why are constants needed in an experiment

What Is an Experimental Constant?

Explanation and Examples of Constants

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A constant is a quantity that does not change. Although you can measure a constant, you either cannot alter it during an experiment or else you choose not to change it. Contrast this with an experimental variable , which is the part of an  experiment that you change or that is affected by the experiment. There are two main types of constants you may encounter in experiments: true constants and control constants. Here is an explanation of these constants, with examples.

Physical Constants

Physical constants are quantities which you cannot change. They may be calculated or defined.

Examples: Avogadro's number, pi, the speed of light, Planck's constant

Control Constants

Control constants or control variables are quantities a researcher holds steady during an experiment. Even though the value or state of a control constant may not change, it is important to record the constant so the experiment may be reproduced.

Examples: temperature, day/night, duration of a test, pH

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• Scientific Variable
• The Difference Between Control Group and Experimental Group
• What Is a Variable in Science?
• The Role of a Controlled Variable in an Experiment
• Understanding Experimental Groups
• Fundamental Physical Constants
• What Is the Rate Constant in Chemistry?
• Physical Constants, Prefixes, and Conversion Factors
• Independent Variable Definition and Examples
• What Is an Experiment? Definition and Design
• How To Design a Science Fair Experiment

Understanding Constants In An Experiment

• December 11, 2020
• Science Facts

Constants In An Experiment

The term ‘constant’ is used to refer to a particular quantity which is not intended to alter or change.

What are Constants?

Amongst the other fields, constants are also used in during various situations of an experiment. Such constants which are implemented in experiments are referred to as experimental constants.

Like other constants, experimental constants are also measurable. However, they cannot be changed in the due course of the experiment or in-between them.

There are various values which are considered as constants during experiments.

Constituents of natural forces such as the speed of light or the atomic weight of gold are considered as experimental constants.

Additionally, there are various properties which are considered to be experimental constants. The prime example of this is the boiling point of water.

The boiling point of water depends may alter depending on the altitude and the decrease in acceleration due to gravity.

However, experiments involving water in a single location consider its boiling point as constants.

The Need for Constants

There are various reasons why we need to implement constants within our experiments. However, they all stem out from the same characteristic, that is duplication of results or consistency in results.

Whenever we perform any experiment, we do so carefully ensuring that the process can be duplicated again as required.

If we involved a plethora of variables it would that we would receive a ton of variable results as well. This would completely defeat the purpose of experimenting.

Factors Considered as Constant in an Experiment

When we are on a lookout for constants, we essentially look for such factors which are considered to be similar in all states or conditions.

Irrespective of the time or the nature of this aforementioned factor, it will never change its state.

This stems out from the fact that a constant never changes its state in the duration of an experiment.

Understanding a constant becomes easier when one contrasts the constant factor with a mathematical constant.

In the field of mathematics, a constant refers to a particular factor which has a fixed numerical value.

In the same way, a constant in an experiment does not change its state and is universally equal all-around.

The only situation in which a mathematical constant and an experimental constant differ is that a mathematical constant does not involve any physical measurement.

Examples of Constants in Experiments

When you consider the factors used for determining an experimental constant, there are various constants that you might come across.

A few good examples of experimental constants include:

• The acceleration due to gravity
• Gravitational constant
• Avogadro’s constant
• The Gas constant
• Boltzmann’s constant
• The Stefan-Boltzmann constant
• Elementary charge
• Electron rest mass
• Proton rest mass
• Unified atomic mass unit
• Solar constant, and much more.

Apart from these constants, there are various other factors which are also considered as constants like Planck’s constant, the permittivity of free space, etc.

In essence, if you want to determine whether or not a particular factor is considered as a constant, you might want to think about such factors which contain a form of measurement that is universal.  

Constants In A Scientific Method

The term scientific method refers to an approach seeking a particular form of knowledge involving the formulation of a hypothesis or testing for proving its validity.

More often than not, scientific methods require intense experimentation for proving their validity. And such experiments often involve constants.

You might ask the purpose of constants in scientific methods. Here’s why we need them.

Whenever you need to perform experiments, you need to test through various factors which often involve a lot of measurable change.

These changes occur due to the presence of the dependent variable. As a result, the changes occurring allude to the dependent variable.

To understand such changes, the experimenters often introduce an independent variable for creating changes in the dependent variables.

However, there should always be only a single independent variable in such experiments.

Even other factors such as the presence of other variables are included in the form of controlled variables. And this is known as a constant in a scientific method.

Controls or Controlled Variables

Now, when you encounter the term ‘variable’, you might refer to such factors which are constantly found to be changing during an experiment. And you are right as well.

For the definition of a variable, states that any factor, trait, or condition which is found to exist in different amounts or types is a variable.

As a result, it would be correct to not refer to them as constants, right? Well, not exactly.

There are various situations in which variables are considered as constants. There are certain experiments in which a person performing the experiments considers certain variables in a constant state.

Such variables are referred to as controlled variables. As a result of this, the experimenter can attain more clarity in isolating the relationship between the independent variables and the dependent variables.

Moreover, by considering certain variables as constants, experimenters are also able to achieve constants results whenever they experiment.

The Prime Example of Controlled Variables

In the due course of an experiment, there are various variables which an experimenter considers to be constant.

The prime example of such controlled variables is the boiling point of water.

As aforementioned in the introductory paragraph, the boiling point of water variably changes when factors such as altitude and the acceleration due to gravity are involved in the experiment.

In spite of this, the temperature is considered constant as it allows the experimenter to derive constant results for a particular location or space.

Other Examples of Controlled Variables

Apart from the boiling point of water, there are various other examples which beautifully explain the concept of controlled variables within experiments.

A few of these include:

• The amount of fertilizer which a plant uses for the crop outgrowth.
• The type of soil is used for planting a particular type of plant.
• The amount of time which is spent by children in trying to learn a new concept.
• The amount of sunlight which a plant uses for its growth.

