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Bhaskara Reddy Sannapureddy

Senior Project Manager|Infosys|B.E(Hons) BITS, Pilani & PGD in ML & AI at IIITB & Master of Science in ML & AI at LJMU, UK | (Building AI for World & Create AICX)(Learn, Unlearn, Relearn)

Bhaskara Reddy Sannapureddy is a Senior Project Manager at Infosys with over 30 years of experience in project, product, and program management.13 He is a highly motivated professional who has demonstrated expertise in his field.

Education and Qualifications

Bhaskara holds impressive educational credentials:

  • B.E. (Hons) from BITS, Pilani
  • Postgraduate Diploma in Machine Learning and AI from IIITB
  • Master of Science in Machine Learning and AI from Liverpool John Moores University (LJMU), UK2

Professional Experience

As a Senior Project Manager at Infosys, Bhaskara is responsible for various aspects of project management and application support. His role likely involves:

  • Maintaining and operating applications supported by Infosys
  • Designing and implementing solutions
  • Managing projects, products, and programs

Skills and Expertise

Given his extensive experience and educational background, Bhaskara likely possesses strong skills in:

  • Project management
  • Machine learning and artificial intelligence
  • Product development
  • Program management

Bhaskara's LinkedIn profile suggests that he is actively engaged in the field of AI, as he mentions "Building AI" in his recent post.2 This indicates his involvement in cutting-edge technology projects within Infosys.

Highlights

Sep 2 · linkedin

Explore Qwen2-VL Released: The Latest Version of the Vision Language Models based on Qwen2 in the Qwen Model Familities

Researchers at Alibaba have announced the release of Qwen2-VL, the latest iteration of vision language models based on Qwen2 within the Qwen model family.

This new version represents a significant leap forward in multimodal AI capabilities, building upon the foundation established by its predecessor, Qwen-VL.

The advancements in Qwen2-VL open up exciting possibilities for a wide range of applications in visual understanding and interaction, following a year of intensive development efforts.

The researchers evaluated Qwen2-VL’s visual capabilities across six key dimensions: complex college-level problem-solving, mathematical abilities, document and table comprehension, multilingual text-image understanding, general scenario question-answering, video comprehension, and agent-based interactions.

The 72B model demonstrated top-tier performance across most metrics, often surpassing even closed-source models like GPT-4V and Claude 3.5-Sonnet.

Notably, Qwen2-VL exhibited a significant advantage in document understanding, highlighting its versatility and advanced capabilities in processing visual information......

Read at:https://lnkd.in/getQnFae

Model:https://lnkd.in/g3hH_c6q

Sep 2 · linkedin

Explore, Read 76-page survey paper on Prompting Techniques ✨

Explores structured understanding and taxonomy of 58 text-only prompting techniques, and 40 techniques for other modalities.

📌 The paper focuses on discrete prefix prompts rather than cloze prompts, because prefix prompts are widely used with modern LLM architectures like decoder-only models. It excludes soft prompts and techniques using gradient-based updates.

📌 The paper identifies 58 text-based prompting techniques broken into 6 major categories:

  1. In-Context Learning (ICL) - learning from exemplars/instructions in the prompt

  2. Zero-Shot - prompting without exemplars

  3. Thought Generation - prompting the LLM to articulate reasoning

  4. Decomposition - breaking down complex problems

  5. Ensembling - using multiple prompts and aggregating outputs

  6. Self-Criticism - having the LLM critique its own outputs

📌 For ICL, it discusses key design decisions like exemplar quantity, ordering, label quality, format, and similarity that critically influence output quality. It also covers ICL techniques like K-Nearest Neighbor exemplar selection.

📌 Extends the taxonomy to multilingual prompts, discussing techniques like translate-first prompting and cross-lingual ICL. It also covers multimodal prompts spanning image, audio, video, segmentation, and 3D modalities.

📌 More complex techniques like agents that access external tools, code generation, and retrieval augmented generation are also taxonomized. Evaluation techniques using LLMs are discussed.

📌 Prompting issues like security (prompt hacking), overconfidence, biases, and ambiguity are highlighted. Two case studies - benchmarking techniques on MMLU and an entrapment detection prompt engineering exercise - are presented.

Paper:https://lnkd.in/g-3XEi39

Related Questions

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How did Bhaskara Reddy Sannapureddy transition into a career in project management?
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What skills does Bhaskara Reddy Sannapureddy specialize in?
How has Bhaskara Reddy Sannapureddy contributed to the AI and ML field?
Bhaskara Reddy Sannapureddy
Bhaskara Reddy Sannapureddy, photo 1
Bhaskara Reddy Sannapureddy, photo 2
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Location

Hyderabad, Telangana, India