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GPT Weekly - 19the June Edition - OpenAI's function calling, Meta's free LLM, EU Regulation and more.

This is a recap covering the major news from last week. 🔥Top 3 news - OpenAI’s updates, Meta’s upcoming free LLM and EU Regulation 🗞️Interesting reads include PSA about protecting your keys, The GPT ouroboros, Reddit - OpenAI’s moat, and more.. 🧑‍🎓Learning includes a Step-by-step guide from a non-technical founder who launched his MVP, Chatbot for your Gdrive and more 🔥Top 3 AI news in the past week OpenAI: New Pricing, Models, & Functions OpenAI has been on a roll. Last week we saw the release of OpenAI best practice on using GPT. This week we saw some amazing updates. Three major buckets were: First, the price decreases for both embeddings and GPT-3.5 tokens. Second, new models for gpt-4 and gpt-3.5. A new longer context model for gpt-3.5. Third, a new function calling capability. Why is it important? Previously, the output from OpenAI was all text. So, calling an external API from GPT was quite difficult. You had to parse the text data and things were often incorrect. Langchain created the Agents and Tools feature to tackle this problem. It was still unreliable and prone to issues. Now you get native support to generate a fixed format output. You can use the output to generate functional calls and also pass functions which need to be called. For example, if your app has multiple API endpoints then you can use GPT to generate the API calls with parameters. You can also pass the endpoints as function calls to ensure the correct function is executed. This functionality can further be used to generate structured data (JSON) out of GPT. So, you can generate data from GPT and load it into your backend. What’s next? This functionality allows turning natural language responses into structured data. This can be used to create “intelligent” backends using LLMs. We might see implementations in no-code tools to allow more robust and natural-language tools for non-technical folks. The structured data process goes both ways. You can also feed structured data into GPT for better responses. This feature also has its share of issues. Function calling suffers from the same prompt injection issues. Malicious actors can pass malicious code in function or the responses. For example, creation of queries using functions might contain malicious code to delete data. Without proper user validation this code will be executed automatically and delete data. So, using LLM as the back-end layer needs proper security implementation. Meta's LLM: Commercial Use Ahead Llama has been a boon for the open source community. Many of the open source models rely on Llama. The issue is that Llama is research-only and cannot be used commercially. So, no one can use it to build any product. Meta is now working on the next version of the model. This model will be available for commercial use. This is in stark contrast to both OpenAI and Google. Both safe-guarde their models and make it available through API. Why is it important? Certain industries cannot use LLM APIs because of strict restrictions on data privacy. These companies would want to run their own instance of a foundational model. A commercially available foundational model is also going to help people who want to keep their “API call” costs next to 0. A commercially available free-for-all model will also help push the open source community further. Just like Llama. What’s next? Sam Altman has said OpenAI didn’t release GPT-3 as open-source because they didn’t think people would be able to run it. Now OpenAI is working on an open-source model. This is going to be weaker than GPT-4. Let the battle of LLMs begin. EU's Proposed Legislation and Its Impact on AI Usage The EU parliament voted to move ahead with the E.U. AI Act. This act aims to ensure consumer protection against the dangers of AI. Why is it important? OpenAI and Sam Altman want regulations for models. They have proposed a IAEA-type of agency to stop the proliferation of LLM models. As per OpenAI, all models should be regulated and monitored. The suggestion of a license based regulation has led to significant backlash. Many people have called it “regulatory capture” - with the aim of shutting down competing LLMs. Licensing based regulations might not really be effective. The EU is approaching regulation from a different angle. It doesn’t focus on how models are developed. Rather focuses on how AI will/can be used. They have broken down use cases into 4 categories - unacceptable (prohibited), high, medium and low risk. For example, Building a Pre-Crime software,on%20crimes%20not%20yet%20committed.) to predict crimes? Building a Social credit system? Unacceptable. Using tools to influence elections or recommendation algorithms? High (Highly regulated). Using generative AI tools to create text or images on news sites? Medium (Add label that the content is AI generated) AI providers also need to disclose their training source. To me this sounds like good legislation. What do you guys think? But, OpenAI has warned that EU regulations might force them to pull out completely. What’s next? The disclosure requirements might help various publishing companies. AI and media companies are in talks to pay for training data. Google has been leading the charge. Additionally, OpenAI and Deepmind will open their models for safety and research purposes to the UK government. 🗞️10 AI news highlights and interesting reads PSA: If you are using Repl to write code, you might want to check your OpenAI API keys. If you have left them embedded then people can pirate and steal the keys. LLMs rely on human annotation or human feedback to learn. And one way to generate human annotation is crowdsourcing. But what if the crowdsource human annotators use LLMs? Research shows 33-46% workers used LLMs. So, basically we go from Human -> AI -> Human -> AI. The AI ouroboros. Researchers also say generated data to train models might cause serious issue. All the talks about moats \- Reddit might be OpenAI’s \future\ moat. Given the amount of complaints about how Google search experience has deteriorated during the blackout, this might be true? Doctors are using ChatGPT but not to diagnose.Rather to be more empathetic. We discussed this just a month ago. And guess where the data for this study came from? Reddit AskDocs. Moat FTW?! Beatles to make a comeback…using Generative AI. SnapFusion - Text to Image diffusion on mobile phones. Large context lengths are important for better GPT experience. The secret sauce for 100k context length. There is a lot of bad AI research out there. Some border on snake oil. Most AI “research” should be double checked and challenged. A new research on huggingface said that GPT-4 can ace MIT curriculum. Now someone is replicating the results and say that GPT-4 can’t beat MIT. Are we seeing peak AI? Especially when people from Deepmind and Meta are involved? Mistral AI raised $113 million in seed round with no product. Some might say this funding is for the team and the team is really solid. The issue though is whether the valuation is justified when OpenAI and Google already have a head start. The AI Hype Wall of Shame. \- Collection of articles which mislead people about AI in various aspects. 🧑‍🎓3 Learning Resources Building and Launching a company using GPT-4 with prompts. (The author didn’t know how to code but created and launched the MVP in a month). Chatbot for your Gdrive - https://www.haihai.ai/gpt-gdrive/ Building ChatGPT plugin using Supabase - https://supabase.com/blog/building-chatgpt-plugins-template That’s it folks. Thank you for reading and have a great week ahead. If you are interested in a focused weekly recap delivered to your inbox on Mondays you can subscribe here. It is FREE!

