#dataaggregation — Public Fediverse posts

How Data Collection Services Transform Raw Data Into Clear, Actionable Insights

Gain clarity from complexity with data collection services that unify, clean, and refine your data. Reduce errors, spot trends faster, and make smarter decisions supported by accurate, real-time insights that keep your business ahead.

Know more: https://peerlist.io/jagadishthakar/articles/how-data-aggregation-services-clear-insights

#datacollectionservices #dataaggregation #datainsights #datamanagement #realtimedata #bigdatasolutions #businessgrowth #usa #germany #uk #canada

Reliable Data Aggregation Services to Clean, Validate & Enrich

Data aggregation services clean, check, and enrich information from different sources to turn unorganized data into a reliable dataset. This helps teams study markets, follow competitors, find opportunities, and make decisions with clarity and confidence.

Know More: https://www.hitechdigital.com/data-aggregation

#dataaggregation #datacleansing #dataenrichment #b2bdata #dataquality

OpenAI and Mixpanel’s recent breach shows how aggregating API metadata can fuel sophisticated phishing and identity‑theft attacks. When data silos merge, social engineering gets a dangerous boost. Learn why tighter controls matter. #OpenAI #Mixpanel #Phishing #DataAggregation

🔗 https://aidailypost.com/news/openai-mixpanel-breach-highlights-risk-data-aggregation-phishing

OpenAI and Mixpanel’s recent breach shows how aggregating API metadata can fuel sophisticated phishing and identity‑theft attacks. When data silos merge, social engineering gets a dangerous boost. Learn why tighter controls matter. #OpenAI #Mixpanel #Phishing #DataAggregation

🔗 https://aidailypost.com/news/openai-mixpanel-breach-highlights-risk-data-aggregation-phishing

OpenAI and Mixpanel’s recent breach shows how aggregating API metadata can fuel sophisticated phishing and identity‑theft attacks. When data silos merge, social engineering gets a dangerous boost. Learn why tighter controls matter. #OpenAI #Mixpanel #Phishing #DataAggregation

🔗 https://aidailypost.com/news/openai-mixpanel-breach-highlights-risk-data-aggregation-phishing

OpenAI and Mixpanel’s recent breach shows how aggregating API metadata can fuel sophisticated phishing and identity‑theft attacks. When data silos merge, social engineering gets a dangerous boost. Learn why tighter controls matter. #OpenAI #Mixpanel #Phishing #DataAggregation

🔗 https://aidailypost.com/news/openai-mixpanel-breach-highlights-risk-data-aggregation-phishing

Unlock the power of #Odoo with the read_group method! Learn how to aggregate data like a pro. #OdooDev #Python #DataAggregation

https://teguhteja.id/odoo-read-group-method-hacks/?utm_source=mastodon&utm_medium=jetpack_social

Unlock the power of #Odoo with the read_group method! Learn how to aggregate data like a pro. #OdooDev #Python #DataAggregation

https://teguhteja.id/odoo-read-group-method-hacks/?utm_source=mastodon&utm_medium=jetpack_social

Unlock the power of #Odoo with the read_group method! Learn how to aggregate data like a pro. #OdooDev #Python #DataAggregation

https://teguhteja.id/odoo-read-group-method-hacks/?utm_source=mastodon&utm_medium=jetpack_social

Unlock the power of #Odoo with the read_group method! Learn how to aggregate data like a pro. #OdooDev #Python #DataAggregation

https://teguhteja.id/odoo-read-group-method-hacks/?utm_source=mastodon&utm_medium=jetpack_social

Unlock the power of #Odoo with the read_group method! Learn how to aggregate data like a pro. #OdooDev #Python #DataAggregation

https://teguhteja.id/odoo-read-group-method-hacks/?utm_source=mastodon&utm_medium=jetpack_social

This diagram, taken from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power. This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it shuts the door on competition. #DataAggregation #BigTech #NetworkEffects

This diagram, taken from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power. This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it shuts the door on competition. #DataAggregation #BigTech #NetworkEffects

This diagram, taken from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power. This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it shuts the door on competition. #DataAggregation #BigTech #NetworkEffects

This diagram, taken directly from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power:

Data Aggregation → Market Dominance

This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it becomes a moat. And without regulatory intervention, it shuts the door on competition.

