Artificial Intelligence#
Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. It includes learning, reasoning, and self-correction.
Examples of AI applications include expert systems, natural language processing (NLP), speech recognition, machine vision, and generative tools like GPT and Perplexity.
Enhanced efficiency and productivity.
Automation of repetitive tasks.
24/7 availability.
Faster devision-making.
Reduction of human error.
Data analysis and insights.
Privacy concerns and ethical problems.
Cost of implementation and maintenance.
Reduced jobs for humans.
Types of AI#
This makes up most current AI models, where the system is designed to perform a single, specific task (such as coding, image generation, or writing) but can’t expand or apply their skills outside of what they’re programmed to do.
A theoretical step further, where AI surpasses human intelligence and the top human minds in every field, able to even perform tasks and solve problems that would be impossible for humans.
As the name suggests, this next type of machine has the ability to evaluate past inputs and actions to learn and improve over time.
The endgame of AI evolution in many sci-fi stories is a self-aware system that has devleoped consciousness and goes a step beyond understanding the emotions and feelings of others and would hypothetically understand its own existence and internal process.
Used to describe models that have broader cognitive capabilities, able to match or slightly surpass humans across a variety of tasks, from recommending restaurants and booking your flights to driving your car–all in one model.
These systems respond to user inputs but have no memory to enable previous mistakes to inform future decisions. Because of this, a specific inpute will always have the same input.
These would be able to understand that other entities ahve thoughts, emotions, desires, and intentions, and also how its own actions influence those. This would represent a huge step towards more personalized, human-like interactions with AI.
Machine Learning#
The subset of Artificial Intelligence focused on algorithms that can “learn” the patterns of training data and, subsequently, make accurate inferences about new data. This pattern recognition ability enables machine learning models to make decisions without explicit, hard-coded instructions.
Types of Machine Learning#
The machine is given a set of training data, including input data and the corresponding expected output. The machine then learns to map the input to the output using a function so it can produce the expected output for any new input.
The data isn’t labeled. The algorithm must find patterns in the data. This type of machine learning is used to find groups in data and to make predictions about new data points. It’s also used for tasks such as clustering and dimensionality reductions.
A type of machine learning that involves training models to make decisions in environments where there are no clear consequences of their actions. In other words, the model is reinforced with feedback based on its performance so it learns which actions lead to the best outcomes.
Examples of Popular Machine Learning Applications
Self-driving cars
Encountering spam e-mails
Detecting malware
Image recognition
Speech recognition
Anticipate traffic patterns