By Malika Cantor, Developer Community Program Manager
Source | public Google developers
The Lever is a new resource for sharing applied machine learning (ML) to help startups innovate and thrive. The Lever is operated by Launchpad, Google’s global entrepreneurship accelerator program, through a collaboration of experts and leaders from Google and Alphabet. The Lever will publish a variety of Launchpad community experiences on how to integrate machine learning into products, including case studies, insights from mentors, and best practices from Thought leaders at Google and around The world.
Peter Norvig, Director of machine learning research at Google, and Cassie Kozyrkov, chief decision scientist at Google Cloud, are editors of the journal. Let’s hear from them and other Google developers about the importance of developing and sharing products and business methods that apply machine learning:
Peter Norvig (Director of Machine Learning research at Google) : “The software industry has spent 50 years perfecting its approach to software development. But we’re only a few years into machine learning, so companies need to pay more attention to the process of creating products that are reliable, up-to-date, accurate, and respectful of customers’ personal data.
Cassie Kozyrkov, Chief Decision Scientist, Google Cloud: “We’re living in an exciting time where the hard work of researchers is finally making it possible for non-expert users to do amazing things with ARTIFICIAL intelligence. Now anyone can stand on the shoulders of giants. Process-oriented approaches to exploring how machine learning can be applied to the best possible extent are increasingly at the forefront. These include decision intelligence engineering: a new approach to machine learning that focuses on how to discover opportunities and build safe, effective and reliable solutions. Data will be more useful than ever, come on, we’ll see!
Clemens Mewald, Leader of Machine Learning X and TensorFlow X: “Machine Learning or ARTIFICIAL intelligence is having a profound impact in many areas, but I think we’re still in the early stages of this journey of discovery. Many machine learning applications are incremental improvements to existing features and products. Make video recommendations more relevant and ads more targeted and personalized. But, as Sundar says, AI goes deeper than electricity (or firepower). Electricity makes possible modern technology, computing and the Internet. What new products can be made possible by machine learning or artificial intelligence? I am confident that the right approach to machine learning products will help us discover more fantastic products that have never been seen before.
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