Energize the Solar System.
Over the next 15 years, multiple space agencies like NASA and commercial partners like SpaceX intend to mount crewed missions to the Moon and Mars.
The accelerated growth in ecommerce and online marketplaces has led to a surge in fraudulent behavior online perpetrated by bots and bad actors alike. A strategic and effective approach to online fraud detection will be needed in order to tackle increasingly sophisticated threats to online retailers.
These market shifts come at a time of significant regulatory change. Across the globe, new legislation is coming into force that alters the balance of responsibility in fraud prevention between users, brands, and the platforms that promote them digitally. For example, the EU Digital Services Act and US Shop Safe Act will require online platforms to take greater responsibility for the content on their websites, a responsibility that was traditionally the domain of brands and users to monitor and report.
Can AI find what’s hiding in your data? In the search for security vulnerabilities, behavioral analytics software provider Pasabi has seen a sharp rise in interest in its AI analytics platform for online fraud detection, with a number of key wins including the online reviews platform, Trustpilot. Pasabi maintains its AI models based on anonymised sets of data collected from multiple sources.
Using bespoke models and algorithms, as well as some open source and commercial technology such as TensorFlow and Neo4j, Pasabi’s platform is proving itself to be advantageous in the detection of patterns in both text and visual data. Customer data is provided to Pasabi by its customers for the purposes of analysis to identify a range of illegal activities — - illegal content, scams, and counterfeits, for example — - upon which the customer can then act.
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A new computational simulator can help predict whether changes to materials or design will improve performance in new photovoltaic cells.
In the ongoing race to develop ever-better materials and configurations for solar cells, there are many variables that can be adjusted to try to improve performance, including material type, thickness, and geometric arrangement. Developing new solar cells has generally been a tedious process of making small changes to one of these parameters at a time. While computational simulators have made it possible to evaluate such changes without having to actually build each new variation for testing, the process remains slow.
Now, researchers at MIT and Google Brain have developed a system that makes it possible not just to evaluate one proposed design at a time, but to provide information about which changes will provide the desired improvements. This could greatly increase the rate for the discovery of new, improved configurations.
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
Sepsis claims the lives of nearly 270,000 people in the U.S. each year. The unpredictable medical condition can progress rapidly, leading to a swift drop in blood pressure, tissue damage, multiple organ failure, and death.
Prompt interventions by medical professionals save lives, but some sepsis treatments can also contribute to a patient’s deterioration, so choosing the optimal therapy can be a difficult task. For instance, in the early hours of severe sepsis, administering too much fluid intravenously can increase a patient’s risk of death.
To help clinicians avoid remedies that may potentially contribute to a patient’s death, researchers at MIT and elsewhere have developed a machine-learning model that could be used to identify treatments that pose a higher risk than other options. Their model can also warn doctors when a septic patient is approaching a medical dead end — the point when the patient will most likely die no matter what treatment is used — so that they can intervene before it is too late.
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Strategy accelerates the best algorithmic solvers for large sets of cities.
Waiting for a holiday package to be delivered? There’s a tricky math problem that needs to be solved before the delivery truck pulls up to your door, and MIT researchers have a strategy that could speed up the solution.
The approach applies to vehicle routing problems such as last-mile delivery, where the goal is to deliver goods from a central depot to multiple cities while keeping travel costs down. While there are algorithms designed to solve this problem for a few hundred cities, these solutions become too slow when applied to a larger set of cities.
The solver algorithms work by breaking up the problem of delivery into smaller subproblems to solve — say, 200 subproblems for routing vehicles between 2,000 cities. Wu and her colleagues augment this process with a new machine-learning algorithm that identifies the most useful subproblems to solve, instead of solving all the subproblems, to increase the quality of the solution while using orders of magnitude less compute.
Their approach, which they call “learning-to-delegate,” can be used across a variety of solvers and a variety of similar problems, including scheduling and pathfinding for warehouse robots, the researchers say.
The possibility of space mining in future was thrown into sharp relief this weekend as a Near Earth Asteroid (NEA) called 4,660 Nereus passed our planet.
Worth an estimated $5 billion in precious metals and measuring 330 meters across, Nereus at no point came anywhere near being dangerous, getting no closer than 2.4 million miles/3.9 million kilometers at 13:51 UTC on Saturday, December 11, 2021.
That’s about 10 times the distance between the Earth and the Moon.
So why so much attention on Nereus?
There seemed to be a lot of misunderstanding about how dangerous—or otherwise—Nereus could be to Earth.
The predicted existence of an exotic particle made up of six elementary particles known as quarks by RIKEN researchers could deepen our understanding of how quarks combine to form the nuclei of atoms.
Quarks are the fundamental building blocks of matter. The nuclei of atoms consist of protons and neutrons, which are in turn made up of three quarks each. Particles consisting of three quarks are collectively known as baryons.
Scientists have long pondered the existence of systems containing two baryons, which are known as dibaryons. Only one dibaryon exists in nature—deuteron, a hydrogen nucleus made up of a proton and a neutron that are very lightly bound to each other. Glimpses of other dibaryons have been caught in nuclear-physics experiments, but they had very fleeting existences.
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I would prefer it if the data was anonymized and handed back to the patient via an AI interface on the assessment, — Recommended actions and risks involved with each decision. It would then be up to the patient to share the data with a doctor or not, to decide how much data they want to share, and to what extent recommendations can interfere with their day to day life. I’m gonna have a glass of wine. AI: this is your 3rd glass today, do you want to know the risks associated with this decision? No. AI: ok-do you want to monitor vital health statistics in relation to drinking wine instead of water? No. AI; Do you want / Just shut up. Erase all records of my wine drinking and do not monitor this going forward. To live means to die, at least for now. Don’t touch my wine 🍷
Remote technology could save lives by monitoring health from home or outside the hospital. It could also push patients and health care providers further apart.
We’ve come a long way since the days of gunboat diplomacy…
Welcome to datamacy, the international data exchange powering relations between people, companies and countries.
Thailand has its own ways of dealing with such ‘requests’ 😁
If Thailand wants Chinese tourists, it must turn over tracking data, report claims.
NASA is about to launch the world’s most powerful space telescope. Webb’s first year of science could rewrite the history of the universe.
Recently, OpenAI opened public access to GPT-3, one of the world’s most sophisticated AI writing tools. It might fool you in a conversation.