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NEW

Why AI Struggles with Practical Optimization Problems

Traditional AI models face significant challenges in practical optimization settings due to their inherent design limitations. These challenges often stem from their inability to efficiently process high-dimensional data, handle noisy or sparse datasets, and adapt to complex problem structures. As…
Thumbnail Image of Tutorial Why AI Struggles with Practical Optimization Problems
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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NEW

AI evals vs AI Hype: what wins ?

Watch: The State of AI Code Quality: Hype vs Reality. Itamar Friedman, Qodo by AI Engineer Evals win. The number we keep running into is blunt: about 75% of AI projects fail, and the common thread is skipped evaluation. Hype buys you a demo. Evals buy you something that holds up when real users…
Thumbnail Image of Tutorial AI evals vs AI Hype: what wins ?
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
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Why Local Models Are Enough for Enterprise AI

Local models moved from hobbyist territory to a real deployment option for one reason: you can run AI without shipping sensitive data to someone else's servers. For a large share of internal enterprise work, that single property settles the decision. You keep the data, you control the cost, and you…
Thumbnail Image of Tutorial Why Local Models Are Enough for Enterprise AI
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
•Last Updated:
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Vector Databases vs Graph RAG: Picking the Right Memory for AI Agents

Watch: VectorDB vs GraphDB for Gen AI Agents | Databases for AI by AWS Events Use vector databases for semantic memory. Use Graph RAG for structured reasoning. Combine them when your agent needs both recall and explainability. That one line covers most decisions. The rest of this section unpacks…
Thumbnail Image of Tutorial Vector Databases vs Graph RAG: Picking the Right Memory for AI Agents
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
•Last Updated:
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Using ZeRO and FSDP to Scale Large Models on Multiple GPUs

Watch: Ultimate Guide To Scaling ML Models - Megatron-LM | ZeRO | DeepSpeed | Mixed Precision by Aleksa Gordić - The AI Epiphany ZeRO and FSDP solve the same problem the same way: shard the heavy parts of training across your GPUs so no single card has to hold all of it. Where they differ is…
Thumbnail Image of Tutorial Using ZeRO and FSDP to Scale Large Models on Multiple GPUs
Image of Author Dr. Dipen
Dr. Dipen
I am an AI/ML researcher with 150+ citations and 16 published research papers. I have three tier-1 publications, including Internet of Things (Elsevier), Biomedical Signal Processing and Control (Elsevier), and IEEE Access. In my research journey, I have collaborated with NASA Glenn Research Center, Cleveland Clinic, and the U.S. Department of Energy for various research projects. I am also an official reviewer and have reviewed over 100 research papers for Elsevier, IEEE Transactions, ICRA, MDPI, and other top journals and conferences. I hold a PhD from Cleveland State University with a focus on large language models (LLMs) in cybersecurity, and I also earned a master’s degree in informatics from Northeastern University.
•Last Updated:
00
Read Full Article

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