Have you ever admired how smartphone cameras isolate the main subject from the background, adding a subtle blur to the background based on depth? This 'portrait mode' effect gives photographs a professional look by simulating shallow depth-of-field similar to DSLR cameras. In this tutorial, we’ll recreate this effect programmatically using open-source computer vision models, like SAM2 from Meta and MiDaS from Intel ISL. Tools and Frameworks To build our pipeline, we’ll use: Segment Anything Model (SAM2): To segment objects of interest and separate the foreground from the background. Depth Estimation Model: To compute a depth map, enabling depth-based blurring. Gaussian

MarkTechPost

22.01.2025

What are Haystack Agents? A Comprehensive Guide to Tool-Driven NLP with Code Implementation - MarkTechPost

Modern NLP applications often demand multi-step reasoning, interaction with external tools, and the ability to adapt dynamically to user queries. Haystack Agents, an innovative feature of the Haystack NLP framework by deepset, exemplifies this new wave of advanced NLP capabilities. Haystack Agents are built to handle scenarios requiring: Complex multi-step reasoning. Integration of external tools or APIs. Retrieval-augmented workflows that go beyond simple question answering. This article delves deep into the Haystack Agents framework, exploring its features, architecture, and real-world applications. To provide practical insights, we’ll build a QA Agent that uses tools like a search engine and a calculator.

MarkTechPost

22.01.2025

Snowflake AI Research Open-Sources SwiftKV: A Novel AI Approach that Reduces Inference Costs of Meta Llama LLMs up to 75% on Cortex AI - MarkTechPost

Large Language Models (LLMs) have become pivotal in artificial intelligence, powering a variety of applications from chatbots to content generation tools. However, their deployment at scale presents notable challenges. High computational costs, latency, and energy consumption often limit their wider use. Organizations face the difficulty of balancing high throughput with reasonable operating expenses. Additionally, as models grow larger, the need for more efficient solutions becomes increasingly urgent. Addressing these issues is essential to making LLMs more practical and accessible. Snowflake AI Research team introduces SwiftKV, a solution designed to enhance LLM inference throughput while reducing associated costs. SwiftKV uses key-value

MarkTechPost

22.01.2025

Google AI Releases Gemini 2.0 Flash Thinking model (gemini-2.0-flash-thinking-exp-01-21): Scoring 73.3% on AIME (Math) and 74.2% on GPQA Diamond (Science) Benchmarks - MarkTechPost

Artificial Intelligence has made significant strides, yet some challenges persist in advancing multimodal reasoning and planning capabilities. Tasks that demand abstract reasoning, scientific understanding, and precise mathematical computations often expose the limitations of current systems. Even leading AI models face difficulties integrating diverse types of data effectively and maintaining logical coherence in their responses. Moreover, as the use of AI expands, there is increasing demand for systems capable of processing extensive contexts, such as analyzing documents with millions of tokens. Tackling these challenges is vital to unlocking AI’s full potential across education, research, and industry. To address these issues, Google

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Create Portrait Mode Effect with Segment Anything Model 2 (SAM2) - MarkTechPost

What are Haystack Agents? A Comprehensive Guide to Tool-Driven NLP with Code Implementation - MarkTechPost

Snowflake AI Research Open-Sources SwiftKV: A Novel AI Approach that Reduces Inference Costs of Meta Llama LLMs up to 75% on Cortex AI - MarkTechPost

Google AI Releases Gemini 2.0 Flash Thinking model (gemini-2.0-flash-thinking-exp-01-21): Scoring 73.3% on AIME (Math) and 74.2% on GPQA Diamond (Science) Benchmarks - MarkTechPost

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