#Artificial intelligence in the industry

[aiinstitutedelhi]

AI in industry

Numerous industries are using AI to boost production, efficiency, and decision-making. The adoption of AI technology has been driving innovation in industries such as healthcare, retail, finance, and manufacturing. Businesses and independent business owners are making significant investments in AI-related projects.

 As a result nowadays, many teenagers are interested in taking AI courses after completing their 12th. Jeetech Academy is one of the best institute for A artificial intelligence course in Delhi. Check them out if you are interested in taking a course

[drferox]

There’s a couple of things happening on the information technology side of the veterinary industry at the moment:

Practice owners are increasingly aware that they need an online presence (website plus social media), but most of them have minimal interest in actually making one because they want to focus on patients. You know, the work they signed up for in the first place.

Various tech companies sell packages to most vet practices doing some or all of this, including ‘writing SEO optimised articles for your website’.

While many of those articles were copy-paste, now they are often ‘unique’ which looks more and more AI generated.

At best, this looks like shoddy articles written for a machine instead of for people. At worst it generates information which is not current or outright false. In the middle, you get articles reminding you to brush your bird’s teeth.

So I find myself wondering if it’s even worth the effort to write informative content and it mostly feels like it doesn’t. Not compared to how fast and easily AI stuff can be churned out. Seriously, there are so, so many articles and videos out there about how to use AI to automate content generation or digital shops… it’s depressing.

But it probably is still worth writing things because it’s always been worth trying to combat misinformation. It’s just that misinformation and weird information can be generated so much more rapidly.

And I realise that whatever I put out on the internet might be chopped up and rearranged in the AI blender, but somebody has to keep telling the internet that you don’t have to brush your bird’s teeth.

[nando161mando]

La nueva 'apropiación de tierras' para las empresas de IA, desde Meta hasta OpenAI, son los contratos militares

The new 'land grab' for AI companies, from Meta to OpenAI, is military contracts

[iww-gnv]

Voice actors are taking to social media to criticize SAG-AFTRA, the union representing actors and other entertainment professionals, for an agreement it struck Tuesday with an artificial intelligence company that would allow video game developers to use digital replicas of actors’ voices.

[mr-entj]

Hello Mr. ENTJ. I'm an ENTJ sp/so 3 woman in her early twenties with a similar story to yours (Asian immigrant with a chip on her shoulder, used going to university as a way to break generational cycles). I graduated last month and have managed to break into strategy consulting with a firm that specialises in AI. Given your insider view into AI and your experience also starting out as a consultant, I would love to hear about any insights you might have or advice you may have for someone in my position. I would also be happy to take this discussion to somewhere like Discord if you'd prefer not to share in public/would like more context on my situation. Thank you!

Insights for your career or insights on AI in general?

On management consulting as a career, check the #management consulting tag.

On being a consultant working in AI:

Develop a solid understanding of the technical foundation behind LLMs. You don’t need a computer science degree, but you should know how they’re built and what they can do. Without this knowledge, you won’t be able to apply them effectively to solve any real-world problems. A great starting point is deeplearning.ai by Andrew Ng: Fundamentals, Prompt Engineering, Fine Tuning

Know all the terminology and definitions. What's fine tuning? What's prompt engineering? What's a hallucination? Why do they happen? Here's a good starter guide.

Understand the difference between various models, not just in capabilities but also training, pricing, and usage trends. Great sources include Artificial Analysis and Hugging Face.

Keep up to date on the newest and hottest AI startups. Some are hype trash milking the AI gravy train but others have actual use cases. This will reveal unique and interesting use cases in addition to emerging capabilities. Example: Forbes List.

On the industry of AI:

It's here to stay. You can't put the genie back in the bottle (for anyone reading this who's still a skeptic).

AI will eliminate certain jobs that are easily automated (ex: quality assurance engineers) but also create new ones or make existing ones more important and in-demand (ex: prompt engineers, machine learning engineers, etc.)

The most valuable career paths will be the ones that deal with human interaction, connection, and communication. Soft skills are more important than ever because technical tasks can be offloaded to AI. As Sam Altman once told me in a meeting: "English is the new coding language."

Open source models will win (Llama, Mistral, Deep Seek) because closed source models don't have a moat. Pick the cheapest model because they're all similarly capable.

The money is in the compute, not the models -- AI chips, AI infrastructure, etc. are a scarce resource and the new oil. This is why OpenAI ($150 billion valuation) is only 5% the value of NVIDIA (a $3 trillion dollar behemoth). Follow the compute because this is where the growth will happen.

