Optimize your real estate footprint and improve workplace efficiency. Dojo offers a comprehensive suite of workplace management software solutions.

Dojo | Workplace Optimization and AI Analytics

Dojo is a financial technology company providing card payment solutions for small and medium-sized businesses in the UK and Ireland.

Dojo - Crunchbase Company Profile & Funding

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

Dojo has scaled to become one of Europe&#x27;s fastest-growing payment companies – but our ambitions are far greater than getting people paid. We&#x27;re creating products that enrich everyday interactions, tech that turns passing trade into paying customers, and apps that make grabbing a table completely ...

Are you ready to | Dojo Careers

We set up the very first dojo ... run the dojo. We specialize in DevOps, Agile/Lean processes, product discovery, cloud native architectures, API design and Microservices, Test First and Test Automation. ... To truly increase the effectiveness of your digital development teams, you need to deliver high-quality hands-on learning that takes place within the context of your own company — that’s ...

Dojo & Co.

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

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

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

Information on valuation, funding, cap tables, investors, and executives for Dojo. Use the PitchBook Platform to explore the full profile.

Dojo Company Profile: Valuation & Investors | PitchBook

We&#x27;re thrilled to be one of the most loved companies on Otta.com! Open roles at Dojo were in the top 10% of most saved jobs by Otta candidates last year. Check out Dojo on Otta right here 🔗 https://lnkd.in/eUt-XAVi #LifeatDojo #MostLovedCompanies #Otta

We create the tools and technology that turn transactions into meaningful relationships, and empower businesses to thrive in the experience economy.

We are Dojo | Dojo Careers

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

We&#x27;re making payments effortless, with payment solutions that simplify the everyday. Join Dojo and enjoy technology and services that put you in control.

About us | Effortless Payments | Dojo

DOJO DIGITAL LIMITED - Free company information from Companies House including registered office address, filing history, accounts, annual return, officers, charges, business activity

DOJO DIGITAL LIMITED overview - Find and update company information ...

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 ♥

See insights on Dojo including office locations, competitors, revenue, financials, executives, subsidiaries and more at Craft.

Dojo Company Profile - Office Locations, Competitors, Revenue, ...

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

Why is it some days we feel superhuman and we are amazed at our own abilities? Yet other days we can’t even complete the most basic of tasks?\nDOJO acknowledges that humans are remarkable - capable of abundant creativity and problem solving abilities that can create mind-blowing beauty and sophistication

DOJO

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

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. Get started with a or the official . #crypto-api Writing Contest $1,000 prize pool. new draft #crypto-api template Click for more info. here Wanna See Why Writers Love Publishing With HackerNoon? With mountains of testimonials from across the web about our great editorial team, wide content variety, and superb distribution engine, it’s no wonder thousands of writers chime into the site daily, and millions of readers keep coming back for more. Check out our new to see why readers trust us and why new and returning writers love submitting to us. HackerNoon Page That’s all for today, folks. See you in cyberspace!

Learn How To Backlink Like a Pro From The HackerNoon Editors

হ্যাকারনুন সম্পাদকদের থেকে কীভাবে একজন প্রো লাইক ব্যাকলিংক করবেন তা শিখুন

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从 HackerNoon 编辑那里学习如何像专业人士一样建立反向链接

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. 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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

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