounds and standpoints. Learn how AI is changing how businesses operate, how products are built, and how companies should adapt to the new reality. Understand the risks of AI and what should be done to protect individuals and humanity. View the leaders.
'60 Leaders on Innovation' is the book that brings together unique insights and ‘practical wisdom’ on innovation. The book is organized into 22 chapters, each presenting one question and multiple answers from 60 global leaders. Learn how innovative companies operate and how to adopt effective innovation strategies. Understand how innovation and experimentation methods blend with agile product development. Get insights from the experts on the role of the C-Suite for innovation. Discover ways that Innovation can help humanity solve big problems like climate change.
How could we prevent the unreasonable concentration of AI power?
The unreasonable concentration of power in AI comes from three components: compute, data, and talent.
In terms of compute, the more complex AI systems we choose to build, the more compute infrastructure they require to be successful. This is evidenced in the rising costs for training very large language models like Turing , GPT-3 , and others that frequently touch millions of dollars. This is all but inaccessible except to the most well-funded industry and academic labs. This creates homogenization in terms of ideas that are experimented with and also a severe concentration of power, especially in the hands of a small set of people who determine what the agenda is going to be in research and development, but also in the types of products and services that concretely shape many important facets of our lives.
The larger the systems we prioritize, the more data they need as well - this is particularly the case with some of the more recent ‘internet-scale’ models that require ‘internet-scale’ data such as the CommonCrawl dataset amongst others. But, these raise issues of ‘internet-scale’ biases because these datasets tend to suck up the worst of humanity and amplify that through the models that use these datasets for training.
So, what can we do to combat these two negative trends? On the compute infrastructure front, there are calls from various groups in the US, for example, that are demanding a government-funded national research cloud that would make cheap compute accessible to anyone who would like to experiment in building AI systems. This would lower the barrier to participation and make it easier for anyone who would like to develop AI systems. In particular, this would be a boon for all of society since we would have the potential to empower local entrepreneurs and concerned citizens to develop AI systems and services for their own communities.
Data commons and open-source datasets that are well-maintained such as Common Voice from Mozilla are another way to bolster nascent, independent efforts from local communities to build AI systems that help meet their own needs without having to rely on technology that gets exported from a small geographic region to the rest of the world.
But, all of this requires significant technical expertise to make it a reality, and nurturing talent will play a key role if we’re to combat the pernicious concentration efforts that emerge from Big Tech not only being able to offer lucrative compensation, but also large datasets and accompany access to adequate computational infrastructure that makes it exciting for talent to pursue challenging problems that further their own careers.
A counterpoint to that trend is to offer projects that can provide meaning and purpose beyond just the points that are raised above, as a way to draw talent. In addition, offering educational training programs and engaging in capacity building, both through community-driven programs like those offered by Masakhane NLP and open-access programs and books like D2L.ai offer resources that will upskill the next generation of data scientists, machine learning engineers, and software developers who can bring their expertise towards solving society’s grand challenges while using AI capabilities in an ethical, safe, and inclusive manner.
New models of self-organization and community engagement have shown to be powerful levers to enact change in the world around us and being deliberate in our approach to leaning on these vectors of change can allow us to pursue lofty goals while staying true to community roots and achieving the ultimate goal of having a more fair, just, and equitable society for us all while harnessing all that AI has to offer.