Artificial intelligence (AI) has become one of the most fascinating areas in today’s fast-paced world. We know that artificial intelligence can seem overwhelming in many ways, especially with so many buzzwords. So, I started a new blog series called AI Made Easy for Beginners.
In this blog, we will walk you through the basics of AI. And explain terminologies in a friendly and simpler manner with examples. This beginner-friendly guide aims to help you get started on your AI project. So, if you’re looking to enhance your career, or are just curious about this transformative technology, you’re in the right place.
What is AI? Made Easy
AI, or Artificial Intelligence, in a nutshell, is a technology that aims to teach computers to perform tasks that typically require human intelligence. This includes things like speech recognition and decision-making. And even some very human activities such as writing blogs, playing chess, etc. Technical speaking, AI imitates human behavior by relying on machines to learn and execute tasks without explicit directions on what to output. To achieve this, AI needs a massive volume of data to train machine learning models.
Big data, with its high volume, velocity, and variety, often referred to as the “3Vs“, is a critical ingredient in AI. It provides the raw ingredient for machine learning models to learn from and make predictions. These large datasets include diverse information from sources like social media posts, emails, text messages, sensors in our homes, cars, public infrastructures, and even transaction records.
Data scientists use those data to train machine learning models, allowing these models to recognize complex patterns and relationships within the data and make predictions for what would happen in the future. This process is like teaching it to recognize cats without explicitly telling it what a cat looks like.
Since Machine learning involves the ability to learn from data without being explicitly programmed, this means the machine can analyze data and identify patterns based on those data points.
From that aspect, deep learning is a subset of machine learning. Deep learning employs neural networks mirroring the human brain. Neural networks are computing systems that’s mimic the human brain. These networks can learn from large amounts of data for tasks like image and speech recognition.
Deep learning leverages layers of algorithms in the form of artificial neural networks to return results for more complex use cases. The neural networks dynamically fine-tune internal parameters to enhance comprehension, allowing them to perform tasks such as image recognition, speech analysis, and natural language processing (NLP).
The following YouTube shorts are Unraveling the Secrets of AI, ML, and DL in Just 50 Secs!
Applicable areas in AI Made Easy
So, let’s take a look at the applicable areas of AI in this section :
Computer vision is a subcategory of AI that refers to the capability of interpreting the world visually through various inputs such as live cameras, video, and images. It has the following use cases :
Image classification is an area of machine learning where a model is trained to identify images based on what they contain. The principal is to teach a computer to recognize and categorize different pictures. Imagine you have vacation photos of beaches, mountains, and cities. Instead of sorting them manually, you train a model by showing it categorized images. Once trained, the model can classify new images into these categories. This simplifies organizing digital photos and has potential in fields like medical imaging and autonomous vehicles. It’s a great example of technology making our lives simpler and more efficient! As a matter of fact, it is also widely used in today’s traffic monitoring solution, New York city, for instance.
Image analysis refers to the field that combines machine learning models with advanced techniques to extract meaningful information from images. This approach enables the generation of tags or labels that aid in cataloging images and descriptive captions that summarize the depicted scene.
Such as a social media platform, through image analysis, you can train a machine learning model to automatically identify key elements in each image, such as mountains, rivers, and trees. This information can be used to generate tags that facilitate easy searching for specific images.
Object detection machine learning models are designed to classify and identify individual objects within an image using bounding boxes. These models play a crucial role in various applications, such as traffic monitoring systems ( together with image analysis, as mentioned earlier ). For instance, a traffic monitoring solution can utilize object detection to accurately identify and locate different classes of vehicles on the road. This enables efficient analysis of traffic patterns, congestion management, and even automated traffic control.
Semantic segmentation involves classifying individual pixels in an image based on the object they belong to. This technique enables a more detailed understanding of the image by assigning each pixel a specific label.
For instance, in medical imaging, semantic segmentation can assist in identifying and segmenting different organs or anomalies within an MRI or CT scan. This enables accurate diagnosis and treatment planning by providing detailed insights into the specific regions of interest.
Let’s go back to the example of the traffic monitoring solution (once again!), semantic segmentation can be applied to overlay traffic images with “mask” layers. Each vehicle is highlighted with a specific color, allowing for easy differentiation and analysis of different types of vehicles on the road. This powerful technique enhances the accuracy and granularity of image analysis, and it opens up possibilities for various applications in fields like autonomous driving and surveillance.
Face recognition is a specialized form of object detection that focuses on locating human faces within an image. It is widely used in scenarios such as facial recognition and surveillance systems, and in our daily life, such as FaceID in iPhone. By leveraging machine learning algorithms, face detection models can accurately identify and highlight the presence of faces, regardless of factors like orientation, lighting conditions, accessories, or facial hair, or even mask ( such as FaceID with mask). This doop technology makes it possible to recognize individuals based on their unique characteristics, so it is already widely used in fields like security, biometrics, computer vision, and more.
Optical character recognition (OCR)
Optical Character Recognition (OCR) is a technique used to detect and extract text from images ( like today’s Live Text OCR feature is enabled by default in iOS 16). This technique also enabes the conversion of printed or handwritten text into machine-readable format such as it is in Remarkable 2 devices.
