Artificial Intelligence

Top 10 Free AI Playgrounds For You to Try

Curious about the future of AI? AI playgrounds are interactive platforms where you can experiment with AI models to create text, code, art, and more. They provide hands-on experience with pre-trained models and visual tools, making it easy to explore AI concepts without complex setup.

1. Hugging Face Space
2. Google AI Test Kitchen
3. OpenAI Playground
4. Replit
5. Cohere
6. AI21 Labs
7. RunwayML
8. PyTorch Playground
9. TensorFlow Playground
10. Google Colaboratory

Join Artificial Intelligence 🏆

Top 5 key developments happening today in the AI and tech space.

1. OpenAI raised $6.6 billion, reaching a valuation of $157 billion, highlighting investor interest in generative AI.

2. Nvidia reported record quarterly revenue of $30 billion, with a 154% increase in data center revenue driven by AI demand.

3. New AI coding assistants like Poolside AI ($626M) and Magic ($465M) are enhancing developer productivity through advanced tools.

4. The White House launched a task force to coordinate policies on AI regulation, focusing on economic and environmental concerns.

5. AI adoption is surging across industries, with significant growth seen in healthcare, finance, and customer service sectors.

Hitchhiker's guide to rebranding:

- Machine learning -> statistical mechanics
- Loss function -> energy functional
- Optimize the model -> minimize free energy
- Trained model -> reached equilibrium distribution
- KL divergence -> free energy difference
- Gaussian noise -> random thermal fluctuations
- Random step -> Brownian motion
- SGD -> directional Brownian motion
- GPU -> simulated particle accelerator
- Diffusion models -> Langevin dynamics
- Reinforcement learning -> control theory
- Robotics -> physical computation
- Audio learning -> 1D signal processing
- Image learning -> 2D signal processing
- Video learning -> 3D signal processing
- Multimodal models -> multidimensional signal processing
- Sora -> learned physics engine

🚀 If you’re a Data Scientist, you should learn how to deploy models in production.

📚 Here are 3 books, 3 courses, 3 blogs, and 3 podcasts to level up your MLOps skills:


📖 Books:

📕 Designing Machine Learning Systems by Chip Huyen with actual case studies of ML systems in enterprise: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/
📗 Practical MLOps: Operationalizing Machine Learning Models by Noah Gift & Alfredo Deza: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
📘 Machine Learning Engineering in Action by Benjamin Wilson: https://www.oreilly.com/library/view/machine-learning-engineering/9781617298714/


🎓 Courses:

💻 Deployment of Machine Learning Models by Soledad Galli and Chris Samiullah: Learn to deploy with FastAPI, containers, and AWS: https://www.udemy.com/course/deployment-of-machine-learning-models/
🧠 Machine Learning Specialization by Noah Gift and Alfredo Deza: A comprehensive MLOps course: https://www.coursera.org/specializations/mlops-machine-learning-duke
🎯 MLOps Specialization by Andrew Ng, Laurence Moroney, Robert Crowe, and Cristian Bartolomé: https://www.deeplearning.ai/courses/machine-learning-in-production/


📝 Blogs:

🚗 Uber Engineering Blog: https://www.uber.com/en-US/blog/engineering/
🏠 Airbnb Engineering and Data Science Blog: https://airbnb.io/
💼 Nubank Engineering Blog: https://building.nubank.com.br/


🎙 Podcasts:

🎧 MLOps Community Podcast: Learn more about MLOps: https://podcast.mlops.community/
🎙 AI Stories Podcast Episodes:
How to get into MLOps with Demetrios Brinkmann, CEO of the MLOps Community: https://www.youtube.com/watch?v=nrmOr98uR1M
MLOps and LLMOps with Noah Gift, MLOps Leader and Author: https://www.youtube.com/watch?v=jZGtmCrgTxk
Model deployment and code best practices with Maria Vechtomova, MLOps Tech Lead: https://www.youtube.com/watch?v=Nnq_2uHBUSo (🎉 Credits: Neil Leiser)

Physicists think AI is physics.
Statisticians think AI is statistics.
Mathematicians think AI is mathematics.
Psychologists think AI is psychology.
Neuroscientists think AI is neuroscience.
And they’re all right.

