AI 101: Everything you need to know about AI

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AI 101: Everything You Need to Know About AI - a flat design illustration highlighting key AI concepts with 'AI 101' at the center.

Time to Read: 19 minutes

AI can appear complex and intimidating, especially if you’re a non-techy and are not familiar with technical terms. We’ll clear up the confusion around AI, and break down AI concepts into simple, understandable pieces. This is your ultimate guide, covering everything you need to know about AI.

Table of Contents

What does Artificial Intelligence or AI mean?

Answer: Artificial Intelligence (AI) is the creation of computer systems that can perform tasks typically requiring human intelligence, like recognizing speech, making decisions, and translating languages.

Example: A simple example of AI is a recommendation system on streaming services like Netflix, which suggests movies based on your watching history.

How does artificial intelligence work?

Answer: AI works by analyzing large amounts of data, learning patterns or features from this data, and then making decisions or predictions based on this learning.

Example: Consider a spam filter in your email. AI learns what spam looks like from large datasets of emails and then uses this knowledge to filter out spam from your inbox.

What types of AI are there?

Answer: There are two main types: Weak AI, designed for specific tasks, and Strong AI, which has broader, human-like intelligence (though still theoretical).

Example of Weak AI: Siri on your iPhone, which is programmed to perform specific tasks like setting reminders or answering questions.

What does machine learning mean?

Answer: Machine learning is a part of AI where machines learn from data to make decisions or predictions, without being explicitly programmed for each task.

Example: A weather app predicting tomorrow’s weather based on past weather data.

What is machine learning in AI?

Answer: In AI, machine learning is the method by which AI systems improve their performance on a task over time with more data.

Example: A music streaming service like Spotify recommending new songs based on the ones you’ve listened to before.

How does machine learning work?

Answer: Machine learning algorithms analyze data, learn patterns, and then apply this learning to make informed decisions.

Example: Credit scoring in banking, where an algorithm assesses your transaction history to decide your creditworthiness.

How many types of machine learning are there?

Answer: There are three main types: Supervised, Unsupervised, and Reinforcement Learning.

Example of Supervised Learning: An email classification system that’s been trained on labeled data (like ‘spam’ or ‘not spam’) to categorize incoming emails.

Example of Unsupervised Learning: A market segmentation tool that groups customers with similar buying behaviors without prior labeling.

Example of Reinforcement Learning: A chess-playing AI that improves its game over time by learning from wins and losses.

What does Large Language Model mean?

Answer: A Large Language Model (LLM) is an advanced AI system that processes, understands, and generates human-like text based on vast amounts of language data.

Example: ChatGPT, which can write essays, answer questions, or compose emails, is a type of Large Language Model.

What is large language model in AI?

Answer: In AI, a large language model refers to a machine learning model that has been trained on a massive dataset of text to understand and generate human language.

Example: GPT-3, used for tasks like translation, content creation, and conversation, is a well-known large language model in AI.

How do large language models work?

Answer: Large language models work by analyzing huge amounts of text data, learning patterns and structures in language, and then using this knowledge to generate coherent and contextually relevant text.

Example: When you ask a question to a virtual assistant like Alexa, it uses a large language model to understand your question and generate a human-like response.

What does neural network mean?

Answer: A neural network is a series of algorithms that mimic the operations of a human brain to recognize relationships in a set of data through a process that mimics the way the human brain operates.

Example: Credit card fraud detection systems use neural networks to identify unusual patterns indicative of fraudulent activity.

How do AI Neural networks work?

Answer: AI neural networks work by processing data through layers of interconnected nodes (mimicking neurons in the brain), which can learn to recognize patterns and make decisions.

Example: Netflix recommendations are powered by neural networks analyzing your viewing history to suggest shows you might like.

What is a node in AI?

Answer: In AI, a node is a basic unit of a neural network, similar to a neuron in the human brain, which processes input data and passes its output to the next layer of nodes.

Example: In a facial recognition system, each node might be responsible for recognizing specific features like edges, colors, or shapes.

What does deep learning mean?

Answer: Deep learning is a subset of machine learning involving neural networks with many layers, enabling the learning of complex patterns in large amounts of data.

Example: Facial recognition technology in smartphones uses deep learning to identify and verify individual faces.

