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Biological research and self-driving labs in deep space supported by artificial intelligence - Nature.com

If you can't build a machine learning model from scratch or lack the infrastructure, merely connecting your app to a working model fixes the gap.

Artificial intelligence is here for everyone to use one way or the other. As for OpenAI Gym, there are many explorable training grounds to feed your reinforcement learning agents.

What is OpenAI Gym, how does it work, and what can you build using it?

OpenAI Gym is a Pythonic API that provides simulated training environments for reinforcement learning agents to act based on environmental observations; each action comes with a positive or negative reward, which accrues at each time step. While the agent aims to maximize rewards, it gets penalized for each unexpected decision.

The time step is a discrete-time tick for the environment to transit into another state. It adds up as the agent's actions change the environment state.

The OpenAI Gym environments are based on the Markov Decision Process (MDP), a dynamic decision-making model used in reinforcement learning. Thus, it follows that rewards only come when the environment changes state. And the events in the next state only depend on the present state, as MDP doesn't account for past events.

Before moving on, let's dive into an example for a quick understanding of OpenAI Gym's application in reinforcement learning.

Assuming you intend to train a car in a racing game, you can spin up a racetrack in OpenAI Gym. In reinforcement learning, if the vehicle turns right instead of left, it might get a negative reward of -1. The racetrack changes at each time step and might get more complicated in subsequent states.

Negative rewards or penalties aren't bad for an agent in reinforcement learning. In some cases, it encourages it to achieve its goal more quickly. Thus, the car learns about the track over time and masters its navigation using reward streaks.

For instance, we initiated the FrozenLake-v1 environment, where an agent gets penalized for falling into ice holes but rewarded for recovering a gift box.

Our first run generated fewer penalties with no rewards:

However, a third iteration produced a more complex environment. But the agent got a few rewards:

The outcome above doesn't imply that the agent will improve in the next iteration. While it may successfully avoid more holes the next time, it may get no reward. But modifying a few parameters might improve its learning speed.

The OpenAI Gym API revolves around the following components:

Since OpenAI Gym allows you to spin up custom learning environments, here are some ways to use it in a real-life scenario.

You can leverage OpenAI Gym's gaming environments to reward desired behaviors, create gaming rewards, and increase complexity per game level.

Where there's a limited amount of data, resources, and time, OpenAI Gym can be handy for developing an image recognition system. On a deeper level, you can scale it to build a face recognition system, which rewards an agent for identifying faces correctly.

OpenAI Gym also offers intuitive environment models for 3D and 2D simulations, where you can implement desired behaviors into robots. Roboschool is an example of scaled robot simulation software built using OpenAI Gym.

You can also build marketing solutions like ad servers, stock trading bots, sales prediction bots, product recommender systems, and many more using the OpenAI Gym. For instance, you can build a custom OpenAI Gym model that penalizes ads based on impression and click rate.

Some ways to apply OpenAI Gym in natural language processing are multiple-choice questions involving sentence completion or building a spam classifier. For example, you can train an agent to learn sentence variations to avoid bias while marking participants.

OpenAI Gym supports Python 3.7 and later versions. To set up an OpenAI Gym environment, you'll install gymnasium, the forked continuously supported gym version:

Next, spin up an environment. You can create a custom environment, though. But start by playing around with an existing one to master the OpenAI Gym concept.

The code below spins up the FrozenLake-v1. The env.reset method records the initial observation:

observation, info = env.reset()

Some environments require extra libraries to work. If you need to install another library, Python recommends it via the exception message.

For example, you'll install an additional library (gymnasium[toy-text]) to run the FrozenLake-v1 environment.

One of the setbacks to AI and machine learning development is the shortage of infrastructure and training datasets. But as you look to integrate machine learning models into your apps or devices, it's all easier now with ready-made AI models flying around the internet. While some of these tools are low-cost, others, including the OpenAI Gym, are free and open-source.

The rest is here:
What Is OpenAI Gym and How Can You Use It? - MUO - MakeUseOf

Summary: Using ECG data, a new machine learning algorithm was able to predict death within 5 years of a patient being admitted to hospital with 87% accuracy. The AI was able to sort patients into 5 categories ranging from low to high risk of death.

