Opteran secures £2.1m for insect-based AI
Opteran, a company based in Sheffield, UK, has secured £2.1 million in seed funding to further its plans to put “600 million years of evolution on to silicon”.
The research firm calls its lightweight, silicon-based AI ‘natural intelligence’ because it is inspired by insect learning. It believes its technology “will significantly expand the potential addressable market for autonomy in machines and robotics”.
Opteran: why use insects for AI?
Project leads Prof James Marshall and Dr Alex Cope spent eight years studying insect brains as part of their research at the University of Sheffield. Opteran is a spin-out from that work. The pair noted that while insects have small brains, they are capable of sophisticated decision making and navigation using optic flow to perceive depth and distance.
The company said in a statement: “This is a far more efficient, robust and transparent way to achieve autonomy than current Deep Learning techniques enabling Opteran to reverse-engineer insect brains to produce algorithms requiring no data centre or extensive pre-training. It means Opteran can mimic tasks such as seeing, sensing objects, obstacle avoidance, navigation and decision making.”
The team was able to demonstate a drone weighing less than a stick of butter (250g) with complete autonomy using a single low-resolution (less than 10MP) panoramic camera.
AI and drone market growth
The robotics market is expected to grow to $77 billion by 2022, according to Technavio. Opteran hopes to exploit this with a developer kit called ODK) which will allow its technology to be integrated into third-party robotics. The ODK weighs less than 30g and draws less than one watt of power.
The company said: “Opteran’s technology will transform the use case for a wide variety of autonomous vehicles, drones, mining robots and even off-planet vehicles, as it will enable real-time autonomous decision-making.”
Opteran insect AI plans
Over the next year and a half, Opteran hopes to build functionality of the algorithms and chipsets, and launch several products: Opteran Sense will help with reactive navigation and obstacle avoidance; Opteran Direct for simultaneous localisation and mapping (SLAM); Opteran Decide for autonomous decision-making; and Opteran See, a 360-degree camera. It also hopes to expand both its engineering and commercial teams.
The £2.1 million seed funding round was led by IQ Capital with Episode1, Join and Seraphim Capital.
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.