In spite of these examples, there are cases in which you might feel confused regarding controlled variables.

In simple terms, a controlled variable is referred to as determiners which greatly influence the result.

Usually, experimenters are more focused on understanding whether or not control variables have any significant effects on results.

With the help of control variables, they can do the same while achieving the desired results or outcomes in a particular experiment.

The Control Group

The term ‘control group’ essentially refers to a particular standard used for making comparisons in a particular experiment.

Whenever an experimenter stages an experiment, he or she designs it, particularly to include a control group and one or more experimental groups.

In an ideal sense, both the experimental groups and the control groups are similar.

However, the dissimilarity arises between these groups when the experimental group is subjected to various treatments which are believed to affect the outcome of the treatment.

Conversely, the control group isn’t subjected to any form of treatment or intervention which could affect the outcomes arising from the treatment.  

The Need For A Control Group

When you think about the involvement of a control group within the confinements of an experiment, you might think about the need which gives birth to such a need.

The primary reason why we need a control group is for the experimenters to easily be able to conclude a particular study.

It is only with the help of a control group that experimenters can determine whether or not a particular experiment can have a significant effect on the experimental group that can be recorded.

Moreover, the inclusion of a constant group within an experiment also ensures that the possibilities of making errors during the derivation of results are vastly minimized.

The Differences Between Constants and Controls

People often get confused with the different concepts that are involved with constants and controlled variables.

This is not only because they both begin with the same alphabet and that they sound the same, but it is also due to the similarity in their definitions.

However, you can be assured that these concepts don’t define the same things.

When we talk about constants, we essentially talk about the factors that are non-varying.

These factors are universally fixed and defined so that they are unable to start any changes that occur at the time of the results.

However, the purpose of control or a controlled variable isn’t the same.

Unlike a constant, a control or controlled variable is set aside to ignore the occurrence of any changes in the result that rise from the independent variable.

This ensures that the experimenter can view the experiment from an objective point of view.

When experimenters implement an experimental method, they do so understandingly which variables are controls and which of them are constants.

It is only by differentiating the controls and the constants that they can understand the changes occurring in the dependent variable.

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Biology archive

Course: biology archive   >   unit 1.

• The scientific method

Controlled experiments

• The scientific method and experimental design

Introduction

How are hypotheses tested.

• One pot of seeds gets watered every afternoon.
• The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍   and coral bleaching.

• What your control and experimental groups would be
• What your independent and dependent variables would be
• What results you would predict in each group

Experimental setup

• Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍   around 8.2 ‍   ). The corals in these tanks served as the control group .
• Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍   . One set of tanks was medium-acidity ( pH ‍   about 7.9 ‍   ), while another set was high-acidity ( pH ‍   about 7.65 ‍   ). Both the medium-acidity and high-acidity groups were experimental groups .
• In this experiment, the independent variable was the acidity ( pH ‍   ) of the seawater. The dependent variable was the degree of bleaching of the corals.
• The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍   fragments of coral, and there were 5 ‍   identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍   values were all above the neutral pH ‍   of 7.0 ‍   . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

• Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
• Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
• University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
• Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

Why Should You Only Test for One Variable at a Time in an Experiment?

The point of an experiment is to help define the cause and effect relationships between components of a natural process or reaction. The factors that can change value during an experiment or between experiments, such as water temperature, are called scientific variables, while those that stay the same, such as acceleration due to gravity at a certain location, are called constants.

The scientific method includes three main types of variables: constants, independent, and dependent variables. In a science experiment, each of these variables define a different measured or constrained aspect of the system.

Constant Variables

Experimental constants are values that should not change either during or between experiments. Many natural forces and properties, such as the speed of light and the atomic weight of gold, are experimental constants. In some cases, a property can be considered constant for the purposes of an experiment even though it technically could change under certain circumstances. The boiling point of water changes with altitude and acceleration due to gravity decreases with distance from the earth, but for experiments in one location these can also be considered constants.

Sometimes also called a controlled variable. A constant is a variable that could change, but that the experimenter intentionally keeps constant in order to more clearly isolate the relationship between the independent variable and the dependent variable.

If extraneous variables are not properly constrained, they are referred to as confounding variables, as they interfere with the interpretation of the results of the experiment.

Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as possible. This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship.

Independent Variable

The independent variable in an experiment is the variable whose value the scientist systematically changes in order to see what effect the changes have. A well-designed experiment has only one independent variable in order to maintain a fair test. If the experimenter were to change two or more variables, it would be harder to explain what caused the changes in the experimental results. For example, someone trying to find how quickly water boils could alter the volume of water or the heating temperature, but not both.

Dependent Variable

A dependent variable – sometimes called a responding variable – is what the experimenter observes to find the effect of systematically varying the independent variable. While an experiment may have multiple dependent variables, it is often wisest to focus the experiment on one dependent variable so that the relationship between it and the independent variable can be clearly isolated. For example, an experiment could examine how much sugar can dissolve in a set volume of water at various temperatures. The experimenter systematically alters temperature (independent variable) to see its effect on the quantity of dissolved sugar (dependent variable).

Control Groups

In some experiment designs, there might be one effect or manipulated variable that is being measured. Sometimes there might be one collection of measurements or subjects completely separated from this variable called the control group. These control groups are held as a standard to measure the results of a scientific experiment.

An example of such a situation might be a study regarding the effectiveness of a certain medication. There might be multiple experimental groups that receive the medication in varying doses and applications, and there would likely be a control group that does not receive the medication at all.

Representing Results

Identifying which variables are independent, dependent, and controlled helps to collect data, perform useful experiments, and accurately communicate results. When graphing or displaying data, it is crucial to represent data accurately and understandably. Typically, the independent variable goes on the x-axis, and the dependent variable goes on the y-axis.

