Demystifying AI, ML, DL, and Generative AI — Oracle Cloud AI Foundations Associate Cheat Sheet
I recently took the Oracle Cloud AI Foundations Associate exam and found it very useful to get a clear understanding of the various trending AI topics, including Generative AI.
This article aims to provide a cheat sheet of the concepts I learned during the course related to Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Generative AI (Gen AI).
Artificial Intelligence
AI refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. The goal of AI is to develop systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
AI systems use various techniques, including machine learning, natural language processing, and computer vision, to analyze and interpret data, make decisions, and improve their performance over time.
Machine Learning
Machine learning, is a subset of AI, involves training algorithms on large datasets to enable them to make predictions or decisions without being explicitly programmed.
ML algorithms can be categorized into supervised learning, unsupervised learning, and reinforcement learning. They are trained on data to recognize patterns and make predictions or decisions. For example, spam filters that learn to classify emails as spam or not based on user feedback.
Deep Learning
DL is a specialized field within ML that involves neural networks with many layers (deep neural networks). It aims to model high-level abstractions in data using multiple processing layers.
DL has been particularly successful in tasks such as image and speech recognition, natural language processing, and playing games. For example, Convolutional Neural Networks (CNNs) used in image recognition or Recurrent Neural Networks (RNNs) used in natural language processing.
Generative AI
Generative AI refers to a form of artificial intelligence that has the ability to understand, learn, and apply knowledge across diverse tasks, similar to human intelligence. It is a more advanced and theoretical concept.
Generative AI possess a broad range of cognitive abilities, allowing it to perform human like intellectual tasks. Gen AI models has the ability to learn patterns in a given data set and use that knowledge to create new data. The model can generate both visual and text content.
Models Used in Generative AI
GAN (Generative Adversarial Network)
GAN is a type of generative model in machine learning where two neural networks, a generator, and a discriminator, are trained simultaneously through adversarial training. The generator creates new data instances, and the discriminator evaluates them. The goal is for the generator to produce data that is indistinguishable from real data.
GANs are widely used for image and video generation, style transfer, and other tasks where the generation of realistic data is desired.
LLM (Large Language Model)
LLM generally refers to a language model that is large in scale, often in terms of the number of parameters in the model. These models are trained on vast amounts of text data and can generate human-like text, understand context, and perform various natural language processing tasks.
Large Language Models, such as OpenAI’s GPT (Generative Pre-trained Transformer) series, are used for tasks like language translation, text completion, question answering, and more.
Transformer
The Transformer is a type of neural network architecture introduced in the paper “Attention is All You Need” by Vaswani et al. Transformers use a mechanism called self-attention to process input data in parallel, making them highly effective for tasks that involve sequential or parallel processing.
Transformers have become a fundamental architecture in natural language processing and have been used in various models, including BERT (Bidirectional Encoder Representations from Transformers) for language understanding, GPT for language generation, and more.
Oracle Cloud Infrastructure (OCI) AI services
OCI offers a variety of pre-trained AI services that allow developers to easily add AI capabilities to their applications without the need for deep expertise in machine learning. These include services for speech recognition, language understanding, image analysis, and more.
The OCI AI services are:
OCI Language
Language allows you to perform sophisticated text analysis at scale. Using the pretrained and custom models, you can process unstructured text to extract insights without data science expertise. Pretrained models include sentiment analysis, key phrase extraction, text classification, and named entity recognition. Additionally, you can translate text across numerous languages.
OCI Speech
Speech can transcribe customer service calls, automate subtitling, and generate metadata for media assets. Speech harnesses the power of spoken language enabling you to easily convert media files containing human speech into highly exact text transcriptions.
OCI Vision
Vision is a serverless, multi-tenant service, accessible using the OCI Cloud Console, or over REST APIs. You can upload images to detect and classify objects in them. If you have many images, then you can process them in batch using asynchronous API endpoints.
OCI Document Understanding
Document Understanding allows you to extract text, tables, and other key data from document files through APIs and CLI tools. With Document Understanding, you can automate tedious business processing tasks with prebuilt AI models, and customize document extraction to fit your industry-specific needs. You can upload documents to detect and classify text and objects in them.
OCI Anomaly Detection
Anomaly Detection provides you with a set of tools to identify undesirable events or observations in business data in real time so that you can act to avoid business disruptions. This service is multi-tenant that analyzes large volume of multivariate or univariate time series data.
These are some of the key topics discussed in the Oracle Cloud AI Foundations Associate (2024) course. There is also a section on Oracle’s AI infrastructure and about responsible AI.
Overall, users new to AI or users wishing to know what’s new in the field of AI and OCI can explore this learning path. The Oracle Cloud AI Foundations Associate certification is offered for free by Oracle.
This course can be completed in about 5 hrs and thereafter you can attempt the online exam to get the certificate and badge.
After you complete this course, you can take up the Oracle Cloud Infrastructure 2024 Generative AI Professional
Exam. Check out the cheat sheet I have prepared for this certification.
