We present Myriad, a testbed written in JAX which enables machine learning researchers to benchmark imitation learning and reinforcement learning algorithms against trajectory optimization-based methods in real-world environments. Myriad contains 17 optimal control problems presented in continuous time which span medicine, ecology, epidemiology, and engineering. As such, Myriad strives to serve as a stepping stone towards application of modern machine learning techniques for impactful real-world tasks. The repository also provides machine learning practitioners access to trajectory optimization techniques, not only for standalone use, but also for integration within a typical automatic differentiation workflow. Indeed, the combination of classical control theory and deep learning in a fully GPU-compatible package unlocks potential for new algorithms to arise.

GPU-compatible package unlocks potential for new algorithms to arise.

We present Myriad, a testbed written in JAX which enables machine learning researchers to benchmark imitation learning and reinforcement learning algorithms against trajectory optimization-based methods in real-world environments. Myriad contains 17 optimal control problems presented in continuous time which span medicine, ecology, epidemiology, and engineering. As such, Myriad strives to serve as a stepping stone towards application of modern machine learning techniques for impactful real-world tasks. The repository also provides machine learning practitioners access to trajectory optimization techniques, not only for standalone use, but also for integration within a typical automatic differentiation workflow. Indeed, the combination of classical control theory and deep learning in a fully GPU-compatible package unlocks potential for new algorithms to arise.

We present Myriad, a testbed written in JAX for learning and planning in real-world continuous environments. The primary contributions of Myriad are threefold. First, Myriad provides machine learning practitioners access to trajectory optimization techniques for application within a typical automatic differentiation workflow. Second, Myriad presents many real-world optimal control problems, ranging from biology to medicine to engineering, for use by the machine learning community. Formulated in continuous space and time, these environments retain some of the complexity of real-world systems often abstracted away by standard benchmarks. As such, Myriad strives to serve as a stepping stone towards application of modern machine learning techniques for impactful real-world tasks. Finally, we use the Myriad repository to showcase a novel approach for learning and control tasks. Trained in a fully end-to-end fashion, our model leverages an implicit planning module over neural ordinary differential equations, enabling simultaneous learning and planning with complex environment dynamics.

Flexible SHAVE Processors: the raw performance of Myriad's SHAVE processors achieve the hundreds of GFLOPS compliments the neural compute engine's hardware fixed-function acceleration. As deep neural network layer types and topologies evolve, the programmability of the SHAVE cores provide the balance between efficiency and future proofing. Massively parallel central memory: deep neural networks create large volumes of intermediate data. Keeping all of this on chip enables our customers to vastly reduce the bandwidth that would otherwise create performance bottlenecks. Myriad X features a proprietary on-chip memory design that minimizes the cost of moving intermediate data – a crucial performance requirement as we see data transfer costs beginning to outstrip data compute costs from an energy perspective.

Reaching a fever-pitch in the 1630s, "tulipmania" -- a Dutch Golden Age obsession with the rare and exotic flowers responsible, supposedly, for driving overzealous buyers to financial ruin -- has long been considered the first economic bubble. The tulip craze served as a convenient analogy for stories of our desire to monetize the natural world and our tendency towards speculative absurdity. While the extent of this botanical craze has been vastly exaggerated in books, blockbuster movies, and principles in economics, the idea that flowers might control markets continues to captivate social scientists as well as artists. In her latest work, London-based artist Anna Ridler brings this historic phenomenon into the future, using AI to produce thousands of invented "impossible" tulips, slowly developing the features that early modern collectors considered valuable -- their unpredictable stripes and stipples -- along with the price of bitcoin. Ridler's video installation, "Mosaic Virus," is named for the plant virus that creates the strange variations in color that catapulted the price of some tulips far beyond others for 17th century collectors.

Our experienced team is creating new hardware and software approach to machine learning. Myriads.io - project that aims to decentralize artificial intelligence by leveraging blockchain technology. We believe that decentralized machine learning is a new technological revolution. Lowest price based on a perfect market. No humans setting prices for ML services, the network incessantly regulates prices at the lowest possible costs based on existing resources and users demand.