#AI for demand prediction

[technologyequality]

AI-Powered Business Analytics: Make Smarter Decisions, Faster

AI-Powered Business Analytics Make Smarter Decisions, Faster 💡 AI-powered analytics give you instant insights into what’s working and what’s not. Learn how to use AI to optimize business decisions. The Problem: Are You Guessing or Growing? Let’s be real—making business decisions based on gut feelings is like throwing darts blindfolded. Sure, you might hit the target occasionally, but most of…

[sanjanabia]

How Can Data Science Predict Consumer Demand in an Ever-Changing Market?

In today’s dynamic business landscape, understanding consumer demand is more crucial than ever. As market conditions fluctuate, companies must rely on data-driven insights to stay competitive. Data science has emerged as a powerful tool that enables businesses to analyze trends and predict consumer behavior effectively. For those interested in mastering these techniques, pursuing an AI course in Chennai can provide the necessary skills and knowledge.

The Importance of Predicting Consumer Demand

Predicting consumer demand involves anticipating how much of a product or service consumers will purchase in the future. Accurate demand forecasting is essential for several reasons:

Inventory Management: Understanding demand helps businesses manage inventory levels, reducing the costs associated with overstocking or stockouts.

Strategic Planning: Businesses can make informed decisions regarding production, marketing, and sales strategies by accurately predicting consumer preferences.

Enhanced Customer Satisfaction: By aligning supply with anticipated demand, companies can ensure that they meet customer needs promptly, improving overall satisfaction.

Competitive Advantage: Organizations that can accurately forecast consumer demand are better positioned to capitalize on market opportunities and outperform their competitors.

How Data Science Facilitates Demand Prediction

Data science leverages various techniques and tools to analyze vast amounts of data and uncover patterns that can inform demand forecasting. Here are some key ways data science contributes to predicting consumer demand:

1. Data Collection

The first step in demand prediction is gathering relevant data. Data scientists collect information from multiple sources, including sales records, customer feedback, social media interactions, and market trends. This comprehensive dataset forms the foundation for accurate demand forecasting.

2. Data Cleaning and Preparation

Once the data is collected, it must be cleaned and organized. This involves removing inconsistencies, handling missing values, and transforming raw data into a usable format. Proper data preparation is crucial for ensuring the accuracy of predictive models.

3. Exploratory Data Analysis (EDA)

Data scientists perform exploratory data analysis to identify patterns and relationships within the data. EDA techniques, such as data visualization and statistical analysis, help analysts understand consumer behavior and the factors influencing demand.

4. Machine Learning Models

Machine learning algorithms play a vital role in demand prediction. These models can analyze historical data to identify trends and make forecasts. Common algorithms used for demand forecasting include:

Linear Regression: This model estimates the relationship between dependent and independent variables, making it suitable for predicting sales based on historical trends.

Time Series Analysis: Time series models analyze data points collected over time to identify seasonal patterns and trends, which are crucial for accurate demand forecasting.

Decision Trees: These models split data into branches based on decision rules, allowing analysts to understand the factors influencing consumer demand.

5. Real-Time Analytics

In an ever-changing market, real-time analytics becomes vital. Data science allows businesses to monitor consumer behavior continuously and adjust forecasts based on the latest data. This agility ensures that companies can respond quickly to shifts in consumer preferences.

Professionals who complete an AI course in Chennai gain insights into the latest machine learning techniques used in demand forecasting

Why Pursue an AI Course in Chennai?

For those looking to enter the field of data science and enhance their skills in predictive analytics, enrolling in an AI course in Chennai is an excellent option. Here’s why:

1. Comprehensive Curriculum

AI courses typically cover essential topics such as machine learning, data analysis, and predictive modeling. This comprehensive curriculum equips students with the skills needed to tackle real-world data challenges.

2. Hands-On Experience

Many courses emphasize practical, hands-on learning, allowing students to work on real-world projects that involve demand forecasting. This experience is invaluable for building confidence and competence.

3. Industry-Relevant Tools

Students often learn to use industry-standard tools and software, such as Python, R, and SQL, which are essential for conducting data analysis and building predictive models.

4. Networking Opportunities

Enrolling in an AI course in Chennai allows students to connect with peers and industry professionals, fostering relationships that can lead to job opportunities and collaborations.

Challenges in Predicting Consumer Demand

While data science offers powerful tools for demand forecasting, organizations may face challenges, including:

1. Data Quality

The accuracy of demand predictions heavily relies on the quality of data. Poor data quality can lead to misleading insights and misguided decisions.

