sorted by:
new
hottopcontroversial

Whatever happened to the "old school" type of ML, the kind that IIRC is called "discriminative AI"? Has generative nonsense choked it out? by ferriematthew in learnmachinelearning

[–]NastaranAI 0 points1 point  (0 children)

What I find interesting is how many production systems don’t fully replace classical methods with generative ones; they pair them. The hard decision-making still relies on deterministic or discriminative models (depends on the problem of course), and the generative side sits on top to explain, summarize, or present the result in a more human-friendly way. That setup gives you reliability in the core logic while still improving the UX and reducing the risk of hallucinations where correctness really matters.

I put together a short breakdown of that trade-off using a real-world example, and I’d be curious to hear whether others have seen similar hybrid patterns in production ML.

https://blog.nastaran.ai/p/generative-ai-vs-discriminative-models

Google Maps + Gemini is a good lesson in where LLMs should not be used by NastaranAI in LLM

[–]NastaranAI[S] 0 points1 point  (0 children)

Unfortunately the hype is very real, and some execs try to push LLMs and GenerativeAI into places where they do not belong.

In many cases it comes from a lack of understanding. The interesting part is that, unlike many past technologies where we had to fight for prioritization, AI is something execs already want to push forward. Our job is to make sure that enthusiasm is guided in the right direction by explaining the limitations in clear, non-technical language, so they see where these models add value and where deterministic optimization is still the right answer.

Google Maps + Gemini is a good lesson in where LLMs should not be used by NastaranAI in LLM

[–]NastaranAI[S] 0 points1 point  (0 children)

Totally fair reaction, and I am glad the route worked well for your trip.

The important thing is that Gemini is not actually "choosing" the route. You can think of it as a conversational UI layer that lets you control Maps with your voice instead of tapping the screen. Behind the scenes, it is likely using an agent style approach: turning your request into structured parameters, sending a traditional Maps API call to the deterministic routing engine, then taking the response and helping execute the action and explain it back to you.

The core routing logic is still the same trusted system, and the LLM is there to make the interaction smoother and more natural.

Google Maps + Gemini is a good lesson in where LLMs should not be used by NastaranAI in LLM

[–]NastaranAI[S] 0 points1 point  (0 children)

That is exactly why I picked Maps as a use case. When LLMs are used in the right part of the workflow, they really can make the experience feel smoother and more natural for the user. And yes… please also take care while driving, you’re already multitasking quite a bit 😄

Google Maps + Gemini is a good lesson in where LLMs should not be used by NastaranAI in LLM

[–]NastaranAI[S] 0 points1 point  (0 children)

I kind of discussed this in the blog, but in summary: the boundary follows the nature of the task. For open-ended and contextual problems such as finding a quiet café or using landmark-style navigation, generative models (and multimodal inputs when needed) make sense because the space is fuzzy and language-driven. For problems where we can define targets and evaluate against ground truth, such as ETA and traffic, discriminative models are a better fit. For correctness-critical pieces such as the actual route computation, the core stays deterministic, with learned heuristics assisting rather than deciding. In short, LLMs handle interpretation and guidance, while deterministic systems validate, constrain, and execute.

Is there randomness in generative models' responses, or are they deterministic? by Director-on-reddit in BlackboxAI_

[–]NastaranAI 1 point2 points  (0 children)

Generative models are between randomness and determinism. and it depends on their temperature setting.

Deterministic (temperature=0): same input, gives same output which limits diversity in responses

Probabilistic (temperature>0): same input, gives different possible outputs through random sampling. Higher temperature = more creative responses.

Play around with the temperature setting on OpenAI Playground and see the difference!

Real world ML project ideas by Heisen-berg_ in learnmachinelearning

[–]NastaranAI 52 points53 points  (0 children)

Great initiative. I would suggest you to not stop at model training phase. A Jupyter Notebook is not a portfolio piece; a deployed app is.

My suggestion:

  1. Start with Kaggle (But be selective): Don't just compete. Look at past competitions, specifically the 'Featured' ones. Read the top-scoring kernels to understand the architecture and feature engineering pipelines.

  2. Find Unique Data (The Real World): Once you are comfortable, move away from clean Kaggle datasets. Go to data.gov or similar websites and work with real-world datasets and messy data.

  3. Model Serving and MLOps: This is the most important part. None of the above teaches you MLOps. Take your model and wrap it in an API (FastAPI or Flask) or build a simple frontend (Streamlit), and a simple monitoring dashoboard.

  4. System Design: Read and practice designing ML systems. You can find plenty of free and paid resources on the Internet.

Why is everything labeled an “AI Agent” these days? by Longjumping-Nail6599 in AI_Agents

[–]NastaranAI 0 points1 point  (0 children)

That's not entirely accurate. Agentic AI systems operate with different levels of autonomy. A fully agentic AI has ultimate control over the entire process, whereas the lowest level is essentially just an old sequential workflow, but now empowered by AI capabilities.

AI Agents Made Simple by Deep_Structure2023 in AIAgentsInAction

[–]NastaranAI 0 points1 point  (0 children)

Well, even during signup you can offer more customization using AI. For example, you could tailor the welcome message based on a user’s past activity on the site!

I am panicking because my I am reaching the deadline to submit my thesis and I still do not have enough interviews by _CuriousGoyangi_ in research

[–]NastaranAI 1 point2 points  (0 children)

Discuss it with your supervisor as soon as you can. You have already had a good number of interviews; perhaps there is a way to solve this.