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Google has been developing AI for more than two decades. Some of their most popular products like Lens and Translate were built entirely using artificial intelligence technologies like optical character recognition and machine learning. Google has long relied on Artificial Intelligence-related resources to power, improve, and enhance its core products such as the Google search engine, voice search, and its photos app. Google is one of the leading companies in the field of AI. It has been investing heavily in AI research for many years, and it has made significant progress in a number of areas, including:

  • Natural Language Processing (NLP): Google's AI technologies are used in a variety of products and services, including Google Search, Google Translate, and Google Assistant. These technologies allow Google to understand human language and respond to users' queries in a natural way.
  • Computer vision: Google's AI technologies are also used in computer vision, which is the ability of computers to see and understand the world around them. These technologies are used in products and services such as Google Photos, Google Lens, and Google Maps.
  • Machine Learning (ML): Google is also a leader in machine learning, which is a type of AI that allows computers to learn from data. Google's machine learning technologies are used in a variety of products and services, including Google Ads, Google Cloud Platform, and Google Translate.

Google's AI research is ongoing, and the company is constantly working to improve its technologies. In recent years, Google has made significant progress in developing new AI technologies, such as:

  • LaMDA: is a large language model that can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way.
  • PaLM: is a large language model that is even more powerful than LaMDA. It can understand and respond to complex questions, and it can generate different creative text formats of text content, like poems, code, scripts, musical pieces, email, letters, etc.
  • Imagen: is an AI model that can generate realistic images from text descriptions.
  • MusicLM: is an AI model that can generate music from text descriptions.

These are just a few examples of Google's AI research. The company is constantly working to develop new AI technologies that can make a positive impact on the world. So, when people say that Google is missing the boat on AI, they are simply wrong. Google is one of the leading companies in the field, and it is constantly working to improve its AI technologies.

AI Infrastructure on GCP (Cloud Next ‘19 UK)
Google Cloud’s AI-optimized infrastructure is setting performance records. From a wide range of our GPU accelerators, to our custom-built supercomputers, Cloud TPU Pods, GCP offers a modern infrastructure for your ML workflows. Learn how you to get started with these accelerators through GCP products and services.

Cloud AI & ML (Google Cloud Talks by DevRel)
Our high-quality, scalable, continuously improving, and fully managed AI services put you in a position to innovate faster and more efficiently than the competition. Join our Developer Advocate team to get a comprehensive view of Google Cloud AI platform and solutions and learn how it can help turn your ideas to deployment rapidly and efficiently. Learn more about Google Cloud AI Platform:

The Path From Cloud AutoML to Custom Model (Cloud Next '19)

by Sara Robinson


The Path From Cloud AutoML to Custom Model (Cloud Next '19)
What comes after AutoML? You've created some models in Cloud AutoML, and they've been useful. But you want to see if there's some room for more improvement and customization. Let's see how to start building custom models and deploy them in production. You'll learn how to take advantage of state-of-the-art models, all while advancing your understanding of your data pipelines and machine learning. Cloud AutoML to Custom Model → Watch more: Next '19 ML & AI Sessions here → Next ‘19 All Sessions playlist → Subscribe to the GCP Channel → Speaker(s): Yufeng Guo, Sara Robinson

Building custom models in AutoML (DevFest 2019)
Kevin Nelson (@knelsoncloud), Developer Advocate for Google Cloud, discusses Machine Learning and building custom models in AutoML. Watch Kevin demonstrate building custom vision and structured data models during this talk.

Links: Video Intelligence → Vision → Speech → Natural Language → Translation → AutoML Tables → AutoML → Bank Marketing →

DevFest on demand 2019 → Subscribe to Google Developer Groups →