Introduction to AI Enabling Technologies PDF

INTRODUCTION TO AI ENABLING TECHNOLOGIES OVERVIEW OF AI TECHNOLOGIES  AI (Artificial Intelligence) helps machines think and learn like humans. AI development relies on two key components: 1. Software: The programs and algorithms that "teach" machines how to learn. 2. Hardware: The physical equ...

INTRODUCTION TO AI ENABLING TECHNOLOGIES OVERVIEW OF AI TECHNOLOGIES  AI (Artificial Intelligence) helps machines think and learn like humans. AI development relies on two key components: 1. Software: The programs and algorithms that "teach" machines how to learn. 2. Hardware: The physical equipment (like computers and chips) that processes large amounts of data. SOFTWARE TECHNOLOGIES FOR AI Programming Languages: Special languages help machines understand instructions. Common ones for AI are: Python: Easy to learn and widely used for AI. R and Java: Other languages often used in data analysis and AI. AI Libraries: These are pre-built tools that help make AI easier. Examples: TensorFlow and PyTorch: They help with deep learning (teaching computers to recognize images or understand speech). Scikit-learn: Useful for simpler machine learning tasks.  CLOUD-BASED AI Cloud allows companies to use powerful AI without owning expensive hardware. Cloud platforms: Google, Amazon, and Microsoft offer services that can run AI models on their computers (cloud). Benefits: Cheaper: No need to buy supercomputers. Scalable: Can handle small or very large AI tasks. Examples: Google’s AI services, Amazon’s Alexa, and Microsoft’s chatbots all run on cloud AI. COMPUTER HARDWARE HARDWARE TECHNOLOGIES FOR AI AI needs to process large amounts of data quickly (think about how many images AI needs to learn to recognize a cat). Specialized hardware is needed to make this fast and efficient. The main types of hardware used are: CPUs (Central Processing Units): Basic processors in computers.They handle most tasks but are slower for advanced AI tasks. GPUs (Graphics Processing Units): These are faster for AI because they can process many things at once. Used for tasks like training AI models (e.g., teaching computers to recognize faces). TPUs (Tensor Processing Units): Special chips designed by Google to handle AI tasks even faster than GPUs. Used for things like speech recognition or natural language processing. INTERNAL HARDWARE DIFFERENCE BETWEEN GPUS AND CPUS A CPU, or central processing unit, is a hardware component that is the core computational unit in a server. It handles all types of computing tasks required for the operating system and applications to run.  Example: The processor in your laptop or phone, such as an Intel Core or AMD Ryzen, is a CPU. A graphics processing unit (GPU) is a similar hardware component but more specialized. It can more efficiently handle complex mathematical operations that run in parallel than a general CPU.  While GPUs were initially created to handle graphics rendering tasks in gaming and animation, their uses now extend far beyond that.  Common examples of GPU :  NVIDIA GeForce: Popular in gaming, but also commonly used in AI tasks.  AMD Radeon: Another brand of GPU that’s often used for both graphics and AI workloads. DATA STORAGE FOR AI AI models need lots of data to learn and make predictions. Storage: AI data is stored in databases or cloud storage. Example: When you search for images on Google, the AI uses a massive database of images to show you results. SSD vs. HDD: SSDs (Solid State Drives) are faster and better for AI tasks compared to HDDs (Hard Disk Drives). HOW AI TECHNOLOGIES ACCELERATE BIOLOGICAL RESEARCH  AI Software  Deep Learning Models: Tools like TensorFlow and PyTorch are used to analyze massive biological data, from gene sequencing to disease prediction.  AI Hardware:  GPUs: Accelerate tasks like analyzing genomic data and processing complex biological images (e.g., MRI scans). THANK YOU