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What An Experimental Control Is And Why It’s So Important

Daniel Nelson

An experimental control is used in scientific experiments to minimize the effect of variables which are not the interest of the study. The control can be an object, population, or any other variable which a scientist would like to “control.”

You may have heard of experimental control, but what is it? Why is an experimental control important? The function of an experimental control is to hold constant the variables that an experimenter isn’t interested in measuring.

This helps scientists ensure that there have been no deviations in the environment of the experiment that could end up influencing the outcome of the experiment, besides the variable they are investigating. Let’s take a closer look at what this means.

You may have ended up here to understand why a control is important in an experiment. A control is important for an experiment because it allows the experiment to minimize the changes in all other variables except the one being tested.

To start with, it is important to define some terminology.

Terminology Of A Scientific Experiment

Randomization is important as it allows for more non-biased results in experiments. Random numbers generators are often used both in scientific studies as well as on 지노 사이트 to make outcomes fairer.

Scientists use the scientific method to ask questions and come to conclusions about the nature of the world. After making an observation about some sort of phenomena they would like to investigate, a scientist asks what the cause of that phenomena could be. The scientist creates a hypothesis, a proposed explanation that answers the question they asked. A hypothesis doesn’t need to be correct, it just has to be testable.

The hypothesis is a prediction about what will happen during the experiment, and if the hypothesis is correct then the results of the experiment should align with the scientist’s prediction. If the results of the experiment do not align with the hypothesis, then a good scientist will take this data into consideration and form a new hypothesis that can better explain the phenomenon in question.

Independent and Dependent Variables

In order to form an effective hypothesis and do meaningful research, the researcher must define the experiment’s independent and dependent variables . The independent variable is the variable which the experimenter either manipulates or controls in an experiment to test the effects of this manipulation on the dependent variable. A dependent variable is a variable being measured to see if the manipulation has any effect.

Photo: frolicsomepl via Pixabay, CC0

For instance, if a researcher wanted to see how temperature impacts the behavior of a certain gas, the temperature they adjust would be the independent variable and the behavior of the gas the dependent variable.

Control Groups and Experimental Groups

There will frequently be two groups under observation in an experiment, the experimental group, and the control group . The control group is used to establish a baseline that the behavior of the experimental group can be compared to. If two groups of people were receiving an experimental treatment for a medical condition, one would be given the actual treatment (the experimental group) and one would typically be given a placebo or sugar pill (the control group).

Without an experimental control group, it is difficult to determine the effects of the independent variable on the dependent variable in an experiment. This is because there can always be outside factors that are influencing the behavior of the experimental group. The function of a control group is to act as a point of comparison, by attempting to ensure that the variable under examination (the impact of the medicine) is the thing responsible for creating the results of an experiment. The control group is holding other possible variables constant, such as the act of seeing a doctor and taking a pill, so only the medicine itself is being tested.

Why Are Experimental Controls So Important?

Experimental controls allow scientists to eliminate varying amounts of uncertainty in their experiments. Whenever a researcher does an experiment and wants to ensure that only the variable they are interested in changing is changing, they need to utilize experimental controls.

Experimental controls have been dubbed “controls” precisely because they allow researchers to control the variables they think might have an impact on the results of the study. If a researcher believes that some outside variables could influence the results of their research, they’ll use a control group to try and hold that thing constant and measure any possible influence it has on the results. It is important to note that there may be many different controls for an experiment, and the more complex a phenomenon under investigation is, the more controls it is likely to have.

Not only do controls establish a baseline that the results of an experiment can be compared to, they also allow researchers to correct for possible errors. If something goes wrong in the experiment, a scientist can check on the controls of the experiment to see if the error had to do with the controls. If so, they can correct this next time the experiment is done.

A Practical Example

Let’s take a look at a concrete example of experimental control. If an experimenter wanted to determine how different soil types impacted the germination period of seeds , they could set up four different pots. Each pot would be filled with a different soil type, planted with seeds, then watered and exposed to sunlight. Measurements would be taken regarding how long it took for the seeds to sprout in the different soil types.

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A control for this experiment might be to fill more pots with just the different types of soil and no seeds or to set aside some seeds in a pot with no soil. The goal is to try and determine that it isn’t something else other than the soil, like the nature of the seeds themselves, the amount of sun they were exposed to, or how much water they are given, that affected how quickly the seeds sprouted. The more variables a researcher controlled for, the surer they could be that it was the type of soil having an impact on the germination period.

  Not All Experiments Are Controlled

“It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are. If it doesn’t agree with experiment, it’s wrong.” — Richard P. Feynman

While experimental controls are important , it is also important to remember that not all experiments are controlled. In the real world, there are going to be limitations on what variables a researcher can control for, and scientists often try to record as much data as they can during an experiment so they can compare factors and variables with one another to see if any variables they didn’t control for might have influenced the outcome. It’s still possible to draw useful data from experiments that don’t have controls, but it is much more difficult to draw meaningful conclusions based on uncontrolled data.

Though it is often impossible in the real world to control for every possible variable, experimental controls are an invaluable part of the scientific process and the more controls an experiment has the better off it is.

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What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

• They make it easier to reproduce the experiment.
• The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

• Duration of the experiment
• Size and composition of containers
• Temperature
• Sample volume
• Experimental technique
• Chemical purity or manufacturer
• Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

• Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
• Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
• Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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What Does ‘Constant’ Mean In Science?

Constants play a vital role across scientific disciplines by representing fixed, unchanging values. The fine structure constant in physics or the speed of light are examples of universal constants that remain the same under all conditions.

If you’re short on time, here’s a quick answer to your question: In science, a constant is a fixed, unchanging value or quantity that remains the same under specific conditions for a particular experiment or scenario .

In this detailed guide, we will explore the meaning of constants in science, provide examples from different fields, explain why they are important, and look at some well-known scientific constants used in equations and experiments.

