June 13, 2022
Kannan Subbiah
FCA | CISA | CGEIT | CCISO | GRC Consulting | Independent Director | Enterprise & Solution Architecture | Former Sr. VP & CTO of MF Utilities | BU Soft Tech | itTrident
What customers are no doubt telling Intel and AMD is that they want highly tuned pieces of hardware co-designed with very precise workloads, and that they will want them at much lower volumes for each multi-motor configuration than chip makers and system builders are used to. Therefore, these compute engine complexes we call servers will carry higher unit costs than chip makers and system builders are used to, but not necessarily with higher profits. In fact, quite possibly with lower profits, if you can believe it. This is why Intel is taking a third whack at discrete GPUs with its Xe architecture and significantly with the “Ponte Vecchio” Xe HPC GPU accelerator that is at the “Aurora” supercomputer at Argonne National Laboratory. And this time the architecture of the GPUs is a superset of the integrated GPUs for its laptops and desktops, not some Frankenstein X86 architecture that is not really tuned for graphics even if it could be used as a massively parallel compute engine in a way that GPUs have been transformed from Nvidia and AMD.?
Our goal within each edge computing site is to have a unified hosting environment to make sure we can run as many games as possible as smoothly as possible. Today’s games are designed for GPUs, so we partnered with NVIDIA to build a hosting environment on top of NVIDIA Ampere architecture-based GPUs. As games continue to become more graphically intensive and complex, GPUs will provide us with the high fidelity and low latency we need for loading, running, and streaming games. To run games themselves, we use Twine, our cluster management system, on top of our edge computing operating system. We build orchestration services to manage the streaming signals and use Twine to coordinate the game servers on edge. We built and used container technologies for both Windows and Android games. We have different hosting solutions for Windows and Android games, and the Windows hosting solution comes with the integration with PlayGiga. We’ve built a consolidated orchestration system to manage and run the games for both operating systems.
A typical Transformer comprises several “blocks,” each containing several distinct layers. A feed-forward network is one of these layers (FFN). This single FFN is replaced in LIMoE and the works described above by an expert layer with multiple parallel FFNs, each of which is an expert. A primary router predicts which experts should handle which tokens, given a series of passes to process. ... The model’s price is comparable to the regular Transformer model if only one expert is activated. LIMoE performs exactly that, activating one expert per case and matching the dense baselines’ computing cost. The LIMoE router, on the other hand, may see either image or text data tokens. When MoE models try to deliver all tokens to the same expert, they fail uniquely. Auxiliary losses, or additional training objectives, are commonly used to encourage balanced expert utilization. Google AI team discovered that dealing with numerous modalities combined with sparsity resulted in novel failure modes that conventional auxiliary losses could not solve. To address this, they created additional losses.
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What makes boundaries different from balance? Balance implies two things that aren't equal that you're constantly trying to make equal. It creates the expectation of a clear-cut division. A work-life balance fails to acknowledge that you are a whole person, and sometimes things can be out of balance without anything being wrong. Sometimes you'll spend days, weeks and even whole seasons of life choosing to lean more into one part of your life than the other. Boundaries ask you to think about what's important to you, what drives you, and what authenticity looks like for you. Boundaries require self-awareness and self-reflection, along with a willingness and ability to prioritize. Those qualities help you to be more aware and more capable of making decisions at a given moment. By establishing boundaries grounded in your priorities, you're more equipped to make choices. Boundaries empower you to say, "This is what I'm choosing right now. I need to be fully here until this is done." Boundaries aren't static, either.?
AI systems need both code and data, and “all that progress in algorithms means it's actually time to spend more time on the data,” Ng said at the recent EmTech Digital conference hosted by MIT Technology Review. Focusing on high-quality data that is consistently labeled would unlock the value of AI for sectors such as health care, government technology, and manufacturing, Ng said. “If I go see a health care system or manufacturing organization, frankly, I don't see widespread AI adoption anywhere.” This is due in part to the ad hoc way data has been engineered, which often relies on the luck or skills of individual data scientists, said Ng, who is also the founder and CEO of Landing AI. Data-centric AI is a new idea that is still being discussed, Ng said, including at a data-centric AI workshop he convened last December. ... Data-centric AI is a key part of the solution, Ng said, as it could provide people with the tools they need to engineer data and build a custom AI system that they need. “That seems to me, the only recipe I'm aware of, that could unlock a lot of this value of AI in other industries,” he said.
The main goal of Chaos Engineering is as explained here: “Chaos Engineering is the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production.” The idea of Chaos Engineering is to identify weaknesses and reduce uncertainty when building a distributed system. As I already mentioned above, building distributed systems at scale is challenging, and since such systems tend to be composed of many moving parts, leveraging Chaos Engineering practices to reduce the blast radius of such failures, proved itself as a great method for that purpose. We leverage Chaos Engineering principles to achieve other things besides its main objective. The “On-call like a king” workshops intend to achieve two goals in parallel—(1) train engineers on production failures that we had recently; (2) train engineers on cloud-native practices, tooling, and how to become better cloud-native engineers!