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🎸🎻𝐍𝐨𝐭𝐡𝐢𝐧𝐠 𝐄𝐥𝐬𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 (𝐌𝐞𝐭𝐚𝐥𝐥𝐢𝐜𝐚) : 𝐌𝐨𝐳𝐚𝐫𝐭 𝐇𝐞𝐫𝐨𝐞𝐬 (𝐎𝐟𝐟𝐢𝐜𝐢𝐚𝐥 𝐕𝐢𝐝𝐞𝐨)🌷🌷 ☘️The most beautiful acoustic rendition I’ve heard of this song! These two brilliant classical musicians took it to a completely different level! Bravo!👏👏💖💖

63,205 views • 1 year ago •via X (Twitter)

10 Comments

Gabriel's profile picture
Gabriel1 year ago

👏🌹

Susana Alvarez's profile picture
Susana Alvarez1 year ago

😍

L Douglas Hicks's profile picture
L Douglas Hicks1 year ago

This will be The Song of The Revolution

Eric  Bearden's profile picture
Eric Bearden1 year ago

Wow Metallica Nothing Else Matter, like A Fine Wine!

x's profile picture
x1 year ago

Kippenvel

Mister C .كيران Paddystinian's profile picture
Mister C .كيران Paddystinian1 year ago

@FirstLadyNot Loved that…and I don’t even like Metallica!

Ⲙⲟⲗⲟϧⲏⲁ's profile picture
Ⲙⲟⲗⲟϧⲏⲁ1 year ago

Honestly, it's better than the original.

Ayça ◻️◽▫️'s profile picture
Ayça ◻️◽▫️1 year ago

👏👏🎶

Eric  Bearden's profile picture
Eric Bearden1 year ago

Again Where is your Sister that plays The Piano? Now I know what a Group of Guys Do Together!

papcio 5's profile picture
papcio 51 year ago

Jak pięknie przenika się twórczość.Każdy utwór muzyczny wraca,bo jest w nim siła i każde inne wykonanie ,aranżacja dodaje mu siły PRZEKAZU społecznego.

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Holden Culotta

43,090 views • 6 months ago

Francis Ford Coppola on how young children convinced him to make 'The Outsiders' (1983) & the reason why he loves the movie: "Interviewer: 'Rumble Fish' (1983) is so completely different a film from 'The Outsiders' (1983), not just in black and white and color, but in the tone. And everybody said, “Well, he’s off in Tulsa, making two films,” rather in a way that, once, people would go up to the desert and make two westerns at the same time. 'Rumble Fish' makes 'The Outsiders' seem an odder film than I thought it was when I first saw it. 'The Outsiders' almost felt like a film in which you weren't quite there all the time. Coppola: No, I don’t think so. 'The Outsiders' (1984) was the type of film that I personally liked, a melodrama with a romantic tone. I liked the book a lot when I read it; I thought it was sweet and youthful and had something in its little, simple theme that was of value, and I wanted to make the picture very much as the book was. Maybe that’s what you're interpreting as my not always being there, except that I did make the decision to make it exactly like the book. The key to 'The Outsiders' is the score; the fact that it’s this schmaltzy classical movie score indicates that I wanted a movie told in sumptuous terms, very honestly or carefully taken from the book without changing it a lot, with young actors—putting the emphasis more on that kind of 'Gone with the Wind' (1939) lyricism which was so important to the young girl [Susie Hinton] when she wrote it. I liked the film on that basis. It’s how I made it and why I made it. But if you think about my career, I’ve never made two films that were alike... maybe the two Godfathers. But every one of my films is very different from one another and I was feeling that I was in kind of a journeyman period of my life, approaching a future style of work as a more serious, older man which would be based on a tremendous amount of exploration while I had the chance to do that. To me it’s nothing to say, well, I’m going to make that film and it’s going to be that kind of film. Like 'The Outsiders'—it’s not that I couldn’t make that sixteen other ways. People suggested, “Well, are we really going to make this book like that?” “Well,” I said, “little kids wrote me a letter and wanted me to make it that way.” Of all the letters I get from movies, 'The Outsiders' is the one where all of these cute little fourteen- year-olds . . . so I’m in it for them. I feel I must have gained something, although it wasn’t as challenging, cinematically or even on the level of acting and other things as, say, the one that came afterwards. But I always had this idea that I wanted to make one film that was romantic and schmaltzy, like 'The Godfather' (1972), and one film that was more of an art film, more in the direction of 'Apocalypse Now' (1979)." (Francis Ford Coppola's interview with David Thomson & Lucy Gray, 1983)

