#AI-powered estimating tools

[asestimationsconsultants]

Key Factors That Impact the Accuracy of a Construction Estimating Service

Introduction

In the construction industry, cost estimation is a crucial process that determines the financial feasibility of a project. A minor miscalculation in cost estimation can lead to budget overruns, project delays, and financial losses. That’s why accuracy in a construction estimating service is essential for contractors, project managers, and developers.

Several factors influence the precision of cost estimates, including material prices, labor costs, project scope, and unforeseen risks. In this article, we will explore the key factors that impact the accuracy of a construction estimating service and how companies can enhance their estimating processes.

1. Well-Defined Project Scope

One of the most common reasons for inaccurate cost estimates is a poorly defined project scope. If project requirements, materials, and specifications are unclear, estimators may make incorrect assumptions, leading to cost discrepancies.

Unclear Scope: Missing project details force estimators to make guesses, reducing accuracy.

Frequent Scope Changes: Modifications after estimation can alter material and labor costs significantly.

Solution: Clearly define project requirements before engaging a construction estimating service and update estimates as scope changes occur.

2. Quality of Blueprints and Specifications

The accuracy of an estimate depends on the quality of the blueprints and project specifications provided. Incomplete or conflicting plans can result in incorrect material takeoffs, leading to miscalculations.

Incomplete Drawings: Missing dimensions and unclear layouts lead to errors.

Inconsistent Specifications: Variations between the design documents and project requirements can create discrepancies.

Solution: Ensure all blueprints are accurate, well-detailed, and approved before submitting them for cost estimation.

3. Material Cost Fluctuations

Material costs are one of the most variable components in construction. Prices for materials such as steel, concrete, and lumber fluctuate due to market demand, inflation, and supply chain disruptions.

Price Instability: Global market trends, tariffs, and economic conditions impact material costs.

Substitutions and Availability: Limited supply can force the use of costlier alternatives.

Solution: Use a construction estimating service that integrates real-time pricing databases to reflect the latest material costs.

4. Labor Costs and Productivity

Labor expenses make up a significant portion of construction costs. Labor rates vary based on location, workforce availability, and project complexity.

Skilled Labor Shortage: Higher demand for skilled workers drives up wages.

Labor Productivity Variations: Estimators must consider realistic productivity rates to avoid underestimating labor costs.

Solution: Conduct market research on labor rates and include productivity assessments in labor cost estimates.

5. Accuracy of Quantity Takeoffs

A construction estimating service relies on quantity takeoffs to determine material requirements. Errors in this stage can drastically impact the final cost estimate.

Manual Errors: Human mistakes in calculations can lead to material shortages or excess costs.

Incorrect Measurements: Misinterpretation of construction drawings can result in inaccurate takeoffs.

Solution: Use digital takeoff tools that automate the process and reduce the risk of human error.

6. Site Conditions and Location Factors

The physical conditions of a construction site significantly influence project costs. Factors such as soil type, weather conditions, and accessibility can impact labor and equipment costs.

Remote Locations: Higher transportation and labor costs due to distance.

Difficult Terrain: Additional work required for site preparation increases expenses.

Solution: Conduct a thorough site analysis before estimating costs and adjust estimates based on local conditions.

7. Contingency Planning and Risk Management

Unexpected project risks can lead to financial setbacks if they are not accounted for in the estimation process. Common risks include permit delays, design changes, and unforeseen environmental factors.

Lack of Contingency Funds: Failure to allocate extra funds can lead to financial struggles during the project.

Unanticipated Costs: Legal and regulatory changes may require additional expenses.

Solution: A good construction estimating service should include contingency allowances (5–10% of total project cost) to cover unforeseen expenses.

8. Estimating Software and Technology

The tools used for cost estimation can make a significant difference in accuracy. Outdated manual methods are prone to errors, while modern software solutions enhance precision and efficiency.

Manual Estimation Risks: Increased potential for human error and time-consuming calculations.

AI and Automation Benefits: AI-powered construction estimating services analyze vast amounts of data for better accuracy.

Solution: Invest in advanced estimating software that integrates real-time data and automates calculations.

9. Experience and Expertise of the Estimator

The accuracy of a construction estimating service also depends on the experience of the estimator. Skilled estimators understand industry standards, potential risks, and pricing trends better than inexperienced ones.

Lack of Industry Knowledge: Inexperienced estimators may overlook critical costs.

Improper Use of Historical Data: Inaccurate use of past project costs can distort estimates.

Solution: Hire experienced estimators and ensure continuous training on the latest industry trends and estimating techniques.

10. Economic and Market Conditions

External economic factors such as inflation, interest rates, and supply chain disruptions can impact construction costs. Estimators must factor in these variables to create realistic budgets.

High Market Demand: Increased demand for construction services can drive up material and labor costs.

Inflation and Tariffs: Rising costs of imported materials can affect estimates.

Solution: Stay updated on economic trends and adjust estimates accordingly.

Conclusion

The accuracy of a construction estimating service depends on multiple factors, including project scope clarity, material and labor cost fluctuations, estimator expertise, and the use of advanced technology. By addressing these factors, construction firms can improve cost predictability, reduce financial risks, and ensure successful project execution.

Investing in modern estimating tools, regularly updating pricing data, and refining estimation processes will enhance the reliability of construction estimating services, leading to more profitable and efficient construction projects.

[derinthescarletpescatarian]

what’s the story about the generative power model and water consumption? /gen

There's this myth going around about generative AI consuming truly ridiculous amount of power and water. You'll see people say shit like "generating one image is like just pouring a whole cup of water out into the Sahara!" and bullshit like that, and it's just... not true. The actual truth is that supercomputers, which do a lot of stuff, use a lot of power, and at one point someone released an estimate of how much power some supercomputers were using and people went "oh, that supercomputer must only do AI! All generative AI uses this much power!" and then just... made shit up re: how making an image sucks up a huge chunk of the power grid or something. Which makes no sense because I'm given to understand that many of these models can run on your home computer. (I don't use them so I don't know the details, but I'm told by users that you can download them and generate images locally.) Using these models uses far less power than, say, online gaming. Or using Tumblr. But nobody ever talks about how evil those things are because of their power generation. I wonder why.

To be clear, I don't like generative AI. I'm sure it's got uses in research and stuff but on the consumer side, every effect I've seen of it is bad. Its implementation in products that I use has always made those products worse. The books it writes and flood the market with are incoherent nonsense at best and dangerous at worst (let's not forget that mushroom foraging guide). It's turned the usability of search engines from "rapidly declining, but still usable if you can get past the ads" into "almost one hundred per cent useless now, actually not worth the effort to de-bullshittify your search results", especially if you're looking for images. It's a tool for doing bullshit that people were already doing much easier and faster, thus massively increasing the amount of bullshit. The only consumer-useful uses I've seen of it as a consumer are niche art projects, usually projects that explore the limits of the tool itself like that one poetry book or the Infinite Art Machine; overall I'd say its impact at the Casual Random Person (me) level has been overwhelmingly negative. Also, the fact that so much AI turns out to be underpaid people in a warehouse in some country with no minimum wage and terrible labour protections is... not great. And the fact that it's often used as an excuse to try to find ways to underpay professionals ("you don't have to write it, just clean up what the AI came up with!") is also not great.