#reddit #weekly #19the

Tic Tac Toe Sweetheart Edition Can You Beat the AI? Prove Your Strategy Skills Today!

basavarajavyadav•This week

Tic Tac Toe Sweetheart Edition Can You Beat the AI? Prove Your Strategy Skills Today!

Need your feedback 🙏

#reddit #sweetheart #edition

How do you learn details / potential strategy about technically important new laws in the jurisdictions you operate in?

friendofherschel•This week

How do you learn details / potential strategy about technically important new laws in the jurisdictions you operate in?

I am reading “The Entrepreneur’s Guide to Law and Strategy” and it’s really fantastic so far about giving a pretty great overview of these aspects of business. It was published by Wiley (a reputable textbook publisher) in 2018. In one chapter, the authors go into the EU’s “right to be forgotten” and it got me thinking about complying with laws like that. Unfortunately, the latest edition of the book is still nearly 7 years old and written pre-COVID, pre-genAI, pre-social network and privacy pushback, etc. I assume every time a new law comes out that can impact my business (say, a random privacy law in California) that businesses aren’t just telling their lawyers “use any amount of hours you need to in order to read the San Jose papers every day and then write me a one paragraph brief with an outline and potential changes needed to our business, also all the other papers across the world”. They’d spend a fortune. There has to be something I’m missing. Is there a law review for business that I should be following? I operate in the US only at this time. A more technical newspaper (I take WSJ, but it’s not technical enough for this sort of thing. It might give the “what”, but won’t give a small business owner “what to do with it”)? PS: I’m the type of person who read every word of my mortgage. I am aware the answer might be “don’t worry about it”. But I do worry about it, and am trying to fix that. For example, the insanely popular new lawsuits about website accessibility. I want to avoid things (essentially low hanging lawsuit fruit) like that before they happen to me.

