Olivier Blais of Moov.ai has had a lot of experience building AI initiatives for organizations. He’s been a lot of places and is well aware of the hype and hysteria surrounding AI. He’s here to help you and your company build better AI initiatives, democratize Artificial Intelligence, and alleviate some of the biggest worries your employees and customers may have about the AI revolution.
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Common AI Misconceptions
The understanding of AI from a data scientist perspective is often very different from the business understanding. Democratizing your data within a business context requires you to dispel these common myths.
Data Scientists are magicians
AI is overhyped. Companies are creating these initiatives, but it’s not just about being the next cool thing. Data scientists are regular people and hiring a data scientist won’t magically make your business “AI enabled.” Instead, using statistical methods allows the data scientist to find patterns using the enhanced abilities of AI. You get better predictions for making future decisions. Not magic at all. Instead, it’s targeted, highly informed decision making.
It’s hard for a business to apply AI
It’s not easy, but it’s not as complicated as its reputation. Some of the complexity is because a company doesn’t have a specific question or target, but you can’t expect AI to both write and answer the question. AI is enhanced problem-solving, so defining the right problem leads to the right results. Your AI initiative could be failing because you don’t have the right problem framework. If your team can point to the right problem, you’ll have better success.
It needs millions of observations for model creation
Businesses sometimes get too caught up in the complexity of projects. They follow the newest, coolest technology when simple projects designed to bring customers closer or make better predictions would better serve their business outcomes.
Machine learning is exclusive to data scientists
Businesses hire data scientists and then separate them from the team. Data scientists don’t need to control the full process. You can turn that over to other members of your team, allowing data scientists to integrate with the team itself. A data scientist should be supported. They’re there for methodology and implementation, but other team members can identify the right problems, make data available, or put the model into production.
What Constitutes a Good Project?
Data needs to be clean and accessible. It should build your answers for a specific purpose and not just follow the latest, coolest tech. If you’re building relationships with customers or launching a new product, those initiatives are not complex. They have specific outcomes, clean and accessible data, and don’t require a ton of effort right upfront. These types of products could be a great start to your AI initiative.
How Can We Improve Data Readiness?
If you have better data than your competitor, you have an advantage. You want your data to be clean, and it’s best to work with less clean data than a lot of messy data. You’re moving towards lots of clean data. You can augment your data, create simulations, or generate new data sets from nearest neighbor records. You can also make good use of transfer learning instead of trying to do everything from scratch.
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How Do We Make Data Interpretable?
One of the most significant ways you can make your AI initiative available for everyone is improving the data interpretability. When more than your data scientist can understand and interpret the results from your AI initiatives, your data is not only more trustworthy, but it takes the pressure from your data scientist. It also helps to improve your direction and acceptance because everyone can determine if the initiative is actually useful and not just jumping on the next big thing.
Democratizing your data takes pressure from your data scientist. It removes the mystery behind what your algorithms are doing and allows more eyes to decide if the questions are the right ones. You’ll pivot easier and can build trust for what AI is doing for your organization. It’s vital that you take the time to democratize your AI initiatives both for the wellbeing of your data scientist and for the good of your company’s overall mission.