Medical tech company Viz.ai, a developer of an AI-powered stroke detection and care platform, has pulled in a new investment of $100 million at a valuation of $1.2 billion, making it Israel’s newest unicorn (a private company valued at over $1 billion).
The company said Thursday that the Series D funding will be used to expand the Viz platform to detect and triage additional diseases and grow its customer base globally.
Viz.ai’s newest round was led by Tiger Global Management, a New York-based investment firm focused on software and financial tech, and Insight Partners, a VC and private equity firm also based in New York. Tiger Global has invested in Israeli companies such as cybersecurity companies Snyk and SentinelOne as well as payroll tech companies Papaya Global and HoneyBook. Insight Partners is a very active foreign investor in Israeli companies, with at least 76 local portfolio startups to its name including privacy startup PlainID, bee tech startup Beewise, and music tech startup JoyTunes.
After a wild display of lights, music and futuristic technology, Tesla (TSLA) CEO Elon Musk kicked off the grand opening of the company’s new Texas gigafactory on Thursday.
The Austin plant — Tesla’s fourth globally — will manufacture the Model Y SUV and, next year, the highly-anticipated Cybertruck.
Tesla bull Kevin Paffrath predicts this is only the beginning electric-vehicle maker.
►Is faster-than-light (FTL) travel possible? In most discussions of this, we get hung up on the physics of particular ideas, such as wormholes or warp drives. But today, we take a more zoomed out approach that addresses all FTL propulsion — as well as FTL messaging. Because it turns out that they all allow for time travel. Join us today as we explore why this is so and the profound consequences that ensue. Special thanks to Prof Matt.
THANK-YOU to our supporters D. Smith, M. Sloan, C. Bottaccini, D. Daughaday, A. Jones, S. Brownlee, N. Kildal, Z. Star, E. West, T. Zajonc, C. Wolfred, L. Skov, G. Benson, A. De Vaal, M. Elliott, B. Daniluk, M. Forbes, S. Vystoropskyi, S. Lee, Z. Danielson, C. Fitzgerald, C. Souter, M. Gillette, T. Jeffcoat, H. Jensen, J. Rockett, N. Fredrickson, D. Holland, E. Hanway, D. Murphree, S. Hannum, T. Donkin, K. Myers, A. Schoen, K. Dabrowski, J. Black, R. Ramezankhani, J. Armstrong, K. Weber, S. Marks, L. Robinson, F. Van Exter, S. Roulier, B. Smith, P. Masterson, R. Sievers, G. Canterbury, J. Kill, J. Cassese, J. Kruger, S. Way, P. Finch, S. Applegate, L. Watson, T. Wheeler, E. Zahnle, N. Gebben, J. Bergman, E. Dessoi, J. Alexander, C. Macdonald, M. Hedlund, P. Kaup, C. Hays, S. Krasner, W. Evans, J. Curtin, J. Sturm, RAND Corp, T. Kordell, T. Ljungberg & M. Janke.
::References:: ► Alcubierre, M., 1994, “The warp drive: hyper-fast travel within general relativity”, Classical and Quantum Gravity, 11 L73: https://arxiv.org/abs/gr-qc/0009013 ► Pfenning, M. & Ford, L., 1997, “The unphysical nature of Warp Drive”, Classical and Quantum Gravity, 14, 1743: https://arxiv.org/abs/gr-qc/9702026 ► Finazzi, S., Liberati, S., Barceló, C., 2009, “Semiclassical instability of dynamical warp drives”, Physical Review D., 79, 124017: https://arxiv.org/abs/0904.0141 ► McMonigal, B., Lewis, G., O’Byrne, P., 2012, “Alcubierre warp drive: On the matter of matter”, Physical Review D., 85, 064024: https://arxiv.org/abs/1202.5708 ► Everett, A., 1996, “Warp drive and causality”, Physical Review D, 53, 7365: https://journals.aps.org/prd/abstract/10.1103/PhysRevD.53.
Oscilloscopes were once commonly called CROs, for the fact that they relied on cathode ray tubes for display. Since then, technology has moved quickly, and oscilloscopes these days almost entirely rely on modern screens like LCDs. However, [lonesoulsurfer] went a different route with this fun DIY build, creating an oscilloscope with a low-resolution LED display.