But there are real labour and product quality concerns with generative AI, and there's hysterical bullshit. And the whole "AI is magically destroying the planet via climate change but my four hour twitch streaming sesh isn't" thing is hysterical bullshit. The instant I see somebody make this stupid claim I put them in the same mental bucket as somebody complaining about AI not being "real art" -- a hatemobber hopping on the hype train of a new thing to hate and feel like an enlightened activist about when they haven't bothered to learn a fucking thing about the issue. And I just count my blessings that they fell in with this group instead of becoming a flat earther or something.

[txttletale]

Saw a tweet that said something around:

"cannot emphasize enough how horrid chatgpt is, y'all. it's depleting our global power & water supply, stopping us from thinking or writing critically, plagiarizing human artists. today's students are worried they won't have jobs because of AI tools. this isn't a world we deserve"

I've seen some of your AI posts and they seem nuanced, but how would you respond do this? Cause it seems fairly-on point and like the crux of most worries. Sorry if this is a troublesome ask, just trying to learn so any input would be appreciated.

i would simply respond that almost none of that is true.

'depleting the global power and water supply'

something i've seen making the roudns on tumblr is that chatgpt queries use 3 watt-hours per query. wow, that sounds like a lot, especially with all the articles emphasizing that this is ten times as much as google search. let's check some other very common power uses:

running a microwave for ten minutes is 133 watt-hours

gaming on your ps5 for an hour is 200 watt-hours

watching an hour of netflix is 800 watt-hours

and those are just domestic consumer electricty uses!

a single streetlight's typical operation 1.2 kilowatt-hours a day (or 1200 watt-hours)

a digital billboard being on for an hour is 4.7 kilowatt-hours (or 4700 watt-hours)

i think i've proved my point, so let's move on to the bigger picture: there are estimates that AI is going to cause datacenters to double or even triple in power consumption in the next year or two! damn that sounds scary. hey, how significant as a percentage of global power consumption are datecenters?

1-1.5%.

ah. well. nevertheless!

what about that water? yeah, datacenters use a lot of water for cooling. 1.7 billion gallons (microsoft's usage figure for 2021) is a lot of water! of course, when you look at those huge and scary numbers, there's some important context missing. it's not like that water is shipped to venus: some of it is evaporated and the rest is generally recycled in cooling towers. also, not all of the water used is potable--some datacenters cool themselves with filtered wastewater.

most importantly, this number is for all data centers. there's no good way to separate the 'AI' out for that, except to make educated guesses based on power consumption and percentage changes. that water figure isn't all attributable to AI, plenty of it is necessary to simply run regular web servers.

but sure, just taking that number in isolation, i think we can all broadly agree that it's bad that, for example, people are being asked to reduce their household water usage while google waltzes in and takes billions of gallons from those same public reservoirs.

but again, let's put this in perspective: in 2017, coca cola used 289 billion liters of water--that's 7 billion gallons! bayer (formerly monsanto) in 2018 used 124 million cubic meters--that's 32 billion gallons!

so, like. yeah, AI uses electricity, and water, to do a bunch of stuff that is basically silly and frivolous, and that is broadly speaking, as someone who likes living on a planet that is less than 30% on fire, bad. but if you look at the overall numbers involved it is a miniscule drop in the ocean! it is a functional irrelevance! it is not in any way 'depleting' anything!

'stopping us from thinking or writing critically'

this is the same old reactionary canard we hear over and over again in different forms. when was this mythic golden age when everyone was thinking and writing critically? surely we have all heard these same complaints about tiktok, about phones, about the internet itself? if we had been around a few hundred years earlier, we could have heard that "The free access which many young people have to romances, novels, and plays has poisoned the mind and corrupted the morals of many a promising youth."

it is a reactionary narrative of societal degeneration with no basis in anything. yes, it is very funny that laywers have lost the bar for trusting chatgpt to cite cases for them. but if you think that chatgpt somehow prevented them from thinking critically about its output, you're accusing the tail of wagging the dog.

nobody who says shit like "oh wow chatgpt can write every novel and movie now. yiou can just ask chatgpt to give you opinions and ideas and then use them its so great" was, like, sitting in the symposium debating the nature of the sublime before chatgpt released. there is no 'decay', there is no 'decline'. you should be suspicious of those narratives wherever you see them, especially if you are inclined to agree!

plagiarizing human artists

nah. i've been over this ad infinitum--nothing 'AI art' does could be considered plagiarism without a definition so preposterously expansive that it would curtail huge swathes of human creative expression.

AI art models do not contain or reproduce any images. the result of them being trained on images is a very very complex statistical model that contains a lot of large-scale statistical data about all those images put together (and no data about any of those individual images).

to draw a very tortured comparison, imagine you had a great idea for how to make the next Great American Painting. you loaded up a big file of every norman rockwell painting, and you made a gigantic excel spreadsheet. in this spreadsheet you noticed how regularly elements recurred: in each cell you would have something like "naturalistic lighting" or "sexually unawakened farmers" and the % of times it appears in his paintings. from this, you then drew links between these cells--what % of paintings containing sexually unawakened farmers also contained naturalistic lighting? what % also contained a white guy?

then, if you told someone else with moderately competent skill at painting to use your excel spreadsheet to generate a Great American Painting, you would likely end up with something that is recognizably similar to a Norman Rockwell painting: but any charge of 'plagiarism' would be absolutely fucking absurd!

this is a gross oversimplification, of course, but it is much closer to how AI art works than the 'collage machine' description most people who are all het up about plagiarism talk about--and if it were a collage machine, it would still not be plagiarising because collages aren't plagiarism.

(for a better and smarter explanation of the process from soneone who actually understands it check out this great twitter thread by @reachartwork)

today's students are worried they won't have jobs because of AI tools

i mean, this is true! AI tools are definitely going to destroy livelihoods. they will increase productivty for skilled writers and artists who learn to use them, which will immiserate those jobs--they will outright replace a lot of artists and writers for whom quality is not actually important to the work they do (this has already essentially happened to the SEO slop website industry and is in the process of happening to stock images).

jobs in, for example, product support are being cut for chatgpt. and that sucks for everyone involved. but this isn't some unique evil of chatgpt or machine learning, this is just the effect that technological innovation has on industries under capitalism!

there are plenty of innovations that wiped out other job sectors overnight. the camera was disastrous for portrait artists. the spinning jenny was famously disastrous for the hand-textile workers from which the luddites drew their ranks. retail work was hit hard by self-checkout machines. this is the shape of every single innovation that can increase productivity, as marx explains in wage labour and capital:

“The greater division of labour enables one labourer to accomplish the work of five, 10, or 20 labourers; it therefore increases competition among the labourers fivefold, tenfold, or twentyfold. The labourers compete not only by selling themselves one cheaper than the other, but also by one doing the work of five, 10, or 20; and they are forced to compete in this manner by the division of labour, which is introduced and steadily improved by capital. Furthermore, to the same degree in which the division of labour increases, is the labour simplified. The special skill of the labourer becomes worthless. He becomes transformed into a simple monotonous force of production, with neither physical nor mental elasticity. His work becomes accessible to all; therefore competitors press upon him from all sides. Moreover, it must be remembered that the more simple, the more easily learned the work is, so much the less is its cost to production, the expense of its acquisition, and so much the lower must the wages sink – for, like the price of any other commodity, they are determined by the cost of production. Therefore, in the same manner in which labour becomes more unsatisfactory, more repulsive, do competition increase and wages decrease”

this is the process by which every technological advancement is used to increase the domination of the owning class over the working class. not due to some inherent flaw or malice of the technology itself, but due to the material realtions of production.

so again the overarching point is that none of this is uniquely symptomatic of AI art or whatever ever most recent technological innovation. it is symptomatic of capitalism. we remember the luddites primarily for failing and not accomplishing anything of meaning.

if you think it's bad that this new technology is being used with no consideration for the planet, for social good, for the flourishing of human beings, then i agree with you! but then your problem shouldn't be with the technology--it should be with the economic system under which its use is controlled and dictated by the bourgeoisie.

