ClassDojo is an educational technology company. It connects primary school teachers, students and families through communication features, such as a feed for photos and videos from the school day, and messaging that can be translated into more than 35 languages.

ClassDojo - Wikipedia

With its swanky design and first-class ... us in the heart of Hull, with the perfect view of the River Humber. THIS is where Dojo can do its best work! 💪KEY NOT FOUND: companyUpdatesClampedText.readMore...

Working at Dojo | Glassdoor

Dojo consultancy is an excellent strategy for companies that wish to start building the Data-Driven culture. With the mapping of analytical maturity and opportunities, building of project roadmap, structuring of Data Office and data governance, the construction of the Data Journey and investments ...

Home - Dojo Smart Ways

At Expert DOJO we believe that you cannot achieve what you cannot measure. We allocate a personal sales coach to you so that you are held accountable to hit the customer sales metrics you need to achieve your next round. We cannot express how important this is to building a company which can ...

Homepage - ExpertDojo

Dojo empowers businesses to thrive in the Experience Economy by creating the tools and technology that turn transactions into meaningful relationships.

Dojo Company Profile

Dojo evolved from Paymentsense Limited, a company established in 2009. The payment-tech provider, based in London, was created to resolve customer pain points. From the products the team builds to the services they provide, everything at Dojo is built with a customer-first mindset.

Dojo: Taking a customer-first approach to win the market | FinTech ...

ClassDojo helps teachers, parents, and students build amazing classroom communities.

Learn all about ClassDojo ♥

Dojo | 1,494 followers on LinkedIn. Dojo uses AI to help companies design better workplaces. Start planning your return @ dojo.co | Dojo combines AI and behavioral science to design efficient workplaces that make people more effective.

Dojo | LinkedIn

With no long-term contracts and next day transfers, Dojo&#x27;s card machine solutions enable businesses to accept debit and credit cards securely and efficiently.

Dojo for Business | Card payment solutions for UK businesses

In ten years, Coding Dojo has helped over 13K+ global alumni study to kickstart their career path in tech. Depending on your program and your schedule, our bootcamps range from 16-36 weeks. ... Depending on what you would like to learn and your financing, our bootcamps cost anywhere from $9,995 to $16,995. Dig deeper into Our Course Curriculum— our comprehensive curriculum allows graduates to work at companies ...

Coding Dojo - Software Development Coding Bootcamp

DOJO

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Are you a cybersecurity expert, DevOps engineer, or AI researcher looking to stay on top of the latest trends and insights in your field? Or maybe you’re an enthusiastic learner interested in exploring the cutting-edge of these rapidly evolving fields? Either way, ! Hack Dojo is here to help offers access to over 5,000 research presentations (and counting) on the latest insights and trends in cybersecurity, DevOps, and AI research. Our platform scours the internet for the most insightful and informative presentations, making it the ultimate tool for professionals and enthusiasts alike. With advanced filtering options, our search engine allows you to easily narrow down your search results by topic, speaker, date, and more, so you can find the information you need to stay ahead of the curve. Hack Dojo has everything you need to stay up to date on the latest developments in cybersecurity, DevOps, and AI research! Hack Dojo Examples of cutting-edge research presentations found on Hack Dojo include: “ ” by Tanya Janca, presented at Shifting Security Everywhere Global AppSec Dublin 2023 “ ” by Levente Csikor and Hoon Wei Lim, presented at RollBack — A New Time-Agnostic Replay Attack Against the Automotive Remote Keyless Entry Systems Black Hat USA 2022 “ ” by Eric Schmidt and Alexandr Wang presented at Navigating an AI-Enabled Future with Eric Schmidt (Former Google CEO) Transform X 2022 At Hack Dojo, we’re dedicated to helping you stay up-to-date with the latest trends and insights in cybersecurity, DevOps, and AI research. Whether you’re a seasoned professional looking to stay ahead of the curve or an enthusiastic learner seeking to deepen your knowledge, we have the resources you need to succeed. So why wait? Sign up today and join our community of like-minded individuals who are passionate about staying ahead in today’s fast-paced tech landscape. We can’t wait to see what you’ll achieve!

Hack Dojo: Stay Ahead of the Game with the Latest Research in Cybersecurity, DevOps, and AI

Hack Dojo: manténgase a la vanguardia con las últimas investigaciones en ciberseguridad, DevOps e IA

Hack Dojo : gardez une longueur d'avance grâce aux dernières recherches en matière de cybersécurité, de DevOps et d'IA

हैक डोजो: साइबर सुरक्षा, DevOps और AI में नवीनतम शोध के साथ गेम से आगे रहें

Hack Dojo: サイバーセキュリティ、DevOps、AI の最新の研究でゲームの一歩先を行く

Hack Dojo: fique à frente do jogo com as pesquisas mais recentes em segurança cibernética, DevOps e IA

Hack Dojo: Dẫn đầu cuộc chơi với Nghiên cứu mới nhất về An ninh mạng, DevOps và AI

