Article published on August 10, 2023 on Hubvisory
It's impossible not to have heard about it, AI is invading the web. While most users use it to generate "cute" animal images in a "Pixar style", others seek to explore the potential and opportunities generated by these tools.
It is interesting to anticipate the impacts on the product sector and adapt as quickly as possible to take advantage of these new technologies..
Artificial intelligence is a field of computer science that aims to develop machines capable of performing tasks that normally require human intelligence. This can include tasks such as speech recognition, computer vision, decision-making, and problem-solving.
On the other hand, machine learning is a branch of AI that focuses on using algorithms to enable machines to learn from data, without being explicitly programmed for each task. Machine learning uses statistical techniques to enable computers to recognize patterns in data and make decisions based on them.
AI and machine learning are therefore cutting-edge technologies that allow machines to learn from data and make autonomous decisions. Companies can use them to analyze data about users, such as purchasing habits, feedback, or online reviews. By analyzing this data, companies can better understand users' needs and improve their product designs.
The first work on AI was carried out in the 1950s, with pioneers such as Alan Turing and John McCarthy laying the groundwork for AI theory. However, at that time, computers were not powerful enough to allow significant advances in the field.
During the 1960s and 1970s, researchers worked on algorithms and models to help machines understand natural language, play strategy games, and solve complex mathematical problems. However, these approaches still did not allow for more complex tasks.
In the 1980s and 1990s, advances in chip design and machine learning theory allowed researchers to make progress in AI development. Machine learning techniques began to be used to analyze large amounts of data and to solve complex problems in areas such as speech recognition and image analysis.
In the early 2000s, the emergence of the internet allowed for the collection and sharing of data on an unprecedented scale, leading to an explosion in the use of AI and machine learning in online applications. Recommendation algorithms, chatbots, and personal assistants such as Siri and Alexa began to become commonplace.
This explosion of collectible data has allowed significant advances in the areas of deep learning and natural language processing in recent years. Deep neural networks have become more efficient in image and sound recognition, as well as natural language understanding. Today, AI and machine learning are widely used in fields such as healthcare, finance, retail, and manufacturing. Deep learning algorithms are used for facial recognition, fraud detection, and market trend prediction, among others. This explains the recent excitement and democratization of these technologies.
Currently, there is no consensus on the number of AI categories, with some classifications grouping different AI techniques into only a few categories, while others count several dozen.
However, several major categories can be identified, with deep neural networks (Deep Learning) at the top of the list, widely used by tech giants such as Microsoft, Facebook, and Google. The latter uses them for image recognition in Google Photos, automatic translation in Google Translate, and sentiment analysis in Google News. Microsoft uses them for speech recognition in Cortana and image recognition in Bing. Facebook uses them, among other things, for face detection in photos and language recognition in Messenger.
We also find natural language processing, which is also familiar to us, as it is used by companies such as Amazon, IBM, and Apple. Amazon uses it for voice commands in Amazon Echo, customer service, and product recommendations. IBM uses it for textual data analysis in IBM Watson, and Apple uses it in its voice assistant.
Finally, more recently, reinforcement learning, which is used by Tesla, OpenAI, and DeepMind, is making its appearance. Tesla uses it especially for the autonomous driving of its cars. OpenAI and DeepMind use it for games and simulations.