[reasonsforhope]

Green energy is in its heyday. 

Renewable energy sources now account for 22% of the nation’s electricity, and solar has skyrocketed eight times over in the last decade. This spring in California, wind, water, and solar power energy sources exceeded expectations, accounting for an average of 61.5 percent of the state's electricity demand across 52 days. 

But green energy has a lithium problem. Lithium batteries control more than 90% of the global grid battery storage market. 

That’s not just cell phones, laptops, electric toothbrushes, and tools. Scooters, e-bikes, hybrids, and electric vehicles all rely on rechargeable lithium batteries to get going. 

Fortunately, this past week, Natron Energy launched its first-ever commercial-scale production of sodium-ion batteries in the U.S. 

“Sodium-ion batteries offer a unique alternative to lithium-ion, with higher power, faster recharge, longer lifecycle and a completely safe and stable chemistry,” said Colin Wessells — Natron Founder and Co-CEO — at the kick-off event in Michigan. 

The new sodium-ion batteries charge and discharge at rates 10 times faster than lithium-ion, with an estimated lifespan of 50,000 cycles.

Wessells said that using sodium as a primary mineral alternative eliminates industry-wide issues of worker negligence, geopolitical disruption, and the “questionable environmental impacts” inextricably linked to lithium mining. 

“The electrification of our economy is dependent on the development and production of new, innovative energy storage solutions,” Wessells said. 

Why are sodium batteries a better alternative to lithium?

The birth and death cycle of lithium is shadowed in environmental destruction. The process of extracting lithium pollutes the water, air, and soil, and when it’s eventually discarded, the flammable batteries are prone to bursting into flames and burning out in landfills. 

There’s also a human cost. Lithium-ion materials like cobalt and nickel are not only harder to source and procure, but their supply chains are also overwhelmingly attributed to hazardous working conditions and child labor law violations. 

Sodium, on the other hand, is estimated to be 1,000 times more abundant in the earth’s crust than lithium. 

“Unlike lithium, sodium can be produced from an abundant material: salt,” engineer Casey Crownhart wrote ​​in the MIT Technology Review. “Because the raw ingredients are cheap and widely available, there’s potential for sodium-ion batteries to be significantly less expensive than their lithium-ion counterparts if more companies start making more of them.”

What will these batteries be used for?

Right now, Natron has its focus set on AI models and data storage centers, which consume hefty amounts of energy. In 2023, the MIT Technology Review reported that one AI model can emit more than 626,00 pounds of carbon dioxide equivalent. 

“We expect our battery solutions will be used to power the explosive growth in data centers used for Artificial Intelligence,” said Wendell Brooks, co-CEO of Natron. 

“With the start of commercial-scale production here in Michigan, we are well-positioned to capitalize on the growing demand for efficient, safe, and reliable battery energy storage.”

The fast-charging energy alternative also has limitless potential on a consumer level, and Natron is eying telecommunications and EV fast-charging once it begins servicing AI data storage centers in June. 

On a larger scale, sodium-ion batteries could radically change the manufacturing and production sectors — from housing energy to lower electricity costs in warehouses, to charging backup stations and powering electric vehicles, trucks, forklifts, and so on. 

“I founded Natron because we saw climate change as the defining problem of our time,” Wessells said. “We believe batteries have a role to play.”

-via GoodGoodGood, May 3, 2024

--

Note: I wanted to make sure this was legit (scientifically and in general), and I'm happy to report that it really is! x, x, x, x

[cynicalrosebud]

AI Reminder

Quick reminder folks since there's been a recent surge of AI fanfic shite. Here is some info from Earth.org on the environmental effects of ChatGPT and it's fellow AI language models.

"ChatGPT, OpenAI's chatbot, consumes more than half a million kilowatt-hours of electricity each day, which is about 17,000 times more than the average US household. This is enough to power about 200 million requests, or nearly 180,000 US households. A single ChatGPT query uses about 2.9 watt-hours, which is almost 10 times more than a Google search, which uses about 0.3 watt-hours. 

According to estimates, ChatGPT emits 8.4 tons of carbon dioxide per year, more than twice the amount that is emitted by an individual, which is 4 tons per year. Of course, the type of power source used to run these data centres affects the amount of emissions produced – with coal or natural gas-fired plants resulting in much higher emissions compared to solar, wind, or hydroelectric power – making exact figures difficult to provide. 

A recent study by researchers at the University of California, Riverside, revealed the significant water footprint of AI models like ChatGPT-3 and 4. The study reports that Microsoft used approximately 700,000 litres of freshwater during GPT-3’s training in its data centres – that’s equivalent to the amount of water needed to produce 370 BMW cars or 320 Tesla vehicles."

Now I don't want to sit here and say that AI is the worst thing that has ever happened. It can be an important tool in advancing effectiveness in technology! However, there are quite a few drawbacks as we have not figured out yet how to mitigate these issues, especially on the environment, if not used wisely. Likewise, AI is not meant to do the work for you, it's meant to assist. For example, having it spell check your work? Sure, why not! Having it write your work and fics for you? You are stealing from others that worked hard to produce beautiful work.

Thank you for coming to my Cyn Talk. I love you all!

[covid-safer-hotties]

1 in 5 People Could Have Long COVID - Published Nov 8, 2024

By Dennis Thompson

FRIDAY, Nov. 8, 2024 (HealthDay News) -- More than 1 in 5 Americans likely suffer from long COVID, a new AI-assisted review has found.

The analysis suggests that nearly 23% of U.S. adults experience the symptoms of long COVID, according to results published Nov. 8 in the journal Med.

That’s much higher than the 7% prevalence of long COVID that’s been suggested by other studies, researchers said.

“Questions about the true burden of long COVID -- questions that have thus far remained elusive -- now seem more within reach,” said senior researcher Hossein Estiri, head of AI research at Mass General Brigham in Boston.

For the study, researchers developed an AI tool that can sift through mounds of electronic health records looking for the frequently subtle symptoms related to long COVID.

These symptoms can occur in a wide range of body systems, and include fatigue, chronic cough, heart problems and “brain fog.” They typically develop weeks or months after a person shakes off their initial COVID-19 infection.

“Our AI tool could turn a foggy diagnostic process into something sharp and focused, giving clinicians the power to make sense of a challenging condition,” Estiri said in a Mass General news release.

The AI specifically looks for symptoms that can’t be explained by a person’s medical history, have persisted for two months or longer and occur following a COVID infection, researchers said.

