Proven concepts for scaling AI - From experimentation to the engineering discipline

The proof-of-concept (POC) is gone, but the POC lives on!

• Artificial intelligence (AI) is either overhyped as a digital utopia or demonized as a terrifying threat.
• It is neither in the pragmatic here and now.
• AI is a means to enhance human skills and improve people's and enterprises' outcomes.
• IBV: The number of companies using AI actively has climbed from 26% four years ago to 44% in 2020.
• According to a new survey, about one-third of companies anticipate increasing their AI investment as a result of the epidemic.
• In the next four years, IBM forecasts that investment in AI will quadruple.

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According to Andrew Keen, companies should stop pursuing data science projects and instead embrace AI wisely and holistically. Keen AI must be incorporated into changing corporate operating models and processes in a way that maximizes its long-term value.

Taking AI engineering and operations seriously

• Businesses that are still in the early phases of AI adoption should consider AI as a discipline.
• Manual duties, such as handoffs to developers whose apps their machine learning (ML) will ultimately run, are a headache for data teams.
• This hinders the supply of machine learning-enabled apps and lowers revenue.
• The trail of a company's all-too-common AI program is chronicled, possibly with a tint of cynicism born on years of observation and experience.
• However, we feel that a more systematic strategy might help to avoid the dip.

Red Hat: Artificial Intelligence in Software Using Open Source Concepts

• The Open Data Hub from Red Hat is a meta-project based on AI engineering concepts.
• Without paying large fees or grasping the complexities of ML and AI software stacks, the open source community may experiment and produce intelligent applications.
• Red Hat established the AI Center of Excellence (CoE) to aid in the implementation of its goals.
• Customers can use Red Hat's "Open Innovation Labs" to build AI/ML projects that use open-source technology.
• Red Hat is the world's largest open-source software company.

Using NLP and semantics to create new products

• Companies are incorporating machine learning and deep learning into their operations, but the models and algorithms that arise are frequently based primarily on structured data.
• Natural language processing (NLP) can provide people a human context for how they look at and use data.
• NLP gives AI's learning loops the power of human language.
• In an otherwise distant environment, NLP and Semantic technologies generated a popular boost to the spectator experience at the 2020 US Open.
• Creating more intelligent processes requires using the appropriate tools and algorithms for each case.

Making the case for AI capability development

• The first step toward realizing AI's full potential is to prioritize AI engineering and operations.
• According to Dr. Jodie O'Neill, the further down the road ML programs go without being guided by strong operational processes, the less likely they are to succeed.
• Artificial intelligence (AI) is the way of the future in terms of how we live our lives.
• According to John Sutter, we must not lose sight of the people who will create AI, utilize it, and benefit from it.
• To create an ethical future, he believes that leaders must be courageous, motivated, and intelligent.

Leading practices for less experienced AI adopters (businesses in the contemplating, reviewing, and piloting phases of getting started) are outlined in the Action Guide.

• Begin small, but plan to grow.
• Engineering concepts should be used.
• Establish benchmarks for success
• Strong leadership should be appointed.

Leading practices for more experienced AI adopters (businesses in the AI implementation, operation, and optimization stages):

• Create an AI playbook.
• Document and improve on a regular basis
• Models to keep track of
• Invent at a large scale
• Collaborate with ecosystem partners