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August 1, 2021Google AI Principles
Overview
Google AI is currently developing research in multiple areas, including healthcare and engineering, applying that knowledge to new products and domains, and making that technology available to everyone. Google’s main objective is to provide structure to the world’s information and make it universally approachable and useful. Artificial intelligence (AI) is helping to do that in new exciting ways, solving problems for users, customers, and the world.
This is making people’s daily lives easier, whether it’s searching for the latest news on their search engine, translating a work-related paper using Google Translate, typing emails, or organizing activities with the help of Google Assistant. AI also supplies new ways of approaching existing problems, from evaluating healthcare to advancing scientific discovery.
Google AI is currently developing research in multiple areas, including healthcare and engineering, applying that knowledge to new products and domains, and making that technology available to everyone.
Google’s main objective is to provide structure to the world’s information and make it universally approachable and useful. Artificial intelligence (AI) is helping to do that in new exciting ways, solving problems for users, customers, and the world.
This is making people’s daily lives easier, whether it’s searching for the latest news on their search engine, translating a work-related paper using Google Translate, typing emails, or organizing activities with the help of Google Assistant. AI also supplies new ways of approaching existing problems, from evaluating healthcare to advancing scientific discovery.
Google AI Principles
Be socially beneficial: anticipating risks and downsides is key to improve the quality of life.
Avoid creating or reinforcing unfair bias: avoiding the discrimination of people based on sensitive features like race, ethnicity, gender, nationality, income, sexual orientation, ability, political affiliation, or religious belief.
Be built and tested for safety: testing in controlled environments and constant evaluation as appropriate.
Be accountable to people: provide opportunities for feedback, relevant explanations and appeal, and subject to appropriate direction and control.
Incorporate privacy design principles: ensuring the data privacy of architectures with safeguards, transparency, and control access.
Uphold high standards of scientific excellence: Technological innovation is rooted in the scientific method and a permanent open inquiry, intellectual rigor, integrity, and collaboration.
Make it available for uses that accord with these principles: trying to limit potentially harmful or abusive applications.(1)
AI Applications Google Will Not Pursue
Google has expressed it will not pursue the development of weapons or systems whose purpose could damage other people. Neither will it develop technologies that use private information in a way that violates internationally accepted norms or privacy or technologies whose purpose clearly violates widely accepted principles of international law and human rights.(2)
Google IT Aspects
Google is launching easy access resources and systems to solve people’s daily problems, building a collaborative ecosystem by providing open-source projects for students and developers everywhere.
Among these are ML Kit’s that provide machine learning (ML) expertise for mobile developers in a powerful and easy-to-use package through Firebase; Fairness Indicators designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader TensorFlow toolkit (browser-based JavaScript library for training and deploying ML models). There’s also Colaboratory, created to help disseminate ML research and development; Datasets like Crowdsource and Dataset search that enable users to find information stored in thousands of repositories across the web, universally accessible and helpful for everyone.
Google is launching easy access resources and systems to solve people’s daily problems, building a collaborative ecosystem by providing open-source projects for students and developers everywhere.
Among these are ML Kit‘s that provide machine learning (ML) expertise for mobile developers in a powerful and easy-to-use package through Firebase; Fairness Indicators designed to support teams in evaluating, improving, and comparing models for fairness concerns in partnership with the broader TensorFlow toolkit (browser-based JavaScript library for training and deploying ML models). There’s also Colaboratory, created to help disseminate ML research and development; Datasets like Crowdsource and Dataset search that enable users to find information stored in thousands of repositories across the web, universally accessible and helpful for everyone.
Another project is Cloud AI and Cloud AutoML is a gamma of ML products that empower developers with limited ML experience to improve their abilities using high-quality systems adapted to their specific needs. Cloud AutoML’s simple user interface is designed to train, evaluate, improve and deploy models on different databases.(3)
Google Financial Aspects
Advertising constitutes Google’s primary income, which amounted to $134.81 billion in 2019. The latest economic report from Google revealed that search and advertising tools helped provide $335 billion for more than 1.3 million businesses and nonprofits nationwide.
Currently, more than 64,000 people are employed by Google in the United States. Plus, being a well-known search engine, Google is also successful with marketing and data analytics. In 2018, more than 35% of clicks for US businesses advertising on Google came from outside borders.(4)
Research Areas
Algorithms and Theory Data Mining and Modeling: Looking for more efficient algorithms for working with massive data.
General Science: Large-scale experimentation, data gathering, and machine learning.
Health and Bioscience: Aims to understand biology to diagnose diseases and to analyze epidemiological studies.
Education innovation: Includes online learning and educational technology.
Economics and Electronic Commerce: auction design, advertising, statistics, and others.
Networking: combines the development and establishment of interconnected systems.(5)
Algorithms and Theory Data Mining and Modeling: Looking for more efficient algorithms for working with massive data.
General Science: Large-scale experimentation, data gathering, and machine learning.
Health and Bioscience: Aims to understand biology to diagnose diseases and to analyze epidemiological studies.
