White Dwarf Stars. Remnant of a dead star in space. The core of a sun after his death. iStock
Stars Can Die in Many Forms
At the end of a star’s life cycle,a star may morph into a white dwarf, a red giant, a neutron star, or a black hole. It all depends upon the amount of mass that is contained in the star’s central core, along with the mass’s gravity.
The more mass that a body contains, the more gravity that is produced, so the more mass an object has, the more gravity that is sustained, and consequently, the more pressure on the object because of its gravitational pull.
Illustration of the fusion process. Wikipedia CC
It is this pressure that provides the extreme heat that is generated and subsequently, the fusion of atoms. The types of elements and the density that are fused determine if the dying star will be a dwarf, giant, neutron, or black hole. These rules of physics are universal.
Death Begins
Stars die when the fusion process ceases. Then, depending on its size, it will change into one of the types mentioned above.
Our sun, which is in the category called the main sequence, is not an extraordinary star by any means, although we may feel that is not the case here on Earth, as we mortals cannot even set our eyes on it for very long.
The fact remains that in comparison to other stars in our Milky Way Galaxy and other galaxies, our sun is a mere pea when equated to some of the giants in the universe.
With that said, when our sun dies, it will expand to become a red giant.
Take a telescope, any telescope, or even binoculars and on a clear day you can see some of the most colorful and beautiful objects in space. These objects are nebulas. The birthplace of stars. It is where it all begins.
Planting the Seeds
When we say seeds, what do we mean exactly? Well, these seeds are actually vast clouds of gas and dust that are floating in space. They come from stars that have previously exploded and left their remnants to roam the universe around like lost soles.
Think of dropping seeds into a pond and watching them float around in the water. Some will collide and some will be pulled away from the other seeds but if that is all there is, we would have these particles floating around arbitrarily for infinity.
Fortunately, there is more than just this particle chaos. A force is involved that will put all these disorganized fragments to converge into something meaningful.
Helix Nebula. Photo: NASA Via Wikipedia CC
What is This Force that Pulls the Particals Together?
The easy answer – gravity. Yes, gravity pulls these particles together. So let’s imagine the nebula as a giant, fluffy cloud in space. Deep inside this cloud, there are regions where the gas and dust are getting squished together. The pressure and temperature rise in these squeezed spots, and eventually, a new star is born from the material in that region.
So, in a way, a nebula is like the starting point for a star’s life. It’s where the ingredients for making a star come together, and as they collapse under their gravity, a bright new star is born, lighting up the cosmic neighborhood.
The Helix Nebula above, which some call “The Eye of God” or “Eye in the Sky” because it resembles a cosmic eye, is located 700 light-years away from Earth. A mere speck of a distance when speaking about the vastness of the universe and is 2.5 light-years in diameter.
The nebula was formed because of the death of a star similar to our Sun. As the star depleted its nuclear fuel, it expanded into a red giant, shedding its outer layers into space.
To learn more about the different types of nebulas there are in space, Wikipedia gives a complete list of these fascinating and beautiful clouds of life-forming stars.
The Birth of a Star
This phenomenon is the result of gravity pulling gas and dust together. It is a process that is multiplied millions of times within the nebula and the beautiful objects that are forming are the fetal stages of stars being created.
Specifically, the gas is a combination of hydrogen and helium which clump together to form larger masses and since gravity gets stronger as the mass of the object gets bigger, additional matter is attracted to the object, which eventually becomes massive enough to form a star. In other words, it is the gravitational force of an object that is directly proportional to the object’s mass.
Nebula’s Molecular Breakdown
Photo: Pixaby
Unbeknownst to many, most of the universe is not a complete void. There is much (loose) matter floating around between the stars. And this matter is not visible to the naked eye, as it is in its atomic form; such as the atoms of hydrogen and helium, as well as plasma and other materials. This sub-atomic matter is called the interstellar medium (ISM). More specifically, the interstellar medium is composed primarily of hydrogen, followed by helium with trace amounts of carbon, oxygen, and nitrogen.
In areas of the ISM where the atomic particles are densely populated, the formation of molecules begins most commonly hydrogen (H2). The more the molecular masses clump together, the greater their gravitational attraction will be to other bodies and particles in their vicinity. As the particles clump further to form larger and more massive structures, they attract more dust and gas.
The Nuclear Element
Enter nuclear fusion, since the gravitational pressure becomes so high that the fusion of hydrogen atoms occurs. This results in the emission of high-energy electromagnetic radiation, which in turn ionizes the outer layers of gas. Ionization is the process by which an atom or a molecule acquires a negative or positive charge by gaining or losing electrons to form ions.
