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Astromony, Technology

Star Trek and Its Influence on Space Exploration

Star Ship Enterprise from the original Star Trek series with stars in the background
Image by p2722754 from Pixabay

The original franchise was not just a great sci-fi series. It catalyzed innovation, inspiration, and collaboration for people worldwide. Concepts such as warp drive, transporter technology, and deflector shields have sparked discussion, and terms such as dilithium crystals initiated conversations. Let’s take a look at what Star Trek has done for space exploration and society as a whole.

Inspiring Future Generations

The franchise ran from 1966-1969 and became a milestone in ingenuity. Many people looked to the skies, wondering what was out there and how they could be part of the science that would lead them and others to space exploration.

Star Trek was and still is an inspiration to countless individuals seeking careers in science, technology, engineering, mathematics (STEM), and of course, space exploration. Many astronauts, scientists, engineers, and space enthusiasts have cited Star Trek as a formative influence on their interest in space and their decision to pursue careers in related fields.

Woman scientist lookiing up
iStock

Fans were fascinated as they watched the Enterprise crew encounter new civilizations. Some were more advanced and some whose evolution was considered primitive by 23rd century Earth standards. That is where the Prime Directive came into play. A central tenet of Starfleet and the United Federation of Planets’ principle prohibits interference with the internal development of alien civilizations, particularly pre-warp ones, meaning they have not yet developed the capability for faster-than-light travel.

The stories captivated audiences as to how the crew would struggle to maintain the directive when encountering obstacles they had to overcome. One of the most enduring encounters was in “The City on the Edge of Forever” where Dr. McCoy accidentally went back in time and somehow changed Earth’s history. It was up to Kirk and Spock to find McCoy and reverse what he did. Shatner mentioned this episode as his favorite during an interview with renowned astrophysicist Neil Degrasse Tyson in what is believed to be Tyson’s office at the Museum of Natural History in New York.

It wasn’t just the prime directive the Enterprise crew had to deal with. There were also villainous civilizations that found Starfleet to be a threat to them, namely the Klingons as well as other aliens such as the Gorns – a reptilian humanoid species who Kirk was forced to fight on an alien planet in the episode “Arena“.

The crew had numerous encounters with the Klingons. In “Day of the Dove,” the crew becomes embroiled in a conflict with Klingons, controlled by a mysterious entity that thrives on hatred and conflict.

The Klingons were always a thorn in the side of Star Fleet; however, that changed in the Next Generation when a peace treaty was consummated with Lt. Commander Worf as the security officer on the bridge. But we’ll venture into Captain Picard’s universe in a separate article, focusing only on the original series here.

Overall, “Star Trek” is a cultural phenomenon that has inspired generations of fans by exploring bold ideas, emphasizing inclusivity and diversity, and envisioning a future where humanity’s potential knows no bounds.

The Characters

A Lesson in Diversity

The show’s optimistic vision of humanity’s future in the 23rd century depicted people from diverse backgrounds working together. This resonated with audiences worldwide and represented a peaceful coexistence between all parties back on Earth. 

Lieutenant Uhura

Most notably, in the 10th episode of the third season titled “Plato’s Stepchildren,” Captain Kirk (William Shatner) kissing Lieutenant Uhura (Nichelle Nichols) broke barriers that echoed across the country as the first interracial kiss on TV, and still today, it is looked upon as a milestone that has advanced racial relations in TV and across all media platforms.

Nichols was the first African-American woman to play a lead role on television as Communication Officer Lieutenant Uhura. She met Dr. Martin Luther King, who was inspired by her character on Star Trek and said to her, “Do you not understand what God has given you? … You have the first important non-traditional role, a non-stereotypical role. … You cannot abdicate your position. You are changing the minds of people across the world, because for the first time, through you, we see ourselves and what can be.”

Chekov

Star Trek was aired during the Cold War, and the Russian national Chekov, played by Walter Koenig, no doubt sent a clear message that there was mutual peace between the US and the Soviet Union in the 23rd century. Interestingly enough, this did become a reality after the collapse of the Soviet regime in 1991. 

Unfortunately, recent current events are proving otherwise, as Valdimar Putin’s attack on Ukraine is no doubt reminiscent of past Soviet colonization. Despite this, the International Space Station (ISS) is still strong. It is a collaborative project involving multiple space agencies, including NASA (United States), ESA (European Space Agency), JAXA (Japan Aerospace Exploration Agency), CSA (Canadian Space Agency) and yes, Roscosmos (Russia).

What happens after the Ukraine war remains to be seen, but let’s hope that when the 23rd century arrives, Gene Roddenberry’s view of future world peace becomes a reality.

Lieutenant Sulu

George Takei portrayed Hikaru Sulu in the original Star Trek series and the first six Star Trek films. He served as the helm officer aboard the USS Enterprise under Captain Kirk. He’s known for his calm demeanor and exceptional piloting skills, expertly navigating the Enterprise through dangerous situations.



