Emily Fox’s Post

If you have been navigating the job market recently (first of all, my best wishes to you!) then, chances are, you have used Generative AI one way or other in your journey - improving your resume, crafting tailored cover letters or refining email communications. And why not, the LLMs are surprisingly good at these writing tasks. But is that all there is? I believe we are just scratching the surface here. There has to be more ways AI can empower candidates to present their best selves! The other side of this is even less discussed. With companies grappling with dwindling recruiter resources, hiring managers having to wear many hats, and having hundreds of applicants to sift through for any role to fill - we see the hiring workflow everywhere is creaking under pressure. If, like me, you have pondered over ways improve this vital function of finding the right person for a job, I bet you felt a sense of cautious optimism with the recent developments in Generative AI. From creating better interview plans, to better screening, help during live interviews, deep understanding of candidates, the potential applications of Generative AI offer a glimmer of hope for a more efficient and equitable future. By leveraging data-driven, bias-free decision-making, we can foster a hiring landscape that is not only more effective but also more inclusive. I'll talk more about this at Voxel51's March 21 AI Meetup. If you're interested to dive deeper on this topic, register (link below) so you can put it on your calendar. #artificialintelligence #aiforall #aiforgood

Thank you so much to Greylock for this wonderful piece about Snorkel's journey! This is obviously a piece about the entire Snorkel team (including the extended Snorkel team of Saam Motamedi et al at Greylock). But it's also a narrative about the AI space as a whole, where data has always been spoken to as an obviously key ingredient, but rarely focused on proportionate to its absolutely critical role. After all, who wants to label and munge data when there are fancy new algorithms/models/frameworks to play with?? But data has *always* been the key to real production AI. Especially today, as models (LLMs) and algorithms standardize and commoditize, the programming interface & key differentiator for AI is data. Whether going into a prompt, or for evaluation, post-training/fine-tuning, pre-training, or now tuning of inference time search/reasoning- labeled, curated data is the key. And as more and more enterprises beyond the major LLM developers take data development into their own hands, to put their own unique data and expertise to work, and to own the result- it is an exciting time to be working in data-centric AI!

Data scientists and ML engineers often post their blogs on Medium or Towards Data Science. Research papers find their home in academic journals, conference proceedings, or arXiv. But what about all the projects in between? That's why we created the Ready Tensor AI Project Showcase. We are thrilled by the diverse and innovative submissions we have received from around the globe. From healthcare breakthroughs to innovations in finance, the creativity of our AI community is truly inspiring. A few noteworthy entries: - MobileNetV3 for breast histopathology image classification: https://lnkd.in/eCUyS2yd - AI-powered invoice validation for scalable document processing: https://lnkd.in/ecxtz_VG - SumBot FreeCode - An AI assistant in Slack that summarizes long texts: https://lnkd.in/e7PqjHPq In response to overwhelming community interest, we have extended the submission deadline to September 20th, 2024! Why participate? - Showcase your AI projects (past or present) - Gain recognition in the global AI community - Build a professional portfolio for career advancement - Connect with fellow innovators and industry experts - Chance to win prizes and certificates of achievement Whether you are a student, professional, or AI enthusiast, this is your opportunity to shine. It takes just about an hour to submit your project! Ready to join? Start here: https://lnkd.in/e7qez3KT #AIShowcase #ReadyTensor #AIInnovation #AICommunity #MachineLearning #DataScience

When Alexander Ratner joined the Stanford Artificial Intelligence Lab as a PhD student in 2014, he planned on spending most of his time researching better machine learning modeling techniques and algorithms. But soon after starting in the lab, a variety of people from across the Stanford campus began coming to him with similar requests: help with labeling and curating training data. “At first, we ignored these requests, thinking - like most did in AI then - that this wasn’t our problem,” says Ratner. “Labeling and developing the data used for training and evaluating AI models was viewed as an upstream janitorial task. All people cared about in AI was the fancy new model.” Turns out, the process of training models with the right data became far more important than Ratner and his team initially surmised. He now regards data to be the deciding factor in whether an ML project succeeds or fails. Today, Snorkel AI, which Ratner and four colleagues spun out of SAIL in 2019, works with everyone from Memorial Sloan Kettering to Wayfair to seven of the top US banks. In each case, Snorkel has helped shrink the time to prepare and label raw data from months down to just weeks. Read on for more about Snorkel's journey, and how the company stayed true to their original insight of the importance of the data during the GenAI boom. https://bit.ly/4fKtDQ2