What is Deep Learning in AI?

Answer: In AI, deep learning refers to algorithms inspired by the structure and function of the brain (neural networks) to learn from large amounts of data.

Example: Voice assistants like Google Assistant use deep learning to understand spoken language and respond accurately.

What Does Natural Language Processing Mean?

Answer: Natural Language Processing (NLP) is a field of AI that focuses on enabling computers to understand, interpret, and respond to human language in a way that is both meaningful and useful.

Example: Grammar checking tools like Grammarly use NLP to understand and suggest corrections in your writing.

How does natural language processing work?

Answer: NLP works by combining computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models.

Example: Voice-activated GPS systems use NLP to understand your spoken directions and provide the correct route.

Is natural language processing machine learning?

Answer: Yes, natural language processing often utilizes machine learning to improve its ability to understand and respond to human language accurately.

Example: Email spam filters use NLP with machine learning to learn from a variety of email messages to identify and filter out spam.

What does big data mean?

Answer: Big data refers to extremely large datasets that are too complex to be handled by traditional data-processing software, but which can be analyzed computationally to reveal patterns, trends, and associations.

Example: Social media platforms use big data to analyze millions of user interactions and personalize content on your feed.

What is big data in AI?

Answer: In AI, big data is used to train models, helping them to learn and make more accurate predictions or decisions based on the analysis of large and complex datasets.

Example: Netflix uses big data to analyze viewing preferences of millions of users to recommend personalized TV shows and movies.

What does algorithm mean?

Answer: An algorithm is a set of rules or instructions given to a computer to help it perform a specific task or solve a problem.

Example: A recipe app suggesting recipes based on the ingredients you have is using an algorithm to make those suggestions.

What is an algorithm in AI?

Answer: In AI, an algorithm is a complex set of instructions that guides the AI system in processing data, learning from it, and making decisions or predictions.

Example: Google’s search algorithm uses AI to understand your search query and return the most relevant search results.

What does cognitive computing mean?

Answer: Cognitive computing refers to systems that simulate human thought processes in a computerized model, using self-learning algorithms that use data mining, pattern recognition, and natural language processing to mimic the human brain.

Example: IBM’s Watson, which can process and analyze large amounts of data to assist in decision-making, is an example of cognitive computing.

What is cognitive computing in AI?

Answer: In AI, cognitive computing is the creation of computer systems that can solve complex problems without constant human oversight, similar to how a human brain would approach and solve problems.

Example: Health monitoring systems that analyze patient data and suggest diagnosis or treatment plans.

What is a prompt in AI?

Answer: In AI, a prompt is an input given to an AI system, usually in the form of text or voice commands, which guides the AI in generating a response or performing a task.

Example: Asking a voice assistant like Alexa to play your favorite song is a prompt.

What is prompt engineering in AI?

Answer: Prompt engineering in AI involves designing and refining the input (or ‘prompt’) given to an AI system to effectively guide the AI in generating the desired output or response.

Example: Crafting specific questions or statements to get the most relevant and accurate responses from an AI language model like ChatGPT.

How does prompt engineering work?

Answer: Prompt engineering works by strategically formulating prompts to leverage the AI’s language understanding capabilities, thereby guiding it to produce more accurate, relevant, and useful responses or outputs.

Example: Adjusting the wording of a query to a language model to get more detailed or specific information on a topic.

What is an AI chatbot?

Answer: An AI chatbot is a software application used to conduct an online chat conversation via text or text-to-speech, simulating a conversation with a human user.

Example: Customer service chatbots on websites that help answer questions and resolve issues without human intervention.

How do AI chatbots work?

Answer: AI chatbots work by using natural language processing to understand user queries and machine learning to provide accurate responses based on learned data from previous interactions.

Example: A banking chatbot that assists customers with account inquiries, transaction histories, and service issues by understanding and responding to customer inputs.

What is ChatGPT?

Answer: ChatGPT is a variant of the GPT (Generative Pre-trained Transformer) language model, specifically fine-tuned for conversational responses.

Example: ChatGPT can be used to answer questions, write essays, or even create scripts, engaging in a dialogue that is coherent and contextually relevant.

What does GPT mean?

