Artificial intelligence has transformed many industries, and transcription is no exception. While AI transcription software offers un-human speed, it’s far from perfect. Anyone who has used these tools knows they can produce some truly baffling results. These moments of machine misinterpretation are not just amusing but also a reminder of why human transcription remains essential.

Recently, while doing some transcription work, we’ve encountered some examples of AI transcription gone hilariously wrong. From bizarre substitutions to completely nonsensical sentences, these errors highlight the limitations of relying solely on algorithms to understand HUMAN language. Let’s dive into a few of these blunders that show why the human touch is still irreplaceable in transcription.

So we took off in convoy back to the Suez Canal, through the Suez Canal, back to Missouri Bizerte.

I guess you’ve heard of Anahita Enewetak.

My father, his name was Mokosak Markus Zack.

Did you find out when he was transported to Terezin, stat Theresienstadt?

We had a tick tock to tiptoe all the way back to the Philippines.

And the chaplain at Meredith’s that married us was a Catholic chaplain who was from South Portland, Maine.

So, anyway, then when they came with the draft, as I said, I was a for declassification 4D classification.

And this dwarf And Düsseldorf was just like some of the pictures I saw here.

We were at a village called Wingen sur Moder. Wingen on the motor River. Wingen-sur-Moder—Wingen on the Moder River.

They were bored with bartered everything for a piece of food.

No, Mr. Battle fatigue, Pietroforte, don’t do that.

And we’re just biding our time before the attack on Hawken Aachen.

It’s similar to a picture that I have for Michelle Hall from a shell hole.

Artificial intelligence has transformed many industries, and transcription is no exception. While AI transcription software offers un-human speed, it’s far from perfect. Anyone who has used these tools knows they can produce some truly baffling results. These moments of machine misinterpretation are not just amusing but also a reminder of why human transcription remains essential.

Recently, while doing some transcription work, we’ve encountered some examples of AI transcription gone hilariously wrong. From bizarre substitutions to completely nonsensical sentences, these errors highlight the limitations of relying solely on algorithms to understand HUMAN language. Let’s dive into a few of these blunders that show why the human touch is still irreplaceable in transcription.

So we took off in convoy back to the Suez Canal, through the Suez Canal, back to Missouri Bizerte.

I guess you’ve heard of Anahita Enewetak.

My father, his name was Mokosak Markus Zack.

Did you find out when he was transported to Terezin, stat Theresienstadt?

We had a tick tock to tiptoe all the way back to the Philippines.

And the chaplain at Meredith’s that married us was a Catholic chaplain who was from South Portland, Maine.

So, anyway, then when they came with the draft, as I said, I was a for declassification 4D classification.

And this dwarf And Düsseldorf was just like some of the pictures I saw here.

We were at a village called Wingen sur Moder. Wingen on the motor River. Wingen-sur-Moder—Wingen on the Moder River.

They were bored with bartered everything for a piece of food.

No, Mr. Battle fatigue, Pietroforte, don’t do that.

And we’re just biding our time before the attack on Hawken Aachen.

It’s similar to a picture that I have for Michelle Hall from a shell hole.

Q: I was surprised when autocorrect changed “intermittent” to “intermit.” I checked and, lo and behold, there is a word “intermit.” Does it not strike you as odd that the base-form is less known than its “built-up” version?

A: We don’t use, or recommend using, the autocorrect function in a word processor. Our spell-checkers flag possible misspellings but don’t automatically “fix” them. Word processors have dictionaries, but not common sense—at least not yet!

As for the words you’re asking about, the adjective “intermittent” (irregular or occurring at intervals) is indeed more common than the verb “intermit” (to suspend or stop). In fact, the verb barely registered when we compared the terms on Google’s Ngram Viewer.

However, “intermittent” isn’t derived from “intermit,” though both ultimately come from different forms of the Latin verb intermittere (to interrupt, leave a gap, suspend, or stop), according to the Oxford English Dictionary. The Latin verb combines inter (between) and mittere (to send, let go, put).

When “intermit” first appeared in English in the mid-16th century, it meant to interrupt someone or something, a sense the OED describes as obsolete.

The modern sense of the verb—“to leave off, give over, discontinue (an action, practice, etc.) for a time; to suspend”—showed up in the late 16th century.

It means “leave off” in the dictionary’s earliest citation for the modern usage: “Occasions of intermitting the writing of letters” (from A Panoplie of Epistles, 1576, by Abraham Fleming, an author, editor, and Anglican clergyman).

As we’ve said, “intermit” isn’t seen much nowadays. English speakers are more likely to use other verbs with similar senses, such as “cease,” “quit,” “stop,” “discontinue,” “interrupt,” or “suspend.”

When the adjective “intermittent” appeared in the early 17th century, Oxford says, it described a medical condition such as a pulse, fever, or cramp “coming at intervals; operating by fits and starts.”

