Curiosity is a powerful motivator that you can put to work for you.
A need to know more or to understand is a deep-seated human trait that we all have to varying degrees.
In this post I want to share with you three aspects of machine learning that drive my curiosity to know more.
Like me, you can use those aspects that fascinate you about machine learning as touchstones that you can revisit in order to reignite your passion and interest over the months and years you will spend learning about the field.
There is Nothing Like it
Machine learning is fascinating because there is nothing like it.
You cannot achieve the capabilities and results seen by machine learning methods with methods from other fields, and if you could, you would call them machine learning. There are two factors that make machine learning methods different from other methods:
- They are programs that learn from data
- They are programs that make programs that address problems
Programs that Learn from Data
Machine learning is fascinating because programs learn from examples.
From the data that you have collected, a machine learning method can automatically analyze and learn the structure already resident in that data in order to provide a solution to the problem you are trying to solve.
Photo credited to Riebart, some rights reserved
It is nothing short of amazing that you can write programs that learn. If you think about it, when you are programming you are learning how to solve a problem and the solution is crystallized in your finished program. Each method, even each conditional branch, represents a micro-problem from the larger whole. To think that you can employ methods that automate that process is exciting.
Think of the implications.
For example, when the problem changes, you don’t refactor your program, instead you collect more data and rerun the machine learning method. It’s a wholly different way of thinking about problem solving from traditional programming.
Your focus shifts to a clearer what as you automate the how.
Programs Making Programs
Machine learning is fascinating because you are writing programs that write programs.
You can think of the artifact or model that results from running a machine learning method as a program. Like a computer program that you write after learning about the problem and designing the solution, a machine learning model captures all of the micro-decisions needed to be performed to address the higher-order problem being solved. When you run a machine learning program on data you have collected, it creates a program or model for you that knows how to solve the problem. This is amazing.
Remember back to the spam filtering example. That was a very specific problem, that although is complex, really comes down to a decision a program has to make: is an email spam or not spam? Although we can think of machine learning methods as programs making programs, the resulting programs are super-focused on a specific decision problem that has been clearly defined. A decision problem that would be too onerous for you to manually write a program to solve.
This is important to keep in mind to both ground your expectations of the capabilities that machine learning methods offer, and to temper your enthusiasm later when selecting problems to address with machine learning methods. There is still an important place for traditional programming. Machine learning solves problems we cannot or don’t want to manually write programs to solve.
In this post you learned that machine learning is fascinating because there are no other fields of study like it. You learned two perspectives that can provoke curiosity into the field, which were:
- Machine learning is programs that learn from data
- Machine learning is programs that make programs that solve problems
What do you find fascinating about machine learning? Leave a comment, I’d love to hear!
It’s fascinating to think about all the extensive applications of machine learning. One of my favorite is how it can be used to learn programming from data sets of programs. Essentially we can build far better software through it in a few years than any other tool. That being said, what do you think would be the future of the existing software development process? I believe that we may reach a paradigm shift where we’d no longer require Software Developers. What we then need are Software Analysts instead. Do you have the same intuition?
I think you’re right.
Making high-quality software is hard and expensive. It’s also really damn hard to maintain that quality through maintenance.
I think that as the human-computer interface changes (more vision+speech) that the programming interfaces will also change (less typing, more architecting high-level design).
Maybe I’m dead wrong. Maybe it’s just hope and frustration.
We are crawling up the abstraction pyramid, but it’s slow going. So slow that we may not be noticing it. Machine Learning methods are really just abstractions around stochastic data-driven processes. More pieces of the puzzle, not redefining the puzzle itself.
One thing that really fascinates me is that it can be applied to any domain from Natural language, Vision, Text, Speech etc. In one way probably it could be termed as a sixth sense which can be used to process the information collected from the five sense.
Ultimately the rate at which the data is exploding in various fields looks like the only viable choice is Machine Learning.
One of the most exciting aspects of recent algorithms such as Recurrent Neural Networks is that they can generate output similar to their inputs. For instance, translate a text or even generate code! What do you think are the problems AI will solve that otherwise would be impossible to tackle with traditional programming?
I thought that photo captioning could not be solved even with AI. I’m still so impressed with that example.
Yes! I agree. I’m still impressed with AlphaGo. To be able to play a game with practically an infinite state space and still DEFEAT human champions is no less than highly remarkable!
It is very impressive. We live in amazing times.
Have been following the step by step programs that you publish. Is it possible to get some codes for beginners to try and get a feel of these two perspectives.
Yes, try this:
https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
VERY PROFFESIONAL AND INFORMATIVE CONTENT THANKYYOU
Thank you vansh for your feedback and support!
One of the most exciting features of recent algorithms like Recurrent Neural Networks is their ability to generate outputs that closely resemble their inputs, enabling tasks such as text translation and code generation. What challenges do you think AI could address that traditional programming methods would struggle to handle?