2. Complexity of Models

Developing and interpreting predictive models can be complex. Organizations must invest in training and resources to ensure their teams can effectively utilize these models.

3. Rapidly Changing Markets

Consumer preferences can shift rapidly due to various factors, such as trends, economic changes, and competitive pressures. Businesses must remain agile to adapt their forecasts accordingly.

The curriculum of an AI course in Chennai often includes hands-on projects that focus on real-world applications of predictive analytics

Conclusion

Data science is revolutionizing how businesses predict consumer demand in an ever-changing market. By leveraging advanced analytics and machine learning techniques, organizations can make informed decisions that drive growth and enhance customer satisfaction.

For those looking to gain expertise in this field, pursuing an AI course in Chennai is a vital step. With a solid foundation in data science and AI, aspiring professionals can harness these technologies to drive innovation and success in their organizations.

[artisticdivasworld]

Strengthening Foundations:

Navigating Customer Demands and Expectations for Robust Trucking Relationships Type your email… Subscribe The trucking industry stands as a pivotal pillar in the global supply chain, its wheels turning the gears of economy and commerce. Yet, amidst its crucial role, trucking companies face the perpetual challenge of balancing customer demands and expectations with operational efficiency and…

[visenyaism]

Stuff about American election night that you should know:

We’re one week out! Crazy. So I know too much about US politics because I explain this for money, so I figured it might be helpful to talk a bit about what we should expect from election night. If you're not American, are new to our insane election system, or are anxious about what's happening next week, here's the deal with next Tuesday:

1. Most important thing: Do NOT expect to know the winner on election night. Different states have different laws about when they can start counting early/mail-in votes, which often slows down reporting time.

2020 took until the Saturday after to call because of the high mail-in vote count due to Covid, and while that isn't happening this time, it'll take longer than 2016, 2012, or 2008 because the polls are predicting that this one's going to be a lot closer than those. Consider just going to bed instead of staying up for the results.

2. Because of the Electoral College, popular vote doesn't matter as much as who wins each individual state does. Every state has a certain amount of electoral votes based on population, whoever wins a state gets all their votes, whoever gets to 270/538 wins. We know how most states are going to vote. The Electoral College puts the election in the hands of 7 "swing" states that could go either way. This time, that's Pennsylvania, Georgia, North Carolina, Michigan, Wisconsin, Arizona, and Nevada. These are the states to watch. Here's the map:

3. No one will know anything until polls close and states start reporting results. Doomscrolling is kind of pointless anyways, but it's especially pointless before 7pm. here's a map of closure times:

4. Data will shift throughout the night. Rural counties report results first because fewer people live there. This means the earlier you check, the more conservative the state maps might look. Do not look at the election results for any state with less than 90% reporting and freak out, especially if the state hasn't been called (deemed mathematically impossible for the other candidate to win) by multiple news outlets.

5. Voter fraud happens way less than you think it does. Pretty much never, actually. One study claims you're more likely to get struck by lightning than you are to witness actual, impersonation-based voter fraud in a modern US election. Be extremely skeptical of any voter fraud claims you might see.

6. Avoid getting news from social media accounts that aren't news outlets. There's a lot of disinformation out there, especially as AI/Deepfake tech is getting worse. Fact-check everything you might see. Anyone can make a destiel meme about the election. make sure it's true before you reblog it.

7. The electoral college sucks shit and does allow for a 269-269 vote tie. In this case, it goes to the House of Representatives, who are majority-Republican and will pick Trump. Some states might be within 1% (like 49.3%-49.7%) and candidates can demand recounts, which might delay official results by weeks or months. It HAS to be over by mid- December when the Electoral College officially votes.

8. take care of yourselves. if we're not going to know on election night, you may as well power down your phone and go to bed at a reasonable hour.

[tangibletechnomancy]

The reason I took interest in AI as an art medium is that I've always been interested in experimenting with novel and unconventional art media - I started incorporating power tools into a lot of my physical processes younger than most people were even allowed to breathe near them, and I took to digital art like a duck to water when it was the big, relatively new, controversial thing too, so really this just seems like the logical next step. More than that, it's exciting - it's not every day that we just invent an entirely new never-before-seen art medium! I have always been one to go fucking wild for that shit.

Which is, ironically, a huge part of why I almost reflexively recoil at how it's used in the corporate world: because the world of business, particularly the entertainment industry, has what often seems like less than zero interest in appreciating it as a novel medium.

And I often wonder how much less that would be the case - and, by extension, how much less vitriolic the discussion around it would be, and how many fewer well-meaning people would be falling for reactionary mythologies about where exactly the problems lie - if it hadn't reached the point of...at least an illusion of commercial viability, at exactly the moment it did.