The earliest OED citation is from an English translation of Plutarch’s Ἠθικά (Ethica, Ethics), commonly known by its Latin title Moralia (The Morals), a collection of essays and speeches originally published in Greek around the end of the first century:

“Beating within the arteries here and there disorderly, and now and then like intermittent pulses” (from The Philosophie, Commonly Called, The Morals, 1603, translated by Philemon Holland).

The adjective later took on several other technical senses involving irregular movement, but we’ll skip to its use in everyday English to mean occurring at irregular intervals. The earliest OED citation for this “general use” is expanded here:

“Northfleet a disunited Village of 3 Furlongs, with an intermittent Market on Tuesdays, from Easter till Whitsuntide only” (Britannia, or, An illustration of the Kingdom of England and Dominion of Wales, 1675, by the Scottish geographer John Ogilby).

Q: I was surprised when autocorrect changed “intermittent” to “intermit.” I checked and, lo and behold, there is a word “intermit.” Does it not strike you as odd that the base-form is less known than its “built-up” version?

A: We don’t use, or recommend using, the autocorrect function in a word processor. Our spell-checkers flag possible misspellings but don’t automatically “fix” them. Word processors have dictionaries, but not common sense—at least not yet!

As for the words you’re asking about, the adjective “intermittent” (irregular or occurring at intervals) is indeed more common than the verb “intermit” (to suspend or stop). In fact, the verb barely registered when we compared the terms on Google’s Ngram Viewer.

However, “intermittent” isn’t derived from “intermit,” though both ultimately come from different forms of the Latin verb intermittere (to interrupt, leave a gap, suspend, or stop), according to the Oxford English Dictionary. The Latin verb combines inter (between) and mittere (to send, let go, put).

When “intermit” first appeared in English in the mid-16th century, it meant to interrupt someone or something, a sense the OED describes as obsolete.

The modern sense of the verb—“to leave off, give over, discontinue (an action, practice, etc.) for a time; to suspend”—showed up in the late 16th century.

It means “leave off” in the dictionary’s earliest citation for the modern usage: “Occasions of intermitting the writing of letters” (from A Panoplie of Epistles, 1576, by Abraham Fleming, an author, editor, and Anglican clergyman).

As we’ve said, “intermit” isn’t seen much nowadays. English speakers are more likely to use other verbs with similar senses, such as “cease,” “quit,” “stop,” “discontinue,” “interrupt,” or “suspend.”

When the adjective “intermittent” appeared in the early 17th century, Oxford says, it described a medical condition such as a pulse, fever, or cramp “coming at intervals; operating by fits and starts.”

The earliest OED citation is from an English translation of Plutarch’s Ἠθικά (Ethica, Ethics), commonly known by its Latin title Moralia (The Morals), a collection of essays and speeches originally published in Greek around the end of the first century:

“Beating within the arteries here and there disorderly, and now and then like intermittent pulses” (from The Philosophie, Commonly Called, The Morals, 1603, translated by Philemon Holland).

The adjective later took on several other technical senses involving irregular movement, but we’ll skip to its use in everyday English to mean occurring at irregular intervals. The earliest OED citation for this “general use” is expanded here:

“Northfleet a disunited Village of 3 Furlongs, with an intermittent Market on Tuesdays, from Easter till Whitsuntide only” (Britannia, or, An illustration of the Kingdom of England and Dominion of Wales, 1675, by the Scottish geographer John Ogilby).

In the initial days of the Trump administration, officials scoured federal websites for any mention of what they deemed “DEI” keywords — terms as generic as “diverse” and “historically” and even “women.” They soon identified reams of some of the country’s most valuable public health data containing some of the targeted words, including language about LGBTQ+ people, and quickly took down much of it — from surveys on obesity and suicide rates to real-time reports on immediate infectious disease threats like bird flu.

The removal elicited a swift response from public health experts who warned that without this data, the country risked being in the dark about important health trends that shape life-and-death public health decisions made in communities across the country.

—Dylan Scott for Vox Future Perfect

Last week’s newsletter from Vox made me think about how basic health information that we have readily available can be taken for granted. Public health data is a cornerstone of effective policy-making, research, and intervention strategies aimed at improving community health. These datasets provide critical insights into health trends, risk factors, and disparities, helping guide decisions that shape healthcare services and public health initiatives.

So, Why Public Health Data Matters?

The collection and analysis of public health data enable researchers and policymakers to track health trends, allocate resources efficiently, and implement targeted interventions. Without access to comprehensive and reliable data, communities risk being uninformed about pressing health concerns, which can lead to delays in addressing emerging issues such as disease outbreaks, mental health crises, and lifestyle-related conditions.

In the U.S., a variety of national surveys collect essential health data to provide a comprehensive view of public health. Below are some of the most impactful surveys and how they have been put into good use.

