Devin Bhushan

On amazing thing on LinkedIn in is I don’t need to state it I can share what others have already stated and add my thoughts. My thoughts are 👇 is a great factual post!#LLM #API #vectorbased #AI #data

Prompting for scale will soon be obsolete... i think...i hope. Prompting is an error-prone, discovery-based process, unsuitable for scalable AI. Many folks rely on black-box LLMs behind API walls (e.g., Claude, GPT-4), lacking direct control over LLM model weights. Programmatically custom-pruned LLMs will be the future of controllable, production AI, not prompting. hint--> #steeringvectors

16

6 Comments

𝐆𝐫𝐨𝐪 + 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 = 𝐅𝐚𝐬𝐭𝐞𝐫, 𝐒𝐦𝐚𝐫𝐭𝐞𝐫 𝐀𝐠𝐞𝐧𝐭𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐇𝐨𝐫𝐢𝐳𝐨𝐧 The future of AI Agents is here, and it's built for speed. Groq's innovative compiler technology is optimizing AI models for unprecedented efficiency. This means next-gen AI Agents that can: 𝐓𝐡𝐢𝐧𝐤 𝐅𝐚𝐬𝐭𝐞𝐫: Real-time interactions and lightning-quick responses 𝐒𝐜𝐚𝐥𝐞 𝐔𝐩: Handle complex tasks and multiple users with ease 𝐁𝐞𝐜𝐨𝐦𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐬: Fine-tuned for specific applications, like language mastery 𝐏𝐨𝐰𝐞𝐫 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬: From stock trading to online gaming, Groq chips enable instant analysis Groq's approach is revolutionizing the way AI Agents are built. Get ready to see a new era of intelligent assistants that seamlessly integrate into our lives. #aiagents #aiandml #ai #groq #aimodels #aiapps #aiapplications #aiinbusiness #enterpriseai #mlalgorithms #aidevelopment

7

My latest blog post details how to set up your local Windows computer to fine-tune Llama3 and similar models. #generativeai #llm #windows #artificialintelligence

5

*Mixture of Agents (MoA)* is an innovative way to boost Large Language Models’ performance by leveraging the strengths of multiple agents as a team. Layered Architecture: MoA arranges many LLMs in layered architecture, each layer composed of numerous individual agents(LLMs). Collaboration: These agents generate responses that are based on outputs from the previous layer’s agents. They repeatedly refine and enhance the last output. Diverse Insights: By integrating different capabilities and insights from various models, MoA achieves a more powerful combined model that is secure against diverse risks. Performance Boost: MoA outperforms standalone LLMs by a wide margin. For instance, it set a new benchmark with 65.1% on AlpacaEval 2.0 as against GPT-4 Omni at 57.5%, using only open-source models. In short, if we combine language modelers’ strengths then it results in increased efficiency and versatility. Here is a nice implementation of MOA by Together AI Github source: https://lnkd.in/dMCVRS3D #AI #LLM #LargeLanguageModels #MachineLearning

59

3 Comments

if you know, you know NVIDIA annual shareholder meeting today: june 26 2024 09:00 pst in about an hour we'll have a chance to listen to new developments. it's breath-taking. the speed of not only NVIDIA's ability to navigate (well i guess they must be using their own (ai) to decide what where how to do and then some. huh?) more importantly just as Apple Microsoft defined the pervasiveness of computers. their applications. we believe NVIDIA holds that position currently. they have been handsomely rewarded in the markets. a valuation as the most valuable in the world. 3.3 trillys. and going. so every and all announcements are truly where the centre of the universe of chip design, manufacturing, exists. it's more than simply offering state of the art chip design for (ai) modelling. where Alphabet Inc. organizes the world's information. NVIDIA makes sense of it. and everyone else applies it, to their subject matter expertise. this is the change that is afoot. at this moment. where it was a race to offer advertisers of services/products a better understanding of their clientele, now the lens is sharper. there is no need to suggest age/sex/ethnicity/generational differences. there are behaviours. theses behaviours are independent of what data was used to point adverts to. to get these people to buy more sh^t. that's the base of the u.s. economy. it's apparently consumer driven. and so in this post pandemic world where it literally stopped. and we were forced to sit still. wait it out as the storm clouds passed. in hopes no one. not us. or loved ones would perish. that time was a generational shift. we all felt. it. no matter what class you were. NVIDIA is the fulcrum Google is the waterfall or Google is the head NVIDIA the neck wherever the neck turns that is where the head has their attention. the neck keeps the attention of the head there. so it appears as if in many ways Google has in essence offered the search part of it's business up for grabs. this is why Perplexity exists, and others. a new dynamic pricing auction model is about to emerge. it will as it is by nature adversarial. let's not fool ourselves. we are designed to be adversarial. it's our nature. and in coveting $$$$$ as the prime objective. money for the sake of money this will destroy a lot of thoughtful, mindful, intentionally beneficial innovation. why? well. it's not playing according to the whims of a recommendation machine that rewards those that are part of it's invisible army. today is a crowning. it's a crown. upon the head of jensen. this is akin to king charles and his crowning. we wonder who will be at the crowning. to recognize this eventful day. it's good to be king. ;) < . > source: https://lnkd.in/eumX4num

