What Does Julia Programming Language Mean?

Julia is an open source high-level, high-performance dynamic programming language designed at MIT for large-scale, partial-differential equation simulations and distributed linear algebra.

Julias ability to support scientific computing makes it a good choice for designing machine learning models and AI simulations.

The Julia programming language has a sophisticated compiler and supports distributed parallel execution. It is known for its numerical accuracy and mathematical function library, as well as its robust ecosystem of tools for optimization, statistics, parallel programming and data visualization.

Julia is expected to play an important role in the future of data science and artificial intelligence because it combines Pythons user-friendly scripting features with the high performance of compiled languages like C++.

Julia is one of the few open-source platforms for training machine learning models. (Until recently, machine learning models have been trained or developed primarily in R and Python.)

While Julia is considered to be a general-purpose language, data scientists are using many of its features for numerical analysis and computational science.

Compared to other platforms, Julia is known for being easy to use. It is also known for being:

Julia is made available under the MIT license and the source code is available on GitHub.

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What is Julia Programming Language? - Definition from Techopedia - Techopedia

Anaconda Inc., provider of a data science platform, is partnering with Oracle Cloud Infrastructure to offer secure open-source Python and R tools and packages. By embedding and enabling Anacondas repository across OCI Artificial Intelligence and Machine Learning Services customers will have access to Anaconda services directly from within OCI without a separate enterprise license.

Together, Anaconda and Oracle are looking forward to bringing open-source innovation to the enterprise, helping apply ML and AI to the most important business and research initiatives.

We are committed to helping enterprises secure their open-source pipelines through the ability to use Anaconda anywhere, and that includes inside the Oracle Cloud, said Peter Wang, CEO and co-founder of Anaconda. By combining Anacondas package dependency manager and curated open-source repository with OCIs products, data scientists and developers can seamlessly collaborate using the open-source Python tools they know and trustwhile helping meet enterprise IT governance requirements.

Python has become the most popular programming language in the data science ecosystem, it is a widely-accessible language that facilitates a wide variety of programming-driven tasks. Because the velocity of innovation powered by the open-source community outpaces any single technology vendor, more and more organizations are adopting open-source Python for enterprise use.

For more information about this news, visit http://www.oracle.com.

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How do you ban an open-source software project and make it stick?

Thats the question facing the Treasury Department, which last week added open-source cryptocurrency mixer Tornado Cash to a U.S. government list of individuals and entities blacklisted for violating sanctions. In this case, Tornado Cash which helps keep cryptocurrency transactions private made the list for violating sanctions against North Korea.

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But Tornado Cash isnt a company. Its an open-source software project based on the Ethereum blockchain, maintained by people and servers spread around the globe. As the team wrote in a 2020 blog post, From now on, Tornado.cash is largely living by the precepts that code is law. No one can modify the smart contracts and the protocol is decentralized and unstoppable, as long as Ethereum isnt changed or taken down.

The U.S. action raises a host of questions about whether any government can effectively sanction open-source code, rather than individuals, and what widespread effects that might have for not just future open-source projects, but anyone who has used Tornado Cash. There have been 12,243 unique user deposits on Tornado Cash, according to Dune Analytics, a blockchain analytics platform.

They werent just sanctioning a specific entity or user like from, in this case, North Korea, said Seth For Privacy, the pseudonym of a privacy educator whose work focuses on the cryptocurrency ecosystem.

Instead, theyre sanctioning the entire tool, the entire open-source tool of decentralized smart contracts on [the cryptocurrency] Ethereum, he said. They went after the entire tool itself that had been used by an entity that was sanctioned. So that was a big, big shift from previously where normally sanctions are targeting an entity using a tool.

The Treasury Department added Tornado Cash to the sanctions list known as the Specially Designated Nationals and Blocked Persons List (SDN list) for allegedly facilitating millions of dollars in cryptocurrency transactions to the North Korean government at the hands of government-affiliated hackers.

In its statement, the Treasury Department said Tornado Cash has been used to launder more than $7 billion worth of virtual currency since its creation in 2019. This includes over $455 million stolen by the Lazarus Group, a state-sponsored North Korean hacking group that was sanctioned by the U.S. in 2019, which the department described as the largest-known virtual currency heist to date.