See, the groundwork was laid in 2020, back during covid lockdowns, when we saw a massive spike in people relying on TV, games, books, movies, etc. to compensate for the lack of outdoor, physical, social entertainment. This was, seemingly, wonderful for the whole industry - but under late-stage capitalism, it was as much of a curse as it was a gift. When industries are run by people whose sole brain process is "line-go-up", tiny factors like "we're not going to be in lockdown forever" don't matter. CEOs got dollar signs in their eyes. Shareholders demanded not only perpetual growth, but perpetual growth at this rate or better. Even though everyone with an ounce of common sense was screaming "this is an aberration, this is not sustainable" - it didn't matter. The business bros refused to believe it. This was their new normal, they were determined to prove -

And they, predictably, failed to prove it.

So now the business bros are in a pickle. They're beholden to the shareholders to do everything within their power to maintain the infinite growth they promised, in a world with finite resources. In fact, by precedent, they're beholden to this by law. Fiduciary duty has been interpreted in court to mean that, given the choice between offering a better product and ensuring maximum returns for shareholders, the latter MUST be a higher priority; reinvesting too much in the business instead of trying to make the share value increase as much as possible, as fast as possible, can result in a lawsuit - that a board member or CEO can lose, and have lost before - because it's not acting in the best interest of shareholders. If that unsustainable explosive growth was promised forever, all the more so.

And now, 2-3-4 years on, that impossibility hangs like a sword of Damocles over the heads of these media company CEOs. The market is fully saturated; the number of new potential customers left to onboard is negligible. Some companies began trying to "solve" this "problem" by violating consumer privacy and charging per household member, which (also predictably) backfired because those of us who live in reality and not statsland were not exactly thrilled about the concept of being told we couldn't watch TV with our own families. Shareholders are getting antsy, because their (however predictably impossible) infinite lockdown-level profits...aren't coming, and someone's gotta make up for that, right? So they had already started enshittifying, making excuses for layoffs, for cutting employee pay, for duty creep, for increasing crunch, for lean-staffing, for tightening turnarounds-

And that was when we got the first iterations of AI image generation that were actually somewhat useful for things like rapid first drafts, moodboards, and conceptualizing.

Lo! A savior! It might as well have been the digital messiah to the business bros, and their eyes turned back into dollar signs. More than that, they were being promised that this...both was, and wasn't art at the same time. It was good enough for their final product, or if not it would be within a year or two, but it required no skill whatsoever to make! Soon, you could fire ALL your creatives and just have Susan from accounting write your scripts and make your concept art with all the effort that it takes to get lunch from a Star Trek replicator!

This is every bit as much bullshit as the promise of infinite lockdown-level growth, of course, but with shareholders clamoring for the money they were recklessly promised, executives are looking for anything, even the slightest glimmer of a new possibility, that just might work as a life raft from this sinking ship.

So where are we now? Well, we're exiting the "fucking around" phase and entering "finding out". According to anecdotes I've read, companies are, allegedly, already hiring prompt engineers (or "prompters" - can't give them a job title that implies there's skill or thought involved, now can we, that just might imply they deserve enough money to survive!)...and most of them not only lack the skill to manually post-process their works, but don't even know how (or perhaps aren't given access) to fully use the software they specialize in, being blissfully unaware of (or perhaps not able/allowed to use) features such as inpainting or img2img. It has been observed many times that LLMs are being used to flood once-reputable information outlets with hallucinated garbage. I can verify - as can nearly everyone who was online in the aftermath of the Glasgow Willy Wonka Dashcon Experience - that the results are often outright comically bad.

To anyone who was paying attention to anything other than please-line-go-up-faster-please-line-go-please (or buying so heavily into reactionary mythologies about why AI can be dangerous in industry that they bought the tech companies' false promises too and just thought it was a bad thing), this was entirely predictable. Unfortunately for everyone in the blast radius, common sense has never been an executive's strong suit when so much money is on the line.

Much like CGI before it, what we have here is a whole new medium that is seldom being treated as a new medium with its own unique strengths, but more often being used as a replacement for more expensive labor, no matter how bad the result may be - nor, for that matter, how unjust it may be that the labor is so much cheaper.

And it's all because of timing. It's all because it came about in the perfect moment to look like a life raft in a moment of late-stage capitalist panic. Any port in a storm, after all - even if that port is a non-Euclidean labyrinth of soggy, rotten botshit garbage.

Any port in a storm, right? ...right?

All images generated using Simple Stable, under the Code of Ethics of Are We Art Yet?

[hellsite-proteins]

AlphaFold Nobel Prize!

