InfoQ Homepage Machine Learning Content on InfoQ
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Anthropic Unveils Claude 3 Models, Highlighting Opus and Its Near-Human Capabilities
Anthropic has introduced the Claude 3 family models, surpassing other industry models such as GPT-4. The Claude 3 family consists of three distinct models: Haiku, Sonnet, and Opus, arranged in ascending order of capability, each designed to cater to diverse user needs in terms of intelligence, speed, and cost.
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Baseline OpenAI End-to-End Chat Reference Architecture
Microsoft published the baseline OpenAI end-to-end chat reference architecture. This baseline contains information about components, flows and security. There are also details about performance, monitoring and deployment guidance. Microsoft also prepared the reference implementation to deploy and run the solution.
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Google Announces Multi-Modal Gemini 1.5 with Million Token Context Length
One week after announcing Gemini 1.0 Ultra, Google announced additional details about its next generation model, Gemini 1.5. The new iteration comes with an expansion of its context window and the adoption of a "Mixture of Experts" (MoE) architecture, promising to make the AI both faster and more efficient. The new model also includes expanded multimodal capabilities.
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NVIDIA Unveils Chat with RTX, a Locally Run AI Chatbot
NVIDIA has introduced Chat with RTX, allowing users to build their own personalized chatbot experience. Unlike many cloud-based solutions, Chat with RTX operates entirely on a local Windows PC or workstation, offering enhanced data privacy and control.
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Instacart Creates Real-Time Item Availability Architecture with ML and Event Processing
Instacart combined machine learning with event-based processing to create an architecture that provides customers with an indication of item availability in near real-time. The new solution helped to improve user satisfaction and retention by reducing order cancellations due to out-of-stock items. The team also created a multi-model experimentation framework to help enhance model quality.
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Hugging Face and Google Cloud Announce Collaboration
Hugging Face and Google Cloud have announced a strategic alliance to advance machine learning and open AI research. Google Cloud customers, Hugging Face Hub users, and open source are the three main focuses of the strategic partnership. Google wants to make cutting-edge AI discoveries available through Hugging Face's open-source frameworks.
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Spotify's Approach to Leverage Recursive Embedding and Clustering to Enhanced Data Explainability
One of the main challenges of any online business is to get actionable insight from their data for decision-making. Spotify shares its methodology and experience to solve this problem by clustering diverse data sets through a unique method involving dimensionality reduction, recursion, and supervised machine learning.
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Custom GPTs from OpenAI May Leak Sensitive Information
After it was reported that OpenAI has started rolling out its new GPT Store, it was also discovered that some of the data they’re built on is easily exposed. Multiple groups have begun finding that the system has the potential to leak otherwise sensitive information.
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How LinkedIn Uses Machine Learning to Address Content-Related Threats and Abuse
To help detect and remove content that violates their standard policies, LinkedIn has been using its AutoML framework, which trains classifiers and experiments with multiple model architectures in parallel, explain LinkedIn engineers Shubham Agarwal and Rishi Gupta.
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Griffin 2.0: Instacart Revamps Its Machine Learning Platform
Instacart created the next-generation platform based on experiences using the original Griffin machine-learning platform. The company wanted to improve user experience and help manage all ML workloads. The revamped platform leverages the latest developments in MLOps and introduces new capabilities for current and future applications.
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Cloudflare's Journey in ML and AI: MLOps Platform and Best Practices
Cloudflare's blog described its MLOps platform and best practices for running Artificial Intelligence (AI) deployment at scale. Cloudflare's products, including WAF attack scoring, bot management, and global threat identification, rely on constantly evolving Machine Learning (ML) models. These models are pivotal in enhancing customer protection and augmenting support services.
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Google Launches New Multi-Modal Gemini AI Model
On December 6, Alphabet released the first phase of its next-generation AI model, Gemini. Gemini was overseen and driven by its CEO, Sundar Pichai and Google DeepMind. Gemini is the first model to outperform human experts on MMLU (Massive Multitask Language Understanding), one of the most popular methods to test the performance of language models.
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Anthropic Announces Claude 2.1 LLM with Wider Context Window and Support for AI Tools
According to Anthropic, the newest version of Claude delivers many “advancements in key capabilities for enterprises—including an industry-leading 200K token context window, significant reductions in rates of model hallucination, system prompts and our new beta feature: tool use.” Anthropic also announced reduced pricing to improve cost efficiency for our customers across models.
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Grafana Cloud Kubernetes Monitoring with Machine Learning Predictions
Managing cloud costs can be challenging as Kubernetes fleets scale. To address this issue, Grafana Cloud has introduced a cost-monitoring feature within Kubernetes Monitoring. In particular, Grafana Cloud’s Kubernetes Monitoring now offers ML predictions for CPU and memory usage.
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Mojo Language SDK Available: Mojo Driver, VS Code extension, and Jupyter Kernel
Mojo SDK is available for developers. It contains the mojo driver, the Visual Studio Code extension and the Jupyter kernel. For now, SDK is available for MacOS and Linux.