The AI Canvas Newsletter #10
Delve into the latest AI advancements by examining the showdown between leading LLMs: Claude 2.1, Inflection-2, and Orca 2.
The AI Canvas Newsletter #10
The AI Canvas: Your weekly palette of inspiration, insights, and innovation in the world of AI.
- 🔍 Explore Claude 2.1: Advancing AI with a broader context window and halving hallucination rates for more accurate analyses.
- 🚀 Meet Inflection-2: Surpassing AI benchmarks with enhanced knowledge and efficiency, powered by NVIDIA's latest GPUs.
- 🧠Dive into Orca 2: Microsoft's leap in compact AI, proving size isn't everything in achieving advanced reasoning.
Written by Oli Wilkins.
Claude 2.1: Expanding AI Capabilities with Enhanced Context and Reduced Hallucination Rates
Claude 2.1 introduces a significant upgrade with a 200K token context window, allowing for detailed analysis of extensive documents up to 500 pages. This model boasts a 2x reduction in hallucination rates, enhancing reliability for enterprise applications. Additionally, the new beta feature of tool use enables seamless integration with existing processes and APIs, further optimising operational efficiency.
Find out more Anthropic’s blog here.
Inflection-2 Unveiled: Elevating AI Performance and Efficiency
Inflection introduces Inflection-2, a new AI model surpassing its predecessor in knowledge, style, and reasoning, and outperforming Google's PaLM 2-Large in standard benchmarks. With a focus on efficiency, Inflection-2 is set to enhance the Pi platform, benefiting from advanced NVIDIA H100 GPUs and optimized inference implementation.
Read more here.
Enhancing Reasoning in Compact AI: Microsoft’s Orca 2
Orca 2 advances the field of AI by demonstrating that smaller language models can be trained to exhibit advanced reasoning abilities akin to their larger counterparts. Utilising a novel training dataset, the model shows remarkable performance in complex zero-shot reasoning tasks, challenging the notion that size is paramount for sophisticated AI capabilities.
Read more here.
Posts and Projects
Exponentially Faster Language Modelling – ETH Zurich
“Language models only really need to use an exponential fraction of their neurons for individual inferences. As proof, we present UltraFastBERT, a BERT variant that uses 0.3% of its neurons during inference while performing on par with similar BERT models.”
A guide to LLM inference and performance – Baseten
“We want to use the full power of our GPU during LLM inference. To do that, we need to know if our inference is compute bound or memory bound so that we can make optimizations in the right area. Calculating the operations per byte possible on a given GPU and comparing it to the arithmetic intensity of our model’s attention layers reveals where the bottleneck is: compute or memory. We can use this information to pick the appropriate GPU for model inference and, if our use case allows, use techniques like batching to better utilize our GPU resources.”
Large-scale pancreatic cancer detection via non-contrast CT and deep learning
A recent study in Nature Medicine unveils a deep learning model, PANDA, that significantly improves the detection of pancreatic ductal adenocarcinoma (PDAC) using non-contrast CT scans. The model outperforms average radiologist sensitivity by over 30% and showcases a potential for large-scale, non-invasive cancer screening with high specificity, offering a promising tool for early detection in both symptomatic and asymptomatic individuals.
“Build ChatGPT over your data, all with natural language.”
“Explanation to key concepts in ML”
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