From AI to AGI: The Next Evolution in Intelligence

Today I woke at 7:20 am, It was hectic day, I had full classes today, so didn’t study so much. I also did some of the editing work in Canva. I listened a podcast about ChatGPT, I will share it’s learning in my tomorrow’s blog.

Likewise, I read about Artificial General Intelligence, the evolution of AI.

AGI is Artificial General Intelligence.

AGI is the hypothetical intelligence of machine, which posses the ability to understand intellectual task that human being can. It is the type of Artificial Intelligence, which aims to mimic the human brain. It will be able to perform those kinds of tasks for which they are not trained for.

Currently, Artificial Intelligence functions on the pre-determined parameters.

For example, if I have trained the model with the data of images and just the videos of animals or something else, then It can’t create a website or essay.

Now ChatGPT doesn’t have actual emotions, they are not conscious, they are just good at pretending and better the model will become at pretending. As the humans have evolved, we can similarly evolve the Artificial Intelligence. The efforts to build AGI system is ongoing.

Technologies that are driving Artificial General Intelligence research:

-Deep Learning, it focuses on training neural networks with multiple hidden layers to extract and understand complex relationship from the raw data.

-Generative AI, it can produce unique ad realistic content from the things it has learned. It trains with massive datasets, which makes them to respond like human with text, audio or visuals.

-NLP, it’s Natural Language Processing, which helps computers to understand and generate the human language.

-Computer Vision, allows extracting, analyse and comprehends the information in the form of visuals. Deep learning helps computer vision to automate large-scale object recognition, classification, monitoring, and other image-processing tasks.

-Robotics, In AGI, robotics allow machine intelligence to manifest physically.

Challenges in AGI:

Achieving AGI requires a broader spectrum of technologies, data, and interconnectivity than what powers AI models today. Creativity, perception, learning, and memory are essential to create AI that mimics complex human behaviour.

Computer scientists face some of the following challenges in developing AGI.

Make connections

Current AI models are limited to their specific domain and cannot make connections between domains. Humans can apply the knowledge and experience from one domain to another.

For example, educational theories are applied in game design to create engaging learning experiences. Humans can also adapt what they learn from theoretical education to real-life situations.

Emotional Intelligence

Creativity requires emotional thinking, this cannot be replicated by neural networks as of now.

For example, we humans always respond to a conversation based on our emotional senses, but the NLP they generate the output based on their trained datasets.

My today’s learning is:

Don’t be afraid of walking alone, if you are doing so, that doesn’t mean you are in the wrong path because fewer people are following that path.