Intelligent, thinking, and acting computers: understand more about Artificial Intelligence and delve into the world of Machine Learning.
From the Alexa personal assistant, intelligent product recommendations on the internet, Waze route optimization, tags, and photo grouping on the iPhone, to getting answers to the most varied questions in ChatGPT and self-driving cars. You may not have realized it yet, but all these different products work with the use of Artificial Intelligence.
Despite having gained the spotlight more recently with the great innovations of Generative AI (with ChatGPT and Adobe Firefly), machines that can think and act like human beings, automating various tasks of our daily lives, are already part of our lives in many ways.
In this article, we will talk about how the Artificial Intelligence area has developed and become popular, about Machine Learning and Deep Learning, technologies that have driven the recent evolution of AIs, and their various applications in the business world. Good reading!
What is Artificial Intelligence: a brief historical summary
It is common to define Artificial Intelligence as the field of science that seeks to make a computer capable of simulating human intelligence. The development of technologies that allow an application to be able to emulate all human thinking is known today as Artificial General Intelligence and was the engine of the first explorations with AI and neural networks.
Over time, technology has evolved in particular themes, creating specialized applications for certain tasks. Then came the term Specific AI (also known as Restricted), which refers to intelligence capable of performing a task with high precision and efficiency.
This type of development allowed the creation of increasingly advanced and specialized models, which, combined, are capable of generating complex products. For example, it is through the joint work of Specific AIs for image recognition, language processing, neural networks, and automotive mechanics that the autonomous car was created.
Currently, it is more common to refer to AI in this context: that of an application capable of learning and operating complex tasks autonomously.
AI often works assertively (“I was looking for just this product and it popped up for me”). In others, it generates frustrations (“This ad has nothing to do with me”; “This translation is completely wrong” etc.).
The fact is that, although we have come a long way in success, Artificial Intelligence is still being developed and improved.
See a summary timeline about Artificial Intelligence:
- 1948: Alan Turing introduces the concepts of what would later be called Artificial Intelligence.
- 1956: A conference of scholars at Dartmouth College (USA) begins formal studies in the field of AI.
- 1965: ELIZA appears, the first expert system, a chatbot that simulated a conversation with a therapist.
- 1993: Polly appears, in a robotics project at MIT (USA) based on behavior patterns.
- 1997: A machine (IBM Deep Blue) defeats a human being (Soviet champion Garry Kasparov) in a game of chess.
- 2002: iRobot launches Roombla, the first autonomous robot that cleans the house.
- 2005: TiVo, a cable television company, introduces the first recommendation technology based on preference algorithms to the market.
- 2008: Google and iPhone launch voice recognition for searches
- 2011: Apple, Google, and Microsoft enter the era of voice search-based mobile recommendation apps; Siri appears, followed by Alexa and Google Assistant.
- 2012: Google deepens the use of neural networks in its products and trains an algorithm to “recognize kittens in YouTube videos”.
- 2013: Advanced Machine Learning and Deep Learning technologies emerge, driven by projects such as Google DeepMind and IBM Watson, among others.
- 2015: OpenAI is founded in San Francisco, raising $1 billion in initial investment, with a mission to accelerate the development of digital intelligence.
- 2016: Google DeepMind – AlphaGo defeats Lee Sedol in the game Go, a complex board game of Chinese origin. The algorithm learned the rules and strategies of the game by watching other matches and was able to beat the number 1 in the world
- 2020: OpenAI’s GPT-3 language model becomes capable of producing code, poetry, and other language-related tasks in a form that is almost indistinguishable from human form.
- 2022: ChatGPT, a chatbot developed by OpenAI using a more advanced version of the GPT-3 model called 3.5, is released to the public in November and gains worldwide attention.
- 2023: OpenAI signs a $10 billion partnership with Microsoft and launches GPT-4, the most advanced iteration of its algorithm, capable of interpreting images, among other features. Google announces Project Bard and other AI products to compete with OpenAI tools.
How does a Specific Artificial Intelligence work?
I’m sure that by now you’ve been able to identify that there’s a lot behind Artificial Intelligence as we know it today. Its construction involves years of research and the development of a series of algorithms (instructions in codes to be followed) and a framework (combination of tools).
Let’s now delve into two technologies that enable modern Artificial Intelligence: Machine Learning and Deep Learning.
Machine Learning
Machine Learning is a subcategory of Artificial Intelligence. It is the science of making computers perform actions without explicit programming, that is, algorithms learn from data to guide decisions.
Or, in other words, the ability of computers to learn on their own, from user interaction and observation.
Want some examples?
- Personalization of content from Netflix, Spotify, and Amazon Prime Video.
- Uber with blocking trips considered dangerous, with the definition of the best route and estimated time of arrival.
- Ads targeted according to user preferences and habits on Google, Instagram, and Facebook.
- IBM’s Watson Health, is a huge database of the health of people around the world that guides medical decisions.
Deep Learning
In free translation, Deep Learning means deep learning. It is a Machine Learning technique that uses neural networks to perform classification tasks.
