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Productivity (incl. AI)

AI | Ultimate Guide to AI - How to get started on AI in layman’s terms.

AI | Ultimate Guide to AI - How to get started on AI in layman’s terms.
Photo by Andrea De Santis / Unsplash

ChatGPT is taking over our dinner table conversations right now. A couple of weeks ago, as a total novice to AI, I was able to create this AI video in Exhibit 1 using three applications in a couple of minutes out of my laptop. No coding was required. I can code, but the point being, the entry barrier to AI for a normal individual like you and me has been significantly lowered and the use case is endless now. Upon releasing ChatGPT, Satya Nadella, CEO of Microsoft, noted that the power of AI models is growing exponentially, which is why there is so much excitement about AI this year compared to last year.

"You know we're still waiting for the Industrial Revolution to reach some large parts of the world 250 years after the first one and so in some sense I do think about technology as having the ability to democratize the means of production as Marx might have said it but but the point being that there is an opportunity here to truly not just create economic growth but to create economic growth that is inclusive and I think that's really what we should all aspire to do.” - Satya Nadella at Davos 2023

So, I wanted to explore the technology of AI with you and share my journey as I learn more about this subject each day in this brave new world where web 3.0 is the new norm. We are now living on the brink of the fourth industrial revolution. Check out this interview between Satya Nadella, CEO of Microsoft, and Klaus Schwab at Davos (2023) and Satya Nadella’s WSJ Interview on chatGPT if you want to understand what I mean.

Exhibit 1. This is the AI-generated video, image, and voice reading the script I provided.

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⚠️Disclaimer: You might be itchy to discuss if AI is a good thing for humanity or not. The moral perspective of AI will be discussed separately. In this post, we will mainly explore AI from a technical perspective. I have my own opinions on AI - short-term (beneficial) vs long-term (probability of dystopian future: could be the end of humanity if not curated carefully), but this can wait till later.

🤖 I. What is AI? Why should we care?

AI, or artificial intelligence, is a branch of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as recognizing patterns, making decisions, and learning from data.

AI is important in today's world for several reasons. First, it has the potential to transform many industries and improve our daily lives. For example, AI is already being used to develop self-driving cars, diagnose medical conditions, and personalize recommendations on social media platforms.

Second, AI has the ability to process and analyze vast amounts of data, which is becoming increasingly important in a world that is generating more data than ever before. With AI, we can extract insights from this data that can help us make better decisions and improve our understanding of complex systems.

Finally, AI has the potential to address some of the world's biggest challenges, such as climate change, poverty, and disease. By using AI to analyze and solve these problems, we can potentially make progress more quickly and efficiently than through traditional approaches.

Overall, AI is important in today's world because of its potential to transform industries, process and analyze data, and address some of the world's biggest challenges. As AI technology continues to advance, it is likely that we will see even more applications and benefits in the years to come.

💗 II. In a nutshell, there are three types of AI

1. Supervised learning: This is a type of machine learning where the algorithm is trained on labeled data, meaning that the correct output is provided for each input. The algorithm then uses this labeled data to learn how to make predictions on new, unseen data.Example: Image recognition software that can identify objects in a picture based on previous labeled examples.

2. Unsupervised learning: This is a type of machine learning where the algorithm is not given labeled data, but instead is tasked with finding patterns and relationships within the data on its own.Example: Clustering algorithms that can group similar items together based on patterns in the data.

3. Reinforcement learning: This is a type of machine learning where the algorithm learns by interacting with an environment and receiving feedback in the form of rewards or punishments. The algorithm then adjusts its behavior based on the feedback it receives. Example: A robot that learns to navigate a maze by receiving rewards for successful navigation.

Examples of AI in various industries:

1. Healthcare: AI is being used to develop new treatments and therapies, improve diagnostic accuracy, and streamline healthcare operations. For example, machine learning algorithms are being used to analyze medical images and identify potential health issues, such as cancerous tumors.

2. Finance: AI is being used to develop trading strategies, detect fraud, and assess creditworthiness. For example, banks are using machine learning algorithms to analyze credit reports and other financial data to make more accurate lending decisions.

3. Transportation: AI is being used to develop self-driving cars, optimize logistics and supply chains, and improve traffic flow. For example, self-driving cars use machine learning algorithms to analyze their surroundings and make decisions about how to navigate the road.

😍 III. Let’s get started on AI

So, you want to get started on AI? Here's some more information on getting started with AI:

How to get started with AI:If you're interested in getting started with AI, there are a few key steps you can take.

It's important to gain a basic understanding of the technology and its applications. This can be done through online tutorials, blogs, and courses, as well as by reading books and attending conferences and events.

Once you have a basic understanding of AI, you can start to explore different programming languages and tools used in the field. Python and R are two popular languages used in AI, but there are many others as well. It's important to choose a language that fits your needs and skill level.

Next, you can start to experiment with different AI techniques and algorithms, using tools like TensorFlow, Keras, and PyTorch. There are many online resources and tutorials available to help you get started with these tools.

