Although artificial intelligence looks today like a fully formed technology—ready to transform the way we live, work and interact with the world—the reality is that its evolution has been a long journey, full of research, testing and breakthroughs that began decades ago. From the first steps in machine learning and natural language processing to the most recent developments, the history of AI is far from new.
Bill Gates may not have had AI in mind on that first day of 1975 when he founded Microsoft, but the fact is that many of the innovations the company has driven over the past 50 years have been key pieces on the road to today's era of artificial intelligence.
From the launch of its first operating system in 1985 to Gates's famous "Tidal Wave" memo in 1995, which anticipated the impact of the internet, Microsoft has shown a unique ability to adapt to change and always look ahead. That vision was also reflected in the pioneering work of Microsoft Research, such as its studies on Bayesian networks and the Z3 theorem prover, which laid important groundwork for advances in AI.
Today, as artificial intelligence becomes ever more embedded in our daily lives, it is worth looking back at the key moments that shaped Microsoft's approach. And, above all, understanding how the company plans to build a trustworthy AI platform, with tools and infrastructure designed for the future.
Bing takes the leap with natural language
It all started with Windows Live Search, but it was in 2009 that Bing marked a turning point. From its launch, the search engine included machine learning-based features that improved the user experience.
Among them were suggestions offered while a query was being typed and the "Explore" pane, which displayed related searches. These improvements did not come out of nowhere: they were built on the semantic technology of Powerset, a company Microsoft had acquired a year earlier, in 2008.
Project Oxford: the starting point for AI in Azure
Many of Microsoft's advances in artificial intelligence rest on a solid foundation: Azure. In 2015, the company launched Project Oxford, a set of intelligent technologies that allowed developers to build more advanced apps. The focus? Facial recognition, speech and language understanding.
Today, that project is known as Azure AI Foundry.
"A lot of this comes from Bing," explains Eric Boyd, corporate vice president of Microsoft Azure AI Platform. Boyd started out at Microsoft working on Bing Ads. "We built an entire infrastructure to train AI models, test them and see which one worked best. That foundation became the components we now offer with Azure AI."
Since then, Azure has powered key products such as:
- Conversational AI applications
- Microsoft Cognitive Services (tools for responsible AI)
- Azure OpenAI Service (language models + enterprise capabilities)
Today, more than 60,000 organizations use Azure AI Foundry. In addition, 65% of the companies on the Fortune 500 list have already adopted Azure OpenAI Service.
ResNet: the engine behind more powerful neural networks
In 2015, Microsoft Research introduced a key breakthrough: Deep Residual Networks, better known as ResNet. This new residual learning framework changed the rules of the game.
Its contribution? It improved the training of deep neural networks. Thanks to that, it became possible to build more complex architectures, with better performance and greater accuracy.
ResNet opened the door to many practical applications. Today, many of them are part of our daily lives.
"ResNet set the standard in computer vision," says Peter Lee, president of Microsoft Research. "If you use a self-driving car, it is powered by AI based on ResNet. If you get an MRI, that machine also uses ResNet technology."
A leap toward an AI that learns the way we do
Between 2015 and 2020, Microsoft reached an impressive milestone: it matched human performance in five key areas of artificial intelligence. Which ones? Conversational speech recognition, machine translation, question answering, reading comprehension and image captioning.
These advances marked a turning point. They allowed AI to learn in a way closer to humans: using multiple senses and multiple languages.
That progress led to the development of Microsoft's XYZ code. What does it mean?
- X: text in a single language
- Y: sensory signals (such as audio or images)
- Z: multilingual content
This combination mirrors the way we understand the world. And it is now part of Azure's AI services, helping companies build systems that are more powerful, more integrated and closer to the human way of learning.
