Vision: the superpowers of Machine Learning
Whoever followed the saga on Data Science so far already knows that Vision is the next hero to come on the scene.
*Vision Image: Marvel/Disney
Created by Tony Stark, the android is such an advanced artificial intelligence that it has awakened self-consciousness in the events of Avengers: the Age of Ultron. In our Data Science universe, it represents Machine Learning.
Machine learning is nothing more than a model of data analysis. The method starts from the premise that the machine can learn from the data collected, that is, it is able to identify patterns and make decisions on its own, with little human intervention. We are experiencing this great learning curve.
In today’s post, we’ll talk a bit more about the relationship between Vision and Machine Learning, and show how companies already use this tool to generate more business. Read closely!
Vision beyond our reach: what can we expect from the machines?
Vision is one of the most powerful Avengers in the MCU. His powers? Telepathy; technopathy; Super strength; bursts of energy; flight… we could continue this list until the end of the post, but you did not come here for that, right? For Data Science, what interests us is the hero’s super-genius intellect.
The air of superiority and wisdom of the Vision does not exist idly: he has stored in his memory more data than we mere mortals would ever be able to guard, process, and analyze. In other words, the “hardware” of the hero is far superior to our biological potential, so he analyzes everything faster.
We are still a long way away from creating conscious machines like Vision, but we have already experienced the potential of Machine Learning to automate a number of analytical processes. Organizations have lost the (manual) operational ability to process and analyze everything: that is why they need automation.
What are the benefits of Machine Learning in companies?
After all, how has this method been used to promote improvements in organizations? How can you help the data scientist? We are still far from having the support of intelligences like Vision, but, let’s agree, we are almost reaching the level of J.A.R.V.I.S.
Here are some examples of the practical use of Machine Learning in enterprises:
Machine Learning is widely used in financial institutions, health plan operators and insurers to aid in the detection of fraud. This has always been a problem in these markets – to give you an idea, health plan operators have already spent more than R$ 27 billion just by paying for irregular processes or covering fraud.
In this endless “universe” of data, machines come as a great ally, as they can process and analyze all of this, identifying patterns and applying solutions to the problem. By the way, MJV already has expertise in the area: we have developed a system focused on fraud detection for one of the largest insurers in the country.
Have you ever imagined predicting what your client wants and delivering what they need before they even ask for it? Yeah. Companies like Netflix and Amazon, for example, are investing heavily in this type of system – you should have already received the product or series suggestions from both companies, right?
These personalized offerings are based on Artificial Intelligence and Machine Learning, of course. The system is capable of collecting and crossing data to identify patterns of behavior and consumption, anticipating needs and delivering what you need most at the right time.
Internet of Things (IoT)
Autonomous cars and smart cities are already a reality and will be even more popular with the advent of 5G in the coming years. Companies already take advantage of these solutions to develop new products, services, processes and even strategies to get closer to their customers.
With so many things connected and exchanging data, it is clear that Machine Learning plays a key role. As we saw in the Iron Man post, with machine learning, we can automate data processing and analysis, something indispensable to keep up with this new moment Big Data: more expansive and faster.
Chatbot is the subject of the moment, after all, the impression is that we are talking more and more with the bots, is it not? David Marcus, Vice President of Products for Facebook Message, has already revealed that the number of active bots in Messenger has risen from 100 to 300 thousand in just one year.
These tools benefit greatly from Machine Learning processes. While they still need human support to really learn, conversational robots already perform a series of learning processes on their own, using the history of conversations with clients to continually improve service.
We cannot think of Machine Learning as a nested tool, intended only for data scientists. In fact, its impacts can be felt in all sectors of the organization.
If we think of all the benefits we have seen so far, we can conclude that the method supports any kind of process involving data processing and analysis. It is a support that helps reduce costs, ensure scalability and, above all, optimize internal processes.
The action plan for your company
Vision was the last hero in our series. Now is the time for action! After all, how do you implement Data Science in your company? What are the steps needed for this? How to identify needs? All of this will be explored in our next post. So stick around for that!
Most importantly, you have understood that Data Science provides a number of solutions for your company. We’re talking about a solution that helps you manipulate data to your advantage, exploring new markets, driving business value and, above all, profitability for your business.
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