• Martina Banyay

How do I get started using AI?

If one of the biggest risks with AI today is not to use AI, how do I get started?

Well, you need to get going on two levels: On a personal level and on a company level (or whatever context you are in).

If you haven’t already, you should start to orient and learn more on a personal level. You can watch this 7-days series I've created on what AI is, how it can be used and how to get started. There are also excellent (and free!) online courses for non-experts that covers the basics of AI (like this and this). You can subscribe to newsletters (The Algorithm & The Download from MIT Tech Review, Futurism, Singularity Hub, Wired, just to name a few), read books - for example the ones by Harari, Tegmark and Bostrom - listen to podcasts, start a discussion group where you explore different questions, this you can do at work or with friends. There are tons of good places to start, so please help each other out by sharing links to good resources in the comment section below.

If you are a corporation or an organization that haven’t started using AI yet, but would like to, you are in good company. The adoption of AI in business is still in its early stages. This year, Gartner indicates that 46 percent of CIOs plan to deploy AI, but only 4 percent have.

So how do I get started?

Educate, educate, educate…

First of all you need to make sure to understand the basics. Kick start by creating a cross functional group that meets once a week to explore the basics of AI together. This is a concept I have tried myself, and it is very effective. People get a mutual understanding of where you’re heading and hands-on experience needed to act. In addition, it is a great teambuilding exercise. You can use someone from the outside to help out in this process, or you can do it yourself. Just make sure someone takes lead and have a theme for every discussion. You can either start by defining some questions you have, prioritize and then start exploring. Or you can take a MOOCs and meet once a week to discuss.

Then you should start train the whole organization. Everyone will be affected, and everyone should get some kind of training. It’s important everyone has the same understanding of the concepts and principles of how AI can be used as a tool to solve business problems, why it’s a good idea to educate the whole organization. This is something they have done at Google, where every single person in the organization - all the way from Machine Learning experts to people in HR with no prior knowledge within the field – have received the same kind of training on a more conceptual level. In addition, it will be much easier to implement changes when people understand why and how. Find translators within your organization, folks that can dumb it down for people based on their skill level. AI is not an IT-thingy. AI is not the goal itself, it is a tool to reach the goal. The business value must come from the people on the business side. They need to understand AI well enough to come up with relevant business problems to address.

Start experimenting using real business problems and keep it small

As soon as you understand enough, start experimenting. Identify a real business need and start on a small scale. You will understand the potential of the technique as you go along, and you will only become better and better from that.

Understand and manage your data

Understand your data, how it is collected and handled. Clean and structure the data and make it accessible. Screen the data: what data do you have, what is structured/unstructured, do you own the data, where is the data, what kind of data is needed for different applications, do you have enough data to train your models? Do you need to complement your data? And finally, consider the risk of bias in data (there will be!) and make sure you include ethical considerations already at start.

Get started quickly by using AI in the cloud.

The easiest way to experiment with AI-related services is probably via the cloud. All of the major tech firms offer various AI services, from the infrastructure to build and train your own machine-learning models through to web services that allow you to access AI-powered tools such as speech, language, vision and sentiment recognition on demand. AI-as-a-service is estimated to increase from 1 bUSD today to 20 bUSD in 2025. Amazon Web Services, Microsoft Azure and Google Cloud Platform provide access to tools for training and running machine learning models.

And finally, see it as a start.

AI is a General Purpose Technology that is developing at exponential speed, and on top of that, is converging with other emerging technologies, which creates a lot of unexpected consequences. In other words, it’s a field that is constantly evolving, so you will have to develop your capabilities along the way. Let the continuous exploration be your goal.

If you want to know more about AI and can spare 5 minutes a day, then you should watch this 7-days series I've created on what AI is, how it can be used and how to get started.

Let's Talk.

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© 2020 by Martina Banyay