Achieving Next-Level Value From AI By Focusing On The Operational Side Of Machine Learning Forbes
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Achieving Next-Level Value From AI By Focusing On The Operational Side Of Machine Learning - Forbes
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Deep Learning | NVIDIA Developer
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Deep learning is a subset of AI and machine learning that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation, and others.
Deep learning differs from traditional machine learning techniques in that they can automatically learn representations from data such as images, video or text, without introducing hand-coded rules or human domain knowledge. Their highly flexible architectures can learn directly from raw data and can increase their predictive accuracy when provided with more data.
Deep learning is commonly used across apps in computer vision, conversational AI and recommendation systems. Computer vision apps use deep learning to gain knowledge from digital images and videos. Conversational AI apps help computers understand and communicate through natural language. Recommendation systems use images, language, and a users interests to offer meaningful and relevant search results and services.
Deep learning has led to many recent breakthroughs in AI such as Google DeepMinds AlphaGo, self-driving cars, intelligent voice assistants and many more. With NVIDIA GPU-accelerated deep learning frameworks, researchers and data scientists can significantly speed up deep learning training, that could otherwise take days and weeks to just hours and days. When models are ready for deployment, developers can rely on GPU-accelerated inference platforms for the cloud, embedded device or self-driving cars, to deliver high-performance, low-latency inference for the most computationally-intensive deep neural networks.
Developing AI applications start with training deep neural networks with large datasets. GPU-accelerated deep learning frameworks offer flexibility to design and train custom deep neural networks and provide interfaces to commonly-used programming languages such as Python and C/C++. Every major deep learning framework such as PyTorch, TensorFlow, JAX and others, are already GPU-accelerated, so data scientists and researchers can get productive in minutes without any GPU programming.
For AI researchers and application developers, NVIDIA Hopper and Ampere GPUs powered by tensor cores give you an immediate path to faster training and greater deep learning performance. With Tensor Cores enabled, FP32 and FP16 mixed precision matrix multiply dramatically accelerates your throughput and reduces AI training times.
For developers integrating deep neural networks into their cloud-based or embedded application, Deep Learning SDK includes high-performance libraries that implement building block APIs for implementing training and inference directly into their apps. With a single programming model for all GPU platform - from desktop to datacenter to embedded devices, developers can start development on their desktop, scale up in the cloud and deploy to their edge devices - with minimal to no code changes.
NVIDIA provides optimized software stacks to accelerate training and inference phases of the deep learning workflow. Learn more on the links below.
For developers looking to build deep learning applications, NVIDIA Pretrained AI models eliminate the need of building models from scratch or experimenting with other open source models that fail to converge. These models are pretrained on high quality representative datasets to deliver state-of-the-art performance and production readiness for a variety of use cases like computer vision, speech AI, robotics, natural language processing, healthcare, cybersecurity, and many others.
Deep learning frameworks offer building blocks for designing, training and validating deep neural networks, through a high level programming interface. Every major deep learning framework such as PyTorch, TensorFlow, and JAX rely on Deep Learning SDK libraries to deliver high-performance multi-GPU accelerated training. As a framework user, its as simple as downloading a framework and instructing it to use GPUs for training. Learn more about deep learning frameworks and explore these examples to getting started quickly.
Tensor Core Optimized Model Scripts
Deep learning frameworks are optimized for every GPU platform from Titan V desktop developer GPU to data center grade Tesla GPUs. This allows researchers and data scientist teams to start small and scale out as data, number of experiments, models and team size grows. Since Deep Learning SDK libraries are API compatible across all NVIDIA GPU platforms, when a model is ready to be integrated into an application, developers can test and validate locally on the desktop, and with minimal to no code changes validate and deploy to Tesla datacenter platforms, Jetson embedded platform or DRIVE autonomous driving platform. This improves developer productivity and reduces chances of introducing bugs when going from prototype to production.