Hey everyone :) this isn't a structure, but there is some protein news that is pretty relevant to this blog that I felt I had to share. This article gives a nice overview of AI-predicted protein structures and what sorts of things they can do for research. It's not too long, and I recommend taking a look

If you've been seeing my posts for any amount of time, I've absolutely given you a flawed view of how useful AF can be. Experimentally determining protein structures is a demanding and difficult process (I've never done it, but I've learned the overview of how x ray crystallography works, and I can only imagine how much work it would take). AI-generated structures are not going to make structural biology obsolete, but they are massively helpful in making predictions that go on to guide further research.

While in many fields (especially creative areas like art and writing) AI has significant ethical concerns, I feel like this sort of use of AI in science is an overwhelmingly positive thing. The data used to train it is publicly available, and science works by building on the work done by those before us. Furthermore, while AI may not be great at generating new ideas or copying humans, it is very good at sorting large amounts of data and using it to make predictions. It's more akin to very complicated statistics than an attempt at the Turing test, and in this case it is a valuable tool to expand the ways we can do science!

[globalnewscollective]

AI and Donald Trump Are Watching You—And It Could Cost You Everything

Imagine this: You post your thoughts online. Or you express support for human rights. Or you attend a peaceful protest. Months later, you find yourself denied a visa, placed on a watchlist, or even under investigation—all because an algorithm flagged you as a ‘threat.’ This isn’t a dystopian novel. It’s happening right now in the U.S.

How AI Is Being Weaponized Against Protesters and Online Speech The Trump administration has rolled out AI-driven surveillance to monitor and target individuals based on their political beliefs and activities. According to reports, these systems analyze massive amounts of online data, including social media posts, protest attendance, and affiliations.

The goal? To identify and suppress dissent before it even happens.

Here’s what this means:

Attending a Protest Could Put You on a Government Watchlist – AI systems are being trained to scan for ‘suspicious behavior’ based on location data and social media activity.

Your Social Media History Can Be Used Against You – The government is using algorithms to flag people who express opinions that don’t align with Trump’s agenda.

Expressing Your Opinion Online Can Have Consequences – It’s not just about attending protests anymore. Simply posting criticism of the government, sharing articles, or even liking the ‘wrong’ post could get you flagged.

Dissenters Could Face Harsh Consequences – In some cases, simply supporting the wrong cause online could lead to visa denials, surveillance, or worse.

AI and Student Visa Bans: A Dangerous Precedent Recently, AI was used to screen visa applicants for supposed ‘Hamas support,’ leading to students being denied entry to the U.S. without due process. This is alarming for several reasons:

False Positives Will Ruin Lives – AI systems are not perfect. Innocent people will be flagged, denied entry, or even deported based on misinterpretations of their online activity.

This Can Be Expanded to Anyone – Today, it’s foreign students. Tomorrow, it could be U.S. citizens denied jobs, housing, or government services for expressing their political views.

It Sets a Dangerous Global Example – If the U.S. normalizes AI-driven political suppression, other governments will follow.

Marco Rubio’s ‘Catch and Revoke’ Plan: A New Threat Senator Marco Rubio has proposed the ‘Catch and Revoke’ plan, which would allow the U.S. government to scan immigrants’ social media with AI and strip them of their visas if deemed a ‘threat.’ This raises serious concerns about surveillance overreach and algorithm-driven repression, where immigrants could be punished for harmless or misinterpreted online activity. This policy could lead to:

Mass Deportations Based on AI Errors – Algorithms are prone to bias and mistakes, and immigrants may have no recourse to challenge these decisions.

Fear-Driven Self-Censorship – Many may feel forced to silence themselves online to avoid government scrutiny.

A Precedent for Broader Use – What starts with immigrants could easily be expanded to citizens, targeting dissenters and activists.

What’s at Stake?

The ability to speak freely, protest, and express opinions without fear of government retaliation is a fundamental right. If AI surveillance continues unchecked, America will become a place where thought crimes are punished, and digital footprints determine who is free and who is not.

The Bigger Picture

Technology that was meant to make life easier is now being turned against us. Today, it’s AI scanning protest footage. Tomorrow, it could be predictive policing, social credit systems, or AI-driven arrest warrants.

What Can You Do?

Be Mindful of Digital Footprints – Understand that what you post and where you go could be tracked.

Support Digital Rights Organizations – Groups like the ACLU and EFF are fighting against mass surveillance.

Demand Transparency – Governments must be held accountable for how they use AI and surveillance.

Freedom dies when people stop fighting for it. We must push back before AI turns democracy into an illusion.

