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Recent advances of artificial intelligence and deep learning have been undoubtedly driven by a large amount of data amassed over the years, helping firms, researchers, and practitioners achieve many amazing feats, most notably in recognition tasks often surpassing human ability in several benchmarks.
How did we get to today’s technology which now supports a trillion dollar AI industry? What were the key scientific breakthroughs? What were the surprises and dead-ends along the way...
How did we get to today’s technology which now supports a trillion dollar AI industry? What were the key scientific breakthroughs? What were the surprises and dead-ends along the way...
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
Child labor remains prevalent in Ghana’s cocoa sector and is associated with adverse educational and health outcomes for children.
The AI Inflection Point: What, How, and Why We Learn
The yield, however, doesn’t seem equally distributed to all who aspire to repeat the success of others in their respective domains, due to the data themselves. A selected few are running away with the infrastructure and the competence they’ve built over time to collect and process the data, leaving many others behind. For some, it’s a struggle to find ways how to get them in the first place, and for some others it’s about figuring out what to do with them. And while many give their data away without knowing what they get in return, the growing awareness of the issue by the public and the thought leaders is being materialized into new regulations and suggestions on how the data should be governed and shared. In this seminar, Bongjun Ko, an AI Engineering Fellow at Stanford HAI, would like to share his thoughts on the this issue, drawing from the experience as an engineer who’s been trying to overcome the lack of data when building data-driven solutions, and as an individual who’s been providing the “new oil in 21st century”. Some of the open questions he would like to cast include: What can you do to remain competitive without data? Is data really a new oil? How much is a piece of data worth, and can it be measured?
