Algorithms and computer programs are sometimes used interchangeably, but they refer to two distinct but interrelated concepts.
In classification, the goal is to assign input data to specific, predefined categories. The output in classification is typically a label or a class from a set of predefined options.
In regression, the goal is to establish a relationship between input variables and the output. The output in regression is a real-valued number that can vary within a range.
In both supervised learning approaches the goal is to find patterns or relationships in the input data so we can accurately predict the desired outcomes. The difference is that classification predicts categorical classes (like spam), while regression predicts continuous numerical values (like age, income, or temperature).
You can use ChatGPT to assist in the writing process for your research paper, thesis, or dissertation in the following ways:
Algorithms are valuable to us because they:
Algorithms and artificial intelligence (AI) are not the same, however they are closely related.
In computer science, an algorithm is a list of unambiguous instructions that specify successive steps to solve a problem or perform a task. Algorithms help computers execute tasks like playing games or sorting a list of numbers. In other words, computers use algorithms to understand what to do and give you the result you need.
A key challenge that arises in reinforcement learning (RL) is the trade-off between exploration and exploitation. This challenge is unique to RL and doesn’t arise in supervised or unsupervised learning.
Exploration is any action that lets the agent discover new features about the environment, while exploitation is capitalizing on knowledge already gained. If the agent continues to exploit only past experiences, it is likely to get stuck in a suboptimal policy. On the other hand, if it continues to explore without exploiting, it might never find a good policy.
An agent must find the right balance between the two so that it can discover the optimal policy that yields the maximum rewards.
Deep reinforcement learning is the combination of deep learning and reinforcement learning.
Some real-life applications of reinforcement learning include:
AI writing tools can be used to perform a variety of tasks.
Generative AI writing tools (like ChatGPT) generate text based on human inputs and can be used for interactive learning, to provide feedback, or to generate research questions or outlines.
These tools can also be used to paraphrase or summarise text or to identify grammar and punctuation mistakes. You can also use Scribbr’s free paraphrasing tool, summarising tool, and grammar checker, which are designed specifically for these purposes.
Unsupervised learning should be used when your data is unlabeled and your goal is to discover the inherent structure or pattern in the data.
This approach is helpful for tasks like clustering, association, and dimensionality reduction.
Supervised learning should be used when your dataset consists of labeled data and your goal is to predict or classify new, unseen data based on the patterns learned from the labeled examples.
Tasks like image classification, sentiment analysis, and predictive modeling are common in supervised learning.
Generative art is art that has been created (generated) by some sort of autonomous system rather than directly by a human artist. Nowadays, the term is commonly used to refer to images created by generative AI tools like Midjourney and DALL-E. These tools use neural networks to create art automatically based on a prompt from the user (e.g., “an elephant painted in the style of Goya”).
However, the term has been in use since before this technology existed, and it can also refer to any technique use by an artist (or writer, musician, etc.) to create art according to a process that proceeds autonomously – i.e., outside of the artist’s direct control. Examples of generative art that does not involve AI include serialism in music and the cut-up technique in literature.
A real-life application of machine learning is an email spam filter. To create such a filter, we would collect data consisting of various email messages and features (subject line, sender information, etc.) which we would label as spam or not spam. We would then train the model to recognize which features are associated with spam emails. In this way, the ML model would be able to classify any incoming emails as either unwanted or legitimate.
Traditional programming and machine learning are essentially different approaches to problem-solving.
In traditional programming, a programmer manually provides specific instructions to the computer based on their understanding and analysis of the problem. If the data or the problem changes, the programmer needs to manually update the code.
In contrast, in machine learning the process is automated: we feed data to a computer and it comes up with a solution (i.e. a model) without being explicitly instructed on how to do this. Because the ML model learns by itself, it can handle new data or new scenarios.
Overall, traditional programming is a more fixed approach where the programmer designs the solution explicitly, while ML is a more flexible and adaptive approach where the ML model learns from data to generate a solution.
Although the terms artificial intelligence and machine learning are often used interchangeably, they are distinct (but related) concepts:
In other words, machine learning is a specific approach or technique used to achieve the overarching goal of AI to build intelligent systems.
Our research into the best summary generators (aka summarisers or summarising tools) found that the best summariser available in 2023 is the one offered by QuillBot.
While many summarisers just pick out some sentences from the text, QuillBot generates original summaries that are creative, clear, accurate, and concise. It can summarise texts of up to 1,200 words for free, or up to 6,000 with a premium subscription.
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| Types of editing | Available at Scribbr? |
|---|---|
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