Lifeboat Foundation

If you’ve been closely following the progress of Open AI, the company run by Sam Altman whose neural nets can now write original text and create original pictures with astonishing ease and speed, you might just skip this piece.

If, on the other hand, you’ve only been vaguely paying attention to the company’s progress and the increasing traction that other so-called “generative” AI companies are suddenly gaining and want to better understand why, you might benefit from this interview with James Currier, a five-time founder and now venture investor who cofounded the firm NFX five years ago with several of his serial founder friends.

Currier falls into the camp of people following the progress closely — so closely that NFX has made numerous related investments in “generative tech” as he describes it, and it’s garnering more of the team’s attention every month. In fact, Currier doesn’t think the buzz about this new wrinkle on AI isn’t hype so much as a realization that the broader startup world is suddenly facing a very big opportunity for the first time in a long time. “Every 14 years,” says Currier, “we get one of these Cambrian explosions. We had one around the internet in ’94. We had one around mobile phones in 2008. Now we’re having another one in 2022.”

Managing data with AI-powered automation and data product managers.

ARTIFICIAL intelligence has predicted what “the last human on Earth” will look like.

The U.S. Sun asked DALL·E, an artificially intelligent software, to make the prediction and it came up with some rather creepy results.

DALL·E is a machine learning model that was created by OpenAI to conjure up unusual images from keyword prompts.

Next, you seem to assume that when I catch a ball, my mind solves equations unconsciously, brining together inertia, gravity, air resistance to calculate my response. You may be right, but I don’t think most neuroscientist agree with you. That’s another computationalist prejudice. Rather than solving equations, my nervous system uses experience and extrapolation through repeated trial and improvement to hone a skill in extrapolating paths; no equations involved. As I say, I could be wrong, it’s an empirical question. But as far as I know, the balance of evidence and theory supports my interpretation.

The meaning of semantics is not just that it means something, but that it can be used to make statements about the world, beyond the formal system used to express that meaning. That, too, is definitional.

Your main argument seems like a really desperate move to sustain the computationalist faith that you assert at the beginning in the face of huge, perhaps insuperable difficulties.

The recipe for the Imaginarium is locked behind the ancient doors. Three brave hunters are sent on a mission to get the three mysterious scrolls needed to open them… but somebody doesn’t like it at all.

Official Music Video for “Imaginarium” by Fish Basket.

Continue reading “Fish Basket — Imaginarium (Official Music Video)” »

Would an advanced AI try to take over the world? If it uses a common approach called ‘reinforcement learning’, the answer is almost certainly yes.

So even insects like to play and have fun.

Bumble bees enjoy playing with balls, suggesting insect minds are far more sophisticated than previously thought, researchers have found.

Continue reading “Bumble bees like to ‘play’: new video study shows them moving balls for fun” »

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying the use of the phrase. But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us — enthralling us and frightening us in equal measure. And, unfortunately, it distracts us.

There is a different narrative that one can tell about the current era. Consider the following story, which involves humans, computers, data and life-or-death decisions, but where the focus is something other than intelligence-in-silicon fantasies. When my spouse was pregnant 14 years ago, we had an ultrasound. There was a geneticist in the room, and she pointed out some white spots around the heart of the fetus. “Those are markers for Down syndrome,” she noted, “and your risk has now gone up to 1 in 20.” She further let us know that we could learn whether the fetus in fact had the genetic modification underlying Down syndrome via an amniocentesis. But amniocentesis was risky — the risk of killing the fetus during the procedure was roughly 1 in 300. Being a statistician, I determined to find out where these numbers were coming from.

Art is a fascinating yet extremely complex discipline. Indeed, the creation of artistic images is often not only a time-consuming problem but also requires a significant amount of expertise. If this problem holds for 2D artworks, imagine extending it to dimensions beyond the image plane, such as time (in animated content) or 3D space (with sculptures or virtual environments). This introduces new constraints and challenges, which are addressed by this paper.

Previous results involving 2D stylization focus on video contents split frame by frame. The result is that the generated individual frames achieve high-quality stylization but often lead to flickering artifacts in the generated video. This is due to the lack of temporal coherence of the produced frames. Furthermore, they do not investigate the 3D environment, which would increase the complexity of the task. Other works focusing on 3D stylization suffer from geometrically inaccurate reconstructions of point cloud or triangle meshes and the lack of style details. The reason lies in the different geometrical properties of starting mesh and produced mesh, as the style is applied after a linear transformation.

The proposed method termed Artistic Radiance Fields (ARF), can transfer the artistic features from a single 2D image to a real-world 3D scene, leading to artistic novel view renderings that are faithful to the input style image (Fig. 1).