Definition and Explanation of a Scientific Constant

In the realm of science, a constant refers to a value that remains unchanging and fixed. It is a fundamental concept that provides stability and reliability to scientific theories, experiments, and calculations.

Constants play a crucial role in the development of scientific laws, formulas, and calculations, as they allow scientists to make accurate predictions and draw meaningful conclusions.

Unchanging, fixed value

A scientific constant is a value that does not vary under normal circumstances. It remains consistent regardless of the conditions or variables involved. This stability allows scientists to establish a reliable baseline against which other measurements and observations can be compared.

For example, the speed of light in a vacuum, denoted by the symbol ‘c’, is considered a constant because it remains the same value, approximately 299,792,458 meters per second, regardless of the observer’s frame of reference.

Applies under defined conditions

Scientific constants apply within specific conditions or contexts. They are valid within a defined range of parameters, such as temperature, pressure, or composition. For instance, Avogadro’s constant, represented by ‘NA’, is a fundamental constant in chemistry that relates the number of atoms or molecules in a given amount of substance.

It is approximately equal to 6.022 x 10^23 particles per mole and is applicable in calculations involving gases and chemical reactions at standard temperature and pressure.

Used in scientific laws, formulas, and calculations

Scientific constants are utilized in the formulation of laws, formulas, and calculations in various scientific disciplines. They serve as building blocks for understanding and predicting natural phenomena.

For example, in physics, the gravitational constant ‘G’ is used in Newton’s law of universal gravitation to calculate the force of attraction between two objects. This constant allows scientists to accurately predict the motion of celestial bodies and explain phenomena such as planetary orbits and gravitational interactions.

It is important to note that scientific constants are not arbitrary values chosen by scientists. They are determined through extensive experimentation, observation, and mathematical analysis. These constants are often based on years, if not centuries, of research and are accepted within the scientific community as reliable and accurate representations of the natural world.

Examples of Famous Scientific Constants

In science, a constant is a value that does not change under specific conditions. Constants play a crucial role in various scientific theories and equations, providing a foundation for understanding the natural world. Here are some examples of famous scientific constants:

Speed of light (c)

The speed of light, denoted by the symbol ‘c’, is a fundamental constant in physics. It represents the maximum speed at which information or energy can travel in the universe. According to the current scientific understanding, the speed of light in a vacuum is approximately 299,792,458 meters per second.

Gravitational constant (G)

The gravitational constant, denoted by the symbol ‘G’, is a fundamental constant that characterizes the strength of the gravitational force between two objects. It plays a crucial role in Isaac Newton’s law of universal gravitation and Albert Einstein’s theory of general relativity.

The value of the gravitational constant is approximately 6.67430 x 10^-11 N(m/kg)^2.

Planck’s constant (h)

Planck’s constant, symbolized by ‘h’, is a fundamental constant in quantum mechanics. It relates the energy of a photon to its frequency and is used to calculate the energy levels of particles at the atomic and subatomic scale.

The value of Planck’s constant is approximately 6.62607015 x 10^-34 joule-seconds.

Gas constant (R)

The gas constant, denoted by the symbol ‘R’, is a universal constant that relates the properties of an ideal gas. It appears in the ideal gas law equation, which describes the behavior of gases under various conditions.

The value of the gas constant depends on the units used and is approximately 8.314 J/(mol·K).

Avogadro’s number

Avogadro’s number, denoted by ‘N A ‘, is a constant that represents the number of atoms or molecules in one mole of a substance. It is an essential constant in chemistry and is used to convert between the mass of a substance and the number of particles it contains.

The value of Avogadro’s number is approximately 6.02214076 x 10^23.

Universal constants in physics

Besides the examples mentioned above, there are several other universal constants in physics, such as the Boltzmann constant (k), the elementary charge (e), and the magnetic constant (μ 0 ). These constants have significant roles in various scientific disciplines and are used in a wide range of calculations and equations.

Mathematical constants like pi

In addition to the physical constants, there are also mathematical constants that hold a special place in science. One of the most famous mathematical constants is pi (π), which represents the ratio of the circumference of a circle to its diameter.

Pi is an irrational number, approximately equal to 3.14159, and it appears in numerous mathematical formulas and calculations.

These examples of famous scientific constants demonstrate the importance of constants in scientific research. They provide a solid foundation for understanding the laws of nature and enable scientists to make accurate predictions and calculations.

By using these constants, scientists can unlock the mysteries of the universe and push the boundaries of human knowledge.

Importance and Use of Scientific Constants

In the world of science, constants play a crucial role in understanding and explaining natural phenomena. These values remain fixed and unchanged, allowing scientists to make accurate calculations and draw meaningful conclusions.

Here are some key reasons why scientific constants are of utmost importance:

Allow precise quantitative analysis

Scientific constants provide a foundation for precise quantitative analysis. By having a known and consistent value, scientists can make accurate measurements and calculations in their experiments. This precision allows for more reliable and meaningful results.

For example, the speed of light, a fundamental constant in physics, enables scientists to calculate distances in space and time with great accuracy.

Enable the creation of scientific laws and formulas

Scientific constants are essential in the creation of scientific laws and formulas. These laws describe the relationships between different variables and constants, allowing scientists to make predictions and understand the underlying principles of nature.

For instance, Newton’s law of universal gravitation, which includes the gravitational constant, G, allows scientists to predict the gravitational force between two objects.

Support reproducibility in experiments

Reproducibility is a cornerstone of scientific research. Scientific constants contribute to the reproducibility of experiments by providing fixed reference points. With known constants, scientists can compare their results with those obtained by other researchers, ensuring that their findings are consistent and reliable.

This promotes transparency and helps to build upon previous knowledge. A well-known example is Avogadro’s constant, which allows scientists to determine the number of atoms or molecules in a given sample.