Education innovation: Includes online learning and educational technology.
Economics and Electronic Commerce: auction design, advertising, statistics, and others.
Networking: combines the development and establishment of interconnected systems.(5)
Artificial Intelligence, Machine Learning, and Deep Learning for Eye Care Specialists
AI is defined as technology that produces intelligent behavior. ML models focus on implementing a variety of algorithms to generate unsupervised forms.
It involves constructing artificial neural networks, including connected computer nodes that can process information to describe concepts in detail.
Health care practitioners using ML have developed high-quality models for detecting diabetic retinopathy and diabetic macular edema; and have high hopes for the future development of systems capable of detecting age-related macular degeneration, glaucoma, and retinopathy of prematurity.(6)
AI is defined as technology that produces intelligent behavior. ML models focus on implementing a variety of algorithms to generate unsupervised forms.
It involves constructing artificial neural networks, including connected computer nodes that can process information to describe concepts in detail.
Health care practitioners using ML have developed high-quality models for detecting diabetic retinopathy and diabetic macular edema; and have high hopes for the future development of systems capable of detecting age-related macular degeneration, glaucoma, and retinopathy of prematurity.(6)
First Steps for the Use of AI in the Diagnosis of Prostate Cancer
Another application for AI in the diagnosis of medical conditions includes prostate cancer. One of the principal causes of morbidity and mortality in men around the world. In another study developed by Google Health, the deep learning system (DLS) evaluated almost 800 prostate biopsies.
The results showed that the agreement rate with subspecialists was similar for the DLS and the general pathologists, showing how AI could improve health care systems by improving diagnostic accuracy, costs, and turnaround delays. More studies need to be done, but these results are promising.(7)
The Use of AI for the Diagnosis of Skin Disease
A group of scientists led by Google Health developed a deep learning system (DLS) capable of providing differential diagnoses using a database of diagnosed cases.
The system works by taking input of identified photographs and metadata. Then the DLS analyzes the information using two processing modules before giving an output.
The results showed the DLS top-1 diagnosis was non-inferior to dermatologists, superior nurse practitioners (NP), and primary care physicians (PCP). When analyzing the accuracy of the top-3 diagnosis, a difference was seen where the system performed better than dermatologists, NPs, and PCPs.
These results are promising, with the potential of improving diagnostic accuracy for non-dermatologists and alerting dermatologists of diagnoses they may not have considered.(8)
A group of scientists led by Google Health developed a deep learning system (DLS) capable of providing differential diagnoses using a database of diagnosed cases.
The system works by taking input of identified photographs and metadata. Then the DLS analyzes the information using two processing modules before giving an output.
The results showed the DLS top-1 diagnosis was non-inferior to dermatologists, superior nurse practitioners (NP), and primary care physicians (PCP). When analyzing the accuracy of the top-3 diagnosis, a difference was seen where the system performed better than dermatologists, NPs, and PCPs.
These results are promising, with the potential of improving diagnostic accuracy for non-dermatologists and alerting dermatologists of diagnoses they may not have considered.(8)
Predicting Inpatient Medications with the Use of AI
A promising feature of AI and machine learning technologies is the development of reliable prediction models. A group of researchers from California developed a long short-term memory sequence model using predictive features from earlier data in the patient’s timeline.
The system could predict 55% of medications ordered by physicians at the time of order placement. The results show that these models could facilitate patient decisions or even assist in detecting abnormal orders and preventing medical errors.(9)
A promising feature of AI and machine learning technologies is the development of reliable prediction models. A group of researchers from California developed a long short-term memory sequence model using predictive features from earlier data in the patient’s timeline.
The system could predict 55% of medications ordered by physicians at the time of order placement. The results show that these models could facilitate patient decisions or even assist in detecting abnormal orders and preventing medical errors.(9)
Deep Learning-Based Survival Prediction for Multiple Cancer Types Using Histopathology Images
Predicting patient prognosis in oncology significantly impacts clinical management decisions such as treatment and monitoring. Although one of the most accurate systems used to provide prognostic information in oncology is the “TNM” cancer staging system, it is not perfect. The system basis focuses on learning morphologic features associated with survival without reliance on expert annotation for known pathologic features or regions of interest.
DLS aims to improve significantly prognostic information in multiple cancer types by recognizing objects and diagnosing diseases from medical images with impressive accuracy. In this context, a DLS was developed using 9,086 slides from 3,664 cases and later evaluated using 3,009 slides from 1,216 cases.(10)
Predicting patient prognosis in oncology significantly impacts clinical management decisions such as treatment and monitoring. Although one of the most accurate systems used to provide prognostic information in oncology is the “TNM” cancer staging system, it is not perfect. The system basis focuses on learning morphologic features associated with survival without reliance on expert annotation for known pathologic features or regions of interest.
DLS aims to improve significantly prognostic information in multiple cancer types by recognizing objects and diagnosing diseases from medical images with impressive accuracy. In this context, a DLS was developed using 9,086 slides from 3,664 cases and later evaluated using 3,009 slides from 1,216 cases.(10)
References