Answer: GPT stands for “Generative Pre-trained Transformer.” It’s a type of language processing AI model designed to generate text and understand and respond to language inputs.

Example: GPT-3, a type of GPT which can generate human-like text based on the prompts it receives.

Is ChatGPT a LLM?

Answer: Yes, ChatGPT is considered a Large Language Model (LLM) as it’s trained on a vast corpus of text data and can generate detailed and contextually relevant text based on that training.

Example: ChatGPT being used to generate a well-structured article on a specific topic.

How does ChatGPT work?

Answer: ChatGPT works by using its training on a large dataset of text to generate responses that are contextually relevant to the input it receives. It uses patterns it has learned to construct coherent and context-appropriate replies.

Example: When you ask ChatGPT a question, it analyzes the question and generates a response based on its training, aiming to match the style and content of the input it received.

What is generative AI?

Answer: Generative AI refers to AI systems that can generate new content or data that is similar but not identical to the data it was trained on, such as text, images, or music.

Example: AI creating new, original pieces of music based on existing genres and styles.

How does generative AI work?

Answer: Generative AI works by using algorithms to analyze and learn from existing data, then using that learned structure to generate new, similar data.

Example: DeepArt, an AI that creates new artworks by learning styles from existing paintings.

What is AI ethics?

Answer: AI ethics is the branch of ethics that examines the moral implications and responsibilities of using artificial intelligence in society.

Example: Debating whether an AI should make decisions in life-critical situations like autonomous driving.

What are the ethical considerations in AI?

Answer: Ethical considerations in AI include issues like privacy, bias, transparency, accountability, and the impact of AI on employment and society.

Example: Ensuring AI in recruitment processes doesn’t discriminate based on gender or ethnicity.

What are the limitations of AI?

Answer: Limitations of AI include a lack of understanding of context or common sense, inability to perform tasks outside of its training, and the need for large amounts of data.

Example: AI struggling to understand sarcasm or idiomatic expressions in language.

Is AI biased or unbiased?

Answer: AI can be biased if the data it’s trained on is biased. Since AI learns from existing data, any prejudices in that data can lead to biased outcomes.

Example: An AI facial recognition system performing poorly with certain demographic groups due to lack of diverse data in its training set.

Is ChatGPT sentient?

Answer: No, ChatGPT is not sentient. It is a program that processes and generates language-based responses based on its training and algorithms, without consciousness or self-awareness.

Example: ChatGPT responding to queries is based on patterns in data, not personal experience or emotions.

Will AI take over jobs?

Answer: AI will likely automate some jobs, especially those involving repetitive tasks, but it also creates new job opportunities, particularly in AI development and oversight.

Example: Automation of routine data entry jobs but creation of new roles in AI maintenance and ethics.

What jobs will AI not replace?

Answer: Jobs requiring complex human interactions, empathy, creativity, and advanced problem-solving are less likely to be replaced by AI.

Example: Jobs like therapists, artists, and strategic management roles.

What jobs will AI create?

Answer: AI will create jobs in AI development, data science, AI ethics, AI system maintenance, and sectors where AI tools augment human capabilities.

Example: Prompt Engineer, AI ethics officer, AI-assisted healthcare diagnostics, and AI system trainers to name a few.

Should I learn to use AI?

Answer: Yes, learning to use AI is beneficial as it’s becoming increasingly important in various fields, offering new career opportunities and enhancing existing job roles.

Example: Marketers using AI tools for customer data analysis and personalized marketing strategies.

Can you use AI to write a resume?

Answer: Yes, AI can be used to write a resume. AI tools, like GradSimple’s “GradSimple Career Coach,” can assist in structuring, formatting, and suggesting content based on industry standards and job descriptions. However, it’s important to personalize and review the AI-generated resume to ensure it accurately reflects your unique skills and experiences.

Limitations: AI may not effectively capture the nuances of individual experiences or tailor the resume to reflect personal achievements and specific job requirements. It might also miss the subtleties of human language that convey personality and individuality.

GradSimple’s Perspective: While AI tools like the “GradSimple Career Coach” are helpful in the initial stages of resume creation, GradSimple strongly recommends a human review to ensure the resume authentically represents your unique skills and experiences. GradSimple’s 1 on 1 career counseling sessions can provide this personalized review, enhancing your resume’s effectiveness.