Source:

https://www.fastcompany.com/91295390/how-the-trump-administration-plans-to-use-algorithms-to-target-protesters

[sanshofox]

At this point I am really wondering how the entertainment industry, especially gaming industry, is going to uphold/maintain themselves.

One layoff after another. How are people from that industry supposed to find a new job there when layoffs are happening everywhere? Do studios really think there’s longevity when they aren’t even willing to hire newcomers/juniors so there‘s adequate supply in the work force? Because look at how it’s currently going: investors want more and more money, the workload increases, but people are getting fired, leaving a smaller team to do said work, even distributing them for 2 or 3 projects at the same time, only to crash in a burnout or in later years go into retirement. Then who’s left? AI? Are you kidding me? As if games aren’t becoming more and more repetitive anyway, because of some „safe recipe for good numbers“ strategy. Creativity and the people behind it are suffering.

It’s been almost 2 years since I saw a junior 3D character artist offer. Ever since then it’s been a desert. And it’s not looking all too bright in other departments either. It’s now even a thing in job descriptions where they want you to have „AI abilities“. So as a junior or regular they want you to feed their machine, so in a few years they can fire you. The audacity.

Another audacity are those layoffs just to rehire people for a smaller price (can’t tell me otherwise. For me this is a tactic to put pressure on the work force to say yes to less money otherwise they will stay jobless). People that made projects what they are today, who are seniors and leads for a reason, out of a job just like that. Make it make sense (it doesn’t).

Studios like ubisoft now openly saying that they want to focus on AI, like assets completely made by AI to „save time and money“ and expand AI onto more fields. Shame on them.

The way creative industries like gaming finance themselves is also their biggest poison. And I only see a solution in that by regulating investors demands and upper positions sheaningans. They can’t have „absolute power“ anymore. It’s destructive and greedy and not realistic. Games can not be linearly successful. For the game design „recipe“ to improve it needs iteration just like when you work in a project for example and work on a design that needs to be iterated until it‘s improved or solid even. We see time and time again that „business/numbers people“ and creatives do not go hand in hand. We see an extreme imbalance.

I would predict that with less creative new input and letting mainly AI do the work consumers will be less and less entertained because everything seems to be and look the same. It will stagnate. And then crumble. And the industry needs to start like it did before. And that’s what I guess for the big companies.

With the layoffs happening and not enough job offers in return I could see that big talents get together to build their own studios now and we may get an era of new successful and growing studios happening that may even replace the current triple A studios one day in the future. They may even change the financing game. We saw successful games happening through platforms like kickstarter more often. So it might lead back to a „power to the people“ thing. Having an idea for a project and seeing if enough people agree and invest to see it happening. There’s room for improvement in that system. That’s all what it leads back to; in the end the consumers need to be satisfied to make it a creative and monetary success. BG3 and larian studios was a good example for that. It’s what made coral island grow and grow too. So there‘s potential.

Feel free to comment your theories. I really would like to see what others think about the current state of gaming studios and how it will or could develop.

[mariacallous]

AI’s energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining.

The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

“The money that bitcoin miners had to get to where they are today is peanuts compared to the money that Google and Microsoft and all these big tech companies are pouring in [to AI],” he says. “This is just escalating a lot faster, and it’s a much bigger threat.”

The development of AI is already having an impact on Big Tech’s climate goals. Tech giants have acknowledged in recent sustainability reports that AI is largely responsible for driving up their energy use. Google’s greenhouse gas emissions, for instance, have increased 48 percent since 2019, complicating the company’s goals of reaching net zero by 2030.

“As we further integrate AI into our products, reducing emissions may be challenging due to increasing energy demands from the greater intensity of AI compute,” Google’s 2024 sustainability report reads.

Last month, the International Energy Agency released a report finding that data centers made up 1.5 percent of global energy use in 2024—around 415 terrawatt-hours, a little less than the yearly energy demand of Saudi Arabia. This number is only set to get bigger: Data centers’ electricity consumption has grown four times faster than overall consumption in recent years, while the amount of investment in data centers has nearly doubled since 2022, driven largely by massive expansions to account for new AI capacity. Overall, the IEA predicted that data center electricity consumption will grow to more than 900 TWh by the end of the decade.

But there’s still a lot of unknowns about the share that AI, specifically, takes up in that current configuration of electricity use by data centers. Data centers power a variety of services—like hosting cloud services and providing online infrastructure—that aren’t necessarily linked to the energy-intensive activities of AI. Tech companies, meanwhile, largely keep the energy expenditure of their software and hardware private.