Ionized gas is known as plasma, and plasma along with electromagnetic radiation is now added to this mixture. This then materializes into the early stages of star formation.
Hence, the formation of stars occurs exclusively within these molecular clouds. This is a natural result of their low temperatures and high densities because the gravitational force acting to collapse the cloud must surpass the forces that are working to push the particles outward and the molecular cloud is now a nebula.
We have previously talked about gem hunting, but we have not discussed the steps as to how to approach the prospecting for gemstones, so let’s get right into how you start your gem-hunting adventure:
Research Your Locations
Different types of gemstones are found in a variety of regions, so it’s important to identify areas where the stones you’re interested in are found.
Start with the Ineternal Gem Society. They can provide you with some of the top locations around the country where you can dig for gemstones.
Make Sure You Have the Right Equipment for Your Gem Search
Depending upon the location you select, they should be able to provide you with the necessary tools for your hunt, most probably for a fee, but you could bring your own equipment as well. That would consist of a pair of gloves, a shovel, a bucket, a screen or sifter, and a magnifying glass. Additionally, when you are there, ask for a gemstone identification guide.
If yes, then you know that you have to travel to a certain spot of a particular destination to view a specific species of bird. To find the right destination for bird watching, one has to find out the species’ habitat, migration patterns, food choices, etc.
Knowing these things will help you figure out the location where a particular species of bird is likely to be found. You cannot simply wander around the forest in the hope of finding the types you are looking for; it would be nothing more than wasting time.
Experts say that gem hunting is much like bird watching. You most likely will not find minerals dug in the soil outside your home; however, the practical approach is to first research the areas where the gems are naturally found and then use the right technique to access the deposits.
For example, since diamonds are formed as a result of extreme pressure, they are either found deep inside the earth, in areas where various geological processes have pushed the mantle rocks from the depths of the earth to the surface, or alongside the rivers that flow from such areas.
Similarly, if you are looking for malachite, you have to look for it near copper and limestone deposits.
The occurrence of gemstones may also vary across countries, depending upon their geological processes, volcanoes, storms, and earthquakes, as they cause shifts in the tectonic plates and bring the buried bedrock to the surface of the earth.
Methods for Gemstones Mining
From basic to advanced, there are various mining methods. They include:
Underground Mining
When hunting for your stones is done within the pipe and alluvial deposits, it is called underground mining. The methods used for underground mining are:
Block caving
Tunneling
Chambering
Open Cast Mining
Open-cast mining uses different techniques. Here removal of the upper layer of rocks is required in order to reach the bedrock, which is buried deep inside the earth that contains the gems. Any of the following methods are used to excavate gems from the deepest layers of the earth:
Terrace Mining
Pit Mining
Open-cast mining methods are widely used in various parts of the world including the United States, Sri Lanka, Brazil, and Myanmar. etc.
Sea Mining
Sea mining, marine or undersea mining, as they are alternatively called, is used in areas where marine deposits are present.
River Digging
As evident from the name, river digging is performed in and around rivers and lakes to excavate the gems that have been buried in the river soil and rocks naturally, by the water current or geological processes over time. It can further be classified into two types:
Wet Digging
Dry Digging
Gem Hunting Tools
Photo: iStock
As with any other specialized task, you cannot expect to have a successful gem-hunting experience if you don’t have the right tools and equipment.
For example, there is no point in going fishing without a fishing tackle and/or bait. It is highly unlikely to catch a fish with your hands. Similarly, searching for gemstones without the proper gem-hunting tools is nothing more than wasting your time. Tools for gem hunting are easily available at affordable prices, which means that even occasional hunters can easily buy them without exceeding their budgets.
Hydraulic Mining, where jets of water are used to loose the rocks from the dirt,
River Panning is where you essentially wash away the gravel to find the minerals,
Open Pit Mining, where you physically remove rocks, possibly in a quarry to search for the gems.
But this just scratches the surface (pun intended). Do some research to find the best method you prefer.
Learn Gemstone Identification
Familiarize yourself with the characteristics and properties of the gemstones you’re hunting for. Look for distinguishing features like color, luster, hardness, and crystal structure. Using a mineral identification guide or app can help determine the gemstones you find.
Artists drawing of astronomer Joannes Kelper. Wikipedia (Public Domain)
Johannes Kepler was a 17th-century German astronomer who discovered the systematic rotation of planets around stars, called the Laws of Planetary Motion, it states the following:
All planets revolve around the Sun in elliptical orbits.
A radius of the planets moves out in equal areas and in equal lengths of time.
The squares of the sidereal periods (of revolution) of the planets are directly proportional to the cubes of their mean distances from the Sun. (You don’t have to concern yourself with this law for our article here).
