EITC/AI/GCML Google Cloud Machine Learning

Google Cloud is highly focused on delivering AI services and performing as high-end machine learning platform.

Some of the Google Cloud AI services include:

• Cloud AutoML – Service to train and deploy custom machine, learning models. As of September 2018, the service is in Beta.
• Cloud TPU – Accelerators used by Google to train machine learning models.
• Cloud Machine Learning Engine – Managed service for training and building machine learning models based on mainstream frameworks.
• Cloud Job Discovery – Service based on Google’s search and machine learning capabilities for the recruiting ecosystem.
• Dialogflow Enterprise – Development environment based on Google’s machine learning for building conversational interfaces.
• Cloud Natural Language – Text analysis service based on Google Deep Learning models.
• Cloud Speech-to-Text – Speech to text conversion service based on machine learning.
• Cloud Text-to-Speech – Text to speech conversion service based on machine learning.
• Cloud Translation API – Service to dynamically translate between thousands of available language pairs
• Cloud Vision API – Image analysis service based on machine learning
• Cloud Video Intelligence – Video analysis service based on machine learning

As an example check out the AutoML Vision features (Google Cloud’s automatic machine learning for computational understanding of vision) and continue with a comprehensive curriculum of this EITC programme.

Google AI is a spcial division of Google dedicated to artificial intelligence. It was announced at Google I/O 2017 by CEO Sundar Pichai. The mains projects of Google AI include

• Serving cloud-based TPUs (tensor processing units) in order to develop machine learning software.
• Development of TensorFlow.
• The TensorFlow Research Cloud will give researchers a free cluster of one thousand cloud TPUs to perform machine learning research on, under the condition that the research is open source and they put their findings and publish it in a peer-reviewed scientific journal.
• Portal to thousands of research publications by Google staff.
• Magenta: a deep learning research team exploring the role of machine learning as a tool in the creative process. The team has released many open source projects allowing artists and musicians to extend their processes using AI.
• Sycamore: a 54-Qubit Programmable Quantum Processor.

Another project is Google Brain. It is a deep learning artificial intelligence research team at Google, formed in the early 2010s, combining open-ended machine learning research with information systems and large-scale computing resources. The Google Brain project began in 2011 as a part-time research collaboration between Google Fellow Jeff Dean, Google Researcher Greg Corrado, and Stanford University professor Andrew Ng. Ng had been interested in using deep learning techniques to crack the problem of artificial intelligence since 2006, and in 2011 began collaborating with Dean and Corrado to build a large-scale deep learning software system, DistBelief, on top of Google’s cloud computing infrastructure. Google Brain started as a Google X project and became so successful that it was graduated back to Google: Astro Teller has said that Google Brain paid for the entire cost of Google X. In June 2012, the New York Times reported that a cluster of 16,000 processors in 1,000 computers dedicated to mimicking some aspects of human brain activity had successfully trained itself to recognize a cat based on 10 million digital images taken from YouTube videos. Since the early years of the project, Google Brain has significantly advanced and finds many applications in Google AI products.

To have a glimpse on the progress check out the examplary demonstration of the Google Assistant capabilities: