Ann Zhang

🌟 Attention Boston Area Students Passionate About AI! 🌟 Are you intrigued by Artificial Intelligence, Machine Learning, or Data Science? Want to drive innovation at your campus? Let’s make it happen together! 🚀 During my time at University of Massachusetts Amherst, I founded the UMass Amherst Data Science Club and was President for 2 years. Not only did I enjoy it, but it opened up so many doors for me with things like job opportunities, industry networking, etc... 🤝 I’m collaborating with AI expert Paul Baier to compile a list of student groups relating to AI and data science in the Boston area and we’ve noticed some schools still on the sidelines without any dedicated groups in this thrilling field. I would really love to help any student at schools such as Bentley University, Suffolk University, UMass Boston, Emerson College, Brandeis University, and Boston College in setting up their first Artificial Intelligence (or similar) club! 📝 Interested in getting involved or starting a group at your school? Just drop a comment below or send me a direct message! 📊 Know of an existing group that's not on our list? Let us know! We aim to connect and empower student communities across Boston. Let’s innovate and lead together in AI! #BostonAI #Innovation #DataScience #MachineLearning #AICommunity #UMassAmherst #Boston #AI #ArtificialIntelligence #ML #MachineLearning #DataScience #AIClub

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Week 4 of CUNY Tech Prep: I'm excited to be working with Daniel Chen and Andrew Lam, RT(R) throughout this semester in building a simple reinforcement learning data science project. Daniel is amazing at coding and has won several Hackathons in the past, including the CTP Hackathon that I participated over this past summer. Andrew and I met through a mutual friend, and we have many interests in common, including a background working in healthcare. This week, I watched a video on Clustering in Data Science, which I have attached below for your reference. This video introduced me to the concept of Clustering, which is the grouping of data based off a measure of similarity. The video continues to show a demo of creating dendrograms from a dataset in R. I thought that it was interesting because I recently worked with R and RStudio in a research project for Collegiate Science and Technology Entry Program (CSTEP). I am excited to recreate the same demonstration using Pandas and NumPy in Python.

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🚀 BostonHacks 2024! Last weekend, Andrew Sun and I won 2nd place for the CometCare track at BostonHacks 2024, which was our first Hackathon. We had a lot of fun working from the ground up to create a final product called GoodDeeds, a platform designed for NGOs, non-profits, and other local organizations looking to post about events requiring community involvent across the spectrum of urgency, ranging from severe post-disaster recovery efforts to more relaxed community service initiatives. 🏢 For Organizations Organizations can sign up to create events, specifying a location, urgency rating, volunteer needs, and a description of the event. 🙋 For Volunteers Individuals like you and me can sign up as volunteers with a specified “home” location and a distance radius we’re willing to travel. Volunteers can view all events within their set radius, filter by date and start time, and sign up. Their email is added to a mailing list for updates, and they can reach out to event organizers or ask questions to an OpenAI chatbot directly within the app. 🎮 XP Levels To encourage community involvement, we introduced a game-like feature: XP levels for individual users that increase with volunteering attendance. Higher XP points are awarded for events with higher severity ratings (like post-hurricane cleanup). Users and organizers can see a leaderboard displaying top volunteers with their XP points and approximate location. 📣 Real-Time Notifications We integrated a notification system, allowing organizers to alert individuals within a set radius about upcoming events or updates for events they’ve signed up for, powered by the Twilio SendGrid API. The emails look nice, too! 💻 Tech Stack We had a lot of fun learning Streamlit, a frontend library built in Python, along with building a Flask backend and using a PostgreSQL database hosted on Neon. The frontend was deployed on Streamlit Cloud, while the backend was deployed on Render. Given the short time constraint, we’re thrilled with what we accomplished, and we look forward to future development! 🌐 Check It Out! Demo Video: https://lnkd.in/ecJyGufP Live Site: https://lnkd.in/ey5ht6yG (starts cold, may take a while on first signup) Source Code: https://lnkd.in/ew7PJySZ

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CUNY Tech Prep: This week, we explored open-source models with Hugging Face. For my wordle group project, I’m planning to incorporate Meta's NLLB (No Language Left Behind) translation model, which supports over 200 languages. The model uses Torch tensors, essentially multi-dimensional arrays that efficiently process large-scale data, to handle translations smoothly. By integrating NLLB, I can offer translations and personalized hints in real-time, making the game accessible to a diverse audience. Checkout Meta's model here: https://lnkd.in/eCbZJz6x #MachineLearning #DataScience #CTP #HuggingFace #Meta