Five researchers from the CMU Human-Computer Interaction Institute – Hong Shen, Wesley Hanwen Deng, Motahhare Eslami, Ken Holstein, and Jason Hong – have been named 2024 Microsoft Research AI & Society Fellows. Congratulations! Our 5 Research AI & Society Fellows will join researchers at Microsoft on the following interdisciplinary collaborations: -Hong Shen, assistant research professor, will be working on the research challenge “Supporting the Responsible AI Red-Teaming Human Infrastructure." -Wesley Deng, HCI PhD student; Ken Holstein, assistant professor; Motahhare Eslami, assistant professor; and Jason Hong, professor, will be collaborating on the research challenge “Regulating AI in Light of the Challenges of Doing Responsible AI in Practice.” 🔗 Details: https://lnkd.in/epDXxukY #cmuhcii #CarnegieMellon #HumanCenteredAI #ResponsibleAI #ArtificialIntelligence Carnegie Mellon University School of Computer Science

Join us at the #Healthcare #NLPSummit 2024 on April 2-3, where Rupinder Singh CTO at COZEVA - Applied Research Works, Inc,, will share his insights on ‘Integrating Multimodal RAG and Human-in-the-Loop for Scalable Healthcare IT Applications: From Analytics to Dx Coding’ Rupinder (Rosh) Singh serves as the Chief Technology Officer at Cozeva, bringing a wealth of expertise with over 15 years in product management, artificial intelligence, machine learning, and mathematical modeling. His previous role as Vice President of Product Management at Cozeva saw him leading the charge on developing innovative analytics, business intelligence, and new technology solutions to enhance payer-provider risk-sharing agreements. Singh’s notable contributions include creating decision science technology that underpins multibillion-dollar transactions and patented methods to advance cardiac care. His career also includes a tenure at Amazon, and he holds a Ph.D. in Biomedical Engineering from Cornell University. Don't miss this chance to learn from his expertise and insights. Register now for free: nlpsummit.org Read more: https://lnkd.in/dRNvC3nu #LLMs #HealthcareLLMs #nocode #GenerativeAI #HealthcareAI #MedicalLLMs #nlp #naturallanguageprocessing #DataScience #DataEngineering #DataAnalysis #DataInsights #TextProcessing #DeepLearning #DataScience

Come find us at the AI FedLab conference and let's talk ServiceNow Gen AI and what that means in the Fed Space! #servicenow #nowassist #servicenowfed

Unlock the future of technology with our Artificial Intelligence, Data Science & Machine Learning program! Enroll now and embark on a journey of innovation and discovery. Don't Wait, Admissions are now open! ➡ For Registration, log on to: https://lnkd.in/drS2pns2 ➡ To Contact, call on to: 8184923333 . #datascience #machinelearning #artificialintelligence #AI #techology #engineering #bechelorofengineering #engineering #btech #be #becheloroftechnology

Advancing AI: With funding from U.S. Department of Energy (DOE), two Argonne scientists will develop cutting-edge AI technologies that promise to accelerate discovery while preserving data privacy and improving energy efficiency - https://bit.ly/3YkKWBB Computational mathematician, Kibaek Kim, was awarded funding for his project, "Privacy-Preserving Federated Learning for Science: Building Sustainable and Trustworthy Foundation Models." This project aims to innovate the way large-scale AI models are trained on distributed datasets across multiple institutions without centralizing the datasets. Franck Cappello, senior computer scientist, received funding for "Tensor-Compressed Sustainable Pre-Training of Extreme-Scale Foundation Models," a project that focuses on making AI models more energy-efficient while maintaining their ability to handle complex scientific problems.

🌠 We're proud to share that our latest paper has been published in the renowned journal Patterns, alongside our esteemed Roche and NVIDIA AI co-authors. 🔍 In this publication, we discuss how Federated Learning (FL) is reaching a tipping point - not just in academic research, but also in real-world industry applications. By sharing our experiences from multi-hospital and multi-pharma collaborations, we illustrate how FL is moving beyond theoretical frameworks into practical, production-grade solutions. These solutions are built with cross-disciplinary collaboration to ensure compliance, privacy, and security, making FL a viable and scalable approach for healthcare and life sciences. 🙌 A huge thank you to our authors: Eric Boernert (Roche), Holger R. Roth (NVIDIA), and our own Inken Hagestedt, Ian Hales, , Michael Höh, Robin Röhm, Ellie Dobson, and José-Tomás (JT) Prieto. We also extend our thanks to Cell Press for this opportunity to contribute to such an important body of work! 🔗 Read the full article and explore the real-world possibilities to work on distributed, proprietary life science and health data while keeping data fully protected: https://hubs.li/Q02Xx8KM0 #FederatedLearning #HealthcareAI #LifeSciences #DataPrivacy #DataSecurity #DataCollaboration #AI