• AI systems teaching themselves
— Baptiste (@BaptisteVicini) November 19, 2024
• Models developing unexpected capabilities
• Programs showing signs of emergent behavior
But the scariest part? pic.twitter.com/AzeU7xA2bt
Very much focused agents on each Topic can make magic if trained well :)
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In all my GenAI product-building efforts, these questions consistently arise across various tasks: Data, ETL, Marketing, NER, Fashion, Design, and ESG.
There are different categories of people based on their work hours.
Some will work 100 hours, others will work 8 hours, and some will find a balance. Some work more when they find the work interesting. It's hard to say whether working 100 hours equals productivity or working 8 hours means mediocrity.
I suggest spending more time on activities you enjoy and less on those you don't. Remember, life is about choices and how you live each day. Personally, I need to read the same subject multiple times to understand it. This doesn't mean I do it all within 100 hours. My learning involves repeating experiments and gaining new perspectives each time.
Understanding a subject, connecting with it, and seeing it from different perspectives are unique learning moments. These cannot be measured simply by the hours spent. Instead of counting hours, focus on the new ideas you discover and how engaging they are. Ask yourself if you are connecting with your work and if your experiments are satisfying.
It's not just about money. Everything in life is finite. Evaluate whether you are productive and if your techniques are effective.
Thank you.!!!
Solutions can be built with different levels of accuracy/scalability based on Talent, Time, and Money
Steve Jobs: The difference between good people and great people in software is 50-to-1
— Startup Archive (@StartupArchive_) November 17, 2024
“I’ve always considered part of my job was to keep the quality level of people in the organizations I work with very high. I mean that’s what I consider one of the few things I can contribute… pic.twitter.com/NBlk3UoWSC
Keep Experimenting!!!
How to Implement the Use Case Correctly
Prediction:
OpenAI is set to shift towards domain-centric solutions, making 2025 a transformative year for AI. This transition is based on the data collected and learned from APIs serving different domains, focusing on context window improvements, reasoning patterns, and cross-modal integration. This will significantly enhance decision-making in critical sectors like FinTech and healthcare. By tackling technical challenges and integrating user feedback, these advancements will result in more powerful, tailored AI applications that will reshape entire industries.
Expanding Beyond Language Models
Today, OpenAI is primarily recognized as a leading provider of large language models, but its true capabilities extend much further. Its question-answering abilities, for instance, are exceptionally powerful and evolving rapidly. As clients integrate this technology into critical sectors like FinTech and healthcare, they will unlock new levels of context window improvements, and cross-modal integration and reasoning by adopting techniques like tree of thought, chain of thought, and graph-based approaches, enabling AI to think and deduce more effectively. Feedback from users will be pivotal in this journey, guiding organizations on how best to structure information flows and assess when to fine-tune models, use Retrieval-Augmented Generation (RAG), or determine the optimal use of short-term and long-term memory. This constant feedback loop will allow AI to achieve unprecedented levels of contextual understanding and adaptive reasoning, creating models that align more closely with complex real-world needs.
"OpenAI's journey is no longer just about language—it's about thought and contextual adaptation."
Building Resilient and Adaptive Systems
These advancements will likely lead to the development of more resilient and adaptable systems. Future systems will not only enhance decision-making but also push reasoning capabilities into new territories, setting the stage for increasingly sophisticated agents and refined RAG architectures. These improved architectures are expected to reduce hallucinations, boost accuracy, and lead to products that are more responsive to real-world challenges. Overcoming issues like catastrophic forgetting, hallucinations, and knowledge manipulation will be critical, positioning these systems as robust, reliable solutions across industries.
"Resilient, adaptive AI systems will transform decision-making and redefine industry standards."
Addressing Technical Challenges
Currently, accuracy challenges remain in areas such as domain-relevant embedding, balancing retrieval techniques against accuracy and latency, chunking methods based on usage or query types, contextualization, and routing or re-ranking processes. Yet, these elements are essential for advancing the capabilities of AI models. Despite these ambiguities, ongoing data processing and analysis are paving the way for more focused, domain-specific AI products. Within the next six to eight months, we’re likely to see a new wave of AI-driven applications, from highly specialized agents to RAG applications and APIs crafted for specific industries.
"Technical hurdles are simply steps toward the next wave of AI-driven, domain-specific innovation."
The Transformative Potential of 2025
The year 2025 is set to be a pivotal moment in AI, marking the dawn of domain-centric solutions that will reshape how AI interacts with our world. As more industry-specific applications emerge, OpenAI’s technologies will bring powerful, tailored solutions closer to reality.
"2025: The year AI becomes truly domain-centric, reshaping industries with precision, customized models, and highly accurate agents and RAG systems."
Keep Exploring!!!
#AI #OpenAI #DomainSpecificAI #Innovation #MachineLearning #FinTech #Healthcare #FutureOfAI