Being that Kepler was a cosmologist who focused his studies on planets, it is fitting that NASA named a spacecraft after him which looks for planets outside of our solar system, called exoplanets.
Specifically, the Kepler Space Telescope is designed to locate exoplanets that exist in the habitable zones, also called the Goldilocks Zone, where conditions are not too hot and not too cold for life as we know it, and which subsequently provides the ingredients for the possibility of liquid water on the planet’s surface. Liquid water is the ingredient that sets the stage for life to cultivate. Water can be found on many planets, some in the form of solid ice, but without water, the possibility of life to develop is minute.
A Perfect Find!
The Kepler spacecraft has not disappointed us. It has located numerous planets that fit this habitual category. Not the least is Kelper-186f, which not only contains an abundance of water but is also similar to Earth in significant ways.
Wikipedia (NASA) Public Domain
It is anexoplanet that orbits the star Kepler-186 in the constellation Cygnus and is only 500 light-years away from Earth. A mere ‘drop of the bucket’ in distance when considering how incomprehensibly large the universe is.
Kepler’s Sun
Numerous methods are employed to locate these planets. The Kepler telescope uses the transit method which finds celestial objects by observing the periodic dimming of their star’s light as the planet passes in front of it. In other words, it measures changes in the lightness of stars where periodic dips in brightness occur.
Kepler-186f’s star is an M dwarf, which is a red dwarf. Red dwarfs are smaller and cooler than our Sun, and this star is about half the size and half the temperature of our Sun.
The Planet
This orbital body is approximately the same size as Earth, making it one of the first Earth-size, habitable-zoned planets discovered outside of our solar system.
The time it takes Kepler-186f to complete one orbit around its star is approximately 130 Earth days. This is shorter than Earth’s orbit around our sun, which is 365 days, but this does not diminish the possibility of life existing there.
Future Research
Due to the current limitations of technology at this time, the Kepler-186f’s distance, although only 500 light years away, our ability to obtain more detailed information remains a significant challenge.
So further advancements in observational planetary technology are needed to acquire the specifics of distant worlds such as Kepler-186f, but we should look forward to obtaining more information about this exoplanet as it has so much to offer considering its close resemblance to our planet and other physical factors that exist there.
Conclusion
Kepler-186f may not be a perfect match to Earth, but we should not expect it to be. The existence of life is still a good possibility and if we expand our horizons a bit more, we can consider the potential of intelligent life as well; although these beings might not look exactly as we do.
Despite the planet’s location in the habitable zone, several factors could affect a being’s habitability there. One circumstance refers to the planet’s closeness to its red dwarf sun, which might expose it to increased stellar activity (sun spots, solar flares, plasma eruptions) that are greater than from our sun.
This could impact the planet’s atmosphere and surface conditions, resulting in life forms that could have much thicker skin than us, in order to avoid the dangers associated with ultraviolet radiation and x-rays common from stellar actions.
AI Image Generator extraterrestrial alien with thick skin. Fotor.com
Even a slight change in any external factor on the planet (temperature, light exposure, gravitational pull, etc) may make their appearance look different from us in one way or another. But does it matter? We should welcome them anyway, or should we?
It’s August and you just bought an electric car. You charged it up to 80% capacity (that is the recommended maximum charging) and your dashboard shows 230 miles of available for your car.
Now it is December and your car still shows 230 miles when charged to 80%, but when you start to drive, you notice that the mileage diminishes faster than when you were driving it during the summer. Why is that? Let’s take a look.
Why Do EV (Lithium) Batteries Decrease in Capacity Faster in Winter?
Photo: Pixaby
Ion Depletion: Cold weather reduces the chemical activity of the lithium ions. Ions are atoms that have either gained or lost electrons, allowing them the ability to bond with other atoms. This is the normal process in battery charging, but when cold weather comes, the amount of ions in the atoms decreases, thereby reducing the charging process. In other words, the battery can’t store as much energy as it would normally do when in warmer weather.Photo: Pixaby
Viscosity: Cold weather increases the thickness of the electrolyte, known as viscosity. This makes it harder for the ions to move around within the battery, which reduces the battery’s energy, e.g. its ability to deliver power.
Plating: Over repeated charge and discharge cycles, some of the ions can stick onto the surface of the anode, known as lithium plating, which forms a solid layer of lithium metal.
This can reduce the capacity of the battery and potentially lead to short circuits and is more likely to occur at low temperatures or when the battery is charged or discharged too quickly.
Note: At temperatures below freezing, some lithium batteries can lose up to 50% of their juice.
What Can I Do to Compensate for This Loss of Energy?