Although “Hikaru” is a typical Japanese first name, “Sulu” isn’t a Japanese surname. It refers to the Sulu Sea, near the Philippines. This is reportedly what Gene Roddenberry wanted – some ambiguity to his origin. Since Sulu was never referred to as specifically Japanese, it allowed Roddenberry to represent him with a broader Asian background, allowing him to represent all Asians within the entire Star Trek universe.

It is no coincidence that in real life, George Takei is full Japanese and felt the wrath of the American internment camps after the Pearl Harbor attacks. He has since become a vocal advocate for racial and social justice and has talked about his experience at previous Star Trek conventions. 

George Takei was born on April 20, 1937, and is 86 years old.

Scotty

Lieutenant Scott, or “Scotty” (James Doohan), as Captain Kirk called him, was the chief engineer of the Enterprise. He is known for his technical expertise and his ability to keep the enterprise running smoothly, even under the most challenging circumstances. Scotty’s catchphrase, “I’m giving her all she’s got, Captain!” has become iconic in popular culture.

His character is depicted as fiercely loyal to the captain and the crew of the Enterprise. He is often portrayed as having a gruff exterior but also demonstrates compassion and camaraderie with his fellow crew members.

Scotty’s loyalty takes control with support from Dr. McCoy

Scott’s background was from Scotland. His strong Scottish accent and colorful expressions are instantly recognizable. In addition to his engineering skills, Scotty is known for his fondness for Scotch whisky, which adds depth to his character and provides moments of levity in the series. The term “Beam me up Scotty” is a common idiom known worldwide.

Dr. McCoy

Dr. Leonard “Bones” McCoy (DeForest Kelly) is a prominent character in the Star Trek franchise, known for his role as the ship’s chief medical officer. He is a highly skilled physician with expertise in various fields of medicine and surgery, but, at times, he finds himself faced with challenging situations, especially when confronted with alien physiology,

Star Trek Enterprise sick bay
Sick Bay Star Trek Museum Ticonderoga, NY

Despite his occasional verbal battles with fellow crewmembers, mainly when addressed by Mr Spock’s lack of human emotion, McCoy can get very poignant and is not afraid to speak his mind, particularly when he feels that ethical principles are at stake. He often serves as a voice of reason, questioning authority and advocating for what he believes is right, even if it means challenging orders from superiors. Overall, however, he shows compassion for the well-being of his fellow crew members.

McCoy shares a close friendship with Captain Kirk, and this dynamic is characterized by playful banter, mutual respect, and unwavering loyalty to one another. His personality is often remembered for his remarks when confronted with events that force him to step outside his expertise, which prompts him to yell out his memorable catchphrase, “I’m a doctor, not a … “.

McCoy’s background is not directly mentioned; however, there are subtle implications that he comes from the southern United States.

Spock
Leonard Nimoy’s most iconic role as Spock, the half-Vulcan, half-human science officer, has become a pop culture phenomenon, admired for his logic, loyalty, and the Vulcan salute he originated. “Live Long and Prosper” were the words that accompanied Spock’s salute. He used two fingers to donate it. Although not explicitly addressed in the series, the Vulcan salute represents the Hebrew letter Shin (ש) and returns to the Jewish Priestly Blessing. A blessing from the Old Testament that Nimoy witnessed as a child.

This author was privileged to meet Nimoy while visiting the Jewish Museum in NYC. I asked him what he thought of the other Star Trek series. He responded, “I think of them as my grandchildren. They come, and then they go away”. 

Leonard Nimoy passed away in 2015 from COPD, but his influence on science fiction and popular culture remains immense, especially among NASA engineers. His mindset and discipline helped bridge the gap between human emotions and logic, enlightening children and adults to think clearly and logically when encountering problems.

Kirk

Captain James Tiberius Kirk, portrayed by William Shatner, is one of the most iconic characters in science fiction history.  As the captain of the starship USS Enterprise, his persona became synonymous with strength, leadership, and exploration of the unknown, along with the phrase “Boldly go where no man has gone before“. And boldly, they did travel through the galaxy, encountering new life and civilizations. Some friendly, some not so friendly, and some war-like.

Black and white portrait of William Shatner
Wikipedia Public Domain

William Shatner always had a passion for space travel. In 1978, he recorded his version of Rocket Man at the Sci-fi Awards. But his passion did not end there. On October 13, 2021, at age 90, he went from fictional to real spaceman aboard billionaire Jeff Besos’s Blue Origin spacecraft and became the oldest human to ever set foot into space.

“I saw a cold, dark, black emptiness. It was unlike any blackness you can see or feel on Earth. It was deep, enveloping, all-encompassing. I turned back toward the light of home. I could see the curvature of Earth, the beige of the desert, the white of the clouds, and the blue of the sky. It was life. Nurturing, sustaining, life. Mother Earth. Gaia. And I was leaving her,” reads an excerpt from “Boldly Go” that was first published in Variety Magazine.”