#reddit #learn #details

Running and selling multiple side projects alongside a 9-5

leanpreneur1•This week

Running and selling multiple side projects alongside a 9-5

My current side project started 56 days ago when I started writing 1,000 words per day. My core businesses are an agency and job board, and I just needed a creative outlet. The likes of Chris Guillebeau and Nathan Barry attribute their progression to writing so I thought I’d see if it might do the same for me. At first I was just vomiting words onto the screen, I made a blog and wrote mainly technical guides related to my skills. Over time I realised I was writing more and more about running a business as a solopreneur, or lean operator. There is tons of content out there giving you the Birds Eye of going from 0 to £10m. Inspiring stuff, but I think there is a void in real content, explaining the nuts and bolts of the how. What is the day-to-day like for the solopreneurs who make a good living and have plenty of free time? That’s what I’m striving for anyway. I’m not talking about the 7-figure outliers. Or the ones teaching you to make content so you can have a business teaching others how to make content, and so on. I’m also sick of the ‘I made $X in 5 minutes and how you can too’ So, I started chatting to people in my network who run lean businesses and/or side hustles. I ask them a bit about their journey and ask them to teach something - how they operate, or a skill/process/system/tool that other people like you/me will find useful. One of my first chats was with Sam Dickie, who runs multiple side projects so thought I’d share here, see if others find it useful and get some feedback. I’ve removed all links as I’ve never posted on Reddit before so conscious of not being promotional, I’m posting this stuff to a tiny email list of friends with no upsells. Just finding my feet on whether others find it useful or not: — Sam is a serial entrepreneur who builds projects in his spare time whilst working a 9-5. He’s scaled and sold multiple ventures and currently runs one of the best newsletters out there for builders and entrepreneurs. Building audience through newsletters has always been a cornerstone strategy for him, so, along with sharing his advice on solopreneurism, he’s also generously shared his lean newsletter writing process. About Sam Sam is a Senior Product Manager who has spent the last 15 years working in the tech sector after starting his career as a town planner. In addition to his job he spends some of his spare time building side projects. These have included a 3D printing startup, a tech directory, a newsletter, a beta product directory, and consultancy. Sam is the epitome of making a success out of following your interest and curiosity. It’s clear he enjoys his business ventures and builds in a risk-free way. It’s often touted by business gurus to avoid building around your interests, but Sam bucks the trend successfully. I think he’s someone who has already found his 1,000 true fans. Descending rabbit holes, Sam’s journey of invention and curation 3D printing Sam’s first foray into launching a startup was with Fiilo, a 3D printing business. This was at the height of the 3D printing craze and he self-admits that he used the launch as an excuse to buy a 3D printer. He ended up with two and launching a product called GrowGo. GrowGo is a sustainable 3D-printed product that turns any bottle into somewhere that you can grow plants and herbs. He eventually sold this business and the printers, making around £10k. Along the way, he was exposed to various business tasks, including building a website in Weebly, the biggest nocode website builder of the time, and built an API that enabled print on demand for his product. NoCode.Tech The experiences of building as someone non-technical led to numerous friends asking how he built all of this tech. Back then, nocode wasn’t popular, and it had almost zero search volume, so Sam created a basic directory. A quick landing page on Weebly with a basic value prop, a short explanation and a list of the tools he had used before. It hit the top spot on Product Hunt, and he landed 2,000 subscribers in the first 48 hours. But, he hadn’t built it at this point, so he set about getting to work. He built the directory and list to 30,000 subs and monetised the site through advertising. At its peak with Sam, it was receiving about £2,000 per month in ad revenue. He was still working his 9-5 at this point, so thought it might be a good time to exit. The site was still growing, but it was becoming anxiety inducing whilst he was still working full-time. So, he ended up selling the site and making friend’s with the buyer. Fast forwarding a bit, Nocode.tech was eventually acquired by Stackr, a nocode app. Sam was working for their competitor at the time and ended up being offered a job by his friend who acquired the site. All of this from a side project in his area of passion. Creator Club After selling the directory, Sam lost his outlet for sharing his tools and learnings. Being fascinated with curation and loving sifting through for nuggets, he invested more time into his personal website and launched Creator Club newsletter. Sam writes monthly and currently has over 8,000 subs. It’s one of the few newsletters that I let bypass my email filters and land in my main inbox. Life as a Part-Time Multipreneur Side Hustler If it’s not obvious already Sam is a curiosity led business creator. He’s found that the products without a revenue focus or intention have ironically outperformed those created for the sole purpose of creating money. He enjoys working on his side hustles. He could have run the