#Google #Antitrust #PlatformPower #DataAggregation #BigTech #DigitalMonopoly #DataEconomy #NetworkEffects #Regulation

This diagram, taken directly from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power:

Data Aggregation → Market Dominance

This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it becomes a moat. And without regulatory intervention, it shuts the door on competition.

#Google #Antitrust #PlatformPower #DataAggregation #BigTech #DigitalMonopoly #DataEconomy #NetworkEffects #Regulation

This diagram, taken directly from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power:

Data Aggregation → Market Dominance

This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it becomes a moat. And without regulatory intervention, it shuts the door on competition.

#Google #Antitrust #PlatformPower #DataAggregation #BigTech #DigitalMonopoly #DataEconomy #NetworkEffects #Regulation

This diagram, taken directly from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power:

Data Aggregation → Market Dominance

This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it becomes a moat. And without regulatory intervention, it shuts the door on competition.

#Google #Antitrust #PlatformPower #DataAggregation #BigTech #DigitalMonopoly #DataEconomy #NetworkEffects #Regulation

This diagram, taken directly from the U.S. v. Google judgment, illustrates a fundamental dynamic of digital market power:

Data Aggregation → Market Dominance

This self-reinforcing loop is the core issue with Big Tech platforms: scale isn’t just an advantage — it becomes a moat. And without regulatory intervention, it shuts the door on competition.

#Google #Antitrust #PlatformPower #DataAggregation #BigTech #DigitalMonopoly #DataEconomy #NetworkEffects #Regulation

📥 Need to aggregate data from multiple Google Sheets into one? Use Apps Script to pull data from different sheets and compile it into a central master sheet.

#GoogleSheets #AppsScript #DataAggregation #Efficiency

📥 Need to aggregate data from multiple Google Sheets into one? Use Apps Script to pull data from different sheets and compile it into a central master sheet.

#GoogleSheets #AppsScript #DataAggregation #Efficiency

📥 Need to aggregate data from multiple Google Sheets into one? Use Apps Script to pull data from different sheets and compile it into a central master sheet.

#GoogleSheets #AppsScript #DataAggregation #Efficiency

📥 Need to aggregate data from multiple Google Sheets into one? Use Apps Script to pull data from different sheets and compile it into a central master sheet.

#GoogleSheets #AppsScript #DataAggregation #Efficiency

@hacks4pancakes You overestimate how many people in the USA even understand the gap in privacy protections between here and the EU. If you're on #LinkedIn, check out the current brouhaha about LinkedIn opting in all non-EU residents into their #AI_ML #dataaggregation and training without notice and before updating their terms of service.

In the US, most non-enterprise #TOS contracts of adhesion basically make it our responsibility to stay on top of the changes anyway; most of the time individuals aren't even provided advanced notice. Simply continuing to use the service in ignorance implies agreement with any new terms. In addition, those terms almost always say you agree that they can be changed at any time, with or without notice, at the service provider's sole discretion.

I've spent this entire week explaining to people why LinkedIn is allowed to do that to us but not to you; why they won't get more than a slap on the wrist, if that, from any oversight body; and why our current copyright laws and precedents around software licensing basically ensure that LinkedIn, #Microsoft, #Google, and #OpenAI will continue to get a free pass on literally stealing people's data and intellectual property, making it "proprietary" and sealing people's data up behind an impenetrable paywall, and then selling a slurry of appropriated data (including their own) back to them at the highest price the market will bear.

That's not free-market capitalism. It's just corporate welfare for large companies, institutional stockholders, and chip makers, plus a dash of good ol' fashioned "Robber Baron" economics.

@hacks4pancakes You overestimate how many people in the USA even understand the gap in privacy protections between here and the EU. If you're on #LinkedIn, check out the current brouhaha about LinkedIn opting in all non-EU residents into their #AI_ML #dataaggregation and training without notice and before updating their terms of service.

