Across industries, AI is supercharging innovation with machine-powered computation. In finance, bankers are using AI to detect fraud more quickly and keep accounts safe, telecommunications providers are improving networks to deliver superior service, scientists are developing novel treatments for rare diseases, utility companies are building cleaner, more reliable energy grids and automotive companies are making self-driving cars safer and more accessible.
The backbone of top AI use cases is data. Effective and precise AI models require training on extensive datasets. Enterprises seeking to harness the power of AI must establish a data pipeline that involves extracting data from diverse sources, transforming it into a consistent format and storing it efficiently.
Data scientists work to refine datasets through multiple experiments to fine-tune AI models for optimal performance in real-world applications. These applications, from voice assistants to personalized recommendation systems, require rapid processing of large data volumes to deliver real-time performance.
As AI models become more complex and begin to handle diverse data types such as text, audio, images, and video, the need for rapid data processing becomes more critical. Organizations that continue to rely on legacy CPU-based computing are struggling with hampered innovation and performance due to data bottlenecks, escalating data center costs, and insufficient computing capabilities.
Many businesses are turning to accelerated computing to integrate AI into their operations. This method leverages GPUs, specialized hardware, software, and parallel computing techniques to boost computing performance by as much as 150x and increase energy efficiency by up to 42x.
Leading companies across different sectors are using accelerated data processing to spearhead groundbreaking AI initiatives.
Finance Organizations Detect Fraud in a Fraction of a Second Financial organizations face a significant challenge in detecting patterns of fraud due to the vast amount of transactional data that requires rapid analysis. Additionally, the scarcity of labeled data for actual instances of fraud poses a difficulty in training AI models. Conventional data science pipelines lack the required acceleration to handle the large data volumes associated with fraud detection. This leads to slower processing times that hinder real-time data analysis and fraud detection capabilities.
To overcome these challenges, American Express, which handles more than 8 billion transactions per year, uses accelerated computing to train and deploy long short-term memory (LSTM) models. These models excel in sequential analysis and detection of anomalies, and can adapt and learn from new data, making them ideal for combating fraud.
Leveraging parallel computing techniques on GPUs, American Express significantly speeds up the training of its LSTM models. GPUs also enable live models to process huge volumes of transactional data to make high-performance computations to detect fraud in real time.
The system operates within two milliseconds of latency to better protect customers and merchants, delivering a 50x improvement over a CPU-based configuration. By combining the accelerated LSTM deep neural network with its existing methods, American Express has improved fraud detection accuracy by up to 6% in specific segments.
Financial companies can also use accelerated computing to reduce data processing costs. Running data-heavy Spark3 workloads on NVIDIA GPUs, PayPal confirmed the potential to reduce cloud costs by up to 70% for big data processing and AI applications.
By processing data more efficiently, financial institutions can detect fraud in real time, enabling faster decision-making without disrupting transaction flow and minimizing the risk of financial loss.
Telcos Simplify Complex Routing Operations Telecommunications providers generate immense amounts of data from various sources, including network devices, customer interactions, billing systems, and network performance and maintenance.
Managing national networks that handle hundreds of petabytes of data every day requires complex technician routing to ensure service delivery. To optimize technician dispatch, advanced routing engines perform trillions of computations, taking into account factors like weather, technician skills, customer requests and fleet distribution. Success in these operations depends on meticulous data preparation and sufficient computing power.
AT&T, which operates one of the nation's largest field dispatch teams to service its customers, is enhancing data-heavy routing operations with NVIDIA cuOpt, which relies on heuristics, metaheuristics and optimizations to calculate complex vehicle routing problems.
In early trials, cuOpt delivered routing solutions in 10 seconds, achieving a 90% reduction in cloud costs and enabling technicians to complete more service calls daily. NVIDIA RAPIDS, a suite of software libraries that enables acceleration of data science and analytics pipelines, further accelerates cuOpt, allowing companies to integrate local search heuristics and metaheuristics like Tabu search for continuous route optimization.
AT&T is adopting NVIDIA RAPIDS Accelerator for Apache Spark to enhance the performance of Spark-based AI and data pipelines. This has helped the company boost operational efficiency on everything from training AI models to maintaining network quality to reducing customer churn and improving fraud detection. With RAPIDS Accelerator, AT&T is reducing its cloud computing spend for target workloads while enabling faster performance and reducing its carbon footprint.
Accelerated data pipelines and processing will be critical as telcos seek to improve operational efficiency while delivering the highest possible service quality.
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