“I’m so filled with emotion about what just happened. It’s extraordinary, extraordinary. It’s so much larger than me and life. It hasn’t got anything to do with the little green men and the blue orb. It has to do with the enormity, quickness, and suddenness of life and death.”

Shatner, at age 93, is still going strong. On March 17, 2024, he was the guest at the Alice Tully Hall in Lincoln Center, NYC, hosted by  Neil Degrasse Tyson for a Q&A session after screening his new movie “You Can Call Me Bill.” 

William Shatner speaking with Neil Degrasse Tyson at Lincoln Center
William Shatner and Neil Degrasse Tyson discuss Shatner’s life and philosophy of the universe

Mission Names and Concepts

NASA and other space agencies have drawn inspiration from the show when naming missions or developing mission concepts. The Space Shuttle Enterprise was named after the show. It’s a nod to the franchise, reflecting its enduring influence on the community and its role in shaping the language and imagery of space exploration.

Fueling Public Interest

The series’ portrayal of space travel, alien worlds, and encounters with extraterrestrial life has captured the imagination of millions of people, inspiring them to learn more about the universe. The famous Star Trek conventions and related events have provided forums for enthusiasts to unite, share their passion for space exploration, and engage with real-world space missions. 

Engineering room on the Star Trek Enterprise spacecraft
Engineering room on the Enterprise Museum Ticonderoga, NY

Shaping Spacecraft Design

The original spacecraft, the USS Enterprise, designated by NCC-1701 (Navel Construction Contract, 17 for Starfleet’s 17th starship design, and 01 – the first of this design series), has influenced real-world spacecraft planning. While the functionality of these fictional vessels may differ from actual spacecraft, their sleek and futuristic designs have inspired engineers to think creatively about spacecraft aesthetics and functionality. 

Four views of the Star Ship Enterprise
Image by Gerhard Janson from Pixabay

Promoting Scientific Inquiry

Star Trek’s emphasis on exploration has promoted a culture of curiosity and exploration in society. The series’ portrayal of futuristic technologies and scientific concepts has sparked interest in science and encouraged viewers to learn more about the universe and the possibilities of space exploration. Concepts introduced in Star Trek, such as the Prime Directive and the exploration of strange new worlds, have inspired discussions about ethics, philosophy, and humanity’s role in the cosmos.

Inspiring Technological Innovation

Star Trek has inspired the development of numerous technologies that were once considered futuristic but have since become reality or influenced real-world technology development. Examples include cell phones (inspired by the communicators used by Starfleet officers), tablet computers (similar to the PADD devices seen on the show), voice-activated computers (reminiscent of the ship’s computer), medical imaging devices (such as MRI and CT scanners), and more. The franchise’s imaginative depictions of technology have inspired scientists, engineers, and inventors to push the boundaries of what is possible and strive to turn science fiction into reality.

Exploring Moral and Ethical Dilemmas

Star Trek often explores complex moral and ethical dilemmas through its storytelling. Episodes frequently address issues such as the ethics of scientific experimentation, the consequences of war and violence, and the challenges of diplomacy and cooperation between different cultures and species. By tackling these issues thoughtfully and thoughtfully, Star Trek encourages viewers to reflect on their values and beliefs.

Conclusion

Star Trek’s influence on space exploration is profound and far-reaching, extending from its role as a source of inspiration and imagination to its impact on technology, collaboration, and public engagement.

By envisioning a future where humanity explores the cosmos with curiosity, courage, and cooperation, Star Trek has helped shape the collective aspirations and ambitions of the space exploration community. It continues to inspire generations of space enthusiasts worldwide.

Technology

How Does Liquid Crystal Displays (LCDs) work?

Photo of LCD screen showing the James Web Telescope
Photo: SMS

In 1994, a man walked into a Manhattan audio-visual store and saw something astonishing, A flat screen was hanging on the wall with a TV picture displayed. The width of this display was about 2”, and the cost was $18,000. That’s over $40,000 in today’s market.

Fast forward to 2024 and flat screens are the norm. Nowhere, or perhaps in a museum, would one find those bulky cathode ray tube (CRT) TVs that the world used 50 years ago. When we buy a TV, we look at all kinds of flat screens, technically called Liquid Crystal Displays (LCDs). There are also more advanced technologies, but we will focus on LCDs in this article as they are still trendy in the commercial market.

We will explore the inner workings of these types of TVs, from the liquid crystals, filters, and electricity to how these elements collaborate to produce the stunning images we see on our TV and computer monitors.

Illuminating the Screen

Incandescnt vs. Fluorescent

The  LCD’s source of illumination is known as a ‘backlight.’ Initially, the backlight comprised fluorescent lamps. This is a step above the well-known incandescent light bulbs we use in our homes. In other words, incandescent light provides light through the continual heating of a metallic filament, which constantly uses electricity to heat the filament and produce light. Fluorescent bulbs consume much less electricity than incandescent bulbs because they don’t require continuous electricity output to heat them.