Hack Dojo：利用网络安全、DevOps 和 AI 方面的最新研究保持领先地位

Debugging is often viewed as an art form or a craft. This is true for most engineering-related troubleshooting processes (e.g., art of motorcycle maintenance). We’re usually indoctrinated into the basic moves by a senior developer and are then thrown into the proverbial pool. As a result, even senior engineers sometimes have gaps in their debugging skills. There are very few university courses or books on the subject, so it’s really hard to blame them. In his book, “ ”, Andreas Zeller told a story from his youth working at a computer store. A customer walked into the store with a new Commodore 64 computer. For context: The computers back then booted directly to a basic interpreter; basic would accept line numbers as the first argument. He tried inputting this valid basic line: Why Programs Fail — A Guide to Systemic Debugging 10 print “Hello World” And he got a syntax error. He was surprised since the program seemed correct and it isn’t exactly complex. You can probably understand it without knowing any Basic… In debugging and in programming in general, we need to break down a problem into smaller components. So he typed in: 10 An empty statement. It turned out that the user was used to typewriters where he’d type a lowercase L and the letter O to type the numbers one and zero. He followed the same practice on the computer and just typed “lo.” When I read that story, I laughed out loud, but I also thought: This isn’t a story about debugging. But it is, debugging is about the unexpected. It’s about narrowing (or slicing) the problem until we have a distilled problem we can observe. At this point, the solution presents itself to us. In this article, I want to go over three big challenges we face when debugging modern applications: Polyglot Debugging Debugging the Unreproducible Data Pollution Polyglot Debugging This isn’t a new problem. As a person who used to build JVMs for a living, I would occasionally “meta debug”: debugging the debug support for the JVM. That sent me between Java and native code with both debuggers running and stepping through. That’s to be expected when building low-level VM code. But it’s something that’s becoming more common across the board. A server might be written in Python or Java with a JavaScript front end. We might track an issue through a frontend debugger all the way to the backend. Similarly in a microservice deployment, each service might be implemented in a different language. In theory, we can test everything in isolation. In practice, that’s just unrealistic. Bugs happen. By their definition, they are unexpected. With serverless, this problem has become even worse. Reproducing a serverless environment locally is so challenging we hear arguments that . local debugging serverless is an anti-pattern and it can’t scale for complex deployments. So a lot of developers limit themselves to logging and maybe some observability tools. While that can help with some problems, these are poor replacements to local debugging. Continuous observability tools provide us with a way to go beyond simple monitoring. We can get source-level debugging similar to traditional debuggers on production servers. Remote debugging is problematic, risky, Debugging the Unreproducible There are two types of unreproducible bugs: those we can’t reproduce locally and those we can’t reproduce at all. If we can reproduce the issue in production, we can use a continuous observability tool to inspect the server. However, if we can’t, we’re effectively stuck with log and observability analysis. We end up looking through forensic information like police crime scene investigators. At this point, it’s a bit late to do something, so we need to make sure we have new logs in place for the “next time” this happens. As developers, we need to fight the culture of closing bugs with “can’t reproduce.” This is a cop-out. A can’t-reproduce scenario should add logs or similar guards to verify assumptions held by the developer. That way, we won’t get caught again with the can’t reproduce conundrum. Data Pollution We often think of bugs as failures, crashes and downtime. While those are indeed bad, they are often the best bugs. We know there’s a problem and the solution is usually obvious and immediate. Data pollution is insidious. It’s remarkably hard to debug and incredibly hard to fix after the fact as fixing the code just isn’t enough. So what is a data pollution bug? This is a bug that results in bad data. That in itself isn’t a big problem… The problem is that this data propagates possibly between microservices and into the database. At this point, it becomes a huge problem. The bad data is a problem, but the bug that caused it can be anywhere, even on a different server. It’s like looking for a needle in a haystack. These bugs are particularly insidious since they often only occur in production and the cleanup after them could be worse than the problem itself. A good example of this is “undefined,” which pollutes databases everywhere as it propagates from bad JavaScript code and somehow wiggles its way into databases. The way these are usually debugged after the fact is by placing a stack log in the place that writes or sends the data. Use a condition there to verify that this is indeed invalid data and detect that violation. This can be done with code and also with continuous observability tools such as . Lightrun TL;DR Debugging is a skill that we use daily but still don’t invest enough time honing. We end up using the same tools and techniques over and over. We fall back to using logs and don’t use sophisticated capabilities that have been around for years. Unfortunately, bugs aren’t standing still. As we scale our infrastructure with amazing container technology, the bugs scale with our distributed solutions. They become more insidious at scale. We need new tools and new technologies to handle the bug scalability in the same way we handle container scaling. Originally published at . The New Stack

Enter the Cloud Native Dojo: Blackbelt-Level Debugging

The future belongs to those who believe in the beauty of their dreams. -Eleanor Roosevelt Image credits: Tesla There's been a lot of talk by Tesla and Elon Musk for a while now about a new supercomputer, and on Tesla's Open AI day, the company introduced a model of its d1 chip tile. So in this article, we are going to talk about the dojo supercomputer, what it is, why it was created, and how it works. You can also check out I wrote about Tesla's new robot. this article Before we get to the cool stuff, we are going to talk about the meaning of supercomputers and Tesla so we can get a sense of completion😉(emoji is pointless) What is a Supercomputer According to , a supercomputer is a computer with a high level of performance as compared to a general-purpose computer. Wikipedia In simple words, a supercomputer is a computer that has a lot more processing power than your laptop at home. Supercomputers are built for relatively large-scale tasks like flying a rocket, quantum modeling, predictive modeling, large-scale game theory, and other stuff. In essence, a supercomputer is a computer built for complex, time-consuming tasks which can't be done efficiently on a normal computer. What is Tesla If you are in the 21st century, you probably know what Tesla is. According to , Tesla, Inc. is an American electric vehicle and clean energy company based in Palo Alto, California, United States. Tesla designs and manufactures electric cars, battery energy storage from home to grid-scale, solar panels and solar roof tiles, and related products and services. Wikipedia In simple everyday words, Tesla is a company that makes electric cars, electric car batteries, solar panels, and everything in-between. That probably aligns with what you know Tesla as. But according to Elon, Tesla doesn't see itself as a car company because of the unique way its cars are built. To paraphrase Elon, Tesla is actually a robotics company because to them; their cars are actually "semi-sentient" robots on wheels. Like I said in about Tesla's robot, talking about the difference between what we know Tesla as and what Tesla sees itself as is way out of the scope of this article. Click to watch a video of the entire Tesla Open AI day (👌). my last article here Now to the fun part What is Dojo The Dojo is a supercomputer built by tesla to train its AI deep neural networks and Machne Learning algorithms. In other words, Dojo is a supercomputer built by tesla to train its neural networks algorithms and Machine Learning models in a faster and more efficient way. According to Elon, the Dojo supercomputer is going to be the fastest AI training supercomputer built. Why? We'll get to that. Why was it created To understand why Dojo was created, we have to first understand how AI works. Basically, AI is a bunch of statistical models and algorithms implemented and trained on data so it can think and make decisions for itself. Data can be in any form, video, images, text corpus, or plain old numbers. Now for AI to be efficient, it needs to be trained on a lot of data. In fact, it's a perfectly linear relationship, i.e., the more data AI is trained on, the more efficient and better it becomes. Now for you to train AI on data, you need a lot of processing power; the more data you have, the more processing power you need, and for this purpose, AI engineers (like myself) make use of things called which are like usual CPUs but far faster. , GPUs But your data gets to a point where training them on GPUs are no more efficient, and at that point, your data becomes to train AI on big data; you make use of software which is essentially sharing processing responsibility among different computers, you can read more on that as it is way out of the scope of the video. big data distributed computing here To put this into perspective, Tesla, as a car/robotics😅company, makes use of AI, especially for their self-driving feature. Tesla's AI self-driving feature is made of neural networks which are trained on millions of video data so it can become better (remember the more data, the better the AI), and these millions of video data easily become big data and so using GPUs would be inefficient and distributed computing would be great but to train AI on data as big as millions of data videos which are updated daily you'd need a lot of computers as essentially distributed computing is just sharing processing work between a lot of computers and so tesla decided that building a supercomputer would be more efficient both tech-wise and cost-wise and so the Dojo supercomputer was born😎. How does it work Below is a picture of self-driving AI in action To understand exactly how the Dojo supercomputer will work, you'd need to understand Tesla's data pipeline, which unfortunately is a trade secret😅(for some reason😒). But from the little publicly available information, it goes something like this when you make use of AI, and it makes mistakes (it doesn't work as it should), it makes note of that mistake and improves on it. Now let's take for instance, you own a Tesla (I don't😅), and you're driving it, and you're tired of taking the wheel and want to make use of the self-driving feature (I mean, it's just sitting there right), and it goes smoothly the self-driving AI is taking note of its surroundings using an inbuilt camera and sending that video back to Tesla's servers so that data can be used to train it more and in so that way the more people who use the self-driving AI, the more it gets better. The cool thing also is the data collected when making use of the self-driving feature is also used to train the AI of new Tesla cars just being produced and so in essence, the AI just gets better and better, and that way, when you buy a new Tesla its self-driving will be better than the last one's. Now back to the Dojo, in theory, when the video data gotten from the car is sent back to Tesla's servers, the Dojo supercomputer then gets hold of that data and uses it to train the neural network which powers the Tesla self-driving AI. Another fun fact you should know is the data is being transmitted in real-time, so as a Tesla's self-driving feature is being used, the data is being transmitted, and so the Dojo supercomputer works full time. Summary The new Tesla Dojo supercomputer is very cool, and it helps improve the way AI is trained efficiently and it that way helps promote the way AI is used, and this is really good news for mankind. AI is slowly integrating itself into our lives like Siri, Google assistant, Netflix movie recommendation, etc., and the Dojo supercomputer might well serve to improve the way our AI systems serve us. Also Published Here