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More often than not, I’m here reflecting: What are the implications of making LLMs endlessly larger? My mind visualizes impossible levels of energy consumption, increasing exponentially each day. It feels like a scene from The Matrix, and not a pretty one. Recently, I’ve been researching about RAG (Retrieval-Augmented Generation), and it sounds promising, there are key points showing potential to reshape how we develop and deploy AI. The aspect that stands out the most to me is the potential to shift away from the “Texas approach” (remember my last post?), RAG could let us move towards smaller(!), modular, specialized models that integrate only the data they need, when they need it, rather than scaling endlessly. It’s like my dream of sibling models working together, creating a more targeted approach for better results. Why am I against ginormous models, you might ask again? LLMs can consume as much energy as powering a small city. City. RAG could allow us to significantly cut energy consumption, by eliminating constant retraining and enormous parameter counts. Have I mentioned I dislike the massive cash burning? Yes, is not my money. Yes, who cares about the overlords money. But honestly, I just hate waste. Did you know the cost of training foundation models have been doubling every 6-10 months? Doubling. Read again. RAG offers the potential to invest once in a base model and then build on it through knowledge retrieval, rather than constant retraining. This approach could make AI access more affordable, opening doors for smaller organizations and underserved communities. Of course, the cynic in me asks: are we simply shifting the computational burden elsewhere? RAG could reduce training costs, but it would introduce database management and retrieval overhead. My cent and a half? What do you think is more costly, monetarily and environmentally, that overhead, or energy consumption? I think you all know my answer. But let’s not forget: RAG doesn’t rely solely on LLM parameters; it retrieves data in real-time from external sources. Meaning APIs, websites, organizational databases, and beyond, and this retrieval approach possess risks and requires robust data security. However, again, the cost will still likely so much lower than today’s soaring energy bills and carbon footprint. So, the question for the 15$ in my wallet is: will AI’s future depend on more power or will be ruled by better processes? My other half cent? A leaner, privacy-conscious, eco-friendlier AI isn’t just a possibility; it should be our goal. All of us. #AI #SustainableTech #EnergyEfficientAI #DataPrivacy

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Exciting news from Cornell University! 🌟 Cornell has joined the U.S. Commerce Department's AI Safety Institute Consortium (AISIC) to support the development and deployment of trustworthy and safe artificial intelligence technologies. As a Computer Science freshman at Cornell's College of Engineering, this initiative resonates deeply with my aspirations in AI and machine learning. 🤖 The AISIC consortium, established by the National Institute of Standards and Technology (NIST), aims to bring together AI creators and users, academics, nonprofit organizations, and government and industry researchers. With more than 200 members, including companies and startups of all sizes, research teams, and representatives of professions deeply engaged in AI's use, the consortium is set to develop evidence-based guidelines and standards for AI measurement and policy. 🌍 Cornell's involvement in the consortium underscores its commitment to advancing safe and trustworthy methods, practices, and policies in AI, contributing to positive global impact. Across its campuses in Ithaca and New York City, Cornell is a hub of AI developers and users, including researchers who are creating new ways to use knowledge, ideas, and tools to have a positive global impact. 🌟 I am proud to be part of a university that is at the forefront of addressing the critical need for safe and trustworthy AI. This initiative aligns perfectly with my goal of becoming a Machine Learning engineer and exploring how AI can enhance productivity and competitiveness in business. 📈 #AI #MachineLearning #CornellUniversity #AISIC #AIethics #TechInnovation https://lnkd.in/e9rYfbtf

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CUNY Tech Prep update: the past few weeks in the data science track, we've brushed up on how to use some python packages, data engineering techniques and good practices, logistic regression, as well as creating simple linear models. Our group, Ye Htut Maung and Sophia Yau have been looking into Convolutional Neural Networks, an architecture widely used for image recognition, in hopes to build our own CNN model.

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I presented a tutorial at the ICDCS '24 conference in Jersey City. "Tutorial: Object as a Service (OaaS) Serverless Cloud Computing Paradigm"

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Ilya Sutskever confirmed at NeurIPS '24 pre-training scaling law will not be the same. Internet data, the AI fossil fuel, is running out. It is not the end of the AI hype, the rapid rise of foundational AI has outpaced our ability to fully grasp their long-term societal impact. AI agents and applications are poised to transform every facet of our lives, and we will see it happening in coming few years. There will still be improvements in model architecture, and synthetic data creation, so models will improve, just not at the lightning speed they did till now. Looking forward, the single most important milestone in AI is creating systems capable of advancing themselves—AI researchers that can innovate and improve foundational models autonomously. We need to assess how far are we from this stage.

