You don't need a title to learn/work in data scienceYou don't need to start with what everyone is recommending.Focus on a few areas of AI, not the complete landscape.Seek out mentors to guide you.Learn by collaborating, solving problems, and asking for help in AI-related issues.Do not believe that the growth path is the same for everyone; avoid a copycat approach.Sometimes, you need to work ..read more

In the corporate world, navigating the hierarchy of influence often poses a challenge, particularly when it comes to balancing the time required to sell personal ideas against completing assigned tasks. Jobs typically compensate us for specific deliverables, not for the incubation or pitching of our innovative ideas.The process of advancing personal ideas depends heavily on the organizational ..read more

I have worked for Microsoft Xbox supply chain and UPS. Now If I have to add a GenAI lens to my past. Here are my perspectives.At Microsoft's Xbox division, I managed both forward and reverse supply chain logistics, covering repairs, refurbishment, and warranty services. By integrating GenAI, we can significantly enhance product defect detection, implement targeted product improvements, and ..read more

Hoping to share more insights on below Vision + GenAI Journey1. Captivating Success: Harnessing Vision and Generative AI to Mitigate Food WasteUnveiling a remarkable deployment where vision technologies, coupled with Generative AI, are being leveraged in a significant initiative to reduce food waste. A partnership between a renowned condiment brand and a leading technology provider exemplifies ..read more

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The following compares two contrasting approaches: a common sense AI framework that focuses on ethical ideals, and the business-focused reality that often prioritizes profitability and market demands:People-Centric vs Business-Centric ApproachCommon Sense AI: Prioritizes individuals' needs and well-being.Business Reality: Focuses on enhancing business profitability and operations.Encouraging ..read more

Congratz Khusbhu, Mastering Vision + Beauty domain takes well calculated approach. This implementation is great example.Selecting the right solution approach is the keyDecision of Build vs Buy Model Market testingModel EvaluationData ComplianceBuild is an expensive route in this case as it needs Deep Expertise in Vision plus Data Collection to culture.Build vs Buy Solutionshaut Perfect ..read more

Over the last few days, there has been intense discussion around product architecture, design, and adoption. Here are some key points:Balancing the adoption of Large Language Models (LLMs) with considerations of cost, consistency, and latency.The architecture should be flexible enough to allow plug-and-play integration of different models.Every solution must have some differentiation, value add ..read more

The interesting thing about OpenAI is the prompt history. The information below is a gold mine:Commonly used prompts and their responses.Ranking responses based on user feedback.Distribution of prompts across different domains. (Health, History, News, Tech)Caching of prompts and answers for quicker access.Low latency approach to handling cache versus read operations.Asynchronous processes ..read more

THE LANDSCAPE OF EMERGING AI AGENT ARCHITECTURES FOR REASONING, PLANNING, AND TOOL CALLING: A SURVEYKey Summary notesAI agent architectures are either comprised of a single agent or multiple agents working together to solve a problem.Agent Persona. An agent persona describes the role or personality that the agent should take on, including any other instructions specific to that agentReAct. In the ..read more