Source: University of Alberta

If youve ever been admitted to hospital or visited an emergency department, youve likely had an electrocardiogram, or ECG, a standard test involving tiny electrodes taped to your chest that checks your hearts rhythm and electrical activity.

Hospital ECGs are usually read by a doctor or nurse at your bedside, but now researchers are using artificial intelligence to glean even more information from those results to improve your care and the health-care system all at once.

Inrecently published findings, the research team built and trained machine learning programs based on 1.6 million ECGs done on 244,077 patients in northern Alberta between 2007 and 2020.

The algorithm predicted the risk of death from that point for each patient from all causes within one month, one year and five years with an 85 percent accuracy rate, sorting patients into five categories from lowest to highest risk.

The predictions were even more accurate when demographic information (age and sex) and six standard laboratory blood test results were included.

The study is a proof-of-concept for using routinely collected data to improve individual care and allow the health-care system to learn as it goes, according to principal investigatorPadma Kaul, professor of medicine and co-director of theCanadian VIGOUR Centre.

We wanted to know whether we could use new methods like artificial intelligence and machine learning to analyze the data and identify patients who are at higher risk for mortality, Kaul explains.

These findings illustrate how machine learning models can be employed to convert data collected routinely in clinical practice to knowledge that can be used to augment decision-making at the point of care as part of a learning health-care system.

A clinician will order an electrocardiogram if you have high blood pressure or symptoms of heart disease, such as chest pain, shortness of breath or an irregular heartbeat. The first phase of the study examined ECG results in all patients, but Kaul and her team hope to refine these models for particular subgroups of patients.

They also plan to focus the predictions beyond all-cause mortality to look specifically at heart-related causes of death.

We want to take data generated by the health-care system, convert it into knowledge and feed it back into the system so that we can improve care and outcomes. Thats the definition of a learning health-care system.

Author: Ross NeitzSource: University of AlbertaContact: Ross Neitz University of AlbertaImage: The image is in the public domain

Original Research: Open access.Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms by Padma Kaul et al. npj Digital Medicine

Abstract

Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms

The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason.

Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (20072020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality. The models for 30-day, 1-year, and 5-year mortality were trained on 146,173, 141,072, and 111,020 patients and evaluated on 97,144, 89,379, and 55,650 patients, respectively. In the evaluation cohort, 7.6%, 17.3%, and 32.9% patients died by 30-days, 1-year, and 5-years, respectively.

ResNet models based on ECG traces alone had good-to-excellent performance with area under receiver operating characteristic curve (AUROC) of 0.843 (95% CI: 0.8380.848), 0.812 (0.8080.816), and 0.798 (0.7920.803) for 30-day, 1-year and 5-year prediction, respectively; and were superior to XGB models based on ECG measurements with AUROC of 0.782 (0.7760.789), 0.784 (0.7800.788), and 0.746 (0.7400.751).

This study demonstrates the validity of ECG-based DL mortality prediction models at the population-level that can be leveraged for prognostication at point of care.

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Machine Learning Programs Predict Risk of Death Based on Results From Routine Hospital Tests - Neuroscience News

New Amazon EC2 P5 Instances Deployed in EC2 UltraClusters Are Fully Optimized to Harness NVIDIA Hopper GPUs for Accelerating Generative AI Training and Inference at Massive Scale

GTCAmazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), and NVIDIA (NASDAQ: NVDA) today announced a multi-part collaboration focused on building out the world's most scalable, on-demand artificial intelligence (AI) infrastructure optimized for training increasingly complex large language models (LLMs) and developing generative AI applications.

The joint work features next-generation Amazon Elastic Compute Cloud (Amazon EC2) P5 instances powered by NVIDIA H100 Tensor Core GPUs and AWSs state-of-the-art networking and scalability that will deliver up to 20 exaFLOPS of compute performance for building and training the largest deep learning models. P5 instances will be the first GPU-based instance to take advantage of AWSs second-generation Elastic Fabric Adapter (EFA) networking, which provides 3,200 Gbps of low-latency, high bandwidth networking throughput, enabling customers to scale up to 20,000 H100 GPUs in EC2 UltraClusters for on-demand access to supercomputer-class performance for AI.

