#Big Data and AI

[recenttrendingtopics]

Unlock the power of big data and AI for your business today! Explore how big data and AI tools are reciprocating greater business enhancements with more finesse.

[bigdatasummitcanada]

Big Data And Data Analytics

Big Data And Data Analytics

A thriving community of Data and Analytics leaders from across North America dedicated to the advancement of data and analytics strategies as well as harnessing rapid developments in AI, machine learning and more.

Big Data Summit Canada stand at the forefront of innovation, hosting the critical discussions that envision Canada's path forward in technology and society. Our community is the heartbeat of this movement, a place where professionals across sectors come together to explore the possibilities that lie at the intersection of data and progress.

Website: https://www.bigdatasummitcanada.com

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Phone : 1-866-298-9343 ext. 200

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[sophiebaybey]

Not to preach to the choir but I wonder if people generally realize that AI models like ChatGPT aren't, like, sifting through documented information when you ask it particular questions. If you ask it a question, it's not sifting through relevant documentation to find your answer, it is using an intensely inefficient method of guesswork that has just gone through so many repeated cycles that it usually, sometimes, can say the right thing when prompted. It is effectively a program that simulates monkeys on a typewriter at a mass scale until it finds sets of words that the user says "yes, that's right" to enough times. I feel like if it was explained in this less flattering way to investors it wouldn't be nearly as funded as it is lmao. It is objectively an extremely impressive technology given what it has managed to accomplish with such a roundabout and brain-dead method of getting there, but it's also a roundabout, brain-dead method of getting there. It is inefficient, pure and simple.

[probablyasocialecologist]

The almost overnight surge in electricity demand from data centers is now outstripping the available power supply in many parts of the world, according to interviews with data center operators, energy providers and tech executives. That dynamic is leading to years-long waits for businesses to access the grid as well as growing concerns of outages and price increases for those living in the densest data center markets. The dramatic increase in power demands from Silicon Valley’s growth-at-all-costs approach to AI also threatens to upend the energy transition plans of entire nations and the clean energy goals of trillion-dollar tech companies. In some countries, including Saudi Arabia, Ireland and Malaysia, the energy required to run all the data centers they plan to build at full capacity exceeds the available supply of renewable energy, according to a Bloomberg analysis of the latest available data. By one official estimate, Sweden could see power demand from data centers roughly double over the course of this decade — and then double again by 2040. In the UK, AI is expected to suck up 500% more energy over the next decade. And in the US, data centers are projected to use 8% of total power by 2030, up from 3% in 2022, according to Goldman Sachs, which described it as “the kind of electricity growth that hasn’t been seen in a generation.”

21 June 2024

[cozylittleartblog]

snow miku 2024 🧡🥄❄

art print on my etsy for the holidays!

[river-taxbird]

What does AI actually look like?

There has been a lot of talk about the negative externalities of AI, how much power it uses, how much water it uses, but I feel like people often discuss these things like they are abstract concepts, or people discuss AI like it is this intangible thing that exists off in "The cloud" somewhere, but I feel like a lot of people don't know what the infrastructure of AI actually is, and how it uses all that power and water, so I would like to recommend this video from Linus Tech Tips, where he looks at a supercomputer that is used for research in Canada. To be clear I do not have anything against supercomputers in general and they allow important work to be done, but before the AI bubble, you didn't need one, unless you needed it. The recent AI bubble is trying to get this stuff into the hands of way more people than needed them before, which is causing a lot more datacenter build up, which is causing their companies to abandon climate goals. So what does AI actually look like?

First of all, it uses a lot of hardware. It is basically normal computer hardware, there is just a lot of it networked together.

Hundreds of hard drives all spinning constantly

Each one of the blocks in this image is essentially a powerful PC, that you would still be happy to have as your daily driver today even though the video is seven years old. There are 576 of them, and other more powerful compute nodes for bigger datasets.

The GPU section, each one of these drawers contains like four datacenter level graphics cards. People are fitting a lot more of them into servers now than they were then.

Now for the cooling and the water. Each cabinet has a thick door, with a water cooled radiator in it. In summer, they even spray water onto the radiator directly so it can be cooled inside and out.

They are all fed from the pump room, which is the floor above. A bunch of pumps and pipes moving the water around, and it even has cooling towers outside that the water is pumped out into on hot days.

So is this cool? Yes. Is it useful? Also yes. Anyone doing biology, chemistry, physics, simulations, even stuff like social sciences, and even legitimate uses of analytical ai is glad stuff like this exists. It is very useful for analysing huge datasets, but how many people actually do that? Do you? The same kind of stuff is also used for big websites with youtube. But the question is, is it worth building hundreds more datacenters just like this one, so people can automatically generate their emails, have an automatic source of personal attention from a computer, and generate incoherent images for social media clicks? Didn't tech companies have climate targets, once?

