Though AI is a powerful technology already providing deep insight and business value, it is not magic. Understanding AI’s limitations will help you choose realistic and attainable AI projects. Below are some common myths about AI and pitfalls to avoid when evaluating it as a potential tool.
Myth about AI:
AI will replace humans in the workplace.
Reality:
AI is more likely to replace tasks within a job, not the entire job itself. Almost all present-day AI systems perform specific tasks rather than entire jobs. The purpose of AI and automation is to make low-value tasks faster and easier, thus freeing up people to focus on high-value work that requires human creativity and critical thinking.
Historically, automation has created more jobs than it replaces. AI will mostly replace tasks, not jobs. It is more appropriate to think in terms of human-machine teams where each does the tasks for which it is best-suited. Many forecasts predict that new jobs will be created, i.e. people are and will continue to be needed for certain tasks and jobs.
Myth about AI:
AI can think like a human and learn on its own.
Reality:
AI uses mathematical models and finite computing power to process information. Though some AI techniques might use ”neural nets,” these algorithms only remotely resemble human biology. Their outputs are still entirely based on data and rules prepared by humans.
Myth about AI:
AI is always more objective than humans.
Reality:
AI applications are a product of data and algorithms combined into models. Data is collected, prepared, and managed by humans. Combining it with algorithms may still produce unfair and biased results. Machines and humans have different strengths and limitations. Humans are good at general tasks and big-picture thinking. Machines are good at doing specific tasks precisely. Human plus machine combinations are almost always superior in performance to a human alone or a machine alone.
Myth about AI:
You can just buy AI solutions that will work across the board.
Reality:
Identifying AI use cases and the data required for them can be specific and localized. Further, the nature of algorithms and model training can require varying degrees of customization as the data is aggregated, cleansed, assimilated, and the outcomes are generated. Barriers to consider beyond technology include organizational culture, appetite for risk, the acquisition process, and agency willingness to experiment. Buy vs. build decisions require careful assessment.
Myth about AI:
Artificial General Intelligence (AGI) is just around the corner.
Reality:
Artificial General Intelligence refers to AI that achieves general human-level intelligence. For most systems, there is a trade-off between performance and generality. An algorithm can be trained to perform one specific task really well, but not every possible task. Whether AGI takes decades or centuries to achieve, it’s more complex than most imagine. The more tasks we want a single machine to perform, the weaker its general performance becomes.
Myth about AI:
A large team of data scientists is required to implement an AI project.
Reality:
Developing AI solutions might require only a couple of people a few weeks, or it could take years with a large team. It all depends on the nature of the objective, data, required technical infrastructure, and integration into the existing environment. Depending on the maturity of the AI applications related to the specific problem of interest to your agency, the level of data science involvement can vary significantly. Examples of how this may depend based on agency need are:
- Some applications, such as voice recognition, can be deployed from commercial-of-the-shelf (COTS) products.
- Some AI applications require training of an existing algorithm using agency-specific data, needing a small data science team.
- Some AI applications are still in the research and development stage. A relatively large data science team is needed to explore the data characteristics and identify the suited AI method to solve the problem.