America and China will lead in the rapid development and deployment of AI technology; the EU will lead in regulation. Keep your eye on these 3 regions depending on what you're looking to better understand.

[river-taxbird]

The Four Horsemen of the Digital Apocalypse

Blockchain. Artificial Intelligence. Internet of Things. Big Data.

Do these terms sound familiar? You have probably been hearing some or all of them non stop for years. "They are the future. You don't want to be left behind, do you?"

While these topics, particularly crypto and AI, have been the subject of tech hype bubbles and inescapable on social media, there is actually something deeper and weirder going on if you scratch below the surface.

I am getting ready to apply for my PhD in financial technology, and in the academic business studies literature (Which is barely a science, but sometimes in academia you need to wade into the trash can.) any discussion of digital transformation or the process by which companies adopt IT seem to have a very specific idea about the future of technology, and it's always the same list, that list being, blockchain, AI, IoT, and Big Data. Sometimes the list changes with additions and substitutions, like the metaverse, advanced robotics, or gene editing, but there is this pervasive idea that the future of technology is fixed, and the list includes tech that goes from questionable to outright fraudulent, so where is this pervasive idea in the academic literature that has been bleeding into the wider culture coming from? What the hell is going on?

The answer is, it all comes from one guy. That guy is Klaus Schwab, the head of the World Economic Forum. Now there are a lot of conspiracies about the WEF and I don't really care about them, but the basic facts are it is a think tank that lobbies for sustainable capitalist agendas, and they famously hold a meeting every year where billionaires get together and talk about how bad they feel that they are destroying the planet and promise to do better. I am not here to pass judgement on the WEF. I don't buy into any of the conspiracies, there are plenty of real reasons to criticize them, and I am not going into that.

Basically, Schwab wrote a book titled the Fourth Industrial Revolution. In his model, the first three so-called industrial revolutions are:

1. The industrial revolution we all know about. Factories and mass production basically didn't exist before this. Using steam and water power allowed the transition from hand production to mass production, and accelerated the shift towards capitalism.

2. Electrification, allowing for light and machines for more efficient production lines. Phones for instant long distance communication. It allowed for much faster transfer of information and speed of production in factories.

3. Computing. The Space Age. Computing was introduced for industrial applications in the 50s, meaning previously problems that needed a specific machine engineered to solve them could now be solved in software by writing code, and certain problems would have been too big to solve without computing. Legend has it, Turing convinced the UK government to fund the building of the first computer by promising it could run chemical simulations to improve plastic production. Later, the introduction of home computing and the internet drastically affecting people's lives and their ability to access information.

That's fine, I will give him that. To me, they all represent changes in the means of production and the flow of information, but the Fourth Industrial revolution, Schwab argues, is how the technology of the 21st century is going to revolutionize business and capitalism, the way the first three did before. The technology in question being AI, Blockchain, IoT, and Big Data analytics. Buzzword, Buzzword, Buzzword.

The kicker though? Schwab based the Fourth Industrial revolution on a series of meetings he had, and did not construct it with any academic rigor or evidence. The meetings were with "numerous conversations I have had with business, government and civil society leaders, as well as technology pioneers and young people." (P.10 of the book) Despite apparently having two phds so presumably being capable of research, it seems like he just had a bunch of meetings where the techbros of the mid 2010s fed him a bunch of buzzwords, and got overly excited and wrote a book about it. And now, a generation of academics and researchers have uncritically taken that book as read, filled the business studies academic literature with the idea that these technologies are inevitably the future, and now that is permeating into the wider business ecosystem.

There are plenty of criticisms out there about the fourth industrial revolution as an idea, but I will just give the simplest one that I thought immediately as soon as I heard about the idea. How are any of the technologies listed in the fourth industrial revolution categorically different from computing? Are they actually changing the means of production and flow of information to a comparable degree to the previous revolutions, to such an extent as to be considered a new revolution entirely? The previous so called industrial revolutions were all huge paradigm shifts, and I do not see how a few new weird, questionable, and unreliable applications of computing count as a new paradigm shift.

What benefits will these new technologies actually bring? Who will they benefit? Do the researchers know? Does Schwab know? Does anyone know? I certainly don't, and despite reading a bunch of papers that are treating it as the inevitable future, I have not seen them offering any explanation.

