Vineet Khosla

Very insightful work by Anthropic’s interpretability team. And an amazing paper, with outstanding writing and figures. The idea is very simple: interpret LLMs by leveraging sparse autoencoders as surrogate models of the MLP of transformer blocks, which allow one to disambiguate the superposition of features captured by a single neuron. A simple idea, but a very careful and complex execution, as it is often the case in our line of work. The paper goes into many details and provide a large array of insights, although the gist of the implementation remains obfuscated due to the closed source nature of Claude. Too bad, because this is the kind of work that we need to better understand and eventually trust LLMs. This is demonstrated by the authors in the section ‘Influence on Behavior’, where they show that clamping some features to either high or low value during inference is “remarkably effective at modifying model outputs in specific, interpretable ways”. Hopefully this kind of work is going to be replicated and generalized to open-weights models, such that we have new ways to steer their behavior. https://lnkd.in/eVym7f_f #interpretability #xai #explainableai #steerableai #anthropic #claude #anthropic

3

Ai : Hunanity's greatest creation for Good and Bad. To secure ourself #zerotrustai In the journey, MS brings up VASA-1 will AI model which essentially spins a selfie image and turn it into a talking clip of you. All you have to do is upload a photo along with a voice note and let the AI model do the talking for you.

2

Taking GenAI and LLMs from POCs to Production 🚀 Join us for an enlightening webinar hosted by CrossML Pvt Ltd and presented by Ankit Aggarwal, as we dive deep into the journey of taking Large Language Models (LLMs) from Proof of Concept (POC) to full-scale production. This webinar is perfect for CTO, AI enthusiasts, data scientists, product managers, and anyone interested in harnessing the power of LLMs to transform business operations. #LLM #AIWebinar #MachineLearning #GenerativeAI #TechnologyEvent

16

Excellent report from General Dynamics Information Technology John P. Curran regarding adoption and trends of AI in the Govt. I used Meta to generate summary below and included link to original article. Main AI use cases are chat and creating/updating computer code. Note also the 23% failure rate of AI projects due to user expectation misalignment. This can be overcome with thorough protoyping and establishing use case metrics. - Successful AI project completion timelines are narrowing, with an average of 14 months from pilot to production. - Future demand for AI projects is building around chat and coding-related functions. - 54% of surveyed AI experts said projects currently under development involve improving computer code. - 59% of projects involve "research and understanding", which may relate to creating chat functions. - 23% of AI projects fail due to misalignment between AI solutions and user expectations. - Partnership efforts are required for successful AI projects. Data diversity is crucial, and incorporating diverse data sources can lead to better AI outcomes. Trends: - AI adoption is increasing across the Federal government, with a growing focus on chat and coding-related functions. - Agencies are leveraging cloud-native services, tooling, and other capabilities to accelerate AI project development. - There is a growing interest in translating older code to newer languages, such as Python or cloud-based analytic tools. - Domain-specific chat functionality is becoming a priority, with many agencies planning to implement chat services in the next two years. Statistics: - 325 AI experts surveyed across the Federal government - 14 months: average time from pilot to production for AI projects - 54%: percentage of projects currently under development that involve improving computer code - 59%: percentage of projects that involve "research and understanding" - 23%: percentage of AI projects that fail due to misalignment between AI solutions and user expectations. Bill Casola John Grim Leslie Anne McIndoe Kim Giffin Michael S. Freeman III Tobias Quaranta https://lnkd.in/eqvmAVFG

4

You're Invited to join our LLM Reading Group on May 28: Efficient RLHF for Large Language Models with Hakim Sidahmed of Google Research. Paper: Parameter Efficient Reinforcement Learning (PERL) with Low-Rank Adaptation (LoRA). Why it matters: Reduced costs of training large language models make Reinforcement Learning with Human Feedback more accessible, enhancing model alignment with human preferences. You will hear from the lead author, Hakim Sidahmed of Google Research. RSVP here: https://lnkd.in/gwzYG5p2 #ai #ml #rlhf #humanfeedback #opensource #opensourceAI #data

9

Metrics for LLM. Nice article explaining the Grounding, hallucination, data correctness for fine-tuning, Prompt correctness, and RAG effectiveness by computing relevant Context awareness. https://lnkd.in/gFQMQ_ZR

32

Just announced by #OpenAI -- read more here: https://lnkd.in/gVsBPRBx Data Analysis is one place OpenAI already really shines thanks to the ADA feature -- can't wait to ee what this adds. #GenAI #AIforBusiness #DataAnalysis

2

Thanks to Datanami for sharing my thoughts. Gen AI is amazing, and we are in the golden age of AI development. For AI to be adopted by the masses, we need a robust way to identify and mitigate risks. A lot of work is being done through the open-source community and leading tech providers around AI safety. With regulations coming in, there is optimism for a responsible AI future.

17

The new voice input for ChatGPT has much improved latency, real-time interactions, you can interrupt it and it does ton of voice styles. (Like it or not, the "interactive xfriend" apps that are going to spring from this will be insane, esp. if there's memory.)

