Not All AI Is Created Equal: How To Select The Right Type For Your Business Needs – Forbes

Were on the cusp of a revolution led by artificial intelligence and automation thats set to radically change how we work. New AI tools are constantly emerging, with the promise of slicing through the complexities of modern problems plaguing todays workforce. AI has the potential to optimize the entire digital value chain in any organization, especially in areas where employees are struggling with manual processes. Its no surprise then that spending forecasts for AI are skyrocketing, with worldwide spending estimated at $35.8 billion. This uptick could yield $2.9 trillion of business value and 6.2 billion hours of worker productivity by 2021.

However, AI comes in many different forms, and identifying which type is most suitable for a particular use case isnt always easy. We hear terms like "machine learning," "deep learning" and "deterministic AI" used, but its important for the industry to avoid generalization. If these technologies are just categorized under the umbrella of AI, theres a risk that organizations will buy into the wrong thing and miss the promised benefits.

Before investing in AI, organizations need to be very clear about what challenges theyre looking to solve. Businesses must first look at where their workforce is under strain and how AI can offer value. Repeatable, manual processes, or those that require a huge volume and variety of data to be processed at a velocity beyond human capabilities, can be improved with AI. More importantly, organizations must consider what data they have available, which can influence the type of AI they should adopt.

Machine Learning

Machine learning-based AI takes a statistical approach, with systems adhering to this approach and ingesting data to understand and make decisions. It requires a data scientist to train an algorithm so that it can learn to make decisions, which could take months or years to develop as AI learns the rules of its environment. As such, machine learning AI is at its best when its working in environments where the rules dont change often because every significant change requires relearning.

Machine learning is well equipped to help automate business operations. In the world of banking, for example, machine learning could be used to determine whether someone should be given a loan or credit card. When a customer applies, a machine learning AI tool could evaluate that application against a database containing the outcomes of thousands of previous applicants, as well as against the criteria for approving a loan. However, machine learning approaches are very limited in dynamic environments where the rules change constantly and there isnt time to "learn." They can also be prone to bias, as seen when Apple Card was reported to be favoring applications by men within its approval process, with men receiving much greater credit limits than women.

Deep Learning

Deep learning is a subcategory of machine learning that uses a neural network approach. This approach is similar to memory foam: Once an object, or in this case a rule, has been introduced, it leaves an imprint that the AI can recall. This makes it effective for rules-based decision making, and it can also work from different types of unstructured data. In the workplace, deep learning has applications for use cases such as predictive maintenance, where, for example, it can take audio or visual data to predict when a piece of equipment, might fail. In healthcare, deep learning can look at scans to identify anomalies or shadows.

However, like machine learning, it takes time to train. While you could argue the training happens by itself, the problem is that the definition of what is good or bad behavior takes too much time and effort to instill. For example, the use of deep learning in facial recognition systems at airports to identify suspicious individuals could introduce bias that could do more harm than good. Faces dont always follow "rules," and its difficult for AI to identify features accurately, as evidenced when a U.K. passport application system rejected someones photograph after mistaking his lips for an open mouth.

Deterministic AI

Deterministic AI is another subcategory of machine learning, but takes a very different approach. It performs a step-by-step fault-tree analysis based on directed dependency graphs (e.g., from real-time topology discovery), similar to a safety engineering approach. As a result, it can provide precise answers and map the evolution of a problem back to the underlying cause. It can do this in near real time, without requiring humans to analyze and interpret data. While its likely too advanced for more repetitive tasks such as robotic process automation on automotive assembly lines, deterministic AI would be well suited to environments where the rules change constantly.

Deterministic AI has great applications for helping organizations overcome the complexity thats exploded in the shift to the enterprise cloud (Full disclosure: My company uses deterministic AI to power the cloud software I helped develop). Todays IT environments are highly dynamic and web-scale, containing hundreds of technologies, millions of lines of code and billions of dependencies. As a consequence, its beyond human capabilities to manage digital service performance effectively.

For instance, if theres an anomaly in a large microservice application that triggers a storm of alerts, it can be impossible for IT teams to find the root cause. Deterministic AI can help IT teams by accurately providing the root cause of the anomaly and the solution in real time while precisely suppressing millions of unrelated events. Such intelligence could be used to trigger auto-remediation procedures before users are impacted. Because of this, I believe deterministic AI will usher in a new era of AI-driven IT operations, where organizations run on an autonomous IT ecosystem that responds to changing rules in real time, and problems are resolved before users notice theres an issue.

Ultimately, it's important to understand the benefits and drawbacks of the different types of AI. Not all AI is created equal, and the IT industry has a responsibility to ensure it doesnt become another buzzword. Otherwise, AI may never become the true game-changer it promises to be.

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Not All AI Is Created Equal: How To Select The Right Type For Your Business Needs - Forbes