Healthcare
The world’s leading organizations are equipping their doctors and scientists with AI, helping them transform lives and the future of research. With AI, they can tackle interoperable data, meet the increasing demand for personalized medicine and next-generation clinics, develop intelligent applications unique to their workflows, and accelerate areas like image analysis and life science research. Uses cases include:
- Pathology. Each year, major hospitals take millions of medical scans and tissue biopsies, which are often scanned to create digital pathology datasets. Today, doctors and researchers use AI to comprehensively and efficiently analyze these datasets to classify a myriad of diseases and reduced mistakes when different pathologists disagree on a diagnosis.
- Patient care. The challenge today, as always, is for clinicians to get the right treatments to patients as quickly and efficiently as possible. This is more of an acute need in intensive care units. There, doctors using AI tools can leverage hourly vital sign measurements to predict eight hours in advance whether patients will need treatments to help them breathe, blood transfusions, or interventions to boost cardiac functions. .
Retail
An Accenture report estimates that AI has the potential to create $2.2 trillion worth of value for retailers by 2035 by boosting growth and profitability. As it undergoes a massive digital transformation, the industry can increase business value by using AI to improve asset protection, deliver in-store analytics, and streamline operations.
- Demand prediction. With over 100,000 different products in its 4,700 U.S. stores, the Walmart Labs data science team must predict demand for 500 million items-by-store combinations every week. By performing forecasting with the NVIDIA RAPIDS™ suite of open-source data science and machine learning libraries built on NVIDIA CUDA-X™ AI and NVIDIA GPUs, the Walmart team is able to engineer machine learning features 100X faster and train algorithms 20X faster.
- AiFi is currently pilot testing NanoStore, their 24/7, autonomous, checkout-free store, with retail giants and universities. NanoStores hold over 500 different products and use image recognition, powered by NVIDIA T4 Tensor Core GPUs, to capture merchandise choices and add those to the customer’s tab.
Telecommunications
AI is opening up new waves of communication in the telecommunications industry. By tapping into the power of GPUs and the 5G network, smart services can be brought to the edge, simplifying deployment and enabling them to reach their full potential.
- 2Hz, Inc., is bringing clarity to live calls with noise-suppression technology powered by NVIDIA T4 and V100 GPUs. 2Hz’s deep learning algorithms scale up to 20X more than CPUs, and by running NVIDIA® TensorRT™ on GPUs, 2Hz meets the 12 millisecond (ms) latency requirement for real-time communications
- 5G will deliver multiple computing capabilities, including gigabit speeds with latencies under 20ms. This has led the Verizon Envrmnt team to deploy powerful NVIDIA GPUs to beef up Verizon’s high-performance computing operations and create a distributed data center. 5G will also enable devices to become thinner, lighter, and more battery efficient, opening the door to memory-intensive parallel processing that can power rendering, deep learning, and computer vision.
Financial Services
AI solutions have found a welcoming home in the dynamic world of financial services, with scores of established and startup vendors rushing these solutions to market. The most popular applications to date include:
- Portfolio management and optimization. Historically, calculating portfolio risk has been a largely manual and therefore extremely time-consuming process. Using AI, banks can undertake highly complex queries in seconds without having to move sensitive data.
- Risk management. Like portfolio management, risk management calculations are often done in batch overnight, resulting in lost opportunities that occur 24/7. AI tools can calculate risk using available data virtually in real-time, resulting in increased portfolio performance and improved customer experience.
- Fraud detection. With the ability to ingest tidal volumes of data and search instantly for anomalies, AI solutions can then flag suspect patterns and trigger specific actions.
Industrial
One of the most common AI use cases is the crunching of enormous data streams from various IoT devices for predictive maintenance. This can pertain to the monitoring of the condition of a single piece of equipment, such as an electrical generator, or of an entire manufacturing facility like a factory floor. AI systems harness data not only gathered and transmitted from the devices, but also from various external sources, such as weather logs. Major railways use AI to predict failures, applying the fixes before failure occurs—thereby keeping the trains running on time. AI predictive maintenance on factory floors has been shown to reduce production line downtimes dramatically.
AI as a tool
Data scientists think of AI as a tool and as a procedure that rests on top of other procedures or methodologies used for deep analysis of data. In addition to languages like R and Python, data scientists work with data from conventional databases, often extracting data using SQL queries. Using certain AI tools, they can quickly undertake tasks to classify and perform predictions on these more conventional data sources.