The environmental impact of artificial intelligence (AI) has been a hot topic as of late—and I believe it will be a defining issue for AI this decade.
Last month, researchers at OpenAI in San Francisco revealed an algorithm capable of learning, through trial and error, how to manipulate the pieces of a Rubik's Cube using a robotic hand. It was a remarkable research feat, but it required more than 1,000 desktop computers plus a dozen machines running specialized graphics chips crunching intensive calculations for several months.
As the decade wraps up, many of the most evident shifts in technology have already taken place in the AI, DL, and ML landscape. Billions in venture financing have been raised to chase AI opportunities, and media outlets now have dedicated beat reporters focused on the market. Though the space has come a long way, it’s also clear that there’s still a long way to go.
Decades ago, Japan faced an unavoidable, long-term economic challenge. Even as its economy reached record highs in the late 1980s (fueled by strong auto sales, the rise of innovative companies like Nintendo, and real estate speculation), it was preparing for the coming day when more than a quarter of its population would be over age 65.
AI talent is either going for huge money at the top companies or for a moonshot at a startup.
Eye On A.I.
Automated machine-learning tools – or tools that automate the creation of machine-learning applications – are increasingly important in the current talent-scarce environment. Expensive ML engineers shouldn't spend their time doing stuff that machines can do quicker and cheaper.
When it comes to the compute-intensive field of AI, hardware vendors are reviving the performance gains we enjoyed at the height of Moore's Law. The gains come from a new generation of specialized chips for AI applications like deep learning.
The New Stack
The field of Machine Learning continues to move fast in terms of research and what organizations with expertise can achieve but is it maturing in the sense of becoming easier for the mainstream to adopt, operate and get value from?
We considered companies in “artificial intelligence, machine learning” and adjacent categories. Included in the list are active startups that have raised at least one venture capital round since January 1, 2016.
O'Reilly Press Release
O’Reilly, the premier source for insight-driven learning on technology and business, and Cloudera today announced the finalists for the 2019 edition of the annual Strata Data Awards.
Deep learning can be expensive and time-consuming, often taking weeks to fashion the right model. New startup Determined AI wants to change that by making the process faster, cheaper and more efficient.
Deep learning management platform Determined AI has raised a $11 million funding round led by GV formerly Google Ventures.
Determined AI Inc., a new startup set on easing artificial intelligence development, today exited stealth mode with $11 million in fresh funding. The round was led by Alphabet Inc.’s GV investment arm.