Artificial intelligence today is the real hype of the business world. All this euphoria has created a distorted understanding that this kind of technology works miracles. But not quite.
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Robots and systems that work autonomously still need to learn a lot to finally turn science fiction into reality. Before undertaking high-performance solutions, companies still need to do a steady job and assess in which areas it will be worth investing in technology.
So before you implement artificial intelligence to improve your productivity and reduce costs, calm down.
The scenario of artificial intelligence
It is inevitable to say that this ecosystem has been growing at exponential rates. So much so that analysts are already discussing a possible future bubble, such as the Internet, in the 1990s. The global artificial intelligence market was estimated at $ 7.35 billion by 2018, according to the Statistician . This value includes applications such as:
- image recognition and writing
- speech processing
- identification of objects
The prediction about the future of artificial intelligence is constant. And it’s not risky to bet that among the next unicorns (companies that hit $ 1 billion), there will be startups with technology-based models. In China, 48% of total startup contributions in the world in 2018. While the United States accounted for 38%, putting more fuel in the war for the dominance of new technologies caught between the two.
Many of the biggest technology companies, such as Google and Amazon, are already buying startups and developing artificial intelligence products and solutions. In the article ” Things That Are in Deep Learning” by data scientist William Vorhie , the birth and advancement of CNN (Convolutional Neural Network) is highlighted. In addition to how technology will replace RNN (Recurrent Neural Network) in translation programs with high potential.
He also points out a number of obstacles that still need to be overcome in order for AI to be effective. Neural network and artificial intelligence models can perform tasks well in environments in which all variables are controlled. And that’s what’s causing a misperception.
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Introduction of technology
Companies willing to deploy models of the neural network mistakenly understand that they will be able to magically launch automation models in their operations. To get the implementation done right, there are five important steps that must be followed.
- Identify what pain the neural network model will cause.
- Map all processes and seek to understand how to replicate the business rule in algorithms.
- Be prepared for problem solving . In artificial intelligence systems, thousands of algorithms are generated that generate the same result. What impacts on results is productivity , accuracy and development time .
- Prepare the company’s infrastructure . The impact of technology is great in both the technical and the human resources aspects. Therefore, it is important to implement change management based on the results achieved.
- Do not forget to do maintenance . An IA system is different from conventional software that a company installs. An AI model evolves naturally, which leads to new results, understandings and accuracy. So this requires ongoing maintenance and throughout the entire system life cycle.
Do not wait to start your walk to make artificial intelligence an important strategy of your business. But do not forget to control your expectations . It is important to be aware that the machines are still being tested and only in a few years from now will they be able to realize their future dreams.