Google

YouTube ... Quora ...Google search ...Google News ...Bing News

• Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Grok | xAI ... Groq ... Ernie | Baidu
• Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
  • Google Labs
• Google's Tools and Resources
• Vertex AI
• Gato
• Duet
  • Google’s Duet AI is now available in Gmail, Docs, and more for $30 a month | David Pierce - The Verge ... Now, you can use Google’s AI to make spreadsheets, whip up slide decks, and summarize all those documents you were never going to actually read.
• Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)
  • Colaboratory (Colab) - Jupyter notebooks
    • Google Developers Codelabs
  • Hands-on labs
  • Google Cloud
    • Google Cloud Platform Marketplace
    • Google Cloud Code and Google Kubernetes Engine (GKE)
• Pixel phone with TPU - Tensor Processing Unit / AI Chip
• TensorFlow
  • TensorBoard ...TensorFlow's visualization toolkit
  • TensorFlow Hub is a library for reusable machine learning modules that you can use to speed up the process of training your model. A TensorFlow module is a reusable piece of a TensorFlow graph. With transfer learning, you can use TensorFlow modules to preprocess input feature vectors, or you can incorporate a TensorFlow module into your model as a trainable layer. This can help you train your model faster, using a smaller dataset, while improving generalization.
• Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services:
  • Gemini ... multimodality — reasoning seamlessly across text, images, video, audio, and code
  • LaMDA (Language Model for Dialogue Applications): our breakthrough conversation technology
  • Language Interpretability Tool (LIT) | Google - GitHub
    • Google open-sources LIT, a toolset for evaluating natural language models | Kyle Wiggers - VentureBeat
  • Google Natural Language
  • Google Semantic Reactor
  • Bidirectional Encoder Representations from Transformers (BERT)
  • Gboard on Pixel phones now uses an on-device neural network for Speech Recognition | Kyle Wiggers - VentureBeat
  • Universal Sentence Encoder lite a model that encodes text into 512-dimensional embeddings. These embeddings can then be used as inputs to natural language processing tasks such as sentiment classification and textual similarity analysis. This module is a TensorFlow.js
• AI Platform
  • All 29 AI announcements from Google Next ‘19: the smartest laundry list
  • Google launches AI Platform, a collaborative model-making tool for data scientists | Khari Johnson - VentureBeat
• AI Hub - explore and use a variety of AI asset categories. AI Hub offers a collection of components for developers and data scientists building artificial intelligence (AI) systems. Use AI Hub to: [1] Find, deploy, and use Kubeflow Pipelines and components, [2] explore code and learn in interactive Jupyter notebooks, [3] explore and reuse TensorFlow modules, Explore, deploy, and use trained models, [4] use prepackaged virtual machine (VM) images to quickly set up your AI environment, and [5] share AI components with your colleagues.
• Kubeflow Pipelines ML workflows on Kubernetes
• Google AutoML
  • Cloud Vision
  • Cloud Vision API - drag & drop picture on webpage
• Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
• We’re making tools and resources available so that anyone can use technology to solve problems | Google AI
• Dopamine - reinforcement learning algorithms
• Google AI Experiments
• ML Engine
• Prediction API
• Grow with Google
• Learn from ML experts at Google
• Google Internet of Things (IoT)
• Psychlab ...testing virtual agents in 3D environments
• Deep Reinforcement Learning (DRL) ...Importance Weighted Actor-Learner Architecture (IMPALA)
• Google AIY Projects Program ... connect Vision or Voice Kit to a Wi-Fi network right from mobile device
• Android Neural Networks API ...machine learning on Android devices
• Big\Table ...designed with a storage engine for machine learning applications leading to better predictions
• Pub/Sub ...messaging and ingestion for event-driven systems and streaming analytics.
• BiqQuery ML (BQML) ...enables users to create and execute machine learning models in BigQuery by using standard SQL queries.
• Firebase ...mobile and web functionality like analytics, databases, messaging and crash reporting
• DialogFlow ...creating conversational AI applications, including chatbots, voicebots, and IVR bots
• Google Analytics ...measure your web traffic
• Teachable Machine ...train a computer to recognize your own images, sounds, & poses
• Google Coral ...toolkit to build products with local AI. Our on-device inferencing capabilities
• Google Sheets ...access, create, and edit your spreadsheets wherever you go — from your phone, tablet, or computer
• Google Facets ...contains two robust visualizations to aid in understanding and analyzing machine learning datasets
• DeepMind - Open Source
  • Google DeepMind AlphaGo Zero
  • Google DeepMind AlphaStar
  • Protein Folding & Discovery ...Google DeepMind AlphaFold
• People + AI Research (PAIR) ... devoted to advancing the research and design of people-centric AI systems.
• Towards a Human-like Open-Domain Chatbot | D. Adiwardana, M. Luong, D. So, J. Hall, N. Fiedel, R. Thoppilan, Z. Yang, A. Kulshreshtha, G. Nemade, Y. Lu, Q. Le - arCXiv - Cornell University ... We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token.

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: https://cloud.google.com/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 → https://bit.ly/2UhnfL7 Watch more: Next '19 ML & AI Sessions here → https://bit.ly/Next19MLandAI Next ‘19 All Sessions playlist → https://bit.ly/Next19AllSessions Subscribe to the GCP Channel → https://bit.ly/GCloudPlatform 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 → https://goo.gle/2RqFWYv Vision → https://goo.gle/2u7h7ss Speech → https://goo.gle/2R32gYX Natural Language → https://goo.gle/2sEDvsY Translation → https://goo.gle/2R32CPh AutoML Tables → https://goo.gle/2FYeZWu AutoML → https://goo.gle/2R3nUwj Bank Marketing → https://goo.gle/2R2aH6Q DevFest on demand 2019 → https://goo.gle/372VJ69 Subscribe to Google Developer Groups → https://goo.gle/GDevGroups