All You Need to Know About the Tesla Dojo Supercomputer

Todo lo que necesita saber sobre la supercomputadora Tesla Dojo

It’s important to start by mentioning that this isn’t my first university experience. Back in 2014, I enrolled in a Biological Sciences program. Attending college was a transformative experience that positively impacted my life. Although I didn’t complete the degree due to a move to another country, which made it impossible to continue remotely, I have no regrets about that choice. At the end of 2018, my wife and I moved to a new country. For a while, I worked in cafes, restaurants, and as a martial arts instructor. However, when the pandemic hit, I had to close my dojo, leaving me without a job. I started delivering food on a bicycle for Glovo (similar to Uber Eats). It was a financially challenging time with no clear prospects for professional growth. A few months later, a friend of my wife told me about a job opening at a cafe in a shopping mall, where I started working. While the job brought me financial stability, it was one of the most difficult periods of my life. I felt intellectually unstimulated, which caused me a great deal of discomfort. One day, while reading about Artificial Intelligence — a topic that had fascinated me since my university days — I thought, “Why not learn to code?” However, there was one big obstacle: I didn’t have a computer and couldn’t see how I could afford one. So, I decided to learn to code using my phone. I started by watching YouTube tutorials and purchased a Python course on Udemy. I printed out the exercise notebook and tried to solve the problems manually. Months passed, and my mother sent me a netbook. With it, I could finally install VS Code and start programming for real. The more I studied, the more certain I became that this was what I wanted to do. But working at the cafe limited my study time. Then, in an act of incredible generosity, my mother’s employer changed my life by offering me the chance to focus exclusively on my studies for three months, paying my salary during that period. I quit the cafe job and dove into my studies. I studied day and night, reading extensively and developing personal projects. I hired a tutor to help me with the more challenging parts, which proved to be immensely helpful. The agreement was that at the end of the three months, I would secure a job. I sent out hundreds of resumes, received many rejections, and only a few interviews. But as they say, you only need one opportunity to work out, and fortunately, it did. I was called in for an interview at CTW, a company within the BMW group. During the technical interview, I realized I couldn’t answer most of the questions. I stopped the interviewer and said, “I don’t know the answers to these questions now, but I promise that I’ll study and learn them. Next time you ask, I’ll know the answers.” There was silence, but then the interviewer smiled and asked a few simpler questions before ending the interview. A few days later, I received an offer to become an intern. Even though there wasn’t an open position, they liked my attitude and gave me an opportunity to learn within the company. The initial proposal was for six months as an intern, with the possibility of being hired as a developer at the end of that period. After four months of the internship, I was hired as a developer. My eagerness to learn and determination were recognized, and my contract was accelerated because I had already demonstrated alignment with the company’s values and work model. Since 2022, I’ve been involved in two projects and am very pleased with what I’m learning. However, something is still missing. The Importance of Higher Education: I notice a significant difference between myself and my colleagues who have completed a university degree. They grasp complex concepts more quickly and dive deeper into code with greater ease. It’s common to hear them mention that they learned a certain topic in college. I hold higher education in high regard. I believe it’s possible to enter the workforce and find a position without a university degree, but to advance in a career and deepen your understanding of the work, a degree is indispensable. Even completing just a few semesters provides a solid and valuable foundation. I often feel like I’m missing out on something during discussions with my colleagues. They see me as someone who works very hard, but I don’t want to be just hard-working. I want to be excellent, to be a reference, and to help my colleagues with the more complex problems we face daily. That’s why I enrolled in the Computer Science program at the University of London, in a remote format, so I can balance work and study conveniently. I’ve attended a traditional university in the past, so I’ll have a good basis for comparing the two models (remote and in-person) and will share my thoughts on the pros and cons of each. I also plan to describe what I’m learning in the course and whether I feel it positively impacts my professional performance. It will be challenging to balance a full-time job with a degree, but I’m excited about this new challenge. The Subjects: My semester starts in October, and I’ve chosen two subjects: Discrete Mathematics and Introduction to Programming I. Additionally, I’ve already completed the Google IT Support course on Coursera, which grants me credits equivalent to the “How Computers Work” subject offered by the university, making a total of three courses this semester. Since I entered through performance-based admission, my spot will only be secured if I pass the two courses I’m enrolled in. At the moment, my biggest concern is Discrete Mathematics, as I’ve never been particularly strong in math. However, I’m working to overcome this difficulty and ensure everything goes as smoothly as possible. Next Steps: My goal is to keep a consistent record of my learning journey to share with everyone interested in this experience. If you want to know more about the application process, why I chose this course, or have any other questions, feel free to ask in the comments. Thank you so much for reading this far, and see you next time! If you enjoy my work and want to support it, you can buy me a coffee! Feature image source: Unsplash - State Library of New South Wales, Shakespeare Place, Sydney NSW, Australia It’s important to start by mentioning that this isn’t my first university experience. Back in 2014, I enrolled in a Biological Sciences program. Attending college was a transformative experience that positively impacted my life. Although I didn’t complete the degree due to a move to another country, which made it impossible to continue remotely, I have no regrets about that choice. At the end of 2018, my wife and I moved to a new country. For a while, I worked in cafes, restaurants, and as a martial arts instructor. However, when the pandemic hit, I had to close my dojo, leaving me without a job. I started delivering food on a bicycle for Glovo (similar to Uber Eats). It was a financially challenging time with no clear prospects for professional growth. A few months later, a friend of my wife told me about a job opening at a cafe in a shopping mall, where I started working. While the job brought me financial stability, it was one of the most difficult periods of my life. I felt intellectually unstimulated, which caused me a great deal of discomfort. One day, while reading about Artificial Intelligence — a topic that had fascinated me since my university days — I thought, “Why not learn to code?” However, there was one big obstacle: I didn’t have a computer and couldn’t see how I could afford one. So, I decided to learn to code using my phone. I started by watching YouTube tutorials and purchased a Python course on Udemy. I printed out the exercise notebook and tried to solve the problems manually. Months passed, and my mother sent me a netbook. With it, I could finally install VS Code and start programming for real. The more I studied, the more certain I became that this was what I wanted to do. But working at the cafe limited my study time. Then, in an act of incredible generosity, my mother’s employer changed my life by offering me the chance to focus exclusively on my studies for three months, paying my salary during that period. I quit the cafe job and dove into my studies. I studied day and night, reading extensively and developing personal projects. I hired a tutor to help me with the more challenging parts, which proved to be immensely helpful. The agreement was that at the end of the three months, I would secure a job. I sent out hundreds of resumes, received many rejections, and only a few interviews. But as they say, you only need one opportunity to work out, and fortunately, it did. I was called in for an interview at CTW, a company within the BMW group. During the technical interview, I realized I couldn’t answer most of the questions. I stopped the interviewer and said, “I don’t know the answers to these questions now, but I promise that I’ll study and learn them. Next time you ask, I’ll know the answers.” There was silence, but then the interviewer smiled and asked a few simpler questions before ending the interview. A few days later, I received an offer to become an intern. Even though there wasn’t an open position, they liked my attitude and gave me an opportunity to learn within the company. The initial proposal was for six months as an intern, with the possibility of being hired as a developer at the end of that period. After four months of the internship, I was hired as a developer. My eagerness to learn and determination were recognized, and my contract was accelerated because I had already demonstrated alignment with the company’s values and work model. Since 2022, I’ve been involved in two projects and am very pleased with what I’m learning. However, something is still missing. The Importance of Higher Education: The Importance of Higher Education: I notice a significant difference between myself and my colleagues who have completed a university degree. They grasp complex concepts more quickly and dive deeper into code with greater ease. It’s common to hear them mention that they learned a certain topic in college. I hold higher education in high regard. I believe it’s possible to enter the workforce and find a position without a university degree, but to advance in a career and deepen your understanding of the work, a degree is indispensable. Even completing just a few semesters provides a solid and valuable foundation. I often feel like I’m missing out on something during discussions with my colleagues. They see me as someone who works very hard, but I don’t want to be just hard-working. I want to be excellent, to be a reference, and to help my colleagues with the more complex problems we face daily. That’s why I enrolled in the Computer Science program at the University of London, in a remote format, so I can balance work and study conveniently. I’ve attended a traditional university in the past, so I’ll have a good basis for comparing the two models (remote and in-person) and will share my thoughts on the pros and cons of each. I also plan to describe what I’m learning in the course and whether I feel it positively impacts my professional performance. It will be challenging to balance a full-time job with a degree, but I’m excited about this new challenge. The Subjects: The Subjects: My semester starts in October, and I’ve chosen two subjects: Discrete Mathematics and Introduction to Programming I. Additionally, I’ve already completed the Google IT Support course on Coursera, which grants me credits equivalent to the “How Computers Work” subject offered by the university, making a total of three courses this semester. Since I entered through performance-based admission, my spot will only be secured if I pass the two courses I’m enrolled in. At the moment, my biggest concern is Discrete Mathematics, as I’ve never been particularly strong in math. However, I’m working to overcome this difficulty and ensure everything goes as smoothly as possible. Next Steps: Next Steps: My goal is to keep a consistent record of my learning journey to share with everyone interested in this experience. If you want to know more about the application process, why I chose this course, or have any other questions, feel free to ask in the comments. Thank you so much for reading this far, and see you next time! If you enjoy my work and want to support it, you can buy me a coffee! If you enjoy my work and want to support it, you can buy me a coffee ! buy me a coffee Feature image source: Unsplash - State Library of New South Wales, Shakespeare Place, Sydney NSW, Australia Feature image source: Unsplash - State Library of New South Wales, Shakespeare Place, Sydney NSW, Australia