In the US, most non-enterprise #TOS contracts of adhesion basically make it our responsibility to stay on top of the changes anyway; most of the time individuals aren't even provided advanced notice. Simply continuing to use the service in ignorance implies agreement with any new terms. In addition, those terms almost always say you agree that they can be changed at any time, with or without notice, at the service provider's sole discretion.

I've spent this entire week explaining to people why LinkedIn is allowed to do that to us but not to you; why they won't get more than a slap on the wrist, if that, from any oversight body; and why our current copyright laws and precedents around software licensing basically ensure that LinkedIn, #Microsoft, #Google, and #OpenAI will continue to get a free pass on literally stealing people's data and intellectual property, making it "proprietary" and sealing people's data up behind an impenetrable paywall, and then selling a slurry of appropriated data (including their own) back to them at the highest price the market will bear.

That's not free-market capitalism. It's just corporate welfare for large companies, institutional stockholders, and chip makers, plus a dash of good ol' fashioned "Robber Baron" economics.

@hacks4pancakes You overestimate how many people in the USA even understand the gap in privacy protections between here and the EU. If you're on #LinkedIn, check out the current brouhaha about LinkedIn opting in all non-EU residents into their #AI_ML #dataaggregation and training without notice and before updating their terms of service.

In the US, most non-enterprise #TOS contracts of adhesion basically make it our responsibility to stay on top of the changes anyway; most of the time individuals aren't even provided advanced notice. Simply continuing to use the service in ignorance implies agreement with any new terms. In addition, those terms almost always say you agree that they can be changed at any time, with or without notice, at the service provider's sole discretion.

I've spent this entire week explaining to people why LinkedIn is allowed to do that to us but not to you; why they won't get more than a slap on the wrist, if that, from any oversight body; and why our current copyright laws and precedents around software licensing basically ensure that LinkedIn, #Microsoft, #Google, and #OpenAI will continue to get a free pass on literally stealing people's data and intellectual property, making it "proprietary" and sealing people's data up behind an impenetrable paywall, and then selling a slurry of appropriated data (including their own) back to them at the highest price the market will bear.

That's not free-market capitalism. It's just corporate welfare for large companies, institutional stockholders, and chip makers, plus a dash of good ol' fashioned "Robber Baron" economics.

@hacks4pancakes You overestimate how many people in the USA even understand the gap in privacy protections between here and the EU. If you're on #LinkedIn, check out the current brouhaha about LinkedIn opting in all non-EU residents into their #AI_ML #dataaggregation and training without notice and before updating their terms of service.

In the US, most non-enterprise #TOS contracts of adhesion basically make it our responsibility to stay on top of the changes anyway; most of the time individuals aren't even provided advanced notice. Simply continuing to use the service in ignorance implies agreement with any new terms. In addition, those terms almost always say you agree that they can be changed at any time, with or without notice, at the service provider's sole discretion.

I've spent this entire week explaining to people why LinkedIn is allowed to do that to us but not to you; why they won't get more than a slap on the wrist, if that, from any oversight body; and why our current copyright laws and precedents around software licensing basically ensure that LinkedIn, #Microsoft, #Google, and #OpenAI will continue to get a free pass on literally stealing people's data and intellectual property, making it "proprietary" and sealing people's data up behind an impenetrable paywall, and then selling a slurry of appropriated data (including their own) back to them at the highest price the market will bear.

That's not free-market capitalism. It's just corporate welfare for large companies, institutional stockholders, and chip makers, plus a dash of good ol' fashioned "Robber Baron" economics.

@hacks4pancakes You overestimate how many people in the USA even understand the gap in privacy protections between here and the EU. If you're on #LinkedIn, check out the current brouhaha about LinkedIn opting in all non-EU residents into their #AI_ML #dataaggregation and training without notice and before updating their terms of service.

In the US, most non-enterprise #TOS contracts of adhesion basically make it our responsibility to stay on top of the changes anyway; most of the time individuals aren't even provided advanced notice. Simply continuing to use the service in ignorance implies agreement with any new terms. In addition, those terms almost always say you agree that they can be changed at any time, with or without notice, at the service provider's sole discretion.

