Whisper raises $35mn for AI hearing aids
San Francisco, California-based Whisper has developed a hearing aid which uses AI for continual improvement.
Whisper’s Hearing System, however, is linked to a phone, allowing updates to features such as its Sound Separation Engine, which use AI algorithms to optimise sound in different environments.
It’s a model similar to Tesla’s approach to the automotive industry, with its cars capable of getting over-the-air updates to unlock new features
The company, which was founded in 2017, has so far raised $53mn, with its latest , announced this week, seeing the company raise $35mn. The round was led by lead investor Quiet Capital, alongside Sequoia Capital, IVP, First Round Capital and Arrive.
In , Dwight Crow, Co-Founder & CEO of Whisper, said: “Technology should be used to improve people’s lives. Many of the problems people face in hearing – whether hearing in a loud restaurant or having a device that quickly gets outdated – are solvable with recent advancements in consumer electronics and artificial intelligence. With the Whisper Hearing System, consumers get a state-of- the-art device designed in Silicon Valley that doesn’t stay static but continually improves with artificial intelligence for better performance.”
The company said it would use the funds to support the launch of the flagship system, which consists of earpieces, a hub called the Whisper Brain, and the aforementioned app
“Software-defined hearing technology is the future,” said Mike Vernal, Partner at Sequoia and Whisper Board Member. “By building the Whisper Hearing System around software, the Whisper team will be able to improve patient care with a device that adapts, upgrades, and improves continuously for the wearer’s benefit. This is the start of a new paradigm for delivering hearing technology, and we’re thrilled to partner with Whisper on this journey.”
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.