Aussie Healthtech develops AI training approach using privacy by design
We know that the more data it has to train on, the more efficient and accurate machine learning algorithms can get. Presagen, a healthtech scaleup from South Australia, has pioneered a federated learning technique that builds in privacy by design.
Using this decentralised and patent-pending approach, it trains AI algorithms at the data source (eg private patient data) wherever they are stored, without having to store or process the private information locally or sharing it further.
Elisity, a US based startup in identity and behavior-based enterprise cybersecurity, closed a USD$26M Series A funding round. It is one of many cyber startups benefiting from the increased need in zero trust architecture and security management approach.Using combination of configuration and techniques, access is monitored by AI algorithms that track, monitor, and analyze flows and user behavior to make recommendations and discover an organisation’s assets to build an encrypted mesh overlay between a cloud services panel and network probes.https://blog.elisity.com/news/elisity-closes-26-million-funding-roundimage credit: bleepingcomputer.com
Lululemon's retail strategy took an advanced tech step with the acquisition of Mirror (at-home-fitness mirror - voice activated 'smart mirror') mid last year. And their latest patents point to a strategy of embedding biosensors into their fitness clothing to determine stress and other wellness indicators. With data driven AI models and more its clearly early days but the possibilities for retail are significant particularly in post COVID times - is this a brand reputation play or a bigger product-market opportunity?https://www.retaildive.com/news/whats-next-for-lululemons-tech-ambitions/601500/image credit: lululemon