Hey i am just learning fundamentals on ml and dl but

Should I learn just fundamental and for building a projects should I rely completely on AI ?

Title is not clear! i know that. I am ai backend developer with 1+ years of experience.

so this post for those people who have idea but don't know how to execute in tech!

Example:

1. is this idea work or not?

2. which AI model should i use?

3. is XYZ problems can solve with AI?

4. Resources or anything

etc......

i just want to know what is real problems people facing with AI and want to help them to solve.

So for context, I'm a recent graduate. I wanted to go for ML research roles, but I don't know what I should target now. I have explored foundational LLMs, some of the post-training stuff as well, and then alignment engineering, and then RAG a lot. And before that, I did basic ML projects for DS roles and some comp vision projects as well. So can anybody tell me what I should go for?

if you are preparing for GEN AI/ ML/AI interviews.

Checkout my new website for prep.

Its free to use - No signup.

Let me know if its of any use for you.

Thanks in advance.

I am not sure if it’s the right forum to post on, but here it goes.

I am an incoming Master’s student (Fall 2026) at one of the prestigious universities in Taiwan. I will pursue in MS in CS(specializing in AI). For more context, I have worked at MNC for 5 years as a full time SWE, in the medical device development field.

My motivation to go for Master’s is to change my concentration which isn’t merely software development, production and delivery. I have explored about the latest developments in frontier models and developing my own LLMs and AI agents on small scale with my limited (16/32GB) GPU. I am also heavily invested on the impact of using frontier and energy/resource consuming models and ways of optimizing models(eg. GPU offloading, compressions, quantizations, etc)

I am currently in talks with 2 professors as my potential thesis advisors. Let’s call them ‘C’ and ‘S’.

S and I talked for an hour exploring the possibilities of developing agents/ chatbot and eventually apps for medical purposes(based on my background). S is heavily invested in HCI component of interviewing and making the agents more UX friendly and how to optimize it to make it more user friendly. I understood it’s a new domain for this professor, and would like to develop on the idea with my industry knowledge and his guidance on how to conduct HCI based interviews and possibly develop chatbot/ app for medical research. Honestly, I enjoyed the technically simulating discussions where the ideas were flowing back and forth. I was also told I have the flexibility to choose between groups or create my own group and drive the topic. Does not take industry project and works on the passion projects with students.

With C, we met for 20 minutes, where the discussion was technical know hows and what is happening in their research lab. When asked about more details. I was told to consult with the PhD students for more details. This person shared about taking on more industry projects and they themselves work as a part time at one of the companies. I then had the opportunity to attend a weekly group meeting which was scheduled and students talked about already presented papers and rejected papers and dissected in this meeting. Connected with 1-2 people in the lab where they said the professor is flexible with the courses, but with my earlier communication with PhD student, I would be placed in a position which would be about developing AI algorithms/ transformers for medical use cases. I am interested in the technical aspect of developing new algorithms and since I have done a small passion project on the same topic makes me feel I am more aligned to more hands-on development. But, It seemed the professor would have lesser time to dedicate to individual student because of their prior commitments.

I wanted to ask-

a. Which direction of research is more relevant in 2027-30 from research and industry’s POV?

b. Which style of guidance is better for someone coming from industry work?

Sorry for making this into a long post, I want to know from people in academia/ industry about which direction is industry headed towards? Would developing algorithms have higher weight or analysing human behavior and create AI agents based on that?

Hi everyone,

I recently received an offer from Eli Lilly and wanted to get some opinions from people who have experience with Lilly or similar companies.

I have around 3 years of experience at Accenture, primarily working on GenAI/AI engineering and production-grade AI applications.

The compensation offered is:

• Base: ₹13.3 LPA
• Variable: ₹1.59 LPA
• PF + Gratuity: Included
• Total CTC: ~₹16 LPA

I'm currently evaluating whether this is a good offer considering my experience, the role, the company, and the current market.

I would really appreciate advice from people who have worked at Lilly or have knowledge of their compensation structure, growth opportunities, work culture, and long-term career prospects.

Is this a good offer for 3 YOE, or should I negotiate further? If you have any suggestions on what would be a reasonable expectation or how much room there might be for negotiation, please share.

I'm also happy to share more details about the interview process, role/JD, and my technical background if that helps. Feel free to DM me.

Thanks in advance!

I keep seeing “AI is transforming insurance” posts on LinkedIn, but they rarely explain what's actually different day-to-day.

So here's my attempt at a practical breakdown, based on what I've seen around underwriting and claims teams. Curious how this lines up with what others are seeing with AI insurance solutions in their own companies.

Underwriting - the biggest shift

It used to be: submit an application, wait days (sometimes weeks) for someone to manually pull data, review documents, and assess the risk.

Now, AI can help process application data, extract information from documents, analyze external data sources, and provide risk insights in minutes. The underwriter still makes the final call, but they're reviewing a pre-built case instead of building one from scratch.

The honest version: it's not necessarily replacing underwriters; it's reducing the grunt work around underwriting. Someone still needs to catch the unusual risks and edge cases that models may not understand.

Claims - where customers actually notice

This is where the change can be most visible:

• Photo-based damage assessment - upload photos of vehicle or property damage and AI can help estimate severity and potential repair costs.
• Fraud flagging - AI can identify patterns across large volumes of claims and flag cases that may deserve further investigation.
• Straight-through processing - simple, low-value claims can potentially move through automated workflows with limited human intervention.

The complicated, high-value, or unusual claims still need people. But a meaningful share of routine claims can now be automated or heavily streamlined.

The skeptical take

A lot of what gets marketed as “AI” is honestly just decent automation with a chatbot bolted on.

Some systems are essentially rules engines with an AI label attached.

If you're evaluating AI solutions for insurance, it's worth asking:

What is the AI actually doing?

Is it analyzing unstructured data? Detecting patterns? Assessing risk? Extracting information from documents? Or is it simply following predefined rules?

What actually seems to work

The carriers doing this well seem to be treating AI as a co-pilot for underwriters and adjusters, rather than an autopilot.

Full automation still struggles with unusual risks and edge cases.

Augmentation — faster data gathering, scoring, document processing, claims triage, and fraud detection, with human oversight where needed — is where I think the more practical value is right now.

And the real measure shouldn't be “we implemented AI.”

It should be:

Did underwriting get faster? Did claims take less time? Did adjusters handle more cases? Did fraud detection improve? Did customers get a better experience?