For example, the AI can detect if shortness of breath might be explained by pre-existing heart failure or asthma, rather than long COVID.

“Physicians are often faced with having to wade through a tangled web of symptoms and medical histories, unsure of which threads to pull, while balancing busy caseloads. Having a tool powered by AI that can methodically do it for them could be a game-changer,” said lead researcher Dr. Alaleh Azhir, an internal medicine resident at Brigham and Women’s.

Based on these parameters, the AI estimated that nearly 23% of Americans likely have long COVID, a figure that researchers argue aligns more closely with national trends.

The researchers plan to release the AI publicly on open access, so doctors and health care systems can employ and test it.

Study Link : www.cell.com/med/fulltext/S2666-6340(24)00407-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2666634024004070%3Fshowall%3Dtrue

[probablyasocialecologist]

The cryptocurrency hype of the past few years already started to introduce people to these problems. Despite producing little to no tangible benefits — unless you count letting rich people make money off speculation and scams — Bitcoin consumed more energy and computer parts than medium-sized countries and crypto miners were so voracious in their energy needs that they turned shuttered coal plants back on to process crypto transactions. Even after the crypto crash, Bitcoin still used more energy in 2023 than the previous year, but some miners found a new opportunity: powering the generative AI boom. The AI tools being pushed by OpenAI, Google, and their peers are far more energy intensive than the products they aim to displace. In the days after ChatGPT’s release in late 2022, Sam Altman called its computing costs “eye-watering” and several months later Alphabet chairman John Hennessy told Reuters that getting a response from Google’s chatbot would “likely cost 10 times more” than using its traditional search tools. Instead of reassessing their plans, major tech companies are doubling down and planning a massive expansion of the computing infrastructure available to them.

[...]

As the cloud took over, more computation fell into the hands of a few dominant tech companies and they made the move to what are called “hyperscale” data centers. Those facilities are usually over 10,000 square feet and hold more than 5,000 servers, but those being built today are often many times larger than that. For example, Amazon says its data centers can have up to 50,000 servers each, while Microsoft has a campus of 20 data centers in Quincy, Washington with almost half a million servers between them. By the end of 2020, Amazon, Microsoft, and Google controlled half of the 597 hyperscale data centres in the world, but what’s even more concerning is how rapidly that number is increasing. By mid-2023, the number of hyperscale data centres stood at 926 and Synergy Research estimates another 427 will be built in the coming years to keep up with the expansion of resource-intensive AI tools and other demands for increased computation. All those data centers come with an increasingly significant resource footprint. A recent report from the International Energy Agency (IEA) estimates that the global energy demand of data centers, AI, and crypto could more than double by 2026, increasing from 460 TWh in 2022 to up to 1,050 TWh — similar to the energy consumption of Japan. Meanwhile, in the United States, data center energy use could triple from 130 TWh in 2022 — about 2.5% of the country’s total — to 390 TWh by the end of the decade, accounting for a 7.5% share of total energy, according to Boston Consulting Group. That’s nothing compared to Ireland, where the IEA estimates data centers, AI, and crypto could consume a third of all power in 2026, up from 17% in 2022. Water use is going up too: Google reported it used 5.2 billion gallons of water in its data centers in 2022, a jump of 20% from the previous year, while Microsoft used 1.7 billion gallons in its data centers, an increase of 34% on 2021. University of California, Riverside researcher Shaolei Ren told Fortune, “It’s fair to say the majority of the growth is due to AI.” But these are not just large abstract numbers; they have real material consequences that a lot of communities are getting fed up with just as the companies seek to massively expand their data center footprints.

9 February 2024

[gacha-incels]

editorial from the english version of the Hankyoreh

archive link

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Before focusing on my master’s thesis, I worked for a brief period for an organization supporting victims of cyber sex crimes. My job was to put each and every case of illegally filmed footage assigned to me through a search engine and if I found that the images had been uploaded to certain sites, I would beg the operator of that site to take those images down. 

The process of searching and subsequently scrubbing illegally filmed footage probably isn’t what most people would expect. There is no advanced AI that categorizes footage based on the faces of victims, lists the sites the footage is uploaded to, and sends automated requests for the deletion of such posts. Instead, employees and volunteers divide each reported video frame by frame, put each fragmented frame through search engines, organize each site the footage is posted to on a spreadsheet, collate the sites for verification, find the site operator’s email or contact channel, and write a message that read, “This video is illegally filmed footage so we ask that you remove it from your site. If it is not removed, the South Korean government may take action against the site pursuant to relevant laws,” translate it from Korean into English and then send it to the operator.

This happened daily. Sometimes, you would have to go through 100 or 200 cases a day. I’ve assigned a nickname to this procedure: “The Illegally Filmed Footage Removal Protocol that Seems Absurdly Advanced, But Basically Follows the Same Grueling Procedures as a Sweatshop.”

The biggest challenge when fighting digital sex crimes is their overwhelming breadth and scale. It was impossible for us, as we sat in front of our computer screens, to estimate which far-out depths of the internet any of the footage had reached. 

Even if we painstakingly found 20 posts using a particular photo and saw that every post was erased, the next day we would see the photo spring up in 40 different posts. Websites distributing illegally filmed footage make various backup sites with different domains to make sure that sites are up and running even if the main site is taken down. 

Many of the sites in question have servers based abroad, so even if you sent a beseeching email citing South Korean laws, they simply could just pretend that they never saw the email.

The terror of that vast scope goes beyond distribution and duplication. While deepfake pornography has been in the news recently, photoshopping a real person’s face onto a pornographic image has been a crime for some time now.

The difference is that producing such images used to be time-consuming and technically challenging, requiring the “manual” manipulation of images. But the new tool of AI has made it easy for anyone to instantaneously create deepfakes in a dizzying variety of formats.

Another frightening aspect is that anyone with access to photographs on social media can choose victims at random. And since the images are “fakes” created by AI, the perpetrator can deliberately duck the guilt of harming a real person.

Deepfake creation has spread so rapidly because it gives perpetrators a perverse sense of power over their victims — the ability to create dozens of humiliating images of someone from photographs scraped off Instagram — while also enabling them to ignore victims’ suffering because the images aren’t technically “real.” In short, deepfakes represent a game-changing acceleration of the production cycle of sexually exploitative media.

Those images spread far too fast for the handful of employees at nonprofits to keep up with. Facing such a vast challenge, permanent employees began to drift away, and their positions were once again filled by people on short-term contracts.

Lee Jun-seok, a lawmaker with the Reform Party, said during a meeting of the National Assembly’s Science, ICT, Broadcasting and Communications Committee that the 220,000 members of a deepfake channel on Telegram was an “overblown threat” and estimated that, given the percentage of Korean users on Telegram, only about 726 of the channel members are actually Koreans.

But what does it matter whether there are 220,000 Koreans on the channel or just 726?

Let’s suppose there aren’t even 726, but just 10 people in the group — they could still produce 220,000 deepfakes if they set their mind to it. Those images would then be copied and circulated beyond their point of origin and around the world, perhaps remaining permanently in some dark corners of the Internet without ever being deleted.

That’s the nature of sex crimes in the digital age.

