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The Cerebras Wafer-Scale Engine is 8.5 inches wide and contains 1.2 trillion transistors. The next biggest chip, the NVIDIA A100 GPU, measures one inch at a time and has only 54 billion transistor. The WSE has made its way into a handful of supercomputing labs, including the National Energy Technology Laboratory. Researchers pitted the chip against a supercomputer in a fluid dynamics simulation and found it to be faster than the supercomputer. The team said that the chip completed a combustion simulation in a power plant approximately 200 times faster.Joule is the 81st fastest supercomputer in the world, with a price tag of $1.2 billion. The WSE is bigger than the average supercomputer, and it's all about design. The company uses couriers to send and collect documents from other branches and archives across the city. It's like an old-fashioned company doing all its business on paper, but on silicon wafers, and the process takes place within a silicon wafer, not a sheet of paper. The CS-1 is the world's largest supercomputer.Cerebras has developed a chip that can handle problems small enough to fit on a wafer. The megachip is far more efficient than a traditional supercomputer that needs a ton of traditional chips to be networked. Next-generation chip will have 2,6 trillion transistors, 850,00 cores, and more than double the memory. It still remains to be seen whether wafer-scale computing really does take off, but Cerebras is the first to seriously pursue it.
The next problem is the accuracy of the information. Let's start with a non-numeric case, such as finding Waldo in a picture.
The next generation of computing is on the horizon, and several new machines may just smash all the records...with two nations neck and neck in a race to get there first.The ENIAC was capable of about 400 FLOPS. FLOPS stands for floating-point operations per second, which basically tells us how many calculations the computer can do per second. This makes measuring FLOPS a way of calculating computing power.So, the ENIAC was sitting at 400 FLOPS in 1945, and in the ten years it was operational, it may have performed more calculations than all of humanity had up until that point in time—that was the kind of leap digital computing gave us. From that 400 FLOPS we upgraded to 10,000 FLOPS, and then a million, a billion, a trillion, a quadrillion FLOPS. That’s petascale computing, and that’s the level of today’s most powerful supercomputers.But what’s coming next is exascale computing. That’s zeroes. 1 quintillion operations per second. Exascale computers will be a thousand times better performing than the petascale machines we have now. Or, to put it another way, if you wanted to do the same number of calculations that an exascale computer can do in ONE second...you’d be doing math for over 31 billion years.
Three-dimensional printing promises new opportunities for more sustainable and local production. But does 3D printing make everything better? This film shows how innovation can change the world of goods.Is the way we make things about to become the next revolution? Traditional manufacturing techniques like milling, casting and gluing could soon be replaced by 3D printing -saving enormous amounts of material and energy. Aircraft maker Airbus is already benefiting from the new manufacturing method. Beginning this year, the A350 airliner will fly with printed door locking shafts. Where previously ten parts had to be installed, today that’s down to just one. It saves a lot of manufacturing steps. And 3D printing can imitate nature's efficient construction processes, something barely possible in conventional manufacturing. Another benefit of the new technology is that components can become significantly lighter and more robust, and material can be saved during production. But the Airbus development team is not yet satisfied. The printed cabin partition in the A350 has become 45 percent lighter thanks to the new structure, but it is complex and expensive to manufacture. It takes 900 hours to print just one partition, a problem that print manufacturers have not yet been able to solve. The technology is already being used in Adidas shoes: The sportswear company says it is currently the world’s largest manufacturer of 3D-printed components. The next step is sustainable materials, such as biological synthetic resins that do not use petroleum and can be liquefied again without loss of quality and are therefore completely recyclable. This documentary sheds light on the diverse uses of 3D printing.
Precision of an information should be considered as the amount of uncertainty that it can remove. Number of bits alone is not adequate. Here is an example. 2.99999999... ≤ π ≤3.9999999...3 ≤ π ≤ 4Many bits in first statement don't remove more uncertainty compared to fewer bits in the second statement. So, we can't say that the first statement has higher precision than the second, although it contains many more bits.