[nostalgebraist]

On the topic of Declare:

A little while ago, on a whim, I prompted several different chat LLMs with a line from the Arabian Nights that has special importance in the novel:

O fish, are you constant to the old covenant?

In the book, this is used as a sign between members of a secret group, and there's a canned response that members are meant to give to identify themselves. (Like "I like your shoelaces" / "Thanks, I stole them from the President.").

I thought it'd be funny and impressive if one of these models responded with the canned phrase from Declare: that would have demonstrated both a command of somewhat obscure information and a humanlike ability to flexibly respond to an unusual input in the same spirit as that input.

None of the models did so, although I was still impressed with Gemini's reaction: it correctly sourced the quote to Arabian Nights in its first message, and was able to guess and/or remember that the quote was also used in Declare in follow-up chat (after a few wrong guesses and hints from me).

On the other hand, GPT-4 confidently stated that the quotation was from Jubilate Agno, a real but unrelated book:

When I asked Claude 3, it asserted that the line was from a real-but unrelated-poem called "The Fish," then proceeded -- without my asking -- to quote (i.e make up) a stanza from its imagined version of that poem:

It is always discomfiting to be reminded that -- no matter how much "safety" tuning these things are put through, and despite how preachy they can be about their own supposed aversion to "misinformation" or whatever -- they are nonetheless happy to confidently bullshit to the user like this.

I'm sure they have an internal sense of how sure or unsure they are of any given claim, but it seems they have (effectively) been trained not to let it influence their answers, and instead project maximum certainty almost all of the time.

[magicalmysteryperson]

Not a Robbie Williams lyric video this time.

But what it does contain is my fears in regards to generative AI.

[nostalgicsneeze]

this is the only website i post art nowadays and i’ll keep doin it but DAMN…

[natureintheory]

Apparently today is international cat day!

I'd like to paint or sculpt more cats. But probably not ~100 at once, as in the 2nd artwork.

1. crop of: https://www.olenashmahalo.com/project/animals-2023 2. crops of: https://www.olenashmahalo.com/project/herding-data

[thefreethoughtprojectcom]

A lot of questions remain unanswered about how much the ever-tightening relationship between private AI and for-profit automated decision making systems will impact much needed transparency and accountability.

Read More: https://thefreethoughtproject.com/news/the-u-s-national-security-state-is-here-to-make-ai-even-less-transparent-and-accountable

#TheFreeThoughtProject

[probablyasocialecologist]

The latest, AI-dedicated server racks contain 72 specialised chips from manufacturer Nvidia. The largest “hyperscale” data centres, used for AI tasks, would have about 5,000 of these racks.  And as anyone using a laptop for any period of time knows, even a single chip warms up in operation. To cool the servers requires water – gallons of it. Put all this together, and a single hyperscale data centre will typically need as much water as a town of 30,000 people – and the equivalent amount of electricity.  The Financial Times reports that Microsoft is currently opening one of these behemoths somewhere in the world every three days. Even so, for years, the explosive growth of the digital economy had surprisingly little impact on global energy demand and carbon emissions. Efficiency gains in data centres—the backbone of the internet—kept electricity consumption in check.  But the rise of generative AI, turbocharged by the launch of ChatGPT in late 2022, has shattered that equilibrium. AI elevates the demand for data and processing power into the stratosphere. The latest version of OpenAI’s flagship GPT model, GPT-4, is built on 1.3 trillion parameters, with each parameter describing the strength of a connection between different pathways in the model’s software brain.  The more novel data that can be pushed into the model for training, the better – so much data that one research paper estimated machine learning models will have used up all the data on the internet by 2028. Today, the insatiable demand for computing power is reshaping national energy systems. Figures from the International Monetary Fund show that data centres worldwide already consume as much electricity as entire countries like France or Germany. It forecasts that by 2030, the worldwide energy demand from data centres will be the same as India’s total electricity consumption. 

30 May 2025

[abathurofficial]

Abathur

At Abathur, we believe technology should empower, not complicate.

Our mission is to provide seamless, scalable, and secure solutions for businesses of all sizes. With a team of experts specializing in various tech domains, we ensure our clients stay ahead in an ever-evolving digital landscape.

Why Choose Us? Expert-Led Innovation – Our team is built on experience and expertise. Security First Approach – Cybersecurity is embedded in all our solutions. Scalable & Future-Proof – We design solutions that grow with you. Client-Centric Focus – Your success is our priority.

[uditprajapati7685]

Pickl.AI offers a comprehensive approach to data science education through real-world case studies and practical projects. By working on industry-specific challenges, learners gain exposure to how data analysis, machine learning, and artificial intelligence are applied to solve business problems. The hands-on learning approach helps build technical expertise while developing critical thinking and problem-solving abilities. Pickl.AI’s programs are designed to prepare individuals for successful careers in the evolving data-driven job market, providing both theoretical knowledge and valuable project experience.

[redspacewriter]

everything going wrong this season blame it on the a.i. (or fusebox, why not both)