Provide fixed reference points

Scientific constants provide fixed reference points against which other measurements can be made. They serve as benchmarks that allow scientists to establish standardized units of measurement. For instance, the Planck constant, h, is a fundamental constant in quantum mechanics that defines the relationship between energy and frequency.

Its precise value provides a reference point for measuring the energy of particles and electromagnetic waves.

Allow measurement of other variables

Scientific constants enable the measurement of other variables that may be difficult to quantify directly. By incorporating known constants into equations, scientists can indirectly determine the values of various quantities.

This indirect measurement technique is often employed in fields like astrophysics and quantum mechanics. For example, the Boltzmann constant, k, allows scientists to determine the average kinetic energy of particles in a gas based on temperature.

Constants vs. Variables in Science Experiments

Constants remain fixed, variables can change.

In scientific experiments, constants and variables play crucial roles. A constant is a factor that remains unchanged throughout the entire experiment, while a variable is a factor that can be manipulated or changed.

Constants are carefully chosen to provide a stable and consistent baseline for comparison.

For example, imagine conducting an experiment to test the effect of temperature on the rate of a chemical reaction. In this case, the amount of reactants, the pressure, and the concentration of the solution would all be considered constants.

These variables are kept constant to ensure that any changes observed in the reaction rate can be attributed solely to the temperature.

Constants provide controls in experiments

Constants serve as controls in scientific experiments. By keeping certain factors constant, researchers can isolate the effects of the variable they are studying. This allows them to accurately determine the relationship between the variable and the observed results.

Continuing with the example of the temperature experiment, if the concentration of the solution were not kept constant, it could introduce confounding variables that would cloud the results. By controlling the concentration, researchers can confidently attribute any changes in the reaction rate solely to the temperature.

Changes in variables are measured against constants

Changes in variables are measured and compared against the constants in a scientific experiment. This comparison allows researchers to draw meaningful conclusions about the relationship between the variable and the outcomes.

Returning to the temperature experiment, the reaction rate at different temperatures would be measured and compared against the constant factors such as the concentration and pressure. By analyzing the data, researchers can determine how changes in temperature affect the rate of the chemical reaction.

Understanding the distinction between constants and variables is essential in scientific research. It ensures that experiments are conducted with precision and accuracy, allowing for reliable and reproducible results.

Establishing and Measuring Scientific Constants

In the field of science, constants play a crucial role in understanding and explaining the natural world. These constants are values that remain unchanged under specific conditions and are used as a foundation for scientific research and experimentation.

One such important aspect of establishing and measuring scientific constants is through high precision empirical measurements.

High precision empirical measurements

Scientists use advanced instruments and techniques to make precise measurements of physical quantities. These measurements are based on empirical evidence obtained through rigorous experimentation and observation.

By repeatedly measuring a specific phenomenon, scientists can establish the value of a constant with a high degree of accuracy. For example, the speed of light, denoted by the symbol ‘c,’ is a well-known constant that has been determined through meticulous measurements and experiments.

Consensus of scientific community

Scientific constants are not established arbitrarily; they require a consensus among the scientific community. When multiple researchers and scientists independently obtain similar results for a specific constant, it strengthens the reliability and validity of that value.

This consensus is crucial in ensuring the accuracy and credibility of scientific constants. It also allows scientists to build upon existing knowledge and theories, leading to further advancements in their respective fields.

Technological advances enabling greater accuracy

Technological advancements have played a significant role in enabling scientists to measure constants with greater precision. Improved instruments, such as atomic clocks and particle detectors, have revolutionized the way scientists can measure and understand the physical world.

These technological advancements have allowed for the refinement of existing constants and the discovery of new ones. For instance, advancements in quantum mechanics have led to the establishment of the Planck constant, which plays a fundamental role in understanding the behavior of subatomic particles.

Importance of exact, standardized values

Exact and standardized values of constants are of paramount importance in scientific research and development. They provide a common language for scientists to communicate and collaborate effectively. Standardized values allow for consistency and reproducibility in experiments, ensuring that results obtained by different researchers are comparable.

This is essential for advancing scientific knowledge and developing practical applications. For example, the value of the gravitational constant, denoted by ‘G’, is crucial in various fields, including astrophysics and engineering, where accurate calculations and predictions are required.

Establishing and measuring scientific constants is a meticulous process that combines empirical evidence, consensus among the scientific community, and technological advancements. These constants serve as the building blocks of scientific knowledge, enabling researchers to understand the natural world and make significant advancements in various fields.

They provide a solid foundation for scientific theories and practical applications, contributing to the progress of society as a whole.

Constants serve an important role in science by representing fixed, known values that remain unchanged under defined conditions. They enable quantitative analysis, precise formulas and reproducible experiments.

Famous constants like the speed of light, gravitational constant and Planck’s constant are fundamental to physics and other areas of science. Understanding what constants represent and how they are established, measured and used is key for scientific learning.

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• Control Variables | What Are They & Why Do They Matter?

Control Variables | What Are They & Why Do They Matter?

Published on March 1, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A control variable is anything that is held constant or limited in a research study. It’s a variable that is not of interest to the study’s objectives , but is controlled because it could influence the outcomes.

Variables may be controlled directly by holding them constant throughout a study (e.g., by controlling the room temperature in an experiment), or they may be controlled indirectly through methods like randomization or statistical control (e.g., to account for participant characteristics like age in statistical tests). Control variables can help prevent research biases like omitted variable bias from affecting your results.

Table of contents

Why do control variables matter, how do you control a variable, control variable vs. control group, other interesting articles, frequently asked questions about control variables.

Control variables enhance the internal validity of a study by limiting the influence of confounding and other extraneous variables . This helps you establish a correlational or causal relationship between your variables of interest and helps avoid research bias .

Aside from the independent and dependent variables , all variables that can impact the results should be controlled. If you don’t control relevant variables, you may not be able to demonstrate that they didn’t influence your results. Uncontrolled variables are alternative explanations for your results and affect the reliability of your arguments.

