The widespread use of artificial intelligence (AI) has the potential to revolutionize industries across the board and better the lives of people everywhere. In reality, every day brings news of a new achievement by artificial intelligence, whether it be in cancer detection, playing Minecraft, the development of “sentient” chatbots, or the generation of appealing artistic works. AI’s mission is straightforward: to speed up the process of “data to insights.” The three cornerstones of artificial intelligence—”data, computation, and algorithms”—have all undergone exponential development, leading to significant advancements in the field.
Data size is expressed in “zettabytes,” which is a unit of measure for the total amount of bytes (1021). Algorithms, measured by the number of parameters in a neural network, have surpassed a trillion, while compute, as measured by hardware execution capability of operations per second, is in the petaflops (1015) to exaflops (1018). (1012).
However, studies show that for several reasons—including performance, infrastructure, and multi-vendor software and tooling—87% of AI ideas never see deployment. New difficulties arise in AI adoption and execution as data sets expand and systems get more complicated. Business goals end up taking much longer to achieve because developers have to spend time and money fixing technical, procedural, and organizational problems, resolving abandoned projects, and upgrading code.
Because AI affects the whole system, it must be supported in every possible way. In order for AI to permeate every aspect of our lives, it is essential that developers and data scientists work in the field unite computation, data, and algorithms. Expanding the application of artificial intelligence inside a company requires paying attention to the fundamentals of both human productivity and computer performance.
Bringing people together
For millions of data scientists and developers, whose AI apps are utilized by billions of users, the bridge between “data to insights” with the help of “compute and algorithms” must be built in software.
Artificial intelligence software can increase worker efficiency, allowing AI to be used globally. In order to accelerate the spread of AI, the industry must implement practices that reduce the time and effort required for programmers and data scientists to improve upon existing AI tools and processes, or to create whole new ones. Widespread implementation of AI shouldn’t call for a Ph.D. in the field. This highlights the need of maintaining easily available data and infrastructure.
The correct data and AI platform and toolset may boost productivity by, for example, improving the performance of widely used industry-standard AI frameworks or providing open tools to support end-to-end AI workflows. Tools like AutoML toolkits, reference toolkits, end-to-end distributed AI toolkits, and development and deployment toolkits are all possible examples.
Tools for data labeling and augmentation, tools for detecting bias in data, tools for transfer learning and federated learning, and so on are all included in this category.
These are open, standards-based, unified, and secure in order to simplify the processes of data engineering and AI solution development and deployment for software engineers and data scientists. Some tools, for instance, have been shown to enhance worker output by a factor of 10 or more.
The Quickening of AI Programs
The software used in each stage of an AI application’s lifecycle varies by industry and application type, hence there is no “one size fits all” solution. Therefore, industry leaders need to work together on open-source tools.
Example: Intel and Accenture are working together to bring open source AI reference kits to the market, which will enable businesses innovate and speed up their digital transformation path. These reference kits may cut the time to solution from weeks to days, letting data scientists and developers train models quicker and at a lesser cost by overcoming the limits of proprietary environments.
Artificial intelligence programs may speed up a computer by making tweaks to the code on the fly. Software AI acceleration may have a huge effect, often increasing performance by a factor of 10 to 100. The resource and computation-intensive nature of AI workloads results in cost or compute time limits, making computer performance a fundamental need that IT teams aim toward.
Hardware AI acceleration has to be paired by software AI acceleration because of the performance enhancements that it allows. It is possible that even when new hardware is launched, the usage of petaflops or exaflops will be rather low if not optimized by means of highly sophisticated software. That’s the equivalent of leaving more than half of the hardware’s processing power unused.
Training time, inference time, energy use, memory use, and cost may all be minimized using software AI acceleration, while performance and accuracy are maintained. This is crucial for facilitating the development and deployment of intelligent apps.
Reaching the Goal of Pervasive AI
Hardware, which is directly related to the performance of these models, benefits most from a heterogeneous architectural plan that offers more options to users, given the variety of workloads in AI. CPUs with built-in AI acceleration, GPUs, bespoke AI accelerators and even FPGAs all have a role to play. Also, depending on the workloads, AI software can deliver a consistent user experience across different hardware accelerators.
Artificial intelligence is expanding across all markets. Gardener predicts that by 2022, sales of artificial intelligence software will have increased by 21.3% globally, reaching $62.5 billion.
There is a direct correlation between the rise in AI software’s ability to boost both human and machine efficiency. Developers and data scientists need to identify solutions that can maximize performance of AI workloads in open ecosystems and secure cross-architecture environments in order to realize the full potential of AI in every aspect of life. Only then businesses can truly implement AI system-wide.
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