Companies won't go from 100 developers down to 0 because of AI.

They will, however, solve 10x more problems with the same team. 10x more software, 10x higher quality.

We may see a net gain in software jobs, not a decrease.

OpenAI released GPT-4o🔥

It's the JARVIS we all dreamed of.

It can reason across text, audio, and video in real time. It's extremely versatile, fun to play with and is a step towards a much more natural form of human computer interaction (and even human computer computer interaction)
Checkout: https://openai.com/index/hello-gpt-4o/

💡Ai research Funding opportunity—share with your ML networks💡

CSET Georgetown just opened their 2nd call for research ideas, this time on expanding the toolkit for frontier model releases. Expressions of interest due Jul 3. This is going to be a good opportunity for students and researchers. Full details ➡️ https://cset.georgetown.edu/wp-content/uploads/FRG-Call-for-Research-Ideas-Expanding-the-Toolkit-for-Frontier-Model-Releases.pdf

Microsoft releases PCs ‘designed for AI, going to be completely new experience!

It will be called "Copilot+ PC”, which uses chips made by Qualcomm rather than Intel, and will have a battery life of 22 hours, Microsoft said, which is slightly ahead of what Apple delivers with its MacBook Pro and MacBook Air. Their new feature called RECALL is going to be very exciting.
https://blogs.microsoft.com/blog/2024/05/20/introducing-copilot-pcs/

One of the best courses on LLMs

The LLM course is divided into 3 parts:

🧩 LLM Fundamentals covers essential knowledge about mathematics, Python, and neural networks.
🧑‍🔬 The LLM Scientist focuses on building the best possible LLMs using the latest techniques.
👷 The LLM Engineer focuses on creating LLM-based applications and deploying them.
https://github.com/mlabonne/llm-course

Doordarshan launched AI Anchor named AI KRISHI and AI BHOOMI 🚀

These anchors can speak upto +50 language, These AI anchors making Doordarshan Kisan the first government TV channel in India to employ artificial intelligence in this way. The AI anchors are designed to look and function just like human presenters, capable of delivering news 24/7 without breaks.

🎉 Google has an open sourced Python based rapid UI creation library: mesop.

It is also used internally at Google to create internal apps by devs who are not well versed with frontend development.

pip3 install mesop

👉 Docs: https://google.github.io/mesop/

⚙️ GitHub: https://github.com/google/mesop

⚡️ Checkout the Colab Notebook: https://colab.research.google.com/github/google/mesop/blob/main/notebooks/mesop_colab_getting_started.ipynb

🦋 GenAI/LLM support straight out of the box for Chat app in mesop 👉 Demo: https://google.github.io/mesop/demo/

"Elon Musk said if Apple integrates OpenAI at the OS level, then Apple devices will be banned at my companies. That is an unacceptable security violation.

It's utterly ridiculous that Apple can't create their own AI but somehow trusts OpenAI to safeguard your security and privacy!

Apple is clueless about what happens to your data once it's handed over to OpenAI. They are betraying your trust."

🔉 𝐓𝐨𝐩 𝐌𝐮𝐬𝐭-𝐖𝐚𝐭𝐜𝐡 𝐀𝐈 𝐓𝐞𝐝 𝐓𝐚𝐥𝐤𝐬

🌐 The inside story of ChatGPT's astonishing potential by Greg Brockman. https://youtu.be/C_78DM8fG6E?si=kdGNA1PvO1lb7L8t

📚 How AI could save (not destroy) education by Sal Khan
https://youtu.be/hJP5GqnTrNo?si=wlD-SOjr5ZxLQ0vQ

⚖️ How to keep AI under control by Max Tegmark
https://youtu.be/xUNx_PxNHrY?si=e8JDz9up3IRYmBo5

🧠 How to think computationally about AI, the universe, and everything by Stephen Wolfram
https://youtu.be/fLMZAHyrpyo?si=5O1b63qgga89rEOb

