Why is everyone talking about AI?

June 23, 2023

Artificial intelligence (AI) is the hottest innovation these days. The debut of ChatGPT earlier this year put AI in everybody's radar. Every single corporate press room is talking about AI. No one wants to stay behind or miss out. Is AI a temporary fad, or is it here to stay for the long run? 

AI is definitely not a fad. AI is here to stay.

AI can be seen as the third phase of the computer era. The first phase was standalone computers. Taking a man to the moon was perhaps the greatest achievement of standalone computers. The second phase of the computer era began with the worldwide web, or internet. Think of web pages and social media as the greatest contribution of the internet phase. AI is the third phase and will be ultimately characterized by an internet of smart robots. 

How smart can AI robots become? 

There is practically no limit to how smart the internet-connected AI robots of the near future can become. Think about C3PO and R2D2 from Star Wars. We are only about three decades away from that technology becoming a full blown reality in human society. By the 20250, robotic AI assistants should be the most valuable product in the marketplace. The opportunity to get involved in the industry is now. Invest time daily in learning more about AI. For example, read about neural networks and how they are powering current day AI.

What are neural networks?

Neural networks, also known as artificial neural networks (ANNs), are a class of machine learning models inspired by the structure and function of biological neural networks in the human brain. They are designed to recognize patterns, learn from data, and make predictions or decisions.

Neural networks consist of interconnected nodes, called artificial neurons or "units," organized in layers. The layers typically include an input layer, one or more hidden layers, and an output layer. Each neuron in a layer receives input from the previous layer and performs a computation, producing an output that is passed to the next layer. The connections between neurons are represented by weights, which are adjusted during the learning process to optimize the network's performance.

The key steps in the functioning of a neural network are as follows:

• Input: The network receives input data, which can be numerical, categorical, or even images and text.
• Forward Propagation: The input data is fed forward through the network, with computations performed in each neuron, using the weights assigned to the connections. This process is known as forward propagation, and it produces an output prediction.
• Activation Function: Each neuron typically applies an activation function to the computed output, introducing non-linearities into the network. Common activation functions include sigmoid, ReLU (Rectified Linear Unit), and tanh.
• Loss Calculation: The output of the neural network is compared to the desired or target output, and a loss or error value is calculated. The loss quantifies the discrepancy between the predicted output and the true output.
• Backpropagation Training: The network adjusts the weights of the connections to minimize the loss. This is done through a process called backpropagation, where the error is propagated backward through the network, and the weights are updated using optimization algorithms like gradient descent.
• Prediction or Decision: After training, the neural network can make predictions or decisions based on new input data. The network takes the input, performs forward propagation, and produces an output that represents the predicted result.

Neural networks excel at tasks involving pattern recognition, classification, regression, and even complex tasks like natural language processing and image recognition. They have been applied to various domains, including computer vision, speech recognition, recommendation systems, autonomous vehicles, and many others.

The field of neural networks has seen significant advancements over the years, with the development of different architectures, such as feedforward neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more recently, transformer models like the popular BERT and GPT models. These architectures and techniques have contributed to the success of deep learning, which involves training neural networks with multiple hidden layers, enabling the extraction of high-level features and representations from data.

Keep learning. AI is the present and the future.

Creatix.one, AI for everyone