Enter Light Emitting Diodes

In more recent years, light-emitting diodes (LEDs) have become the standard due to their improved energy efficiency and better control over brightness levels. This energy savings is due to the LEDs not being needed to generate the amount of heat that fluorescent lighting does.

With LCDs, the backlight uniformly illuminates the entire display panel, providing brightness for image formation.

Liquid Crystal Layer 

Directly in front of the backlight lies the liquid crystal layer. Liquid crystals are unusual in that they can possess the properties of both liquids and solids. They have the ability to flow like a liquid. This flow is random by nature, but temperature changes can cause these crystals to bypass their natural random state of flux and move in a certain direction. Additionally, adding an electric current through the crystals will also cause them to ‘straighten out’, and in so doing, one can harness the crystal flow allowing a certain degree of light to materialize. 

How the Crystals are Harnessed to Produce Light

When electricity is transmitted through the crystals, they become polarized, which causes the molecules to straighten and move in one direction. Similar to when an electric field is sent through a wire, the electrons become polarized and move in one direction from one pole to the other. In the case of crystals, it is the molecules that are affected. They will align and move in a specific direction.

Polarizing the liquid crystals is a crucial component in controlling the amount of light being emitted; in other words, it controls the orientation of the molecules to produce the appropriate amount of luminescence on the TV display, which forms images on the screen.

This amount of luminescence is controlled by a polarizing filter. By adding a polarizing filter to the electrically charged molecules, the crystals will align either horizontally or vertically. One direction will block the light and the other direction will allow the light to pass through.

But this is just a black-and-white situation (pun intended). 🙂 What happens between the pure black or pure white luminance that passes through the crystals is significant. In other words, the shades of black and white produce what we see on the screen. Let’s discuss this in more detail.

Enter the Pixel

Copilot AI Generated image of a woman's face
Each square in this image is a pixel. Copilot AI-Generated Image

There is a polarizing filter for each of the LCD molecules. This combination of a crystal and filter is called a pixel – a liquid crystal cell. (The actual components and how the components react within these cells are beyond the scope of this article).

Rotating the filter regulates the amount of light that will be released. Another way of putting it is that the rotation of the filter controls the intensity of the light; thus, the filter can make the pixels very bright, not too bright, or have no brightness at all (blackness), depending upon how much the filter is rotated either way.

Close up view of pixels in an image of an eye.
Close up view of how each pixel contains a different shade of black and white due to the amount of light that is emitted through the pixel from the polarizing filter

For LCDs, this specific control of light transmission forms the basis of how images are displayed on the screen since some pixels will be brighter or darker than their neighbors.

An analogy would be If you look at any black and white photograph, the images are little dots of pure white or pure black and everything in between which forms the figures we see. 

Next, we will discuss adding color, but understanding how light is released through pixels is a prerequisite. 

Color Filters 

Ai generated color image of a woman's face
Notice how each pixel has a different shade of color and light intensity. On a live screen, the pixels will not be visible

In addition to the polarization filters attached to each cell, there are the color filters. These filters, typically red, green, and blue (RGB), determine the color of the light transmitted through them. 

Just as the rotation of the polarization filters determines the shades of black and white for the image, the color filters go one step further and choose the correct combination of colors to obtain for each pixel. 

Forming the Image

Whether the initial signal comes from a cable box, streaming device, or computer screen, a set of algorithms in the TV determines the appropriate amount of electrical current for each pixel. The desired image is then formed on the screen by selectively activating or deactivating the brightness levels of the pixels. 

Conclusion

LCD screens produce images using liquid crystals, which have the unique ability to react to electrical current in a way that permits just the right amount of light to be emitted from each pixel.

The pixels are cells that contain polarization filters and color filters. By fine-tuning the intensity of the electrical current applied to each pixel and carefully manipulating the polarization of light, the TV can reproduce a vast array of colors and shades.

Technology

Neuralink – The Brain’s Sixth Sense

AI generated illustration of neurons connecting to a cell phone

Lucy Has Arrived!

In 2014, the sci-fi thriller Lucy was released in theaters across the country. It starred Scarlett Johanson, whose brain became so powerful that she could move objects with nothing but a thought.

This may sound far out, but it is much closer than you think. Enter the ‘Link’. A computer chip that is implanted inside the human brain. It can read and convert our thoughts into digital signals that a computer will understand and respond to.

Although the Link is in its fetal stages, the results are so promising that we can confidently say that Lucy is here to stay. No more is it a thought of the future (pun intended 🙂)

One example would be a person who wants to browse the web on their iPhone, and he/she would control the device by simply thinking about it. This can be particularly useful for those who have paralysis, neurological disorders, or prosthetic limbs, as well as assisting with a range of other disorders where a person is medically incapacitated. 