⚔️ The dark side of competition in AI by Liv Boeree
https://youtu.be/WX_vN1QYgmE?si=QDMlKkrxqrSCdFkr

🖼️ How AI art could enhance humanity's collective memory by Refik Anadol
https://youtu.be/iz7diOuaTos?si=iyQOF20jZp78hfo2

🤖 Why AI is incredibly smart and shockingly stupid by Yejin Choil
https://youtu.be/SvBR0OGT5VI?si=rLhDzmohC_dPfrtM

🌍 Will superintelligent AI end the world by Eliezer Yudkowsky
https://youtu.be/Yd0yQ9yxSYY?si=JqN2yNgP0IOTnjN1

Kai-Fu Lee: How AI can save our humanity | TED Talk
https://bit.ly/44SC1ss

All Developers are Now AI Developers

Worldwide, there are 30 million developers, 300,000 ML engineers, and only 30,000 ML researchers.

For those innovating at the very forefront of ML, the references estimate there may only be ~100 researchers in the world who know how to build a GPT-4 or Claude 3-level system.

The good news is that in the face of these talent shortages, tasks that used to require years of fundamental research and sophisticated ML expertise can now be accomplished in days or weeks by mainstream developers building on top of powerful pre-trained language models.

The future belongs to those who can effectively apply existing AI, not just pioneer new AI. Credits - Armand R

This is a class from Harvard University:

"Introduction to Data Science with Python."

It's free. You should be familiar with Python to take this course.

The course is for beginners. It's for those who want to build a fundamental understanding of machine learning and artificial intelligence.

It covers some of these topics:

• Generalization and overfitting
• Model building, regularization, and evaluation
• Linear and logistic regression models
• k-Nearest Neighbor
• Scikit-Learn, NumPy, Pandas, and Matplotlib

Link: https://pll.harvard.edu/course/introduction-data-science-python

AI Engineers can be quite successful in this role without ever training anything.

This is how:

1/ Leveraging pre-trained LLMs: Select and tune existing LLMs for specific tasks. Don't start from scratch

2/ Prompt engineering: Craft effective prompts to optimize LLM performance without model modifications

3/ Implement Modern AI Solution Architectures: Design systems like RAG to enhance LLMs with external knowledge

Developers: The barrier to entry is lower than ever.

Focus on the solution's VALUE and connect AI components like you were assembling Lego! (Credits: Unknown)

Microsoft creates an AI speech tool VALL-E 2 so realistic they decide not to release it.

Microsoft has created VALL-E 2, a text-to-speech AI tool that is so realistic that they have decided not to release it to the public, fearing misuse of the ability to impersonate other people’s voices.
https://www.microsoft.com/en-us/research/project/vall-e-x/vall-e-2/

Upstage Global Online Ai Hackathon! 🚀

Submit your innovative ideas around LLM and Win South Korea trip. ✈️

Checkout: https://go.upstage.ai/4f4JMk1

💰 Prize worth $14K.
💳 $200 Upstage Credits.
🤖 Workshops on LLM fine-tuning .
📚 Join the FREE workshop by DeepLearning AI on LLM training, register: http://go.upstage.ai/3Wo6dbf

Share with AI enthusiasts! 🚀✨

Huge announcement from Meta. Welcome Llama 3.1!

This is all you need to know about it:

The new models:
- The Meta Llama 3.1 family of multilingual large language models (LLMs) is a collection of pre-trained and instruction-tuned generative models in 8B, 70B, and 405B sizes (text in/text out).

- All models support long context length (128k) and are optimized for inference with support for grouped query attention (GQA).

- Optimized for multilingual dialogue use cases and outperform many of the available open source chat models on common industry benchmarks.

- Llama 3.1 is an auto-regressive language model with an optimized transformer architecture, using SFT and RLHF for alignment. Its core LLM architecture is the same dense structure as Llama 3 for text input and output.

