February 11, 2024
Absolutely, this is the best time in history to learn about computer programming, especially artificial intelligence (AI) machine learning programming. No human is late to this party. The AI Era is just getting started.
Many humans fancy the idea of being able to travel back in time to make the correct investments knowing how well they will work in the future. For example, going back 20 years to buy Apple’s stock at 42 cents a share, knowing that today it will be at about $189 a share. Every $1 invested in Apple 20 years ago would be $450 today. $1K invested then would be $450k today; $1M then would be worth $450M today; $2.22M then would be worth $1B.
Other humans fancy even more world domination by going back in time to the beginning of the information era, to the beginning of the industrial revolution, or even the beginning of the agricultural era. Well, time travel is probably impossible. In any event, humans today are at the beginning of a new era, the AI Era.
AI Era
The AI Era carries the inherent promise of being more profoundly disruptive than all of those previous eras together. That sounds like a gross exaggeration, but if you think about it all those previous eras were the ones putting humans on top of the animal kingdom on Earth. The AI Era carries the promise (or threat) of dethroning humans from the top of the evolution ladder. Humans climbed to the top because they are the smartest and most intelligent species on the planet. AI can change things relatively quickly.
Hopefully humans can find ways to slow down and contain the AI “arms race” before it is too late. Otherwise, humans face a quasi extinction threat not unlike the one suffered by Neanderthals when smarter Africans caught up to them in Europe. Neanderthals were African primates who migrated to European caves and were king and queens of the caves for about 350,000 years.
About 40,000 years another group of African primates, the homo sapiens, walked and navigated their way into Europe. They were smarter and more intelligent than Neanderthals. The newer African species was intellectually superior than the older version. It would be like an iphone 15 displacing an iphone 5. Some Neanderthals and homo sapiens interbred. In any event and long story short, Neanderthals went extinct. Homo sapiens are the kings and queens of planet Earth today.
AI may gradually spell the beginning of the end of the reign of homo sapiens as the smartest life form on Earth. AI can lead to transhumanism. AI can help humans transcend into a more advanced and dominant species of sapiens (the AI Sapiens?), who will dethrone the current homos. Current homos may be able to coexist like pets, or may go extinct like Neanderthals. All that is yet to be created. The universe is a creative matrix, a creatix. No one knows what will happen in the future because the future has not been created yet.
Machine Learning In the meantime, learning more about AI and AI machine learning programming can be fun. It can help humans stay relevant. It can help humans learn more about how they themselves learn so that they can maximize their potential. It can help humans become entrepreneurs and investors in the lucrative AI industry. There is nothing to lose with learning more about AI and machine learning, and there is much to potentially win and earn.
Machine learning turns traditional computer programming on its head. In traditional programming, the programmers create rules that computer apply to data to return results (answers). In AI machine learning programming, programmers provide the answers that the computers analized against the data to decipher the applicable rules in play.
Traditional Programming:
Rules + Data => Answers
AI Machine Learning Programming
Answers + Data => Rules
In machine learning, the computer learns what the correct answers are and then figures out what are the data characteristics, patterns, and correlations that make up the rules. For example, to learn to recognize the image of a shoe, the computer is fed the answers by showing it say 10,000 images of different shoes. The computer finds out what similarities in the binary data correspond to the 10,000 shoes. The computer can learn to recognize whether a new image represents a show or not based on whether the image binary data includes the patterns and characteristics shared by the other 10,000 samples.
More on AI Programming vs Traditional Programming
AI (Artificial Intelligence) and machine learning programming differ from traditional computer programming in several key aspects:
Problem-solving approach:
- Traditional programming typically involves writing explicit instructions to solve a specific problem or perform a task. Developers specify the rules and algorithms directly.
- In AI and machine learning programming, the focus is on creating algorithms that can learn from data and make predictions or decisions without being explicitly programmed. The emphasis shifts from explicit instruction to learning patterns and relationships from data.
Data-driven:
- Traditional programming may involve processing predefined inputs and producing predetermined outputs based on predetermined logic.
