AI is ushering in a new era of global innovation. From powering human ingenuity to counter the spread of infectious diseases, to building smart cities, and revolutionizing analytics for all industries, AI is providing teams with the super-human power needed to do their life’s work.
In its most fundamental form, AI is the capability of a computer program or a machine to think and learn and take actions without being explicitly encoded with commands. AI can be thought of as the development of computer systems that can perform tasks autonomously, ingesting and analyzing enormous volumes of data then recognizing patterns in that data. The large and growing AI field of study is always oriented around developing systems that perform tasks that would otherwise require human intelligence to complete, only at speeds beyond any individual’s or group’s capabilities. For this reason alone AI is broadly seen as both disruptive and highly transformational.
Moreover, a basic precept of AI systems is the ability to actually learn from experiences or learn patterns from data, adjusting on its own when new inputs and new data are fed into these systems. This self-learning allows AI systems to accomplish a stunning variety of tasks, including image recognition; natural language speech recognition; language translation; crop yield predictions; medical diagnostics; navigation; loan risk analysis; flawlessly perform error-prone boring human tasks; and hundreds of other use cases.
Though the theory and early practice of AI go back three-quarters of a century, it wasn’t until the 21st century that practical AI business applications blossomed. This was the result of a combination of huge advances in computing power and the availability of enormous amounts of data available. AI systems combine vast quantities of data with ultra-fast iterative processing hardware and highly intelligent algorithms that allow the computer to ‘learn’ from data patterns or data features.
The hardware in demand for the heavy work of AI systems is graphical processing units or GPUs. These specialized, superfast processors make parallel processing very fast and powerful. And massive amounts of data - essentially the fuel for AI engines, data comes from a wide variety of sources such as the Internet of Things; social media; historical databases; operational data sources; various public and governmental sources; the global science and academic communities; even genomic sources - to name just a few. Combining GPUs with enormous data stores and almost infinite storage capabilities, AI is positioned to make an enormous impact on the business world.
Among the many and growing technologies propelling AI to broad usage are application programming interfaces or APIs. These essentially highly portable bundles of code allow developers and data scientists to pack AI functionality to current products and services, expanding the value of existing investments. For example, APIs can add Q&A capabilities describing data or calling out interesting insights and patterns in data.
It is not an overstatement to say that artificial intelligence or AI holds the capability to transform the productivity potential of the entire global economy. A study by PwC found that AI’s contribution to the global economy will total nearly $17 trillion within 10 years. To participate in this AI-inspired economy, organizations need to overcome AI challenges.
The processing power needed to build AI systems and leverage techniques like machine learning and image processing or language understanding is enormous. NVIDIA is the choice of AI development teams around the world seeking to infuse AI into existing products and services as the build-out new and exciting ‘native AI’ services for GPUs and AI SDKs.
As with any other computer system, AI systems are as good as the data fed into them. Bad data can come from business, government, or other sources and contain racial, gender, or other biases. Developers and data scientists must take extra precautions to prevent bias in AI data or risk the trust people have in what AI systems actually learn.
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:
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.
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.
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:
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, and thereby keeping the trains running on time. AI predictive maintenance on factory floors has been shown to reduce production line downtimes dramatically.
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, extracting data there often using SQL queries. Using certain AI tools, they can quickly undertake tasks to classify and perform predictions on these more conventional data sources.
Machine Learning Researchers
Most of the researchers are working on AI since it can be applied to almost any problem. and the availability of large datasets and huge computation power has helped ML researchers create breakthrough research in various domains and revolutionizing industries such as Autonomous Vehicles, Finance, Agriculture, etc.
Software Developers
AI has not advanced to the point where it can write software on its own, though enthusiasts say that day isn’t far off. But various organizations already use AI to help develop and then test software solutions, particularly custom software. In the past two years, software vendors have brought to market an ever-growing number and variety of AI-enabled software development tools. Some of the hottest and best-funded startups are those pioneering AI development tools.
And in one particularly exciting application of AI development tools, AI boosted project management by ingesting enormous quantities of data from previous development projects. Then the tools accurately predicted the various tasks, resources, and schedules that would be needed to manage new projects. This doesn’t mean, AI can write software or replace developers, but it is making the time these valuable developers spend creating custom software far more efficient.
AI models can be very large, especially Deep Neural Networks(DNNs), and require massive computing power. Training these AI models is highly parallelized tasks since the computations are independent of each other, which makes it a good use case for distributed processing on GPUs. With the recent advancements in GPUs, several Vision and Language AI models can now be trained under a minute.
NVIDIA invented the GPU in 1999. Then with the creation of its CUDA programming model and Tesla GPU platform, NVIDIA brought parallel processing to general-purpose computing. With AI innovation and high-performance computing converging, NVIDIA GPUs powering AI solutions are enabling the world’s largest industries to tap into accelerated computing and bring AI to the edge.
Building game-changing AI applications begin with training neural networks. NVIDIA DGX-2 is the most powerful tool for AI training, utilizing 16 GPUs to deliver 2 petaflops of training performance to data teams. Adding in the extreme IO performance of Mellanox InfiniBand networking, DGX-2 systems quickly scale up to supercomputer-class NVIDIA SuperPODs. DGX-2 set world records on MLPerf, a new set of industry benchmarks designed to test deep learning. NVIDIA DGX™ A100 is the most powerful system for all AI workloads, offering high performance compute density, performance, and flexibility in the world’s first 5 petaFLOPS AI system, adding in the extreme IO performance of Mellanox InfiniBand networking, DGX-A100 systems quickly scale up to supercomputer-class NVIDIA POD.
Trained AI applications are deployed in large scale and highly complex cloud data centers serving up voice, video, image, and other services to billions of users. With the rise of Conversational AI, the demand is increasing for these systems to work extremely fast to make these services truly useful. NVIDIA TensorRT software and its T4 GPU combine to optimize, validate, and accelerate such demanding networks.
Meanwhile, as AI spills out of the cloud and into the edge where mountains of raw data are generated by industries worldwide, the NVIDIA EGX platform puts AI performance closer to the data to drive real-time decisions when and where they are needed.