We can all agree that the use of AI in business is at its infancy and may be long until it becomes widespread. Businesses of all sizes may find it easier than thought to run early AI experiments to clear their vision on how to accelerate their competitiveness. However, several myths will be on the way and need to be reflected upon. Let’s dive into the most common ones.

1. My business isn’t sophisticated enough to require AI

AI is humanity’s attempt to simulate our brain’s intuition and put it on the fast track to experience and interpret the world for us. In the early 90s, the development of very narrow applications using AI concepts gave birth to what we now call machine learning (ML). Think of a computer playing checkers or an e-mail spam filter. Deep learning (DL) is making a comeback from its debut in the early 50s. Think of a computer telling you what is in an image or video or translating languages.

In summary, we say that DL is a subset of ML which is a subset of the broad field we call AI. Your business can and eventually will use AI. The reflection about which approach to use will depend on the problem to be solved and the data available.

2. AI is a magic box, just throw another problem at it

While there is something magical about predicting an outcome from an input that the computer never saw, the magic ends there. If you try to use machine learning without minimally understanding the problem you want to solve, you will fail miserably. It’s very important to think of your AI strategy as a portfolio of approaches to solving very hard problems you can’t solve with traditional programming. Each problem may require completely different datasets and approaches to achieve meaningful results.

3. Only the big companies have enough data

While it’s true that whoever has the data will have an advantage in solving certain problems, no business should be trapped in the analysis paralysis around the question “do I have enough data?” Maybe you don’t, but that doesn’t mean you shouldn’t try to attack a business problem using AI. There are some scenarios to keep in mind:

  1. Sometimes you can augment the necessary datasets with public or purchased data
  2. By creating the first version of your application, you may get your users to generate the data you need to improve your ML model
  3. Depending on the problem being mapped, you can hire people to generate the data you need (crowdsourcing, Mechanical Turk, etc.)
  4. It’s not uncommon to use computers to generate data that can be used to augment your dataset.

4. Models improve with new data ‘automagically’

Most of the machine learning models are trained offline. Surprised? Things can get widely out of control if you just feed more data to your model. By keeping humans in the loop, you can make sure your models will keep performing well. So, every time Siri, Alexa or the Google Assistant tell you they can’t help you, but they are learning, it doesn’t mean they are learning with you right then. However, the collection of inputs that didn’t map to any result is highly valuable data to help you fill the important gaps with users. You will need to use them to retrain your model.

5. A low accuracy model can’t be used

During training, a typical machine learning model will have an accuracy that asymptotically increases with the number of data used to train it. After training, you will test the model with your evaluation set (a subset of the data you had at the beginning) and see how the model performs. You want a model that behaves well with both training data and new data. Sometimes an accuracy above 70% will be more than enough for practical applications as long as you have a good plan to work out the situations where the model doesn’t work well and improve your model over time.

6. UX is irrelevant for machine learning

The image above is from a mobile application that implements the imagenet model for image recognition. As you can see, the photo on the left, from above the mouse, led to an unexpected result. By tilting the camera I managed to catch the right category, albeit at a small confidence percentage. Now imagine if the mobile application used the device sensor information like gyroscope data, and it told me that I should tilt the camera in order to get a better result. It would’ve guided me to a better experience because it would’ve provided the machine learning model a better input. Depending on how you design your application, you can also get valuable information from users that will help improve your model.

7. I don’t have budget for an AI project

The cost of building your first AI project should be equivalent to the cost you had when you built your first mobile app, just to give you a tangible reference. In contrast, the cost of not building your first AI project soon, rest assured, will be much higher as time goes by.

Companies who will treat AI as part of their portfolio of problem-solving tools will probably achieve compounding gains over time. They will have, however, to manage internal expectations around early results and consider experiments as bets worth making.

Mars Cyrillo is the VP of Machine Learning and Product Development at CI&T, a digital tech agency.

Above: The Machine Intelligence Landscape This article is part of our Artificial Intelligence series. You can download a high-resolution version of the landscape featuring 288 companies by clicking the image.