There are plenty of other criticisms, and I found a nice summary from ICT Works here, it is a revolutionary view of history, an elite view of history, is based in great man theory, and most importantly, the fourth industrial revolution is a self fulfilling prophecy. One rich asshole wrote a book about some tech he got excited about, and now a generation are trying to build the world around it. The future is not fixed, we do not need to accept these technologies, and I have to believe a better technological world is possible instead of this capitalist infinite growth tech economy as big tech reckons with its midlife crisis, and how to make the internet sustainable as Apple, Google, Microsoft, Amazon, and Facebook, the most monopolistic and despotic tech companies in the world, are running out of new innovations and new markets to monopolize. The reason the big five are jumping on the fourth industrial revolution buzzwords as hard as they are is because they have run out of real, tangible innovations, and therefore run out of potential to grow.

[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.

[disobedyent]

You're lost inside a lucid dream

Longing for a timeline that goes your way

Such a maladaptive drifter

Lusting for someone intangible

They're so close but out of reach

What does it mean to be alive?

You've turned yourself off, but you're not fading away

[fuckyeahmarxismleninism]

By Gary Wilson

The United States military has shifted toward AI and “data driven” warfare. A new revolving door is putting senior Pentagon officials into executive positions or as advisors to the Big Tech companies.

Over the past two years, global events have further fueled the Pentagon’s demand for Silicon Valley technologies, including the deployment of drones and AI-enabled weapon systems in Ukraine and Gaza, as well as the Pentagon’s AI arms race directed against China.

[savage-kult-of-gorthaur]

"SHEILA, OPEN."

PIC INFO: Spotlight on the absolute state of visual FX in 2001 -- Canadian actress Sabrina Grdevich as Sheila the mecha secretary, from the American science fiction film "A.I.: Artificial Intelligence" (2001), written & directed by Steven Spielberg.

Source: www.inverse.com/article/9529-spielberg-s-a-i-artificial-intelligence-feels-right-despite-bad-science.

[avant-greendecor]

The City's Embrace: An Industrial Haven for Modern Explorers

Visit my website for more inspiration 🌿

[travels-with-independent-music]

Your Dystopia, My Utopia

[nando161mando]

"No Tech for Apartheid’s protest is as much about what the public doesn’t know about Project Nimbus as what it does. The contract is for Google and Amazon to provide AI and cloud computing services to the Israeli government and military, according to the Israeli finance ministry, which announced the deal in 2021.

Nimbus reportedly involves Google establishing a secure instance of Google Cloud on Israeli soil, which would allow the Israeli government to perform large-scale data analysis, AI training, database hosting, and other forms of powerful computing using Google’s technology, with little oversight by the company.

Google documents, first reported by the Intercept in 2022, suggest that the Google services on offer to Israel via its Cloud have capabilities such as AI-enabled facial detection, automated image categorization, and object tracking."

[worlds-of-imaginations]

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[askagamedev]

On the topic of generative AI, what jobs are game companies looking to replace with it first? I imagine that concept art is going to be one the easiest things to replace and I harass some games are using AI to fill out voice over work ( AI is a major sticking point between game companies and the voice actor union right now), but what other jobs are at risk of being replaced?

Honestly, right now it's actually rather difficult to replace entire jobs with generative AI. It's much more of a situation where AI would be used to augment and fill in small knowledge gaps rather than replace contributions from individual developers.

Recently, a development company called Keywords attempted to build a 2D game internally using only generative AI tools. Keywords is a well-established co-development studio that has helped out with development on many large projects like Alan Wake, CoD: MW3 (2023), Super Mario Bros Wonder, Mortal Kombat 1, Starfield, Madden, Diablo IV, Skull and Bones, Baldur's Gate 3, Elden Ring, and so on. After six months the Keywords team [reported on their findings]:

Whilst the project team started small, it identified over 400 tools, evaluating and utilising those with the best potential. Despite this, we ultimately utilised bench resource from seven different game development studios as part of the project, as the tooling was unable to replace talent. One of the key learnings was that whilst Gen AI may simplify or accelerate certain processes, the best results and quality needed can only be achieved by experts in their field utilising Gen AI as a new, powerful tool in their creative process.

This gels with my own experience with Gen AI - it's an expanded Dunning-Kruger situation. Gen AI can create all kinds of content or results but it requires actual expertise in the field in order to separate the wheat from the chaff. Without having the skills needed to determine if something is good or not, the Gen AI results aren't (yet) good enough to use to build something.

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