Nocode.Tech for 10 more years and wouldn’t have tired of it as it’s a byproduct of his interest. For this reason, he has also created the Beta Directory, simply because he loves unearthing early-stage products. He admits he gets the fear when he thinks about quitting his 9-5, although he suspects if he devoted the same energy to one of his projects it could replace his income (no doubts from me here). This same fear means that he can run his ventures with less fear. This way, he can experiment with freedom and isn’t risking the ranch with a young family to consider. For example, recently he stopped paid sponsors on his newsletter as it was more stress than the value of the income to him. Sam divides his time on evenings and weekends (unequally) between the following: Creator Club Validation Co Beta directory Consultancy The pure side hustle status magnifies the need to run lean, let’s jump into his process…. Sam’s lean newsletter curation and creation process Starting out publishing his personal newsletter Going against his expertise, Sam originally over-engineered his process. He curated with Feedly and tried to automate the full writing process with Zapier. The trouble is that there are too many points of failure which can lead the whole chain to break down, and you spend more time fixing the system. For a 200 subscriber newsletter, he needed to pare things back. His set-up now Sam scaled back and now simple builds automations when he needs them. He keeps the process simple, right down to the design and any welcome automations. Keeping things real We touched on the trend that keeping things raw is better. Content has come full circle with the advent of AI. Everything looks too perfect and consequently, people’s tastes are changing. Sam mentioned watermarks that show content isn’t AI written, and we referenced content such as Greg Isenberg’s sketches, and Chris Donnelly’s image posts. \\Step by Step Process:\\ Using Stoop Inbox to manage sources Curation with Pocket Managing content with Airtable and Zapier Using Bearly to summarise Substack for writing Monitoring content sources Sam uses Stoop Inbox, an RSS curation tool, to manage his content sources. It gives him a dedicated email address for newsletters and he follows an Inbox Zero methodology. He checks in daily in Stoop, and on X, Reddit and IndieHackers. With X, he just uses the standard interface but has been careful to curate his feed, sometimes adding in extra notifications to hear from interesting people. Highlighting content When curating links, Sam uses Arc browser and the Pocket extension to save links. It’s super simple and lightweight. He creates tags which trigger an automation that curates the link to Airtable. If you watch the video, here’s a shoutout to Alice, the AI interface I use which has recently featured on Product Hunt. It’s a fantastic tool with bags of potential to enhance a solopreneur’s life. Ranking and sorting content He sends the links indexed using Pocket to a basic Airtable base via Zapier. From there, he grades the content and sets aside some time to read it in more depth. Pocket pulls through the title, metadata, and URL link. Review Sam does this manually but has used a tool as a shortcut for digesting long form content — Bearly.ai. Bearly.ai was created by Trung Phan and linking back to raw content, Trung is 1/3 of the hosts on the Not Investment Advice podcast. Its irreverent style and thumbnail are an example of a successful podcast that doesn’t over polish. Writing it all up Being a huge Notion fan (check out the free templates on his site), Sam originally used Notion for writing and linked it into Revue. When Elon sunsetted Revue, he switched to Substack. He loves the Substack interface so drafts in Substack based on a duplication of last month’s edition. Before publishing, Sam runs through a 10-point Notion checklist, which he shared with me. Parting Advice Keep your tool stack as lean as possible. Avoid tool switching to the shiny new object. Getting launched quickly is key. Don’t think that you have to be everywhere for distribution, Sam sticks with what he knows on X and LinkedIn. Overall, he advises just keeping things simple and therefore minimising risk. Resources He says they’re cliche, but I don’t agree; they’re timeless. Paul Graham of Y Combinator is someone Sam recommends following. He doesn’t write much, which is great as Sam gets anxiety when someone good often writes and he can’t keep up with the writing. His content is well thought out and distills complex concepts in entrepreneurship and startups. In addition, Sam loves Naval Ravikant’s approach. He mentions checking out the Almanac of Naval Ravikant for collected wisdom. Follow Sam’s Journey Again, not going to link here but you can find Sam’s stuff easily enough if you want to. His personal website is beautiful and contains loads of free downloads. He has also curated personal websites he admires if you need some inspiration. Sam is a super nice guy so reach out to him, I did before I started my personal blog recently, and he gave me some great advice. Also, worth keeping an eye on Validation Co, where he aims to help early-stage makers and creators validate their ideas. He’s building super slow — trying to enjoy the process without unachievable deadlines. Maintaining his stamina and passion. Amazing, I hope he writes more about that soon! -- That’s my second shot at an interview, hope you enjoyed it and found something useful in it. I’m talking to a marketplace founder who spends 2–3 hours per month his project, a multiple job board owner with a 9-5 and a leading book designer next. As this is my side project, should I keep going?