If you have a garage, use it. Even if the garage is not heated, it would still be a bit warmer than if the car was in your driveway or on the street.
Charge your batteries regularly. This will help to prevent them from discharging too deeply.
Avoid fast charging. Fast charging can generate heat, which can damage the battery and reduce its capacity. That doesn’t mean that you shouldn’t use a fast EV charger, but be cognitive about how often you use one. Maybe in the future, as this technology advances, this won’t be as much of a problem as it is now.
Summary
Lithium batteries, whether in a car or for any device diminish in capacity when in winter time. This is because of the decrease in ion capabilities when in cold weather. There are however a number of things you can do to circumvent this decrease, but they are not 100% reliable after you take the vehicle out for a drive.
Best bet would be to move to a warm climate. Then you never have this problem ????.
Picture yourself lying in your backyard on a warm June evening and all of a sudden, a bright flash begins to show up in the sky! No doubt it is an explosion of some kind and your hope is that it is nothing where any lives were lost. On the contrary, it is where life begins as you have just witnessed a supernova explosion!
So What Exactly is a Supernova?
Is this a galaxy? No, it is a supernova explosion!
A supernova, also called supernovea, represents the explosion of a star after it has exhausted all of its energy. This loss of energy occurs when the star is no able to longer withstand the force of its gravity, thereby causing the star’s core to collapse and subsequently, unleashing an extraordinary burst of energy.
This explosion is a powerful stellar explosion that occurs at the end of a star’s life cycle and is one of the most dramatic events in the universe. Its explosion is so powerful that it outshines entire galaxies, at least for a short time.
According to NASA, a supernova is the largest known explosion in space. The last recorded supernova in the Milky Way occurred in 1604, known as Kepler’s Supernova, and remained visible to the naked eye for an astounding 18 months.
The Seeds of Life
At the time of a supernova explosion, the energy that is released is so extraordinary that, for a short time period, the star will outshine entire galaxies, which is equivalent to a combination of billions of stars combined into one.
This outburst is not just that of light, rather it contains elements like carbon, iron, calcium, and gold, which are the seeds of life via the creation of new planets and stars, called stellar nurseries or nebulas as the term used mostly when referring to the beginning of life in the universe.
In our Artificial Intelligence 101article, we spoke about binary classification with supervised learning using the fly example. Then we discussed the limitations of this type of clarification because it has only two data sets to compare with the unknown data.
In the case of the fly example, we are only able to determine if it is a flying or a crawling insect. If we want to get more precise, such as determining what type of fly it is, we need to acquire more categories of labeled data. This is called multiclass classification.
As we proceed with the multiclass classifications, we are also going to delve into the types of models that are used for this process, but before we begin, let’s clarify a couple of AI terms so that everything is clear, starting with data points which we scratched the surface within our AI 101 article.
A data point is a specific attribute that is input into the machine learning algorithm (AKA the model). It is a component that is part of a complete unit. The more data points there are, the more precise the model will be in its conclusion.
What is a Dataset?
A dataset is a collection of data points. A data set can contain any number of data points, from a few to billions.
Data Point and Dataset Usages
Our fly example is a representation of AI data points and datasets, but in the real world, these factors work for a large variety of conditions. Below are just a few of them.
Financial predictions
Using a self-driving car
Facial recognition
Medical diagnosis models
Agriculture
Predictions for better sales
Fraud detection systems
A customer service chatbot
Together, the algorithm reads the unknown data points that are given to it and compares those data points to the labeled model The more data points that are supplied, the more accurate the model will be.
Now, let’s look at the AI models that are available.
Honor Thy Neighbor! The K-Nearest Neighbor Model
One of the models is called K-Nearest Neighbor (KNN). This algorithm will look at the unknown piece of data and compare it to the marked data. This is nothing new. We learned about this in our previous lesson on supervised learning, but now the comparisons will be matched against more than two classes.
In our fly example, let’s create classes that will include four types of flies: house fly, horse fly, fruit fly and horn fly. Each one of these flies have specific characteristics or patterns of data points that distinguish them from the other classes.
Example 1: Imagine you have a big puzzle with different pieces. Each piece of the puzzle represents a data point. Just like how each puzzle piece is unique and contributes to the overall picture, a data point is a single piece of information or observation that helps us understand or solve a problem.
Example 2: Let’s say we want to know the favorite color of each student in a class. Each student’s favorite color is a data point. We can collect all these data points to find patterns or make conclusions about the class’s preferences.
In simpler terms, a data point is like a puzzle piece that provides us with a small part of the whole picture or information we are trying to understand. By putting all the data points together, we can learn more about a situation, solve problems, or make decisions based on the available information.