Despite public assurances otherwise, Tornado Cash has repeatedly failed to impose effective controls designed to stop it from laundering funds for malicious cyber actors on a regular basis and without basic measures to address its risks, said Undersecretary of the Treasury for Terrorism and Financial Intelligence Brian E. Nelson in a statement. Treasury will continue to aggressively pursue actions against mixers that launder virtual currency for criminals and those who assist them.

Contrary to popular belief, few cryptocurrency transactions are private.

Public blockchains, which can be thought of as digital ledgers, keep a record of all transactions. While cryptocurrency wallets or alphanumeric addresses where funds are sent are pseudonymous, the people behind them can be identified.

Indeed, people publicly post their wallet addresses online, and blockchain analytics or analysis companies like Chainalysis and Elliptic have made whole business models off of opening up the curtains and tracking cryptocurrency transactions.

They do things like identify, categorize and track addresses in real time, using modeling and visual representations to track changes on a blockchain and identify behaviors. In a sense, they follow the money.

Tornado Cash is a mixer, meaning that it helps obfuscate the origins and destinations of cryptocurrency transactions and makes them harder to trace, even for law enforcement. People can send funds to a smart contract on the Ethereum blockchain, which then mixes the funds, which are then withdrawn from another address. That contract address was on the sanctions list even though no one owns it; its merely a series of ones and zeros executing a task.

Chainalysis, a blockchain analytics company that has done multimillion-dollar business with the U.S. military and law enforcement, estimated that 18 percent of the funds received by Tornado Cash were from sanctioned entities, but said almost entirely, we should note, before those entities were sanctioned.

Detractors of the mixer service argue that its used solely by criminals for money laundering. Proponents tout the privacy-preserving function, which is also used by a significant number of law-abiding people.

While we and many others have been working alongside both sides in the aisle in a positive direction on crypto and privacy, this move blindsided everyone, said Josh Swihart, senior vice president of growth, product strategy and regulatory affairs at Electric Coin Company, creators and supporters of the anonymity-enhancing cryptocurrency Zcash.

After the government announced the sanctions against Tornado Cash, Microsoft deleted the accounts of Tornado Cash contributors and the project itself from GitHub, a platform where developers collaboratively create and maintain open-source software. It has over 83 million users.

Thirty years of hard legal work to establish first amendment protections around software distribution, blown up in a day by GitHub/Microsoft, tweeted Johns Hopkins University cryptography professor Matthew Green.

Trade laws require GitHub to restrict users and customers identified as Specially Designated Nationals (SDNs) or other denied or blocked parties, or that may be using GitHub on behalf of blocked parties, said a GitHub spokesperson in a statement. At the same time, GitHubs vision is to be the global platform for developer collaboration. We examine government sanctions thoroughly to be certain that users and customers are not impacted beyond what is required by law.

The move to sanction a tool, rather than, for example, a cryptocurrency wallet address directly affiliated with a national security threat, has sent shock waves through the cryptocurrency community.

The implications of [the Treasury Department] adding the Tornado Cash protocol to the sanction list was actually greater for the world beyond crypto than for crypto itself, said Omid Malekan, an adjunct professor at Columbia Business School who teaches courses on crypto and blockchain.

The U.S. government took the drastic step of sanctioning an open-source, decentralized protocol specifically actually adding the Ethereum addresses of the smart contracts where the code lives, along with the addresses to access the service, he said.

That effectively criminalizes the act of seeking financial privacy, Malekan said, and opens up a can of worms around open source such as whether the government will charge someone who wrote code because a criminal later used that code.

Seth For Privacy said there may also be risks for users of the Tornado Cash service. He wonders what will happen with any of their funds that interacted with Tornado Cash and whether that money would be subject to criminal action.

On Friday, Dutch authorities announced they had arrested a 29-year-old for being suspected of involvement in concealing criminal financial flows and facilitating money laundering through the mixing of cryptocurrencies through the decentralized Ethereum mixing service Tornado Cash.

