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Artificial intelligence (AI) is rapidly being adopted by organizations of all sizes and yielding impressive results, transforming businesses and industries globally. While senior executives, managers and senior IT architects are often catalysts for change, it is the developers who are deciding which specific products and technologies individual companies will use on their journey to AI innovation.
Interestingly, while many AI projects get started following executive mandates, often it is the AI developers and data scientists who shepherd new and innovative AI projects along in the organization. AI developers and data scientists aren’t as motivated by executive mandates as they are by the combination of experimentation, the desire to proactively satisfy new client demands, and of course by the fun realized in delivering new and innovative AI solutions. In many cases, it’s the AI developers and data scientists who also can quickly establish which specific AI tools and algorithms will immediately help an organization solve business problems.
Traditional developers normally measure success in terms of “application code”-centric metrics, such as the lines of new code shipped, the number of application releases shipped per year with new features and functions, the number of software defects fixed, and more. Many traditional developers work with programming languages optimized for general purposes systems, such as Java and C. In contrast, AI developers and data scientists first and foremost focus their time and energy on unlocking hidden insights within an organization’s data. The objective of an AI developer is to bring more value to an organization’s data, not just to produce more lines of code. When you talk to AI developers, they often report that a large amount of their time spent on AI projects involves data selection, data preparation, AI model development and AI model training (often on accelerated hardware).
AI developers create new solutions with an entirely new approach to problems: Identifying desired client experiences, better leveraging a wider variety of big data, selecting a subset of deep learning and machine learning frameworks, and then developing, training and creating new AI models with the desired outcome of creating new and innovative client experiences.
All developers need tools, and experienced AI developers will typically leverage specialized programming languages (and skills) in products like Python or R or SAS. Newer AI development product suites are enabling less technical professionals to leverage machine learning and deep learning, for example, by utilizing products from companies like H2O.ai. Finally, AI developers often use open source technologies and tend to favor newer cloud-native development approaches.
Big data combined with next-generation AI algorithms is emerging as the “currency” of the new global economy, and company leaders would be wise to consider adding AI-skilled developers and data scientists to their IT teams. If you want to further accelerate your organization’s AI journey, empower your AI developers with the next generation of modern AI tools. One good way to do that is to check out the IBM PowerAI Developers Portal.