1

Exciting News! Anthropic has just released their most intelligent model to date, Claude 3.5 Sonnet, on Amazon Bedrock. The model exhibits near-human levels of comprehension, fluency, and reasoning, and represents a significant leap in understanding nuance, humor, and complex instructions. ✅ Improved performance to support complex use cases like multi-step customer service workflows ✅ Better visual understanding to extract insights from images, charts, and graphs ✅ More natural and creative writing capabilities ✅ Enhanced data science expertise to generate insights from unstructured data ✅ Improved autonomous coding skills #GenerativeAI #Startups https://lnkd.in/gzg8rnRR

204

1 Comment

Dive into the world of #ecommerce innovation with the latest article by Sriram P H., CEO and Co-Founder, DaveAI, as we explore the integration of large language models (LLMs) with recommendation engines. Discover how this cutting-edge technology is revolutionizing customer experiences and driving sales in the digital marketplace. From personalized shopping assistants to predictive product recommendations, the possibilities are endless.   Read the full article exclusively on Express Computer to uncover the future of conversational commerce and personalized recommendations in e-commerce: https://lnkd.in/gNtNjzeh #ai #ecommerceinnovation #customerexperience

56

1 Comment

One of the biggest challenges in language models today is making them more interpretable. We often treat AI models as black boxes: data goes in, a response comes out, and the reasoning behind that response remains unclear. I remember an interview with Google CEO, where he was asked to explain how Gemini works. He said he didn’t know. This response resonated with the scientific community, as deep learning often similar to the human brain, but the interviewer was shocked! How can a model, released to millions, be so poorly understood? Two weeks ago, Anthropic released an important paper on model interpretability. They used a technique called "dictionary learning," borrowed from classical ML, which isolates patterns of neuron activations that recur across many different contexts. This paper sheds some light on this important challenge, which, if solved, will create more trust in these models and thus ease the integration of AI into our everyday lives. Highly recommend reading: https://lnkd.in/gPzEePx8

12

1 Comment

Ethical AI applies to inference as well as upstream data sources. Amazon is emphasizing structure and responsibility over unchecked innovation spanning from foundational models to consumer applications. It is good to see this mentality being adopted. https://buff.ly/445KcS0 #ArtificialIntelligence #AmazonAI #TechResponsibility

11

There has been a series of #largelanguagemodels launches in the last few weeks. One such #llm is Meta's #llama3, which was released last week(https://shorturl.at/duQT2). Today Snowflake and its CEO Sridhar Ramaswamy along with the company's AI head Baris Gultekin, launched its #Arctic LLM under the Apache 2.0 license, which is more open than Meta's Llama 3. How does the Arctic model differ from most LLMs, such as xAI's #Grok-1 and #DBRX? Why did Snowflake choose the open-source route? How can the models be accessed and what can it be used to do? We at Foundry InfoWorld decode with the help of IDC's Michele Rosen, Forrester's Charlie Dai, AnswerRocket's Mike Finley, Omdia's Bradley Shimmin, The Futurum Group's Paul Nashawaty, Amalgam Insights' Hyoun Park, and West Monroe's Scott Rozen-Levy #OpenAI, #Microsoft, #Databricks, #IBM, #Amazon Web Services (AWS) #NVIDIA

9

1 Comment

So much fun talking about the power of LLMs with Amazon Bedrock and Anthropic Claude. In summary, the ever-evolving landscape of GenAI, it's becoming clear that there won't be a single LLM (Large Language Model) to rule them all. The rise of application-specific LLMs heralds a new era, where various models will carve out niches to excel in. This diversity opens up a wide playing field for innovation and tailored solutions. When it comes to deploying AI models, striking the right balance in evaluation is paramount. We must weigh factors like cost, speed, and precision to ensure optimal performance and resource utilization. Finding this equilibrium ensures that our GenAI solutions are not just efficient but also effective in real-world scenarios. Anthropic Claude emerges as a frontrunner in the realm of LLMs, distinguished by its exceptional capabilities and groundbreaking advancements. Boasting unparalleled scale, efficiency, and adaptability, Anthropic Claude represents a pinnacle of GenAI innovation. Its ability to seamlessly integrate with Amazon Bedrock amplifies its prowess, providing users with an unparalleled ecosystem for knowledge integration and generation. With state-of-the-art architecture and a commitment to pushing the boundaries of what's possible, Anthropic Claude stands as a testament to the transformative potential of GenAI in shaping the future of technology and beyond. Prompt engineering holds the key to maximizing the potential of applications. Crafting prompts that consider persona, context, tags, and positioning can significantly enhance the model's ability to comprehend and generate relevant responses. This strategic approach empowers AI systems to better serve their intended purposes. In the realm of GenAI architectures, a robust knowledge base is essential. Amazon Bedrock stands out as a key component for architectures like RAG (Retrieval-Augmented Generation), providing a solid foundation to fuel intelligent decision-making and content generation. As we march towards AI integration, bridging the gap with legacy applications is crucial. Agents equipped with open APIs offer a seamless way to facilitate communication and interoperability, ensuring a smooth transition into the AI-driven future. Let's continue to explore, innovate, and collaborate as we navigate the exciting frontier of GenAI together! #aws #LanguageModels #Innovation #TechTrends #AIIntegration #webdevcon #claude #bedrock

68

Small models becoming smarter! Phi-3 reinforces the importance of data quality.

24