How is AI helping the response to COVID-19?
As the world struggles on with the coronavirus pandemic, every ounce of technology is being utilised to try and fight off the threat and restore normality.
AI is playing an integral role in being able to understand the pandemic, machine learning enables computers to be able to mimic human intelligence and take in large quantities of data to quickly identify patterns.
Machine learning has been implemented for many purposes; identifying how the disease spreads, scaling customer communications and speeding up research.
Every business, whether it is small or global, is finding new ways to operate efficiently and meet the needs of both employees and customers as social distancing measures remain in place.
One example of how machine learning is being used is chat bots for contactless COVID-19 screenings and to answer questions for any concerned members of the public.
It also helps researchers discover how the coronavirus spreads by analysing a large volume of data, it also helps to predict the spread and act as an early warning system.
It may be too early to tell the extent of which artificial intelligence aided the fight against the coronavirus however we know that it has played a major role in identifying trends in the virus to decipher how it spreads.
What is machine learning?:
Simply put, machine learning is the study of computer algorithms that aim to improve themselves through experience.
Machine learning powers some of the tools that we use day to day that we may not even know about, for example, Netflix uses machine learning, in addition to Twitter, Spotify and Google. Less shockingly, Siri and Alexa both utilise machine learning.
Each platform collects as much data about you as they can for example, what genres you like to listen to/watch, what links you click and even which statuses you interact with and react to most.
What is artificial intelligence?
Artificial intelligence is the simulation of human intelligence in machines that are programmed to think like humans and also mimic human actions. Artificial intelligence technology has many applications, the uses of it are endless. Such as self-driving cars and even computers that play games like chess.
Artificial intelligence is also used in banking to help detect fraud and flag unusual activity.
Google launches Visual Inspection AI tool for manufacturers
Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped.
Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale.
How does it work?
The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.
Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI.
- Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor.
- Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps.
- Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers.
- Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
- Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.
Industry use cases
The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing.
Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”
Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture.