One of the biggest unknowns regarding the upcoming Tesla Cybertruck was and still is its size, but a set of new photos posted online on the Cybertruck Owners Club forum shed some light on the matter.

In the images (embedded below), a release candidate (RC) Cybertruck is stopped at a Tesla Supercharger, and a Rivian R1T is parked right next to it, giving us an idea of what to expect when the angular all-electric pickup hits the market toward the end of this year (probably).

Judging from the photos, the Cybertruck seems a bit wider and longer than the R1T. For reference, Rivian’s pickup measures 217.1 inches long, 81.8 in wide (with the side mirrors folded), and 78.2 in tall (with the antenna accounted for and the suspension in its highest setting).

While the stock market may love the term “AI,” companies haven’t figured out how to optimally monetize the services that go along with it.

ML only contributing branch of AI elements ML missing in intelligent agent View of AI by Norvig, Russell. View of AI by John McCarthy. View by Nils Nilsson.

Colorado-based Prometheus Materials has developed masonry blocks from a low-carbon cement-like material grown from micro–algae.

The blocks, which meet the American Society for Testing and Materials (ASTM) standards, were made using an organic cement-like material grown in bioreactors that reproduces itself in ways similar to coral.

“Coral reefs, shells, and even the limestone we use to produce cement today show us that nature has already figured out how to bind minerals together in a strong, clever, and efficient way,” said Prometheus Materials co-founder Wil V Srubar III.

Zaha Hadid Architects has won a competition for the design of a new harborfront district in the Chinese city of Sanya. Located on the southern tip of Hainan island, the area attracts 80 million tourists each year for its forests and beaches, a demand that is driving the development of the new harbor district.

Let’s say you typically eat eggs for breakfast but were running late and ate cereal. As you crunched on a spoonful of Raisin Bran, other contextual similarities remained: You ate at the same table, at the same time, preparing to go to the same job. When someone asks later what you had for breakfast, you incorrectly remember eating eggs.

This would be a real-world example of a false memory. But what happens in your brain before recalling eggs, compared to what would happen if you correctly recalled cereal?

In a paper published in Proceedings of the National Academy of Sciences, University of Pennsylvania neuroscientists show for the first time that electrical signals in the human hippocampus differ immediately before recollection of true and false memories. They also found that low-frequency activity in the hippocampus decreases as a function of contextual similarity between a falsely recalled word and the target word.

In this article we’ll use a Q-Former, a technique for bridging computer vision and natural language models, to create a visual question answering system. We’ll go over the necessary theory, following the BLIP-2 paper, then implement a system which can be used to talk with a large language model about an image.

Who is this useful for? Data scientists interested in computer vision, natural language processing, and multimodal modeling.

How advanced is this post? Intermediate. You might struggle if you don’t have some experience in both computer vision and natural language processing.

For a long time, each ML model operated in one data mode – text (translation, language modeling), image (object detection, image classification), or audio (speech recognition).

However, natural intelligence is not limited to just a single modality. Humans can read and write text. We can see images and watch videos. We listen to music to relax and watch out for strange noises to detect danger. Being able to work with multimodal data is essential for us or any AI to operate in the real world.

OpenAI noted in their GPT-4V system card that “incorporating additional modalities (such as image inputs) into LLMs is viewed by some as a key frontier in AI research and development.”

Today’s most advanced AI models have many flaws, but decades from now, they will be recognized as the first true examples of artificial general intelligence.

We are arguably at “the knee” of the curve. More breakthroughs have happened in the first 9 months of 2023 than all previous years from the turn of the century (2001 — 2022).

Will AGI kill us all? Will we join with it? Is it even close? Is it just “cool stuff”? Will we have bootstrapping self-improving AI?

The podcast crew today:
On the panel (left to right): Stefan Van Der Wel, Oliver Engelmann, Brendan Clarke.
Host camera: Roy Sherfan, Simon Carter.
Off camera: Peter Xing.