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Making Your Pictures Worth a Thousand Labels! (with Cloud Vision API)

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Discover the power of Google Cloud Vision API in extracting valuable insights from your images, automating workflows, and enhancing data interpretation. Learn more...

In this post, I’ll be showing some amazing ways the Vision API can extract meaning from your images  – keep reading, or jump directly into a tutorial using PythonNode.jsGo, or Java! This tutorial can be completed at no cost within the Google Cloud Free Tier.

They say a picture is worth a thousand words. But how do you make those words available and useful? Around the world, we are generating more images than ever before, and it’s no surprise that businesses are turning to image recognition technology to help meet the immense opportunities created with this growing set of data.

Cloud Vision API is a powerful tool that enables you to perform a variety of tasks including label detection, text recognition, and object tracking on your image data. Whether it’s identifying products in a retail store, analyzing social media posts for brand mentions, or scanning through millions of images to find a specific object, the Cloud Vision API can help businesses automate their image analysis workflows and gain valuable insights from their visual data.  To protect privacy, and help you build responsibly, the Cloud Vision API offers features to limit personal identification, such as person blur, which hides identifiable features. 

Let’s explore a few of the key features of the Cloud Vision API.

Detect famous landmarks

Landmark detection allows you to analyze images to identify specific landmarks such as buildings, natural features, and other recognizable locations. Cloud Vision API recognizes landmarks and provides information about them, including their name, location, and other relevant details. Perhaps you are trying to identify the landmarks in images shared by customers as part of social campaigns, or want to build a mobile app that provides information to tourists on famous landmarks.

In the below left-hand side image, Cloud Vision API has detected the Eiffel Tower, shown in the visualized response. Not shown in this visualization here, but also detected, were Pont de Bir-Hakeim (the bridge) and Champs de Mars (the park in front of the Eiffel Tower).

Response from landmark detection feature visualized. Original image courtesy of John Towner.

Detect objects and label images

Object detection and labels are two related features that enable you to identify and classify objects within an image. Object detection detects and locates objects within an image, and provides information such as the position, size, and orientation of each object.  Labels, on the other hand, provide a general classification of the content within an image.

Object detection has practical applications in many industries such as self-driving vehicles (where it’s critical), retail, manufacturing and more, while labels can be used to help classify and organize large collections of images, or to categorize and filter content.

You can see the similarities and differences in the responses provided by the object detection and labeling features in this image taken in Setagaya.

Response from object detection feature visualized. The green bounding boxes were added to the original image with the response data from the Cloud Vision API. Original image courtesy of Alex Knight.
Response from labels feature visualized. Original image courtesy of Alex Knight.

Detect text

Cloud Vision API detects and extracts text from any image, even if it’s handwritten or in different languages. Once it detects text, the API can provide information about the position, orientation, and size of each text element, as well as individual words, and their bounding boxes.

In this image of a traffic sign, Cloud Vision API has detected the text and provided it in the response.

Response from text detection feature visualized.

Detect explicit content

Cloud Vision API can automatically identify and flag explicit or inappropriate content within an image using five categories: adult, spoof, medical, violence, and racy. The API provides a score that indicates the likelihood for each category in the image, which you can use to set thresholds in your application and decide how to handle those that exceed them. This feature is particularly useful for filtering or moderating user-generated content. 

Luckily for the images I shared here, each category has been deemed “very unlikely” to be present. Phew!

Responses from explicit content feature visualized.

Next Steps

These are just a few features of the Cloud Vision API and how it can help your business with automating image analysis workflows and gaining valuable insights from your visual data. 

Head to the interactive walkthrough tutorials in PythonNode.jsGo, and Java to see step-by-step how to access the API and learn more about all the features that you can integrate into your own applications! Again, this tutorial can be completed at no cost within the Google Cloud Free Tier.

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Google’s Lesson on Leveraging AI and More to Optimize Cloud Value for Innovation

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Google has managed to stay ahead of the curve and demonstrate its ‘growth while staying innovative’ approach using advanced open technologies, AI/ML based analytics solutions, as well as tools for team collaboration and skill development, automation and storage security. Businesses too can achieve the same by deriving maximum value from its people and technology by following Google’s simple guide to stay innovative. Download to learn more.

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Three Data Insights That Set Marketing Leaders Apart from Marketing Laggards

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Google partnered with MIT Sloan Management Review and MIT Technology Review for a deep dive into the mindsets of marketing leaders who use machine learning and AI to get better results from their marketing activities, compared to marketing executives who don't.

With insights directing the bulk of today’s marketing decisions, leading marketers are driving growth by embracing three core mindset shifts. Marketing leaders are working toward a holistic view of consumers; they are investing in machine learning to support their activities; and they believe how they apply their data is crucial to success. Google partnered with MIT Sloan Management Review and MIT Technology Review for a deeper dive into the mindsets around these beliefs. Here’s what we found.