In other words: A data point is a small piece of information or a single example that helps us understand or learn about a larger group or class of things. It’s like having one item or measurement from a collection that represents the whole group.
The k-nearest neighbors (KNN) algorithm uses data points of specific marked classes to compare to the unknown (given) data. The more data points of a specific class, the more likely the unknown data will match that class.
The algorithm will scan the data points of the unknown fly and ask itself which known fly category looks to be the closest neighbor to the unknown fly? Technically speaking, which set of data points of a specific class is the closest match to the set of data points to the unknown data? Looking at it in reverse, which class is the most distant match to the unknown data?
This is the KNN process, which finds the closest pattern of data points of the unknown data. The more accurate the data points that match the unknown data, called votes, the better of a match you have, and those classes will be its closest neighbors.
Another way of explaining KNN is once the K nearest neighbors are identified, the unknown data point is assigned the class label that is most prevalent among its neighbors. This means that the majority class among the k nearest neighbors determines the classification of the unknown data point.
Math is used (don’t worry. It is simply high school math) to determine which neighbors are the closest in proximity to the unknown data and those neighbors are designated by the letter K.
The math that is used is the distance between two points. If you don’t remember how to calculate the distance between two points, you can go to this refresher course. This procedure is called the Euclidean Distance and the computer instructions are based upon this concept.
So the data points that match the unknown data get more votes and subsequently are given a number that represents the distance to the unknown entity. The lower the number, the closer the data class resembles the unknown.
To relate Euclidean Distance to our fly example, it would mean what fly category has the line with the least distance to the unknown fly.
The KNN algorithm is based on the concept that similar things exist in close proximity, so the best match would be those where the lines in the graph are the shortest distance.
What is a Predictor?
A predictor is the output that an algorithm releases based on a learned dataset that it uses to make further predictions.
The Regression Model
This algorithm is a supervised learning model used when future predictions are required. It takes the input data, also known here as independent variables and makes predictions based on the patterns it sees from what it learned from the dataset. In other words, Regression models are trained on a dataset of historical data. The model learns the relationship between the independent and dependent variables from the data. Then it can be used to predict the value of the dependent variable for new data points.
Conclusion
A major advantage of AI lies in its ability to improve efficiency. Similar to the Industrial Revolution, AI is streamlining the manufacturing process, increasing productivity and reducing human error.
Artificial Intelligence enhances decision-making through data analysis and predictive capabilities. In healthcare, AI can analyze a vast amount of medical datasets, aiding doctors in diagnosing diseases and suggesting treatment plans. Financial institutions rely on AI for fraud detection, increasing security and efficiency. and governments use machine learning to predict criminal activities and allocate resources for improved public safety.
Machine learning algorithms can generate art, compose music, and write literature. In design and engineering, it assists in more efficient and aesthetically pleasing products.
AI is expediting scientific research by rapidly analyzing extensive datasets, accelerating discoveries in genomics, drug development, and climate science.
This technology also holds promise in addressing global challenges such as in agriculture, where it can enhance crop yields. Disaster prediction and response are also improved through AI analytics.
Natural Language Processing (NPL) gives us voice recognition that enables better interaction with digital devices, especially for people with disabilities.
As AI continues to advance, the potential to reshape industries and improve the quality of life for people around the world is extremely promising, but we must ensure that the utilization of machine learning does not fall into the wrong hands. Ethical considerations and responsible development must remain at the forefront so that artificial intelligence benefits are harnessed responsibly and equitably throughout the world!
As computers gained momentum in the 1980s, the need to store information on a mobile platform was intensifying. Floppy disks were the first portable devices that were invented. They were invented by a team of IBM engineers led by Alan Shugart in 1971 but they didn’t gain popularity until the early 1980s. The disks were very light in weight and would “flop” if you waved them; hence, ‘floppy disks.’
Comparison of sizes of the floppy disks. Photo: iStock
They were large 8″ in diameter disks and could store a maximum of 100 KB of data. That’s about 10 full pages of words plus maybe a few small pictures. So if you had a thesis to write or hundreds of pictures to save, you would have been out of luck.
5.25″, 1.44 MB ‘floppy’ disks. iStock
In 1981, the 3.5-inch floppy disk was introduced, which stored up to 1.44 MB of data. They were hard disks, meaning that they didn’t “flop” but their storage capacity was over 100 times more than the 8″ floppy disks that were initially created.
Floppy Disk Issues
Floppy disks were not without their flaws. They were susceptible to damage from magnets and dust, and could easily be corrupted by physical damage or exposure to heat. They were also slow, with read and write speeds that could be frustratingly slow for users.Despite these limitations, these disks played an important role in the history of computing. They enabled the widespread distribution of software and documents and helped establish the personal computer as a powerful tool for individuals and small businesses.
