Google gets aggressive in Artificial Intelligence with new acquisitions, new products, new APIs, a 30 billion platform and new hires like A.I. guru Fei Fei Li, now Chief Scientist of Google Cloud and Machine Learning.
“My name is Fei Fei Li, and the chief scientist of Google Cloud Artificial Intelligence and Machine Learning. In Google’s code word, I’m still a noogler and it’s quite an honor and privilege to be on the stage to share with you some of my thoughts about AI, Machine Learning and Google Cloud.“
“The world is changing incredibly fast. Some say that we’re living in a fourth Industrial Revolution. Much of it is propelled by the phenomenal force of computing. As an AI technologist for nearly 20 years working on machine learning computer vision, I’ve witnessed my field growing from a lofty but academic pursuit to the biggest driver of this change. The change happens at meaning scales and it takes imagination to see them all.”
“Let’s take a familiar example the self-driving car.. It’s easy to understand to appeal with the help of sensors and algorithms. A self-driving car reduces accident risks and gives us more time to work, socialize and just relax.”
“This is great for a single driver. But what happens when thousands of people have self-driving car? Suddenly through the coordination of vehicles, traffic congestion is reduced and parking is dramatically simplified. What about millions of people having them? Entire cities will be reshaped by to reflect the fundamental shift in the use of its infrastructure?”
“So the difference between each scale is participation. As a technology reaches more people, its impact becomes more profound. This is why, the next step for AI must be the democratization. Lowering the barriers to entry and making it available to the largest possible community of developers, users and enterprises.. Speaking of democratization and reaching many people, Google’s cloud platform already delivers our customers applications to over a billion users every day. That’s a lot of participation.. Now if you can only imagine combining the massive reach of this platform with the power of AI, making it available to everyone.”
“We stand to witness a greater improvement in quality of life that any other time in history from finance to education, from manufacturing to healthcare, from retail to agriculture..you name it..”
“This is why delivering AI machine learning through Google Cloud excitement and means finally sharing the technology and insights. I’ve been involved in for years as AI researcher at Stanford. That’s also where by the way, I began a collaboration and partnership in AI with Dr.Jia Li who was one of my first PhD student many years ago. I’m very excited that she has joined Google with me as the head of our R&D AI/ML, Google Cloud. As speaking of International Women’s Day today this is another back as woman in CS and AI..”
Google Cloud’s Li Sees Transformative Time for Enterprise
Fei-Fei Li, Google Cloud chief scientist, discusses the transformation to machine learning in cloud technology and the diversity challenges in artificial intelligence. She speaks with Bloomberg’s Caroline Hyde on “Bloomberg Technology.”
Artificial intelligence is playing an increasingly essential role in the enterprise, however, more and more businesses find themselves struggling to keep up. One of our most important goals is to make machine learning a transformational tool for organizations of any size, industry or sophistication.
We’re seeing customers making it part of their wider data analytics strategy, with early adopters like Airbnb, Airbus, Disney and Ocado serving as inspirational use cases.Today at Google Cloud Next ‘17 we’re excited to announce new products, research and education programs to ensure machine learning is accessible to all businesses, data scientists and developers. We’re also thrilled to welcome Kaggle to Google Cloud. Home to the world’s largest community of data scientists and machine learning enthusiasts, Kaggle is used by more than 800,000 data experts to explore, analyze and understand the latest updates in machine learning and data analytics.
Understanding videos with Cloud Video Intelligence API
Cloud Video Intelligence API (now in Private Beta) uses powerful deep-learning models, built using frameworks like TensorFlow and applied on large-scale media platforms like YouTube. The API is the first of its kind, enabling developers to easily search and discover video content by providing information about entities (nouns such as “dog,” “flower” or “human” or verbs such as “run,” “swim” or “fly”) inside video content. It can even provide contextual understanding of when those entities appear; for example, searching for “Tiger” would find all precise shots containing tigers across a video collection in Google Cloud Storage.
Google has a long history working with the largest media companies in the world, and we help them find value from unstructured data like video. This API is for large media organizations and consumer technology companies, who want to build their media catalogs or find easy ways to manage crowd-sourced content, and for partners like Cantemo to build it into their own video management software.
With this announcement, Google Cloud Machine Learning adds to a growing set of Cloud Machine Learning APIs: Vision, Video Intelligence, Speech, Natural Language, Translation and Jobs. These APIs let customers build the next generation of applications that can see, hear and understand unstructured data —greatly expanding the use cases for machine learning for everything from next-product recommendations, to medical-image analysis, to fraud detection and beyond.
Cloud Machine Learning Engine in GA
Cloud Machine Learning Engine, now in GA, is an attractive option for organizations that want to train and deploy their own models into production in the cloud. It has the advantages of a managed service for building custom TensorFlow-based machine-learning models that interact with any type of data, at any scale. It’s also integrated with Google Cloud Platform’s complete data analytics pipeline that includes Cloud Dataflow (for data processing), Cloud Datalab (for data science workflow) and Google BigQuery (for SQL analytics).
We’re also working with technology partners to power their own solutions with Cloud Machine Learning Engine. Two recent examples are: SpringML, which uses Cloud Machine Learning Engine to provide real-time analytics for its end-users, and SparkCognition, which uses it to identify and block zero-day threats.
By Fei-Fei Li, Chief Scientist, Google Cloud AI and Machine Learning