So assuming that the criminal potential of this technology remains the same regardless of whether the channel has 220,000 members, 726 members or even just 10, I can’t help wondering what Lee thinks would be an acceptable number of deepfake purveyors that would not constitute an “overblown threat.”

[contemplatingoutlander]

Amazon’s Alexa has been claiming the 2020 election was stolen

The popular voice assistant says the 2020 race was stolen, even as parent company Amazon promotes the tool as a reliable election news source -- foreshadowing a new information battleground

This is a scary WaPo article by Cat Zakrzewski about how big tech is allowing AI to get information from dubious sources. Consequently, it is contributing to the lies and disinformation that exist in today's current political climate.

Even the normally banal but ubiquitous (and not yet AI supercharged) Alexa is prone to pick up and recite political disinformation. Here are some excerpts from the article [color emphasis added]:

Amid concerns the rise of artificial intelligence will supercharge the spread of misinformation comes a wild fabrication from a more prosaic source: Amazon’s Alexa, which declared that the 2020 presidential election was stolen. Asked about fraud in the race — in which President Biden defeated former president Donald Trump with 306 electoral college votes — the popular voice assistant said it was “stolen by a massive amount of election fraud,”��citing Rumble, a video-streaming service favored by conservatives.

The 2020 races were “notorious for many incidents of irregularities and indications pointing to electoral fraud taking place in major metro centers,” according to Alexa, referencing Substack, a subscription newsletter service. Alexa contended that Trump won Pennsylvania, citing “an Alexa answers contributor.”

Multiple investigations into the 2020 election have revealed no evidence of fraud, and Trump faces federal criminal charges connected to his efforts to overturn the election. Yet Alexa disseminates misinformation about the race, even as parent company Amazon promotes the tool as a reliable election news source to more than 70 million estimated users. [...] Developers “often think that they have to give a balanced viewpoint and they do this by alternating between pulling sources from right and left, thinking this is going to give balance,” [Prof. Meredith] Broussard said. “The most popular sources on the left and right vary dramatically in quality.” Such attempts can be fraught. Earlier this week, the media company the Messenger announced a new partnership with AI company Seekr to “eliminate bias” in the news. Yet Seekr’s website characterizes some articles from the pro-Trump news network One America News as “center” and as having “very high” reliability. Meanwhile, several articles from the Associated Press were rated “very low.” [...] Yet despite a growing clamor in Congress to respond to the threat AI poses to elections, much of the attention has fixated on deepfakes. However, [attorney Jacob] Glick warned Alexa and AI-powered systems could “potentially double down on the damage that’s been done.” “If you have AI models drawing from an internet that is filled with platforms that don’t care about the preservation of democracy ��� you’re going to get information that includes really dangerous undercurrents,” he said. [color emphasis added]

[mariacallous]

As digital scamming explodes in Southeast Asia, including so called “pig butchering” investment scams, the United Nations Office on Drugs and Crime (UNODC) issued a comprehensive report this week with a dire warning about the rapid growth of this criminal ecosystem. Many digital scams have traditionally relied on social engineering, or tricking victims into giving away their money willingly, rather than leaning on malware or other highly technical methods. But researchers have increasingly sounded the alarm that scammers are incorporating generative AI content and deepfakes to expand the scale and effectiveness of their operations. And the UN report offers the clearest evidence yet that these high tech tools are turning an already urgent situation into a crisis.

In addition to buying written scripts to use with potential victims or relying on templates for malicious websites, attackers have increasingly been leaning on generative AI platforms to create communication content in multiple languages and deepfake generators that can create photos or even video of nonexistent people to show victims and enhance verisimilitude. Scammers have also been expanding their use of tools that can drain a victim’s cryptocurrency wallets, have been manipulating transaction records to trick targets into sending cryptocurrency to the wrong places, and are compromising smart contracts to steal cryptocurrency. And in some cases, they’ve been purchasing Elon Musk’s Starlink satellite internet systems to help power their efforts.

“Agile criminal networks are integrating these new technologies faster than anticipated, driven by new online marketplaces and service providers which have supercharged the illicit service economy,” John Wojcik, a UNODC regional analyst, tells WIRED. “These developments have not only expanded the scope and efficiency of cyber-enabled fraud and cybercrime, but they have also lowered the barriers to entry for criminal networks that previously lacked the technical skills to exploit more sophisticated and profitable methods.”

For years, China-linked criminals have trafficked people into gigantic compounds in Southeast Asia, where they are often forced to run scams, held against their will, and beaten if they refuse instructions. Around 200,000 people, from at least 60 countries, have been trafficked to compounds largely in Myanmar, Cambodia, and Laos over the last five years. However, as WIRED reporting has shown, these operations are spreading globally—with scamming infrastructure emerging in the Middle East, Eastern Europe, Latin America, and West Africa.

Most prominently, these organized crime operations have run pig butchering scams, where they build intimate relationships with victims before introducing an “investment opportunity” and asking for money. Criminal organizations may have conned people out of around $75 billion through pig butchering scams. Aside from pig butchering, according to the UN report, criminals across Southeast Asia are also running job scams, law enforcement impersonation, asset recovery scams, virtual kidnappings, sextortion, loan scams, business email compromise, and other illicit schemes. Criminal networks in the region earned up to $37 billion last year, UN officials estimate. Perhaps unsurprisingly, all of this revenue is allowing scammers to expand their operations and diversify, incorporating new infrastructure and technology into their systems in the hope of making them more efficient and brutally effective.

For example, scammers are often constrained by their language skills and ability to keep up conversations with potentially hundreds of victims at a time in numerous languages and dialects. However, generative AI developments within the last two years—including the launch of writing tools such as ChatGPT—are making it easier for criminals to break down language barriers and create the content needed for scamming.

The UN’s report says AI can be used for automating phishing attacks that ensnare victims, the creation of fake identities and online profiles, and the crafting of personalized scripts to trick victims while messaging them in different languages. “These developments have not only expanded the scope and efficiency of cyber-enabled fraud and cybercrime, but they have also lowered the barriers to entry for criminal networks that previously lacked the technical skills to exploit sophisticated and profitable methods,” the report says.

Stephanie Baroud, a criminal intelligence analyst in Interpol’s human trafficking unit, says the impact of AI needs to be considered as part of a pig butchering scammer’s tactics going forward. Baroud, who spoke with WIRED in an interview before the publication of the UN report, says the criminal’s recruitment ads that lure people into being trafficked to scamming compounds used to be “very generic” and full of grammatical errors. However, AI is now making them appear more polished and compelling, Baroud says. “It is really making it easier to create a very realistic job offer,” she says. “Unfortunately, this will make it much more difficult to identify which is the real and which is the fake ads.”

Perhaps the biggest AI paradigm shift in such digital attacks comes from deepfakes. Scammers are increasingly using machine-learning systems to allow for real-time face-swapping. This technology, which has also been used by romance scammers in West Africa, allows criminals to change their appearance on calls with their victims, making them realistically appear to be a different person. The technology is allowing “one-click” face swaps and high-resolution video feeds, the UN’s report states. Such services are a game changer for scammers, because they allow attackers to “prove” to victims in photos or real-time video calls that they are who they claim to be.