Control variables in experiments

In an experiment , a researcher is interested in understanding the effect of an independent variable on a dependent variable. Control variables help you ensure that your results are solely caused by your experimental manipulation.

The independent variable is whether the vitamin D supplement is added to a diet, and the dependent variable is the level of alertness.

To make sure any change in alertness is caused by the vitamin D supplement and not by other factors, you control these variables that might affect alertness:

• Timing of meals
• Caffeine intake
• Screen time

Control variables in non-experimental research

In an observational study or other types of non-experimental research, a researcher can’t manipulate the independent variable (often due to practical or ethical considerations ). Instead, control variables are measured and taken into account to infer relationships between the main variables of interest.

To account for other factors that are likely to influence the results, you also measure these control variables:

• Marital status

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There are several ways to control extraneous variables in experimental designs, and some of these can also be used in observational studies or quasi-experimental designs.

Random assignment

In experimental studies with multiple groups, participants should be randomly assigned to the different conditions. Random assignment helps you balance the characteristics of groups so that there are no systematic differences between them.

This method of assignment controls participant variables that might otherwise differ between groups and skew your results.

It’s possible that the participants who found the study through Facebook use more screen time during the day, and this might influence how alert they are in your study.

Standardized procedures

It’s important to use the same procedures across all groups in an experiment. The groups should only differ in the independent variable manipulation so that you can isolate its effect on the dependent variable (the results).

To control variables , you can hold them constant at a fixed level using a protocol that you design and use for all participant sessions. For example, the instructions and time spent on an experimental task should be the same for all participants in a laboratory setting.

• To control for diet, fresh and frozen meals are delivered to participants three times a day.
• To control meal timings, participants are instructed to eat breakfast at 9:30, lunch at 13:00, and dinner at 18:30.
• To control caffeine intake, participants are asked to consume a maximum of one cup of coffee a day.

Statistical controls

You can measure and control for extraneous variables statistically to remove their effects on other types of variables .

“Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

A control variable isn’t the same as a control group . Control variables are held constant or measured throughout a study for both control and experimental groups, while an independent variable varies between control and experimental groups.

A control group doesn’t undergo the experimental treatment of interest, and its outcomes are compared with those of the experimental group. A control group usually has either no treatment, a standard treatment that’s already widely used, or a placebo (a fake treatment).

Aside from the experimental treatment, everything else in an experimental procedure should be the same between an experimental and control group.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

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Experimental Design - Independent, Dependent, and Controlled Variables

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Scientific experiments are meant to show cause and effect of a phenomena (relationships in nature).  The “ variables ” are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment.

An experiment can have three kinds of variables: i ndependent, dependent, and controlled .

• The independent variable is one single factor that is changed by the scientist followed by observation to watch for changes. It is important that there is just one independent variable, so that results are not confusing.
• The dependent variable is the factor that changes as a result of the change to the independent variable.
• The controlled variables (or constant variables) are factors that the scientist wants to remain constant if the experiment is to show accurate results. To be able to measure results, each of the variables must be able to be measured.

For example, let’s design an experiment with two plants sitting in the sun side by side. The controlled variables (or constants) are that at the beginning of the experiment, the plants are the same size, get the same amount of sunlight, experience the same ambient temperature and are in the same amount and consistency of soil (the weight of the soil and container should be measured before the plants are added). The independent variable is that one plant is getting watered (1 cup of water) every day and one plant is getting watered (1 cup of water) once a week. The dependent variables are the changes in the two plants that the scientist observes over time.

Can you describe the dependent variable that may result from this experiment? After four weeks, the dependent variable may be that one plant is taller, heavier and more developed than the other. These results can be recorded and graphed by measuring and comparing both plants’ height, weight (removing the weight of the soil and container recorded beforehand) and a comparison of observable foliage.

Using What You Learned: Design another experiment using the two plants, but change the independent variable. Can you describe the dependent variable that may result from this new experiment?

Think of another simple experiment and name the independent, dependent, and controlled variables. Use the graphic organizer included in the PDF below to organize your experiment's variables.

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Why do we need constants? [closed]

This question is driving me crazy because I cannot find a straightforward answer. I want to know what a physical constant exactly is . I know that it’s a value that doesn’t change, but what is it? Why do we need them in our equations?

For example what is the gravitational constant? Why do we need it in the universal gravitation formula? What is Planck’s constant? I don’t want to know the value, I want to what its use is. What does it allow us to do or find? What exactly does Planck’s constant tell us?

• physical-constants

• 5 $\begingroup$ Your question is hard to answer because it doesn't make sense. A constant is just a constant. It would be worth haveing a look through the existing questions related to this area . $\endgroup$ –  John Rennie Commented Apr 17, 2016 at 13:49
• $\begingroup$ To only add an example to Jeff's already good answer: If you want to convert foot to meter, you also use constants: 1 foot is 0.3048 m. You can also use for own system with your own units (like foot-pound-second for imperial units) where most equation constants change because in many equations they really only have conversion meanings. But there are also constants like the fine-structure constant, the ratio of eletromagnetic interaction which are given by nature and have a deeper meaning. Physicists are also not completely sure that these values are really constant over time ! $\endgroup$ –  Thorsten S. Commented Apr 17, 2016 at 21:40

3 Answers 3

Physical constants arise from the way we define units. Let's take the gravitational constant $G$ as an example. According to Newton's law of universal gravitation: $$F_{g} = G \frac{m_1 \times m_2}{r^2}$$ If you were to take two spheres, both with mass 1 kilogram, 1 meter apart, it turns out the gravitational attraction between them is not 1 newton: it would be $6.674 \times 10^{-11}\ N$. Thus, the gravitation equation needs a conversion factor with value $6.674 \times 10^{-11}$, which we call $G$. We need it just because of the way our unit of force, the newton, is defined: 1 newton is defined as the force needed to give a mass of 1 kilogram an acceleration of 1 meter per second squared.