Authorities said multiple arrests could not be ruled out.

Because crypto wallets cannot reject incoming transactions, an anonymous Twitter user out to prove a point started sending a slew of incredibly small, unsolicited transactions of Ethereum that had interacted with Tornado Cash to the public wallets of celebrities, in theory implicating them in potential violations of sanctions laws.

Malekan performed a similar public experiment on Twitter by donating a small amount of Ethereum, via Tornado Cash, to Planned Parenthood and to a secret group of Russians helping Ukrainian refugees. In both cases, he said, he committed a crime, but did so to illustrate that privacy itself should not be criminalized.

There are 10,000 vanilla reasons why somebody would want to use Tornado Cash for something completely mundane in a way that is not remotely criminal or illicit, he said.

Hailey Lennon, a shareholder at the law firm Anderson Kills Technology, Media and Distributed Systems Group, said the further sanctions regimes get from a direct connection to helping terrorists and covering the source of funds, the more you get toward developers and open source that gets really sticky.

She also pointed out that there is a tension between national security and privacy in this case, with national security used as a justification for intruding on privacy. Similar debates play out around encrypted communications, for example.

When 9/11 happened, it gave the Patriot Act sharper teeth, she said. It changed the way we travel and how financial institutions surveil transactions.

The governments actions have already made it harder for Tornado Cash users to access the service, although whether sanctions can truly eliminate an open-source project remains to be seen. In addition to Microsoft removing the code and contributors from GitHub, two major application programming interface and infrastructure providers, Alchemy and Infura, have blocked API access to Tornado Cashs front-end interface. That means users trying to access it through these APIs software intermediaries that let apps talk to each other cannot see Tornado Cash. Users can still reach the Tornado Cash service, but its going to get increasingly harder and more complicated over time.

I think the main things for a project to be prepared for when building their project is to make sure its built for adversarial environments, said Seth for Privacy. Not assuming that the current environment will last forever, or that their tool itself will always be considered above board and OK.

Thanks to Lillian Barkley and Alicia Benjamin for copy editing this article.

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Tornado Cash's sanction has the tech industry watching nervously - Grid

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Meet the myBuddy 280 Pi, an adorable dual-armed Raspberry Pi-powered robot. The creator of the myBuddy, Elephant Robotics, calls it an "open-source educational essentials collaborative robot" for coding, AI, and robotics enthusiasts.

The little guy features two articulated arms (a first for Elephant Robotics) that can lift 260g of weight, rotate 165 degrees, and perform various tasks like waving hello or conducting a band. The ends of the arms can be fitted with little hands, suction pumps, and grippers. You know, robot stuff. The company's previous robots have featured single-armed creations that seem to lack the personality the myBuddy brings to the table.

The myBuddy is powered by a Raspberry Pi 4 with three ESP32 microcontroller modules. The arms run on a high-performance servo steering gear with six points of articulation.

The seven-inch touchscreen display has a pair of dual-200w pixel cameras that'll help teach the robot how to use visual sorting and facial recognition applications to have it do things like greet you when you arrive. Oh, and it'll show off several cute little facial expressions to give it some personality.

According to the video above, some applications of myBuddy include, but aren't limited to, programming it to play an instrument, wave a flag, and dribble a ball. There's even a way to control the arms remotely via a VR headset and controller.

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Some of the more wild things you can teach it include how to pet a robot cat, pour out candy, and even have it perform fun little dances. What you can do with the myBuddy seems contingent on your skills and creativity as a programmer.

The open-source robot works with Arduino, Python, C++, and Java programming languages. Elephant Robotics provides useful tools and software on its download page for users to experiment with.

The myBuddy 280 Pi starts at $1,699 or $1,749, depending on whether or not you want to add a pair of goofy hands to your new friends. Our colleagues at Tom's Hardware pointed out that myBuddy won't be showing up on Amazon for the next few months because of limited supply. Elephant Robotics also makes a bionic pet cat if you're looking for more robotic companions of the four-legged variety.

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You'll never be as happy as this adorable wiggly-armed robot - PC Gamer

BROOKSTON, Ind. One local teacher has transformed not just her own curriculum, but her school's learning methods with two little robots.

