Lexicon

Machine learning

// noun

EN For starters, it's not the same as artificial Intelligence. But what is it then? The world is full of data. Words, pictures, videos, ... and it doesn't look like this data flood is going to slow down in the near future. Especially not with all the connected products emerging. What machine learning does is try to extract meaning from all this data. Different from classic software, in machine learning not all the commando's are given beforehand. That's a good thing because there is way too much data to manually sift through! Machine learning is all around us: when we tag somebody in a picture, when we say 'I'm not a robot', when we get recommendations on Netflix,... All the data about Netflix viewers and all the data about TV shows and movies is gathered and a Machine learning algoritm will make sensible connections. Orange Is the New Black? 99% match. Stranger Things? 150% !


NL Om te beginnen, machine learning is niet zomaar hetzelfde artificiële intelligentie. Maar wat is het dan wel? De wereld is een broeihaard van data: woorden, foto’s, video’s, … En het ziet er niet naar uit dat die zondvloed aan data in de nabije toekomst gaat verminderen. Zeker niet met alle ‘connected’ producten die de kop opsteken. Wat machine learning doet, is uit al die data betekenis onttrekken. Machine learning kun je eigenlijk vertalen als ‘automatisch leren’. Anders dan klassieke software, worden bij machine learning dus niet alle commando’s op voorhand uitgeschreven. Gelukkig maar, want er bestaat veel te veel data om er manueel door te spitten! Machine learning is overal om ons heen: wanneer we iemand taggen in een foto, wanneer we op een website moeten bewijzen dat we geen bot zijn, wanneer we aanbevelingen krijgen op Netflix, … Alle data over Netflix-kijkers en alle data over series en films is verzameld in een machine learning-algoritme, dat op basis daarvan zinnige links legt. Orange Is the New Black? 99% match. Stranger Things? 150% !

What's with this lexicon?

Digital transformation, Artificial intelligence, Robots takings our jobs, ... my GOD what a scary time we live in! Right? No, not really. We live in a very exciting time of endless possibilities, of genius new answers to questions we haven’t even been able to formulate... Some people just want to make good money by starting a fire and then being hired to put it out. We don’t like that at Bagaar. No apocalyptic vibes for us, we’re too zen for that.

The intelligence game will be won by those who know how to ask & answer questions critically.

Technology is evolving at a crazy pace, absolutely, and sometimes it might be a bit mindblowing, but don’t worry, humans are still the writers of this story, we decide how it goes. At Bagaar we help our clients every day to gear up for any technological challenge they may be up to, we have a very big toolbox of digital answers to their problems and are very happy to help them in writing their own story. This time we wanted to do something not only for our clients but for everyone, our moms, the guy at the busstop, politicians, journalists, whoever may be helped with a little free knowledge.

Core message here: Trojan: Nice for the greeks, not for you.

So, we wrote this lexicon in the assumption that if people would have more information, and a bit of guidance, they wouldn’t have to feel so helpless when hearing about Digital transformation, MVP’s, Frontend, AWS, Trojans, UX, UI, ... (Que?) We assembled all the buzzwords we use on a daily basis and tried to give a down to earth, simple explanation for each of them. Take it with you, read through it when you’re waiting on a train or getting your nails done and next time somebody starts a fire, you just blow it out yourself (or if it’s too big call Bagaar).

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