Behavioral Risk Factor Surveillance System (BRFSS)

The BRFSS is one of the most extensive national health surveys, collecting data annually from approximately 400,000 Americans. Conducted by the Centers for Disease Control and Prevention (CDC), it tracks behavioral health risks such as physical activity, diet, tobacco and alcohol use, and chronic diseases.

Findings from BRFSS have been instrumental in monitoring trends such as the rise in teen vaping. For instance, BRFSS data helped inform decisions on banning flavored e-cigarettes, and subsequent research indicated potential unintended consequences, such as an increase in traditional cigarette use among youth. Additionally, BRFSS has played a crucial role in identifying health disparities among LGBTQ+ populations, shedding light on higher rates of uninsurance and poor self-reported health, prompting targeted health initiatives.

Youth Risk Behavior Survey (YRBS)

The YRBS, conducted by the CDC since 1990, focuses on the behaviors of high school students, collecting data directly from adolescents rather than from parents or teachers. This survey is essential for understanding trends in mental health, substance use, sexual activity, and experiences of violence.

For example, YRBS data has highlighted increasing rates of depression and anxiety among teenagers, contributing to national conversations on youth mental health. It has also been used to explore the relationship between social media usage and teen well-being, informing debates over policies such as phone restrictions in schools.

Social Vulnerability Index (SVI)

The SVI is a specialized dataset that breaks the U.S. into small geographic regions and assesses their vulnerability to public health crises and natural disasters based on socioeconomic factors, disability rates, and housing conditions. Government agencies and emergency planners use this data to allocate resources effectively before, during, and after disasters.

For example, researchers utilized SVI data to evaluate community responses to Hurricane Helene, identifying patterns in disaster preparedness and recovery across different socioeconomic groups. This information has been vital in shaping future emergency response strategies and ensuring equitable disaster relief distribution.

In the initial days of the Trump administration, officials scoured federal websites for any mention of what they deemed “DEI” keywords — terms as generic as “diverse” and “historically” and even “women.” They soon identified reams of some of the country’s most valuable public health data containing some of the targeted words, including language about LGBTQ+ people, and quickly took down much of it — from surveys on obesity and suicide rates to real-time reports on immediate infectious disease threats like bird flu.

The removal elicited a swift response from public health experts who warned that without this data, the country risked being in the dark about important health trends that shape life-and-death public health decisions made in communities across the country.

—Dylan Scott for Vox Future Perfect

Last week’s newsletter from Vox made me think about how basic health information that we have readily available can be taken for granted. Public health data is a cornerstone of effective policy-making, research, and intervention strategies aimed at improving community health. These datasets provide critical insights into health trends, risk factors, and disparities, helping guide decisions that shape healthcare services and public health initiatives.

So, Why Public Health Data Matters?

The collection and analysis of public health data enable researchers and policymakers to track health trends, allocate resources efficiently, and implement targeted interventions. Without access to comprehensive and reliable data, communities risk being uninformed about pressing health concerns, which can lead to delays in addressing emerging issues such as disease outbreaks, mental health crises, and lifestyle-related conditions.

In the U.S., a variety of national surveys collect essential health data to provide a comprehensive view of public health. Below are some of the most impactful surveys and how they have been put into good use.

Behavioral Risk Factor Surveillance System (BRFSS)

The BRFSS is one of the most extensive national health surveys, collecting data annually from approximately 400,000 Americans. Conducted by the Centers for Disease Control and Prevention (CDC), it tracks behavioral health risks such as physical activity, diet, tobacco and alcohol use, and chronic diseases.

Findings from BRFSS have been instrumental in monitoring trends such as the rise in teen vaping. For instance, BRFSS data helped inform decisions on banning flavored e-cigarettes, and subsequent research indicated potential unintended consequences, such as an increase in traditional cigarette use among youth. Additionally, BRFSS has played a crucial role in identifying health disparities among LGBTQ+ populations, shedding light on higher rates of uninsurance and poor self-reported health, prompting targeted health initiatives.

Youth Risk Behavior Survey (YRBS)

The YRBS, conducted by the CDC since 1990, focuses on the behaviors of high school students, collecting data directly from adolescents rather than from parents or teachers. This survey is essential for understanding trends in mental health, substance use, sexual activity, and experiences of violence.

For example, YRBS data has highlighted increasing rates of depression and anxiety among teenagers, contributing to national conversations on youth mental health. It has also been used to explore the relationship between social media usage and teen well-being, informing debates over policies such as phone restrictions in schools.

Social Vulnerability Index (SVI)

The SVI is a specialized dataset that breaks the U.S. into small geographic regions and assesses their vulnerability to public health crises and natural disasters based on socioeconomic factors, disability rates, and housing conditions. Government agencies and emergency planners use this data to allocate resources effectively before, during, and after disasters.

For example, researchers utilized SVI data to evaluate community responses to Hurricane Helene, identifying patterns in disaster preparedness and recovery across different socioeconomic groups. This information has been vital in shaping future emergency response strategies and ensuring equitable disaster relief distribution.