Artificial Intelligence (AI) has come a long way since its first description in the mid-50s in the last century. We have already covered how AI evolved from a basic computing algorithm to now becoming the most highly ranked disruptive technology of the 21st century and powered by the science of Internet, Robotics Automation, and Big Data Science, the AI field is slated to further achieve the pinnacle of scientific superiority.
AI courses are evolving with the industry demands. If you know what AI is, you already have some idea of what technologies are expected to rule the IT and AI ML trends in the coming years. Check out our latest blog on AI courses that highlight the latest application of AI ML.
AI hardware / AI Accelerators
Popularly referred to as the AI optimized hardware, this is a special family of computer systems that extensively use and integrate the traditional AI based hardware, networking, and AI ML applications under a single umbrella. With the rise of GPUs and FPGAs, we are seeing a wide scale interest among all computer chip making companies, to embrace AI as a core technology to boost computing performance.
If you are really interested in knowing about AI optimized hardware and how these work, please check out the detailed project report on IBM’s RaPiD AI Accelerator program.
AI-based Sentiment Analysis
The whole fan following resulting in today’s AI ML dominance is due to the super-computing power of Affective Computing capabilities. What is Affective Computing?
You can call it a simulation of AI behavior, speech, and motion control using computer systems, NLP, sensors, and emotion tracking tools. Affective Computing was first widely discussed in the mid 90s, and since then numerous Internet Marketing and Advertising companies have latched onto the brilliant idea of interaction with humans online using what we now know as “Virtual Assistants”, or “Chatbots”, face detection models, emotion classification, and CV machines.
Distributed Artificial Intelligence (DAI)
DAI is also referred to as Decentralized AI, and it is widely used as a sub-field within AI research for building various decision-making systems, such as LIDAR based location data management, telemetry, AIOps, and financial data management.
In top AI courses, you would learn the basics of AI algorithm models and techniques that are conventionally used to build these advanced computing systems. Some of these techniques are K-nearest Neighbour, Gaussian Mixture Model (GMM), Support Vector Machines (SVM), ANN / CNNs, and LDC models.
No wonder, if the last few decades belonged to the Internet companies, the modern era totally belongs to the league of data science capability providers who specifically delve into AI and Machine Learning services.