Here's Why I Decided to Pursue a CS Degree Even Though I Already Had a Stable Job in the Field

Mastering the Art of Software Development: From Developer to Craftsperson

Hey there, Hackers! Today, we’re talking about the almighty backlink. Although it could sometimes seem like an afterthought, a good set of high-quality backlinks could take a good piece to the next level. If you can backlink like a pro, then all your writing will be more authoritative, look stronger, read more professionally, and, best of all, can help you reach the coveted front page of Google. So, how can you master backlinks? Let's break down a few tips and learn from some of our backlink black belts at HackerNoon. Are you ready to step into the Backlink Dojo? Use our template with backlink tips and more to get started. AP-Style Reporting 1. Keywords Matter (Make It Make Sense!) When backlinking, always remember that you should include any relevant keywords from the website you are linking to in the writing of your HackerNoon article and make that your point of contact. For example, if you’re linking to a source reference about Game of Thrones, make sure that “Game of Thrones” is what gets highlighted and linked in your HackerNoon article. This ensures that your audience knows what they’re getting linked to. In short, make sure your anchor text is reflective of what you’re sourcing. Take a look at ’s new piece about visualizing network graphs as Hamilton characters and how all their backlinks make semantic sense to their reference source about the play and various other media. @iswaryam https://hackernoon.com/analysis-of-network-graphs-visualizing-hamilton-characters-as-a-social-network?embedable=true 2. Backlink Quality Good writers present strong sources to back up their claims, and you should make sure you’re not just taking info from anywhere. If you find yourself writing about news, academic research, or anything that is highly evidence-based or makes strong claims, always make sure you’re linking to authoritative, high-quality sources to reinforce your writing. Plus, the stronger the source, the more Google will like your writing! Check out ’s new piece about AI and extinct species, where he links back to strong sources like the National Library of Medicine, BBC Earth, and CBS News. @zacamos https://hackernoon.com/the-role-of-ai-in-resurrecting-extinct-species?embedable=true 3. Backlink Diversity While it could be tempting to list one place as your one-stop shop for everything you’re writing about, it can actually hinder your writing in the long run if you’re not careful. Linking to one place repeatedly can be considered backlink spamming by Google and our editors, and it could hurt your piece’s chances of becoming a HackerNoon Top Story or reaching search engines. Diversifying your knowledge pool and linking back to a wide variety of places indicates that you’ve researched your topic well and can present useful backlinks for those who would like to follow in your footsteps. For a good example of backlink diversity, read ’s deep dive into the world of PrisonTok, where he finds great interviews and quotes from former prisoners and legal specialists about the world of incarcerated influencers. @drewchapin https://hackernoon.com/the-rise-and-real-value-of-prisontok?embedable=true 4. Use a Lot of Backlinks (But Know When To Trim It Down!) Now you know how to use backlinks effectively; however, the next level is knowing to use them. No one likes to read a mega-highlighted article filled with so many links that it looks like a virtual minefield of redirects for every minor claim and occurrence. A truly skilled writer knows when it’s time to backlink for the benefit of the reader and when to let the writing confidently do its own thing. If you find yourself swamped with important sources and reference material, don’t be afraid to make a nice “References” section at the bottom of your piece to look like a real professional! when deftly links back to important sources like GitHub, Intel, and Nvidia as needed throughout this piece and then intelligently creates a “References” section at the bottom with a whole host of high-quality backlinks should readers need them. @venkat2811 https://hackernoon.com/a-digestible-high-level-overview-of-cpu-and-gpu-capabilities?embedable=true Are you ready to step into the Backlink Dojo? Use our template with backlink tips and more to get started. AP-Style Reporting Now That You’re A Pro, Check Out The #crypto-api Writing Contest by CoinGecko. If you’re an expert in all things crypto and with expertise regarding cryptocurrency data APIs, try your hand at the for your shot at winning from a Submissions are open now and run until July 18th, 2024. 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Learn How To Backlink Like a Pro From The HackerNoon Editors

