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Radar trends to watch: December 2020

This month’s collection of interesting commodities that point to important trends is dominated by AI. That’s not surprising; AI has probably been the biggest single category all time. But its dominance over other topics seems to be increasing. That’s partly because there’s more research into why AI flunks; partly because we’re beginning to see AI in embedded organizations, straddling from giant gas and oil wells to the tiny maneuvers that Pete Warden is working with.

Artificial Intelligence

Teaching AI to operate and sell: Combine NLP with buttres learning, and train in a multiplayer role-playing competition. This is where AI get terrifying, peculiarly since AI methods don’t understand what they’re doing( ascertain the next piece ). GPT-3 is great at producing human-like language, but that’s as far as it goes; it has no sense of what an adequate response to any prompt might be. For example, hinting suicide as a solution to depression. This isn’t a surprise, but it means that GPT-3 actually can’t be incorporated into applications.Why machine learning prototypes fail in the real world, and why it’s a very difficult problem to fix: Any list of training programs data can lead to a huge number of models with same behaviour on the training data, but with very different performance on real-world data. Deciding which of these examples is “best”( and in which places) is a difficult, and unstudied, problem.Tiny NAS: Neural Architecture Search designed to automate building Tiny Neural Networks. Machine Learning on small-scale machines will be an increasingly important topic in the coming years.Pete Warden on the future of TinyML: There will be hundreds of billions of designs in the next few years. Many of them won’t be “smart”; they’ll be more intelligent versions of dumb devices. We don’t need “smart refrigerators” that can order milk automatically, but we do need refrigerators that can use energy more efficiently and notify us when they’re about to fail.The replication crisis in AI: Too numerous academic AI papers are published without system or data, and using hardware that can’t be obtained by other researchers. Without access to code, data, and equipment, academic newspapers about groundbreaking results are little more than corporate marketing.Machine learning to see gas discloses: Granted, this is for oil-well scale natural gas discloses, but we should all be more aware of these invisible applications of machine learning. It’s not just autonomous vehicles and face recognition. And lest we forget, invisible an applicant for ML also have problems with bias, fairness, and accountability.Vokens: What is the case when you compound computer vision with natural language processing? Is it is feasible to isolate the meaningful points in a painting, then use that to inform language sits like GPT-3 to add an element of “common sense”? Using AI to diagnose COVID-1 9 via coughs: MIT has developed an AI algorithm that spies features in a cough that indicate a COVID-1 9 infection. It is at least as accurate as current research, particularly for asymptomatic beings, furnishes develops in real era, and could easily be built into a cell phone app.Over time, models in feedback loops( e.g ., economic competition) tend to become more accurate for a narrower slice of the population, and less precise for the population as a whole. Essentially, a pose that is constantly retraining on current input will, over meter, make itself dishonest.

Robotics

Robots in interpretation: The construction industry has been resistant to automation. Canvas has built a robot that installs drywall. This robot is in use on several major areas, including the renovation of the Harvey Milk terminal at San Francisco Airport.Simplifying the robot’s model of the external macrocosm is the route to better collaborations between robots and humans.Honda earns admiration to sell a level-3 autonomous vehicle. The vehicle is capable of entirely taken away from driving in certain situations , not just assisting. It should be on sale before March.

Programming

Nbdev is a literate programming environment for Python. It is based on Jupyter, but includes the entire application lifecycle and CI/ CD pipe , not just programming.A visual programming environment for GraphQL is another step in getting beyond text-based programming. A visual environment seems like an self-evident alternative for working with graph data.PHP 8 is out! PHP is an old language, and this release isn’t likely to employed it onto the “trendy language” list. But with a huge portion of the Web built with PHP, this new release is important and certainly worth watching.

Privacy and Security

Google is adding end-to-end encryption to their implementation of RCS, which is a standard designed to replace SMS messaging. RCS hasn’t been adopted widely( and, given the dominance of the telephone system, may never be adopted widely ), but the criteria for encrypted messaging are an important step forward.Tim Berners-Lee’s privacy project, Solid, has released its first job: an organizational privacy server. The opinion behind Solid is that people( and organizations) accumulate their own data in secure repositories called Pods that they verify. Bruce Schneier has entered into Inrupt, the company commercializing Solid.CMU has shown that passwords with minimum segment of 12 attributes and that pass some simple experiments can be remembered and resist assault. We can move on from password policies that require obscure combinations of upper and lowercase, punctuation and numerals, and that don’t require converting passwords regularly.

Infrastructure

Remember DNS cache poisoning? It’s back. Regrettably. A public mesh WiFi network for New York City: Mesh networks can provide Internet access in locations where demonstrated providers don’t care to go-but constructing them work at scale is difficult. Technology we first heard about in Cory Doctorow’s very strange Someone Comes To Town, Someone Leaves Town.Hyper-scale indexing: Helios is Microsoft’s reference architecture for the next generation of cloud arrangements. It is capable of handling extremely large data sets( even by modern standards) and incorporates unified shadowed computing with line estimating.

Hardware

The Raspberry Pi 400 looks like a LOT of fun. It’s Raspberry Pi 4 were integrated into a keyboard( like the very early Personal Computers ); 1.8 GHz ARM processor, 4 GB RAM, more I/ O ports than a MacBook Pro; precisely needs a monitor. I merely hope the keyboard is good.I should say something positive about Apple’s M1, but I won’t. I’m disenchanted fairly with them as a company that I actually don’t care how good the processor is.

Covid

Amazon reviews about scented candlesthat don’t smell correlate to Covid. A neat application of data analysis using publicly available beginnings. Data science earns.

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