- Tool use, Llama 3.1 Instruct Model (Text) is fine-tuned for tool use, enabling it to generate tool calls for search, image generation, code execution, and mathematical reasoning, and also supports zero-shot tool use.

International students are having a tough time securing a full time job in Tech.

Reasons

1. Market has limited opportunities for entry level junior data/software engineers.

2. People having 0-3 years of workex are competing against job seekers having 8+ years of seasoned experience.

3. Lot of companies have their operations in off-shore, as companies grow the headcount of offshore>>onshore.

Solution :

Build up relevant skillsets, latch on to the newer technologies that will bring you at par with experienced candidates.

Build apps/ai models/data pipelines/dashboards that tracks live data.

People who have secured job has everything to lose and their routine is restricted and have limitied time to be dedicated to learning. You on the other hand have nothing to lose and everything to gain.

Request for that coffee chat.

Take that certification exam.

Attend that quarterly event.

Success is sweeter when its delayed.

You can download this Data Science and Machine Learning book for free.

There are 4 sections for those who are starting:

• Python Primer
• Linear Algebra and Functional Analysis
• Probability and Statistics
• Multivariate Differentiation and Optimization

The rest of the chapters:

• Importing, Summarizing, and Visualizing Data
• Statistical Learning
• Monte Carlo Methods
• Unsupervised Learning
• Regression
• Regularization and Kernel Methods
• Classification
• Decision Trees and Ensemble Methods
• Deep Learning

Download the book here: https://people.smp.uq.edu.au/DirkKroese/DSML/DSML.pdf

"Artificial Intelligence Bootcamp" 🚀

+90 hrs sessions.
+26 Weeks.
+19 Tools & Technology.
+9 Case studies.
+9 Projects.
+9 Skills.
+7 Domains.
+13 homework assignments

Duration: 6 months

📌 Live Remote & weekend sessions.
📌 Starting from basics.
📌 Get Certificate

For quires: +918275367267 (WhatsApp)

Join group:
https://chat.whatsapp.com/KbFzHamJb5M57qZhMMaThm

For registrations:
https://aiindia.ai/ai-bootcamp/

Starting from 23rd August

To start with Machine Learning:

1. Learn Python
2. Start practicing using Jupyter

There are two main deep learning frameworks that everyone uses:

• TensorFlow
• PyTorch

Don't overthink this. Pick one of them and start practicing with it. I promise you'll end up learning both at some point.

You'll find many tutorials online but I usually struggle putting a good plan together, so I prefer courses that hold my hand from start to end.

Here are two of those programs:

• Introduction to Machine Learning with TensorFlow.
bit.ly/4fFu0wk

• Introduction to Machine Learning with PyTorch.
bit.ly/46JHQd0

These are the same program but one uses TensorFlow and the other uses PyTorch. Choose the one you prefer.

After you are done with this, you'll have accomplish something very important:

1. You'd have a large background on classical machine learning
2. You'd have a bunch of solved problems under your belt

Now, it's time to go much deeper. Here are some of the most advanced classes you can take:

• Udacity's Deep Learning Topics with Computer Vision and NLP
• MIT 6.S191 Introduction to Deep Learning
• DS-GA 1008 Deep Learning
• Udacity's Computing With Natural Language
• UC Berkeley Full Stack Deep Learning
• UC Berkeley CS 182 Deep Learning
• Cornell Tech CS 5787 Applied Machine Learning

I also love books! Look at the attached image. Those are some of my favorite machine learning books that I think you should consider.

Finally, keep these three ideas in mind:

1. Start by working on solved problems so you can find help whenever you get stuck.

2. Use AI to summarize complex concepts and generate questions you can use to practice.

3. Find a community and share your work. Ask questions and help others.

During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.

Here are the good news: ☺️

Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in the space.

Focus on finding your path, and Write. More. Code. ❤️

That's how you win. ✌️