Machine learning, Artificial Intelligence and the crystal ball

What about Machine Learning (ML) and Artificial Intelligence (AI) in 2022? Is this year finally the year of the advent of machines? Until recently, AI was just an overused term in marketing. Many software vendors who sold algorithm-based solutions and like to use fancy regular expressions passed off their products as artificial intelligence when they weren’t.


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Times have changed and the market is broadly divided into two camps: vendors who use a predefined AI framework and those who create their own. Without wanting to discuss the advantages and disadvantages of each camp, let’s try to determine what it means for users.

Two obstacles are slowing the adoption of AI

First, a financial obstacle. Solutions that use real AI can be considered “advanced”, but they are quite expensive. Yet it’s always the same story for companies that want to implement new technologies: they want to limit costs or optimize efficiency. We can notice the same phenomenon with the invention of looms and the production line as well as the increased use of automation in the computer field. We’ve taken the next step, but the math hasn’t changed: can we reduce operational labor costs by using new technologies? Where is the break-even point?

Let’s take the simple example of computer security. How many hours does it take to identify the origin of suspicious behavior? How many analysts scrutinize the log files, and what is the overall price of these operations? This is a task that can easily be delegated to a machine, which will display its results in minutes or even seconds, whereas it would take hours for humans to achieve it. This does not mean that we no longer need analysts. Indeed, they can be given tasks that require creativity, which AI lacks.

However, as we said before, times have changed and the adoption of AI is slightly on the rise. As a result, prices come down and the solutions offered are more affordable. Global players are no longer the only ones with the means to acquire new technologies. Over the next 12 months, we will see an increase in the rate of adoption, especially by SMEs. We no longer hear “we don’t need these technologies”, but rather “it could be interesting. Let’s give it a try. »

Taming this sophisticated technology

This brings us to the second barrier to AI adoption, which is complexity.

Whether off-the-shelf or bespoke, to be effective, an AI-powered solution must be customized. This is a complex task that requires specialized development resources. It does not matter whether they come from a supplier as a package or whether they are available internally. In fact, it is important, because the last option is likely to increase costs even further (see above). However, even then, we are seeing an increased use of technology, or a mix of technologies to be more precise. Some of these solutions include lightweight/no-code interfaces. So anyone who can create talking boards can use such a system.

Then there is the issue of trust. A solution powered by artificial intelligence makes its own decisions. As with humans, this process relies on experience, knowledge and training. But who provides this training? It is not for nothing that the EU proposed in 2021 to regulate the use of AI in the recruitment sector. In a nutshell, the more this learning process is developed internally, the higher the level of trust. But that’s not all. Once implemented, an AI-based solution should take on the toughest tasks and improve efficiency.

As with humans, this process relies on experience, knowledge and training. But who provides this training? It is not for nothing that the EU proposed in 2021 to regulate the use of AI in the recruitment sector. In a nutshell, the more this learning process is developed internally, the higher the level of trust. But that’s not all. Once implemented, an AI-based solution should take on the toughest tasks and improve efficiency.

Welcome computers!

Most of us deal with some kind of software in our daily lives. It can be a tool for customer relationship or resource management, warehousing, or lead management to close sales. All of these programs come with out-of-the-box reports, and while they come in varying levels of quality, they usually do the trick. Still, sometimes we need specific functions and that’s where the problems start. Among these professional solutions, some are unwieldy and do not facilitate the creation of personalized reports. It’s not uncommon for a DBA to have to abandon an ongoing task to create a custom query and extract some data points the CEO needs for a meeting. Wouldn’t it be easier to be able to enter text in a box provided for this purpose? We could simply write, “give me the Middle East sales figures for the last quarter broken down by country,” and we’d have a nicely laid out chart in 20 seconds. This is not science fiction, as in fact many of us have used a chatbot before and this is how it works. We just need to improve the quality so that we no longer receive an “Excuse me, I didn’t understand the command. “We will eventually get there.

AI and AA-based solutions will become more affordable and easy to use, which will increase their adoption rate. It looks like 2022 is the perfect year for this and we don’t need a crystal ball to find out. Right now we are still feeling the effects of the pandemic and we need to limit human interaction.

You robots are our only hope.

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Machine learning, Artificial Intelligence and the crystal ball


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