#reddit #running #selling

[N] TheSequence Scope: When it comes to machine learning, size matters: Microsoft's DeepSpeed framework, which can train a model with up to a trillion parameters

Kseniase•This week

[N] TheSequence Scope: When it comes to machine learning, size matters: Microsoft's DeepSpeed framework, which can train a model with up to a trillion parameters

Hi there! Offering to your attention the latest edition of a weekly ML-newsletter that focusing on three things: impactful ML research papers, cool ML tech solutions, and ML use cases supported by investors. Please, see it below. Reddit is a new thing for me, and I've been struggling a bit with it, so please don't judge me too harsh for this promotion. This weekly digest is free and I hope you'd find the format convenient for you. Your feedback is very appreciated, and please feel free to sign up if you like it. 📝 Editorial The recent emergence of pre-trained language models and transformer architectures pushed the creation of larger and larger machine learning models. Google’s BERT presented attention mechanism and transformer architecture possibilities as the “next big thing” in ML, and the numbers seem surreal. OpenAI’s GPT-2 set a record by processing 1.5 billion parameters, followed by Microsoft’s Turing-NLG, which processed 17 billion parameters just to see the new GPT-3 processing an astonishing 175 billion parameters. To not feel complacent, just this week Microsoft announced a new release of its DeepSpeed framework (which powers Turing-NLG), which can train a model with up to a trillion parameters. That sounds insane but it really isn’t. What we are seeing is a consequence of several factors. First, computation power and parallelization techniques have evolved to a point where it is relatively easy to train machine learning models in large clusters of machines. Second and most importantly, in the current state of machine learning, larger models have regularly outperformed smaller and more specialized models. Knowledge reusability methods like transfer learning are still in very nascent stages. As a result, it’s really hard to build small models that can operate in uncertain environments. Furthermore, as models like GPT-3 and Turing-NLG have shown, there is some unexplainable magic that happens after models go past a certain size. Many of the immediate machine learning problems might be solved by scaling the current generation of neural network architectures. Plain and simple, when it comes to machine learning, size matters. We would love to hear your opinions about the debate between broader-larger vs. smaller and more specialized models. Leave a comment Now, to the most important developments in the AI industry this week 🔎 ML Research GPT-3 Falls Short in Machine Comprehension Proposed by researchers from a few major American universities, a 57-task test to measure models’ ability to reason poses challenges even for sophisticated models like GPT-3 ->read more in the original paper Better Text Summarization OpenAI published a paper showing a reinforcement learning with human feedback technique that can surpass supervised models ->read more on OpenAI blog Reinforcement Learning with Offline Datasets Researchers from the Berkeley AI Research (BAIR) Lab published a paper unveiling a method that uses offline datasets to improve reinforcement learning models->read more on BAIR blog 🤖 Cool AI Tech Releases New Version of DeepSpeed Microsoft open-sourced a new version of DeepSpeed, an open-source library for parallelizable training that can scale up to models with 1 trillion parameters->read more on Microsoft Research blog 💸 Money in AI AI-powered customer experience management platform Sprinklr has raised $200 million (kudos to our subscribers from Sprinklr 👏). Sprinklr's “AI listening processing” solution allows companies to get structured and meaningful sentiments and insights from unstructured customer data that comes from public conversations on different websites and social platforms. Xometry, an on-demand industrial parts marketplace, raises $75 million in Series E funding. The company provides a digital way of creating the right combination of buyers and manufacturers. Another example of AI implementation into matching two sides for a deal. Real estate tech company Orchard raises $69 million in its recent funding round. Orchard aims to digitize the whole real estate market, by developing a solution that combines machine learning and rapid human assistance to smooth the search, match the right deal, and simplify buying and selling relationships. Cybersecurity startup Pcysys raised $25 million in its funding round. Pcysys’ platform, which doesn’t require installation or network reconfiguration, uses algorithms to scan and “ethically” attack enterprise networks. Robotics farming company Iron Ox raised $20 million in a funding round. The system of farming robots is still semi-autonomous, the company’s goal is to become fully autonomous. Insurtech company Descartes Underwriting raised $18.5 million. The company applies AI and machine learning technologies to climate risk predicting and insurance underwriting. Legaltech startup ThoughtRiver raised $10 million in its Series A round. Its AI solution applied to contract pre-screening aims to boost operational efficiency. Medtech startup Skin Analytics raised $5.1 million in Series A funding. Skin Analytics has developed a clinically validated AI system that can identify not only the important skin cancers but also precancerous lesions that can be treated, as well as a range of lesions that are benign. Amazon, along with several government organizations and three other industry partners, helped fund the National Science Foundation, a high-priority AI research initiative. The amount of funding is not disclosed. The content of TheSequence is written by Jesus Rodriguez, one of the most-read contributors to KDNuggets and TDS. You can check his Medium here.