Can you use AI to write a cover letter?

Answer: AI can also assist in writing a cover letter by providing templates, language suggestions, and content optimization. However, like with resumes, the cover letter should be personalized to reflect your individual voice and specifically address the job and company you’re applying to.

Limitations: An AI generated cover letter will lack personalization and have the potential to miss context-specific nuances important for a cover letter. AI-generated letters will also lack your personal touch and specific alignment with the company’s culture and values.

GradSimple’s Perspective: While AI, including GradSimple’s AI tools, can offer a starting point for cover letter writing, it’s crucial to have human input for personalization. GradSimple’s services, including expert reviews and counseling, ensure your cover letter resonates with your personal story and aligns with the employer’s expectations.

Does GradSimple recommend using AI to write a resume?

Answer: GradSimple recommends using AI as a tool to assist with resume creation. AI, such as the GradSimple Career Coach, can offer valuable advice, make suggestions, and help optimize your resume. However, GradSimple advises against relying solely on AI to create a resume from scratch due to its limitations in capturing the unique aspects and personal experiences of an individual.

Does GradSimple recommend using AI to write a cover letter?

Answer: Similar to resumes, GradSimple suggests using AI for guidance and optimization in cover letter writing. AI can provide a good starting point and structure, but it’s crucial to add a personal touch and tailor the letter to the specific job and company, something AI might not fully achieve

How does GradSimple use AI?

Answer: GradSimple utilizes AI, particularly through its GradSimple Career Coach on ChatGPT Plus, to act as a digital career counselor. This AI tool is equipped with GradSimple’s extensive knowledge base and insights from industry professionals, designed to assist college students and new graduates. It offers support in areas like resume and cover letter writing, job search strategies, and interview preparation. Additionally, GradSimple’s AI 101 blog category educates students about AI, preparing them for an AI-integrated future, and plans to offer prompt engineering guides for resumes and cover letters, further empowering users to effectively use AI in their career development.

Can you use AI to practice for job interviews?

Answer: Yes, AI can be used to practice for job interviews. AI-driven interview preparation tools can simulate a variety of interview scenarios, provide questions, and even give feedback on responses.

Limitations: While AI can offer a good starting point for practice, it may not fully replicate the nuances of a real interview, such as understanding complex human expressions, giving personalized feedback based on industry specifics, or adapting to the unique interview style of different companies.

GradSimple’s Perspective: GradSimple acknowledges the benefits of using AI for initial interview practice. However, for a more effective and realistic preparation, GradSimple recommends mock interviews with actual humans. Our service includes one-on-one mock interview sessions with experienced professionals, providing personalized feedback and insights that AI alone cannot offer.

Where can you learn more about AI as a college student or recent graduate?

Answer: College students and recent graduates can learn more about AI through various online resources, university courses, and specialized blogs or publications.

GradSimple’s Approach to AI Learning: GradSimple’s AI 101 blog category is an excellent resource for students to learn about AI in simple, easy-to-understand terms. The blog covers fundamental AI concepts, industry trends, and practical applications tailored for college students. Additionally, GradSimple is embracing AI in its offerings, including upcoming products like a prompt engineering guide for creating effective resumes. These resources are designed to empower students and graduates with the knowledge and skills to navigate an AI-integrated future successfully.

GradSimple’s approach to AI education and career preparation is holistic, combining the practicality of AI tools with the depth of human expertise. Our services and resources are geared towards demystifying AI and making it accessible and relevant to the needs and aspirations of students and recent graduates, preparing them for a future where AI plays an integral role in various professions.

Everything you need to know about AI: concluding thoughts

AI isn’t just a trendy term, but a force that’s reshaping sectors from business to the arts. At GradSimple, our dedication lies in offering resources that not only prepare you for an AI-driven future but also ensure you are equipped to thrive in it.

Subscribe to our newsletter to stay up to date with the latest AI developments, insights, and tips. Be a part of our growing community and stay ahead in your academic and professional journey with GradSimple!

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The human who runs the ship. Occasional writer, occasional web developer. Yes, this is the guy who hired a raccoon (if you know, you know).

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