Mindy Brennan, the computer lab teacher at Frontier Elementary School, has been utilizing the Cozmo and Vector robots from Digital Dream Labs (DDL) to supplement the school's programming studies.

DDL is an "edtechtainment" company that develops consumer robots for people and students of all ages.

"Digital Dream Labs started off as an ed-tech company," Jacob Hanchar, CEO at DDL said, "so we taught coding to kindergarten through 5th grade. And now we are a robotics/AI-companion company. So we make robots for all ages that not only teach coding but also keep you company, count medicine for you, reminds you to take your medicine; go with you on trips, take pictures, answer phone calls...all those things."

The two main products produced by DDL is Cozmo and Vector. Cozmo is the robot focused more so for a younger audience, according to Hanchar. This little bot has its own YouTube channel, "Cozmo & Friends" where Cozmo and its friends teach and learn about STEM-oriented topics.

"We lead with entertainment and fun first," Hanchar said. "So (Cozmo) is more of a fun robot where you control it like a toy. It's more like a toy experience where you have direct remote control access, you're using the phone as that remote control. So it's heavily dependent on the user to get information, whereas Vector's more autonomous and he kind of just hangs out with you a little bit."

Vector does not require a phone to be used as a remote controller. The robot synchronizes to Wi-Fi and functions based on that.

"He can kind of putter around your desk independently," Hanchar said. "And the age group who owns that tends to be (around) 25 (years old). That's more of like your companion robot, more of a quote-unquote 'AI' experience."

Brennan is a retired Navy veteran with a passion for teaching. The DDL bots have factored into many aspects of her own classroom and school.

"The first person that (students) want to see is Vector and Cozmo when they come in my classroom," Brennan said, regarding how the robots influence her students. "...I already use them for our coding curriculum. All of our students start coding in kindergarten."

Brennan explained that the DDL bots help the students learn the terminology of coding, the different parts of computers and more. The social-emotional side of learning is something Cozmo and Vector have shown serious impacts in as well.

"They'll say 'Hey Vector, what is a sequence? What's a loop?' They would rather have Vector tell them what that means versus Mrs. B telling them what that means because it means more coming from Vector," Brennan said, with a laugh. "The other part of (the robots) that I did not see coming was the social-emotional side that these robots have with these students."

Cozmo and Vector gives the students someone who is not a teacher or parent to open up to. This has allowed students to become more open-minded in the classroom - both intellectually and emotionally, according to Brennan.

"I would have students that were nonverbal but, the way that Cozmo's set up, he has a whole (range) of emotions that he can do," Brennan said. "Whether it's frustrated, sad, mad, happy, unsure...And then we have students that actually earn individual time with Cozmo and Vector on a weekly basis."

The impact of the DDL bots has been felt outside of Brennan's classroom as well, from the guidance counselor's office to the music, art and physical education departments.

"So I taught the guidance counselor how to code Cozmo so that these kids can talk with Cozmo about their feelings," Brennan said. "...They're not as scared. They're a lot more open with the robot than they are a human being.

"...I had been attempting to code Cozmo for the music (program)I can't read music. So I went to the person that I knew that could. Well he couldn't code...And so (we taught each other) so more students this year will actually be learning the music notes and coding it into Cozmo in music class."

Brennan also provided an example of Cozmo helping out in the art department. A new 3D printer at Frontier Elementary will allow students to print and attach a drawing accessory for Cozmo that will allow him to hold markers, pencils and more. The students will then be taught how to code Cozmo to draw pictures. The art students in turn will have a unit where they learn to draw Cozmo itself.

This symbiotic relationship between the robots, teachers, students and expanded curriculums continues. In gym class, student can build "weight sets" to go on its lifting mechanism so that Cozmo can shout the number of lifts needed and do the lifts with the students.

The future of edtechtainment bots in the classroom is vast, according to both Brennan and Hanchar.

"We're working hard to improve Cozmo, that's for sure," Hanchar said. "One thing that we know we're gonna do is, we have an application inside of Cozmo (and) we need to build more games like that in the future where you're doing drag-and-drop block coding. And we're gonna integrate that more with some of our other applications."