#reddit #thesequence #scope:

[N] Last Week in AI News Digest - Automated chemical synthesis, using heartbeats to detect deepfakes, and more!

regalalgorithm•This week

[N] Last Week in AI News Digest - Automated chemical synthesis, using heartbeats to detect deepfakes, and more!

Hi there, just sharing the latest edition of our AI news digest newsletter! We're just a couple of AI grad students doing this for fun, so hope the self promotion is not too annoying (also, welcome feedback). See it below, and feel free to subscribe. Mini Briefs Robotics, AI, and Cloud Computing Combine to Supercharge Chemical and Drug Synthesis IBM recently demoed a complex system for chemical testing and drug synthesis. The system has an AI component that predicts the results of chemical reactions, and a fully automated robotic experiment setup that runs chemical tests 24/7. Users can access the remote robotics lab online, and IBM can also install the system on-premise. With these tools working together, IBM is hoping to reduce typical drug discovery and verification time by half. AI researchers use heartbeat detection to identify deepfake videos Researchers from multiple groups are tackling the challenge of detecting deepfake videos by analyzing the apparent heartbeat of the people depicted in the video. This is possible, because a person’s blood flow changes their skin color ever so slightly, and this change is often detectable via a process called photoplethysmography (PPG). Because deepfakes are not currently optimizing to generate realisitic heartbeats, temporal or spatial anomalies in PPG signals allow resesarchers to detect deepfakes with a 97% accuracy. Advances & Business This AI Expert From Senegal Is Helping Showcase Africans In STEM \- Adji Bousso Dieng will be Princeton’s School of Engineering’s first Black female faculty. Google’s AI-powered flood alerts now cover all of India and parts of Bangladesh \- India, the world’s second most populated nation, sees more than 20% of the global flood-related fatalities each year as overrun riverbanks sweep tens of thousands of homes with them. Two years ago, Google volunteered to help. Finding magnetic eruptions in space with an AI assistant \- MMS look for explosive reconnection events as it flies through the magnetopause - the boundary region where Earth’s magnetic butts up against the solar wind that flows throughout the solar system. This know-it-all AI learns by reading the entire web nonstop \- Diffbot is building the biggest-ever knowledge graph by applying image recognition and natural-language processing to billions of web pages. Bosch and Ford will test autonomous parking in Detroit \- Ford, Bosch, and Dan Gilbert’s real estate firm Bedrock today detailed an autonomous parking pilot scheduled to launch in September at The Assembly, a mixed-used building in Detroit’s Corktown neighborhood. Create your own moody quarantine music with Google’s AI \- Lo-Fi Player, the latest project out of Google Magenta, lets you mix tunes with the help of machine learning by interacting with a virtual room. Apple launches AI/ML residency program to attract niche experts \- As Apple’s artificial language and machine learning initiatives continue to expand, its interest in attracting talent has grown - a theme that’s barely under the surface of the company’s occasionally updated Machine Learning Research blog. Dusty Robotics CEO Tessa Lau Discusses Robotics Start-Ups and Autonomous Robots for Construction \- Tessa Lau is Founder/CEO at Dusty Robotics, whose mission is to increase construction industry productivity by introducing robotic automation on the jobsite. Concerns & Hype Google Offers to Help Others With the Tricky Ethics of AI \- Companies pay cloud computing providers like Amazon, Microsoft, and Google big money to avoid operating their own digital infrastructure. The Peace Dividends Of The Autonomous Vehicle Wars \- The rapid growth of the mobile market in the late 2000s and early 2010s led to a burst of technological progress. Ethics must be part of the development process’ \- The increasing use of AI (artificial intelligence) in the development of new medical technologies demands greater attention to ethical aspects. Analysis & Policy China’s new AI trade rules could hamper a TikTok sale \- TikTok’s attempt to sell itself and avert a possible US ban may run into some complications. The Wall Street Journal reports that China has unveiled new restrictions on AI technology exports that could affect TikTok. Podcast Check out our weekly podcast covering these stories! Website | RSS | iTunes | Spotify | YouTube