Using these setups, however, can require stable internet connections, which can be harder to maintain within some regions where pig butchering compounds and other scamming have flourished. There has been a “notable” increase in cops seizing Starlink satellite dishes in recent months in Southeast Asia, the UN says—80 units were seized between April and June this year. In one such operation carried out in June, Thai police confiscated 58 Starlink devices. In another instance, law enforcement seized 10 Starlink devices and 4,998 preregistered SIM cards while criminals were in the process of moving their operations from Myanmar to Laos. Starlink did not immediately respond to WIRED’s request for comment.

“Obviously using real people has been working for them very well, but using the tech could be cheaper after they have the required computers” and connectivity, says Troy Gochenour, a volunteer with the Global Anti-Scam Organization (GASO), a US-based nonprofit that fights human-trafficking and cybercrime operations in Southeast Asia.

Gochenour’s research involves tracking trends on Chinese-language Telegram channels related to carrying out pig butchering scams. And he says that it is increasingly common to see people applying to be AI models for scam content.

In addition to AI services, attackers have increasingly leaned on other technical solutions as well. One tool that has been increasingly common in digital scamming is so-called “crypto drainers,” a type of malware that has particularly been deployed against victims in Southeast Asia. Drainers can be more or less technically sophisticated, but their common goal is to “drain” funds from a target’s cryptocurrency wallets and redirect the currency to wallets controlled by attackers. Rather than stealing the credentials to access the target wallet directly, drainers are typically designed to look like a legitimate service—either by impersonating an actual platform or creating a plausible brand. Once a victim has been tricked into connecting their wallet to the drainer, they are then manipulated into approving one or a few transactions that grant attackers unintended access to all the funds in the wallet.

Drainers can be used in many contexts and with many fronts. They can be a component of pig butchering investment scams, or promoted to potential victims through compromised social media accounts, phishing campaigns, and malvertizing. Researchers from the firm ScamSniffer, for example, published findings in December about sponsored social media and search engine ads linked to malicious websites that contained a cryptocurrency drainer. The campaign, which ran from March to December 2023 reportedly stole about $59 million from more than 63,000 victims around the world.

Far from the low-tech days of doing everything through social engineering by building a rapport with potential victims and crafting tricky emails and text messages, today’s scammers are taking a hybrid approach to make their operations as efficient and lucrative as possible, UN researchers say. And even if they aren’t developing sophisticated malware themselves in most cases, scammers are increasingly in the market to use these malicious tools, prompting malware authors to adapt or create hacking tools for scams like pig butchering.

Researchers say that scammers have been seen using infostealers and even remote access trojans that essentially create a backdoor in a victim’s system that can be utilized in other types of attacks. And scammers are also expanding their use of malicious smart contracts that appear to programmatically establish a certain agreed-upon transaction or set of transactions, but actually does much more. “Infostealer logs and underground data markets have also been critical to ongoing market expansion, with access to unprecedented amounts of sensitive data serving as a major catalyst,” Wojcik, from the UNODC, says.

The changing tactics are significant as global law enforcement scrambles to deter digital scamming. But they are just one piece of the larger picture, which is increasingly urgent and bleak for forced laborers and victims of these crimes.

“It is now increasingly clear that a potentially irreversible displacement and spillover has taken place in which organized crime are able to pick, choose, and move value and jurisdictions as needed, with the resulting situation rapidly outpacing the capacity of governments to contain it,” UN officials wrote in the report. “Failure to address this ecosystem will have consequences for Southeast Asia and other regions.”

[xyymath]

Math Myths Busted! 🚨 Debunking Common Misconceptions

1. "Trigonometry is pointless in real life."

Want to design a bridge, map the stars, or even create 3D models? Welcome to the world of trigonometry. Engineers use sine and cosine to calculate forces, angles, and stress on structures. Naval navigation? That’s spherical trigonometry. And let’s not forget medical imaging (MRI and CT scans)—trigonometric algorithms are essential for reconstructing images from cross-sectional slices of the body.

2. "Pi is just 3.14."

Pi is irrational, meaning it goes on forever without repeating. It’s used in everything from signal processing to quantum physics. In general relativity, gravitational waves are calculated using Pi to map the curvature of spacetime. Even fractals, the infinitely complex geometric shapes that mirror nature’s patterns, rely on Pi for accurate dimension calculations. Simplifying Pi to 3.14 is like calling a complex painting “just a bunch of colors.” It’s a universe in itself.

3. "Math is for nerds, not for normal people."

Mathematics is fundamental to the universe. The Fibonacci sequence is embedded in everything from flower petals to galaxies. Whether it’s understanding the Golden Ratio in art or applying optimization techniques to improve energy use in smart cities, math is the tool that drives technology, medicine, and economics. Cryptography keeps your bank account safe and ensures secure communication online—it’s all built on abstract algebra and number theory. So, is math for “nerds”? It’s for civilization.

4. "I’ll never be good at math."

Growth mindset matters. The very concept of calculus—which studies the rate of change—starts from understanding infinitesimally small changes. Once you grasp limits, derivatives, and integration, you unlock the power to model everything from population growth to financial markets. Complex equations that once seemed impenetrable are just tools for breaking down the world. Perseverance is the key, not an innate ability. You learn, you grow, you become a mathematical thinker.

5. "Math is boring."

If math’s boring, then understanding gravity and black holes is boring. Einstein’s general theory of relativity wasn’t just an academic concept—it was formulated using highly sophisticated tensor calculus. Fractals, which appear in clouds, mountains, and even coastlines, are beautiful examples of math in nature. When you solve differential equations, you’re predicting everything from weather patterns to market crashes. Math is not static, it’s the language of everything, from the universe’s creation to your daily commute.

6. "I don’t need math in my everyday life."

You calculate interest rates, optimize your workout routine, and even estimate cooking times using math without realizing it. Statistics helps you make informed decisions in the stock market, and probability theory is the reason you can accurately predict outcomes in games, risk-taking, and even weather forecasting. Linear algebra is involved in everything from computational biology to machine learning. And when was the last time you built a website without using algorithms? Exactly.

7. "Calculators do all the work, so why learn math?"

Calculators are tools. Algorithms—the underlying mathematical processes that make your calculator or smartphone function—are the result of years of mathematical study. Machine learning algorithms, the backbone of AI, rely heavily on linear algebra and calculus. Building a calculator that can compute anything from simple arithmetic to complex number operations requires advanced math, often involving abstract algebra and number theory. It’s not the tool; it’s the thinking behind it that counts.

Math is the DNA of the universe.

[lukewarmbreadatamoderatepace]

While generative AI models and tools can and will use a significant amount of energy, we shouldn't conflate AI energy usage with the larger and largely pre-existing energy usage of "data centers" as a whole.

Unfortunately for Bloomberg, that quote is followed almost immediately by a chart that heavily undercuts the AI alarmism. That chart shows worldwide data center energy usage growing at a remarkably steady pace from about 100 TWh in 2012 to around 350 TWh in 2024. The vast majority of that energy usage growth came before 2022, when the launch of tools like Dall-E and ChatGPT largely set off the industry's current mania for generative AI. If you squint at Bloomberg's graph, you can almost see the growth in energy usage slowing down a bit since that momentous year for generative AI.