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Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Threat Model Background and Related Work Methodology Experiments Setup Results Discussion Conclusion, Acknowledgments, and References Appendix 8 Conclusions By employing model-based reinforcement learning, MEME generates adversarial malware samples that successfully evade antivirus systems and train a surrogate model mimicking the target classifier accurately. Our experiments show that MEME surpasses existing methods in evasion rate, suggesting its potential for various applications, such as testing model robustness and enhancing cyber security against advanced persistent threats. Future work may involve exploring ensemble surrogates and other optimizations to enhance MEME’s performance further. Acknowledgments The authors acknowledge support from the Strategic Support for the Development of Security Research in the Czech Republic 2019–2025 (IMPAKT 1) program, by the Ministry of the Interior of the Czech Republic under No. VJ02010020 – AI-Dojo: Multi-agent testbed for the research and testing of AI-driven cyber security technologies. The authors acknowledge the support of NVIDIA Corporation with the donation of a Titan V GPU used for this research. References 1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 2623–2631. KDD ’19, Association for Computing Machinery, New York, NY, USA (Jul 2019). https://doi.org/10.1145/3292500.3330701 2. Anderson, H.S., Kharkar, A., Filar, B., Evans, D., Roth, P.: Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning (Jan 2018). https://doi.org/10.48550/arXiv.1801.08917, arXiv:1801.08917 [cs] 3. Anderson, H.S., Roth, P.: EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models (Apr 2018). https://doi.org/10.48550/arXiv.1804.04637, arXiv:1804.04637 [cs] 4. Bergstra, J., Bardenet, R., Bengio, Y., K´egl, B.: Algorithms for Hyper-Parameter Optimization. In: Advances in Neural Information Processing Systems. vol. 24. Curran Associates, Inc. (2011) 5. Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: OpenAI Gym (Jun 2016). https://doi.org/10.48550/arXiv.1606.01540, arXiv:1606.01540 [cs] 6. Ceschin, F., Botacin, M., Gomes, H.M., Oliveira, L.S., Gr´egio, A.: Shallow Security: on the Creation of Adversarial Variants to Evade Machine Learning-Based Malware Detectors. In: Proceedings of the 3rd Reversing and Offensive-oriented Trends Symposium. pp. 1–9. ROOTS’19, Association for Computing Machinery, New York, NY, USA (Feb 2020). https://doi.org/10.1145/3375894.3375898 7. Chandrasekaran, V., Chaudhuri, K., Giacomelli, I., Jha, S., Yan, S.: Exploring connections between active learning and model extraction. In: Proceedings of the 29th USENIX Conference on Security Symposium. pp. 1309–1326. SEC’20, USENIX Association, USA (Aug 2020) 8. Correia-Silva, J.R., Berriel, R.F., Badue, C., de Souza, A.F., Oliveira-Santos, T.: Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data. In: 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1–8 (Jul 2018). https://doi.org/10.1109/IJCNN.2018.8489592, iSSN: 2161-4407 9. Demetrio, L., Biggio, B., Lagorio, G., Roli, F., Armando, A.: FunctionalityPreserving Black-Box Optimization of Adversarial Windows Malware. IEEE Transactions on Information Forensics and Security 16, 3469–3478 (2021). https://doi.org/10.1109/TIFS.2021.3082330, conference Name: IEEE Transactions on Information Forensics and Security 10. Demetrio, L., Coull, S.E., Biggio, B., Lagorio, G., Armando, A., Roli, F.: Adversarial exemples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection. ACM Transactions on Privacy and Security (TOPS) 24(4), 1–31 (2021), publisher: ACM New York, NY, USA 11. Dowling, S., Schukat, M., Barrett, E.: Using Reinforcement Learning to Conceal Honeypot Functionality. In: Brefeld, U., Curry, E., Daly, E., MacNamee, B., Marascu, A., Pinelli, F., Berlingerio, M., Hurley, N. (eds.) Machine Learning and Knowledge Discovery in Databases. pp. 341–355. Lecture Notes in Computer Science, Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-10997-4 21 12. Fang, Y., Zeng, Y., Li, B., Liu, L., Zhang, L.: DeepDetectNet vs RLAttackNet: An adversarial method to improve deep learning-based static malware detection model. PLOS ONE 15(4), e0231626 (Apr 2020). https://doi.org/10.1371/journal.pone.0231626, publisher: Public Library of Science 13. Fang, Z., Wang, J., Geng, J., Kan, X.: Feature Selection for Malware Detection Based on Reinforcement Learning. IEEE Access 7, 176177–176187 (2019). https://doi.org/10.1109/ACCESS.2019.2957429, conference Name: IEEE Access 14. Fang, Z., Wang, J., Li, B., Wu, S., Zhou, Y., Huang, H.: Evading Anti-Malware Engines With Deep Reinforcement Learning. IEEE Access 7, 48867–48879 (2019). https://doi.org/10.1109/ACCESS.2019.2908033, conference Name: IEEE Access 15. Harang, R., Rudd, E.M.: SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection. arXiv:2012.07634 [cs] (Dec 2020), arXiv: 2012.07634 16. Hu, W., Tan, Y.: Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN. In: Tan, Y., Shi, Y. (eds.) Data Mining and Big Data. pp. 409–423. Communications in Computer and Information Science, Springer Nature, Singapore (2022). https://doi.org/10.1007/978-981-19-8991-9 29 17. Huang, L., Zhu, Q.: Adaptive Honeypot Engagement Through Reinforcement Learning of Semi Markov Decision Processes. In: Alpcan, T., Vorobeychik, Y., Baras, J.S., D´an, G. (eds.) Decision and Game Theory for Security. pp. 196– 216. Lecture Notes in Computer Science, Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-030-32430-8 13 18. Institute, A.T.: AV-ATLAS - Malware & PUA (2023), https://portal.av-atlas.org/malware 19. Jagielski, M., Carlini, N., Berthelot, D., Kurakin, A., Papernot, N.: High Accuracy and High Fidelity Extraction of Neural Networks. pp. 1345–1362 (2020) 20. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.Y.: LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In: Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017) 21. Kurutach, T., Clavera, I., Duan, Y., Tamar, A., Abbeel, P.: Model-Ensemble TrustRegion Policy Optimization. In: International Conference on Learning Representations (2018) 22. Labaca-Castro, R., Franz, S., Rodosek, G.D.: AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learning. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds.) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. pp. 37–52. Lecture Notes in Computer Science, Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-86514-6 3 23. Li, X., Li, Q.: An IRL-based malware adversarial generation method to evade anti-malware engines. Computers & Security 104, 102118 (May 2021). https://doi.org/10.1016/j.cose.2020.102118 24. Ling, X., Wu, L., Zhang, J., Qu, Z., Deng, W., Chen, X., Qian, Y., Wu, C., Ji, S., Luo, T., Wu, J., Wu, Y.: Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art. Computers & Security 128, 103134 (May 2023). https://doi.org/10.1016/j.cose.2023.103134 25. Lundberg, S.M., Lee, S.I.: A Unified Approach to Interpreting Model Predictions. In: Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017) 26. Nguyen, T.T., Reddi, V.J.: Deep Reinforcement Learning for Cyber Security. IEEE Transactions on Neural Networks and Learning Systems pp. 1–17 (2021). https://doi.org/10.1109/TNNLS.2021.3121870, conference Name: IEEE Transactions on Neural Networks and Learning Systems 27. Orekondy, T., Schiele, B., Fritz, M.: Knockoff Nets: Stealing Functionality of BlackBox Models. pp. 4954–4963 (2019) 28. Pal, S., Gupta, Y., Shukla, A., Kanade, A., Shevade, S., Ganapathy, V.: ActiveThief: Model Extraction Using Active Learning and Unannotated Public Data. Proceedings of the AAAI Conference on Artificial Intelligence 34(01), 865–872 (Apr 2020). https://doi.org/10.1609/aaai.v34i01.5432, number: 01 29. Phan, T.D., Duc Luong, T., Hoang Quoc An, N., Nguyen Huu, Q., Nghi, H.K., Pham, V.H.: Leveraging Reinforcement Learning and Generative Adversarial Networks to Craft Mutants of Windows Malware against Black-box Malware Detectors. In: Proceedings of the 11th International Symposium on Information and Communication Technology. pp. 31–38. SoICT ’22, Association for Computing Machinery, New York, NY, USA (Dec 2022) 30. Quertier, T., Marais, B., Morucci, S., Fournel, B.: MERLIN – Malware Evasion with Reinforcement LearnINg (Mar 2022), arXiv:2203.12980 [cs] 31. Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: StableBaselines3: Reliable Reinforcement Learning Implementations. Journal of Machine Learning Research 22(268), 1–8 (2021) 32. Rigaki, M., Garcia, S.: Stealing and evading malware classifiers and antivirus at low false positive conditions. Computers & Security 129, 103192 (Jun 2023). https://doi.org/10.1016/j.cose.2023.103192 33. Rosenberg, I., Meir, S., Berrebi, J., Gordon, I., Sicard, G., Omid David, E.: Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–10 (Jul 2020). https://doi.org/10.1109/IJCNN48605.2020.9207168, iSSN: 2161-4407 34. Sanyal, S., Addepalli, S., Babu, R.V.: Towards Data-Free Model Stealing in a Hard Label Setting. pp. 15284–15293 (2022) 35. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) 36. Security.org, T.: 2023 Antivirus Market Annual Report (Feb 2023), https://www.security.org/antivirus/antivirus-consumer-report-annual/ 37. Severi, G., Meyer, J., Coull, S., Oprea, A.: Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers. In: 30th USENIX Security Symposium (USENIX Security 21). pp. 1487–1504. USENIX Association (2021) 38. Song, W., Li, X., Afroz, S., Garg, D., Kuznetsov, D., Yin, H.: MAB-Malware: A Reinforcement Learning Framework for Blackbox Generation of Adversarial Malware. In: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security. pp. 990–1003. ASIA CCS ’22, Association for Computing Machinery, New York, NY, USA (May 2022). https://doi.org/10.1145/3488932.3497768 39. Sussman, B.: New Malware Is Born Every Minute (May 2023), https://blogs.blackberry.com/en/2023/05/new-malware-born-every-minute 40. Sutton, R.S., Barto, A.G.: Reinforcement Learning, second edition: An Introduction. MIT Press (Nov 2018) 41. Total, V.: VirusTotal - Stats, https://www.virustotal.com/gui/stats 42. Uprety, A., Rawat, D.B.: Reinforcement Learning for IoT Security: A Comprehensive Survey. IEEE Internet of Things Journal 8(11), 8693–8706 (Jun 2021). https://doi.org/10.1109/JIOT.2020.3040957, conference Name: IEEE Internet of Things Journal 43. Wu, C., Shi, J., Yang, Y., Li, W.: Enhancing Machine Learning Based Malware Detection Model by Reinforcement Learning. In: Proceedings of the 8th International Conference on Communication and Network Security. pp. 74–78. ICCNS 2018, Association for Computing Machinery, New York, NY, USA (Nov 2018). https://doi.org/10.1145/3290480.3290494 44. Zolotukhin, M., Kumar, S., H¨am¨al¨ainen, T.: Reinforcement Learning for Attack Mitigation in SDN-enabled Networks. In: 2020 6th IEEE Conference on Network Softwarization (NetSoft). pp. 282–286 (Jun 2020). https://doi.org/10.1109/NetSoft48620.2020.9165383 45. Sembera, V., Paquet-Clouston, M., Garcia, S., Erquiaga, M.J.: Cybercrime Spe- ˇ cialization: An Expos´e of a Malicious Android Obfuscation-as-a-Service. In: 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW. pp. 213–236 (2021). https://doi.org/10.1109/EuroSPW54576.2021.00029 This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Enhancing Cybersecurity with MEME: Reinforcement Learning for Adversarial Malware Evasion