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In 2011, Meena Bakhai, a first-generation immigrant and former electrical engineer, took a bold step. She invested $10,000 in Nvidia at just $0.37 per share. Today, that investment is worth a staggering $3.5 million. What was her secret? A combination of technical expertise and long-term conviction. Ms Bakhai, leveraging her professional expertise as an engineer in the space, saw the potential in semiconductors and trusted her instincts. 💡 Her career knowledge created her conviction. ↩ 💡 Her conviction, in turn, allowed her to hold out over the past 13 years, through all the peaks and troughs. ↩ 💡 That conviction has created an enviable fortune for Meena and her family. The question is — what are you doing with your money to create such opportunities? You don’t need to be an engineer to make smart financial choices, but tapping into your own expertise can give you an edge. Whether it’s your industry knowledge or insights from your career, it’s about putting your skills to work and making your money do the same. Long-term strategy, thoughtful risk, and making informed decisions — these are the keys. What plans are you making to unlock your potential? #sydneyfinance #sydneyfinancialplanner #financialadviser #financialadvisor #financialfreedom #financialindependence #mindset #lifeplanning #nvidia #stocks #sharetrading #semiconductors

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Machine Learning (unsupervised): I tried to clean up an old DEA license. The original, scanned into my computer, had lots of faint blue background noise. Machine learning (Scikit-learn, KMeans) got rid of most of the noise, but at a cost of making the lettering uneven. This may represent a limitation of the basic process. See side-by side comparisons of original and the cleaned versions.

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I wrote about about the state of compute in the world of AI and what that means for individuals. I also included my usual dump of ML resources and updates in a streamlined fashion. I'm changing up the format of my writing this week. Let me know what you think! https://lnkd.in/dS_EXsK5

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Join Daniel G. and Jacob Marks, PhD for an exciting computer vision hackathon in NYC: https://lnkd.in/gCFbrxTd … where ML enthusiasts and college students alike will come together to tackle real-world challenges in the field and win cash prizes and swag. Participants can expect an immersive experience filled with learning, networking, and the opportunity to showcase their skills. Whether you’re a beginner eager to explore foundational concepts or an intermediate looking to add flair to your projects, this event offers something for everyone. What you can expect: 🚀 Tech talks: Deep dive tech talks on Computer Vision and Data-Centric AI. 🔧 Hands-on Workshops: Learn how to build AI applications through code examples 🏅 Engaging Challenges: Tackle real-world problems with AI solutions. 🤝 Networking Opportunities: Connect with fellow developers, builders, and industry experts. 🎁 Exciting Prizes: Demo your projects and compete for cash prizes. #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

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Join Daniel G. and Jacob Marks, PhD for an exciting computer vision hackathon in NYC: https://lnkd.in/gCFbrxTd … where ML enthusiasts and college students alike will come together to tackle real-world challenges in the field and win cash prizes and swag. Participants can expect an immersive experience filled with learning, networking, and the opportunity to showcase their skills. Whether you’re a beginner eager to explore foundational concepts or an intermediate looking to add flair to your projects, this event offers something for everyone. What you can expect: 🚀 Tech talks: Deep dive tech talks on Computer Vision and Data-Centric AI. 🔧 Hands-on Workshops: Learn how to build AI applications through code examples 🏅 Engaging Challenges: Tackle real-world problems with AI solutions. 🤝 Networking Opportunities: Connect with fellow developers, builders, and industry experts. 🎁 Exciting Prizes: Demo your projects and compete for cash prizes. #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

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Join Daniel G. and Jacob Marks, PhD for an exciting computer vision hackathon in NYC: https://lnkd.in/gCFbrxTd … where ML enthusiasts and college students alike will come together to tackle real-world challenges in the field and win cash prizes and swag. Participants can expect an immersive experience filled with learning, networking, and the opportunity to showcase their skills. Whether you’re a beginner eager to explore foundational concepts or an intermediate looking to add flair to your projects, this event offers something for everyone. What you can expect: 🚀 Tech talks: Deep dive tech talks on Computer Vision and Data-Centric AI. 🔧 Hands-on Workshops: Learn how to build AI applications through code examples 🏅 Engaging Challenges: Tackle real-world problems with AI solutions. 🤝 Networking Opportunities: Connect with fellow developers, builders, and industry experts. 🎁 Exciting Prizes: Demo your projects and compete for cash prizes. #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