I've spent this entire week explaining to people why LinkedIn is allowed to do that to us but not to you; why they won't get more than a slap on the wrist, if that, from any oversight body; and why our current copyright laws and precedents around software licensing basically ensure that LinkedIn, #Microsoft, #Google, and #OpenAI will continue to get a free pass on literally stealing people's data and intellectual property, making it "proprietary" and sealing people's data up behind an impenetrable paywall, and then selling a slurry of appropriated data (including their own) back to them at the highest price the market will bear.

That's not free-market capitalism. It's just corporate welfare for large companies, institutional stockholders, and chip makers, plus a dash of good ol' fashioned "Robber Baron" economics.

In a recent post, I perpetrated the fallacy that the notion of a #flatEarth was endemic during the #MiddleAges. Someone correctly pointed out that this was not 100% factually accurate, and suggested that no one actually thought this during the Middle Ages. While "no one" may also be a misrepresentation of how widespread the knowledge of a spherical Earth was at the time, let me explain why my factually flawed lead-in about a putatively widespread belief actually reinforces the original post's central point about the inherent bias caused by large-scale #dataAggregation when training #AI.

Thanks to the Greeks, scholars and the well-educated knew about a spherical Earth since about 500 BCE, and that even during the Middle Ages the educated elite (who were nevertheless a minority) widely accepted it as fact. What the typically uneducated general public thought about it at the time may be a different story, though. Regardless, this inaccurate assumption about the beliefs of the time actually reinforces my original point about how certain factual inaccuracies and data biases, especially when amplified by repetition, negatively impact the usefulness of the current generation of #LLM and #GenAI systems.

In some ways, geocentrism may have been a better example. However, whichever example you choose, in this context its veracity is less important than how often the statement is made, impacting the frequency or weighting of the statement within the corpus used to train an #AI or #ML system.

The references to historical beliefs in a flat Earth or the solar system revolving around the Earth are widely repeated, and that's all that's necessary for it to become a data point within the statistical mean of a large and uncurated #ML #dataset. In other words, if enough separate data sources repeat a given statement frequently enough, that's often sufficient to skew the resulting data set. This problem is closely related to the very human cognitive bias that people have for believing commonly heard statements.

To support the historical points about who may or may not have believed in a flat Earth or geocentrism during the Middle Ages, I've attached some relevant links. Meanwhile, I'll post later about why any "belief in a belief" or frequently-repeated datum actually defines an existential problem with many of today's very large AI/ML systems, and what we can collectively do about it.

• https://en.wikipedia.org/wiki/Myth_of_the_flat_Earth
• https://en.wikipedia.org/wiki/Geocentric_model

In a recent post, I perpetrated the fallacy that the notion of a #flatEarth was endemic during the #MiddleAges. Someone correctly pointed out that this was not 100% factually accurate, and suggested that no one actually thought this during the Middle Ages. While "no one" may also be a misrepresentation of how widespread the knowledge of a spherical Earth was at the time, let me explain why my factually flawed lead-in about a putatively widespread belief actually reinforces the original post's central point about the inherent bias caused by large-scale #dataAggregation when training #AI.

Thanks to the Greeks, scholars and the well-educated knew about a spherical Earth since about 500 BCE, and that even during the Middle Ages the educated elite (who were nevertheless a minority) widely accepted it as fact. What the typically uneducated general public thought about it at the time may be a different story, though. Regardless, this inaccurate assumption about the beliefs of the time actually reinforces my original point about how certain factual inaccuracies and data biases, especially when amplified by repetition, negatively impact the usefulness of the current generation of #LLM and #GenAI systems.

In some ways, geocentrism may have been a better example. However, whichever example you choose, in this context its veracity is less important than how often the statement is made, impacting the frequency or weighting of the statement within the corpus used to train an #AI or #ML system.

The references to historical beliefs in a flat Earth or the solar system revolving around the Earth are widely repeated, and that's all that's necessary for it to become a data point within the statistical mean of a large and uncurated #ML #dataset. In other words, if enough separate data sources repeat a given statement frequently enough, that's often sufficient to skew the resulting data set. This problem is closely related to the very human cognitive bias that people have for believing commonly heard statements.

