Thinking about Artificial Intelligence has been a topic of science and literature since the 1950s – at the same time that Alan Turing was devising the first chatbot, Frank Herbert, author of Dune, was also exploring a future that saw it as too dangerous to exist. The truth is that today, artificial intelligence is being used in business – and this is increasingly a differentiator for companies and careers that want to succeed.
What is Artificial Intelligence, or AI?
Artificial Intelligence, or AI, began to be theorized by the father of computing, Alan Turing. Responsible for breaking Nazi ciphers during World War II, Turing posed the question: how can you tell the difference between a machine and a human? Thus, the Turing Test was devised : if a human cannot tell the difference between one and the other, the machine can be considered human. Artificial Intelligence is a fuzzy term, but most who work with it agree that Artificial Intelligence is the ability of machines to solve problems autonomously . Therefore, we use “artificial intelligence” to refer to a variety of different aspects: from Amazon’s virtual assistant Alexa to self-driving cars and stock-investing robots.
Big Data and Artificial Intelligence
When we apply for a job, we look for something close to our experience—an internship for undergraduates, a junior position for graduates, and a senior position for those with years of experience. This is because experience says a lot about the quality of our work. The same goes for Artificial Intelligence, and this is where Big Data comes in. Big Data, gigantic volumes of data available at all times, are the raw material for an artificial intelligence’s “reasoning.” What makes an artificial intelligence as smart (or dumb) as its data.
If we want to program a self-driving car , for example, it can’t crash or take the wrong turn. Therefore, we need to feed it data—images of objects to avoid, possible routes, real-time traffic data, etc. From this, it could decide the best route from point A to point B, taking you to the bar and back without worrying about driving under the law. Even if the car is a diesel.
AI in business management
Just as a car can make the best decision about where to go, an organization can also anticipate problems and identify better trends through data. Based on a large database, an AI can realize that every truck, after three years of operation, decreases its fuel efficiency by 25%, identifying that it’s more worthwhile to replace them than to keep them, and immediately allocate funds for this. All this without an executive needing to look at a spreadsheet.
Data-Driven Management
Data-driven management relies on available information to inform decision-making and theories. Methodologies and philosophies like Lean Six Sigma help critically understand the position and relevance of each indicator within a strategy, making them essential for leaders seeking success.
Uses of AI today
AI has many applications in business, and new ones are being discovered daily. To help you see how this trend is already a part of your everyday routine, we’ve put together a few examples.
AI for Chatbots:
Talking about AI in chatbots (automated chats, where you submit questions answered in real time by a robot) is almost redundant: the first recorded AI, Eliza, was a robot therapist capable of identifying over 250 phrases, in 1966, by Joseph Weizenbaum. Although Eliza failed the Turing Test, many customer service services have been optimized through the use of Artificial Intelligence, especially with the help of Natural Language Processing (NLP). Customer service professionals’ work hours, as well as the capture of more data, are added to these 24/7 robots.
Natural Language Processing, or NLP
Natural language processing is the mechanism through which robots or artificial intelligence understand human languages and are able to respond to them. It is a field of computing that combines linguistics and programming, and is seen as a trend for the coming years.
IoB – Internet of Behavior
Following the Internet of Things (IoT), today we work with the concept of the Internet of Behavior, or IoB. To create intelligent business solutions, this study seeks to assimilate differences in user behavior data. Realizing that when a sales app user unlocks their phone screen three times within five minutes, this may indicate a greater propensity to purchase, could lead you to push your app to this audience, optimizing your sales. This type of reasoning, which requires tens of thousands of data points, is only possible with the algorithmic processing of Artificial Intelligence.
Credit Granting
It’s no wonder banks ask for your date of birth when conducting a credit analysis – powerful AIs calculate how your generation typically handles money, linking it to your state of origin, your parents’ financial history, and bank statements through predictive algorithmic models. Every banking transaction we make is included in this machine’s calculation, which assesses whether we’re worth the risk of the loan.
Logistics Management
The coronavirus pandemic has placed logistics in its most perilous position yet: on the one hand, there was a sharp increase in demand for home deliveries, while on the other, international trade was stalled by health restrictions. However, it was necessary to develop a logistics solution for transporting products, taking into account tens of thousands of routes and variables. Here, Artificial Intelligence emerges as a facilitator: capable of calculating routes taking into account vehicle speed, fuel consumption, customs duties, and product expiration dates. It can pinpoint logistics solutions in minutes—a task that would otherwise require a dedicated team several days.
Innovation with AI
Never before has so much data been available in such a short space of time and so easily accessible. The availability of information today would put the Library of Alexandria to shame. However, knowing how to use this data for concrete solutions determines the success or failure of a business’s innovation using AI – we’ve outlined some points to consider.
Question that AI will answer
First, consider the problem you want to solve with Artificial Intelligence. This is the starting point for deciding which data to feed your AI, how to structure it, and, above all, what degree of autonomy is worth dedicating to this activity.
Biased Data
One of the risks for AIs, which even puts them in the crosshairs of some regulatory projects, is the existence of biased data that makes AIs act like humans—which undermines the project’s usefulness. Ensure that the data assigned to AI is clean and free of computational bias.
Metadata
We’re not talking about the metaverse, a trend in recent conversations. Rather, metadata is data about data. In this sense, when we walk into a library, we find the book Blade Runner (data) in the science fiction section (metadata). The use of metadata in information processing is the next step that differentiates Big Data from “lots of data