AWS and NVIDIA have collaborated for more than 12 years to deliver large-scale, cost-effective GPU-based solutions on demand for various applications such as AI/ML, graphics, gaming, and HPC, said Adam Selipsky, CEO at AWS. AWS has unmatched experience delivering GPU-based instances that have pushed the scalability envelope with each successive generation, with many customers scaling machine learning training workloads to more than 10,000 GPUs today. With second-generation EFA, customers will be able to scale their P5 instances to over 20,000 NVIDIA H100 GPUs, bringing supercomputer capabilities on demand to customers ranging from startups to large enterprises.

Accelerated computing and AI have arrived, and just in time. Accelerated computing provides step-function speed-ups while driving down cost and power as enterprises strive to do more with less. Generative AI has awakened companies to reimagine their products and business models and to be the disruptor and not the disrupted, said Jensen Huang, founder and CEO of NVIDIA. AWS is a long-time partner and was the first cloud service provider to offer NVIDIA GPUs. We are thrilled to combine our expertise, scale, and reach to help customers harness accelerated computing and generative AI to engage the enormous opportunities ahead.

New Supercomputing ClustersNew P5 instances are built on more than a decade of collaboration between AWS and NVIDIA delivering the AI and HPC infrastructure and build on four previous collaborations across P2, P3, P3dn, and P4d(e) instances. P5 instances are the fifth generation of AWS offerings powered by NVIDIA GPUs and come almost 13 years after its initial deployment of NVIDIA GPUs, beginning with CG1 instances.

P5 instances are ideal for training and running inference for increasingly complex LLMs and computer vision models behind the most-demanding and compute-intensive generative AI applications, including question answering, code generation, video and image generation, speech recognition, and more.

Specifically built for both enterprises and startups racing to bring AI-fueled innovation to market in a scalable and secure way, P5 instances feature eight NVIDIA H100 GPUs capable of 16 petaFLOPs of mixed-precision performance, 640 GB of high-bandwidth memory, and 3,200 Gbps networking connectivity (8x more than the previous generation) in a single EC2 instance. The increased performance of P5 instances accelerates the time-to-train machine learning (ML) models by up to 6x (reducing training time from days to hours), and the additional GPU memory helps customers train larger, more complex models. P5 instances are expected to lower the cost to train ML models by up to 40% over the previous generation, providing customers greater efficiency over less flexible cloud offerings or expensive on-premises systems.

Amazon EC2 P5 instances are deployed in hyperscale clusters called EC2 UltraClusters that are comprised of the highest performance compute, networking, and storage in the cloud. Each EC2 UltraCluster is one of the most powerful supercomputers in the world, enabling customers to run their most complex multi-node ML training and distributed HPC workloads. They feature petabit-scale non-blocking networking, powered by AWS EFA, a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS. EFAs custom-built operating system (OS) bypass hardware interface and integration with NVIDIA GPUDirect RDMA enhances the performance of inter-instance communications by lowering latency and increasing bandwidth utilization, which is critical to scaling training of deep learning models across hundreds of P5 nodes. With P5 instances and EFA, ML applications can use NVIDIA Collective Communications Library (NCCL) to scale up to 20,000 H100 GPUs. As a result, customers get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of AWS. On top of these cutting-edge computing capabilities, customers can use the industrys broadest and deepest portfolio of services such as Amazon S3 for object storage, Amazon FSx for high-performance file systems, and Amazon SageMaker for building, training, and deploying deep learning applications. P5 instances will be available in the coming weeks in limited preview. To request access, visit https://pages.awscloud.com/EC2-P5-Interest.html.

With the new EC2 P5 instances, customers like Anthropic, Cohere, Hugging Face, Pinterest, and Stability AI will be able to build and train the largest ML models at scale. The collaboration through additional generations of EC2 instances will help startups, enterprises, and researchers seamlessly scale to meet their ML needs.

