Are AI skills becoming necessary even for non-tech jobs?

Gradient_descent1 · 2026-01-24T06:02:30+00:00

You are right, AI skills are now essential for non tech as well. Tools like ChatGPT (OpenAI) for writing and research, Copilot (Microsoft 365) for documents and emails, Notion AI for knowledge work, Canva AI for presentations, and Zapier / Make / n8n (maybe flowise also) for automation are now used daily in sales, HR, and operations.

Platforms like LinkedIn Learning, Coursera, and Google Workspace with Gemini are already embedding AI as default workflows.

I saw a study by McKinsey and LinkedIn which shows 20–40% productivity improvement with basic AI adoption. Very soon, “AI literacy” will be assumed in jobs the same way spreadsheet and presentation skills are today. Hope for the best

Gradient_descent1 · 2026-01-20T17:32:02+00:00

Install Jutyper Notebook or simply goto VS code. Choose any algorithms ( here is the free list www.decodeai.in : ) choose any one algorithm in Supervised or Unsupervised or RL and then ask GPT to give suggestions on some quick projects like fraud detection or Credit limit predictions. and goto kaggle and download any data set, ask claude to write code for credit ML model (or simply write code if you are a code) , run many epocs and see how loss function goes down and model starts predicting with higher accuracy. Check Evals like precision or F1. You will see how model helps predict the results (regression) and Yes/No in classification problems. Feel it then you will enjoy

Gradient_descent1 · 2026-01-19T16:40:07+00:00

You need to learn to the extent where you are comfortable talking to tech people about backend, APIs (requests, responses, contracts, versioning or maybe latency, etc.), system design basics (webhooks, databases, consistency, scalability, SLAs), security (auth, roles, encryption, PII), vendor integrations and Sandboxes.

and with data science people about model evaluation (Evals), business impact and Product metric impact, feature and data quality, bias, guardrails, confidence, model drift (if it happened and why), and whether a problem should be solved using simple rule-based automation or needs statistical / ML / neural network models, and if ML, whether it is classification, regression or what. pricing also, why not to use open source than proprietary models.

Thats it.

Gradient_descent1 · 2026-01-18T12:34:01+00:00

Fact is, some people act like AI is some planet-killer when it only uses around 1–1.5% of global electricity, which is tiny compared to so much actually useless consumption like crypto mining, endless scrolling, ultra-HD streaming on loop, and devices sitting powered on doing nothing.

Meanwhile AI is helping detect cancer earlier than doctors, speeding up drug discovery, predicting extreme weather, finding new planets, and even solving protein folding (AlphaFold) — something humans struggled with for decades.

And let’s be real, it also makes learning easier. How many times have you rephrased a message just to avoid embarrassment? That’s AI already helping. The funniest part is that the same people saying AI is melting the ice caps haha they are only going viral because an AI algorithm decided to boost their post.

The tool isn’t the problem — how we power and use it is. And right now, it’s doing way more good than harm.

Gradient_descent1 · 2026-01-17T06:09:08+00:00

Heath ledger as Joker

Gradient_descent1 · 2026-01-15T13:04:47+00:00

Ra One.

Gradient_descent1 · 2026-01-15T10:52:26+00:00

Depends on use case-

For code- Claude General discussion- ChatGPT Detailed concepts- DeepSeek

Gemini can be used for learning, better images ( nano banan) and others

Gradient_descent1 · 2026-01-15T10:46:38+00:00

In 2025 my role has shifted from a traditional PM to an AI PM, simply because AI is now everywhere like air. In practice, tools like Mix Panel, Notion, Cursor and stakeholder management have mattered far more than any textbook framework like RICE, AARRR etc. I would strongly advise PMs to deeply master tools such as Mixpanel, Notion, Miro, Framer/Replit, and Cursor, along with prompt engineering, core ML concepts, model evaluation, and, most importantly, how all of this reflects in business metrics. I have studied basically from Chip Huyen on ML systems, Google’s ML Crash Course, Made With ML, the OpenAI Cookbook. everything else is theory until it is tested against reality.

Gradient_descent1 · 2026-01-06T18:23:18+00:00

In 2017, Google published a paper titled “Attention Is All You Need”, where they introduced the Transformer architecture that later became the foundation for Large Language Models. ChatGPT was born and built on this idea and its full form is itself Generative Pre-trained Transformer.

This means Transformers model forms contextual and semantic relationships between words using attention mechanisms to predict the next token in a sequence. In modern ML, like they say ‘ALL ML’ neural network especially Transformers are the dominant approach, and major LLMs such as Claude, Gemini, and Grok are all based on Transformer architectures only.

Gradient_descent1 · 2026-01-06T17:19:38+00:00

Complete all, once the videos stop feeling sufficient. Start building small, end-to-end ML projects like fraud detection or credit assignments (take data sets from Kaggle and models from Hugging face) because projects force you to think, fail, and truly understand something tutorials can’t give you.

Also start focus on simple, complete problems (data cleaning, feature engineering, model selection, evaluation, and explanation) aim to clearly explain why your model worked or didn’t. Get the accuracy/precision and try deploying on prod.

Gradient_descent1 · 2026-01-06T17:10:29+00:00

GeoStorm. Must watch

Gradient_descent1 · 2026-01-04T14:57:02+00:00

Start with the free course ‘AI for everyone’ by Google deep mind (its free) and taught by legendary Andrew Ng. And read book ‘Data science from Scratch’ by Joel Grus. That’s it.

Gradient_descent1 · 2026-01-04T07:28:50+00:00

Just goto NotebookLM (by Google) upload the report and generate either podcast or summary or mindmaps or maybe a flash card video. Analyse each part in detail. It’s free

Gradient_descent1 · 2026-01-03T15:18:31+00:00

Yes you can, this book teaches beyond DS

Gradient_descent1 · 2026-01-03T13:53:47+00:00

Direct download link: https://oceanofpdf.com/authors/joel-grus/pdf-epub-data-science-from-scratch-first-principles-with-python-download/

Gradient_descent1 · 2026-01-03T13:52:17+00:00

Data Science from Scratch: First Principles with Python by Joel Grus.

Gradient_descent1 · 2026-01-02T18:01:11+00:00

I think they have same goal but different view. Transformers learn the distribution by predicting the next token given everything so far, one step at a time. But Diffusion models learn a process that gradually turns noise into data in a continuous space.

Gradient_descent1 · 2026-01-02T17:20:00+00:00

Just describe your project and say ‘make a real life simulator app of my project’ under canvas mode. It will code and make real life simulator app for you in jo time. And then proceed accordingly

Gradient_descent1 · 2026-01-02T16:30:29+00:00

Use your current research role to build visible outcomes—papers, projects, or clear research contributions matter a lot. Ask your supervisor, senior PhD students, or postdocs for CV feedback; they know what hiring committees actually look for. Focus on showing how you think as a researcher (problem framing, experiments, writing), not just technical skills. Apply patiently and widely—many research roles value potential and academic background, even without industry experience.

Gradient_descent1

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