Besides acquiring mammoth of data through the years, these tech giants are among the pioneers currently mastering the incorporate artificial intelligence in devising new solutions.
As much as everyone likes to talk about AI and associate with machine learning, data science, and deep learning technology, only few companies are actually using AI in their core operations.
According to MMV Ventures, in Europe, only 60% of the startups are using the AI technology that’s truly useful for their operations and value proposition.
the concept of AI, the requirement of top-notch, complex skills, coding,
statistics modelling, data analysis, and other mathematical and programming execution is not easy to find.
Even when such resources are do found, their affordability is not everyone’s cup of tea. Furthermore, corporations require tonnes of data in order to train their AI systems. Setting the right infrastructure to create an AI-based system is quite an investment not many companies are willing to make, despite acknowledging the benefits it could bring for their business.
In order to harness the true potential of artificial intelligence, the technology needs to come within the grasp of a variety of business worldwide.
This is what we call the “Democratization of AI”.
What do we mean by democratization of AI and why it is required?
The idea behind the democratization of AI means that the technology should be made easily accessible for various businesses.
Apparently, not many businesses today have the resources and prerequisites to understand the application of AI, however, “knowledge is power”, and it surely benefits those who lay their hands on it.
Making AI accessible, in other words, indicates that more
people will have the chance to interact with it. In doing so, mobile
application developers will enter other, untapped sectors, while the AI experts will have the liberty to move onto high-end projects.
What are the steps to democratize artificial intelligence?
Data accessibility and quality
“Reap what your sow”, “garbage in, garbage out”, and similar associations are often tagged with data today. As we know that data is the new oil, and the results are only as good as the data input, but maintaining the quality and management of data is easier said than done.
Collecting tonnes of data is now more convenient and affordable as compared to the past, however, majority of enterprises still endure one of the following situations.
· Due to limited availability of the required data, it is difficult to build AI-based systems.
· Poor data quality creates equally unstable and somewhat misleading AI systems.
· The use of improperly managed and handled data results in time-consuming and expensive automation of AI systems.
Only the right use of data will produce reliable results through AI models. Therefore, democratizing the process won’t be it, but democratizing the data management aspect is also relevant to get the job done.
Several industry experts have advised for the culture of data preservation, quality assurance and management should be taught through every hierarchy of the social and corporate system, i.e., from basic to higher
education and connecting the important aspects of data science in all major industries worldwide.
As we know how the first computers were considered and handled at the time of their introduction in the world, only selected individuals knew how to run the early machines. Today, even toddlers are aware of how to open YouTube, use entertainment applications, etc. on iPads and smartphones.
The one major reason enabling this massive ease is the evolution of user interface of smartphones and desktops.
As cloud service providers like Microsoft and releasing top self-service analytics, like Azure ML, the interaction with AI is becoming more and more intuitive
Explanation of results
Let’s suppose a company has completed the preliminary considerations and accessed, collected, and filtered the data and created the initial predictive model. Now the team will be presenting the results to the upper management of the company.
How will you convince the team to acknowledge the model and work on the predictions of your model?
This is where the results explanations comes in! You might be forced to compromise certain accuracy of the model for better, understandable output.
In this case, using a black box model won’t be appropriate, where you know the input and acquire the output, but are unaware of what occurs
in between. Apparently, you want the management to act on the results and not only trust them.
Comprehending what drives a certain actions into results and its influence on your business through a particular behavior is important. Therefore, besides focusing on the data in hand and what information it holds, also determine the drivers behind the action. This is why it is advised to keenly work with members of cross-functional teams.
Alleviating the challenges associated with AI democratization
We are on course of automating the whole process from data processing and modelling, and empower companies and teams to create their own predictive models.
Yes, being realistic is the key, a wide range of skill set is required to be on board. For example, activities like drag-and-drop, top-notch modelling functionalities, etc. can be moderated and automated. Albeit, a machine is bound to follow only the rules in the end.
Remember, if you cheat or incorporate the wrong instructions out of inexperience or carelessness, the machine will follow the incorrect instructions and produce unusable results.Catching such mistakes or locating the restrictions of certain algorithms, is a skill that a data scientist must possess.
In short, handing over AI control to people will insufficient knowledge in data science will facilitate inappropriate interpretation of results.Moreover, there is not one, grand metric that indicates which model to use, and well productively will it function.The trick is to keep experimenting and making comparisons among various algorithms,metrics, and parameters.
Obviously, it all comes down to the experience and specialized knowledge in the end.
In order to avoid such issues, it is vital for people to have a significant knowledge of data science, how to interact with self-service analytics, and what’s the game play behind the regular user friend interfaces we see on different platforms.
For instance, automating some more easily understood and frequently used use cases including prediction of churn, credit default, and similar tasks will aid users is learning how to correctly interpret and analyze the data and results in hand, as for the businesses, they could exhaust their efforts on more complex use cases instead.