Today, floppy disks are essentially a relic of the past, but their impact on computing history cannot be overlooked.
The Introduction of the Compact Disk
By the early 2000s, floppy disks were being phased out as other storage options, such as CDs were becoming more popular and they were a revolution in data storage capacity. From 1.44 MB of the 3.5,” floppies came 50 megabytes (MB) to 700 MB of data storage on a CD.
This capacity not only allowed users to store text and image data but also music and videos.
Enter the Flash Drive, AKA USB
Not to be confused with USB cables, these are plastic devices, about an inch long that plug into the USB port, the same port that those cables connect to.
A typical flash drive is a hard plastic device about the size of your thumb, which is why they are sometimes called thumb drives. Their storage capacity blows away any of their predecessor’s CDs or floppies with storage starting with 4 GB up to 256 GB. That is over 1,000 times more storage space than the first hard drives that came onto the market.
Comparison of computer storage devices. Photo: Wikimedia CC
OK so you got a bunch of those wires with different looking ends and you don’t know which one of these connects to the device you want to connect to. Here we will unfold that mystery for you!
USB Overview
They are called USB (Universal Serial Bus) cables. There are several types of USB connectors that have been developed over the years, but it is worth noting that USB connectors can have different versions, indicating the supported USB specification and data transfer speeds.
For example, USB 2.0, USB 3.0 (also known as USB 3.1 Gen 1), USB 3.1 (also known as USB 3.1 Gen 2), and USB 3.2 are different versions with varying capabilities.
USB (Universal Serial Bus) cables come in various types, each designed for specific purposes and compatibility with different devices.
Here’s an overview of the most common types of USB cables:
USB, HDMI, ethernet icon set. Mini, micro, lightning, type A, B, and C connectors. Photo: iStock
USB Type-A: USB Type-A is the standard and most recognizable USB connector. It has a rectangular shape and a flat, rectangular end. These are most commonly found on computers, laptops, and game consoles. They are used to connect peripherals such as keyboards, mice, printers, external hard drives, and flash drives.
USB Type-B: These connectors are larger than Type-A. They are square-shaped and have beveled corners. You would see them on laptops that connect to a printer or external hard drives.There are various subtypes of Type-B connectors. Let’s take a look.
Standard-B: Standard-B connectors are the ones you would be most familiar with. They connect printers, scanners, and other peripheral devices. They have a square shape with two rounded corners but are less common in modern devices.
Mini-B: Mini-B connectors are smaller than Standard-B and were commonly used with older cameras, MP3 players, and other small electronic devices. They are gradually being phased out in favor of Micro-B connectors.
Micro-B: These connectors are smaller than both Standard-B and Mini-B connectors. They are commonly used with smartphones, tablets, portable hard drives, and other compact devices. Micro-B connectors are reversible, making them more user-friendly. There are two subtypes of Micro-B connectors: Micro-B USB 2.0 and Micro-B USB 3.0.
USB Type-C: Type-C is a newer, versatile, and increasingly popular connector. It features a small, reversible design that allows for easy plug orientation. Type-C cables can be plugged in either way, eliminating the frustration of trying to find the correct orientation. They are used in a wide range of devices, including smartphones, tablets, laptops, desktop computers, gaming consoles, and peripherals. Type-C cables have numerous advantages over their predecessors. They support faster data transfer speeds and higher power delivery and can transmit audio and video signals through alternate modes like DisplayPort or HDMI. Type-C connectors are backward compatible with USB 2.0 and USB 3.0 standards using appropriate adapters or cables.
USB Mini-A and Mini-AB: Mini-A connectors are smaller and less common than Type-A connectors. They were primarily used in older digital cameras, MP3 players, and other portable devices. USB Mini-AB connectors combine the features of both Mini-A and Mini-B connectors, allowing devices to function as either a USB On-The-Go (OTG) host or a peripheral device.
USB 3.0 Type-A and Type-B: USB 3.0, also known as USB 3.1 Gen 1, introduced faster data transfer speeds compared to USB 2.0. USB 3.0 Type-A connectors are backward compatible with USB 2.0 Type-A ports, while USB 3.0 Type-B connectors provide improved speeds for compatible devices such as external hard drives.
USB 3.1 Type-C: USB 3.1, also known as USB 3.1 Gen 2, further improved data transfer speeds over USB 3.0. USB 3.1 Type-C connectors offer faster speeds, higher power delivery, and support for alternate modes for audio, video, and other protocols. USB 3.1 Type-C cables are backward compatible with USB 3.0 Type-A and Type-B connectors using appropriate adapters or cables.