Brennan is looking forward to expanded use of DDL AI in her classroom and school.

"Right now the kids are wanting me to also bring in and work on video editing," Brennan said. "So we have a green screen...it's something we're going to be working on towards Christmas time (and) they get to do a skit of whatever kind they want and we also work on the video editing and teaching them how to do all of that.

"...The possibilities are endless because they are so excited."

Margaret Christopherson is a reporter for the Journal & Courier. Email her at mchristopherson@jconline.com and follow her on Twitter @MargaretJC2.

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Teacher inspires early love of technology through robotics - Journal & Courier

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AI that analyzes data to help you make decisions is set to be an increasingly big part of business tools, and the systems that do that are getting smarter with a new approach to decision optimization that Microsoft is starting to make available.

Machine learning is great at extracting patterns out of large amounts of data but not necessarily good at understanding those patterns, especially in terms of what causes them. A machine learning system might learn that people buy more ice cream in hot weather, but without a common sense understanding of the world, its just as likely to suggest that if you want the weather to get warmer then you should buy more ice cream.

Understanding why things happen helps humans make better decisions, like a doctor picking the best treatment or a business team looking at the results of AB testing to decide which price and packaging will sell more products. There are machine learning systems that deal with causality, but so far this has mostly been restricted to research that focuses on small-scale problems rather than practical, real-world systems because its been hard to do.

SEE: How to become a machine learning engineer: A cheat sheet (TechRepublic)

Deep learning, which is widely used for machine learning, needs a lot of training data, but humans can gather information and draw conclusions much more efficiently by asking questions, like a doctor asking about your symptoms, a teacher giving students a quiz, a financial advisor understanding whether a low risk or high risk investment is best for you, or a salesperson getting you to talk about what you need from a new car.

A generic medical AI system would probably take you through an exhaustive list of questions to make sure it didnt miss anything, but if you go to the emergency room with a broken bone, its more useful for the doctor to ask how you broke the bone and whether you can move your fingers rather than asking about your blood type.

If we can teach an AI system how to decide whats the best question to ask next, it can use that to gather just enough information to suggest the best decision to make.

For AI tools to help us make better decisions, they need to handle both those kinds of decisions, Cheng Zhang, a principal researcher at Microsoft, explained.

Say you want to judge something, or you want to get the information on how to diagnose something or classify something properly: [the way to do that] is what I call Best Next Question, said Zhang. But if you want to do something, you want to make things better you want to give students new teaching material, so they can learn better, you want to give a patient a treatment so they can get better I call that Best Next Action. And for all of these, scalability and personalization is important.

Put all that together, and you get efficient decision making, like the dynamic quizzes that online math tutoring service Eedi uses to find out what students understand well and what they are struggling with, so it can give them the right mix of lessons to cover the topics they need help with, rather than boring them with areas they can already handle.

The multiple choice questions have only one right answer, but the wrong answers are carefully designed to show exactly what the misunderstanding is: Is someone confusing the mean of a group of numbers for the mode or the median, or do they just not know all the steps for working out the mean?

Eedi already had the questions but it built the dynamic quizzes and personalized lesson recommendations using a decision optimization API (application programming interface) created by Zhang and her team that combines different types of machine learning to handle both kinds of decisions in what she calls end-to-end causal inferencing.

I think were the first team in the world to bridge causal discovery, causal inference and deep learning together, said Zhang. We enable a user who has data to find out the relationship between all these different variables, like what calls what. And then we also understand their relationship: For example, how much the dose [of medicine] you gave will increase someones health, by how much which topic you teach will increase the students general understanding.

We use deep learning to answer causal questions, suggest whats the next best action in a really scalable way and make it real world usable.

Businesses routinely use AB testing to guide important decisions, but that has limitations Zhang points out.

You can only do it at a high level, not an individual level, said Zhang. You can get to know that for this population, in general, treatment A is better than treatment B, but you cannot say for each individual which is best.

Sometimes its extremely costly and time consuming, and for some scenarios, you cannot do it at all. What were trying to do is replace AB testing.