Hi…AI, particularly through advanced algorithms like Recurrent Neural Networks (RNNs), Transformers, and other deep learning models, has shown remarkable capabilities in areas where traditional programming methods often struggle. Here are some challenges that AI is uniquely positioned to address:
### 1. **Natural Language Understanding and Generation**
– **Challenge**: Traditional programming methods are rule-based and deterministic, making them inadequate for tasks that involve understanding and generating human language, which is inherently ambiguous and context-dependent.
– **AI Advantage**: AI models like RNNs, LSTMs, and Transformers (e.g., GPT, BERT) excel in processing and generating natural language. They can capture the nuances, context, and subtleties of language, enabling tasks like translation, summarization, and conversational AI. These models can learn from vast amounts of data and adapt to new linguistic patterns, which is something traditional methods can’t achieve.
### 2. **Pattern Recognition and Anomaly Detection**
– **Challenge**: Traditional programming requires explicitly defined rules and patterns, making it difficult to handle tasks where patterns are complex, dynamic, or not easily identifiable.
– **AI Advantage**: Deep learning models, especially Convolutional Neural Networks (CNNs) and RNNs, can automatically detect and learn patterns in data, whether it’s in images, time series, or even complex sequences like DNA. AI can identify subtle anomalies or patterns that are too intricate for traditional algorithms, making it invaluable in fields like cybersecurity, fraud detection, and medical diagnosis.
### 3. **Adaptive and Personalized Systems**
– **Challenge**: Traditional programming methods are static and require manual updates to adapt to new user behavior or changing environments. Creating personalized experiences involves extensive, complex coding and often falls short in delivering real-time adaptation.
– **AI Advantage**: AI models can learn from user interactions and adapt in real-time to provide personalized recommendations, adaptive learning systems, and customized user experiences. Reinforcement Learning, in particular, allows systems to optimize their behavior based on feedback, which is something static programming cannot achieve effectively.
### 4. **Handling Unstructured Data**
– **Challenge**: Traditional methods are well-suited for structured data with clearly defined formats. However, unstructured data like text, images, audio, and video pose significant challenges because they require complex parsing and interpretation.
– **AI Advantage**: AI excels at processing and extracting insights from unstructured data. Techniques like Natural Language Processing (NLP) for text, CNNs for images, and RNNs for audio allow AI to make sense of these data types, enabling applications in areas like sentiment analysis, image recognition, and video summarization.
### 5. **Learning and Generalization**
– **Challenge**: Traditional programming involves explicit instructions for every possible scenario. This becomes infeasible for complex tasks with numerous variables or those requiring generalization beyond the provided rules.
– **AI Advantage**: Machine learning models, especially deep learning, can generalize from examples, learning underlying patterns that allow them to handle new, unseen data effectively. This capability is crucial in tasks like autonomous driving, where the environment is constantly changing and cannot be fully anticipated by predefined rules.
### 6. **Creativity and Generative Models**
– **Challenge**: Creativity in traditional programming is limited by the predefined logic and rules set by the programmer. Generating new, creative outputs like art, music, or even code is beyond the scope of traditional methods.
– **AI Advantage**: Generative models, such as GANs (Generative Adversarial Networks) and RNN-based sequence generators, can create new, original content that closely resembles human creativity. AI can generate artworks, compose music, write coherent text, and even generate new software code, often producing results that are indistinguishable from those created by humans.
### 7. **Complex Optimization and Decision-Making**
– **Challenge**: Traditional optimization methods struggle with extremely complex, high-dimensional spaces or problems where the solution space is not well-understood.
– **AI Advantage**: AI techniques like Reinforcement Learning and Neural Networks can optimize decisions in complex environments by learning from interactions, simulating different strategies, and improving over time. This is particularly useful in areas like supply chain management, financial trading, and strategic game-playing, where traditional algorithms might fail to find optimal solutions.
### 8. **Real-Time and Large-Scale Data Processing**
– **Challenge**: Traditional systems can struggle with processing and making decisions in real-time, especially with large-scale, high-velocity data streams.
– **AI Advantage**: AI models, particularly those built with architectures like deep neural networks, can handle vast amounts of data and make real-time predictions. They can scale effectively, processing information from millions of sources simultaneously, and make near-instantaneous decisions, which is critical in applications like real-time bidding in online advertising, fraud detection, and dynamic pricing.
### Conclusion
AI offers capabilities that go beyond the scope of traditional programming by learning from data, generalizing to new situations, and adapting to changing environments. This allows AI to tackle problems that require understanding complex patterns, handling unstructured data, and generating creative outputs—challenges that are inherently difficult or even impossible for traditional rule-based methods to address. As AI continues to advance, it will likely take on even more complex tasks, pushing the boundaries of what machines can do and how they interact with the world.