Summary
It may be confusing in the beginning, but keep in mind that the most used one is the Type-A, and then you can take it from there.
You are a robot, but like the scarecrow in the Wizard of Oz, you have no brain. John the human wants to change that, so he filled your brain with a model of a fire engine.
But John also wants you to identify the fire engine by knowing the components that comprise it, so he provides you with this knowledge.
In addition, he provides you with information as to other variations of the fire engine vehicle, meaning that if the parts do not entirely match that of a fire engine, the components may be more closely matched to that of an ambulance or possibly some other type of vehicle.
Now you have the data necessary to identify a fire engine and know what the parts are that encompass it. You can use this knowledge to compare the model to other objects and determine if any of those objects are fire engines or decide that it is something else entirely, and if so, what else could it be?
Congratulations! You are now a machine that can differentiate between objects, or more specifically, you are artificial intelligence!
Ok, we admit this scenario is quite simplified but the idea is to provide the concept of artificial intelligence. So now, let’s dwell into the details of exactly how this works, but before we continue, here are a few technical terms that you should familiarize yourself with. We will be discussing them in more detail further into this article.
Datapoint = The components that make up the model (parts of the fire engine).
Dataset = The combination of all the components together that make up the model (the vehicle as a whole unit).
Supervised Learning = The ability to look at a particular object and compare it to the object (model) that you have in your possession.
AI is Learning
The basic premise behind AI is to create algorithms (computer programs) that can scan unknown data and compare it to data that it is already familiar with. So let’s start by looking at another example.
Is this a fork or a spoon? Is it a knife? Well, they both have handles, but this one has spikes. Let me look up what pieces of information I have in my database that look like this item. Oh, I have a piece that resembles this spike pattern, so it must be a fork!
AI algorithms scan the unknown data’s characteristics, called patterns. It then matches those patterns to data it already has recognized, called pattern recognition. The data it recognizes is called labeled data or training data and the complete set of this labeled data is called the dataset. The result is that it decides as to what that unknown item is.
The patterns within the dataset are called data points, also called input points. This whole process of scanning, comparing, and determining is called machine learning. (There are seven steps involved in machine learning and we will touch upon those steps in our Artificial Intelligence 102 article).
For example, if you are going to write a computer program that will allow you to draw a kitchen on the screen, you would need a dataset that contains data points that make up the different items in the kitchen; such as a stove, fridge, sink, as well as utensils to name a few; hence our analysis of the fork in the image above.
Note: The more information (data points) that is input into the dataset, the more precise its algorithm’s determination will be.
Now, let’s go a bit deeper into how a computer program is written.
Writing the Computer Program
Photo: iStock
We spoke about how computers are programmed using instructions in our bits and bytes article, but as a refresher, let’s recap!
Computer programs, called algorithmstell the computer to do things by reading instructions that a human programmer has entered. One of our examples was a program that distributes funds to an ATM recipient. It was programmed to distribute the funds if there was enough money in the person’s account and not if there wasn’t.
But THIS IS NOT AI since the instructions are specific and there are no variations to decide anything other than “if this, then that”.
In other words, the same situation will occur over and over with only two results. There is no determination that there may be more issues, such as the potential for fraudulent activity.
Bottom line – There is no learning involved.
Writing a Learning Program
The ATM example is limited to two options, but AI is much more intensive than that. It is used to scan thousands of items of data to determine a conclusion.
How Netflix Does It
Did you ever wonder how Netflix shows you movies or TV shows that are tuned to your interests? It does this by examining what your preferences are based on your previous viewings.
The algorithm analyzes large amounts of data, including user preferences, viewing history, ratings, and other relevant information to make personalized recommendations for each user.
It employs machine learning to predict which movies or TV shows the user is likely to enjoy.
It identifies patterns and similarities between users with similar tastes and suggests content that has been positively received by those users but hasn’t been watched by the current user.
For example, if a user has watched science fiction movies, the recommendation might be to suggest other sci-fi films or TV shows that are popular among those users with similar preferences.
The program will learn and adapt as the user continues to interact with the platform, incorporating feedback from their ratings and viewings to refine future recommendations.
By leveraging machine learning, streaming platforms like Netflix can significantly enhance the user experience by providing tailored recommendations, increasing user engagement, and improving customer satisfaction.
This can’t be done using the non-learning ‘if-else’ program we previously spoke about in the ATM example.
A Gmail AI Example
As you type your email, Google reads it and then offers words to accompany the sentence that would coincide with what you are about to type before you have even typed it.
This is called language modeling which uses the Natural Language Process (NPL) model.
