CLEAR Global’s Post

Considering the internet is still predominantly English, it’s not surprising to see more articles emerging around the topic of AI and cultural bias. Various languages carry expressions not found in English, many of those are culturally relevant and will inevitably get lost in translation. For example, in Polish:* **Kilkanaście**. It means “some,” specifically between 11 and 19. “Several” falls short, “a dozen or so” is roughly 12, and “many” is too many. Why don’t you have a word for that? Luckily, seems like for every problem there exists an effort to solve it. “Aya is an open science project that aims to build a state of art multilingual generative language model; that harnesses the collective wisdom and contributions of people from all over the world.” Let’s hope we can preserve more than just a kilkanaście of the thousands of unique words from other cultures. https://lnkd.in/e3NdGgjF @aya

The future of large language models holds immense promise beyond the immediate challenges of the AI brickwall. Multimodal language models integrating information from various sources could revolutionize human-AI interactions, enabling novel applications in education, healthcare, and creative industries. Additionally, the development of more human-like reasoning abilities in language models could augment human intelligence and accelerate scientific discovery. Integrating language models with other AI technologies like reinforcement learning and robotics has significant potential, paving the way for more adaptable and user-friendly robots in industries such as manufacturing, transportation, and healthcare. However, realizing the full potential of these models will require a deep commitment to addressing societal and ethical implications, ensuring responsible and equitable development and deployment with safeguards against misuse and bias. Collaboration between researchers, policymakers, and stakeholders is essential to develop governance frameworks and best practices for language models. Proactive public engagement and education are crucial to ensure a widespread understanding and debate on the broader societal implications of these powerful technologies.

🚀 Enhancing Large Language Model Capabilities with Mixture-of-Agents (MoA) 🚀 Together AI https://lnkd.in/gBwHuiH6 https://lnkd.in/gfj2NTRx Exciting advancements in AI! The Mixture-of-Agents (MoA) methodology leverages the collective strengths of multiple Large Language Models (LLMs) to enhance natural language understanding and generation. 🌐23 🔍 Key Highlights: Collaborative Approach: MoA uses a layered architecture where each layer comprises multiple LLM agents4. Each agent refines responses based on outputs from previous layers, leading to more robust and comprehensive results5. State-of-the-Art Performance: MoA has achieved top scores on benchmarks like AlpacaEval 2.0, MT-Bench, and FLASK, surpassing even GPT-4 Omni6. Diverse Expertise: By harnessing the unique strengths of different LLMs, MoA creates a more capable and versatile model. This innovative approach is a significant step towards more powerful and efficient AI systems. 🌟 #AI #MachineLearning #NaturalLanguageProcessing #Innovation #Tech

Quantization impacts languages differently. Cohere paper finds: "(1) harmful effects of quantization are apparent in human evaluation, and automatic metrics severely underestimate the detriment: a 1.7% average drop in Japanese across automatic tasks corresponds to a 16.0% drop reported by human evaluators on realistic prompts; (2) languages are disparately affected by quantization, with non-Latin script languages impacted worst; and (3) challenging tasks such as mathematical reasoning degrade fastest." Presentation (30 min): https://lnkd.in/ebq2pB6V Paper: https://lnkd.in/emyYWjM4 #MultingualAI

Revolutionizing AI Evaluation: The HELM Framework Language models (LMs) are critical across sectors, yet their capabilities and risks remain poorly understood. HELM (Holistic Evaluation of Language Models), developed by Stanford researchers, aims to revolutionize LM assessment. How HELM Works 1. Evaluate 30 LMs across 42 diverse scenarios 2. Assesses seven metrics: accuracy, calibration, robustness, fairness, bias, toxicity, efficiency 3. Improves evaluation coverage from 17.9% to 96.0% Key Findings 1. Instruction-tuned models outperform in accuracy, robustness, fairness 2. Top models struggle with demographic disparities 3. Highlights risks in generating biased/toxic content Implications * Enables data-driven LM selection for high-stakes environments * Promotes responsible, equitable AI systems * Open-source toolkit for community contributions HELM represents a significant step towards transparent, responsible AI development. #AI #MachineLearning #ResponsibleAI #LanguageModels #AIEthics

Decoding the DNA of Large Language Models: A Comprehensive Survey on Datasets, Challenges, and Future Directions Developing and refining Large Language Models

pub.towardsai.net: Stay updated with the latest research on large language models by reading the article on Towards AI.

How will AI and large language models reduce damage? Jason Adams, Executive Vice President of Irth, answered this question at CGA this year. “I'm telling you right now, in my view, what we’re going to do with AI will change the game substantially more than GIS,” Jason said. He shared how a learning model and AI use Irth’s comprehensive dataset in sophisticated ways to determine risk scores. Irth’s Insights for Damage Prevention calculates risk scores based on variables extracted from data on ticket details, organizations, industry trends, locations, facilities, and more. Risk scores account for more than one variable; they also consider the interaction and conditionality of data. For example, when two variables are present simultaneously, it might trigger a higher risk score than if those two variables existed individually. Listen to Jason’s full presentation: https://hubs.la/Q02Cmf0Y0 #damageprevention #AI #GIS

pub.towardsai.net: Stay updated with the latest research on large language models by reading the article on Towards AI.

Check out this article https://lnkd.in/gu4zC56T, which discusses global gaps in language models and some relevant work from Stanford Trustworthy AI Research (STAIR) and collaborators.