Artificial Intelligence

2023-11-22 16:03:28 AI Engineers vs Software Engineers:

Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.

1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a “Novice” is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.

The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.

Here is an example.

Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft.

Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.

In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved." Open / Comment

2023-11-21 06:31:07 10 Things you need to become an AI/ML engineer:

1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models

Join our WhatsApp Channel. Open / Comment

2023-11-18 21:45:26 Open / Comment

2023-11-18 03:19:14 Now here is something unexpected,

OpenAI's Sam Altman exits as CEO because 'board no longer has confidence' in ability to lead.
https://www.bloomberg.com/news/articles/2023-11-17/sam-altman-to-depart-openai-mira-murati-will-be-interim-ceo Open / Comment

2023-11-14 17:31:18 Copilot just works

You need zero effort to benefit from it. You install it and forget about it.

Copilot is one example of what I call "magic technology." It's completely transparent to you until you don't have it. Then, you realize how much it helps.

Privacy concerns aside, every developer can increase their productivity by 40% - 50% by installing Copilot.

At $10 every month, this is the most ridiculously cheap investment one can make.

One reason I love Github Copilot is I rarely spend time purely prompting it and describing my structure/software/goals. It just sort of knows and offers suggestions while I code.

One thing I absolutely hate about ChatGPT is describing, outlining, and giving a history of the problem, maybe even uploading docs... and then waiting for a solution.

This is just for writing code, but IMO this applies to anything you might use ChatGPT/LLMs for. We need less prompt engineering/agents crap and more deeply integrated things like Copilot is for writing code.
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2023-11-10 05:30:53 NVIDIA just made Pandas 150x faster with zero code changes.

All you have to do is:
%load_ext cudf.pandas
import pandas as pd

Their RAPIDS library will automatically know if you're running on GPU or CPU and speed up your processing.

You can try it in this colab notebook

GitHub repo: https://github.com/rapidsai/cudf Open / Comment

2023-11-08 16:37:00 It takes $2-$3M to train a custom model from scratch using OpenAI!!

GPT-4 finetuning service cost starts at "$2-3 million" and requires "billions of tokens at minimum", It sounds terrifying, but could actually be a good deal to medium-sized companies. Think about how much resources you need to set up the pipeline in-house:

- Pay big salaries to top AI engineers ($300k+/yr). At least 5 of them.
- Pay eye-watering cloud bills or buy GPUs and rent facilities.
- Set up training infrastructure - really good distributed systems engineer required.
- Iterate lots of times on open-source models. You won't get it right in the first few tries.
- Scale up deployment pipelines.
- Monitor reliability.
- Worry about efficient serving.
- And even after all this: your finetuned Llama-2 will still trail far, far behind a finetuned GPT-4.

The amount of work that goes into a reliable LLM in production is mind-boggling. Open / Comment

2023-11-06 10:12:21 Early access for grok.x.ai is out!

Elon Musk has introduced Grok, a direct rival to ChatGPT.

Grok is an AI model that is "intended to answer almost anything, and far harder, even suggest what questions to ask. Grok is designed to answer questions with a bit of wit and has a rebellious streak, so please don't use it if you hate humour"

It is beating gpt-3.5 in less than 2 months.

Gonna be interesting.

Go signup and get on waitlist: https://grok.x.ai/ Open / Comment

2023-11-04 17:31:56 Generative AI for Beginners
Another great effort by Microsoft on AI education. This one contains a series of lessons on generative AI, including an introduction to LLMs, prompt engineering fundamentals, building text generation/chat applications, and more. Open / Comment

2023-11-01 16:56:01 PyTorch or TensorFlow?

You talk to people online, and everyone is a die-hard PyTorch fan. You talk to people offline, and everyone is a die-hard TensorFlow fan.

Many people ask me the same question: which of these frameworks should you learn?

The research community rallied around PyTorch for a good reason: it's much easier to understand than TensorFlow. PyTorch code feels and looks like Python code. TensorFlow 2 is much better, but the first version left a bad aftertaste.

TensorFlow, however, built a more extensive ecosystem for productizing Machine Learning systems. What they lacked in clarity, they made up with tooling. They also added Keras, which improved the developer experience by 100x.

If you are starting today, which one should you learn?

I usually recommend that people pick the one everyone else around them uses. If you start working for a company that uses TensorFlow, learn TensorFlow. If you join a research lab that uses PyTorch, learn PyTorch.

You can also decide based on the material you are using. For example, these are my three favorite technical books (in no particular order):

1. Deep Learning with Python by François Chollet

2. Hands-on Machine Learning with Scikit-Learn, Keras, and Tensorflow by Aurélien Géron

3. Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, PhD

The first two use TensorFlow and Keras. The last one uses PyTorch. If you have any of these books, learn the framework they use.

Ultimately, you may need both, and switching is easier than you think. The fundamental principles of building machine learning don't change. Everything else is a stylistic choice.

By the way, Keras 3.0 is coming out soon in the November, and it's a big deal. You can write your code in Keras and swap the backend to TensorFlow, PyTorch, or JAX without any changes. You can also combine different frameworks in the same codebase.

Keras 3.0 will be a standalone library, and you won't need TensorFlow anymore. I'm a huge fan, and I can't wait for everyone to try it. Open / Comment