Land Rover is teaming up with Virgin Galactic for the Adventure of a Lifetime sweepstakes, which will see one lucky Land Rover owner literally fly to space at some point in the near future.
Anyone who currently owns a Land Rover can register online by submitting their vehicle’s VIN and some other bits of info, which counts as one entry. Placing an order for a new Land Rover nets 50 entries, while actually buying or leasing a vehicle gets you 100 entries. Anyone who enters also gets credit for referring people to the sweepstakes, and there’s no limit to how many entries you can get.
This isn’t the first time Land Rover has partnered with Richard Branson’s space-tourism venture. Range Rover SUVs have been used to tow the company’s space planes at various events and presentations, and in 2019 a special Range Rover Astronaut Edition was unveiled. That model could only be purchased by those who signed up to be an astronaut with Virgin, and it came with unique styling elements. As the Astronaut Edition was based on the now-old Range Rover, we wouldn’t be surprised if Land Rover releases a new version based on the fifth-gen Range Rover that was revealed last year.
Scientists are gearing up to once more push the boundaries of the cutting edge of particle physics with the reopening of the Large Hadron Collider (LHC) at CERN after a three-year shutdown.
Visit https://brilliant.org/Veritasium/ to get started learning STEM for free, and the first 200 people will get 20% off their annual premium subscription. Digital computers have served us well for decades, but the rise of artificial intelligence demands a totally new kind of computer: analog.
▀▀▀ References: Crevier, D. (1993). AI: The Tumultuous History Of The Search For Artificial Intelligence. Basic Books. – https://ve42.co/Crevier1993 Valiant, L. (2013). Probably Approximately Correct. HarperCollins. – https://ve42.co/Valiant2013 Rosenblatt, F. (1958). The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain. Psychological Review, 65, 386–408. – https://ve42.co/Rosenblatt1958 NEW NAVY DEVICE LEARNS BY DOING; Psychologist Shows Embryo of Computer Designed to Read and Grow Wiser (1958). The New York Times, p. 25. – https://ve42.co/NYT1958 Mason, H., Stewart, D., and Gill, B. (1958). Rival. The New Yorker, p. 45. – https://ve42.co/Mason1958 Alvinn driving NavLab footage – https://ve42.co/NavLab. Pomerleau, D. (1989). ALVINN: An Autonomous Land Vehicle In a Neural Network. NeurIPS, 1305-313. – https://ve42.co/Pomerleau1989 ImageNet website – https://ve42.co/ImageNet. Russakovsky, O., Deng, J. et al. (2015). ImageNet Large Scale Visual Recognition Challenge. – https://ve42.co/ImageNetChallenge. AlexNet Paper: Krizhevsky, A., Sutskever, I., Hinton, G. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NeurIPS, (25)1, 1097–1105. – https://ve42.co/AlexNet. Karpathy, A. (2014). Blog post: What I learned from competing against a ConvNet on ImageNet. – https://ve42.co/Karpathy2014 Fick, D. (2018). Blog post: Mythic @ Hot Chips 2018. – https://ve42.co/MythicBlog. Jin, Y. & Lee, B. (2019). 2.2 Basic operations of flash memory. Advances in Computers, 114, 1–69. – https://ve42.co/Jin2019 Demler, M. (2018). Mythic Multiplies in a Flash. The Microprocessor Report. – https://ve42.co/Demler2018 Aspinity (2021). Blog post: 5 Myths About AnalogML. – https://ve42.co/Aspinity. Wright, L. et al. (2022). Deep physical neural networks trained with backpropagation. Nature, 601, 49–555. – https://ve42.co/Wright2022 Waldrop, M. M. (2016). The chips are down for Moore’s law. Nature, 530144–147. – https://ve42.co/Waldrop2016
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Biologists from ETH Zurich have discovered speargun-like molecular injection systems in two types of bacteria and have described their structure for the first time. The special nanomachines are used by the microbes for the interaction between cells and could one day be useful as tools in biomedicine.
The CRISPR gene-editing system is a powerful tool that could revolutionize medicine and other sciences, but unfortunately it has a tendency to make edits to the wrong sections of DNA. Now, researchers at the University of Texas at Austin have identified a previously unknown structure of the protein that drives these mistakes, and tweaked it to reduce the likelihood of off-target mutations by 4,000 times.
CRISPR tools use certain proteins, most often Cas9, to make precise edits to specific DNA sequences in living cells. This can involve cutting out problematic genes, such as those that cause disease, and/or slotting in beneficial ones. The problem is that sometimes the tool can make changes to the wrong parts, potentially triggering a range of other health issues.
And in the new study, the UT researchers discovered how some of these errors can happen. Usually, the Cas9 protein is hunting for a specific sequence of 20 letters in the DNA code, but if it finds one where 18 out of 20 match its target, it might make its edit anyway. To find out why this occurs, the team used cryo-electron microscopy to observe what Cas9 is doing when it interacts with a mismatched sequence.
CNBC’s Morgan Brennan joins The News with Shepard Smith to report that Russia is refusing to deliver satellites to space without guarantees they won’t be used against Moscow in its war against Ukraine.