Last Monday, a nurse suggested we try a wireless monitor to track my vitals and those of my unborn baby. the nurse told me. “We call this device “Monica, the monitor!” It’s either a dream to work with or a total nightmare,” On that day, “Monica” (actually the Novii Wireless Patch System) performed exceptionally well. I was able to move freely, without the encumbrance of wires, while giving birth to my daughter. This technology harnesses passive signal acquisition to differentiate between fetal and maternal heart signals and to detect uterine contractions. Data is wirelessly transmitted to a monitoring unit for real-time observation. This system enhances accuracy and reduces false alarms, offering so much-needed mobility during labor. I thought: that writing and theorizing about technologies is one thing, but experiencing their remarkable capabilities firsthand is quite another, especially when a device functions flawlessly. A question arose: What can foundation models add to wearables? Right after my experience with “Monica”, a recent paper from Google Research and MIT researchers caught my attention. Titled ‘ ,’ and authored by Kim et al., this paper delves into the application of LLMs in the health sector, focusing on interpreting data from wearable sensors for health prediction. Intriguingly, these models are fed data not from medical records or doctor’s notes, but from wearable devices like Fitbits, which track daily steps, heart rate, sleep patterns, and more — akin to ‘Monica.’ Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data The research evaluated eight cutting-edge LLMs: Med-Alpaca, PMC-Llama, Asclepius, ClinicalCamel, Flan-T5, Palmyra-Med, GPT-3.5, and GPT-4, across six public health datasets. They conducted experiments on thirteen health prediction tasks related to mental health, activity, metabolism, sleep, and cardiac assessments. The team experimented with various methods, including zero-shot and few-shot prompting (teaching the model with minimal or no examples), instructional fine-tuning (tailoring the model to specific tasks), and even some parameter-efficient fine-tuning for computational efficiency. Particularly fascinating is the effectiveness of context enhancement in prompts, which involves adding user context, health knowledge, and temporal information. This approach yielded up to a 23.8% improvement in performance. Healthcare is an exceedingly sensitive field, but the potential benefits of generative AI for humans are immense, especially with the power of foundation models. Health-LLM explores the future where wearables are not just passive trackers but proactive health guardians. Another recent groundbreaking paper in healthcare comes from Stanford and Stability AI researchers, titled . The most fascinating aspect of this paper is the development of CheXagent, an advanced foundation model specifically designed for interpreting chest X-rays. This model uniquely combines a clinical LLM, a specialized vision encoder, and a vision-language bridging network, demonstrating exceptional performance in interpreting complex medical images. Its ability to outperform existing models in accuracy and fairness evaluations marks a significant advancement in medical imaging AI technology. It can save so much time! And possibly lives. CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation (The newborn girl — Reason Leeloo Joy — sends her regards. We took a week off last week but are now back on track, exploring the AI world to understand how she and her four brothers will live in it and navigate it.) News from The Usual Suspects © Sam Altman and OpenAI OpenAI two new embedding models (text-embedding-3-small and text-embedding-3-large) and updated versions of GPT-4 Turbo, GPT-3.5 Turbo, and a text moderation model. The new embedding models represent content as numerical sequences, enhancing machine learning tasks like clustering or retrieval. They are also more efficient and cost-effective. released Meanwhile, Sam Altman is with Middle Eastern backers, including wealthy investors and chip fabricators like TSMC, to launch a new chip venture. This move aims to meet OpenAI’s growing semiconductor needs and reduce dependence on Nvidia. The venture’s structure is unclear, and it might be a separate entity or a subsidiary of OpenAI. in discussions Blackstone steps in Another big player is investing heavily in the AI revolution. Blackstone is building a $25 billion network of power-intensive data centers across America. Following its $10 billion acquisition of QTS, a major data center operator, Blackstone is developing massive facilities to meet tech giants' growing digital and AI demands. These projects, consuming electricity equivalent to millions of homes, are reshaping communities and sparking debates over resource use and local benefits. Despite challenges, including strained power supplies and public backlash, Blackstone views this venture as one of its potentially best investments, illustrating the increasing significance and complexity of data infrastructure in the AI era. Elon Musk, xAI and Tesla Elon Musk has been making headlines recently, seeking a from global investors in the Middle East, Hong Kong, Japan, and Korea. If successful, xAI’s valuation could reach $20 billion, surpassing Anthropic’s $18.4 billion but falling behind OpenAI’s $100 billion. However, Musk’s recent unless he secures 25% control has stirred dissatisfaction among current investors and might affect talks with potential new backers. Meanwhile, in a “Dojo” supercomputer at its Buffalo, New York facility, underscoring the company’s commitment to advancing AI technology. $6 billion investment for xAI threat to remove Tesla AI projects Tesla is planning a $500 million investment Google and Hugging Face The recently announced aims to make AI more accessible. It focuses on shared initiatives in open science and source, leveraging both Hugging Face’s open models and Google Cloud’s technology. The goal is to facilitate the development of AI technologies for a wider range of users and applications. partnership between Hugging Face and Google Cloud Meanwhile, Google Bard has on HuggingFace’s Chatbot Arena Leaderboard, overtaking GPT-4 and now only behind GPT-4 Turbo in the community-driven LLM rankings. ascended to the second position The freshest research papers, categorized for your convenience Model Compression and Efficiency : A technique for efficiently compressing large language models by removing parameters while retaining performance SLICEGPT →read the paper : Focuses on developing high-performing, multi-language code generation models with an extensive parameter range DeepSeek-Coder →read the paper : Introduces an efficient pre-training method for T5 models, reducing computational requirements SPACTOR-T5 →read the paper : A framework for accelerating large language model inference using multiple decoding heads MEDUSA →read the paper LLM Capabilities and Evaluation : Evaluates MLLMs for generalizability, trustworthiness, and causality across multiple modalities From GPT-4 to Gemini and Beyond →read the paper : Develops a multilingual LLM supporting over 500 languages, enhancing language model accessibility MaLA-500 →read the paper : Introduces a method for zero-shot detection of text generated by large language models Spotting LLMs with Binoculars →read the paper Multimodal and Specialized Models : Examines the decoding mechanism in masked autoencoders for improved image processing Rethinking Patch Dependence for Masked Autoencoders →read the paper : A comprehensive survey on the advancements and capabilities of multimodal large language models MM-LLMs →read the paper : Establishes a benchmark for evaluating large multimodal models in the Chinese context CMMMU →read the paper : Enhances vision-language models with advanced spatial reasoning capabilities SpatialVLM →read the paper AI Training and Data Generation Techniques : Explores training neural networks for universal prediction strategies, approaching Solomonoff Induction Learning Universal Predictors →read the paper : A Python library for flexible and reproducible data preparation in generative NLP Unitxt →read the paper : A method for generating high-quality, content-grounded synthetic data using large language models GENIE →read the paper : Investigates a token-free language model that learns directly from raw bytes MambaByte →read the paper : Enhances language models with a task-agnostic scaffolding technique for better performance Meta-Prompting →read the paper : An approach for aligning large language models with human preferences in reinforcement learning WARM →read the paper Language Models and Role-Playing : Presents a compact model integrating enhanced vision vocabulary for efficient visual information encoding Small Language Model Meets with Reinforced Vision Vocabulary →read the paper : Develops a method for role-playing dialogues using large language models Large Language Models are Superpositions of All Characters →read the paper : Introduces a collection of multilingual large language models for conversational applications Orion-14B →read the paper In other newsletters Great dive into Apple’s “Update on apps distributed in the European Union” from Hardcore Software Fun read from about Model Merging “When what seems like pure LLM black magic is supported by the literature” Interconnects Is This The Year Apple Awakens in AI? Madrona investors’ opinion. describes his experience in Davos and World Economic Forum. It’s about AI but in Ng’s signature humanistic style. Andrew Ng