In NLP, the algorithm uses a factor of probability that is designed to predict the most likely next word in a sentence based on the previous entry.
AI algorithms feed on data to learn new things.
The more data (data points) that exist, the easier it will be for the model to identify the patterns of an unknown entity.
AI: How it All Works
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Click CC above for closed caption
Supervised Learning
This is the most common type of machine learning. It involves feeding a computer a large amount of data to enable it to recognize patterns from the labeled dataset and make predictions when confronted with new data.
In other words, supervised learning consists of training a computer program to read from a data sample (dataset) to identify what the unknown data is.
How the Machine Thinks with Supervised Learning
Photo: iStock
Show and Tell: A human labels a dataset with data points that identify the sample set to be a building.
Then the human does the same thing to identify a bridge. This is another classification different from the building classification and is identified with specific patterns that make up a bridge.
The program takes note of the patterns of each classification. If computer instructions were written in plain English, this is what it would say:
This is a bridge. Look at the patterns that make up the bridge. And this is a building. Look at the patterns that make up the building. I can see distinguishable differences in the characteristics between the two. Let me match them up to the unknown data and make a decision on whether this new data is a bridge or a building.
Supervised learning is used in many applications such as image recognition, speech recognition, and natural language processing.
Supervised learning uses a data sample to compare unknown data. The data sample is called a data model.
It’s Raining Cats and Dogs
A supervised learning algorithm could be trained using a set of images called “cats” and “dogs”, and each cat and dog are labeled with data points that distinguish each.
The program would be designed to learn the difference between the animals by using pattern recognition as it scans each image.
A computer instruction (in simplified terms) might be “If you see a pattern of thin lines from the face (whiskers), then this is a cat”.
The result would be that the program would be able to make a distinction of whether the new image it is presented with is that of a cat or dog!
This type of learning involves two categories – cats and dogs. When only two classifications are involved, it is called Binary Classification.
Suppose you are studying insects and you want to separate flying insects from crawling ones. Well, that’s easy. You take a bug that you found in your backyard and compare it to the ant and fly you already stored on your insect board. In AI terms, this is supervised binary classification.
You immediately know, based on the pattern configuration of the insect which classification it belongs to – the crawlers or the flies. Now you grab more flies and put them in the fly category and do the same with the creepy crawlers for their category.
Let’s say you want to go deeper in the fly classification and find out what type of fly it is, (e.g. house fly, horse fly, fruit fly, horn fly, etc.); but you only have two classifications to compare them two – flies and crawlers, so what do we do? You create more classifications for the fly class.
This is multi-classifications, or more technically called multi-class classifications, which provide additional labeled classes for the algorithm to compare the new data to.
We will delve more into multi-class classifications and how this works in our next article, but for now, just know what binary classifications and multi-class clarifications are.
Unsupervised learning involves training a computer program without providing any labels or markings to the data. The aim is to enable the program to find (learn) patterns and relationships on its own.
It does this by reading millions of pieces of information and grouping them into categories based on their characteristics or patterns, then making decisions on what the new entity is by matching it up to one of those categories.
In other words, it matches patterns of the unknown data to the groups it created and then labels them without human intervention. This is called clustering.
Anomaly detection is the task of identifying data points that are unusual or different from the rest of the data. This can be useful for tasks such as fraud detection and quality control.
Reinforcement Learning
Reinforcement learning (RL) learns by trial and error, receiving feedback in the form of rewards or penalties for their actions. Any negative number that gets assigned means it is punished.
The higher the negative number, the more the algorithm will learn not to pursue that particular circumstance and will subsequently try again until positive numbers are assigned, called a reward. It will continue this process until it is properly rewarded. The goal of RL is to maximize its rewards over time by finding a sequence of actions that leads to the highest possible reward.
One of the defining features of RL is the use of a feedback loop in which the agent’s actions (an agent is the decision-making unit that is responsible for choosing actions in the environment that was provided to it). The loop permits the agent to learn from experience and adjust its behavior accordingly.
The feedback loop works as follows:
The agent takes an action in its environment.
The environment provides the agent with feedback about the action, such as a reward or punishment.
The agent then updates its policy based on the feedback.
The agent will repeat steps 1-3 until it learns to take actions that lead to desired outcomes (rewards).
RL has been applied to a wide range of problems, such as games, robotics, and autonomous driving. It is particularly useful in scenarios where the action may not be immediately clear and where exploration is necessary to find the best solution.
Conclusion
Overall, these AI methods are widely used in various industries and applications. We will continue to see growth and development as artificial intelligence technology advances.
What are the advances or dangers that AI can bring to the future? Read our article on the Pros and Cons of AI to find out.