Some attempts to quantify AI’s energy consumption have started from the user side: calculating the amount of electricity that goes into a single ChatGPT search, for instance. De Vries-Gao decided to look, instead, at the supply chain, starting from the production side to get a more global picture.

The high computing demands of AI, De Vries-Gao says, creates a natural “bottleneck” in the current global supply chain around AI hardware, particularly around the Taiwan Semiconductor Manufacturing Company (TSMC), the undisputed leader in producing key hardware that can handle these needs. Companies like Nvidia outsource the production of their chips to TSMC, which also produces chips for other companies like Google and AMD. (Both TSMC and Nvidia declined to comment for this article.)

De Vries-Gao used analyst estimates, earnings call transcripts, and device details to put together an approximate estimate of TSMC’s production capacity. He then looked at publicly available electricity consumption profiles of AI hardware and estimates on utilization rates of that hardware—which can vary based on what it’s being used for—to arrive at a rough figure of just how much of global data-center demand is taken up by AI. De Vries-Gao calculates that without increased production, AI will consume up to 82 terrawatt-hours of electricity this year—roughly around the same as the annual electricity consumption of a country like Switzerland. If production capacity for AI hardware doubles this year, as analysts have projected it will, demand could increase at a similar rate, representing almost half of all data center demand by the end of the year.

Despite the amount of publicly available information used in the paper, a lot of what De Vries-Gao is doing is peering into a black box: We simply don’t know certain factors that affect AI’s energy consumption, like the utilization rates of every piece of AI hardware in the world or what machine learning activities they’re being used for, let alone how the industry might develop in the future.

Sasha Luccioni, an AI and energy researcher and the climate lead at open-source machine-learning platform Hugging Face, cautioned about leaning too hard on some of the conclusions of the new paper, given the amount of unknowns at play. Luccioni, who was not involved in this research, says that when it comes to truly calculating AI’s energy use, disclosure from tech giants is crucial.

“It’s because we don’t have the information that [researchers] have to do this,” she says. “That’s why the error bar is so huge.”

And tech companies do keep this information. In 2022, Google published a paper on machine learning and electricity use, noting that machine learning was “10%–15% of Google’s total energy use” from 2019 to 2021, and predicted that with best practices, “by 2030 total carbon emissions from training will reduce.” However, since that paper—which was released before Google Gemini’s debut in 2023—Google has not provided any more detailed information about how much electricity ML uses. (Google declined to comment for this story.)

“You really have to deep-dive into the semiconductor supply chain to be able to make any sensible statement about the energy demand of AI,��� De Vries-Gao says. “If these big tech companies were just publishing the same information that Google was publishing three years ago, we would have a pretty good indicator” of AI’s energy use.

[aa-predictions]

Economy Predictions: Part 1 (Post-Trump)

.　 . • ☆ . ° .• °:. *₊ ° . ☆

Prices will go up by at least 1.5% (accounting for inflation)

Social security collapse or severe downgrade

Trump tries tariffs, they backfire, and he backtracks

Hoover 2.0

We have the equivalent of a second Great Depression Reincarnated

Gas prices temporarily go down, while grocery and other cost-of-living prices skyrocket

Investments in AI companies will perform well, and investments in small businesses will perform more poorly

Some notable companies that haven’t shifted enough online for misc goods will collapse or go bankrupt

It will be much more difficult to be successful as a small business

More pressure on domestic factories causes an increase in demand for Hispanic workers, except ICE depleted available workers

The U.S dollar tanks in value and brings down other currencies dependent on it

The job market is garbage so American citizens and graduates emigrate for work

Massive hit to the agriculture industry as a result of immigration laws

Initially strict regulation becomes more lax again

They never address the temporary food shortage caused by lack of immigrant workers

[physalian]

Hello there!

Welcome to my little corner of the internet.

Here we talk about the writing process, worldbuilding, tropes, character design, and being an author. Also queerness and asexuality specifically, with the occasional bit of shower thoughts, AI-bashing, and a few mini essays here and there on whatever I'm feeling like.

—

Interested in some vampire fantasy? How 'bout some merfolk?

Eternal Night of the Northern Sky

Three hundred  years ago, the vampire covens of the North took the sun from the sky. In the frozen wasteland that remains, clans of the living cling to warmth in a way the vampires never predicted—hunting the undead down for the fire  in their veins. When Clan Maewag’s last blood slave takes their own life, Elias joins the hunting party in braving the surface. When the worst happens, Elias finds himself trusting a vampire to save him from the Freeze. Conflict brews between a coven thriving in a glittering utopia of equality and a coven enduring on blood slave servitude, with Elias trapped in the middle. What makes a monster in the sunless, frozen North, where survival demands Elias betray home, his people, and every truth he’s ever known?

