Today's Tech Digest - May 23, 2020

A new artificial eye mimics and may outperform human eyes

This device, which mimics the human eye’s structure, is about as sensitive to light and has a faster reaction time than a real eyeball. It may not come with the telescopic or night vision capabilities that Steve Austin had in The Six Million Dollar Man television show, but this electronic eyepiece does have the potential for sharper vision than human eyes, researchers report in the May 21 Nature. “In the future, we can use this for better vision prostheses and humanoid robotics,” says engineer and materials scientist Zhiyong Fan of the Hong Kong University of Science and Technology. The human eye owes its wide field of view and high-resolution eyesight to the dome-shaped retina — an area at the back of the eyeball covered in light-detecting cells. Fan and colleagues used a curved aluminum oxide membrane, studded with nanosize sensors made of a light-sensitive material called a perovskite (SN: 7/26/17), to mimic that architecture in their synthetic eyeball. Wires attached to the artificial retina send readouts from those sensors to external circuitry for processing, just as nerve fibers relay signals from a real eyeball to the brain.

Prepare to be tracked and tested as you return to work

Given the clear finding that people with covid-19 can be highly contagious even if they display few or no symptoms, a growing number of companies and health experts argue that reopening plans must also include wide-scale and continual testing of workers. “It’s less a question of if testing becomes a part of workplace strategies, than when and what will prompt that,” says Rajaie Batniji, chief health officer at Collective Health. Measures like temperature checks may even do more harm than good by giving workers and employers a false sense of confidence, he says. The San Francisco company, which manages health benefits for businesses, has developed a product called Collective Go that, among other things, includes detailed health protocols for companies looking to reopen. Developed in partnership with researchers at Johns Hopkins, the University of California, San Francisco, and elsewhere, the guidelines include when and how often workers in various job types and locations should be tested.

How effective security training goes deeper than ‘awareness’

While the approach may be up for debate, its effectiveness is not. Almost 90% of organisations report an improvement in employee awareness following the implementation of a consequence model. The model itself is secondary here. The key takeaway is that time and effort matter. The more hands-on training workers receive, the better they are at spotting phishing attempts. Organisations must strive to develop training programmes that leave employees equipped with the skills to spot and defend against attacks – before anyone is left to face the consequences. The goal of any security training programme is to eradicate behaviours that put your organisation at risk. The best way to achieve this is through a mix of the broad and the granular. Start by cultivating a security-first culture. This means a continuous, company-wide training programme that acknowledges everyone’s role in keeping your organisation safe.

Gaming: A gold mine for datasets

While working on a project, I came across a problem where the object detector that I was using did not recognize all the objects in the image frame. I was trying to index all the objects present in the images frame, which later would make searching of images easier. But all the images are labeled human, not being able to detect the other objects in the image frames, the search was not working as I wanted. The ideal solution for this problem would be to gather data for those objects and re-train the object detector to also identify the new objects. This would not only be boring but time-consuming. I could use GANs, a type of machine learning algorithm famous for its use of creating artificial and similar examples to its inputs, to create more samples after organizing a few samples manually, but this is also boring and will require resources to train the GANs to generate more data. Now the only thing I could do was using internet services, like ScaleAI and Google Cloud Platform, to create a dataset.

Identity Silos: shining a light on the problem of shadow identities

It’s important to stress that identity silos – sometimes referred to as ‘shadow identities’ because, similar to shadow IT, they are created without central organisational approval – come about during routine business expansion. If a business unit wishes to roll out a new digital service, in the absence of an existing centralised identity management function that can do the job, they often end up either buying an off-the-shelf identity and access management (IAM) system or create their own. When a business merges or acquires a new organisation, the new unit often keeps its own IAM infrastructure. In both cases, the result is the same: hundreds of invisible silos of duplicated user identities. The chances are you’ve experienced the problems caused by identity silos. If you use the same broadband, mobile, and television provider, you’ve probably had to update the same information multiple times for each account, rather than just once. Or if you’ve been subjected to marketing calls (even though you’re already a customer!) that try to sell you products you already have. This is all because your customer data is siloed in each department throughout the company, thereby ruling out cohesive customer experiences.

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