Demystifying Artificial Intelligence
Sharing knowledge on Data Science and A.I. -especially towards non-techie’s- is crucial to bring opportunities to everyone
Recently, I read an anecdote about the film “A.I.”, which was screened in 2001. This project was originally developed by Stanley Kubrick in the 70's, but remained “in the freezer” for almost 25 years, until his death. Afterwards, Steven Spielberg rekindled the project, bringing the public attention to a topic which-at that time- seemed to only be part of the science fiction world.
The first time I watched “A.I.” -in the period following the 2000 dotcom bust-, the concept of Artificial Intelligence seemed distant and surreal, but having a film with the “A.I.” acronym as title was an initial action to give visibility to such important topic. The ugly truth was that, at that moment, the general interest in Computer Science topics was not really vibrant: the mainstream public thought about this domain as something which could only be of interest to “lonely geeks” just to ensure that the “operational stuff” (meaning: transferring data and bringing websites to life) would be up and running. That that was “all the magic” about it.
In less than a decade, the view had dramatically changed. At that moment, our interaction with apps, smartphones and social networks had significantly shifted the perception of technology. The mantra changed from “that stuff done by and for nerds because they are the only understanding it” to “technology brings zest into my life and I enjoy it”. Many started thinking on how to create the next BIG social network, or how to become rich via making the next super app… many times, ending up with deceiving ideas and results (just think about some Shark Tank episodes that went bloodily awful). Due to scalability, competing against the big players in the tech arena became a thorny challenge: as ABBA said, “the winner takes them all”. So… what could be next?
Gradually, the concept (and value) of “data” started being seen through a different lens. The breakthrough took place in 2012, when Harvard Business Review claimed that “Data Scientist was the sexiest job of the 21st Century”. For many, that was the first time to ever hear the term “Data Scientist”. Same as the “A.I.” film was brought to life after being kept in the freezer, the hidden value of Data Science and Artificial Intelligence was unveiled.
People started realising that, instead of copying existing business models and trying to compete against giants, a more pragmatic approach to optimise the value of technology was using algorithms, and started getting acquainted with “a brave new world”. The data frenzy started, with many questions popping into scene by those who have mainly relied on Excel worksheets to analyse data:
“Why do we use those slow, complex VBA macro’s with multiple regression incapable of handling tons of data rows, instead of following a faster and more accurate approach by using Python to generate predictive analytics?
Why do we present boring charts on business performance, while we can be more persuasive (and funnier) using real-time analytics and data visualisations, which all management levels can understand and play with?
Why do we only use SQL queries with relational databases, while we can now spot connections using graph databases, like a journalist did to unravel the Panama Papers?
Why do we only talk about classical analytics, while we can now have access to natural language processing (NLP) and mobile sensor data tools to also keep an eye on the behavioural aspect of my customers?”
In a nutshell: in order to use our budget in a wiser way, why haven’t we really exploited the potential of Data in the way we should?
Little by little, the perception on algorithms has changed from an abstract concept to a tool to help all kinds of organisations, bringing opportunities to everyone. For instance, fields like Marketing and HR -traditionally limited to the Business discipline- have become more technical, requiring a higher level of programming to handle larger volumes of data and deliver more accurate forecasts. Contrary to some comments I have heard, this is not a hype: it is crucial -no matter which is our background- to spot where can we learn more about this topic, which is shaping our lives in a tangible manner.
Ten years back from today, who would have really thought that our political decisions, love lives, or chances to get selected for a job could be so much impacted by algorithms?
As a former Training Manager in a tech co-creation center, one of my main interests was to share the knowledge and to explain the interrelationship among Data Science, Machine Learning, and Artificial Intelligence. I witnessed the urgent need to demystify them in a way that everyone -especially those who have never hear these terms before- could understand their basic principles. Furthermore, considering their close relationship to privacy and cybersecurity, as well as with other technologies (Blockchain, Virtual Reality and Internet of Things), it is key to keep on spreading the word on these concepts.
Rather than being the topic of a science fiction film, today’s reality is that we need to proactively and continuously learn about these concepts: it is the only way to ensure that we understand the benefits and challenges they are bringing along. For many, this need is clear, and the question is: which is the starting point to learn about this? Well, here are a few tips:
If you live in Belgium -no matter what your background is- you can join the major Data Science and A.I. event of the year: the diSummit, taking place on June 27th., at ULB (in Brussels). In this fourth edition, the organisers are bringing top-notch speakers, who offer valuable takeaways from a theoretical, practical, and ethical standpoint. It has also been designed to meet the needs of all kinds of attendees, ranging from the data newbie, to the expert. The cherry on the cake is the opportunity to network while enjoying some very nice beers 🍻 (Belgian, of course 🇧🇪 !). Join this event, and extend the invitation to your friends, colleagues, and to your boss! Check out the tickets and special offers offered in the event’s website.
You can have a look at the lectures of the previous diSummit editions via the Data Science Community Youtube channel. Additionally, you can join any of the meetups of the Data Science communities (mostly active in Antwerp, Brussels, Ghent, Leuven and Liège). You don’t need to be a Data Scientist or expert in A.I. to join them! Just be curious and open, and I’m sure that you will find the proper people to clarify your questions: that is how I started learning about these topics.
If you live outside of Belgium, feel free to search for your closest Data Science and A.I. meetup. Other useful places are universities and tech hubs. If you do not find any option nearby, initiate a community! Don’t be afraid to do so: I have met people who have created very interesting and tight-knit communities who were driven by their curiosity to learn and to share than by their level of expertise. There is plenty to learn from each other!
In the journey to become more technology savvy, we all need to jump into the boat! Let’s keep on being proactive and keep a (very open) eye on the many ways we can learn through about Data Science and A.I.!