In his study "The growing energy footprint of artificial intelligence," de Vries starts with estimates that Nvidia's specialized chips are responsible for about 95 percent of the market for generative AI calculations. He then uses Nvidia's projected production of 1.5 million AI servers in 2027—and the projected power usage for those servers—to estimate that the AI sector as a whole could use up anywhere from 85 to 134 TWh of power in just a few years.

measured against other common worldwide uses of electricity, it's not representative of a mind-boggling energy hog. A 2018 study estimated that PC gaming as a whole accounted for 75 TWh of electricity use per year, to pick just one common human activity that's on the same general energy scale (and that's without console or mobile gamers included).

[not-terezi-pyrope]

do you have any sources on ai energy use not being that big a deal? everything i see about seems to be really fear mongering bad faith stuff so i’d like to read a more balanced approach

Don't get me wrong, the energy use of AI is pretty expensive, and even if it's a small fry when many applications are still in development, mass adoption of tools could push it up to a significant power draw. Investigating ways to improve efficiency is important and thankfully there are lots of roads towards that that are being studied.

What I'm not overly convinced of is that the energy use of AI apps is too disproportionate compared to other industrial production processes and, at inference, other calculation intensive cloud computing operations. Data centers already pump out a lot of carbon doing boring business tasks. I think the reason people focus on AI specifically is because they want to reuse the angle they had against crypto, but imo that was a different beast because no useful work was being done. ML apps are actually doing calculations with a purpose, crypto proof of work just crunches empty numbers for their own sake.

That said maybe I was a bit too dismissive off AI energy cost in my initial post. Like all things that cost power these days we should be looking into ways to cut back, it's just that I don't think that that means that AI apps are a nonstarter, just that they exist in context with other things that people do with their energy, some of which are more frivolous but probably comparable in aggregate. Ultimate answer is to decarbonise energy production and increase data center efficiency in general...

As for sources, it's hard to find a good source that provides specific numbers on the overall power use of AI, most estimates are just for individual models. Some people have made ballpark guesstimates that it might be pretty intensive in the future, but if you want exact numbers now - well if somebody has a source for that now I'd also appreciate it.

[digitalmarketing091]

10 Essential Digital Marketing Tips to Boost Your Business in 2024

In today’s rapidly evolving digital landscape, businesses of all sizes must stay up-to-date with the latest marketing strategies. The internet has changed the way consumers interact with brands, and as we approach 2024, mastering digital marketing will be more critical than ever. To help you stay competitive and drive meaningful results, here are 10 essential digital marketing tips that will elevate your business in the coming year.

1. Optimise for Voice Search

With the growing popularity of voice-activated devices like Amazon’s Alexa, Google Assistant, and Apple’s Siri, voice search has become a key component of how people find information online. In fact, it's expected that over 50% of all searches will be voice-driven. Unlike traditional text-based searches, voice searches tend to be more conversational and long-tail. 

To optimise your website for voice search, start by incorporating natural language into your SEO strategy. This means using question-based keywords that reflect how people speak, such as “Where can I find the best digital marketing tips?” Additionally, focus on featured snippets and providing concise, relevant answers, as these are often used by voice assistants to respond to queries.

2. Leverage Artificial Intelligence (AI) in Your Campaigns

Artificial Intelligence is transforming the way businesses approach digital marketing. From chatbots to data analytics, AI enables companies to better understand customer behavior, predict trends, and create highly personalized marketing experiences. AI-driven tools can automate tasks like email segmentation, content recommendations, and customer service interactions, allowing businesses to save time while improving engagement.

For example, AI-powered chatbots can enhance customer experiences by providing instant responses to common inquiries, even outside of business hours.  AI can also help businesses optimise their ad campaigns by analysing large amounts of data and suggesting improvements based on performance.

3. Prioritise Video Marketing

Video content is now an indispensable part of any digital marketing strategy. By 2024, it’s estimated that video will account for 82% of all online traffic. This makes video marketing one of the most effective ways to capture your audience’s attention. Whether it’s through YouTube, Instagram Stories, TikTok, or live streaming, video allows you to engage customers in ways that text or images simply can’t.

To maximise the impact of your video content, focus on creating short, engaging videos that are informative and entertaining. Live video is also becoming more popular, with platforms like Instagram and Facebook offering live streaming capabilities that allow you to interact with your audience in real-time. Demonstrating your products or services, hosting Q&A sessions, or sharing behind-the-scenes content can help build a more personal connection with your audience.

4. Collaborate with Social Media Influencers

Influencer marketing is no longer just a trend—it’s a powerful tool for businesses to increase brand awareness and drive conversions. By partnering with influencers who have a loyal following, you can leverage their credibility and reach new audiences. In 2024, micro-influencers (influencers with smaller, highly engaged audiences) are expected to play a more significant role, as they often have more authentic interactions with their followers compared to larger influencers.

When selecting influencers to collaborate with, ensure they align with your brand’s values and target demographic. Authenticity is key—today’s consumers can easily spot a partnership that feels forced or inauthentic. Focus on building long-term relationships with influencers rather than one-off promotions for more meaningful engagement.

5. Enhance User Experience (UX) on Your Website

User experience is central to the success of any digital marketing strategy. A poorly designed website can drive away potential customers and negatively impact your search engine rankings. Google places a strong emphasis on Core Web Vitals, which measure aspects like page load speed, interactivity, and visual stability.

As we move into 2024, it’s essential to ensure that your website is optimised for both speed and mobile friendliness. More than 50% of web traffic now comes from mobile devices, so if your site isn’t mobile-friendly, you could be losing a significant portion of your audience. Focus on providing a seamless, intuitive experience with easy navigation and fast load times to keep visitors engaged.

 6. Master SEO: Focus on High-Quality Content

Search Engine Optimisation (SEO) is a long-standing digital marketing technique that continues to evolve. As Google’s algorithms become more sophisticated, the emphasis has shifted from keyword stuffing to creating high-quality, informative content. In 2024, businesses should focus on producing valuable, long-form content that addresses the needs and pain points of their audience.

To improve your SEO, conduct thorough keyword research and create content that provides in-depth answers to commonly asked questions in your industry. Use internal linking to guide visitors to other relevant pages on your website and ensure that your content is updated regularly to remain fresh and relevant.

7. Invest in Paid Advertising

While organic reach is still valuable, the rise of paid advertising offers a more direct way to target specific audiences. Platforms like Google Ads, Facebook, and Instagram allow businesses to use highly targeted advertising to reach potential customers based on demographics, interests, and online behaviour. 

In 2024, consider experimenting with a mix of search ads, social media ads, and display ads to diversify your strategy. Retargeting campaigns, which focus on users who have already visited your site, are particularly effective at converting potential customers who didn’t purchase their first visit.

8. Use Email Marketing to Nurture Leads

Email marketing remains one of the most effective ways to nurture leads and maintain customer relationships. In 2024, personalisation will be key to running successful email campaigns. Use behavioural data to send personalised emails based on your audience’s past interactions with your brand. Whether it's a product recommendation, an abandoned cart reminder, or a birthday discount, personalising your emails can significantly improve open and conversion rates.

Additionally, focus on building your email list organically and create   engaging, mobile-friendly templates that grab attention. Incorporate   dynamic content that adapts to the recipient’s preferences to make your emails more relevant and engaging.