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Building with Large Language Models (LLMs) can be incredibly frustrating! The hype is real, but the reality? It's like we're in the Lotus Notes era of LLMs—promising, yet limited and painful to manage. Integrating LLMs into your tech stack, alongside your compute cluster, relational database, and object storage, reveals their quirks and limitations, making them just another complex piece of the puzzle. They're not magic 🧙‍♂️! For anyone diving into the world of LLMs, here are some critical challenges to be aware of: 1. Unpredictable Behaviour: LLMs aren't deterministic, meaning getting them to perform specific tasks can feel like an endless struggle. 2. Shaky Developer Features: Many infrastructure providers boast about their developer features, but in practice, these tools often fail to deliver. The documentation may promise full support, but real-world usage tells a different story. 3. Provider Reliability: OpenAI is the only truly reliable provider for developers, yet their models are typically too large and slow for real-time applications like mine. They're great for the chat modality, if you're into building wrappers 🎁. 4. The Groq Hype: People are excited for Groq because of its speed and impressive chips, but their software stack is still in a very beta state. Anyone claiming to use Groq in production is either lying or taking a huge risk with their customer experience. That said, it's awesome having a new tool in our toolbelt for building new, creative software capabilities we've never had before. It's just really hard to merge the creativity with the need for most business use cases to be deterministic!

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Join Daniel G., Allen Lee and Jacob Marks, PhD for an exciting computer vision hackathon in #NYC on October 27: Registration is mandatory: https://lnkd.in/gYDVuaVS … where ML enthusiasts and college students alike will come together to tackle real-world challenges in the field and win cash prizes and swag. Participants can expect an immersive experience filled with learning, networking, and the opportunity to showcase their skills. Whether you’re a beginner eager to explore foundational concepts or an intermediate looking to add flair to your projects, this event offers something for everyone. What you can expect: 🚀 Tech talks: Deep dive tech talks on Computer Vision and Data-Centric AI. 🔧 Hands-on Workshops: Learn how to build AI applications through code examples 🏅 Engaging Challenges: Tackle real-world problems with AI solutions. 🤝 Networking Opportunities: Connect with fellow developers, builders, and industry experts. 🎁 Exciting Prizes: Demo your projects and compete for cash prizes. Join one of our three tracks 🧭   Level 0 – Beginner: For those who are just getting started on their ML Journey! 🛞  Level 1: – Intermediate: For those familiar with some Data Science or ML practices, but looking to level up! 🔥 Level 2 – Expert: For those who are the frontier of ML or Computer Vision today looking to make a positive impact! When and Where 11:30 AM – 4:30 PM Eastern #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

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Join Daniel G., Allen Lee and Jacob Marks, PhD for an exciting computer vision hackathon in #NYC on October 27: Registration is mandatory: https://lnkd.in/gYDVuaVS … where ML enthusiasts and college students alike will come together to tackle real-world challenges in the field and win cash prizes and swag. Participants can expect an immersive experience filled with learning, networking, and the opportunity to showcase their skills. Whether you’re a beginner eager to explore foundational concepts or an intermediate looking to add flair to your projects, this event offers something for everyone. What you can expect: 🚀 Tech talks: Deep dive tech talks on Computer Vision and Data-Centric AI. 🔧 Hands-on Workshops: Learn how to build AI applications through code examples 🏅 Engaging Challenges: Tackle real-world problems with AI solutions. 🤝 Networking Opportunities: Connect with fellow developers, builders, and industry experts. 🎁 Exciting Prizes: Demo your projects and compete for cash prizes. Join one of our three tracks 🧭   Level 0 – Beginner: For those who are just getting started on their ML Journey! 🛞  Level 1: – Intermediate: For those familiar with some Data Science or ML practices, but looking to level up! 🔥 Level 2 – Expert: For those who are the frontier of ML or Computer Vision today looking to make a positive impact! When and Where 11:30 AM – 4:30 PM Eastern #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

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Join Daniel G., Allen Lee and Jacob Marks, PhD for an exciting computer vision hackathon in #NYC on October 27: Registration is mandatory: https://lnkd.in/gYDVuaVS … where ML enthusiasts and college students alike will come together to tackle real-world challenges in the field and win cash prizes and swag. Participants can expect an immersive experience filled with learning, networking, and the opportunity to showcase their skills. Whether you’re a beginner eager to explore foundational concepts or an intermediate looking to add flair to your projects, this event offers something for everyone. What you can expect: 🚀 Tech talks: Deep dive tech talks on Computer Vision and Data-Centric AI. 🔧 Hands-on Workshops: Learn how to build AI applications through code examples 🏅 Engaging Challenges: Tackle real-world problems with AI solutions. 🤝 Networking Opportunities: Connect with fellow developers, builders, and industry experts. 🎁 Exciting Prizes: Demo your projects and compete for cash prizes. Join one of our three tracks 🧭   Level 0 – Beginner: For those who are just getting started on their ML Journey! 🛞  Level 1: – Intermediate: For those familiar with some Data Science or ML practices, but looking to level up! 🔥 Level 2 – Expert: For those who are the frontier of ML or Computer Vision today looking to make a positive impact! When and Where 11:30 AM – 4:30 PM Eastern #computervision #ai #artificialintelligence #machinevision #machinelearning #datascience

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