1

💥 Finnt is now publicly available! 🌎 Building an AI-native software is a whole new beast - actually that’s why there’s so many great demos but so few amazing experiences in production. The nature of Generative AI makes it difficult to reproduce identical, deterministic outcomes. Where a traditional feature would have two statuses “functional” vs. “bugged”; in the GenAI world, a slightly different prompt or setup can drastically change the output. But this is also what makes AI so powerful and why we are so excited to be building in this space. 🧠 As a reminder, Finnt specializes in extracting crucial data from large, unstructured, and complex documents — including OMs, PPMs, 10-Q/10-K, as well as prospectuses, reports, leases, loans, etc. It then integrates these findings into AI-generated and templated memos, enriching them with real-time market data. 🔑 Key facts: - Ingest any large, complex, and unstructured files - Design custom frameworks tailored towards your specific use cases - Select your preferred LLMs, from closed to open-source options - Customize the goals, structures, and formats of your output reports - Augment your reports with third-party sources and live market data - Ask anything to your files and transform responses into insights - Seamlessly integrate Finnt into your existing workflows 👉 Check the comments to get the link to signup. PS: you might need to get a custom onboarding but we are nice people 😅

179

47 Comments

NVIDIA has a clear “unfair advantage” in the market right now. They are ahead of the game in so many fronts, they sit on the hardware every company working with AI needs and is the preferred partner/CVC for any AI startup at the moment. If I was Jensen Huang, I would issue 10% new stock and raise a $300bn war chest at the same time as they are doing the 1/10 split. I would take $10bn of that money, split it into 40k tickets of $250k and fund basically any new novel AI project (no ChatGPT wrappers). This way NVIDIA would have a stake in ~75% of all AI related startups and the massive amount of talent those companies attract. They would ultimately become unstoppable while still having $290bn in the war chest 🤯

24

1 Comment

NVIDIA's Jensen said "ChatGPT democratized computing, Llama2 democratized generative AI" ...and now Llama3 is the next level 🔥 It's fascinating to watch Meta's strategic moves. With Llama3, they prioritized training on a massive 15T token dataset to pack all into a lean 70B param model, instead of building a massive model. This allows Llama 3 to match trillion+ parameter models like GPT-4, but at 1/10th the compute, storage and inference costs! 💰 This technique was published by Google DeepMind a few years ago https://lnkd.in/gx7VU8aA Meta is not a dark horse in AI anymore. They might be the top dog. https://lnkd.in/gRRJYuxt #llama2 #llama3 #llm #chatgpt #gpt4 #ai #ml

16

5 Comments

I like this idea of "technical taste". It gets into where software engineering is as much creative art as it is science. Four great takeaways (IMHO) for software devs in this era in particular: 1. Aspiring engineers should cultivate a sense of curiosity, experiment with different tools and technologies, and embrace a mindset of continuous learning. 2. AI has the potential to streamline processes and enhance productivity for engineers, but it may also lead to disruptions in traditional software development workflows. 3. Developing technical taste and judgment is essential for making informed decisions about which technologies and approaches to pursue. 4. Collaboration and open-mindedness are key to leveraging the full potential of AI and staying ahead of technological advancements. I also really liked these two quotes from Sam Schillace: 1. "The right time to do something is when you have that feeling in the pit of your stomach that's like, 'oh, this is a great idea and it's going to suck to build because nothing's ready yet.'" 2. "Technical taste is like, 'how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things?'" 3. "It may be the case that very small teams can do very large projects, or like we were talking about before, it may be the case we're just going to get really ambitious about what we try to do with the same size of teams, which is kind of where I would put my money." Great job on continuing to put out great episodes, Brett Berson and team First Round Capital! #ai #engineering #softwaredevelopment

5

Worth reading: Generative AI is not going to build your engineering team for you. So, so good - peppered with gem quotes and observations- chuckle. https://lnkd.in/gZBKtU4X Thanks to Eric Brooke for sharing.