Further, OCR can be employed to digitize and analyze scanned documents like letters, invoices, or forms, making extracting relevant data easier and automating document processing workflows such as Adobe PDF reader. By leveraging advanced algorithms and machine learning, OCR technology continues to enhance efficiency and accuracy in tasks involving text recognition and data extraction from visual content.
Speech analysis in AI is a machine learning algorithm to analyze and extract meaningful information from spoken language. It can transcribe and interpret speech, allowing for applications such as voice assistants, speech-to-text conversion, sentiment analysis, and even emotion detection.
For example, virtual assistants like Siri and Alexa utilize speech analysis AI to understand user commands and provide accurate responses. AI-powered sentiment analysis can analyze customer service calls to gauge customer satisfaction levels.
Combining Conversational AI with Natural Language Processing (NLP), this technique empowers computers to interpret written or spoken language and respond accordingly. It not only involves the capability to understand and generate human-like responses, but also seamlessly connects with various communication channels, like web chat, email, Microsoft Teams, and more.
Video analysis in AI terms, such as Microsoft Video Indexer, involves the use of advanced algorithms to extract valuable insights from video data. It can automatically transcribe spoken words, detect and recognize faces, objects, and scenes, and even generate closed captions and keywords. This technique is used in video summarization, content moderation, and even personalized recommendations.
Anomaly Detection refers to the capability to detect errors or unusual activity in a system automatically. It is a machine learning technique that analyzes data over time and identifies unusual changes.
Anomaly detection is like a detective, but for data, it refers to the capability to automatically detect errors or unusual activity in a workflow or a computer system. This method, powered by machine learning, keeps an eye on your data over time and raises a flag when something doesn’t quite fit the usage pattern, which may indicate fraud. In essence, anomaly detection helps us anticipate problems by spotting the signs early on.
Knowledge mining is the process of extracting valuable information from vast amounts of unstructured data and then transforming it into a searchable knowledge store. It uses in combination with natural language processing(NLP) and text analytics to uncover insights and patterns within the data.
For instance, in the retail industry, knowledge mining can analyze customer reviews, sales data, and competitor information to improve marketing strategies and product offerings.
I will continue to write about concrete use cases and hands-on content in those areas by leveraging Microsoft Azure AI, Google AI, and Amazon Web Service (AI) in the future. Subscribe to this email list for free so you don’t miss anything!
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What is Generative AI? Made Easy
Generative AI is a subcategory of AI that leverages deep learning algorithms and neural networks to create unique content. If you’ve interacted with ChatGPT or used the new Bing from Microsoft, you’ve experienced generative AI in action!
Generative AI doesn’t just recognize patterns in existing data. It goes one step further to create something entirely new when given natural language prompts. It has three key capabilities:
Natural Language Processing (NLP)
Generative AI can understand, interpret, and generate natural human language. This is the technology that powers chatbots and voice assistants, making them more interactive and responsive.
Generative AI can write code on your behalf, since they can take natural language or code snippets and translate them into code.
Generative AI can create new images, which has enormous potential in fields like graphic design and visual arts. It can also be used for tasks like creating realistic video game environments or virtual reality experiences.
This video explains the concept of generative AI in 50 seconds :
Moreover, training generative AI models is a complex process as they do not just analyze and interpret data but create new content from it. This process involves the model learning patterns within the data and then generating similar but distinct data. The computational intensity of this process necessitates a supercomputer equipe with NVIDIA H100 Tensor Core GPU, which is introduced as the world’s most powerful option ( and pricey ) for generative AI training and machine learning inference, wheich is crucial for advancements like GPT4 (you may get this if you have ChatGPT plus membership and OpenAI limits GPT-4 to 25 messages every 3 hours). You can learn more about how AI and supercomputers from this blog post.
If you’re eager to explore generative AI further, I highly recommend “Generative Deep Learning” by David Foster. This comprehensive book delves into the prominent techniques that have shaped the field of generative modeling in recent years. Alongside explaining the fundamental theory behind generative modeling, the book provides hands-on experience through practical examples of key models from the literature with step-by-step guidance. And don’t forget to subscribe to our Youtube channel for more updates in this space !
Opportunities and Risks in AI
We may not picture as bad as it gets in a sci-fi movie, yet Artificial Intelligence (AI) presents numerous opportunities and risks in our reality. AI systems needs to moderate fairness so they can make unbiased decisions. Also regulated data and the use of data, data privacy. Without careful oversight, AI could inadvertently support bias, respond unpredictably, breach data privacy, exclude certain groups, or operate without clear explanations, creating a ‘black box’ effect. Therefore, the importance of responsible AI cannot be overstated. It is crucial to balance these opportunities and risks to ensure AI systems contribute positively to society. Promoting trust, understanding, and equitable access to technology.
AI is fascinating! While it does present immense opportunities for innovation and advancement. It is crucial also to acknowledge the associated risks and ethical considerations as we continue to delve deeper into the realm of AI. Let us embrace its potential but also approach it with a keen sense of responsibility and ensure its development benefits humanity as a whole.
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In our next post in this series – AI Made Easy for Beginners, we’ll dive deeply into OpenAI and Generative AI. Stay tuned!