99% of Beginners Don’t Know the Basics of AI

Learning the AI basics for beginners can be an eye-opener. Last week, I dedicated five hours and $49 to complete Google’s latest AI Essentials course. My goal was to recoup that expense and, of course, to feed my curiosity about AI. To share my experience, I’m breaking down five key takeaways from the course, discussing the pros and cons, and evaluating whether the certificate you earn is worth the investment. Let’s dive into what I discovered and whether it’s worth your time and money.

We strongly recommend that you check out our guide on how to take advantage of AI in today’s passive income economy.

Understanding AI Tools

Types of AI Tools

Broadly speaking, there are three main types of AI tools that beginners should understand.

First, we have standalone tools. These are AI-powered software designed to work independently with minimal setup. This category includes general-purpose chatbots like ChatGPT, Gemini, Claude, and Perplexity, as well as specialized apps such as Spico, Otter AI, MidJourney, and Gamma.

Although these tools cater to different needs, they are classified as standalone because you can access them directly through their websites or apps without needing to integrate them with other software.

In contrast, the second category includes tools with integrated AI features. These are enhancements built into existing software. For example, after drafting a document in Google Docs, you can either copy and paste the text into a standalone app like ChatGPT for improvements or use the integrated AI feature within Google Workspace, like Gemini for Workspace, to make adjustments directly in Google Docs.

Another instance is MidJourney, which can be used as a standalone tool to generate images for a presentation or integrated directly within Google Slides using Gemini for Workspace. In these scenarios, ChatGPT and MidJourney are standalone AI tools, while Google Docs and Google Slides are tools with integrated AI features.

The third type of AI tool is a custom AI solution. These applications are tailor-made to address specific problems. For instance, Johns Hopkins University developed an AI system specifically to detect sepsis. This custom solution significantly improved diagnostic accuracy from 2 to 5% up to an average of 40%.

If you’re like me and lack a technical background, you might think that custom AI solutions are too complex for everyday use. However, well-designed custom AI solutions are often user-friendly and require little technical knowledge. For example, when I managed over 200 clients each quarter, researching each one was time-consuming. Today, custom AI solutions can analyze all client data, considering factors like seasonality, historical data, and industry trends to prioritize clients who are most likely to need assistance. This helps salespeople focus their efforts more effectively.

Course Enrollment and Sponsorship

Google AI Essentials Course Insights

By the way, if you’re considering buying Google’s AI Essentials course, I have a tip: you can get the course for free by enrolling in the Google Project Management Certification on Coursera, which is sponsored by Coursera.

I currently juggle a full-time job where project management plays a crucial role. I’m mostly self-taught in this area, but I recently started the Google Project Management Certification on Coursera, which has become a standard in the field. Project management skills are applicable across various industries and roles. If you want to improve your organizational skills at work, I highly recommend enrolling in the Google Project Management Certification through the link below to unlock the AI Essentials course for free.

Thank you, Coursera, for sponsoring this part of the article.

Prompt Engineering Tips

Surface Implied Context

One of the key takeaways from the course is about prompt engineering, specifically the importance of surfacing implied context. Imagine you’re asked for restaurant recommendations by a vegetarian friend. You would instinctively provide vegetarian-friendly options, even though your friend didn’t explicitly state this preference.

This implied context needs to be clearly stated when communicating with AI tools like ChatGPT or Google Gemini. For instance, if you’re negotiating a raise with your boss and know that last year you received a 10% increase while the industry average is 12%, you should include all this context when seeking negotiation strategies from an AI tool.

Leaving out such details can lead to less relevant and generic responses. For those interested in learning more about crafting the perfect prompt, I’ve included a link to a video on that topic below. Additionally, you can access my free workspace toolkit with five of my favorite productivity prompts through the same link.

Zero-Shot and Few-Shot Prompting

Using Prompt Examples Effectively

Another important concept covered is knowing when to use zero-shot and few-shot prompting. In this context, “shot” refers to examples provided with the prompt.

A zero-shot prompt involves no examples at all. For example, “Write me a pickup line for Bumble,” is a hypothetical scenario I would never personally engage in.

A one-shot prompt includes a single example: “Write me a pickup line for Bumble. Reference this example my friend used that worked well for him.”

A few-shot prompt includes two or more examples of successful outcomes. The more relevant examples you provide, the better the AI tool’s output.

To clarify, if you’re my future wife reading this, I’ve never used dating apps myself. This example is just for illustrative purposes.

Chain of Thought Prompting

Breaking Down Complex Tasks

Chain of Thought prompting is another useful technique. This method involves breaking a complex task into smaller, manageable steps to help the AI produce accurate and consistent results.

For example, when writing a cover letter, instead of asking the AI to draft the entire letter in one go, you can start by prompting it to write an attention-grabbing hook based on your resume and the job description.

After refining the hook, you can then use the AI to write the body paragraph, and finally, the closing paragraph. This step-by-step approach improves the quality of the output. I have a detailed guide on using Chain of Thought prompting to enhance cover letters and resumes, which you can find linked below.

Understanding AI Limitations

Recognizing AI’s Constraints

The course also emphasizes understanding the limitations of AI. There are three main constraints to consider.

First, the data used to train AI models may be biased. For example, if a text-to-image model is trained only on minimalistic graphics, it might struggle to produce elaborate designs.