The kilogram, meter and second are rather arbitrary quantities, and as such, the newton is an arbitary unit. We could have equally well chosen to use yards instead of meters, and pounds instead of kilograms, and define the newton as the force needed to accelerate a mass of 1 pound by 1 yard per second squared. This would give us other values for the physical constants. Nature, of course, couldn't care less how we humans define our units. Nature has it's own units .

We need the physical constants to convert the effects of nature into the units of our choice. The gravitational constant $G$ converts the gravitational force between masses (in kg) seperated by some distance (in meters) into Newtons. Planck's constant $h$ converts the energy of a photon with some wavelength (in meters) into Joules.

• 1 $\begingroup$ +1 Great answer, just one thing - I think dimensionless constants ought to be mentioned as well when going in this direction! $\endgroup$ –  MPeti Commented Apr 17, 2016 at 19:28
• 1 $\begingroup$ In my world, $G\approx 4.301\times10^{-6}\,({\rm km}\,{\rm s})^{-1}{\rm kpc}\,{\rm M}_\odot^{-1}$ :) $\endgroup$ –  Kyle Oman Commented Apr 17, 2016 at 20:06

Actually, we do not really need constants in the sense that we choose to use them, but it’s just the way the universe works – or more precisely: very well seems to work.

For example, various experiments confirmed that the quotient of the energy of a photon and its frequency (when measured in the same units) is always the same within the accuracy of what can be measured. We did not chose the universe to be this way; it just is. In the above case, we agreed to call the quotient $h$ and we usually also agree on some units to communicate the value of this quotient. This in turn allows us to make accurate predictions about reality – in this case, what energy a certain photon has.

Now, the whole unit concept is again based on constants and, once more, they are brought upon us by the universe, which happens to be very well described by numbers. For a blatant example, suppose we take two blocks of wood and cut two pieces of string such that they just begin and end where one of the blocks begins and ends. If we then tie those strings together, we find that the composite string begins and ends where the two blocks of wood begin and end, when put together. This description is awfully complicated, because I tried to avoid the word length, which denotes the fundamental property of objects derived from such observations and which eventually lead to the invention of mathematics to better describe such properties.

However, for being able to talk mathematically about specific lengths without resorting to actual pieces of string, we need to agree on a unit length, which is nothing but an arbitrarily chosen constant to facilitate communication. While we chose what to use as units, we did not really chose to use units in general: The universe just happens to be such that they are damn useful and without them, we would not have the time to think about such questions.

Many of the units related to physical phenomena were defined before the phenomena in question were particularly well understood. For example, electric current was measured based upon its magnetic effects before it was understood that the amount of current called "1 ampere" represented the flow of some number of electrons per second, and "2 amperes" represented the flow of twice as many electrons per second. Since the units were defined before there was a means of quantifying the number of electrons per second they should represent, the number of electrons per second representing one ampere ends up seeming rather arbitrary.

If one had the luxury of defining units without regard for earlier practices, it may be possible to assign them so that most ratios of interest would have a scaling factor of one (e.g. if time is measured in units called the "jiffy", one could define unit of distance called the "bip" equal to the distance light travels through a vacuum in one jiffy, then the speed of light through vacuum would be one bip/jiffy). From a practical standpoint, common units would need to be scaled by powers of ten (e.g. if the speed of light is one bip/jiffy, expressing a typical highway speed limit as 0.0000001 bip/jiffy would be awkward, but 100 bips/nanojiffy might be less so).

Even if one had the luxury of defining units, however, it would probably be impossible to avoid having some non-unity constants slip in. The constant 2pi, of course, would be one that would arise since something that travels at a 1 bip/nanojiffy around a circle with a 1 bip radius will have a rotational period of 1/2pi nanojiffies since the distance per revolution will be 2pi times the radius. Further, it's convenient to have units which are defined according to some properties of the big rock called Earth. Having the basic unit of time be an integer fraction of the "aparent" rotational period of the Earth relative to the Sun, for example, is generally convenient [though there could arguably be advantages to having a "scientific" time unit which not a convenient multiple of to civil time units derived from the Earth's rotation, since it would mean that "scientific" time could run uniformly without any need for "leap seconds" even if the Earth's rotation speed varies slightly.] Having the earth's rotational period divided into 24x60x60 seconds is more convenient than would be, e.g. having it typically take about (made-up number) 4,291,269 jiffies.

Not the answer you're looking for? Browse other questions tagged units physical-constants or ask your own question .

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What are constants in an experiment?

Constants are the things that are kept the same each time one of the trials in the experiment is repeated. For example, constants could include the amount of water used, the brand of effervescent tablet used, the type of water used, and the fact that the water was not stirred. As many outside factors as possible should be kept constant in an experiment so that the researcher can be sure that any changes that occur do so because of the independent variable.

Constants in an experiment are the variables that don't change. They stay the same throughout the experiment to ensure that results are comparable to the control group and that they are repeatable.

The constant is whatever Evie says it is.

Add your answer:

Conditions that are kept the same in every group in an experiment?

Any factor changed in an experiment is called.

called constants

The factors that are kept the same throughout an experiment is called a?

What is name of all factors that should remain the same in an experiment.

Factors that are kept the same in an experiment are called constants.

What are the constants and control related to science?

A constant is the thing that stays the same. The control is the normal variable or the regular variable. The control will help, in a science experiment, to see if a new object works better than the regular one. The constants helps to keep the experiment fair.

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Why artists are becoming less scared of AI

As people tinker and experiment with it, we’re gaining a clearer understanding of its limitations in creative fields.

• Melissa Heikkilä archive page

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here .

Knock, knock. 

Who’s there? 