#reddit #last #week

[D] Last Week in Medical AI: Top Research Papers/Models 🏅(September 21 - September 27, 2024)

aadityaura•This week

[D] Last Week in Medical AI: Top Research Papers/Models 🏅(September 21 - September 27, 2024)

Last Week in Medical AI: Top Research Papers\/Models 🏅$September 21 - September 27, 2024$ Medical AI Paper of the Week A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor? This paper presents o1, a Large Language Model (LLM) evaluated across 37 medical datasets demonstrating superior performance in clinical understanding, reasoning, and multilinguality compared to GPT-4 and GPT-3.5. Medical LLM & Other Models: DREAMS: Python Framework for Medical LLMs A comprehensive deep learning framework for EEG data processing, model training, and report generation. SLaVA-CXR: A Small Language and Vision Assistant for Chest X-Ray Report Automation This paper introduces SLaVA-CXR, an innovative small-scale model designed for automating chest X-ray reports with high accuracy and efficiency. O1 in Medicine: AI Doctor Potential Genome Language Model : Opportunities & Challenge It highlights key gLM applications like functional constraint prediction, sequence design, and transfer learning, while discussing challenges in developing effective gLMs for complex genomes. Medical LLMs & Benchmarks: MEDICONFUSION: Probing Medical LLM Reliability This paper introduces MediConfusion, a challenging benchmark for probing the failure modes of multimodal large language models (MLLMs) in medical imaging. CHBench: Chinese LLM Health Evaluation This paper introduces CHBench, the first comprehensive Chinese health-related benchmark designed to evaluate large language models (LLMs) on their understanding of physical and mental health. LLMs for Mental Illness Evaluation PALLM: Evaluating Palliative Care LLMs Protein LMs: Scaling Necessity? Frameworks and Methodologies: Digital Twin for Oncology Operations Enhancing Guardrails for Healthcare AI InterMind: LLM-Powered Depression Assessment Conversational Health Agents: LLM Framework Medical LLM Applications: LLMs for Mental Health Severity Prediction Fine-tuning LLMs for Radiology Reports LLMs in Patient Education: Back Pain Boosting Healthcare LLMs with Retrieved Context Continuous Pretraining for Clinical LLMs AI in Healthcare Ethics: Confidence Intervals in Medical Imaging AI Generative AI Readiness for Clinical Use ... Check the full thread in detail: https://x.com/OpenlifesciAI/status/1840020394880667937 Thank you for reading! If you know of any interesting papers that were missed, feel free to share them in the comments. If you have insights or breakthroughs in Medical AI you'd like to share in next week's edition, connect with us on Twt/x: OpenlifesciAI

#reddit #last #week

regalalgorithm•This week

[N] Last Week in AI News Digest - Automated chemical synthesis, using heartbeats to detect deepfakes, and more!

#reddit #last #week

Edition

GPT Weekly - 19the June Edition - OpenAI's function calling, Meta's free LLM, EU Regulation and more.

Tic Tac Toe Sweetheart Edition Can You Beat the AI? Prove Your Strategy Skills Today!

How do you learn details / potential strategy about technically important new laws in the jurisdictions you operate in?

Running and selling multiple side projects alongside a 9-5

[N] TheSequence Scope: When it comes to machine learning, size matters: Microsoft's DeepSpeed framework, which can train a model with up to a trillion parameters

[N] Last Week in AI News Digest - Automated chemical synthesis, using heartbeats to detect deepfakes, and more!

[D] Last Week in Medical AI: Top Research Papers/Models 🏅(September 21 - September 27, 2024)

[N] Last Week in AI News Digest - Automated chemical synthesis, using heartbeats to detect deepfakes, and more!

[N] Last Week in AI News Digest - Automated chemical synthesis, using heartbeats to detect deepfakes, and more!

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