Cambridge Quantum appoints DeepMind alumnus as AI head
Cambridge Quantum Computing (CQC) has appointed a former DeepMind Senior Staff Research Scientist as its Head of Artificial Intelligence.
Professor Stephen Clark’s formerly held roles at the computer science departments of both Oxford and Cambridge University, before joining AI leader DeepMind, a stablemate of Google under the Alphabet umbrella. His work there included investigating AI language learning as part of the company’s quest to create “artificial general intelligence”. DeepMind is, of course, most famous for the victory of its AlphaGo platform over Go grandmaster Lee Sedol, with other projects including an AI that plays the much more complicated game of .
The Cambridge, UK-based CQC specialises in commercialising quantum computers in an enterprise setting. Last December they attracted to the tune of $45mn, from industry leaders including Honeywell Ventures, IBM Ventures, JSR Corporation, Serendipity Capita, Alvarium Investments and Talipot Holdings
“Making the most of quantum computing hardware”
Ilyas Khan, CEO of CQC, : "I am thrilled to welcome Prof. Clark to Cambridge Quantum as a senior member of our amazing scientific team. Steve is a world class scientist of the absolute highest standing, and I am excited at the perspective and leadership he brings at this vital time of our development. As Head of Artificial Intelligence, Steve will advance our mission of making the most of quantum computing hardware in these early stages of their development, as we create applications that will affect humanity as a whole when quantum computers scale and approach fault tolerance."
Quantum and AI possibilities
In his role, Professor Clark will investigate the possibilities of combining AI with quantum computing. "I am delighted to be joining Cambridge Quantum, a cutting-edge world-leading quantum computing company,” he . “I am especially pleased to be reigniting my long-standing collaboration with Prof. Bob Coecke, now Chief Scientist at Cambridge Quantum, and excited about exploring the potential of quantum computers in AI generally with the company's impressive scientific teams.
“I am also very excited that I will be continuing, indeed accelerating a research program in language processing that started 15 years ago when I presented at OASIS (The Oxford Advanced Seminar on Informatic Structures), setting out the problem of composing distributed representations in language, which seems especially well-suited to a solution based on quantum computation."
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.