The API to do that, currently called Best Next Question, is available in the Azure Marketplace, but its in private preview, so organizations wanting to use the service in their own tools the way Eedi has need to contact Microsoft.

For data scientists and machine learning experts, the service will eventually be available either through Azure Marketplace or as an option in Azure Machine Learning or possibly as one of the packaged Cognitive Services in the same way Microsoft offers services like image recognition and translation. The name might also change to something more descriptive, like decision optimization.

Microsoft is already looking at using it for its own sales and marketing, starting with the many different partner programs it offers.

We have so many engagement programs to help Microsoft partners to grow, said Zhang. But we really want to find out which type of engagement program is the treatment that helps a partner grow most. So thats a causal question, and we also need to do it in a personalized way.

The researchers are also talking to the Viva Learning team.

Training is definitely a scenario we want to make personalized: We want people to get taught with the material that will help them best for their job, said Zhang.

And if you want to use this to help you make better decisions with your own data, We want people to have an intuitive way to use it. We dont want people to have to be data scientists.

The open-source ShowWhy tool that Microsoft built to make causal reasoning easier to use doesnt yet use these new models, but it has a no-code interface, and the researchers are working with that team to build prototypes, Zhang said.

Before the end of this year, were going to release a demo for the deep end-to-end causal inference, said Zhang.

She suggests that in the longer term, business users might get the benefit of these models inside systems they already use, like Microsoft Dynamics and the Power Platform.

For general decision-making people, they need something very visual: A no-code interface where I load data, I click a button and [I see] what are the insights, said Zhang.

SEE: Artificial Intelligence Ethics Policy (TechRepublic Premium)

Humans are good at thinking causally, but building the graph that shows how things are connected and whats a cause and whats an effect is hard. These decision optimization models build that graph for you, which fits the way people think and lets you ask what-if questions and experiment with what happens if you change different values. Thats something very natural, Zhang said.

I feel humans fundamentally want something to help them understand If I do this, what happens, if I do that, what happens, because thats what aids decision making, said Zhang.

Some years ago, she built a machine learning system for doctors to predict how patients would recover in different scenarios.

When the doctors started to use the system they would play with it to see if I do this or if I do that, what happens,' said Zhang. But to do that, you need a causal AI system.

Once you have causal AI, you can build a system with two-way correction where humans teach the AI what they know about cause and effect, and the AI can check whether thats really true.

In the U.K., schoolchildren learn about Venn diagrams in year 11. But when Zhang worked with Eedi and the Oxford University Press to find the causal relationships between different topics in mathematics, the teachers suddenly realized theyd been using Venn diagrams to make quizzes for students in years 8 and 9, long before theyd told them what a Venn diagram is.

If we use data, we discover the causal relationship, and we show it to humans its an opportunity for them to reflect and suddenly these kinds of really interesting insights show up, said Zhang.

Making causal reasoning end to end and scalable is just a first step: Theres still a lot of work to do to make it as reliable and accurate as possible, but Zhang is excited about the potential.

40% of jobs in our society are about decision making, and we need to make high-quality decisions, she pointed out. Our goal is to use AI to help decision making.

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Microsoft is teaching computers to understand cause and effect - TechRepublic

It turns out that JavaScript, the programming language that web developers and users alike love to complain about, had a hand in delivering the stunning images that the James Webb Space Telescope has been beaming back to Earth. And no, I dont mean that in some snarky way, like that the website NASA hosts them on uses JavaScript (it does). I mean that the actual telescope, arguably one of humanitys finest scientific achievements, is largely controlled by JavaScript files. Oh, and its based on a software development kit from 2002.

According to a manuscript (PDF) for the JWSTs Integrated Science Instrument Module (or ISIM), the software for the ISIM is controlled by the Script Processor Task (SP), which runs scripts written in JavaScript upon receiving a command to do so. The actual code in charge of turning those JavaScripts (NASAs phrasing, not mine) into actions can run 10 of them at once.

