The method uses microneedles smaller than a grain of sand.
Tattooing went from a subculture to pop culture in the past decades. Tattoo artists use a mechanized needle to puncture the skin and inject ink into the dermis or second layer of skin-this is not only painful but it’s time-consuming.
The patch consists of microneedles that are each smaller than a grain of sand and are made of tattoo ink encased in a dissolvable matrix.
GEORGIA TECH
Now researchers at the Georgia Institute of Technology have developed a painless and bloodless tattoo patch that’s simple enough for people to stick to themselves, according to a press release published by EurekAlert on Sept .14.
Many companies developed blood tests for cancer, but none of them have FDA approval so far.
The U.S. is preparing to launch trials of blood tests that can improve the detection of multiple kinds of cancer, according to a report.
Solarseven/iStock.
The majority of the multi-cancer early detection tests (MCEDs) function by searching for tumor cell remnants that explode after being attacked by the immune system. Debris from dead tumors can be found in the bloodstream, where it can be identified as a cancer warning before symptoms appear. And if imaging confirms the result, a biopsy follows the process.
He also has advice on how to bring things back on track.
Bill George, a senior fellow at Harvard Business School, thinks that Mark Zuckerberg has “really lost his way” and is slowly dragging his company Meta to failure. George made these comments while speaking to CNBC
George, a former CEO at a medical technology company himself, has spent the last two decades of his life studying leadership failures in workplaces. His recent book is a compilation of his work, where he has found that bosses who lose sight of their values and purpose are doomed to fail.
You won’t be able to blame it on your genetics anymore: with CRISPR, it’s so easy to hacn into your DNA. CRISPR technology is our future, and experiments with DNA hacking are booming. CRISPR biotechnology is not science fiction anymore, it is our very near future. Would you hack and reprogram your own DNA with CRISPR? Breaking the code of life, hacking DNA at home.
Welcome to the world of a new nature. We can now literally cut and paste DNA with the new CRISPR technology. There is a revolutionary development going on that will have major consequences for humans, plants and animals. The new biotechnology is here.
‘Bio is the New Digital’. We are able to accurately reprogram the genetic code of our body cells, embryos, bacteria, viruses and plants. With the CRISPR technology we can adjust the characteristics of each organism to our needs. This allows us to permanently ban diseases, improve our body conditions and adapt plants to our food needs.
The special feature of CRISPR technology is that it is relatively simple. In the past year, the number of experiments and applications has exploded. Around the world, people have been tinkering with CRISPR: experimenting at home with the ‘Do it Yourself CRISPR kits’.
Scientists call for new ethical frameworks. The demand for the (un)desirable so-called designer babies is imminent. Although this is not yet the case, we can put an end to hereditary diseases in the short term. We may also want to make bacteria that can eat oil or plastic, pigs in which human organs can grow or bring extinct animals back to life. It looks like science fiction but it is now closer to our reality than ever. With: Emmanuelle Charpentier (geneticist), John van der Oost (microbiologist), Andrew Hessel (biotechnologist), Niels Geijsen (cell biologist), Josiah Zayner (biohacker) and Ivan van der Meij (FSHD patient).
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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▀▀▀ Written by Derek Muller, Stephen Welch, and Emily Zhang. Filmed by Derek Muller, Petr Lebedev, and Emily Zhang. Animation by Iván Tello, Mike Radjabov, and Stephen Welch. Edited by Derek Muller. Additional video/photos supplied by Getty Images and Pond5 Music from Epidemic Sound. Produced by Derek Muller, Petr Lebedev, and Emily Zhang.
The millennia-old idea of expressing signals and data as a series of discrete states had ignited a revolution in the semiconductor industry during the second half of the 20th century. This new information age thrived on the robust and rapidly evolving field of digital electronics. The abundance of automation and tooling made it relatively manageable to scale designs in complexity and performance as demand grew. However, the power being consumed by AI and machine learning applications cannot feasibly grow as is on existing processing architectures.
THE MAC In a digital neural network implementation, the weights and input data are stored in system memory and must be fetched and stored continuously through the sea of multiple-accumulate operations within the network. This approach results in most of the power being dissipated in fetching and storing model parameters and input data to the arithmetic logic unit of the CPU, where the actual multiply-accumulate operation takes place. A typical multiply-accumulate operation within a general-purpose CPU consumes more than two orders of magnitude greater than the computation itself.
GPUs. Their ability to processes 3D graphics requires a larger number of arithmetic logic units coupled to high-speed memory interfaces. This characteristic inherently made them far more efficient and faster for machine learning by allowing hundreds of multiple-accumulate operations to process simultaneously. GPUs tend to utilize floating-point arithmetic, using 32 bits to represent a number by its mantissa, exponent, and sign. Because of this, GPU targeted machine learning applications have been forced to use floating-point numbers.
ASICS These dedicated AI chips are offer dramatically larger amounts of data movement per joule when compared to GPUs and general-purpose CPUs. This came as a result of the discovery that with certain types of neural networks, the dramatic reduction in computational precision only reduced network accuracy by a small amount. It will soon become infeasible to increase the number of multiply-accumulate units integrated onto a chip, or reduce bit-precision further.
During the middle ages, the concept of the perpetual motion machine would develop. The first law, known as the Law of Conservation of Energy, would prohibit the existence of a perpetual motion machine, by preventing the creation or destruction of energy within an isolated system.
MAXWELL’S DEMON
In 1,867 James Clerk Maxwell, the Scottish pioneer of electromagnetism, conceived of a thermodynamic thought experiment that exhibited a key characteristic of a thermal perpetual motion machine. Because faster molecules are hotter, the “beings” actions cause one chamber to warm up and the other to cool down, seemingly reversing the process of a heat engine without adding energy.
ENTROPY
Despite maintaining the conservation of energy, both Maxwell’s demon and thermal perpetual motion machines, contravened, arguably one of the most unrelenting principles of thermodynamics. This inherent, natural progression of entropy towards thermal equilibrium directly contradicts the behavior of all perpetual motion machines of the second kind.