The Makers of the Link 

Elon Musk discussing the NeuralinkElon Musk discussing the Neuralink

Neuralink is an advanced neurotechnology company founded by Elon Musk. It specializes in developing brain-computer interfaces (BCIs). These interfaces allow communication between the human brain and external devices by translating neural activity (movement of brain cells) into digital signals (the electrical impulses (1s and 0s) that computer systems use, called “bits). 

The Neuralink Device

The Link is a tiny, flexible device about the size of a small coin surgically embedded into the human skull. It contains thousands of hair-thin electrodes that interface directly with the brain cells. These electrodes read the neural activity and translate them into digital data (the 1s and 0s mentioned above). 

This is quite fascinating because there are roughly 86 billion cells in the brain, each measuring about 680 microns, which is extremely small. One micron equals 0.000039 inches or 1/100 the size of a human hair.

Groundbreaking Medical Features

Illustration of thoughts coming out of the brain

Wireless Charging

From cell phones to earbuds to EV cars, we all have some device that needs routine charging, maybe twice a day, depending upon its use. With the Link, it gets its charge from the skin.   

A Robotic Miracle

If you think AI is cool, imagine a robot that surgically implants the device in the brain! That might sound scary, but it has been proven to work more efficiently than any human can do, no matter how skilled the surgeon might be.

How Does It Work?

The process involves several steps.

  1. Recording Neural Activity: The Link has thousands of thin, flexible electrodes embedded in the brain tissue. These terminals capture the electrical pulses of nearby neurons and their voltage fluctuations. The fluctuations are in analog format, meaning that they act like a sine wave. Digital data is in the form of whether a signal is on (represented by a computer bit of 1) or off (represented by a computer bit of 0). The size of the voltage fluctuations determines which instance it is and is subsequently converted to the appropriate computer bit format.

    Ilustration of two neurons communicating with each other
    Neuron cells send electrical chemical signals. 3d illustration
  2. Analog-to-Digital Conversion: This is a common practice for many devices we use every day, and the Neuralink device is no different, except that the translation process occurs within the tiny Link chip. The captured analog signals are changed into digital data via the chip’s electronics, which amplifies the weak signals, filters out the noise, and then converts the voltage changes into a series of digital bits.
  3. Feature Extraction: Not all neural activity is converted. The Link’s processing unit analyzes the digital data stream and extracts specific features that are known to be associated with the desired output, such as movement, speech, or sensory perception. This could involve identifying patterns in the timing and frequency of the electrical spikes or the activity of specific groups of neurons.
  4. Machine Learning Algorithms: Now, the AI part. The extracted data is fed into machine learning algorithms trained on a large dataset of brain activity. These algorithms map the neural patterns to specific commands, thoughts, or sensations; in other words, they decode the brain’s messages. 
  5. Output Generation: Based on the decoded information, the Link can trigger specific actions (e.g., controlling a computer cursor or prosthetic limb) or generate external signals (e.g., synthetic speech or electrical stimulation for sensory restoration).

How the Link Will Be Applied 

Neuralink’s technology has the potential to transform medical technology into the 24th century and beyond.

AI generated image showing a man's brainwaves connecting to his body

  • Human-Computer Interaction: The ability to control devices directly through thought is closer now than ever before.
  • Medical Applications: Restoring lost sensory and motor functions in individuals with paralysis or neurological disorders.
  • Cognitive Enhancement: Humans may be able to retain information at an exceptional level, called Augmenting Memory. The possibility of one having extremely long-term memory can have significant advantages for everyone, from students to the elderly who would gain the most benefits.

The Future of Neuralink

The Neuralink technology holds immense potential to reshape our understanding of the brain and its interaction with technology.  While challenges remain, ongoing research and development efforts are bringing us closer to a future where brain-computer interfaces will become a reality and the potential for advanced human abilities and our interaction with the world around us will be within our reach!

 

 

 

Astromony, Technology

One More Step to Mars!

The Next Step!

The world watched in awe as Neil Armstrong put his foot on the moon’s surface on July 21, 1969, and his famous words, “That’s one small step for man, one giant leap for mankind,” resonated across the globe.

Now, 50 years later, we begin our lunar quest again. This time with advanced technology only dreamed of in the mid-20th century. A sci-fi fantasy then, but not anymore. Let’s look at what’s in store for this new, exciting journey!

Artemis

The Orion rocket. Part of the Artemis System
The Orion rocket. Part of the Artemis System

Unlike Neil Armstrong’s day, the Artemis project is led by NASA but includes a collaboration of international partners and is designed for greater ventures beyond the moon. A stepping stone if you will, with the final destination – Mars.

Named after Apollo’s twin sister, Artemis is a fitting name for this venture, as one of its plans is to put the first woman on the moon. The moon will act as a testing ground for the new technologies, and if successful, it will pave the way for these systems for deep space exploration.