To support the historical points about who may or may not have believed in a flat Earth or geocentrism during the Middle Ages, I've attached some relevant links. Meanwhile, I'll post later about why any "belief in a belief" or frequently-repeated datum actually defines an existential problem with many of today's very large AI/ML systems, and what we can collectively do about it.

• https://en.wikipedia.org/wiki/Myth_of_the_flat_Earth
• https://en.wikipedia.org/wiki/Geocentric_model

In a recent post, I perpetrated the fallacy that the notion of a #flatEarth was endemic during the #MiddleAges. Someone correctly pointed out that this was not 100% factually accurate, and suggested that no one actually thought this during the Middle Ages. While "no one" may also be a misrepresentation of how widespread the knowledge of a spherical Earth was at the time, let me explain why my factually flawed lead-in about a putatively widespread belief actually reinforces the original post's central point about the inherent bias caused by large-scale #dataAggregation when training #AI.

Thanks to the Greeks, scholars and the well-educated knew about a spherical Earth since about 500 BCE, and that even during the Middle Ages the educated elite (who were nevertheless a minority) widely accepted it as fact. What the typically uneducated general public thought about it at the time may be a different story, though. Regardless, this inaccurate assumption about the beliefs of the time actually reinforces my original point about how certain factual inaccuracies and data biases, especially when amplified by repetition, negatively impact the usefulness of the current generation of #LLM and #GenAI systems.

In some ways, geocentrism may have been a better example. However, whichever example you choose, in this context its veracity is less important than how often the statement is made, impacting the frequency or weighting of the statement within the corpus used to train an #AI or #ML system.

The references to historical beliefs in a flat Earth or the solar system revolving around the Earth are widely repeated, and that's all that's necessary for it to become a data point within the statistical mean of a large and uncurated #ML #dataset. In other words, if enough separate data sources repeat a given statement frequently enough, that's often sufficient to skew the resulting data set. This problem is closely related to the very human cognitive bias that people have for believing commonly heard statements.

To support the historical points about who may or may not have believed in a flat Earth or geocentrism during the Middle Ages, I've attached some relevant links. Meanwhile, I'll post later about why any "belief in a belief" or frequently-repeated datum actually defines an existential problem with many of today's very large AI/ML systems, and what we can collectively do about it.

• https://en.wikipedia.org/wiki/Myth_of_the_flat_Earth
• https://en.wikipedia.org/wiki/Geocentric_model

In a recent post, I perpetrated the fallacy that the notion of a #flatEarth was endemic during the #MiddleAges. Someone correctly pointed out that this was not 100% factually accurate, and suggested that no one actually thought this during the Middle Ages. While "no one" may also be a misrepresentation of how widespread the knowledge of a spherical Earth was at the time, let me explain why my factually flawed lead-in about a putatively widespread belief actually reinforces the original post's central point about the inherent bias caused by large-scale #dataAggregation when training #AI.

Thanks to the Greeks, scholars and the well-educated knew about a spherical Earth since about 500 BCE, and that even during the Middle Ages the educated elite (who were nevertheless a minority) widely accepted it as fact. What the typically uneducated general public thought about it at the time may be a different story, though. Regardless, this inaccurate assumption about the beliefs of the time actually reinforces my original point about how certain factual inaccuracies and data biases, especially when amplified by repetition, negatively impact the usefulness of the current generation of #LLM and #GenAI systems.

In some ways, geocentrism may have been a better example. However, whichever example you choose, in this context its veracity is less important than how often the statement is made, impacting the frequency or weighting of the statement within the corpus used to train an #AI or #ML system.

The references to historical beliefs in a flat Earth or the solar system revolving around the Earth are widely repeated, and that's all that's necessary for it to become a data point within the statistical mean of a large and uncurated #ML #dataset. In other words, if enough separate data sources repeat a given statement frequently enough, that's often sufficient to skew the resulting data set. This problem is closely related to the very human cognitive bias that people have for believing commonly heard statements.

To support the historical points about who may or may not have believed in a flat Earth or geocentrism during the Middle Ages, I've attached some relevant links. Meanwhile, I'll post later about why any "belief in a belief" or frequently-repeated datum actually defines an existential problem with many of today's very large AI/ML systems, and what we can collectively do about it.