In summary, neural networks are the simulation of the operation of neurons by computational units. Do you remember when we told you that Google announced an algorithm capable of finding cat videos on YouTube? It was the beginning of the popularization of Deep Learning.
Want some examples?
- Speech recognition of virtual assistants.
- Image/facial recognition as a security action in banks, for example.
- Satellite mining done in Australia, where geologists were able to use mineral indices integrated with algorithms to find trace minerals (like gold) deep in the Earth’s crust.
- Medical discoveries such as that of eye diseases through guidance based on historical information.
- Pattern recognition for self-driving cars such as license plates and pedestrians.
Machine Learning and Deep Learning: what do you need to know
As we have seen so far, Machine Learning is the use of algorithms to organize data, recognize patterns and make computers learn from models and generate insights without the need for pre-programming. Simply put, it’s the computer looking for answers without being programmed to do so.
Deep Learning is the part of machine learning that mimics the neural network of the human brain. The data is subjected to several layers of non-linear processing to simulate the way humans think. Thus, the machine becomes capable of recognizing images and speech; and performing advanced tasks without a human around. Some of its most advanced applications are in image recognition, and it is also the technology used at the base of ChatGPT.
Deep Learning algorithms are often considered evolutions of Machine Learning algorithms because they are more efficient, given their operation similar to our neural network.
With these two solutions, the machine absorbs knowledge through experience, avoiding the need for a human being to specify and/or program the tasks to be performed.
Deep Learning is Machine Learning. But, not all Machine Learning is Deep Learning. And that’s why these concepts are so confusing. Both are algorithms with data analysis capabilities, learning and using them to make decisions in much less time than a human being would have. And AI contains Machine Learning, which contains Deep Learning.
Understand better in the table below:
 | Machine Learning | Deep Learning |
| What it is | Computers perform actions without being programmed to do so. Human beings teach the main patterns and the machine works from them, recognizing and labeling new patterns (supervised learning). | Machine Learning algorithms built from the principle of neural networks, which imitate the way human neurons work. There is no human training; the machine learns on its own to detect patterns (unsupervised learning). |
| How it is | We supply the algorithms with data. With this, they begin to learn on their own, make predictions, and guide decisions. | It works like a mind of its own. It captures Big Data and, by overlaying non-linear layers of data processing, arrives at the desired results. |
Main differences between Machine Learning and Deep Learning
Now, let’s get straight to the point to understand the differences between these two very important sciences:
| Machine Learning | Deep Learning |
| Automated to some extent, it needs the “kick off”. | Autonomous, learns without much guidance. |
| Uses structured data, which requires less data. | Need more data. So it grew up in the era of Big Data and better-performing computers. |
| It does not apply to solving complex problems. | It has more complex applications like chatbots, fraud detection, virtual assistants, etc. |
| It doesn’t always correct itself. | Itself corrects. |
How are these technologies used in practice?
The applications of these technologies are very wide. They go through industry, retail, logistics, sales, marketing, and even health. Below are some of the most common uses in broad business activities:
More effective sales
With AI, there is a productivity gain in sales. The seller saves time and can focus on other tasks to prioritize sales. There may be, for example, the automatic capture of customer data, recording of calls, and navigation. Thus, the seller does not need to fill in anything.
With this, the systems suggest dates for the next contact and even the subject and approach. There is even the possibility of predicting whether the contact will convert into sales (or how far it is from that).
Fast customer service
For customer service, the system determines which sector the customer should be forwarded to, saving time for both the attendant and the customer. AI even recommends solutions, which increase productivity and reduces service time.
There are also self-service systems, preferred mainly by the new generations. These tools reinforce security, for example, by recognizing the customer’s face for access or by identifying, based on a photo taken by the customer, a product that has a problem.
Accurate marketing
For marketing, AI uses are even more powerful. For example, it is possible that before launching a campaign, professionals already know which customers are more likely to click, which will unsubscribe, and which will not open, through a predictive analysis based on historical data.
More security
Facial recognition is a secure Machine Learning (and Deep Learning) system that uses a database of faces. To grant access, the system identifies similarities with the registered face, generating an accurate result. Ideal for Internet Banking and other online access.
Quick translation
Simultaneous and automatic translations, using neural networks, are currently made even from images.
Resolution of everyday tasks
Virtual assistants such as Alexa, Siri, and Google Assistant work with Deep Learning. They recognize human speech and perform tasks quickly, making everyday life easier.
Conclusion
The world of Artificial Intelligence is very broad. As we’ve seen, the origins of this technology date back to the beginning of computing, but in recent years it has grown exponentially, becoming both more refined and accessible.
Many applications for Artificial Intelligence are at an early stage, while others are already incorporated into our daily lives. The fact is that its use, driven by Machine and Deep Learning algorithms, has enormous business potential.
Therefore, we will see more and more AI-based products emerge, or AI incorporations into existing products, in the most diverse segments. Accompanying its evolution is undoubtedly a competitive differential that will mark not only the technology market but the most diverse industries in the coming years.
We hope that this article has helped to broaden your knowledge and clarify doubts and preconceptions about this technology. To the next!