ARTIFICIAL INTELLIGENCECan’t Access GPT-3? Here’s GPT-J — Its Open-Source CousinSimilar to GPT-3, and everyone can use it.The AI world was thrilled when OpenAI released the beta API for GPT-3. It gave developers the chance to play with the amazing system and look for new exciting use cases. Yet, OpenAI decided not to open (pun intended) the API to everyone, but only to a selected group of people through a waitlist. If they were worried about the misuse and harmful outcomes, they’d have done the same as with GPT-2: not releasing it to the public at all.It’s surprising that a company that claims its mission is “to ensure that artificial general intelligence benefits all of humanity” wouldn’t allow people to thoroughly investigate the system. That’s why we should appreciate the work of people like the team behind EleutherAI, a “collective of researchers working to open source AI research.” Because GPT-3 is so popular, they’ve been trying to replicate the versions of the model for everyone to use, aiming at building a system comparable to GPT-3-175B, the AI king. In this article, I’ll talk about EleutherAI and GPT-J, the open-source cousin of GPT-3. Enjoy!
GPT-J is 30 times smaller than GPT-3-175B. Despite the large difference, GPT-J produces better code, just because it was slightly more optimized to do the task. This implies that optimization towards improving specific abilities could give rise to systems that are way better than GPT-3. And this isn’t limited to coding: we could create for every task, a system that would top GPT-3 with ease. GPT-3 would become a jack of all trades, whereas the specialized systems would be the true masters.This hypothesis goes in line with the results OpenAI researchers Irene Solaiman and Christy Dennison got from PALMS. They fine-tuned GPT-3 with a small curated dataset to prevent the system from producing biased outputs and got amazing results. In a way, it was an optimization; they specialized GPT-3 to be unbiased — as understood by ethical institutions in the U.S. It seems that GPT-3 isn’t only very powerful, but that a notable amount of power is still latent within, waiting to be exploited by specialization.
GPT-J is 30 times smaller than GPT-3-175B. Despite the large difference, GPT-J produces better code, just because it was slightly more optimized to do the task. This implies that optimization towards improving specific abilities could give rise to systems that are way better than GPT-3.
GitHub and OpenAI have launched a technical preview of a new AI tool called Copilot, which lives inside the Visual Studio Code editor and autocompletes code snippets.Copilot does more than just parrot back code it’s seen before, according to GitHub. It instead analyzes the code you’ve already written and generates new matching code, including specific functions that were previously called. Examples on the project’s website include automatically writing the code to import tweets, draw a scatterplot, or grab a Goodreads rating.
GitHub sees this as an evolution of pair programming, where two coders will work on the same project to catch each others’ mistakes and speed up the development process. With Copilot, one of those coders is virtual.
Since we are babies, we intuitively develop the ability to correlate the input from different cognitive sensors such as vision, audio and text. While listening to a symphony we immediately visualize an orchestra or when admiring a landscape painting, our brain associates the visual with specific sounds. The relationships between images, sounds and texts are dictated by connections between different sections of the brain responsible from analyzing specific cognitive input. In that sense, you can say that we are hardwired to learn simultaneously from multiple cognitive signals. Despite the advancements in different deep learning areas such as image, language and sound analysis, most neural networks remain specialized on a single input data type. A few years ago, researchers from Alphabet’s subsidiary DeepMind published a research paper proposing a method that can simultaneously analyze audio and visual inputs and learn the relationships between objects and sounds in a common environment.
Does PlayStation count.
Although their main purpose may not be directly correlated.
Quote from: hamdani yusuf on 04/07/2021 13:00:11 Although their main purpose may not be directly correlated.What about Xbox.
Quote from: hamdani yusuf on 04/07/2021 13:12:26Although their main purpose may not be directly correlated.I just thought of something. What if we are already in a virtual universe we will have to try and build a real universe.
As long as we have no reliable way to proof otherwise, it's better for us to assume that we're living in reality. Descartes' Cogito tells us that our own consciousness is the only self evident proof of our existence.
I think it is safe to assume that our consciousness is merely a circuit board plugged into the motherboard that is programmed to make some decisions inside the virtual reality life that we only virtually think we have. I could be wrong but if I am then that would be a falt in the electronics of the virtual reality machine. eg. When I get a headache this can be due to computer overload.