The manuscript and the paper (pdf) JWST: Maximizing efficiency and minimizing ground systems, written by the Space Telescope Science Institutes Ilana Dashevsky and Vicki Balzano, describe this process in great detail, but Ill oversimplify a bit to save you the pages of reading. The JWST has a bunch of these pre-written scripts for doing specific tasks, and scientists on the ground can tell it to run those tasks. When they do, those JavaScripts will be interpreted by a program called the script processor, which will then reach out to the other applications and systems that it needs to based on what the script calls for. The JWST isnt running a web browser where JavaScript directly controls the Mid-Infrared Instrument its more like when a manager is given a list of tasks (in this example, the JavaScripts) to do and delegates them out to their team.

The JavaScripts are still very important, though the ISIM is the collection of instruments that actually take the pictures through the telescope, and the scripts control that process. NASA calls it the heart of the James Webb Space Telescope.

It seems a bit odd, then, that it uses such an old technology; according to Dashevsky and Balzano, the language the scripts are written in is called Nombas ScriptEase 5.00e. According to Nombas (now-defunct) website, the latest update to ScriptEase 5.00e was released in January 2003 yes, almost two decades ago. There are people who can vote who werent born when the software controlling some of the JWSTs most vital instruments came out.

This knowledge has been bubbling up on the internet in Hacker News and Twitter threads for years, but it still surprised quite a few of us here at The Verge once it actually clicked. At first blush, it just seems odd that such a vital (not to mention expensive) piece of scientific equipment would be controlled by a very old version of a technology thats not particularly known for being robust.

After thinking about it for a second, though, the softwares age makes a bit more sense while the JWST was launched in late 2021, the project has been in the works since 1989. When construction on the telescope started in 2004, ScriptEase 5 wouldve only been around two years old, having launched in 2002. Thats actually not particularly old, given that spacecraft are often powered by tried-and-true technology instead of the latest and greatest. Because of how long projects like the JWST take to (literally) get off the ground, things that had to be locked in early on can seem out of date by more conventional standards when launch day rolls around.

Its worth noting that, like the project itself, these documents that describe the JWSTs JavaScript system are pretty old; the one written by Dashevsky and Balzano is undated but came out in 2006, according to ResearchGate, and the ISIM manuscript is from 2011. (There does appear to have been a version published in 2010, but the one I read cites papers published in 2011.) Its always possible that NASA couldve changed the scripting system since then, but that seems like a pretty big undertaking that wouldve been mentioned somewhere. Also, while NASA didnt reply to The Verges request for comment, this JWST documentation page published in 2017 mentions event-driven science operations, which is pretty much exactly how the documents describe the JavaScript-based system.

This knowledge base, by the way, also contains a few more details on the telescopes 68 GB SSD, saying that it can hold somewhere between 58.8 and 65 gigabytes of actual scientific data. Wait, did I forget to mention that? Yes, this telescopes solid state drive has around the same capacity as the one that was available in the original 2008 MacBook Air.

Anyways, were not here to talk about the JWSTs storage. I feel like the big question at this point is why Javascript? Sure, theres probably a bit more angst about the language now than there was in the time when the projects engineers were selecting tech for the project, but NASA is famous among some programmers for its strict programming guidelines whats the point of going with web-like scripts instead of more traditional code?

Well, NASAs document says that this way of doing things gives operations personnel greater visibility, control and flexibility over the telescope operations, letting them easily change the scripts as they learn the ramifications and subtleties of operating the instruments. Basically, NASAs working with a bunch of files that are written in a somewhat human-readable format if they need to make changes, they can just open up a text editor, do a bunch of testing on the ground, then send the updated file to the JWST. Its certainly easier (and therefore likely less error-prone) than if every program was written in arcane code that youd have to recompile if you wanted to make changes.

If youre still worried, do note that the Space Telescope Science Institutes document mentions that the script processor itself is written in C++, which is known for being... well, the type of language youd want to use if you were programming a spacecraft. And its obviously working, right? The pictures are incredible, no matter what kind of code was run to generate them. It is, however, a fun piece of trivia next time youre cursing the modern web for being so slow and wishing that someone would just blast JavaScript into space, you can remember that NASA has, in fact, done that.

See the original post here:
JavaScript had a hand in delivering James Webb Space Telescopes images - The Verge