Yes, the signals are shown on a 10×10 matrix made up of red LEDs. The individual pixels look nicely diffused and chunky thanks to the fact that [lonesoulsurfer] was able to source square 5mm LEDs for the build. The whole project only uses four ICs – a decade counter and a LM3914 LED driver to run the display, a 555 timer for clock input, and an LM386 op-amp for amplifying incoming signals.
With a mic fitted onboard, the oscilloscope can act as a simple music visualizer, or be used with a probe to investigate actual circuits. It may not be of great enough resolution or precision for fine work, but it’ll at least tell you if your microcontroller’s clock is running properly if you’re scratching your head about the function of a simple project.
Hannah wraps up the series by meeting DeepMind co-founder and CEO, Demis Hassabis. In an extended interview, Demis describes why he believes AGI is possible, how we can get there, and the problems he hopes it will solve. Along the way, he highlights the important role of consciousness and why he’s so optimistic that AI can help solve many of the world’s major challenges. As a final note, Demis shares the story of a personal meeting with Stephen Hawking to discuss the future of AI and discloses Hawking’s parting message.
Interviewee: Deepmind co-founder and CEO, Demis Hassabis.
Credits. Presenter: Hannah Fry. Series Producer: Dan Hardoon. Production support: Jill Achineku. Sounds design: Emma Barnaby. Music composition: Eleni Shaw. Sound Engineer: Nigel Appleton. Editor: David Prest. Commissioned by DeepMind.
Thank you to everyone who made this season possible!
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
▀▀▀ Special thanks to Patreon supporters: Kelly Snook, TTST, Ross McCawley, Balkrishna Heroor, 65square.com, Chris LaClair, Avi Yashchin, John H. Austin, Jr., OnlineBookClub.org, Dmitry Kuzmichev, Matthew Gonzalez, Eric Sexton, john kiehl, Anton Ragin, Benedikt Heinen, Diffbot, Micah Mangione, MJP, Gnare, Dave Kircher, Burt Humburg, Blake Byers, Dumky, Evgeny Skvortsov, Meekay, Bill Linder, Paul Peijzel, Josh Hibschman, Mac Malkawi, Michael Schneider, jim buckmaster, Juan Benet, Ruslan Khroma, Robert Blum, Richard Sundvall, Lee Redden, Vincent, Stephen Wilcox, Marinus Kuivenhoven, Clayton Greenwell, Michael Krugman, Cy ‘kkm’ K’Nelson, Sam Lutfi, Ron Neal.
Additive manufacturing, or 3D printing, can create custom parts for electromagnetic devices on-demand and at a low cost. These devices are highly sensitive, and each component requires precise fabrication. Until recently, though, the only way to diagnose printing errors was to make, measure and test a device or to use in-line simulation, both of which are computationally expensive and inefficient.
To remedy this, a research team co-led by Penn State created a first-of-its-kind methodology for diagnosing printing errors with machine learning in real time. The researchers describe this framework—published in Additive Manufacturing —as a critical first step toward correcting 3D-printing errors in real time. According to the researchers, this could make printing for sensitive devices much more effective in terms of time, cost and computational bandwidth.
“A lot of things can go wrong during the additive manufacturing process for any component,” said Greg Huff, associate professor of electrical engineering at Penn State. “And in the world of electromagnetics, where dimensions are based on wavelengths rather than regular units of measure, any small defect can really contribute to large-scale system failures or degraded operations. If 3D printing a household item is like tuning a tuba—which can be done with broad adjustments—3D-printing devices functioning in the electromagnetic domain is like tuning a violin: Small adjustments really matter.”
— About ColdFusion – ColdFusion is an Australian based online media company independently run by Dagogo Altraide since 2009. Topics cover anything in science, technology, history and business in a calm and relaxed environment.
In this video we look at the structure of the US Social Security system and how it closely resembles aa Ponzi Scheme. Current retirees are paid from payroll taxes collected from existing workers. In recent years the demographic trends in the US have deteriorated significantly and Social Security expected to become insolvent by 2033.
