How has AI evolved over the years?
The advancement of Artificial Intelligence (AI) has profoundly impacted technology, especially in our digital age. Since the 1950s. AI has slowly evolved with steady development to become an essential component of modern consumer products.
Today, AI-powered algorithms are used in nearly all aspects of digital technology, ranging from UX design to advanced robotics and even space exploration. Their impact on our daily lives is hard to overstate, leading to remarkable innovations in science. So, how did AI evolve and where does it go from here?
Historical Evolution
Before the 20th century, AI as a concept was difficult to fathom, but it still existed. While ideas of digital technology didn’t exist yet, an interest in automation and the creation of sentient life can be traced back as far as 3,000 years ago in the works of Homer where descriptions of Greek gods using automated machinery were first illustrated; yet our modern vision of AI is much more clear:
1950s: The Turing Test, created by Alan Turing was a pivotal moment for computer science that proposed a method of measuring a machine’s ability to exhibit human-like intelligence. Later in the decade, researchers would come together to form the Dartmouth Conference which first explored, and coined, the concept of Artificial Technology.
1960s: Robotics were just coming into their infancy during the 1960s with companies like GM purchasing highly innovative robotic arms produced by Unimate for industrial manufacturing. Natural language processing (NLP) also took a major step forward with ELIZA, a language model that mimicked certain therapy strategies.
1970s: AI development slowed down during the next decade due to the limitation of software at the time. These setbacks halted research and funding while new technology was developed. Humanoid robots, however, were just getting started in Japan at the end of the decade.
1980s: Expert domain systems saw a revival following new innovations in software and computer technology. This led to the emergence of Machine Learning, a powerful subset of AI that is capable of executing algorithms that can learn and improve themselves based on collected data.
1990s: Neural networks came into the scene following the development of machine learning. This advancement helped AI mimic the human brain, creating more capacity for self-learning using backpropagation and reinforcement learning. The internet was also just becoming publicly available at the same time, giving researchers new scientific avenues to explore.
2000s: At the turn of the 21st century, the internet had caused an explosion in data, creating huge amounts of information for neural networks to collect and learn from. This led to Deep Learning, a subfield of Machine Learning, that uses multiple layers of nodes to process information.
2010s: Applications for AI technology using Deep Learning became more noticeable with the growing popularity of smartphones and user-created content generated on social media, causing tech companies like Google and Microsoft to expand their research into AI with programs like DeepMind and Azure.
2020s: While still in the midst of the 2020s, our current decade has started off with a bang as the majority of the world’s population began to rely more heavily on the internet in the wake of COVID-19. Generative AI also took major leaps with the release of ChatGPT, causing public interest in AI to explode.
Major Milestones in AI
Advancements in AI can be measured in many ways, showcasing the growth of AI from simple board games to advanced integration into our society. Here are a few milestones in AI’s history:
Deep Blue defeats Garry Kasparov: Chess is a popular proving ground for AI because of its structured rules and reliance on logical strategies. So, in the late 1990s, IBM developed a supercomputer called Deep Blue which defeated the reigning world chess champion Garry Kasparov. The event proved that artificial technology could execute tasks that many thought only humans could do.
Tesla popularizes self-driving cars: After Elon Musk’s acquisition of Tesla in the mid-2000s, the company slowly grew into a massive technology company that fused automobiles with AI. Using advanced camera vision and sensors, the self-driving cars eventually became roadworthy and grew the company into an AI behemoth.
AlphaGo beats the world champion in Go: Years later in the middle of the 2010s, Google recreated Deep Blue’s achievement with DeepMind and its subsequent AlphaGo bot which defeated Lee Sedol, the world’s Go Champion in a series of five games.
OpenAI releases GPT3: Before the launch of OpenAI’s ChatGPT3, generative AI was still a fledgling technical concept that the general public was unaware of. However, the launch of GPT3 and GPT4 became the fastest program to 100 million users, beating TikTok’s previous record of 6 months in only a matter of weeks.
Current State and the Future
AI has become a hot topic since the launch of ChatGPT, causing many to realize that AI has become a lot more developed than previously realized. The advent of Generative Adversarial Networks and natural language processing are breaking new ground in how AI can be used, but the bigger picture so far has been the possibility of Artificial General Intelligence (AGI).
While we are far, far away from the development of sentient AI, there is no doubt that, at some point in our future, it will exist and it is up to us to prepare for it. However, until then, other exciting prospects in quantum computing and Edge AI are also pushing the limits of what is possible.