In my previous team at IBM, we hired over 450 AI Engineers worldwide. They are working on Generative AI pilots for our IBM customers across various industries.

Thousands applied, and we developed a clear rubric to identify the best candidates.

Here are 8 concise tips to help you ace a technical AI engineering interview:

𝟭. 𝗘𝘅𝗽𝗹𝗮𝗶𝗻 𝗟𝗟𝗠 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 - Cover the high-level workings of models like GPT-3, including transformers, pre-training, fine-tuning, etc.

𝟮. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗽𝗿𝗼𝗺𝗽𝘁 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 - Talk through techniques like demonstrations, examples, and plain language prompts to optimize model performance.

𝟯. 𝗦𝗵𝗮𝗿𝗲 𝗟𝗟𝗠 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 - Walk through hands-on experiences leveraging models like GPT-4, Langchain, or Vector Databases.

𝟰. 𝗦𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗼𝗻 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 - Mention latest papers and innovations in few-shot learning, prompt tuning, chain of thought prompting, etc.

𝟱. 𝗗𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀 - Compare transformer networks like GPT-3 vs Codex. Explain self-attention, encodings, model depth, etc.

𝟲. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀 𝗳𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 - Explain supervised fine-tuning, parameter efficient fine tuning, few-shot learning, and other methods to specialize pre-trained models for specific tasks.

𝟳. 𝗗𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 - From tokenization to embeddings to deployment, showcase your ability to operationalize models at scale.

𝟴. 𝗔𝘀𝗸 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝗳𝘂𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - Inquire about model safety, bias, transparency, generalization, etc. to show strategic thinking.

OpenAI New Model-o1 🚀

Don't let snake-oil salesmen fool you. This new model released by OpenAI today doesn't "think." It just generates an extensive "chain of thought", which is just a discussion of the model with itself that looks like a person talking to themselves.

Previous models were trained to be used in one-shot. You ask a question, you get an answer. Because of randomness in next token generation, if you were unlucky, your answer might be wrong. This model was finetuned to generate a long (and hidden from the user by the UI) discussion on how to better solve the problem, what facts are known, what assumptions need to be made, and what constraints should be respected.

If you explicitly asked previous models to generate this discussion before answering your question, you would get a better quality result, because the final answer would be conditioned on the information contained in this discussion.

They seemingly optimized their model to generate good quality discussions (without the user asking for it) by using reinforcement learning on various problems that have a verifiable solution, so that a reward for finding the right answer could be automatically assigned. For example:

Question: 1+1 = ?
Discussion: we have 1 and we have 1 more. And we have a plus sign, so it's an addition. What happens if we add 1 and 1? It means 1 is incremented by 1. When we increment 1 by 1, what do we get? Let's count: 1, 2, 3, 4,... Ok, 2 comes after 1, so 1 + 1 must be 2.
Answer: 2
Reward: 1

Question: 1+1 = ?
Discussion: It's easy. 1+1=11
Answer: 11
Reward: 0

Once the model is trained, what the user sees:

Question: 1+1 = ?
(Discussion happens behinds the scenes.)
Answer: 2

Sure! Here's a more polished version of the statement:

Don't get swept away by the hype around AI; Stay grounded and approach it thoughtfully. 💯

This year's NVIDIA AI Summit is happening in India 🔥

Today is last day left to save 50% on your NVIDIA #AISummit India conference pass. Register by Sep. 11 to access early-bird pricing for this must-attend #AI event.

NVIDIA AI Summit India, where you can explore the latest breakthroughs in Generative AI, industrial digitalisation, robotics, LLMs, Edge, Future factories, DataCenters, Heath care, Retail, Public services, training, Certification and more. We have special round tables for Startups/ VCs / NVIDIA teams as well arranged.

Register today and save 50% off your conference pass

When: Oct 23-25, 2024
Where: Jio World Convention Centre, Mumbai
Register here: https://nvda.ws/4fucsD1