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Deep Learning | NVIDIA Developer
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Its Not Just About Accuracy – Five More things to Consider for a Machine Learning Model – AZoM
You might think that a machine learning (ML) specialist company likeIntellegens is always pursuing the perfect model - one that takes a new set of system inputs and predicts their outputs correctly every time. But, despite the importance of model accuracy, it is possible to focus on it too much in real-world R&D.
A near-perfect model typically considered a model that predicts outputs reliably to within 5% - could mean thatmachine learning (ML)has found a set of robust relationships not previously observed by cutting through multi-dimensional complexity.
Image Credit: Intellegens Limited
However, this can also mean that experiments were poorly designed or trivial, and the ML is simply confirming the obvious. Such perfection is, in any case, mathematically unachievable in many complex systems with inherent uncertainties.
In the real world of R&D, a typical use case might be designing a set of experiments to find more effective formulations, chemicals, or materials. Here, visualizing the range of possibilities is beyond the capacity of the human brain and even relatively sophisticated Design of Experiments methods still result in large, expensive and time-consuming experimental programs. Users dont want perfection they just want ML to shift the odds in their favor, with predictions that outperform the logic currently driving their work.
Pursuing the ideal model may also waste time that is better spent elsewhere. It may also lead to users inadvertently narrowing down their search space in ways that exclude more innovative solutions.
Instead of asking how accurate a model is, the right question may focus on the models usefulness. Below are Intellegens top five examples of questions that might help a user to shape their model:
1. Can we get to an answer in fewer experiments?
Does the ML that is being used have the ability to understand what missing data could best improve its accuracy? This information can then be deployed to decide what experiment to perform next, resulting in a significantly reduced time-to-market. In some cases, theAlchemitesoftware from Intellegens has reduced experimental workloads by 80%+. More commonly, reductions of 50% are reported.
2. How do we generate new ideas for formulations that achieve our goals?
New concepts with a chance of success can result from a moderately-accurate model. And R&D teams are given a big helping hand if the model comes with a robust estimate of its uncertainty, pointing them towards those most likely to succeed. If the ML can move the dial so that one in three candidate formulations succeed when the previous metric was one in five, this could make a big difference.
3. Can we remove costly or environmentally harmful ingredients?
Questions like this typically derive from consumer, regulatory, or market pressure and require a fast response. ML can screen potential solutions, and an indication of probable success can be given by quantifying the uncertainty of the predictions.
4. Where should we focus which inputs are the most significant?
The absolute accuracy of predictions may be less important than whether useful relationships are identified, for example, between structure, processing variables, and properties. Often, the latter is the most vital piece of information that users need. A series ofanalytical toolsthat enable users to explore the sensitivity of outputs to particular inputs are provided by Alchemite.
5. Can we make better use of the expertise weve already developed?
Insight developed at great expense in R&D projects is often not be re-used. A valuable starting point for future projects can be provided by the ability to capture this insight in anML model.
Alchemite Analytics How Do Changes in Inputs Impacts Outputs?
Rather than focusing on ML as a magic bullet, it is essential to consider its use in informing scientific intuition and functioning alongside it.
Image Credit: Intellegens Limited
It is vital to have the right tools like uncertainty quantification and graphical analytics to interrogate and understand the results. When data is messy, as it often is in R&D, rather than investing up-front effort to clean and enrich the data, it can be valuable to be able to generate an ML model even an imperfect one quickly. By exploring this model, users can gain insight and improve their work iteratively and at a much lower cost.
The team at Intellegens values accurate models, and sometimes, they are, of course, essential. Mostly they also work in the spirit of the aphorism commonly attributed to statistician George Box:All models are wrong; some are useful.
This information has been sourced, reviewed and adapted from materials provided by Intellegens Limited.
For more information on this source, please visit Intellegens Limited.
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Its Not Just About Accuracy - Five More things to Consider for a Machine Learning Model - AZoM