- AI and machine learning programming rely heavily on data. Algorithms are trained on large datasets to recognize patterns and make predictions or decisions based on new, unseen data.
Flexibility and adaptability:
- Traditional programs are typically rigid and follow predefined logic. They may not adapt well to new or changing environments without manual intervention and modification of code.
- AI and machine learning models can adapt and improve over time as they encounter new data. They have the potential to learn from experience and refine their performance without the need for explicit reprogramming.
Complexity of algorithms:
- Traditional programming often involves writing relatively straightforward algorithms based on logical rules, arithmetic operations, and control structures.
- AI and machine learning algorithms can be highly complex, involving sophisticated mathematical and statistical techniques such as neural networks, decision trees, support vector machines, and more. These algorithms may have many parameters that need to be tuned and optimized for optimal performance.
Evaluation and validation:
- In traditional programming, the correctness of the program’s output can often be verified through testing and debugging.
- AI and machine learning models require rigorous evaluation and validation procedures to assess their performance, generalization ability, and robustness. This involves techniques such as cross-validation, test/train splits, and performance metrics specific to the problem domain.
Interpretability and transparency:
- Traditional programs are generally transparent, and developers can easily understand how they work by examining the code.
- AI and machine learning models, especially complex ones like deep neural networks, can be difficult to interpret and understand. They are often referred to as “black boxes,” making it challenging to explain their decisions or behavior, which can be a significant concern in applications where interpretability is crucial, such as healthcare and finance.
In summary, AI and machine learning programming involve creating algorithms that can learn from data and improve over time, whereas traditional programming focuses on writing explicit instructions to solve specific problems. AI programming requires a deep understanding of statistical methods, data processing techniques, and domain knowledge in addition to programming skills.
Human Learning
Machine learning mimics how humans learn. Take human languages as an example. Most humans become proficient in their native languages before they learn the formal rules of the language. Humans are exposed to the “correct” results or answers of their native languages before they are formally trained on grammar and other linguistic rules.
Humans are exposed to thousands and thousands of usage samples until they figure out intuitively the patterns of speech in their native language. They learn those patterns being immersed in their native language since audition is developed in the womb. They are exposed to the “correct” results or answers about how the language is spoken. They naturally and intuitively figure out the applicable rules of the language and begin to gradually begin to speak it fluently. Later on, in grade school and beyond, humans are taught formal rules of the language such as grammar, conjugation, and other conventions.
Nonetheless, when trying to learn another language, most humans are victims of traditional educational methods that rarely ever work. Instead of learning the second language through immersion in a way that would mimic how they learned their native languages, humans are typically subjected to a traditional programming or structured learning program that sets them up for failure in the target language.
Traditional second language education is typically centered on memorized translated vocabulary and memorizing grammatical rules. This puts the cart before the horse, inverting the natural way of learning a language. The traditional method is rarely ever effective and is typically utterly ineffective. Even after years of traditional efforts to learn a second language, most students lack fluidity and never achieve proficiency in the second language. However, when humans are immersed in the new language in a way that mimics the natural immersion of the native language, results are considerably superior.
Humans learn more by doing.
Humans learn better by being exposed to the results desired and let them intuitively find ways of matching those results. In the process, humans figure out naturally what the patterns, conventions, and rules are. Besides languages, sports are another good example. Most humans who learn to play sports well, learn by observing and practicing moves. Later on, they can learn the theory and written rules. However, if the focus on learning the theory and the rules without practicing, they never learn the sport. Imagine a human trying to learn to ride a bicycle, how to throw a basket in basketball, or how to kick a soccer ball by reading books about physics all day without watching the sport and actually trying it at the court or the field.
Google Colab
Just like humans learn to play at the playground, at the court, or at the field, humans can learn AI machine learning programming at the lab. Specifically, the Google Colaboratory or Colab. Begin by wathching YouTube videos to see how the sport is played. Then go to the Colab to play with Python code. While playing and using trial and error, google tutorials for more information and help. You will learn better and more. That’s guaranteed.
