Artificial Intelligence into Practice

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Vendors are rushing with AI solutions to market before the ultimate decision-makers and buyers are up to speed on what they need. Business, Political and Technology leaders, the plot further thickens them on when they are asked by Owners, CEO about the specifics of what AI can accomplish, when and how AI differs from machine learning and deep learning. A gig hoax and mess is growing while virtual and Robot vendors are gearing up to master and divert the true capabilities of AI. Possessional can watch, rather we put into practice this Oxygen that should feed the majority.

Step one: Explaining AI

The first step is communicating what the definitions are for AI, machine learning (ML), and deep learning. There is some argument that AI, ML, and deep learning are each individual technologies. We view AI/ML/deep learning as successive stages of computer automation and analytics that are built on a common platform and serving the same objectives.

On the first tier of this platform sits AI, the broader which analyzes data and quickly delivers analytical outcomes to users, mimicking human intelligence. Machine learning sits on the tier two application of AI that not only analyzes raw data, but it also looks for characteristics, patterns in the data that can yield further insights. Deep learning is a third-tier application that analyzes data and data patterns, but it goes even further. The computer also uses neuronal network advanced algorithms developed by data scientists that ask more questions about the data with the ability to yield even more surrounding insights.

Step two: Putting AI/ML and deep learning into practice

The best way to demonstrate these different layers of increasingly complex analytics is by finding a business example that can show the benefits to the decision making into the business.

Let’s take the sample of traffic planning.

Tier one: AI into civil engineering

You develop an AI application that tells your traffic engineers and planners where the major traffic congestion points are located in the city. This assists them in planning for road repairs, emergency, stop lights, and other infrastructure that, hopefully, can relieve congestion in certain areas.

Tier two: role of Machine learning

You further develop your AI/analytics so that it also looks for patterns in the data. For instance, it notices the traffic at certain intersections is most congested in the morning between 6 am and 8 am, or that traffic queues up in the evening, ahead of a sporting event. Knowledge of the situation gives planners and engineers more insight because now they can plan not only for traffic snarls but also for future events like celebrations, concerts and sport games.

Tier 3: role of Deep learning

Deep learning is where data analytics moves beyond raw data and data patterns. Deep learning adds specific algorithms with models that data engineers develop to further expand the querying and insights derived from the data. Algorithms that could be added to the traffic analysis might include: What areas of the city will see the greatest population growth over the next ten years? Or, which roads will need major repairs in the next five years? Or, do weather projections say that we will have more or less snow, heat, humidity over the next five years? By adding these algorithms on top of pattern and data analyses, users get a more complete picture of the situation they are trying to act on, assess, predict or prescribe better.

Final remarks

The ultimate goal is to ensure that both the business (politics) and IT maintain a firm understanding of what AI (and its embellishments) are and how it will be used to benefit the organization and the general public. This understanding should go beyond buzzwords and definitions. It should be built into strategic plans that are tied to budgets, talent acquisition and development, ROI, and outcomes within new political mind, new fresh thinking that set transformation DNA of their Blood. Mastering AI and Big Data are the oxygen not just oil of the future as others are saying.