Genres: New Adult, Queet Lit, Dark Fantasy

Available in ebook and paperback!

4.1.25: Interested in a free signed copy? Head to my author website and fill out the short form for yours, while supplies last.

—

Tell Me How Long

Finley McCann is an aspiring marine biologist, spending her days watching the ocean she loves die before her feet as the world spins on unmoved to stop it. Three years after a chance meeting with a survivor of a species thought extinct for centuries, Finley stumbles on an opportunity to save a Mer, and shock the world into acting before it’s too late. Tell Me How Long is a twist on the modern merfolk myth, and what it takes to survive under the threat of humanity in the open ocean.

Genres: Contemporary Sci-Fi, Mystery

Available in ebook!

Some notable posts of mine:

How to make your writing less stiff (1-9)

Dialogue Tags (Ohhhhh)

Character Dynamics the world needs more of

10 more of those

5 more of those

Yet another 5 of those

Aaaand 3 more

My stance on generative AI

On Foreshadowing

On Pacing

On Fantasy Worldbuilding

And that thereabouts covers some of the big ones? As always requests are open!

[rideboomindia]

Based on the search results, here are some innovative technologies that RideBoom could implement to enhance the user experience and stay ahead of ONDC:

Enhanced Safety Measures: RideBoom has already implemented additional safety measures, including enhanced driver background checks, real-time trip monitoring, and improved emergency response protocols. [1] To stay ahead, they could further enhance safety by integrating advanced telematics and AI-powered driver monitoring systems to ensure safe driving behavior.

Personalized and Customizable Services: RideBoom could introduce a more personalized user experience by leveraging data analytics and machine learning to understand individual preferences and offer tailored services. This could include features like customizable ride preferences, personalized recommendations, and the ability to save preferred routes or driver profiles. [1]

Seamless Multimodal Integration: To provide a more comprehensive transportation solution, RideBoom could integrate with other modes of transportation, such as public transit, bike-sharing, or micro-mobility options. This would allow users to plan and book their entire journey seamlessly through the RideBoom app, enhancing the overall user experience. [1]

Sustainable and Eco-friendly Initiatives: RideBoom has already started introducing electric and hybrid vehicles to its fleet, but they could further expand their green initiatives. This could include offering incentives for eco-friendly ride choices, partnering with renewable energy providers, and implementing carbon offset programs to reduce the environmental impact of their operations. [1]

Innovative Payment and Loyalty Solutions: To stay competitive with ONDC's zero-commission model, RideBoom could explore innovative payment options, such as integrated digital wallets, subscription-based services, or loyalty programs that offer rewards and discounts to frequent users. This could help attract and retain customers by providing more value-added services. [2]

Robust Data Analytics and Predictive Capabilities: RideBoom could leverage advanced data analytics and predictive modeling to optimize their operations, anticipate demand patterns, and proactively address user needs. This could include features like dynamic pricing, intelligent routing, and personalized recommendations to enhance the overall user experience. [1]

By implementing these innovative technologies, RideBoom can differentiate itself from ONDC, provide a more seamless and personalized user experience, and stay ahead of the competition in the on-demand transportation market.

[materialsscienceandengineering]

Crystallography-informed AI achieves high performance in predicting novel crystal structures

A research team from the Institute of Statistical Mathematics and Panasonic Holdings Corporation has developed a machine learning algorithm, ShotgunCSP, that enables fast and accurate prediction of crystal structures from material compositions. The algorithm achieved world-leading performance in crystal structure prediction benchmarks. Crystal structure prediction seeks to identify the stable or metastable crystal structures for any given chemical compound adopted under specific conditions. Traditionally, this process relies on iterative energy evaluations using time-consuming first-principles calculations and solving energy minimization problems to find stable atomic configurations. This challenge has been a cornerstone of materials science since the early 20th century. Recently, advancements in computational technology and generative AI have enabled new approaches in this field. However, for large-scale or complex molecular systems, the exhaustive exploration of vast phase spaces demands enormous computational resources, making it an unresolved issue in materials science.

Read more.

[rjzimmerman]

Excerpt from this story from Heated:

Energy experts warned only a few years ago that the world had to stop building new fossil fuel projects to preserve a livable climate.

Now, artificial intelligence is driving a rapid expansion of methane gas infrastructure—pipelines and power plants—that experts say could have devastating climate consequences if fully realized.

As large language models like ChatGPT become more sophisticated, experts predict that the nation’s energy demands will grow by a “shocking” 16 percent in the next five years. Tech giants like Amazon, Meta, and Alphabet have increasingly turned to nuclear power plants or large renewable energy projects to power data centers that use as much energy as a small town.