Google Colab is a free, cloud-based machine learning platform. It’s used for machine learning projects, such as training and running models, processing data, creating visualizations, and collaborating. It is easy to use. No setup required. It is flexible allowing you to train and run machine learning models, create visualizations, and collaborate with others. Google Colab is an excellent tool for machine learning projects. It allows users to write and execute Python code collaboratively in a Jupyter Notebook environment. Jupyter notebooks support multiple languages, including Python, Julia, and R. However, Colab currently only supports Python. It provides access to free GPU and TPU resources that can significantly speed up model training. You can also install popular machine learning libraries such as Sklearn, TensorFlow and PyTorch to make use of those in your modeling.
Colab notebooks are saved under your Google Drive account, just like your Google Docs and Google Sheets files.
To get started with Google Colab, you can:
Go to Google Colab and sign in with your Google account
- Click on File > New notebook to create a new notebook
- For deep learning, use a GPUYes, TensorFlow runs on Google Colab. Google Colab is a cloud-based Jupyter notebook environment that allows you to run Python code. It comes with TensorFlow pre-installed, so you can start using it right away.
- To use TensorFlow on Google Colab, simply create a new notebook and import the TensorFlow library. You can then start writing TensorFlow code and running it in the notebook.
Brief notes on TensorFlow
TensorFlow is a software library for numerical computation using data flow graphs. It was originally developed by the Google Brain team and released under the Apache 2.0 open source license nine years ago in 2015.
TensorFlow is a popular choice for machine learning and deep learning applications because it can be used to create and train complex models quickly and efficiently. It is also highly scalable, allowing models to be trained on large datasets using distributed computing. TensorFlow is available in Python, C++, Java, Go, and Rust. It can be run on a variety of platforms, including CPUs, GPUs, and TPUs.
TensorFlow is used by a wide range of companies and organizations, including Google, Facebook, Amazon, and Uber. It is also used by researchers and academics to develop new machine learning and deep learning algorithms.
Here are some of the benefits of using TensorFlow:
- Open source and free to use.
- Scalable from simple models on small data sets to complex models on large datasets.
- Versatile. Available in Python, C++, Java, Go, and Rust. Works in Windows, Mac, and Linux
- Community. Already a large and active community of users and developers.
- Documentation. Well-documented with many tutorials and online learning resources.
How Does TensorFlow Work?
TensorFlow works by representing computations as data flow graphs. Each node in the graph represents a mathematical operation, and each edge between nodes represents a multidimensional data array, or tensor. The graph is executed by feeding data into the input nodes and computing the output values of the output nodes.
It is called TensorFlow because the nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. In a graph diagram, a nodes are usually represented by circles. Nodes can be connected to one another by lines or links known as edges.
TensorFlow can be used to train and deploy machine learning models. It can also be used to perform numerical computation for other tasks, such as AI image processing and AI natural language processing.
Here are the steps on how TensorFlow works:
1. Data preprocessing:
- The first step is to preprocess the data. This may involve cleaning the data, removing outliers, and converting the data into a format that TensorFlow can understand.
2. Building the model:
- The next step is to build the model. This involves defining the architecture of the model and specifying the parameters of the model.
3. Training the model:
- The next step is to train the model. This involves feeding the data into the model and adjusting the parameters of the model until the model can make accurate predictions. For example, you can feed into the model 10,000 images of shoes to let it learn what are the data characteristics that make a data arrangement a shoe.
4. Evaluating the model:
- The next step is to evaluate the model. This involves feeding the model data that it has not seen before and measuring the accuracy of the model’s predictions.
5. Deploying the model:
- The final step is to deploy the model. This involves making the model available to users so that they can use the model to identify data, detect patterns, and make predictions.
TensorFlow is a powerful tool for machine learning and deep learning. It is used by researchers and developers to create state-of-the-art models for a wide variety of tasks.
Yes, get curious about all this. Watch videos on YouTube. Google tips and tutorials.
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