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📢 Join us on 30 October 2024 at 10am PDT/ 1pm EDT/ 5pm BST for our Holistic AI webinar on Bias Detection in Large Language Models - Techniques and Best Practices Our research team ZEKUN WU, Xin Guan, Nathaniel Demchak, and Ze Wang will be discussing: ☞ Bias assessment in traditional machine learning and the specific challenges posed by LLMs ☞ Policy requirements for bias assessments ☞ Types of bias in LLMs and how they manifest 📆 Sign up here below - don't miss it! #datascience #biasaudit #llm #generativeai #algorithmicbias #AIgovernance #AIpolicy

We love a good #Bias Detection webinar! Holistic AI is diving into techniques for tackling bias in LLMs 🤩 Check it out.

⭐ In our upcoming webinar 'Bias Detection in Large Language Models - Techniques and Best Practices', we'll discuss methods for bias detection in traditional machine learning, as well as in large language models, exploring policies like NYC Bias Local Law 144 and research from Holistic AI on addressing these issues. 📆 Date: Wednesday, October 30th ⏰ Time: 10am PDT/1pm EDT/ 5pm BST Register today: https://lnkd.in/e4rJiuav   #Webinar #LLM #AIBias #BiasDetection #LLMBias #AIGovernance #ArtificialIntelligence #EthicalAI

⭐ WEBINAR 'Bias Detection in Large Language Models - Techniques and Best Practices' 🗓 Date: Wednesday, October 30th ⏰ Time: 10am PDT/1pm EDT/ 5pm BST LLMs use raises concerns about bias and perpetuating inequalities. This webinar will discuss bias detection in both traditional machine learning and LLMs, focusing on policies like NYC Bias Local Law 144 and research from Holistic AI addressing these issues. Lead by the brilliant ZEKUN WU Xin Guan Nathaniel Demchak and Ze Wang Register now: https://lnkd.in/dQPaa2hm #Webinar #LLM #AIBias #BiasDetection #LLMBias #AIGovernance

Building your AI strategy to gain marketshare, return dollars to shareholders and increasing you NPS is dependent upon making sure you get AI's into production faster, safer and better. #beAIresponsible #AIgovernance #AIvelocity

🚨 Last change to register to our webinar 'Bias Detection in Large Language Models' 📅 Date: Wednesday, October 30th ⏰ Time: 10am PDT/1pm EDT/ 5pm BST Large Language Models are revolutionizing AI—but they can carry hidden biases, creating unfair outcomes. Detecting these biases early is crucial to making sure LLMs drive positive change. Sign up today: https://lnkd.in/e4rJiuav   #Webinar #LLM #AIBias #BiasDetection #LLMBias #AIGovernance #ArtificialIntelligence #EthicalAI

🚀 Unlocking the Power of Large Language Models (LLMs) - Week 2: Prompting and Prompt Engineering 🚀 This week on my blog, I'm diving into the intricate world of Prompting and Prompt Engineering with Large Language Models—a crucial component for anyone working with AI. 🔍 Understanding Domain Adaptation in AI We start by exploring how general AI models often fall short in specialized domains and how domain-adapted LLMs can bridge this gap. These models are not just more precise and capable of deeper understanding, but they also offer enhanced user experiences and better privacy management. ✨ Techniques Unveiled: - Domain-Specific Pre-Training - Domain-Specific Fine-Tuning - Retrieval Augmented Generation (RAG) Each technique is explained with its type, training duration, and applications, providing a solid foundation for understanding their unique advantages. 🔧 The Art and Science of Prompting: Prompting isn't just about feeding questions; it's about structuring queries that guide LLMs to produce the most accurate and relevant responses. We discuss the nuances of effective prompting—from ensuring contextual understanding to leveraging training data patterns. 🛠️ Deep Dive into Prompt Engineering: From enhancing model performance in complex tasks to ensuring AI's alignment with human intent, prompt engineering is a field that's rapidly gaining importance. I share insights from my personal learning journey, emphasizing how this skill is vital for anyone looking to work closely with AI technologies. #AILanguageModels #MachineLearning #DataScience #PromptEngineering #ArtificialIntelligence #TechnologyInnovation

🚀 The AI Showdown: Claude 3.5 Sonnet vs. the Titans of Language Models 🚀 In the dynamic world of artificial intelligence, staying ahead of the curve is essential. Latest analysis pits some of the leading AI language models against each other, revealing fascinating insights and trends. 🔹 Claude 3.5 Sonnet emerges as the top performer, excelling in graduate-level reasoning, coding, multilingual math, and more. Its versatility and high scores set a new benchmark for AI capabilities. 🔹 GPT-4o and Claude 3 Opus also show strong performances, making them reliable choices for various applications. Their competitive scores in coding and reasoning highlight their robustness. 🔹 Gemini 1.5 Pro and Llama-400b are not far behind, offering competitive performance in specific contexts, particularly in multilingual and grade school math tasks. Industry Insights: - Specialization vs. Generalization: Versatile models like Claude 3.5 Sonnet are invaluable for real-world applications. - Context Matters: Performance improves with more context, showcasing the need for adaptive AI. - Multilingual Capabilities: Essential for global applications, ensuring seamless AI integration across languages. - Coding Proficiency: AI's role in software development is set to revolutionize the tech industry. The AI race is heating up, with each model pushing the boundaries of what's possible. Stay updated with these advancements to leverage the best tools for your business and research needs. 🌟 Let's embrace the future of AI! 🌟 #ArtificialIntelligence #MachineLearning #TechInnovation #AIShowdown #ClaudeAI #GPT4 #GeminiAI #LlamaAI #FutureTech #IndustryInsights #AIRevolution Read my article on Medium for more such insights.

From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on a large body of research in the two fields. We explore the potential of LLMs as tools for ALife research, for example, as operators for evolutionary computation or the generation of open-ended environments. Reciprocally, principles of ALife, such as self-organization, collective intelligence and evolvability can provide an opportunity for shaping the development and functionalities of LLMs, leading to more adaptive and responsive models. By investigating this dynamic interplay, the paper aims to inspire innovative crossover approaches for both ALife and LLM research. Along the way, we examine the extent to which LLMs appear to increasingly exhibit properties such as emergence or collective intelligence, expanding beyond their original goal of generating text, and potentially redefining our perception of lifelike intelligence in artificial systems. https://buff.ly/3WNP9Nb Join the agents community to learn, discuss, and collaborate with 8,000 agent engineers! https://buff.ly/4eufyqf

Investigating various inference platforms for Large Language Models (LLMs) involves a thorough understanding of critical optimization strategies, ranging from diverse Quantization methods to paged attention, alongside different batching techniques and their support within specific inference engines. This knowledge is essential for making informed, long-term decisions about selecting an inference engine. It's encouraging to witness a resurgence of Computer Science within the ML/AI sphere, emphasizing computational optimization. Interestingly, there was a period when a study suggested that conversational AI systems should incorporate intentional delays in their responses to mimic human conversation speeds, supposedly because users preferred the illusion of speaking with a human. This concept gained traction among some developers who began implementing such delays in their systems. However, I have yet to find the original study that quantified this preference for slower interactions. Personally, I'm very skeptical of the claim that users would favor slower over faster, more seamless experiences.