Are algorithms the same as computer programs?

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Frequently asked questions: AI tools

What is the difference between classification and regression in supervised machine learning?

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).

How can I use ChatGPT for my term paper or bachelor thesis?

You can use ChatGPT to assist in the writing process for your research paper, thesis, or dissertation in the following ways:

• Developing a research question
• Creating an outline
• Generating literature ideas
• Paraphrasing text
• Getting feedback

Why do we need algorithms?

Algorithms are valuable to us because they:

• Form the basis of much of the technology we use in our daily lives, from mobile apps to search engines.
• Power innovations in various industries that augment our abilities (e.g., AI assistants or medical diagnosis).
• Help analyse large volumes of data, discover patterns and make informed decisions in a fast and efficient way, at a scale humans are simply not able to do.
• Automate processes. By streamlining tasks, algorithms increase efficiency, reduce errors, and save valuable time.

Are algorithms the same as artificial intelligence (AI)?

Algorithms and artificial intelligence (AI) are not the same, however they are closely related.

• Artificial intelligence is a broad term describing computer systems performing tasks usually associated with human intelligence like decision-making, pattern recognition, or learning from experience.
• Algorithms are the instructions that AI uses to carry out these tasks, therefore we could say that algorithms are the building blocks of AI – even though AI involves more advanced capabilities beyond just following instructions.

What is an algorithm in computer science?

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.

What is the exploration vs exploitation trade off in reinforcement learning?

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.

What is deep reinforcement learning?

Deep reinforcement learning is the combination of deep learning and reinforcement learning.

• Deep learning is a collection of techniques using artificial neural networks that mimic the structure of the human brain. With deep learning, computers can recognize complex patterns in large amounts of data, extract insights, or make predictions, without being explicitly programmed to do so. The training can consist of supervised learning, unsupervised learning, or reinforcement learning.
• Reinforcement learning (RL) is a learning mode in which a computer interacts with an environment, receives feedback and, based on that, adjusts its decision-making strategy.
• Deep reinforcement learning is a specialized form of RL that utilizes deep neural networks to solve more complex problems. In deep reinforcement learning, we combine the pattern recognition strengths of deep learning and neural networks with the feedback-based learning of RL.

What are some real-life applications of reinforcement learning?

Some real-life applications of reinforcement learning include:

• Healthcare. Reinforcement learning can be used to create personalized treatment strategies, known as dynamic treatment regimes (DTRs), for patients with long-term illnesses. The input is a set of clinical observations and assessments of a patient. The outputs are the treatment options or drug dosages for every stage of the patient’s journey.
• Education. Reinforcement learning can be used to create personalized learning experiences for students. This includes tutoring systems that adapt to student needs, identify knowledge gaps, and suggest customized learning trajectories to enhance educational outcomes.
• Natural language processing (NLP). Text summarization, question answering, machine translation, and predictive text are all NLP applications using reinforcement learning.
• Robotics. Deep learning and reinforcement learning can be used to train robots that have the ability to grasp various objects , even objects they have never encountered before. This can, for example, be used in the context of an assembly line.

How can I use AI writing tools?

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.

When should I use unsupervised learning?

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.

When should I use supervised learning?

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.

What is generative art?

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.

What is an example of a machine learning application in real life?

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.

What is the difference between machine learning and traditional programming?

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.

Is artificial intelligence (AI) the same as machine learning (ML)?

Although the terms artificial intelligence and machine learning are often used interchangeably, they are distinct (but related) concepts:

• Artificial intelligence is a broad term that encompasses any process or technology aiming to build machines and computers that can perform complex tasks typically associated with human intelligence, like decision-making or translating.
• Machine learning is a subfield of artificial intelligence that uses data and algorithms to teach computers how to learn and perform specific tasks without human interference.

In other words, machine learning is a specific approach or technique used to achieve the overarching goal of AI to build intelligent systems.

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