16

Jensen: "Most code will become API calls to these powerful LLMs." It really does feel like there are just 2 strategies for building in AI 1) better LM ==> better product 2) better LM ==> product less useful?? At Crosshatch we're all in on (1). We're betting that AI gets faster, cheaper, and more capable. With AI changing so fast, Jeff Bezos' now-famous quote feels important "'What's not going to change in the next 10 years?' [This question] is actually the more important [one] because you can build a business strategy around the things that are stable in time." So with everything changing so fast right now, what's not going to change? - People want to feel known and understood. - We want to save time and money. - We want to feel in control. Excited for AI to help deliver a true hyper-personalized cyberspace.

8

Insightful article by David Cahn, but the assumption that NVIDIA GPUs in the data center are only used for AI is my only critique. Accelerated computing is affecting numerous applications from routing optimization to financial simulations. As we all know, AI is the lion-share of NVIDIA's growth, but the growth of GPUs in the datacenter allows tangential applications to shift more graciously to GPUs because of their ubiquity. Data analytics, and data science more generally, will be predominantly GPU-accelerated in the next decade. Data is growing exponentially, users want to analyze larger dataset per workload, and if workloads were faster and cheaper, users would do more workloads. Thankfully, with GPU-native engines like Theseus, https://lnkd.in/dTurZRMU, large scale jobs complete in seconds and minutes, not hours and days, AND the cost of analytics will reduce by 100x in the next year over CPU systems like Spark. Want to learn more, reach out to us at Voltron Data and let's design your next enterprise data system together.

60

3 Comments

Earn royalties for automating troubleshooting steps? See an AI Assistant use your steps to respond to alerts and questions? If you can be in San Francisco on Sat June 22nd, sign up for SRE Day and use discount code KYLE20 so we can provision you a dev environment for our workshop. Link in the comments. I'm also taking volunteers for anyone who wants to race Eager Edgar, one of our AI Assistants, troubleshooting popular Kubernetes-based apps... #sre #devops #platformengineering #whatdoirunwhen

76

10 Comments

Headed out soon! Come geek out with me and see why I ❤️ Streamlit! (And why it’s a perfect pairing with LaunchDarkly when testing out models from Hugging Face) #AI #MachineLearning #FeatureManagement

1

Is this tech company and transformer technology a potential Nvidia disruptor? I need to dive deeper into the matter. This is an interesting piece of info.

5

2 Comments

📣 With this workflow, we enable our users to effectively A/B test between different versions of a prompt and measure the respective accuracy. This empowers developers to make data driven decisions about choosing a prompt when building with LLMs.

6

I want to highlight a new feature in our latest release also included in our free Community edition ! Offset-Based Retrieval in Kamiwaza's Full Gen AI Stack: Seamless RAG Flow at PB+ Scale! A significant advancement in our Full Gen AI stack at Kamiwaza: out-of-the-box offset-based retrieval support, tailored for Enterprise-scale Retrieval-Augmented Generation (RAG) flow. #### What Does This Mean? - **Seamless Pagination:** Offset-based retrieval enables effortless pagination through massive datasets. This feature lets your enterprise handle enormous data scales, efficiently delivering specific subsets of data based on your application's requirements. - **Optimized Performance:** Our implementation ensures that even with petabyte-plus data volumes, your queries are streamlined for speed. Retrieve exactly what you need, without overloading your infrastructure or consuming unnecessary resources. - **RAG Flow Ready:** Retrieval-Augmented Generation (RAG) is critical for enterprises leveraging AI to augment their workflows. Our stack’s native offset-based retrieval ensures that RAG systems can navigate vast datasets, retrieving relevant information with unparalleled efficiency. #### Why Offset-Based Retrieval? Handling large-scale data is challenging, particularly when your AI systems require real-time responses. Offset-based retrieval allows you to: - **Navigate Efficiently:** Skip irrelevant data and jump straight to the relevant subset for your application. - **Scale Seamlessly:** Manage and manipulate data even at petabyte-plus scale, keeping your AI applications responsive and efficient. - **Maintain Consistency:** For use cases like search, recommendations, and dynamic data exploration, consistency is key. Offset-based retrieval helps manage this seamlessly. #### What to Expect? With Kamiwaza’s Full Gen AI stack, enterprises can: - **Empower AI Workflows:** Quickly and efficiently support retrieval-augmented workflows, ensuring your AI models access the most relevant data. - **Enhance Decision Making:** Our optimized data retrieval enables faster, more informed decisions driven by the power of your data. - **Streamline Operations:** Offset-based retrieval reduces the complexity of managing vast data landscapes, allowing your teams to focus on deriving insights. We believe that this feature will significantly enhance the performance of our AI stack, helping enterprises unlock new levels of productivity and data-driven insights. #GenAI #1TrillionInference #KamiwazAI #EnterpriseAI Check out https://lnkd.in/gAwbGdGH for the latest updates!

3

3 Comments