• https://en.wikipedia.org/wiki/Myth_of_the_flat_Earth
• https://en.wikipedia.org/wiki/Geocentric_model

In a recent post, I perpetrated the fallacy that the notion of a #flatEarth was endemic during the #MiddleAges. Someone correctly pointed out that this was not 100% factually accurate, and suggested that no one actually thought this during the Middle Ages. While "no one" may also be a misrepresentation of how widespread the knowledge of a spherical Earth was at the time, let me explain why my factually flawed lead-in about a putatively widespread belief actually reinforces the original post's central point about the inherent bias caused by large-scale #dataAggregation when training #AI.

Thanks to the Greeks, scholars and the well-educated knew about a spherical Earth since about 500 BCE, and that even during the Middle Ages the educated elite (who were nevertheless a minority) widely accepted it as fact. What the typically uneducated general public thought about it at the time may be a different story, though. Regardless, this inaccurate assumption about the beliefs of the time actually reinforces my original point about how certain factual inaccuracies and data biases, especially when amplified by repetition, negatively impact the usefulness of the current generation of #LLM and #GenAI systems.

In some ways, geocentrism may have been a better example. However, whichever example you choose, in this context its veracity is less important than how often the statement is made, impacting the frequency or weighting of the statement within the corpus used to train an #AI or #ML system.

The references to historical beliefs in a flat Earth or the solar system revolving around the Earth are widely repeated, and that's all that's necessary for it to become a data point within the statistical mean of a large and uncurated #ML #dataset. In other words, if enough separate data sources repeat a given statement frequently enough, that's often sufficient to skew the resulting data set. This problem is closely related to the very human cognitive bias that people have for believing commonly heard statements.

To support the historical points about who may or may not have believed in a flat Earth or geocentrism during the Middle Ages, I've attached some relevant links. Meanwhile, I'll post later about why any "belief in a belief" or frequently-repeated datum actually defines an existential problem with many of today's very large AI/ML systems, and what we can collectively do about it.

• https://en.wikipedia.org/wiki/Myth_of_the_flat_Earth
• https://en.wikipedia.org/wiki/Geocentric_model

ECMAScript 2024 #JavaScript standard approved

ECMAScript 2024 introduces features like:
➡️resizing and transferring ArrayBuffers
➡️a RegExp/v flag for advanced string set operations
➡️Promise.withResolvers for managing asynchronous operations
➡️Object.groupBy and Map.groupBy for data aggregation
➡️Atomics.waitAsync for non-blocking shared memory changes
➡️methods for ensuring well-formed Unicode strings

#ECMAScript #ArrayBuffers #RegExp #Promises #DataAggregation #Unicode

https://www.infoworld.com/article/2514147/ecmascript-2024-javascript-standard-approved.html

ECMAScript 2024 #JavaScript standard approved

ECMAScript 2024 introduces features like:
➡️resizing and transferring ArrayBuffers
➡️a RegExp/v flag for advanced string set operations
➡️Promise.withResolvers for managing asynchronous operations
➡️Object.groupBy and Map.groupBy for data aggregation
➡️Atomics.waitAsync for non-blocking shared memory changes
➡️methods for ensuring well-formed Unicode strings

#ECMAScript #ArrayBuffers #RegExp #Promises #DataAggregation #Unicode

https://www.infoworld.com/article/2514147/ecmascript-2024-javascript-standard-approved.html

ECMAScript 2024 #JavaScript standard approved

ECMAScript 2024 introduces features like:
➡️resizing and transferring ArrayBuffers
➡️a RegExp/v flag for advanced string set operations
➡️Promise.withResolvers for managing asynchronous operations
➡️Object.groupBy and Map.groupBy for data aggregation
➡️Atomics.waitAsync for non-blocking shared memory changes
➡️methods for ensuring well-formed Unicode strings

#ECMAScript #ArrayBuffers #RegExp #Promises #DataAggregation #Unicode

https://www.infoworld.com/article/2514147/ecmascript-2024-javascript-standard-approved.html

