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Understanding AI
Welcome to our comprehensive guide to better understanding AI. Whether you are a business leader, developer or design practitioner embarking on a career in conversational AI.
Jumpstart your career in AI
Through detailed explanations, practical examples, and expert perspectives, we offer a peek at the amazing world of AI. We offer explanations of key concepts like generative AI, conversational AI, and more.
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CDI helps you master the fundamentals of conversational AI and beyond, whether you’re a business leader who wants to learn more or an individual looking to land your next job in AI.
Topics in AI
Where, what and how of AI
What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is the umbrella term that refers to the development of computer programs or machines that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, understanding natural language, and even interacting with the environment in ways that mimic human-like behavior.
AI systems are designed to analyze large amounts of data, recognize patterns, and make decisions based on that data, often with minimal human intervention. They achieve this through various techniques such as machine learning, deep learning, natural language processing, computer vision, and robotics.
Types of AI
Jobs in AI
Conversation Designer
Professionals in this role are responsible for designing human-centric conversational experiences. They make sure conversations are aligned with the brand, resonate with the audience and deliver on the goals of the business.
Prompt Designer
Prompt designers or prompt engineers are known for their ability to instruct LLMs to get the desirable output. Think of classification and summarization tasks, answer generation in a certain tone and style or building and maintaining advanced RAG pipelines.
AI Trainer
They prepare the data used to train NLU models. Ensuring the quality and accuracy of training data that directly impacts the performance of AI Assistants, like chatbots and voice assistants.
Machine Learning Engineer
ML engineers focus on designing, implementing, and optimizing machine learning models and systems.
Data Scientist
Experts who leverage statistical analysis, machine learning, and data visualization techniques to extract insights and patterns from large datasets
AI Ethicist
With the growing importance of ethical considerations in AI development and deployment, ethicists evaluate and advocate for responsible AI practices
AI Product Manager
AI product managers oversee the development and implementation of AI-powered products and services.
Automation in Conversational Ai
Conversational AI helps us automate conversations in a human-centric way. The history of chatbots spans multiple decades. We went from rudimentary rule-based systems to the general-purpose, generative AI chatbots like ChatGPT, Gemini, and Claude, that we have today.
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Declarative chatbots are powered by natural language understanding (NLU). While limited to a pre-defined set of intents, they have the ability to more accurately recognize what people are talking about and can carry context across dialogues or dialogue turns.
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Voice Assistants & Interactive Voice Response (IVR) are a subset of AI Assistants that interact with users primarily through voice. They leverage speech recognition technology, like text-to-speech (TTS) and speech-to-text (STT) to interpret spoken language and respond back in spoken language.
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Generative AI chatbots are powered by LLMs to generate responses on the fly. They can understand context to some extent and produce more flexible and dynamic responses (however, these responses aren’t always accurate).
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Copilots and AI agents are a final category of chatbots that have gained popularity. These are chatbots, usually powered by generative AI, that can either collaborate with you or even take actions on your behalf.
Types of Generative AI
Foundation models generate output based on human language instructions (prompts). Generative AI encompasses various techniques that enable machines to create new content or data that is similar to the input it has been trained on. Here are some types of generative AI:
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Text-to-text: Text generation models are designed to produce human-like text based on the input they receive. These models can be used to augment conversational experiences, summarize long pieces of text, and even generate code.
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Text-to-image: Image generation models create new images based on the patterns and features they have learned from a dataset of existing images, like DALL-E or Stable Diffusion.
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Text-to-music: AI models can also generate music compositions by learning from existing musical pieces. These models can compose melodies, harmonies, and even entire songs in various styles and genres.
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Text-to-video: Video generation AI can create new video sequences based on the visual patterns it has learned from training data. These models can generate realistic animations, visual effects, or even deepfake videos.
What types of people work in AI?
Working in AI requires a combination of technical expertise, creativity, curiosity, and above else a collaborative mindset. There’s no one-size-fits-all personality type for AI professionals, however, certain traits are commonly found among successful individuals in the field:
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Analytical thinking
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Curiosity and creativit
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Adaptability and flexibility
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Problem-solving skills
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Collaboration and communication
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Ethical awareness
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Attention to detail
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Learning mindset
Obviously, this list is non-exhaustive. The key takeaway here is that AI is a dynamic and multidisciplinary field that demands continuous learning and skill development.
How fast is AI growing?
If we look at the field of AI as a whole, we can see immense growth. Here are some key indicators of AI's rapid expansion:
Investment and funding
AI has attracted significant investment from both public and private sectors. Venture capital funding for AI startups has surged, with billions of dollars being invested in AI-related ventures across various industries.
Research publications
The number of research publications in AI has been steadily increasing over the years.
Job market
The demand for AI talent has skyrocketed. Job postings for AI-related roles, such as machine learning engineers, data scientists, and researchers, have seen exponential growth. Companies across industries are actively recruiting AI professionals to develop and deploy AI solutions to gain a competitive edge.
Adoption:
AI technologies are being increasingly adopted across industries to automate processes, improve decision-making, and provide better customer service. From healthcare and finance to manufacturing and retail, organizations are leveraging AI to drive efficiency and growth.
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