Artificial intelligence. The next big step for mankind? A reliable driver for self-driving vehicles? Or is it the drone-driven destruction of humanity? Whichever part of the debate you're on (and there's been some positive ones recently), AI undeniably holds the potential to be the next revolution on the planet that is on the same level as the cognitive, agricultural, and scientific.

Thankfully, the only thing that AI in learning and development will be killing off anytime soon is prescriptive, irrelevant, lowest-common-denominator content. We've developed self-driving technology and all the efficiency and time-saving benefits that come from it.

Pitfalls You May Face in Your AI Strategy

As the business world in general starts to recognize the possibilities of AI in the field of learning We decided it was the perfect time to discuss some of the mistakes to be aware of. Each of them is essential to be aware of in the event you are considering creating or implementing an artificial intelligence-based solution for learning.

Cold Start

AI relies primarily upon data. It's the fuel that drives the machine's wheels (learning). This leads to the well-known cold-starting problem. In essence, if a computer isn't able to gather enough data about its users, it's not able to draw any conclusions (and give good advice) until it is able to.

It's a real issue, and an enormous one. Imagine rolling out your brand new learning initiative and needing to inform your stakeholders "uh... it's not actually going to work that well for 6 months while it collects data and users have a bad experience."

How do you get started fresh and make sure that your suggestions are logical right out of the gate? You need relevant data. Imagine getting a car started with an empty tank of gas. We accomplish this by using vast amounts of anonymous data from experts such as you. We arrange it in an approach that the AI is able to draw upon a previous understanding of the relation between user traits (e.g. the type of role) and requirements for capabilities from an existing master skill framework. This allows us to make fantastic recommendations right out of the box. There's always data that you can utilize for this within every company. Just search for it.

Insisting on the AI to complete everything

One of the greatest advantages to AI solutions is the versatility of its processing capabilities. However, you cannot just throw into whatever data you like and trust that the machine learning process will take care of it. In this regard, the current generation of artificial intelligence is a lot like the human brain garbage in and garbage out.

Furthermore, content that is based on machine learning could also have the potential to go to the extreme. Human desire means that for example, every YouTube video that you're given is just that much more captivating and clickbait-worthy until that adorable cat video is transformed into an explosive flat Earth conspiracy saga. This is why YouTube and Facebook acknowledge the necessity to have human curators.

It is necessary to perform some manual data processing prior to you can start the machine. In our case, this involves a team of experienced curators that are experts in their field, tagging high-quality content according to their expertise. As you see, we're close to achieving a level of AI that could also do this.

Frameworks with Rigidity

It's tempting after you've completed your initial processing to conclude "there we have it, we've got these two datasets that align nicely and tell us exactly what skills each user needs." But how many of your team share the same characteristics and don't change in time?

Like all algorithms the algorithmic framework, your system needs to be a perpetual learner, constantly expanding its capabilities and knowledge. It must also take lessons from a variety of sources.

Every learning centre is accumulating more data, so it is worth trying and find a way to incorporate this information to the AI process. Magpie, for instance analyses a variety of characteristics that learners have, for example:

• Their role
• A task or goal they require assistance with
• their performance goals
• Their recent interest
• Content they have specifically selected to express their interests (their currently logged-in bookmarks)
• the common interests of people

This means that we are able to provide a wide range of needs that range from support for performance to on-boarding, the development of careers to business changes. As well as supporting various goals of the client that range from improving engagement to improving relevance to support for established business goals.

Insignificant Metrics

A large portion of the list above is based on the characteristics of users. However, how can you tell whether your Artificial Intelligence Development is fulfilling its job in a manner that is pleasing to the users? Many AI-based learning programs and even those that require the technical knowledge to integrate AI with fuzzy objectives - or, worse, there is none.

Machine learning requires to understand what looks good in order to optimize for further improvement. Consider the kind of metrics you'll be tracking before the program begins, and then be open to changing them as the program grows. It is important to look for signals that signal the effectiveness of your program and engagement on a more fundamental level. Views/opens are just the beginning.

We can, for instance, improve the effectiveness of our suggestions for learning by considering the numbers of completed tasks, user feedback and reengagements to provide a clearer image of the actual use and effectiveness on an investment, and not only its attractiveness. The algorithm is also geared towards more tangible attributes, such as capabilities, instead of superficial ones like preference to anchor what we do with the benefit of the learner.

Setting and Forgetting

Now that you've put together your content, created your frameworks that are flexible and have relevant metrics. The process is going well and your users are getting decent quality. You're now ready to give yourself a pat on the back and enjoy a pint isn't it? There are dashboards that determine directly the amount and the rate that machine learning is learning.

Make use of the metrics that are meaningful that you have gathered from the previous step to determine how your machine learning algorithm is performing. After that, you are able to modify the algorithm and tweak it again. Test it with an A/B test and check if the score is increasing.

Conclusion

Although the path to success can be laid out with the best of intentions, the fact is that intentions and passion will not suffice. Successful implementation of AI for any organization starts with a knowledgeable and committed management, a culture of alignment across all levels to the business's goals, and a host of carefully crafted pavement stones. It's not easy but the results are definitely worth it provided it's done right.