Artificial Intelligence
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.
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.
What Is AI?
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, AI is broadly seen as both disruptive and highly transformational.
A key benefit of AI systems is the ability to actually learn from experiences or learn patterns from data, adjusting on its own when new inputs and 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; error-prone boring human tasks; and hundreds of other use cases.
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, AI is broadly seen as both disruptive and highly transformational.
A key benefit of AI systems is the ability to actually learn from experiences or learn patterns from data, adjusting on its own when new inputs and 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; error-prone boring human tasks; and hundreds of other use cases.
AI Growth Powered By GPU Advances
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 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 ideal hardware for the heavy work of AI systems are 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— comes from a wide variety of sources, such as the Internet of Things (IoT); social media; historical databases; operational data sources; various public and governmental sources; the global science and academic communities; even genomic sources. 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 are essentially highly portable bundles of code that allow developers and data scientists to integrate AI functionality to current products and services, expanding the value of existing investments. For example, APIs can add Q&A capabilities that describe data or call out interesting insights and patterns.
AI Challenges
It isn’t an overstatement to say that artificial intelligence, or AI, offers 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 ten years. To participate in this AI-inspired economy, organizations need to overcome AI challenges.
Acquiring raw computing power.
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 they build out new and exciting ‘native AI’ services for GPUs and AI SDKs.
Dealing with data bias.
As with any other computer system, AI systems are only 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.
AI Use Cases
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.
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 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 ideal hardware for the heavy work of AI systems are 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— comes from a wide variety of sources, such as the Internet of Things (IoT); social media; historical databases; operational data sources; various public and governmental sources; the global science and academic communities; even genomic sources. 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 are essentially highly portable bundles of code that allow developers and data scientists to integrate AI functionality to current products and services, expanding the value of existing investments. For example, APIs can add Q&A capabilities that describe data or call out interesting insights and patterns.
AI Challenges
It isn’t an overstatement to say that artificial intelligence, or AI, offers 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 ten years. To participate in this AI-inspired economy, organizations need to overcome AI challenges.
Acquiring raw computing power.
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 they build out new and exciting ‘native AI’ services for GPUs and AI SDKs.
Dealing with data bias.
As with any other computer system, AI systems are only 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.
AI Use Cases
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.
NVIDIA Project DIGITS
A Grace Blackwell AI Supercomputer on your desk.
Powered by the NVIDIA GB10 Grace Blackwell Superchip, Project DIGITS delivers a petaflop of AI performance in a power-efficient, compact form factor. With the NVIDIA AI software stack preinstalled and 128GB of memory, developers can prototype, fine-tune, and inference large AI models of up to 200B parameters locally, and seamlessly deploy to the data center or cloud.
A Grace Blackwell AI Supercomputer on your desk.
Powered by the NVIDIA GB10 Grace Blackwell Superchip, Project DIGITS delivers a petaflop of AI performance in a power-efficient, compact form factor. With the NVIDIA AI software stack preinstalled and 128GB of memory, developers can prototype, fine-tune, and inference large AI models of up to 200B parameters locally, and seamlessly deploy to the data center or cloud.