1. Leading marketers are working toward a holistic view of consumers.

  • 63% of leading marketers agree they are using KPIs to develop a single integrated view of the customer.
  • 66% of leading marketers agree they should build teams for end-to-end customer experiences and journeys, across channels and devices.
  • Marketing leaders are 60% more likely than laggards to believe that marketing teams should own a data-driven customer strategy that supports all organizational stakeholders.

2.  Leading marketers are investing in machine learning to support their activities.

  • Measurement-leaders are more than 2X as likely as their measurement-challenged counterparts to agree that their organization is already investing in automation and machine learning technologies to drive marketing activities.
  • 75% of marketers who use machine learning to drive marketing activities said they were satisfied with how their KPIs inform and influence decision-making across their enterprise.
  • 73% of marketing leaders who have invested in machine learning have shifted more than 10% of their time from manual activation to strategic insight generation.

3. Leading marketers believe how they apply their data is crucial to success.

  • 66% of marketing leaders believe how companies apply their data will play a key role in their ability to thrive.
  • 60% of leading marketers believe data-driven attribution is essential to understanding journeys of high-value customers.
  • Marketing leaders are 53% more likely than laggards to say machine learning processes data signals to help marketers better understand consumer intent.
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How Machine Learning Can Cut Support Ticket Resolution Time By Over 80%

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Sure, machine learning is becoming a business imperative, but how does it work in practice—and what are the benefits for IT managers?

That’s the subject of a new step-by-step guide to solving business and IT problems with artificial intelligence and ML, based on insights gathered by IDG Research Services.

Its publication comes at a time when technology departments face growing pressure to embrace these emerging technologies, yet many have questions about how to get started.

It has real-life examples such as the health services company that used ML to reduce support ticket-resolution time from 48 minutes to six.

In another section, a financial services VP explains that cloud-based ML services enable his company to avoid spending money on computing resources that sit idle.

The guide also includes concrete tips for new ML adopters. For example, a real-estate CIO recommends the use of third-party tools that rely on AI and ML technologies, while a financial services VP highlights the challenge and potential of incorporating unstructured data into ML initiatives.

Download the guide now!

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Google Cloud’s No-Cost Skill Badge: Up Your Generative AI Game

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Elevate your tech skills with Google Cloud's new, no-cost Generative AI Fundamentals skill badge. It's an empowering step into the world of AI, no prior knowledge required!

Generative AI is a rapidly expanding technology with a wide range of potential applications. Google Cloud Learning is thrilled to offer a new, no-cost Generative AI Fundamentals skill badge. This skill badge is designed for anyone eager to learn about the power of generative AI. No technical skills or prior knowledge required!

For those new to digital credentials, Google Cloud skill badges are digital credentials issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Individuals can earn skill badges on Google Cloud Skills Boost, and can share their skill badge to their social media profile and resume.

Watch the short videos in the generative AI courses and complete the final quiz to earn the Generative AI Fundamentals skill badge pictured below. In as little as 120 minutes, you will learn the basics of how generative AI works, how Google Cloud AI technology can be used by businesses and individuals, and how the principles of responsible AI lead to ethical decisions about the use of generative AI. 

By earning the skill badge, you will demonstrate your understanding of foundational concepts in generative AI.

The topics covered in the courses include:

https://storage.googleapis.com/gweb-cloudblog-publish/images/gen_ai_fundamentals.0311011704870232.max-700x700.jpg

1. Introduction to Generative AI 

  • Explain how generative AI works
  • Describe generative AI model types
  • Describe generative AI applications

2. Introduction to Large Language Models

  • Define large language models (LLMs)
  • Describe LLM use cases
  • Explain prompt tuning
  • Describe Google’s generative AI development tools

3. Introduction to Responsible AI

  • Identify the need for a responsible AI practice within an organization
  • Recognize that decisions made at all stages of a project make an impact in Responsible AI
  • Recognize that organizations can design an AI infrastructure to fit their own business needs and values

Earn the skill badge and show off your generative AI knowledge today! And for more content to help you stay up to date with generative AI, check out “The Prompt” and our generative AI primer for executives on Transform with Google Cloud.

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Hospitals Can Offer Interconnected Patient Experiences Using Google’s Natural Language Services

Machine Learning (ML) in healthcare helps extract data from conversations, medical records, forms, research reports, insurance claims and other documents across the care value-chain to help care providers have a holistic view of their patients to draw insights for diagnoses and treatments. With Natural Language Processing(NLP), healthcare organizations can program computers and systems to process and analyse large volumes of human communication in form of spoken texts, written documentation and utterances. Watch the video to learn how the healthcare community can leverage Google’s NLP services to process structured and unstructured data to offer interconnected experiences for patients.

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