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Humans have been storing, retrieving, manipulating, and communicating information since the Sumerians in Mesopotamia developed writing in 3000 BCE. Since then, we have continuously developed more and more sophisticated means to communicate and push information. Whether unconsciously or consciously, we seem to always need more data, faster than ever. And with every technological breakthrough that comes along, we also have a set of new concepts that reshape our world.
We can think back, for example, to Gutenberg’s printing press. Invented in 1440, it pushed printing costs down and gave birth to revolutionary concepts like catalogs (the first was published in 1495 in Venice by publisher Aldus Manutiu and listed all the books that he was printing), mass media (which enabled revolutionary ideas to transcend borders), magazines, newspapers, and so on. All these concepts emerged from a single “master” technology breakthrough and have had a great impact on every single aspect of individuals’ lives and the global world picture.
A hundred years later, the core idea of data distribution has not changed much. We still browse catalogs to buy our next pair of shoes, we create catalogs to sell our products and services, and we still browse publications looking for information.
Is the way we currently access and share information efficient?
This question may sound silly, but technology and the quantity of data produced are intrinsically related. The more technology advances, the more data quantity increases, and the more we need new, advanced technologies to process this data and extract valuable meaning from it. Data has three primary uses:
1) It can be used for “action,” such as optimizing the distribution of electricity.
2) It can be used to facilitate social change, such as through donations and online voting.
3) It can be used for innovation, such as helping us understand climate change or molecules, and building and testing new theories.
The ratio of data quantity produced versus data impact is an important indicator in innovation’s engine speed.
It’s all about data production and consumption velocity
Until the beginning of 21st century, the ratio of human data gathered vs. data impact (that is, social change, innovation, etc.) was very high. We are where we are thanks to our brains’ capacity to imagine and build models for the data we’ve collected and to draw lessons from that data. But we long ago reached a “consumption saturation” point, whereby the amount of data is increasing so exponentially that the old way of accessing data has become inefficient and obsolete. For example, in today’s super interconnected world, when people from virtually every culture and language are putting valuable information on the internet, we still rely on keywords searches to find information. This is a rudimentary approach. Instead, we could be using an AI-powered search engine that gives us an answer after considering all the available sources and languages.
Data access and sharing technologies are the most important fuel of the innovation engine.
Don’t get me wrong; we are still innovating and using data in various ways. But the ratio of data quantity produced to data impact is really small because datasets are getting more and more complex and need more and more intelligence to process them.
Storing data is good. Consuming it in a social and efficient way is better
Technology obviously influences the way we interact, how social circles are formed, how we make decisions, and even the way we think, but it has not changed our core nature. We are still social beings, and with all of today’s technologies, word of mouth still plays an important role in our lives. We still ask our friends for advice and recommendations and ask our friends for help when making important decisions in our lives. It’s no wonder that the most successful products out there are the most social ones.
We have always tried to “humanize technology,” and future breakthroughs in AI will go a step further to radically change the way we access and share information. We are truly going to experience AI-powered social technologies everywhere. And, for the first time, everyone will have a smart AI-powered friend that understands them, memorizes their habits and preferences, gathers meaning from the petabytes of data available on the web, gives advice, and so on. For the first time, data access will be social and efficient.
Sure, we haven’t developed “HAL 9000” or “Her” yet, but in the meantime, the recent progress made in machine learning and the democratization of information — combined with the explosion of messaging — has already given us the possibility of throwing most of the current apps in the trash and starting to develop real human interactions via chatbots.
Precursor to true AI
Chatbots are an old concept. Messaging apps are also an old concept. So why are chatbots rising now?
If we really delve into the reasons websites replaced client applications and apps replaced websites and we look at why messaging bots can and will replace mobile apps, we will find that users’ behavior changes when the technology advances significantly and users reach a saturation point in terms of usage. For client applications, it was the widespread availability of the internet and the need to get things done anywhere and everywhere. For websites, it was the smartphone revolution and the need to get things done on the go. For apps now, it is the advances in AI and the need to get everything done immediately. Users no longer tolerate the hassle of finding and downloading an app on the go; it is usually a waste of data, patience, and time. And no matter how great your app’s UI/UX is, there will always be users who simply don’t understand it and get frustrated because the process isn’t natural. With chatbots, you don’t have to download anything. It’s a more natural experience and you can go straight to your goal.
Current limitations in natural language processing and bad implementation have caused some critics to speak out against chatbots. But like any new concept, it takes time to define standards and frameworks (technical and conversational) and put best practices in place. But this is it, we are in the chatbot era. I believe that every era has a purpose, and the purpose of the chatbot era is to get people comfortable interacting with a virtual entity as a way to get things done. That’s it.
This will be, perhaps, the last stop before the arrival of true AI.
This article appeared originally at Chatbots Magazine.
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