Another difference from the 1969 moon landing is that the new spaceship will land on the lunar south pole. This is of particular interest to scientists since this region has water and ice. Water is a critical resource for sustaining life and can also be converted into oxygen for breathing and hydrogen for rocket fuel.

This research will lead to establishing a sustainable infrastructure that can support a long-term human presence.

The Programs Supported by the Artemis Project

The development of the Space Launch System (SLS) and the Orion spacecraft are two of the major developments currently being developed. Let’s take a closer look.

The Space Launch System

Artemis 1 Moon Rocket on the launch pad at Cape Canaveral Florida photograph taken March 2022
Artemis 1 Moon Rocket on the launch pad at Cape Canaveral Florida March 2022. iStock

The SLS is the super heavy rocket that will propel the Orion spacecraft and its crew into deep space. This is the first of the two main components of the Artemis project. The SLS consists of a rocket and its boosters that will blast the astronauts to the moon and later to deep space.

It will lift off with 8.8 million pounds of thrust and is equipped with four RS-25 core engines in two boosters and an upper-stage booster. They will be using liquid hydrogen and oxygen as their fuel.

No other rocket in history will have the advancements of the SLS. With its ambitious design for deep space, it will contain life support technology for long journeys, advancements in navigation and communications, and a powerful radiation shield for re-entry.

The Orion Spacecraft

The Orion Spacecraft
Orion spacecraft. Elements of this image furnished by NASA. iStock

The Orion Spacecraft is the reusable capsule located at the upper component of the SLS where the astronauts will reside and will contain the modules that will land on the moon. Similar to the lunar module that landed on the lunar surface in 1969.

It can provide life support for up to six crew members for up to 21 days. Orion is a critical part of NASA’s Artemis program and will be the rocket used to land on the lunar surface and to prepare for the mission to move on to Mars.

 

Technology

How AI is Changing Traffic

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Artists conception of AI traffic control along a highwat
iStock

We have all been inundated with newscasts about artificial intelligence and how it is changing our lifestyles, and traffic control is no exception. From the Belt Parkway to the Long Island Expressway and from Brooklyn to Montauk, AI is coming to a town near you.

Here are some ways in which AI is contributing to traffic reduction:

  • Traffic Prediction and Management:
    AI algorithms analyze historical traffic patterns, real-time data, and other sources to predict traffic congestion. This information allows authorities to proactively manage traffic flow and implement measures to avoid potential congestion problems.
  • Smart Traffic Lights: How many times have you been stuck at a light and yelled “Why is this light taking so long? It’s 3:00 am and no one is on the road”? AI-powered traffic light control systems can adjust signal timings based on real-time traffic conditions. These systems are designed to keep traffic moving as optimum as possible.
  • Route Optimization:
    Navigation systems use AI algorithms to provide drivers with real-time route recommendations that consider current traffic conditions. This helps distribute traffic across different routes, reducing congestion on commonly used paths.
  • Autonomous Vehicles:
    The development and integration of autonomous vehicles can potentially reduce traffic by improving overall traffic efficiency. AI-driven self-driving cars can communicate with each other to optimize spacing and speed, reducing stop-and-go traffic patterns.
  • Parking Solutions:
    AI can assist in finding parking spaces efficiently. Smart parking systems use sensors and AI algorithms to guide drivers to available parking spaces, reducing the time spent circling for parking, which contributes to traffic congestion.
  • Public Transportation Optimization:
    AI is used to optimize public transportation routes and schedules based on demand and real-time data. This helps ensure that public transportation systems are efficient and can serve more people, potentially reducing the number of individual vehicles on the road.
  • Traffic Incident Detection:
    AI systems can analyze data from various sources, such as surveillance cameras and social media, to quickly detect and respond to traffic incidents. Timely management of accidents or road closures can prevent the buildup of congestion.
  • Dynamic Toll Pricing:
    AI is utilized to implement dynamic toll pricing based on traffic conditions. Higher tolls during peak hours can encourage the use of alternative transportation or off-peak travel, helping to smooth out traffic flow.

Summary

Artist conception of an AI traffic monitoring system
iStock

By combining these AI-driven solutions, cities and transportation authorities can work towards creating more efficient and sustainable transportation systems, ultimately contributing to the reduction of traffic congestion. However, it’s important to note that the effectiveness of these measures depends on their implementation, infrastructure, and public acceptance.

 

Technology

Why do Lithium (EV) Batteries Decrease in Capacity in Winter?

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Illustration of an EV being charged
Photo iStock, Credit: Golden Sikorka

The Summer of EV Love 

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? 

Car driving in winter snow
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. 
      Illustration of an atom's valence electrons
      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 ????.

Technology

Computer Data Storage – How Times Have Changed!

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A Bit of Data Storage History

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.’

old diskettes set and flash disk isolated on white background
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.

Woman holding two 5.25" floppy disks
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.

Comparisions of computer storage devices
Comparison of computer storage devices. Photo: Wikimedia CC
Technology

What are All Those Types of USB Connectors For?