FOD#38: AI May Be Ushering Us Into a New Era of Health Monitoring

FOD#38: AI হয়তো আমাদেরকে স্বাস্থ্য পর্যবেক্ষণের একটি নতুন যুগে নিয়ে যাচ্ছে

FOD#38: KI könnte uns in eine neue Ära der Gesundheitsüberwachung einleiten

FOD#38: La IA puede estar llevándonos a una nueva era de vigilancia de la salud

FOD#38 : L'IA pourrait nous faire entrer dans une nouvelle ère de surveillance de la santé

एफओडी#38: एआई हमें स्वास्थ्य निगरानी के एक नए युग में प्रवेश करा सकता है

FOD#38: AI は私たちを健康モニタリングの新時代に導くかもしれない

FOD#38: AI가 우리를 건강 모니터링의 새로운 시대로 안내할 수 있음

FOD#38: A IA pode estar nos conduzindo a uma nova era de monitoramento da saúde

FOD № 38: ИИ может открыть нам новую эру мониторинга здоровья

FOD#38: Yapay Zeka Bizi Yeni Bir Sağlık İzleme Çağına Götürüyor Olabilir

FOD#38: AI có thể đưa chúng ta bước vào kỷ nguyên mới về theo dõi sức khỏe

FOD#38：人工智能可能将我们带入健康监测的新时代

278 Stories To Learn About Machine Learning

The importance of cybersecurity can't be overstated. It's vital for keeping data private and safe and to support systems running smoothly. However, digital threats get more sophisticated as time goes by. And it’s critical to bolster our defenses, too. And this is where AI comes into play. It’s already widely in use, and it’s only necessary to embrace its inevitable presence in cybersecurity. AI has all the potential to tackle the challenges of cybersecurity. Since it can learn and adapt fast, it offers a powerful solution to react quickly to different types of hacking attempts. But all is not clear and in the right when it comes to AI in online security. We have to look at the ethics of using AI when we don't understand precisely how it works. Especially when it's used to deal with private data on a large scale. We'll look into the advantages and possible drawbacks of integrating AI into cybersecurity frameworks. Let's dive in. The Role of AI in Strengthening Cybersecurity Let’s start by looking at what AI can do for cybersecurity. Understanding this makes it possible for us to weigh the pros and cons of AI in handling data and protecting us from different types of . We can also judge whether it's worth the tradeoff between the advantages and disadvantages. attacks The following are some ways that AI boosts online security measures (note that some of these tips are referenced from ): Data Science Dojo AI systems can spot attacks such as malware, phishing efforts, and brute force attacks. This is through continuous analysis of network traffic which enables the detection of suspicious activity. Intrusion Identification in Real-Time: AI is a game-changer because of the speed of its response - especially when it comes to cyber threats. Once security analytics detect a threat, they can estimate the severity and launch a suitable response. This ability to move fast handles threats before they can cause any major damage. Automated Response to Incidents: Threats from within an organization are a major concern. AI algorithms can learn user behavior patterns and notice if there’s any unusual behavior, like a hacked account or access where permissions are not available. This can help find internal threats as well as manage hacked accounts. Analyzing Behavioral Patterns for Internal Threats: One of the greatest strengths of AI in security is its ability to analyze historical data to predict possible future security threats. AI can also recommend protective measures and help businesses take preventative steps. Proactive Security Measures: AI-driven systems possess the ability to learn from past data, enabling them to adjust and respond to new threats. Their fast "learning" skills make them adept at spotting attacks that haven't been seen before. This flexibility allows AI to manage even when developers are unaware of the existing security issue. Identifying Adaptive Threats: zero-day attacks While the role of AI in cybersecurity is undeniably transformative, it's crucial to understand that every silver lining has a cloud. The following section will delve into the potential drawbacks and ethical implications that come with the integration of AI into our cybersecurity frameworks. The Problems of AI in Cybersecurity When all is said and done, it’s not wise to rely on AI completely. There are problems that can arise from its use. AI, while it offers numerous benefits, also poses significant cybersecurity risks. Here are some : potential issues to consider AI systems process large quantities of personal and sensitive data. This makes them more of a target for hackers and raises concerns. Privacy Concerns: The lack of explainability in AI algorithms can make it challenging to understand the core issues behind vulnerabilities and attacks. Also, AI is not free from bias and discrimination and this can lead to unintended outcomes such as bias in decision-making. Unintended Consequences: The growing prevalence of AI and automation will likely create unemployment and job displacement. Job Displacement and Unemployment: Just as AI can boost cybersecurity, it can also be trained to take on an adversarial role. Hackers can train AI to target cybersecurity defenses by identifying vulnerabilities in systems and networks. It can also be manipulated to produce incorrect outputs or decisions. Adversarial AI: These are just a few of the concerns or problems that arise with the close integration of AI with cybersecurity. However, understanding these issues also suggest steps to support the ethical use of this tool. Ethical Considerations in AI-Powered Cybersecurity So, now we come to the key ethical practices we must incorporate into cybersecurity when implementing AI. These can act as guardrails and guidelines that draw modern security protocols into becoming more effective. Transparency The starting place is transparency and openness. Businesses need to communicate that they use AI  and explain the basics of how it works. This could involve identifying the algorithm used, what training data was used, and what metrics are being monitored. This can help to build trust between the business and its customers. This also allows people to make a conscious choice if they want to conduct business with a data-driven system. This can also show that AI is not being used in a harmful or discriminatory manner. Accountability The use of AI systems means that there must be clear accountability on part of the business and the developers/creators. Who is responsible for the decisions made and who is liable if things go wrong? There must be a clear chain of responsibility that can be followed in case of any ethical violations or issues. Explainability AI is considered a black box. However, we must try to make it more transparent so that its decisions can be explained and justified. , or XAI, is a growing field that is dedicated to making AI more transparent. It aims to create algorithms and systems that can explain their decisions in a human-understandable way. As a result, businesses can better understand and explain why certain decisions or actions were taken. Explainable AI Privacy-by-design As mentioned earlier, AI systems process large amounts of personal and sensitive data. must be a key factor when integrating AI into cybersecurity. This means that from the very beginning, privacy should be built in rather than an afterthought - which is often the case. Developers, pressured by business owners, are more concerned about getting products to market rather than building a secure tool. Privacy Privacy by design means creating AI systems that collect only necessary data and use anonymization techniques wherever possible. Businesses should also have clear policies in place for handling and securing personal data. And there needs to be clear responsibility and accountability to ensure that privacy is respected and protected. There are many other principles to consider, not least issues of fairness and non-discrimination. Despite the fact that AI should be fair and unbiased, it is only based on what it's 'fed'. As such, it's important to bring in a wide range of stakeholder representation right from the design and conceptualization stage. So, the development of AI in cybersecurity has enough diverse input to reduce the risk of bias and alienation of certain groups. Conclusion We’ve laid out the benefits and pitfalls of AI in cybersecurity. And they only highlight the importance of involving ethics in cybersecurity design. I hope this post has helped you think about ways to implement AI ethically in all areas to create a positive experience for users and businesses.

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