Lifeboat Foundation

3D printed rockets save on up front tooling, enable rapid iteration, decrease part count, and facilitate radically new designs. For your chance to win 2 seats on one of the first Virgin Galactic flights to Space and support a great cause, go to https://www.omaze.com/veritasium.

Thanks to Tim Ellis and everyone at Relativity Space for the tour!
https://www.relativityspace.com/
https://youtube.com/c/RelativitySpace.

Continue reading “The Surprising Genius of 3D Printed Rockets” »

Circa 2019

For better or worse, machine learning and big data are poised to transform the study of the heavens.

For drone racing enthusiasts. 😃

If you follow autonomous drone racing, you likely remember the crashes as much as the wins. In drone racing, teams compete to see which vehicle is better trained to fly fastest through an obstacle course. But the faster drones fly, the more unstable they become, and at high speeds their aerodynamics can be too complicated to predict. Crashes, therefore, are a common and often spectacular occurrence.

Continue reading “System trains drones to fly around obstacles at high speeds” »

We combined a machine learning algorithm with knowledge gleaned from hundreds of biological experiments to develop a technique that allows biomedical researchers to figure out the functions of the proteins that turn genes on and off in cells, called transcription factors. This knowledge could make it easier to develop drugs for a wide range of diseases.

Early on during the COVID-19 pandemic, scientists who worked out the genetic code of the RNA molecules of cells in the lungs and intestines found that only a small group of cells in these organs were most vulnerable to being infected by the SARS-CoV-2 virus. That allowed researchers to focus on blocking the virus’s ability to enter these cells. Our technique could make it easier for researchers to find this kind of information.

The biological knowledge we work with comes from this kind of RNA sequencing, which gives researchers a snapshot of the hundreds of thousands of RNA molecules in a cell as they are being translated into proteins. A widely praised machine learning tool, the Seurat analysis platform, has helped researchers all across the world discover new cell populations in healthy and diseased organs. This machine learning tool processes data from single-cell RNA sequencing without any information ahead of time about how these genes function and relate to each other.

The service’s algorithm for cropping photos favors people with slimmer, younger faces and lighter skin.

Natural language processing continues to find its way into unexpected corners. This time, it’s phishing emails. In a small study, researchers found that they could use the deep learning language model GPT-3, along with other AI-as-a-service platforms, to significantly lower the barrier to entry for crafting spearphishing campaigns at a massive scale.

Researchers have long debated whether it would be worth the effort for scammers to train machine learning algorithms that could then generate compelling phishing messages. Mass phishing messages are simple and formulaic, after all, and are already highly effective. Highly targeted and tailored “spearphishing” messages are more labor intensive to compose, though. That’s where NLP may come in surprisingly handy.

At the Black Hat and Defcon security conferences in Las Vegas this week, a team from Singapore’s Government Technology Agency presented a recent experiment in which they sent targeted phishing emails they crafted themselves and others generated by an AI-as-a-service platform to 200 of their colleagues. Both messages contained links that were not actually malicious but simply reported back clickthrough rates to the researchers. They were surprised to find that more people clicked the links in the AI-generated messages than the human-written ones—by a significant margin.

No, it’s not forbidden to innovate, quite the opposite, but it’s always risky to do something different from what people are used to. Risk is the middle name of the bold, the builders of the future. Those who constantly face resistance from skeptics. Those who fail eight times and get up nine.

(Credit: Adobe Stock)

Fernando Pessoa’s “First you find it strange. Then you can’t get enough of it.” contained intolerable toxicity levels for Salazar’s Estado Novo (Portugal). When the level of difference increases, censorship follows. You can’t censor censorship (or can you?) when, deep down, it’s a matter of fear of difference. Yes, it’s fear! Fear of accepting/facing the unknown. Fear of change.

Continue reading “Innovation is a risk!” »

Transformer-based deep learning models like GPT-3 have been getting much attention in the machine learning world. These models excel at understanding semantic relationships, and they have contributed to large improvements in Microsoft Bing’s search experience. However, these models can fail to capture more nuanced relationships between query and document terms beyond pure semantics.

The Microsoft team of researchers developed a neural network with 135 billion parameters, which is the largest “universal” artificial intelligence that they have running in production. The large number of parameters makes this one of the most sophisticated AI models ever detailed publicly to date. OpenAI’s GPT-3 natural language processing model has 175 billion parameters and remains as the world’s largest neural network built to date.

Microsoft researchers are calling their latest AI project MEB (Make Every Feature Binary). The 135-billion parameter machine is built to analyze queries that Bing users enter. It then helps identify the most relevant pages from around the web with a set of other machine learning algorithms included in its functionality, and without performing tasks entirely on its own.

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In the next year and a half, we’re going to see increasing adoption of technologies, which will trigger a broader industry shift, much as Tesla triggered the transition to EVs.

Continue reading “Embodied AI, superintelligence and the master algorithm” »