ECMAScript 2024 #JavaScript standard approved

ECMAScript 2024 introduces features like:
➡️resizing and transferring ArrayBuffers
➡️a RegExp/v flag for advanced string set operations
➡️Promise.withResolvers for managing asynchronous operations
➡️Object.groupBy and Map.groupBy for data aggregation
➡️Atomics.waitAsync for non-blocking shared memory changes
➡️methods for ensuring well-formed Unicode strings

#ECMAScript #ArrayBuffers #RegExp #Promises #DataAggregation #Unicode

https://www.infoworld.com/article/2514147/ecmascript-2024-javascript-standard-approved.html

ECMAScript 2024 #JavaScript standard approved

ECMAScript 2024 introduces features like:
➡️resizing and transferring ArrayBuffers
➡️a RegExp/v flag for advanced string set operations
➡️Promise.withResolvers for managing asynchronous operations
➡️Object.groupBy and Map.groupBy for data aggregation
➡️Atomics.waitAsync for non-blocking shared memory changes
➡️methods for ensuring well-formed Unicode strings

#ECMAScript #ArrayBuffers #RegExp #Promises #DataAggregation #Unicode

https://www.infoworld.com/article/2514147/ecmascript-2024-javascript-standard-approved.html

Joe Rogan thinks Google is eavesdropping on him, but is it?

https://stackdiary.com/joe-rogan-thinks-google-is-eavesdropping-on-him-but-is-it/

#JoeRogan #podcast #privacy #Google #advertising #tech #smartphones #microphones #data #tracking #ads #technology #security #AI #eavesdropping #dataaggregation #crossdevice #targeting #marketing #conspiracy #digital #IPaddress #Wandera #BBC #CoxMedia #ActiveListening #consent #permissions #Apple #AppTracking #surveillance

Joe Rogan thinks Google is eavesdropping on him, but is it?

https://stackdiary.com/joe-rogan-thinks-google-is-eavesdropping-on-him-but-is-it/

#JoeRogan #podcast #privacy #Google #advertising #tech #smartphones #microphones #data #tracking #ads #technology #security #AI #eavesdropping #dataaggregation #crossdevice #targeting #marketing #conspiracy #digital #IPaddress #Wandera #BBC #CoxMedia #ActiveListening #consent #permissions #Apple #AppTracking #surveillance

Joe Rogan thinks Google is eavesdropping on him, but is it?

https://stackdiary.com/joe-rogan-thinks-google-is-eavesdropping-on-him-but-is-it/

#JoeRogan #podcast #privacy #Google #advertising #tech #smartphones #microphones #data #tracking #ads #technology #security #AI #eavesdropping #dataaggregation #crossdevice #targeting #marketing #conspiracy #digital #IPaddress #Wandera #BBC #CoxMedia #ActiveListening #consent #permissions #Apple #AppTracking #surveillance

Joe Rogan thinks Google is eavesdropping on him, but is it?

https://stackdiary.com/joe-rogan-thinks-google-is-eavesdropping-on-him-but-is-it/

#JoeRogan #podcast #privacy #Google #advertising #tech #smartphones #microphones #data #tracking #ads #technology #security #AI #eavesdropping #dataaggregation #crossdevice #targeting #marketing #conspiracy #digital #IPaddress #Wandera #BBC #CoxMedia #ActiveListening #consent #permissions #Apple #AppTracking #surveillance

Joe Rogan thinks Google is eavesdropping on him, but is it?

https://stackdiary.com/joe-rogan-thinks-google-is-eavesdropping-on-him-but-is-it/

#JoeRogan #podcast #privacy #Google #advertising #tech #smartphones #microphones #data #tracking #ads #technology #security #AI #eavesdropping #dataaggregation #crossdevice #targeting #marketing #conspiracy #digital #IPaddress #Wandera #BBC #CoxMedia #ActiveListening #consent #permissions #Apple #AppTracking #surveillance

Regarding why it's important to send most digital messages through a #secure channel.

"Data aggregation is a significant concern in today's digital landscape. It's essential to be aware of how our personal information can be combined and used in ways we might not intend or expect." ~ Meta.ai / #LLaMa #dataaggregation

Regarding why it's important to send most digital messages through a #secure channel.

