Disruptive technology has violated the linear nature of time with innovation and creativity thanks to multiple factors such as scientific research, global hyper-connectivity, and the improvement of resources such as Artificial Intelligence (AI). This has wrought a series of innovations that has impacted markets, offering products and services that are capable of solving specific issues and the possibility of shared solutions for entire industries.
In the case of Artificial Intelligence, there is a world of ideas and concepts that encircle the myth of this field of knowledge. Nevertheless, the idea that best defines the possibilities of this technology looks to understand it as a process of learning through previously programmed codes, and also designed to make their capacity for retaining certain tasks more flexible. For example, we can detect emotions and discursive inconsistencies when we apply AI, whether via the analysis of an image, an audio file, or the combination of both in audiovisual material.
For the purposes of this article, we will dive into AI’s current potential, considering its reach, its possibilities, its uses and applications, and lay out the trends that show the future of Artificial Intelligence as the technology that will determine our way of life in coming years.
What is Artificial Intelligence?
The dawn of AI took its first breath in the early 1950s, when researchers at Dartmouth College began working on a project that replicated human perception, seeking to simulate its cognitive ability.
Artificial Intelligence now has a base of programming that uses a specific language, acquires the capacity to complete activities on command (syntactic actions) that border complementing a contiguous reaction, a semantic body of individual tasks that contribute to executing a process. This will determine the functionality of a software whose innovation is an integral part of the digital expertise of Artificial Intelligence and its respective fields of study.
Uses, reach, and limits of Artificial Intelligence
As we saw in the previous section, the reach of Artificial Intelligence, in terms of its different uses in business, determines the technique that should be used for a particular process, just as the limit should be established considering the main objective one hopes to achieve. For example, if you want to determine a human emotion, you will need to focus on the learning and interpretation of codes for traits that allow us to understand a state of mind; in other words, if we want AI to recognize whether a person is asleep or awake, we must define the reference points on the face that would help yield the answer.
Maybe we should map the intersections of the eyes that demonstrate a state of sleep. Currently, this level of precision corresponds to previously programmed structures with a predetermined language such as Python. Another possibility is offered by multiplatform software libraries such as dlib, which could help detect these facial landmarks.
With the margins of our analysis established, the components to be analyzed are subjected to a decoding process (performed through mathematical operations such as systems and data convolution), which yield different results, which represent the state of the subject of the analysis; this is the ability to identify whether an individual is “awake” or “asleep.”
The technical and practical background that closes the previous paragraph is truly fascinating because, to reach these conclusions, we must create a model that allows us to interact with the data and materials provided. Artificial Intelligence is not yet completely independent when seeking solutions with its own consciousness. This limitation may show that limits to this technology exist, especially if we use new processing techniques such as Deep Learning, the purpose of which is to conduct analysis with an automatic learning process using neural networks (a concept simulated on computer networks) to classify certain codes and patterns. Some possible business applications for this technology include human recognition, translation, processing of written and oral language, or the identification of objects based on their physical characteristics, for example, the recognition of stolen vehicles (even if they have been altered).
The analytic models that circumscribe Artificial Intelligence are a mathematical canon that seek to integrate a logical answer in a context of structured learning and decoding.
What are the limits of Artificial Intelligence?
At this point, given constant technological progress, it would be inaccurate to say that there is a defined limit to the field of Artificial intelligence. Although it is true that there are still areas of opportunity in which specialized research could deepen and improve AI in business, the development of this technology in computing systems has become intensely relevant to the market, which depends on its abilities to operate normally.
In accordance with this idea, many day-to-day operations conducted on digital devices are possible thanks to Artificial Intelligence. That is why it may even be a bit imprudent to try to delineate a complete border, since the efforts of important researchers and computer scientists continue to yield interesting results, like a wave of new applications across multiple sectors.
Finally, we should not forget that the origin of Artificial Intelligence can be traced to the intention to replicate the intellectual activities of a human being; in other words, an attempt to replicate the human brain using circuits, PCBs, and other electronic resources. We should ask ourselves if we, at some point, can let Artificial Intelligence advance of its own accord, on an autochthonous pathway, independent of the human reach. The first steps tell us that it is not such a crazy idea; a quick glance at something as simple as the advertising on a search engine or social media feed show a small fragment of the vast reach of this technology.