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USB Connectors
Photo: iStock

What are These Things?

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 Connectors
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.

Artificial Intelligence, Computer Systems, Technology

AI 101 – How Does Artificial Intelligence Work?

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Illustration of computer chips on a wall with a woman in front
Image by Gerd Altmann from Pixabay

Overview

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.

Photo of a fire engine
Photo by John Torcasio on Unsplash
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.

Image of a fork
Image by Susann Mielke from Pixabay. Text by SMS.

The AI Mindset

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

Computer Program Instructions
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 algorithms tell 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

Poyab Bridge under construction, Freiburg, Switzerland
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.

Supervised Learning Usining Multi Classifications

An Example

Illustration of a fruit fly
Image by Mostafa Elturkey from Pixabay

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

Colorful illustration of AI unsupervised clustering
Photo by Google DeepMind on Unsplash

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:

  1. The agent takes an action in its environment.
  2. The environment provides the agent with feedback about the action, such as a reward or punishment.
  3. The agent then updates its policy based on the feedback.
  4. 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.

Machine Language Terms to Know

  • Computer Instruction
  • Computer Program
  • Algorithm
  • Data Points
  • Patterns
  • Labeled Data
  • Dataset
  • Data Model
  • Pattern Recognition
  • Machine Learning
  • Binary Classification
  • Multiclass Classification
  • Supervised Learning
  • Unsupervised Learning
  • Reinforced Learning
Computer Systems, Technology

How to Optimize for Voice Search in 2023

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Illustration of voice seach with man at microphone
Image: iStock

Voice Search Overview

Voice search is here to stay and will only be gaining momentum as we proceed into the future and for those that are in marketing or SEOs, it is important to stay up to date with these features and optimize accordingly.

The processes behind voice and text search are quite different. Voice search queries may be longer and more complex, as people tend to ask questions in a conversational style, while text queries are typically shorter and more direct.

Another difference is in the way search results are presented. In text search, results are typically displayed on a search engine results page (SERP), with a list of links and brief descriptions. In contrast, voice search typically provides only the most relevant result, read aloud by a virtual assistant or smart speaker; such as Apple Siri, Amazon Alexa, Google Assistant and Microsoft Corona. This means that optimizing for voice search requires a different approach to traditional SEO, with an emphasis on providing clear, concise answers to common voice questions.

Searching by sound is an SEO component that cannot be overlooked and with the accelerating advancements in artificial intelligence, it is imperative that web developers and SEOs keep a watchful eye on this evolving technology.

The Statistics

Laptop computer showing statistics
Photo: iStock

As of the writing of this article, 32% of people between the age of 18 and 64 use a voice search medium (Alexa, Siri, Corona, etc.) and that number will only grow as we move to the future. 

Entering standard text search queries on mobile devices is commonplace, with over 60% of cell phone users text searching and 57% of mobile users taking advantage of voice search. 

It should be no surprise that Google is the most successful interpreter of audio searches with a 95% accuracy.

In a study in 2021, 66.3 million households in the US were forecasted to own a smart speaker and that forecast has become a reality as of 2023.

Voice technology stretches beyond search queries as 44% of homeowners use voice assistants to turn on TVs and lights, as well as an array of other smart home devices currently on the market. 

With statistics as these, speaking to robotic assistants is here to stay and will only be growing with new technologies as we proceed through the 2020s and beyond. 

How Does Voice Search Work?

Woman speaking into a moblie phone
Photo: iStock

The Physics Behind It

If you just need to know that there is an analog-to-digital conversion and are not interested in the specifics of how it’s done, you can skip this part and go to the next section, which is “Where Does the Data Come From?“. 

We will summarize the process of how the sound of human speech is converted into machine language, which is filtering and digitizing.

Filtering: Smart speakers and voice assistants are designed to recognize the human voice over background noise and other sounds; hence, they filter out negative sounds so that they can only hear our voices.

Digitizing: All sound is naturally created in analog frequencies (use of sinewaves). Computers cannot decode analog frequencies. They must be converted to the computer language of binary code.

Below are the details of how an analog signal is converted to digital. 

The Analog Conversion Process

Illustration of a sine wave
Image by Gerd Altmann from Pixabay

|n order to make this conversion, an Analog-to-Digital Converter (ADC) is required. The ADC works by sampling the analog signal at regular intervals and converting each sample into a digital value. 

