Why Now AI and What's Not AI ?
Why AI Now ?
Everyone has this question in mind why AI now ? Lets understand in Detail further the reasons behind AI .
Data Explosion
Explosion of data is a result of largely due to the rise of computers . The Internet and technology getting capable of capturing data from the world we live in e.g. Location data , Interests Data , Medical Data , Fitness Data etc. These days Mostly all Business process and Logic are linked with data and using AI is the best way now to train data Models and provide necessary Data Analytics and Dashboards and much more with the AI generated results .
- Experts have predicted 1000s of % data increase in couple of coming years
- On average, companies use only a fraction of the data they collect and store. In short, if a company is already struggling to store and analyze its own data now . AI will increase this Data usage rate .
- Rise of Big Data and increased Compute powers allows training complex AI Models .
Algorithm Advances
New Techniques like Deep Learning have unlocked more capable AI systems .
Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain. Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions
Three following types of deep neural networks are popularly used today:
- Multi-Layer Perceptrons (MLP)
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
AI algorithms are instructions that enable machines to analyze data, perform tasks, and make decisions
Hardware Improvements
GPUs and specialized chips provide the necessary processing ability to implement advanced algorithms .
An AI chip is a specialized integrated circuit designed to handle AI tasks. Graphics processing units (GPUs), field programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) are all considered AI chips.
As the cutting edge keeps moving and keeps changing, "then the hardware has to change and follow, too.”
GPUs are most often used in the training of AI models. Originally developed for applications that require high graphics performance, like running video games or rendering video sequences, these general-purpose chips are typically built to perform parallel processing tasks.
In short this decade is more focusing on the Algorithms along with Compute and Data unlike past few decades
What’s NOT AI ?
- Rule Based Systems : Encoding Human domain knowledge as conditional rules doesn’t equate to machine intelligence .
- Simple Optimization : Mathematical Optimization techniques like Linear programming is not AI .
- Data Analysis : Basic business intelligence and reporting of data is not intelligent on its own .
- Pre-Programmed Behaviour : An Agent acting via hard coded rules lack the learning and generalization abilities associates with AI .
- Static Predictions : Statistical Model making on-off predictions without adapting is not considered AI .
Thanks .
Network and Cloud Automation Professional with 15 years of experience in Development | Testing | Deployment | Support | Automation on various Technologies like IP-Routing, Cloud, Programming, Containers, Kubernetes, Telemetry, Orchestration, Network-Programmability, YANG, TextFSM, Jinja, RestAPI , Terraform , AWS , Ansible , Cisco NSO , observability and so on .
https://www.linkedin.com/in/ranjeetjangra/
