Welcome to the hive
The second generation
The second generation started the connection economy. Computers have allowed us to approach distant situations. The telephone, coupled to the fax and then to the e-mail, allowed us the remote coordination of activities. You could use a credit card anywhere in the world, call a toll-free number to place an order, activate a service via a web form. Email and the Internet have led to the creation of large databases. Wikipedia, eBay, LinkedIn and PayPal were born. Each company had a website. Obviously we used what we learned in the first generation but the second has, above all, added connection.The third generation
The third generation has combined the first two, allowing however to choose a place and time. We can watch a twenty-year-old movie on YouTube or join a video call with someone on the other side of the world. We can relocate our activities. A person on another continent can retouch our digital photos because we asked him to. Or some Indian programmers can work on the algorithms we have devised on paper. Or Chinese seamstresses can produce dresses on the basis of Italian paper patterns (in reality no longer paper, only patterns).But that's not all. Your phone knows where you are and who has been in that place before you (better: the centralized system your phone is the offshoot of) knows. Obviously, each generation builds on the previous one, so, for example, Google Maps is composed of arithmetic plus data plus remote data entry plus location management.
The fourth generation
And the fourth generation, the one that has been talked about for years but which is now really coming as a mass phenomenon, brings the "prediction" to the table. Bring intelligence and the ability to make decisions for us. If you don't like the word "Intelligence", which is normally reserved for humans, then call it "AI" (Artificial Intelligence) if you want, but, in the harsh reality, it is a combination of analyzing information and predicting what we would do. if we knew what the computer knows. Not a single computer but an interconnected network of computers taking data from a multitude of sources.This prediction is not done in a centralized location, precisely because the previous generation led to putting computers everywhere. So now let's connect all the computers the way we used to connect people before. We are giving those computers the ability to make predictions (and make decisions, or at least make suggestions that greatly influence our decisions) based on what thousands of people have done before us.
By di more, with each generation, the previous generations also improve. More sophisticated databases, more advanced algorithms, greater connectivity, a larger and more qualified number of people sending data, more emphasis on what you are connected to and doing. If you are a mediocre professional, your job is in jeopardy. The rapid succession of these cycles will ensure that the intelligence contained in a computer network will soon do your job better than you. Someone calls this system "hive", just like the tidy cells of bees that make up a complex, intelligent, strongly interconnected and functional whole for a very specific goal.
Are you ready to be part of a hive? But above all are you aware of it? Every time you enter data, consciously or unconsciously (even moving 10 meters with your mobile phone in your pocket is equivalent to providing data, even if you don't realize it), you increase that gigantic database that, in fact, regulates our life. Welcome, therefore, to the fourth generation, that of the hive. Either throw away your smartphone, disconnect from the internet and enter a hermitage or be aware that you are part of the hive. And you are an active part of it, feeding it with your data.
It's a question of algorithms
Friends entrepreneurs and managers, being part of the hive is a bit dangerous (think, for example, of the risks related to always being connected, for example in terms of cybersecurity), but it is also very convenient. It is convenient for Google Maps to inform us which is the best route, also because, a little further on, other users have discovered before us that there was an accident and therefore will allow us to avoid it. It is also convenient for an e-commerce service to suggest books based on our tastes and those of people like us.In the end, we are talking about algorithms. In fact, the algorithms decide for us, even if we think we decide. The truth is that, if there is scarcity, choices have to be made. Who is hired, which website appears at the top of the search results, who gets a loan. In the old days, if there was a problem, it was examined and addressed. In short, ad hoc decisions were made on a case-by-case basis. Today, however, for more and more tasks and more and more on a large scale, we find it convenient to rely on algorithms, benefiting from a series of precoded steps, to inferences and decision-making heuristics that, apparently, become more sophisticated and precise as data is collected and as algorithms refine.
It is clear that algorithms are reforming our culture. They guide the way social media brings out content (and thus win or lose political elections!), The way search engines highlight websites, the way any system makes decisions about who uses it for first of a service, who gets a loan and who doesn't, who gets vaccinated first and who doesn't. The hive is everywhere, always. And the algorithms are not neutral. They can't be. Every decision has consequences and, unlike the Pythagorean theorem, there is no right answer, simply a choice that is never neutral.
Entrepreneurs and managers, you too create algorithms
Friends entrepreneurs and managers, do not think that creating algorithms is a task only for Facebook, Google and Amazon. You too, when you define internal procedures, such as which customer to serve first or which task to prioritize or how and when to pay supplier invoices, create algorithms in your own small way.It has simply become necessary. For example, I have created algorithms that help companies discover talent. We can create them virtuous, informed and efficient or not. We can build algorithms aware of their (small or large) consequences or not. In short, we can generate algorithms for the good of our company and their collaborators and customers, or based on rigid rules and codified bureaucracies, therefore, in fact, unaware of the consequences. We can do bad or we can do well. Do we want to talk about it? Write to me!
