An Intuitive Approach to the Comparison between Machine Learning Models and Human Mind

The rise of GPUs (Graphics Processing Units) and computing devices, particularly those used for parallel processing and high-performance computing, can be traced back to several key milestones in the late 20th century and early 21st century. Early 2000s: Emergence of Consumer GPUs, Mid-2000s: CUDA and Stream Computing, 2010s: Rise of Deep Learning and 2010s-Present: Heterogeneous Computing and AI are some of the important happenings for the rise of Artificial Intelligence and Machine Learning.

However, the human race has always had access to and has been using the most sophisticated computing units on the planet. The computing unit referred to here is the human brain, a wondrous masterpiece of nature, boasting an inspiring network of around 86 billion neurons, each as unique as a star in the night sky. With its boundless synaptic connections, this neural orchestra orchestrates unparalleled feats of learning, creativity, and adaptability, crafting the symphony of human intelligence and innovation. It’s a dazzling testament to the limitless potential of our minds, forever evolving and expanding the horizons of knowledge and imagination!

Researchers, Scientists, and Spiritual experts have always argued about the mind’s true nature. Certainly, the research community has spent an enormous amount of work in identifying the dynamics of the mind. In this article, we will be looking into the comparison between the very basics of the mind and ML algorithms.

Inputs and Data

The concepts of machine learning clearly tell us that mathematical functions and adjustable weights adapt to the patterns and learn from historical or existing data through a process called model training. Similarly, the moment a human is born starting from being an infant he/she is continuously being bombarded with enormous amounts of data through the five senses. On the other hand data scientists and machine learning engineers strive and hustle to bring up enormous amounts of data to build a model for a specified task.

With this continuous input of data at every instant, the mind constantly and gradually rewires itself to get better with understanding, decision-making, etc. Every Machine learning model is corrected on the basis of a loss function or an error function, The goal is to train a model with minimal loss or error. Similarly, the process by which the mind rewires itself to reach a state of minimum error is the fundamental aspect of learning and adaptation. This process involves the brain’s ability to reorganize its neural connections through a phenomenon known as neuroplasticity.

Under-fit and Cognitive Limitations

In the context of machine learning, “underfitting” occurs when a model is too simple to capture the underlying patterns in the data, leading to poor performance.

Analyzing the graphs above, it becomes evident that applying a linear model to a dataset with a non-linear distribution yields unsatisfactory results. This underscores the importance of aligning the model’s complexity with that of the underlying data distribution. When the model’s complexity matches the intricacies of the dataset, it is better equipped to perform effectively, closely mirroring the patterns inherent in the distribution.

Recalling those moments when we struggled to memorize a mere four-line rhyme or found basic arithmetic operations challenging in childhood, it’s evident that our young minds operated with lower complexity levels. This lower complexity hindered our initial learning experiences, akin to a linear model attempting to fit a non-linear distribution. However, we can use the term “cognitive limitations” to describe situations where the human mind fails to adequately process or understand certain information or tasks due to its inherent constraints. As our cognitive complexity grew with age and experience, we became better equipped to grasp more intricate concepts and tasks.

Optimizers and Decision-Making

In the realm of machine learning, optimization algorithms come into play during model training. They are employed to fine-tune a model’s parameters in order to minimize a particular loss or error function. The overarching objective is to identify the most suitable set of parameters that aligns seamlessly with the training data and exhibits strong generalization when applied to novel, unseen data. On the other hand, the human mind undertakes a comparable optimization process in the realm of decision-making. It consistently assesses a multitude of options, assesses their merits and drawbacks, and ultimately selects actions or choices perceived as optimal based on established objectives, personal preferences, and the information available.

The adaptability of the human mind shines through as it excels in learning from past experiences and errors. In a parallel fashion, in the realm of machine learning, optimizers engage in iterative processes, refining model parameters using feedback to enhance overall performance. Additionally, the human mind utilizes an array of heuristics and tactics to streamline decision-making, a concept often referred to as “bounded rationality” in complex scenarios. Similarly, within the field of machine learning, optimization algorithms frequently leverage heuristics and strategies to uncover solutions that are nearly optimal in situations that pose computational challenges.

What is the continuous process all about?

“The mind is not a vessel to be filled, but a fire to be kindled.” — Plutarch

The human mind, much like the fine-tuning of a machine learning model, is in a perpetual state of learning and increasing complexity. Just as an algorithm refines its parameters to optimize performance, the mind evolves through continuous learning from experiences, adapting to new challenges, and fine-tuning its decision-making processes. Both processes exemplify the inherent capacity for growth and adaptability, striving for ever-improving outcomes.

In conclusion, we’ve explored the parallels between machine learning and the human mind, and we’ve uncovered fascinating similarities between machine learning optimization and the human mind’s decision-making processes. Both strive for optimization, adapting based on feedback and employing heuristics to navigate complexity. These comparisons underscore the enduring capacity for growth and learning in both artificial and human intelligence.