DepressedBergman

36,099 views • 6 months ago

Thermodynamic computing is here There is a new computing paradigm emerging from the noise, and its arrival may be as significant as the dawn of deep learning or the advent of cloud virtualization. A new company, Extropic, has just launched its first thermodynamic computer, a device they call a TSU, or Thermal Sampling Unit. While the web is already filling with deep technical dives, what’s more important for most of us is building a clear intuition for what this technology is, how it’s fundamentally different from anything that’s come before, and why it’s generating so much excitement. This isn’t just another chip; it’s a new way to think about computation itself. Seeing is Believing: Solving Puzzles in One Shot To understand what a TSU does, let’s look at two classic, notoriously difficult computer science problems: Sudoku and the Eight Queens problem. When you or I solve a Sudoku, we use a process of sequential logic, guess-and-check, and backtracking. We make an assumption, follow its logical conclusion, and if we hit a dead end, we erase and try again. A classical computer does the same, just much faster. A TSU, however, approaches this in a completely different way. Using a TSU simulator, one can “program” the problem by first clamping the known values—the clues already on the board. Then, you program in the constraints: no duplicate numbers in any row, column, or 3x3 square. With the problem thus defined, the TSU doesn’t “search” for a solution; it anneals one. In a single computational step, the solution simply emerges, backfilling all the empty squares correctly. The same principle applies to the Eight Queens problem, a challenge to place eight queens on a chessboard so that none can attack any other. This is a complex combinatorial problem with 92 distinct solutions. A classical computer would have to iteratively search for these. A TSU, by contrast, can be programmed with the constraints (the “anti-affinity” between queens on the same row, column, or diagonal) and then set to sample the “solution space.” In this context, a valid solution is one with a “problem energy” of zero. The TSU’s physical nature allows it to naturally find these zero-energy states. A simulation of this process shows the TSU discovering all 92 unique solutions, demonstrating its ability to not just find an answer, but to explore the entire landscape of all correct answers. This is a fundamentally new approach, one that bypasses the brute-force, iterative methods we’ve relied on for decades. The Physics of Computation: Using Noise, Not Fighting It This new power comes from a radical design philosophy. For the last 70 years, computing has been about one thing: order. We build chips that are deterministic, logical, and precise. The great enemy has always been noise, heat, and randomness. We spend billions on cooling and error correction to eliminate these very things. Quantum computing, in many ways, is the ultimate expression of this, requiring temperatures near absolute zero to eliminate all thermal noise and achieve quantum coherence. Thermodynamic computing is the polar opposite. It doesn’t fight the noise; it uses it. The TSU is built on the understanding that the natural, stochastic noise from “leaky” transistors—the very randomness we’ve tried to engineer out of existence—is itself a powerful computational resource. Think of it this way: a GPU, which is central to today’s AI, has to simulate noise. When a generative AI model creates a new image or sentence, it’s using complex algorithms to fake randomness. The TSU doesn’t need to fake it; it harnesses the actual physical randomness of thermodynamics. It is a piece of hardware that directly computes with probability. This makes it a hybrid, sitting somewhere between a purely analog computer (which might use light or sound waves to compute) and a digital GPU. It’s a physical device that leverages the laws of physics itself to find solutions, rather than just using logic gates to simulate them. From a Lost Hiker to a Million Bouncy Balls Perhaps the best way to build intuition is with a metaphor. Imagine that solving a complex optimization problem is like trying to find the lowest point of altitude in a 100-square-mile mountainous landscape. Classical computing, using an algorithm like gradient descent, is like being a single hiker dropped into this landscape at night. You have no map or satellite view. All you have is an altimeter and the sensation of the slope under your feet. You can only take one step at a time, always walking downhill, hoping you don’t get stuck in a small local valley when the true, lowest canyon is miles away. Thermodynamic computing is a completely different approach. It’s like having a million bouncy balls and a helicopter. You drop all million balls simultaneously across the entire 100-square-mile landscape. Then, you “turn on an earthquake,” shaking the entire system. The balls bounce and jostle, but as the shaking (the “annealing”) subsides, where do they all end up? They naturally settle into the lowest points. The balls that collect in the deepest valley represent the optimal solution. The TSU is, in essence, a physical device for dropping those million balls at once and letting the laws of thermodynamics find the lowest “energy” state for you, all at the same time. Beyond Puzzles: The Real-World Impact This is far more than just a clever way to solve brain teasers. This ability to instantly find the lowest energy state for a complex, constrained system has staggering real-world applications. One of the most immediate is protein folding. Companies like Google’s DeepMind have made incredible progress with AI like AlphaFold, which predicts protein structures. But this is still a predictive model trained on existing data. A TSU could potentially solve the folding problem directly, treating the protein as a system of atomic affinities and repulsions and finding its most stable, lowest-energy configuration almost instantaneously. This could revolutionize drug discovery and materials science. An even more profound possibility lies in nuclear fusion. One of the greatest engineering challenges in history is controlling the superheated plasma within a tokamak reactor. This requires shaping unimaginably complex magnetic containment fields in real-time to prevent the plasma from touching the reactor walls. This is a real-time optimization problem so complex it’s currently beyond our capabilities. A TSU, however, could be fast enough. Its ability to compute with electricity itself, rather than abstracting the problem through layers of software, might allow it to update the magnetic fields fast enough to stabilize the fusion reaction. One could even imagine a future where thermodynamic computing elements are built directly into the tokamak’s walls, allowing the reactor to physically and intelligently react to the plasma’s state in real time. A ‘GPT-2 Moment’ for a New Era It’s easy to become numb to hype, but what we are witnessing with the TSU feels different. This is what you might call a “GPT-2 moment.” For those who were there, GPT-2 was the first generative AI model that wasn’t just a toy; it was the first time you could play with it at home and see the spark of true generative intelligence. It was the precursor that pointed directly to the GPT-3 and ChatGPT revolution that has since changed the world. This TSU has that same feel. It’s the “SDK” for a new computing paradigm. This technology is as different from classical computing as quantum computing is, but with a critical difference: a team of 15 built this in two years, and it runs at room temperature on your desk. Quantum computing has seen decades of work and billions in funding, and it still hasn’t produced a commercially viable, scalable machine. The TSU is here now. Based on a two-decade-long career at the cutting edge of technology—from seeing the obvious future of virtualization in 2007 to an early conviction in deep learning and GPT—this has all the same hallmarks of a fundamental, world-changing shift. We are not just building faster calculators; we are learning to compute with the universe itself. Pay close attention to this. This is the next big thing.

David Shapiro (L/0)

83,649 views • 9 months ago