REALISTIC EXPECTATION OF ARTIFICIAL


INTELLIGENCE— FOUR THINGS YOU SHOULD KNOW By Susanne Tedrick


While we are still a few decades out from seeing most businesses and industries use artificial intelligence (AI) in their operations, there is certainly greater adoption than there has been in years past. According to a top IT training company, 2021 saw a sharp uptick in respondents for their annual AI Adoption in the Enterprise survey, (nearly three times than the previous year), as well as 61 percent of respondents showing that they are actively considering or evaluating AI solutions.


If you are interested in learning more about AI, here are four things you should know:


AI is Already Embedded in People’s Day to Day


While widespread and more sophisticated uses of artificial intelligence are perhaps slated in the not-so- distant future, people are already using or interacting with some form of AI every day. If you have used web-based email services, used ridesharing services, or shopped on e-commerce platforms, you’ve likely been interacting with artificial intelligence in some capacity— and didn’t know it.


Tere are currently no federal or state laws that require companies to disclose that they are using AI in their applications and, for applications that are used by the public, customers are more likely to be interacting with a very well programmed AI interface than with another human being.


However, many consumer advocacy groups are pushing to have more formal laws instituted for requiring disclosure, as they believe that there is the potential for customers to be deceived and misled. While this may not be a worry for simpler transactions, this can be more concerning where there is more at stake, like for financial services and telemedicine.


AI vs. Machine Learning vs. Deep Learning


Te terms AI, machine learning, and deep learning are often used interchangeably, but each term has a specific meaning and is interrelated.


AI: General, high-level uses of AI, like text chatbots on e-commerce sites, are likely pre-programmed by humans


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to mimic typical human interactions or calculations. Te AI here depends on what is already programmed; it cannot “learn” on its own from mistakes or added information, nor can it start-up or power down on its own. It relies heavily on human interaction.


Machine Learning: Machine learning helps to address these scenarios. In machine learning, tools are put into place where the AI learns from the feedback and information it is given, and then uses it to improve its operations or performance in the future. Human intervention is sometimes necessary, but not at the same level as basic AI. Voice assistants are examples of machine learning in action.


Deep Learning: Deep learning takes this to the next level, where the learning that takes place mimics that of the human brain, using sophisticated algorithms called neural networks. Neural networks are currently being used in several applications, including aircraft fault detectors, guidance systems used by cars, and even ATM machines.


AI is More Accessible Than You Think


Two of the biggest barriers of entry for organizations to use artificial intelligence tools are overall costs and gaining the right set of skills to use these tools properly.


While the overall cost of an AI solution will largely depend on the targeted use case, or problem attempting to be solved, and the level of sophistication needed. Building a general AI chatbot will not be as expensive


TPI Turf News September/October 2022


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