NSO made the news again due to their tools being used to spy on Bahraini and Hungarian activists, which obviously isn’t good. NSO is a cyber security organization that focuses on offensive rather than defence; they sell hacking tools and exploits to target individuals. Anyone with enough money can buy their attack tools, including rich individuals or companies. In Mexico their spying tool was used to intimidate campaigners asking the government to regulate sugar content in sofas.
We know spying on human rights activists is not good for anyone, and three organizations teamed up to expose how NSO supports such spying (and thus abuse). Forensic Architecture, Amnesty International, and Citizen Lab all worked together to create a neat website called Digital Violence which explores the complexity and reach of NSO’s tools.
First detected in 2015, the NSO Groupâ€™s Pegasus malware has reportedly been used in at least 45 countries worldwide to infect the phones of activists, journalists and human rights defenders. Having learnt that our former collaborators and close associates were hacked by Pegasus, Forensic Architecture undertook 15 months of extensive open-source research, interviews assisted by Laura Poitras, and developed bespoke software to present this data as an interactive 3D platform, along with video investigations narrated by Edward Snowden to tell the stories of the individuals targeted and the web of corporate affiliations within which NSO is nested. Supported by Amnesty International and the Citizen Lab, our analysis reveals relations and patterns between separate incidents in the physical and digital sphere, demonstrating how infections are entangled with real world violence, and extend within the professional and personal networks of civil society actors worldwide.
A few years ago Silicon Valley mega corps thought all cities should be made “smart” by tracking all citizen data. There was a concentrated effort by Google to violate privacy rights in Toronto and bullying the city into a finance deal which only benefit the advertising giant. Torontonians protested and the company backed out.
In Columbus, they ran a well funded research project into the smart city only to discover that the “smart” aspects showed mediocre results. We already have solutions to most problems cities face like mass transit and better funded health services. It’s time to fund the boring, old, not “smart” solutions in our cities.
For years engineers tried to prevent flooding, then they realized they can’t stop nature. Now instead of trying to stop it, we try to mitigate flooding by creating spaces that can absorb a lot of water (parks along rivers are an example of this). Still, these attempts don’t always work and with increasing instability in our climate it’s getting harder to deal with more extreme flooding instances. This is where a new startup, Floodmapp, fits in. They are using machine learning and AI to improve how we understand flooding instead of the traditional physics-driven modelling.
The companyâ€™s premise is simple: We have the tools to build real-time flooding models today, but we just have chosen not to take advantage of them. Water follows gravity, which means that if you know the topology of a place, you can predict where the water will flow to. The challenge has been that calculating second-order differential equations at high resolution remains computationally expensive.
Murphy and Prosser decided to eschew the traditional physics-based approach that has been popular in hydrology for decades for a completely data-based approach that takes advantage of widely available techniques in machine learning to make those calculations much more palatable. â€œWe do top down what used to be bottoms up,â€ Murphy said. â€œWe have really sort of broken the speed barrier.â€ That work led to the creation ofDASH, the startupâ€™s real-time flood model.
Machines produce a lot of waste heat, and if we can capture that heat we can convert it into electric energy. Capturing thermal energy is currently inefficient because of thermal dynamics and the lack of super capacitors. Not to be deterred by these obstacles, researchers have found ways to efficiently capture thermal energy. By using an inexpensive material a small thermoelectric can be placed to convert heat to power.
â€œThis looks like a very smart way to realize high performance,â€ says Li-Dong Zhao, a materials scientist at Beihang University who was not involved with the work. He notes there are still a few more steps to take before these materials can become high-performing thermoelectric generators. However, he says, â€œI think this will be used in the not too far future.â€
The result, which they report today in Nature Materials, was not only a thermal conductivity below that of single-crystal tin selenide but also a ZT of 3.1. â€œThis opens the door for new devices to be built from polycrystalline tin selenide pellets and their applications to be explored,â€ Kanatzidis says.
It’s hard to predict the future, but with enough data we can at least get better at it. That’s exactly what digital twins are all about. By using as much real world data as possible to model out anything from a building, to a person, to a city in a digital space we can run simulations on what can work best for the real world counterpart. Of course, since simulations are only as good as the data source (and how we process the data) there are limits to effectiveness of digital twins; still, the idea that we can effectively model solutions and their potential outcomes in higher fidelity is appealing.
With the rise of the internet of things, sensor technology is increasingly being installed in our homes and workplaces, as well as the physical infrastructure that surrounds us. Meanwhile, cloud computing makes it easier than ever for data to be shared across different devices and networks.
As a result, businesses and other organisations have been able to build up huge volumes of data. Not all of this is private either â€“ online sources such as theLondon Datastoreare making live data readily available to anyone who wants to use it.
“We see digital twins as a way of improving decision making,” Hayes told Dezeen.
“A city is effectively a system of systems â€“ water, electricity, housing, schools, hospitals, prisons, natural environment â€“ it all fits together,” she said. “When you start to connect the datasets from these digital twins, you can build a bird’s eye view of a city, which gives you better information about the consequences of your decisions.”