- 4 AI Things
- Posts
- Elon Musk's Neuralink implants brain chip
Another one, DJ Khaled. 😆 I can’t wait to share the new announcements and advancements this week. Big names stealing the show, bootcamps and more.
Here are the 4 interesting AI things that I learned and enjoyed this week.
In language modeling, which is part of natural language processing, we create models to understand and generate human language. Recently, big models called LLMs have really changed the game (ChatGPT). The main issue here is optimizing these models effectively. Typically, this involves a process called gradient descent, where devices update their learning in steps and synchronize but this can be slow. More on this in today’s AI concept below. DeepMind's new method DiLoCo solves this issue. It is like having several cooks in different kitchens working on the same recipe. Each cook (device) makes their own adjustments (Gradient Descent steps) to the recipe (model) based on their ingredients (data). As soon as a cook finishes tweaking, they update the master recipe (global model parameters) with their changes. They don't have to wait for the other cooks to finish; they just add their improvements right away. This way, the recipe(model) gets better very quickly, as each cook (device) contributes independently. This approach makes the LLMs effective.
Elon Musk's Neuralink, has successfully implanted a brain chip in a human. This first product, named 'Telepathy', allows people to operate their phones or computers just by thinking. Initial tests showing promising neuron spike detection meaning chip can accurately pick up when brain cells fire up. Imagine scrolling through your feed or sending texts without lifting a finger! And let's hope it's smart enough to understand when you're thinking about 'incognito mode' and open the right websites😋
Expanding on the definition of Gradient descent introduced in the AI research section. Gradient steps are used in machine learning to improve the model's performance. Imagine the model's error as a hill. The goal is to reach the bottom, where the error is lowest. Gradient steps are like steps down this hill. By calculating the gradient, the model finds out in which direction to take the next step to reduce error. This process is repeated many times, gradually improving the model's accuracy by moving in the direction that makes us descent the most, like finding the lowest point in a valley. Hence, making the error smaller.
Did you know? One million machine learning specialists are needed by 2027. Acquiring skills that are currently in high demand could potentially yield a significant ROI. Here are 4 bootcamps that can help you in AI learning.
1. edX: Machine Learning and AI MicroBootCamps
- Duration: 8-10 weeks
- Prerequisites: Python and math skills; work experience recommended
- Topics: ML Optimization, Neural Networks, Natural Language Processing
2. Fullstack Academy: AI and Machine Learning Bootcamp
- Duration: 6 months
- Prerequisites: Coding, math experience, or computational work experience
- Topics: Applied Data Science, Deep Learning, Generative AI
3. Simplilearn: AI & Machine Learning Bootcamp
- Duration: 6 months
- Prerequisites: Programming, math experience, formal work preferred
- Topics: Deep Learning with Keras, TensorFlow, Data Science with Python
4. Springboard: Machine Learning Engineering & AI Bootcamp
- Duration: ~9 months (15-20 hours/week)
- Prerequisites: Python, Java, or JavaScript skills
- Topics: ML Models, Deep Learning, Ethics in ML
And that's all I have for you today! Start learning with free courses and bootcamps 😝 Stay curious, keep questioning.