Much is said about artificial intelligence and machine learning. Our colleagues mention it casually in their conversations, especially when it comes to new innovation projects and startups working on amazing apps.
Undoubtedly, the advances that exist today in this technological field are having a strong impact on a wide variety of industries. But, if we go to the essentials, how do artificial intelligence and Machine Learning add value to companies?
Even better, how can these technologies add value to your company?
At Shortnews247, promoters of digitization among the members of our Entrepreneurs Club, we want to make it as clear as possible how these technologies and their continuous implementation in our projects can have a profound positive impact.
But first we must clarify the fundamental questions around these technologies in our understanding.
What is Artificial Intelligence?
When we talk about artificial intelligence, also referred to as AI ( also AI for its acronym in English ), we refer to software and hardware development that has the ability to carry out complex tasks that, in analog circumstances, would require the human intervention .
If we are fair, we will refer to artificial intelligence as an entire field of study, a science in which professionals of many types diverge. The history behind the development of this science goes much further back than the appearance of the computer that we use today
Alan Turing, a famous English mathematician whose efforts during World War II succeeded in decrypting German communications using the Enigma device, published a paper a few years after the end of the war. In this paper, Turing posed a profound question that would give way to the development of this field of study: Can machines think?
The fundamental objective of artificial intelligence as a science is to replicate human intelligence and reasoning in the machine. Of course, such a proposal has opened countless debates that today continue to raise questions about the ethical and moral nature of developing such technology.
Post-Turing, Stuart Russell and Peter Norvig publish an academic piece that is now fundamental to the study of AI . In this book, they describe artificial intelligence as “ the study of agents receiving perceptions of their environment and executing actions as a result .”
Much has happened since the publication of Russel and Norvig’s book in 1994. The technological advances of the last 3 decades have given way to an extraordinary expansion of what this particular science can achieve.
Today we see, without looking too far, many examples of AI in action. Smart assistants like Siri and Alexa are probably the clearest examples around us, perhaps because of their interactive nature.
But many times, the implementation of artificial intelligence does not carry with it interfaces that emulate human interaction, but rather rely on a more technical interface. Real examples of this are software that maps and predicts the spread of diseases in a population, robo-advisors for investment in financial markets, spam filters in digital communication, autonomous driving that we see in Tesla products, recommendations on streaming platforms such as Netflix, Amazon Prime Video and Spotify, as well as artificial intelligence currently used in the manufacturing sector to optimize processes.
What is Machine Learning?
As a newer concept, we have been hearing about Machine Learning for a while now. We see it more and more present in our applications and web services. The SaaS industry in particular uses the term in its marketing material , often unjustifiably.
But the truth is that machine learning is a very real concept, most often implemented in the services we enjoy. Now, we explain it.
Machine Learning, also referred to as ML by its acronym , is a branch of the science of artificial intelligence and programming that seeks to create learning models based on data and algorithms, often mimicking or emulating the way humans learn things. new .
By creating better learning models, new possibilities are created for artificial intelligences to become “smarter”, so to speak. Data processing becomes more precise and the quality of the criteria that the software has to make decisions increases.
How do these Technologies Add Value to your Company?
Artificial intelligence and Machine Learning are no longer exclusive technologies of computer multinationals such as IBM or Microsoft. On the contrary, the impressive expansion of the SaaS industry has put these technologies in everyone’s hands . Even its implementation can be, in simple cases or in small companies, carried out at a very low cost.
Let’s start with a common case: content. All modern business projects that compete in the digital economy must have a content strategy that positions them favorably in front of their ideal audience. And since the challenge is that there are too many players doing the same thing, the solution can be found in being creative and taking advantage of the available technologies.
The success of a content strategy lies in their quality. Are you producing and distributing noise and garbage?
Take the popular Quora platform as an example. A few years ago, this project began to gain popularity and today it has more than 300 million active users, positioning Quora among the most active pages on the web.
This platform works as a kind of Yahoo! Answers, publishing questions and answers formulated by the same users. The difference? A key focus on quality responses.
To achieve this, we have two paths: people who work as content curators, reading each answer and determining if they have enough quality to rank the answer… or let an artificial intelligence determine the quality and automatically curate the content for the readers.
Although it sounds complicated, this type of technology is accessible to other companies looking to improve the content they show to their users. Prioritizing quality in this way means that we are not sacrificing efforts in other areas of the business.
Continuing in the case of content, there are tools such as Snazzy AI and Copy.AI that use artificial intelligence to create pieces of informative and commercial content (copywriting) based on minimal data that we provide to the tool. These SaaS products give us a good idea of what the future of content creation may look like.
Another practical example is in advertising. Artificial intelligence is being used by businesses of all sizes to accurately profile online advertising campaigns. Each euro invested in advertising offers greater return by showing ads on sites with a high possibility of conversion. Consuming data and making decisions in milliseconds, this type of software knows where the greatest chance of success exists and can quickly change course to continue experimenting and take care of the budget.
Office productivity is skyrocketing thanks to artificial intelligence that accurately converts speech to text. Many menial tasks performed by trained professionals have disappeared thanks to these technologies.
For warehouses, artificial intelligence is helping with inventory management. The data remains current at all times, with exact precision and without the manual effort of a human.
The user experience can also be improved with a simple implementation of SaaS tools based on artificial intelligence, such as chatbots. Companies of all sizes take advantage of chatbots to increase the satisfaction of their clients, attending to all their needs in real time without the necessary intervention of a professional.
Exploration for Improvement
As entrepreneurs in a digital economy, even if the nature of our projects is not digital per se, we have to take advantage of new technologies to be competitive. Artificial intelligence and Machine Learning are examples of this.
We have a duty to explore the possibilities of improvement for our projects. Technology opens up a multitude of possibilities in this regard.
To get into this world without technical difficulties, we recommend exploring the wide world of SaaS products, many of which already expand their potential by implementing artificial intelligence. The improvement of our processes is imminent, as well as the disappearance of small tasks that waste the time of our trained employees.