Programming languages used for AI: Python and R are two of the most popular programming languages used in AI. Python is known for its simplicity, versatility, and wide range of libraries and tools. R is known for its statistical analysis capabilities and its use in data science.

Other languages commonly used in AI include Java, C++, and MATLAB. It's important to choose a language that fits your needs and skill level.

Resources for learning programming languages: There are many online courses, tutorials, and books available to help you learn programming languages like Python and R. Some popular resources include:

  • Coursera: Offers a range of online courses on AI, machine learning, and programming languages like Python and R.
  • edX: Offers online courses on AI, machine learning, and programming languages like Python and R.
  • Udemy: Offers a range of online courses on AI, machine learning, and programming languages like Python and R.
  • Codecademy: Offers interactive online tutorials on programming languages like Python and R.

Importance of data in AI: Data is a critical component of AI, as machine learning algorithms rely on large datasets to learn patterns and make predictions. Gathering and cleaning data is an important step in the AI process.

To gather data, you can use a variety of sources, including publicly available datasets, data scraped from websites, and data collected from sensors and other devices. Once you have your data, you'll need to clean it to remove any errors or inconsistencies that could affect the accuracy of your AI models.

There are many tools and techniques available to help with data cleaning, including data wrangling and data preprocessing. These techniques help to ensure that your data is clean, accurate, and ready to be used in AI models.

Overall, getting started with AI requires a combination of learning programming languages, experimenting with AI tools and algorithms, and working with data to develop accurate models. By using the resources available online, you can gain the skills and knowledge needed to succeed in this exciting field.

🤩 IV. Building AI Models

Building AI models involves a series of steps to develop an effective algorithm for solving a specific problem. The following are some of the key steps involved in building an AI model:

1. Data preprocessing: The first step in building an AI model is to collect and preprocess the data. This involves cleaning, transforming, and normalizing the data to ensure that it is ready for use in the model.

2. Model selection: Once the data has been preprocessed, the next step is to  select an appropriate model for the problem at hand. There are several different  types of models that can be used, including decision trees, support vector  machines, neural networks, and others.

3. Training the model: Once a model has been selected, it needs to be trained using the preprocessed data. This involves feeding the data into the model and adjusting the parameters to optimize its performance.

4. Evaluation: Once the model has been trained, it needs to be evaluated to determine its accuracy and performance. This can be done using various techniques such as cross-validation or holdout validation.

5. Fine-tuning: To improve the performance of the model, it may be necessary to fine-tune it by adjusting its parameters or tweaking its architecture.

Some popular AI models include:

1. Neural networks: Neural networks are a type of AI model that are inspired by the structure and function of the human brain. They are commonly used for tasks such as image recognition, natural language processing, and prediction.

2. Decision trees: Decision trees are a type of model that uses a tree-like structure to represent a set of decisions and their possible consequences. They are commonly used for classification tasks.

3. Support vector machines: Support vector machines are a type of model that can be used for classification, regression, and outlier detection. They work by finding the optimal hyperplane that separates the data into different classes.

In terms of resources for learning how to build AI models, there are many online courses, books, and tutorials available. Here are some examples:

🎓 Online courses: Platforms like Coursera, Udemy, and edX offer a wide range of online courses on AI and machine learning. Some popular courses include:

📚 Books: There are many books available on AI and machine learning, including:

👩🏽‍🏫 Tutorials: There are many online tutorials available that provide step-by-step instructions for building AI models. Some popular tutorials include:

📚 Books:

📻 Podcasts:

📺 YouTube:

🎓 Online courses:

💡 Blogs:

**I will have a separate review of these courses and materials later.**

Overall, building AI models requires a combination of programming skills, domain expertise, and knowledge of machine learning algorithms. With the right resources and practice, anyone can learn how to build effective AI models for a wide range of applications.

☀️ V. Future of AI

The field of AI is rapidly evolving, with new developments and innovations emerging all the time. Here are some current developments in AI and their potential impact:

1. Natural language processing: This technology allows computers to understand and interpret human language, enabling advancements in areas such as voice recognition and virtual assistants.

2. Autonomous systems: These are AI-powered systems that can operate independently, such as self-driving cars and drones. These systems have the potential to revolutionize transportation and logistics.

The potential impact of AI on society and the economy is significant. AI has the potential to improve productivity and efficiency, creating new opportunities for growth and innovation. However, it also raises important ethical and social questions, such as job displacement and privacy concerns. It's important to ensure that AI is developed and deployed responsibly, with a focus on maximizing benefits while minimizing risks.

In conclusion, AI is an exciting and rapidly developing field with many potential applications in business, finance, healthcare, and other areas. By understanding the basics of AI and exploring its potential, individuals and businesses can stay ahead of the curve and take advantage of this transformative technology. There are many resources available for learning about AI, including online courses, books, and communities of experts and enthusiasts.

Let me know your take on AI and also if there are any other topics in AI/Machine learning or resources you want me to explore further! Have an awesome day!😊

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