Anthropic builds reliable, interpretable, and steerable AI systems that will have many opportunities to create value commercially and for public benefit. At Anthropic, we are working to build reliable, interpretable, and steerable AI systems. While the large, general AI systems of today can have significant benefits, they can also be unpredictable, unreliable, and opaque. Our goal is to make progress on these issues and deploy systems that people find useful, said Tom Brown, co-founder of Anthropic. Our organization is one of the few in the world that is building foundational models in deep learning research. These models are highly complex, and to develop and train these cutting-edge models, we need to distribute them efficiently across large clusters of GPUs. We are using Amazon EC2 P4 instances extensively today, and we are excited about the upcoming launch of P5 instances. We expect them to deliver substantial price-performance benefits over P4d instances, and theyll be available at the massive scale required for building next-generation large language models and related products.

Cohere, a leading pioneer in language AI, empowers every developer and enterprise to build incredible products with world-leading natural language processing (NLP) technology while keeping their data private and secure. Cohere leads the charge in helping every enterprise harness the power of language AI to explore, generate, search for, and act upon information in a natural and intuitive manner, deploying across multiple cloud platforms in the data environment that works best for each customer, said Aidan Gomez, CEO at Cohere. NVIDIA H100-powered Amazon EC2 P5 instances will unleash the ability of businesses to create, grow, and scale faster with its computing power combined with Coheres state-of-the-art LLM and generative AI capabilities.

Hugging Face is on a mission to democratize good machine learning. As the fastest growing open source community for machine learning, we now provide over 150,000 pre-trained models and 25,000 datasets on our platform for NLP, computer vision, biology, reinforcement learning, and more, said Julien Chaumond, CTO and co-founder at Hugging Face. With significant advances in large language models and generative AI, were working with AWS to build and contribute the open source models of tomorrow. Were looking forward to using Amazon EC2 P5 instances via Amazon SageMaker at scale in UltraClusters with EFA to accelerate the delivery of new foundation AI models for everyone.

Today, more than 450 million people around the world use Pinterest as a visual inspiration platform to shop for products personalized to their taste, find ideas to do offline, and discover the most inspiring creators. We use deep learning extensively across our platform for use-cases such as labeling and categorizing billions of photos that are uploaded to our platform, and visual search that provides our users the ability to go from inspiration to action," said David Chaiken, Chief Architect at Pinterest. "We have built and deployed these use-cases by leveraging AWS GPU instances such as P3 and the latest P4d instances. We are looking forward to using Amazon EC2 P5 instances featuring H100 GPUs, EFA and Ultraclusters to accelerate our product development and bring new Empathetic AI-based experiences to our customers.

As the leader in multimodal, open-source AI model development and deployment, Stability AI collaborates with public- and private-sector partners to bring this next-generation infrastructure to a global audience. At Stability AI, our goal is to maximize the accessibility of modern AI to inspire global creativity and innovation, said Emad Mostaque, CEO of Stability AI. We initially partnered with AWS in 2021 to build Stable Diffusion, a latent text-to-image diffusion model, using Amazon EC2 P4d instances that we employed at scale to accelerate model training time from months to weeks. As we work on our next generation of open-source generative AI models and expand into new modalities, we are excited to use Amazon EC2 P5 instances in second-generation EC2 UltraClusters. We expect P5 instances will further improve our model training time by up to 4x, enabling us to deliver breakthrough AI more quickly and at a lower cost.

New Server Designs for Scalable, Efficient AILeading up to the release of H100, NVIDIA and AWS engineering teams with expertise in thermal, electrical, and mechanical fields have collaborated to design servers to harness GPUs to deliver AI at scale, with a focus on energy efficiency in AWS infrastructure. GPUs are typically 20x more energy efficient than CPUs for certain AI workloads, with the H100 up to 300x more efficient for LLMs than CPUs.

The joint work has included developing a system thermal design, integrated security and system management, security with the AWS Nitro hardware accelerated hypervisor, and NVIDIA GPUDirect optimizations for AWS custom-EFA network fabric.

Building on AWS and NVIDIAs work focused on server optimization, the companies have begun collaborating on future server designs to increase the scaling efficiency with subsequent-generation system designs, cooling technologies, and network scalability.

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AWS and NVIDIA Collaborate on Next-Generation Infrastructure for Training Large Machine Learning Models and ... - NVIDIA Blog