Second, there might be insufficient data on certain topics, particularly if AI models have a cutoff date. Asking about recent events might yield incomplete or inaccurate responses.

Third, AI can produce hallucinations, which are outputs that are factually incorrect. These can sometimes be useful for brainstorming but can also perpetuate misinformation. It’s crucial to verify AI-generated information, especially for high-stakes tasks, such as making health decisions based on AI recommendations.

Course Evaluation

Pros and Cons of the AI Essentials Course

Now, let’s discuss who might not benefit from this course. It’s not ideal for those already using AI tools like ChatGPT and Google Gemini extensively and looking for advanced insights. The course provides good explanations of complex topics but often with vague examples.

For instance, one lesson described a company using AI to reduce customer service response times but didn’t detail whether the AI was a standalone tool or a custom solution, how the company trained its employees, or how it ensured the AI’s accuracy.

On the other hand, the course offers significant advantages for beginners. Firstly, you learn from Google experts who provide reliable insights rather than random internet sources.

Secondly, as a visual learner, I appreciate how the course uses simple graphics to explain complex concepts. AI tools and models are analogized to a car and its engine, with the model being the engine providing underlying capabilities and the tool being the car helping you complete tasks.

Thirdly, the interactive elements of the course are genuinely helpful. The activities and graded assignments are designed to reinforce key concepts effectively. You must pay attention to pass the quizzes, which are mostly multiple-choice but require understanding.

Finally, the course offers a curated list of AI tools for beginners and includes a glossary of common AI terms, which are increasingly relevant in our daily lives.

Conclusion

Summary and Recommendations

To sum up, the AI Essentials course is great for beginners and visual learners. It provides a legitimate certificate that can enhance your job prospects or career development. If you found this article helpful, you might also be interested in my summary of Google’s free AI course, which offers more conceptual insights.

In the meantime, dive into the basics of AI and explore how these tools and techniques can enhance your understanding and application of artificial intelligence. Have a great one!

FAQ: AI basics for beginners

How do I start learning AI for beginners?

Starting with AI can seem daunting, but it’s manageable if you break it down into steps.

1. Begin with the Basics: Understand fundamental concepts like machine learning, neural networks, and natural language processing. Online platforms like Coursera, edX, and Udacity offer beginner courses that cover these areas.
2. Choose a Learning Path: Decide whether you want to focus on programming (Python is a popular choice), data science, or specific AI applications. This will help tailor your learning resources.
3. Hands-On Practice: Engage in practical projects and use tools like TensorFlow or PyTorch. Websites like Kaggle provide datasets and competitions to practice your skills.
4. Join AI Communities: Forums, online groups, and local meetups can provide support, answer questions, and offer networking opportunities.
5. Follow Industry News: Stay updated with the latest advancements by reading AI blogs, research papers, and news articles.

What are the basics of AI?

The basics of AI involve understanding key concepts and technologies:

1. Artificial Intelligence (AI): AI refers to the simulation of human intelligence in machines programmed to think and learn. It encompasses various techniques and applications.
2. Machine Learning (ML): A subset of AI, ML involves training algorithms to recognize patterns and make decisions based on data. Examples include supervised learning, unsupervised learning, and reinforcement learning.
3. Neural Networks: Inspired by the human brain, neural networks consist of layers of interconnected nodes (neurons) that process data. They are essential in deep learning.
4. Natural Language Processing (NLP): NLP focuses on the interaction between computers and human language, enabling machines to understand and respond to text or speech.
5. Data Science: The field of data science involves collecting, analyzing, and interpreting large datasets to inform decision-making, often using AI tools.

Are there 4 basic AI concepts?

Yes, there are several fundamental AI concepts that form the foundation of the field. Four key ones include:

1. Supervised Learning: This method involves training a model on labeled data, where the input-output pairs are known. The model learns to make predictions based on this data.
2. Unsupervised Learning: Unlike supervised learning, unsupervised learning deals with unlabeled data. The model identifies patterns and structures within the data without predefined outcomes.
3. Reinforcement Learning: This concept involves training an agent to make decisions by rewarding it for desirable actions and penalizing it for undesirable ones. It’s often used in robotics and game-playing AI.
4. Deep Learning: A subset of machine learning, deep learning utilizes neural networks with many layers to analyze various forms of data, such as images, text, and audio.

How to use AI for the first time?

Using AI for the first time involves several steps:

1. Select a Simple Tool or Platform: Begin with user-friendly AI tools or platforms like Google Colab or IBM Watson. These platforms provide pre-built models and easy-to-use interfaces.
2. Explore AI Demos: Many platforms offer AI demos and tutorials that showcase what AI can do. Experiment with these to get a feel for the technology.
3. Start with Basic Projects: Engage in small projects such as building a simple chatbot or using AI for data analysis. This hands-on approach helps you understand how AI can be applied in real-world scenarios.
4. Use Pre-Trained Models: Many AI platforms offer pre-trained models that you can customize for your needs. This allows you to leverage existing AI capabilities without needing extensive programming knowledge.
5. Seek Tutorials and Courses: Online courses, tutorials, and documentation can guide you through your initial experiments with AI, providing step-by-step instructions and best practices.
6. Experiment and Iterate: AI is an evolving field, so don’t be afraid to experiment with different approaches and continuously refine your skills based on feedback and results.

We strongly recommend that you check out our guide on how to take advantage of AI in today’s passive income economy.