But those cleaner energy sources will not be enough to meet the voracious energy demands of AI, analysts say. To bridge the gap, tech giants and fossil fuel companies are planning to build new gas power plants and pipelines that directly supply data centers. And they increasingly propose keeping those projects separate from the grid, fast tracking gas infrastructure at a speed that can’t be matched by renewables or nuclear.

The growth of AI has been called the “savior” of the gas industry. In Virginia alone, the data center capital of the world, a new state report found that AI demand could add a new 1.5 gigawatt gas plant every two years for 15 consecutive years.

And now, as energy demand for AI rises, oil corporations are planning to build gas plants that specifically serve data centers. Last week, Exxon announced that it is building a large gas plant that will directly supply power to data centers within the next five years. The company claims the gas plant will use technology that captures polluting emissions—despite the fact that the technology has never been used at a commercial scale before.

Chevron also announced that the company is preparing to sell gas to an undisclosed number of data centers. “We're doing some work right now with a number of different people that's not quite ready for prime time, looking at possible solutions to build large-scale power generation,” said CEO Mike Wirth at an Atlantic Council event. The opportunity to sell power to data centers is so promising that even private equity firms are investing billions in building energy infrastructure.

But the companies that will benefit the most from an AI gas boom, according to S&P Global, are pipeline companies. This year, several major U.S. pipeline companies told investors that they were already in talks to connect their sprawling pipeline networks directly to on-site gas power plants at data centers.

“We, frankly, are kind of overwhelmed with the number of requests that we’re dealing with, ” Williams CEO Alan Armstrong said on a call with analysts. The pipeline company, which owns the 10,000 mile Transco system, is expanding its existing pipeline network from Virginia to Alabama partly to “provide reliable power where data center growth is expected,” according to Williams.

[morlock-holmes]

Man, YouTube just recommended the most obnoxiously complacent and poorly thought-out anti-AI-art video. Good job algorithm, I did hatewatch as much as I could stand because that's the cyberpunk future and also I guess the past because EM Forster predicted all of this.

In particular, there is this incredibly poorly thought out attempt to distinguish between the tedious parts of artistic work, which are okay to automate, and the non-tedious parts, which are immoral to automate.

There's also a distinction drawn, equally thoughtlessly, between the kinds of automation that destroy jobs and the kinds of automation that don't.

For example, the idea of an AI cutting a trailer together is bad, because it puts an editor out of a job. The cgi crowds in The Lord of The Rings are good, because they allow one animator to do jobs that once would have had to have been done by hundreds of extras or at the very least, way more animators.

I'm really sick of this bizarre half-luddism. I'm also DEEPLY annoyed by this muddled economic idea that people with tedious, unpleasant jobs like it when automation takes over for them, because I can assure you that they fucking don't.

Like, the whole thing stems from this quixotic attempt to be a pro-automation luddite, but luddism is a reaction to economic conditions.

If you sell skilled labor, you can get more money for it because the supply of laborers capable of producing what you produce is low. If there is a sudden flood of supply that stands to quite possibly lower the cost at which you can sell your labor.

This is an economic process, not a moral one. The guy fucking up his bladder working in the Amazon warehouse without using the bathroom all day is not going to be elated when Amazon announces he's being replaced by a robot, because it means the demand for his labor is getting lower relative to supply, which means the price at which he can sell it gets lower, which means it just got a lot fucking harder for him to pay his rent.

This has nothing whatsoever to do with whether Amazon warehouse work is spiritually fulfilling or not.

[nothlits]

I don't know who needs to hear this, but "mega corporations are responsible for the climate crisis, not individuals" does not mean "you can do whatever you want on purpose with no regard for the climate because your actions have no consequences."

The IPCC has changed their "best case scenario" temperature increase prediction to 2.8 degrees C by the year 2100, up from their original 1.5 degrees C limit. This will have and is already having devastating consequences. Because of innovations in AI and the increased need for data centers, climate scientists are predicting energy demand to double quickly. Many companies are turning to nuclear power to run high-demand data centers, but nuclear requires large amounts of water to operate.

Frivolous use of generative AI causes more power to be drawn from these data centers and using AI software tells these companies that their products are in demand and they should continue scaling up their operations to meet perceived demand.

Your actions do not exist in a vacuum and if you wouldn't throw plastic bags on the street because you realize that, despite plastic waste ultimately being the fault of corporations, your inappropriate disposal of these products also contributes to the issue, you should not be dismissing energy demand related climate concerns because it's "not your fault".