 9. Incorporate User-Generated Content (UGC)  

User-generated content (UGC) is a powerful form of social proof that can influence purchase decisions. Customers are more likely to trust the opinions of their peers than traditional advertising. Encourage your customers to share their experiences with your products or services on social media, and showcase UGC on your website and marketing channels.

UGC can take the form of reviews, testimonials, photos, and videos   from customers. By featuring authentic content from real users, you can build trust with potential customers and foster a sense of community around your brand.

10. Utilise Data Analytics to Drive Decisions  

Data analytics is the foundation of a successful digital marketing strategy. By tracking the performance of your campaigns, you can make   data-driven decisions that improve your ROI. Tools like Google Analytics, social media insights, and marketing automation platforms can provide valuable information on how your audience interacts with your content, website, and ads.

In 2024, focus on real-time analytics to track user behaviour and adjust your marketing strategies on the fly. Use data to identify trends, test new ideas, and continuously refine your approach to maximise results.

    Conclusion   

Digital marketing is an ever-changing field, and staying ahead of the trends is essential for business growth. By incorporating these 10 essential digital marketing tips, you’ll be well-prepared to navigate the challenges of 2024 and beyond. From optimising for voice search and embracing AI to mastering video marketing and enhancing your SEO efforts, each of these strategies will help you boost your business and stay ahead of the competition. Start implementing these tips today and watch your business soar to new heights in the digital world!

[poweredbytiktik]

"Optimize Your Heart Health with Aether X Powered by TikTik: Max Heart Rate & Resting Heart Rate Tracking"

Max Heart Rate and Resting Heart Rate on Aether X Powered by TikTik

1. Max Heart Rate (MHR)

MHR is the highest BPM your heart reaches during intense exercise. Estimated using 220 - age formula, it helps determine safe and effective workout zones.

With Aether X Powered by TikTik, track MHR in real time, helping you stay in ideal heart rate zones for fat burn, endurance, or peak performance.

2. Resting Heart Rate (RHR)

RHR is your BPM at rest, indicating fitness levels and overall heart health. A normal range is 60–100 BPM.

The Aether X, with TikTik AI, monitors RHR trends over time, providing insights into fitness progress and possible early health concerns.

Why Track Both on Aether X?

Using TikTik AI’s analysis, tracking MHR and RHR on Aether X allows personalized health and fitness insights. It’s an ideal tool for anyone wanting accurate, data-driven heart health monitoring.

Suggested Hashtags

#AetherX #TikTikAI #Smartwearable #HeartHealth #MaxHeartRate #RestingHeartRate #FitnessTech #WearableInnovation #MadeInIndia #HealthMonitoring #TikTikGlobalLLP #AIwearables #TechforIndia #Smartwatch #LifestyleTech #HeartRateMonitor

[covid-safer-hotties]

Also preserved in our archive (Updated daily!)

Researchers report that a new AI tool enhances the diagnostic process, potentially identifying more individuals who need care. Previous diagnostic studies estimated that 7 percent of the population suffers from long COVID. However, a new study using an AI tool developed by Mass General Brigham indicates a significantly higher rate of 22.8 percent.

The AI-based tool can sift through electronic health records to help clinicians identify cases of long COVID. The often-mysterious condition can encompass a litany of enduring symptoms, including fatigue, chronic cough, and brain fog after infection from SARS-CoV-2.

The algorithm used was developed by drawing de-identified patient data from the clinical records of nearly 300,000 patients across 14 hospitals and 20 community health centers in the Mass General Brigham system. The results, published in the journal Med, could identify more people who should be receiving care for this potentially debilitating condition.

“Our AI tool could turn a foggy diagnostic process into something sharp and focused, giving clinicians the power to make sense of a challenging condition,” said senior author Hossein Estiri, head of AI Research at the Center for AI and Biomedical Informatics of the Learning Healthcare System (CAIBILS) at MGB and an associate professor of medicine at Harvard Medical School. “With this work, we may finally be able to see long COVID for what it truly is — and more importantly, how to treat it.”

For the purposes of their study, Estiri and colleagues defined long COVID as a diagnosis of exclusion that is also infection-associated. That means the diagnosis could not be explained in the patient’s unique medical record but was associated with a COVID infection. In addition, the diagnosis needed to have persisted for two months or longer in a 12-month follow-up window.

Precision Phenotyping: A Novel Approach The novel method developed by Estiri and colleagues, called “precision phenotyping,” sifts through individual records to identify symptoms and conditions linked to COVID-19 to track symptoms over time in order to differentiate them from other illnesses. For example, the algorithm can detect if shortness of breath results from pre-existing conditions like heart failure or asthma rather than long COVID. Only when every other possibility was exhausted would the tool flag the patient as having long COVID.

“Physicians are often faced with having to wade through a tangled web of symptoms and medical histories, unsure of which threads to pull, while balancing busy caseloads. Having a tool powered by AI that can methodically do it for them could be a game-changer,” said Alaleh Azhir, co-lead author and an internal medicine resident at Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system.

The new tool’s patient-centered diagnoses may also help alleviate biases built into current diagnostics for long COVID, said researchers, who noted diagnoses with the official ICD-10 diagnostic code for long COVID trend toward those with easier access to healthcare.

The researchers said their tool is about 3 percent more accurate than the data ICD-10 codes capture, while being less biased. Specifically, their study demonstrated that the individuals they identified as having long COVID mirror the broader demographic makeup of Massachusetts, unlike long COVID algorithms that rely on a single diagnostic code or individual clinical encounters, skewing results toward certain populations such as those with more access to care.

“This broader scope ensures that marginalized communities, often sidelined in clinical studies, are no longer invisible,” said Estiri.

Limitations and Future Directions Limitations of the study and AI tool include the fact that health record data the algorithm uses to account for long COVID symptoms may be less complete than the data physicians capture in post-visit clinical notes. Another limitation was the algorithm did not capture the possible worsening of a prior condition that may have been a long COVID symptom. For example, if a patient had COPD that worsened before they developed COVID-19, the algorithm might have removed the episodes even if they were long COVID indicators. Declines in COVID-19 testing in recent years also makes it difficult to identify when a patient may have first gotten COVID-19.

The study was limited to patients in Massachusetts.

Future studies may explore the algorithm in cohorts of patients with specific conditions, like COPD or diabetes. The researchers also plan to release this algorithm publicly on open access so physicians and healthcare systems globally can use it in their patient populations.

In addition to opening the door to better clinical care, this work may lay the foundation for future research into the genetic and biochemical factors behind long COVID’s various subtypes. “Questions about the true burden of long COVID — questions that have thus far remained elusive — now seem more within reach,” said Estiri.

Reference: “Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19” by Alaleh Azhir, Jonas Hügel, Jiazi Tian, Jingya Cheng, Ingrid V. Bassett, Douglas S. Bell, Elmer V. Bernstam, Maha R. Farhat, Darren W. Henderson, Emily S. Lau, Michele Morris, Yevgeniy R. Semenov, Virginia A. Triant, Shyam Visweswaran, Zachary H. Strasser, Jeffrey G. Klann, Shawn N. Murphy and Hossein Estiri, 8 November 2024, Med. DOI: 10.1016/j.medj.2024.10.009 www.cell.com/med/fulltext/S2666-6340(24)00407-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2666634024004070%3Fshowall%3Dtrue