Evolution of AI Agents : A Journey Through Time (Part 2)

In the last post, we saw evolution of AI Agents for first 50 years and all the innovation through these years set a solid foundation for further advancement of AI field. In this post, we will see how advancement in AI Agents (between early 2000 to 2019) not just the task executors but self learner from their own environment and then complete the task with success. This phase was fascinating progression of artificial intelligence that has forever altered our interaction with technology.

The Early Days: Robots Learning to Interact

Q1: Who set the stage for emotional AI, and how did it learn to interact with us?

In the late 1990s, the Massachusetts Institute of Technology (MIT) introduced Kismet, a robot designed to exhibit and perceive emotions, marking the advent of affective computing. Created by Dr. Cynthia Breazeal, Kismet could mimic human emotions through expressive facial features and respond to social cues, laying the groundwork for future AI to understand and interact with humans on an emotional level. This early exploration into affective computing showed that robots could go beyond mechanical tasks to engage in social interactions, learning from exchanges much like a child learns from its surroundings.

Q2: How did the concept of practical home robotics come to life?

In 2002, the world met Roomba, an autonomous robotic vacuum cleaner developed by iRobot. It was engineered to navigate the complexities of home environments autonomously, avoiding obstacles and adjusting to different floor types. The creation of Roomba brought the concept of practical home robotics to life, providing a glimpse into a future where household chores could be automated, saving time and effort for users. This innovation made technology a more integral part of everyday life, demonstrating the practical applications of autonomous robotics in the home.

Image Credit : Kindel Media

Q3: What advancements did ASIMO bring to humanoid robotics?

Developed by Honda in 2000, ASIMO (Advanced Step in Innovative Mobility) represented a significant leap in humanoid robotics. It was designed to walk, run, climb stairs, and interact with humans through voice and face recognition. ASIMO showcased the potential of robots to assist with daily tasks and interact with people in a variety of settings. Its development was driven by the desire to create robots that could help people, especially those with mobility challenges, thereby expanding the scope of what humanoid robots could achieve.

The Rise of Virtual Assistants

Q4: How did Siri change the way we use our smartphones?

Launched by Apple in 2011, Siri revolutionized smartphone use by integrating Natural Language Processing (NLP). It allowed users to perform tasks and get information through voice commands, adapting to individual preferences and learning from interactions. Siri’s development marked a shift towards more intuitive and conversational technology, making smartphones even more versatile and personal.

Q5: What did Alexa do to enhance our smart home experience?

Amazon’s Alexa, introduced with the Echo smart speaker in 2014, took voice-activated technology to new heights. It enabled users to control smart home devices, play music, get information, and more, using just their voice. Alexa’s ability to integrate with a wide range of services and devices made it a central hub for smart home management, demonstrating the potential of virtual assistants to make technology more accessible and integrated into our daily lives.

Game-Changing AI: Mastering Strategy Games

Q6: What is AlphaZero, and how did it master complex games like chess and Go?

DeepMind’s AlphaZero, introduced in 2017, marked a groundbreaking moment in AI. It could teach itself to play and excel at chess, shogi, and Go from scratch, using advanced neural networks and Monte Carlo Tree Search (MCTS). AlphaZero’s self-learning capability represented a significant advance in AI, showing that an AI could not only learn complex games without human input but also discover new strategies and play at a superhuman level. This development underscored the potential of AI to learn and excel in areas requiring strategic thought and planning.

Q7: How did AlphaStar raise the bar for AI in real-time strategy games?

AlphaStar, developed by DeepMind for StarCraft II, showcased AI’s potential in real-time strategy games. It could compete at a high level against professional players, managing resources, and executing strategies in a game known for its complexity. AlphaStar’s success in StarCraft II demonstrated the advancements in AI’s decision-making and strategic planning abilities, hinting at future applications beyond gaming.

Image via https://gigazine.net/

Conclusion:

As we look back on the evolution of AI agents, from emotional robots to strategic game masters, it’s evident that the line between human and machine continues to blur. These advancements are not just technological feats; they represent a new era of companionship, assistance, and competition. The journey of AI agents is a testament to human creativity and foresight, offering a glimpse into a future where AI enhances every aspect of our lives. As we forge ahead, embracing these innovations, we must also navigate the ethical landscapes they present, ensuring that as AI becomes more integrated into our world, it does so in a way that enriches rather than diminishes the human experience.

In the next post, I will cover LLM based AI Agents, their technology, application and how to build one.

Thanks for reading!!