The steps are as follows:

    1. Sampling: The first step is to sample the analog signal at a fixed interval. The sampling rate must be high enough to capture all the frequencies of interest in the analog signal. The Nyquist-Shannon sampling theorem states that the sampling rate must be at least twice the highest frequency in the signal. Sampling means taking regular measurements of the amplitude (or voltage) of the signal at specific points in time and converting those measurements into a digital signal. Sampling is necessary in order to convert analog sound waves into digital signals, which are easier to store, transmit, and process using digital systems such as computers or microcontrollers. The rate at which the analog signal is sampled, known as the sampling rate or sampling frequency, is important because it determines the level of detail that can be captured in the digital signal. Sampling an analog signal is an important step in converting it to a digital signal that can be analyzed, manipulated, or transmitted using digital systems.
    2. Quantization: Once the analog signal is sampled, the next step involves assigning a digital value to each sample based on its amplitude. The resolution of the quantization process is determined by the number of bits used to represent each sample. The higher the number of bits, the greater the resolution of the digital signal.
    3. Encoding: The final step is to encode the quantized samples into a digital format. This can be done using various encoding techniques such as pulse code modulation (PCM) or delta modulation.

Overall, the main process of converting analog to digital frequencies involves sampling, quantization, and encoding. The resulting digital signal can then be processed using digital signal processing techniques.

In summary: Smart speakers and voice assistants take in the audio from a person’s speech and convert it to machine language.

Where Does the Data Come From?

Outline of a computer screen wiht a cloud behind it
Image by Gerd Altmann from Pixabay

Information gathered from smart speakers and voice assistants pulls data from an aggregate of sources.

If you want your business to grow, you must be attentive to where content for voice search is collected so that you can make intelligent decisions regarding how to optimize for these devices. 

Amazon Alexa

When Alexa responds to a query, it relies on Microsoft’s Bing search engine for the answer. Why? Because Amazon, as well as Microsoft, are in direct competition with Google, even though Google has the most popular search engine in the world. 

Amazon’s refusal to use Google for audio responses is not something to be concerned about. After all, Bing’s search algorithms are very similar to Google’s.

With that said, if a person speaks to Alexa with a specific request, (e.g. “What’s the weather today?”), Alexa can pull that information from a database associated with that request. In this case, Alexa will connect to Accuweather. The device can access Wikipedia and Yelp if it needs to as well.

Apple Siri

Initially, Apple used Bing as its default search engine, but in 2017, Apple partnered with Google. Now, when you say “Hey Siri”, you can expect Siri to access the immense data repository from Google and supply the answer. This applies to the Safari browser for text searches as well.

There is a caveat though. When it comes to local business searches, Siri will call on Apple Maps data and will use Yelp for review information.

Microsoft Cortona

This one is probably the most straightforward out of all of the search engines, as Cortona relies on what else but Microsoft Bing for its information. 

Google Assistant

OK, this one’s a no-brainer. Google can currently index trillions of pages to retrieve information and since this also applies to Apple’s Siri, this section is of most importance if you want to optimize voice search for these voice assistants.

In most cases, Google and Siri will read from Google’s featured snippet.

So What is a Featured Snippet?

Screenshot of a Google featured snippet
Image: © SMS

Featured snippets are what you see after you run a Google search query. It is a paragraph that appears at the top of the page that summarizes the answer to a question.

The information that Google applies to the snippet is gathered from, what Google determines to be the most reliable source (website) for that information.

How Does Google Determine a Featured Snippet?

For a snippet to be posted by Google, it needs to know that the source is trustworthy via its domain authority, link juice and high-quality content, to name three important organic factors, as any SEOs would already know, but in addition to these factors, Google will defer to “HowTo” and FAQ pages most often to pull in the snippet.

Is Structured Data Needed?

Structured data is extra code that helps Google better understand what the page or parts of the page are about.

One might wonder if structured data has to be used in order to provide the featured snippet? The answer is no. As per Google, as long as the web page is optimized properly and contains the questions that equate to the user’s query or voice search in this case, structured data is not necessary; however, if it wouldn’t hurt to put it in, as we all are aware that nothing is static in the SEO world and this rule can easily change in the future.

The reason why Google focuses on “HowTo” and FAQ pages is that their content reflects that of human speech. For example, an FAQ page on EV cars may have the question “How long do EV batteries last?” – That is exactly how a person would ask a voice assistant that same question!

An ‘Action’ for Google Assistant is created, equivalent to an Alexa Skill and Google will read the snippet back to the user to answer the question he/she asked.

Summarizing Optimization for Voice Search

Alexa

Bing: If you have not already done so, bring Bing into your scope of work for SEO and start optimizing for this search engine.

Yelp: We all know that reviews are of the utmost importance, so check out Yelp for your or your client’s business and build on those reviews! Legitimately of course.

Siri

Google SEO: If you are already optimizing for Google’s search, just keep up the good work.  

Apple Factors: Where you might not be fully optimized is with Apple Maps, so get going. Start by registering with Apple Business Connect.

Yelp: And now Yelp is back in the picture!

Cortona

Bing: As mentioned, become an SEO Bing expert and you are ready to ask Cortona anything.

Google

Besides the standard organic optimization, focus on schema markup for HowTo and FAQ articles for voice search, which, if you’re lucky, will be shown on the SERP as a featured snippet.

There you have it. How to optimize for voice search. Let’s get these robots configured so that our businesses will be the first thing you hear from your voice assistant!