An AI with generic jokes. Researchers from Google DeepMind asked 20 professional comedians to use popular AI language models to write jokes and comedy performances. Their results were mixed. 

The comedians said that the tools were useful in helping them produce an initial “vomit draft” that they could iterate on, and helped them structure their routines. But the AI was not able to produce anything that was original, stimulating, or, crucially, funny. My colleague Rhiannon Williams  has the full story .

As Tuhin Chakrabarty, a computer science researcher at Columbia University who specializes in AI and creativity, told Rhiannon, humor often relies on being surprising and incongruous. Creative writing requires its creator to deviate from the norm, whereas LLMs can only mimic it.

And that is becoming pretty clear in the way artists are approaching AI today. I’ve just come back from Hamburg, which hosted one of the  largest events for creatives in Europe , and the message I got from those I spoke to was that AI is too glitchy and unreliable to fully replace humans and is best used instead as a tool to augment human creativity. 

Right now, we are in a moment where we are deciding how much creative power we are comfortable giving AI companies and tools. After the boom first started in 2022, when DALL-E 2 and Stable Diffusion first entered the scene, many artists raised concerns that AI companies were scraping their copyrighted work without consent or compensation. Tech companies argue that anything on the public internet falls under fair use, a legal doctrine that allows the reuse of copyrighted-protected material in certain circumstances. Artists, writers, image companies, and the New York Times have filed lawsuits against these companies, and it will likely take years until we have a clear-cut answer as to who is right. 

Meanwhile, the court of public opinion has shifted a lot in the past two years. Artists I have interviewed recently say they were harassed and ridiculed for protesting AI companies’ data-scraping practices two years ago. Now, the general public is more aware of the harms associated with AI. In just two years, the public has gone from being blown away by AI-generated images to sharing viral social media posts about how to opt out of AI scraping—a concept that was alien to most laypeople until very recently. Companies have benefited from this shift too. Adobe has been successful in pitching its  AI offerings  as an “ethical” way to use the technology without having to worry about copyright infringement. 

There are also several grassroots efforts to shift the power structures of AI and give artists more agency over their data. I’ve written about  Nightshade , a tool created by researchers at the University of Chicago, which lets users add an invisible poison attack to their images so that they break AI models when scraped. The same team is behind Glaze, a tool that lets artists mask their personal style from AI copycats. Glaze has been integrated into Cara, a buzzy new art portfolio site and social media platform, which has seen a surge of interest from artists. Cara pitches itself as a platform for art created by people; it filters out AI-generated content. It got nearly a million new users in a few days. 

This all should be reassuring news for any creative people worried that they could lose their job to a computer program. And the DeepMind study is a great example of how AI can actually be helpful for creatives. It can take on some of the boring, mundane, formulaic aspects of the creative process, but it can’t replace the magic and originality that humans bring. AI models are limited to their training data and will forever only reflect the zeitgeist at the moment of their training. That gets old pretty quickly.

Now read the rest of The Algorithm

Deeper learning, apple is promising personalized ai in a private cloud. here’s how that will work..

Last week, Apple unveiled its vision for supercharging its product lineup with artificial intelligence. The key feature, which will run across virtually all of its product line, is Apple Intelligence, a suite of AI-based capabilities that promises to deliver personalized AI services while keeping sensitive data secure. 

Why this matters:  Apple says its privacy-focused system will first attempt to fulfill AI tasks locally on the device itself. If any data is exchanged with cloud services, it will be encrypted and then deleted afterward. It’s a pitch that offers an implicit contrast with the likes of Alphabet, Amazon, or Meta, which collect and store enormous amounts of personal data.  Read more from James O’Donnell here . 

Bits and Bytes

How to opt out of Meta’s AI training If you post or interact with chatbots on Facebook, Instagram, Threads, or WhatsApp, Meta can use your data to train its generative AI models. Even if you don’t use any of Meta’s platforms, it can still scrape data such as photos of you if someone else posts them. Here’s our quick guide on how to opt out. ( MIT Technology Review ) 

Microsoft’s Satya Nadella is building an AI empire Nadella is going all in on AI. His $13 billion investment in OpenAI was just the beginning. Microsoft has become an “the world’s most aggressive amasser of AI talent, tools, and technology” and has started building an in-house OpenAI competitor. ( The Wall Street Journal )

OpenAI has hired an army of lobbyists As countries around the world mull AI legislation, OpenAI is on a lobbyist hiring spree to protect its interests. The AI company has expanded its global affairs team from three lobbyists at the start of 2023 to 35 and intends to have up to 50 by the end of this year. ( Financial Times )  

UK rolls out Amazon-powered emotion recognition AI cameras on trains People traveling through some of the UK’s biggest train stations have likely had their faces scanned by Amazon software without their knowledge during an AI trial. London stations such as Euston and Waterloo have tested CCTV cameras with AI to reduce crime and detect people’s emotions. Emotion recognition technology is extremely controversial. Experts say it is unreliable and simply does not work.  ( Wired ) 

Clearview AI used your face. Now you may get a stake in the company. The facial recognition company, which has been under fire for scraping images of people’s faces from the web and social media without their permission, has agreed to an unusual settlement in a class action against it. Instead of paying cash, it is offering a 23% stake in the company for Americans whose faces are in its data sets. ( The New York Times ) 

Artificial intelligence

How to opt out of meta’s ai training.

Your posts are a gold mine, especially as companies start to run out of AI training data.

Apple’s first big salvo in the AI wars makes a bet that people will care about data privacy when automating tasks.

• James O'Donnell archive page

Why does AI hallucinate?

The tendency to make things up is holding chatbots back. But that’s just what they do.

• Will Douglas Heaven archive page

This AI-powered “black box” could make surgery safer

A new smart monitoring system could help doctors avoid mistakes—but it’s also alarming some surgeons and leading to sabotage.

• Simar Bajaj archive page

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