The Robotaxi event had an interesting side-effect. The Cybercab and Cybervan had mixed reviews, with Uber, Lyft and Waymo actually getting a boost post event.
We’ll have to wait to see how that race for autonomous taxis plays out.
What was pretty impressive, however, were the Optimus robots mingling amongst the crowds. Even serving gifts and interfacing with the guests with no apparent issues.
Were these remote controlled? Or are humanoids here? Suddenly, we’re not so bored with humanoids.
In a typical staged production last night, Elon Musk and Tesla showcased Robotaxi.
The concept is of course great. Taxis and Ubers barely need a driver now, are often undersupplied during events or peak times, and could easily be completed by a computer. Tesla has had self-driving for a while so it’s no surprise they want to compete with Uber and Lyft now.
Musk put on the We, Robot party.
The Cybercab, one of the models, has no steering wheel or pedals. Musk describes it as individualized mass transit. Musk explained that the average cost of mass transport is generally $1 or more per mile vs $0.30 per mile with Tesla’s new vehicles. In the past, he’s hinted that Tesla owners could rent their cars out to become taxis and earn income in the future.
The company expects to launch Robotaxi with Model 3 and Model Y in California and Texas in 2025 with the Cybercab model launching towards the end of 2026.
The car uses inductive charging, which means it has no plug.
John Hopfield is a physicist and neuroscientist best known for his contributions to theoretical neuroscience and artificial intelligence. He developed the Hopfield Network in 1982, a type of recurrent neural network that provided a foundation for modern AI and machine learning models. His work bridged physics, biology, and computation, emphasizing how systems of neurons can perform complex computations. Hopfield is also renowned for his work on associative memory and has made significant contributions to molecular biology. He has been a professor at Princeton University and the California Institute of Technology.
Geoffrey Hinton is a cognitive psychologist and computer scientist widely regarded as one of the pioneers of deep learning. He is known for his foundational work on artificial neural networks and the development of backpropagation algorithms, which are central to training modern deep learning models. Hinton’s work laid the groundwork for innovations in image recognition, natural language processing, and AI in general. He is a professor emeritus at the University of Toronto and has worked at Google as a key figure in their AI research division. Hinton has received numerous accolades, including the 2018 Turing Award, often referred to as the “Nobel Prize of Computing,” shared with Yann LeCun and Yoshua Bengio.
What is unique about Hinton: he’s an expert but also sounding alarm bells for society. Hinton believes A.I. will help us in healthcare but is concerned that the technology will eventually take over, and become very good at manipulating us (See Apple Intelligence).
“Free speech is the bedrock of democracy. If they don’t know the truth, how can you make an informed vote….President Trump must win to preserve the constitution, to preserve democracy…this is a must-win situation.”
Watch the full speech below. Musk had one request, there are only 2 days left to register to vote in Georgia and Arizona, everyone needs to register and refer others to vote via a site SwampTheVote.com.
The companies building the building blocks of A.I. provided via Quartr – Financial Research and Investor Relations Platform.
There’s a chart going around that compares Nvidia stock to Tulip Mania growth. Tulip Mania is a historical financial crash that started around a craze for Tulips.
This is different.
A.I. is already more impactful than electricity. What is coming next in terms of wealth creation, societal change and disruption from A.I. isn’t fully understood. Nvidia provides much of the infrastructure for modern A.I. to compute on.
It’s much bigger than anything humans have conceived up until this point.
Imagine you’re building a robot that can recognize pictures of your favorite animals—dogs, cats, maybe even dragons! To help your robot learn to tell the difference between a dog and a cat, you’d need something super smart, like artificial intelligence (AI). But how do you teach your robot? That’s where TensorFlow comes in.
At its core, TensorFlow is a tool that helps computers learn to do things on their own, just like how you learn from practice. But instead of practicing soccer or math, the computer practices by looking at data. TensorFlow makes it easier for computers to practice and get better at tasks like recognizing pictures, understanding speech, or even playing games.
Let’s break it down with an example:
The word “TensorFlow” sounds complicated, but it’s just made up of two words:
There are tons of cool things TensorFlow can help create! Here are some fun examples:
Before TensorFlow, teaching computers was really tricky and took a lot of time. TensorFlow made it easier for people—scientists, engineers, and even students—to build smart systems without having to do all the hard work from scratch. It’s like using a calculator instead of doing long division by hand!
Now, with TensorFlow, AI is more accessible, and we see cool advancements all the time. It’s even used in games, art, and health care!
If you’re curious about TensorFlow and want to build your own smart projects, you can start small:
In a nutshell, TensorFlow is like a super-smart teacher for computers. It helps them learn from examples, improve with practice, and do amazing things—like identifying animals or even driving cars! Whether you’re into robotics, gaming, or science, TensorFlow opens up a world of possibilities for you to explore.
Who is this person that Google essentially acquired back after he left in frustration. The price tag: $2.7B.
The A.I. talent war is heating up!
To start with, Noam Shazeer is a name you’ll probably start hearing more of. He is currently a Google VP but previously was very instrumental in Google’s Gemini project. He is a prominent computer scientist and engineer known for his groundbreaking work in machine learning and natural language processing (NLP). He has made significant contributions to the field of artificial intelligence, particularly in the development of models and architectures that power modern NLP systems.
Some of his notable contributions include:
Shazeer’s innovations have had a profound impact on the development of AI, influencing both research and commercial applications in the field of NLP.
Sam Altman, the founder of OpenAI recently said something interesting about the evolution of A.I…
According to Altman, it will evolve like this:
1. Data: The building blocks of A.I.
2. Models: Intelligence built on data
3. Agents (Current): Semi-intelligent bots using models to assist humans
4. Innovators: Entrepreneurs putting the pieces together to make wealth by solving problems and creating products
5. Organizations: Businesses totally formed and run by A.I.
He thinks we’re ‘months’ away which could mean years but it’s close. Read Sam’s full blog post here.
“It won’t happen all at once, but we’ll soon be able to work with AI that helps us accomplish much more than we ever could without AI”
Sam Altman
September 2024