"Data aggregation is a significant concern in today's digital landscape. It's essential to be aware of how our personal information can be combined and used in ways we might not intend or expect." ~ Meta.ai / #LLaMa #dataaggregation

Regarding why it's important to send most digital messages through a #secure channel.

"Data aggregation is a significant concern in today's digital landscape. It's essential to be aware of how our personal information can be combined and used in ways we might not intend or expect." ~ Meta.ai / #LLaMa #dataaggregation

#USA #FTC #DataProtection #DataAggregation #LocationData #Marketing #Adtargeting: "Data aggregator InMarket Media will be prohibited from selling or licensing any precise location data to settle Federal Trade Commission charges that the company did not fully inform consumers and obtain their consent before collecting and using their location data for advertising and marketing.

Under the proposed order, InMarket will also be prohibited from selling, licensing, transferring, or sharing any product or service that categorizes or targets consumers based on sensitive location data.

“All too often, Americans are tracked by serial data hoarders that endlessly vacuum up and use personal information. Today’s FTC action makes clear that firms do not have free license to monetize data tracking people’s precise location,” said FTC Chair Lina M. Khan. “We’ll continue to use all our tools to protect Americans from unchecked corporate surveillance.”"

https://www.ftc.gov/news-events/news/press-releases/2024/01/ftc-order-will-ban-inmarket-selling-precise-consumer-location-data

#USA #FTC #DataProtection #DataAggregation #LocationData #Marketing #Adtargeting: "Data aggregator InMarket Media will be prohibited from selling or licensing any precise location data to settle Federal Trade Commission charges that the company did not fully inform consumers and obtain their consent before collecting and using their location data for advertising and marketing.

Under the proposed order, InMarket will also be prohibited from selling, licensing, transferring, or sharing any product or service that categorizes or targets consumers based on sensitive location data.

“All too often, Americans are tracked by serial data hoarders that endlessly vacuum up and use personal information. Today’s FTC action makes clear that firms do not have free license to monetize data tracking people’s precise location,” said FTC Chair Lina M. Khan. “We’ll continue to use all our tools to protect Americans from unchecked corporate surveillance.”"

https://www.ftc.gov/news-events/news/press-releases/2024/01/ftc-order-will-ban-inmarket-selling-precise-consumer-location-data

#USA #FTC #DataProtection #DataAggregation #LocationData #Marketing #Adtargeting: "Data aggregator InMarket Media will be prohibited from selling or licensing any precise location data to settle Federal Trade Commission charges that the company did not fully inform consumers and obtain their consent before collecting and using their location data for advertising and marketing.

Under the proposed order, InMarket will also be prohibited from selling, licensing, transferring, or sharing any product or service that categorizes or targets consumers based on sensitive location data.

“All too often, Americans are tracked by serial data hoarders that endlessly vacuum up and use personal information. Today’s FTC action makes clear that firms do not have free license to monetize data tracking people’s precise location,” said FTC Chair Lina M. Khan. “We’ll continue to use all our tools to protect Americans from unchecked corporate surveillance.”"

https://www.ftc.gov/news-events/news/press-releases/2024/01/ftc-order-will-ban-inmarket-selling-precise-consumer-location-data

#USA #FTC #DataProtection #DataAggregation #LocationData #Marketing #Adtargeting: "Data aggregator InMarket Media will be prohibited from selling or licensing any precise location data to settle Federal Trade Commission charges that the company did not fully inform consumers and obtain their consent before collecting and using their location data for advertising and marketing.

Under the proposed order, InMarket will also be prohibited from selling, licensing, transferring, or sharing any product or service that categorizes or targets consumers based on sensitive location data.

“All too often, Americans are tracked by serial data hoarders that endlessly vacuum up and use personal information. Today’s FTC action makes clear that firms do not have free license to monetize data tracking people’s precise location,” said FTC Chair Lina M. Khan. “We’ll continue to use all our tools to protect Americans from unchecked corporate surveillance.”"

https://www.ftc.gov/news-events/news/press-releases/2024/01/ftc-order-will-ban-inmarket-selling-precise-consumer-location-data