Generative AI, in its most basic form, is “trained on vast amounts of data,” according to Ryan Bangert, senior vice president of strategic initiatives at Alliance Defending Freedom. “It ingests petabytes of information in order to learn how human language works, in order to understand how it is that human syntax grammar is structured, and then it predicts what comes next.”

Generative AI is “not a mind, it’s not a consciousness, it’s not a human being,” Bangert says. “It’s a piece of software, a very complex piece of software, that’s fulfilling an algorithmic function.”

Therefore, he adds, generative AI is “not a First Amendment rights-bearing entity.”

In their new paper, “The Ghost in the Machine: How Generative AI Will Test the Bounds of the First Amendment,” Bangert and Jeremy Tedesco, senior vice president of corporate engagement at Alliance Defending Freedom, parse the relationship between AI and the First Amendment.

Bangert and Tedesco join “The Daily Signal Podcast” to discuss the fight to protect free speech amid rapidly changing AI technology use.

Listen to the podcast below:

Government officials, including White House Chief of Staff Jeff Zients, National Economic Adviser Lael Brainard, National Climate Adviser Ali Zaidi, Senior Adviser to the President for International Climate Policy John Podesta, and more, met with numerous tech industry leaders, including Alphabet President Ruth Porat, Amazon Web Services CEO Matt Garman, and OpenAI CEO Sam Altman.

This isn’t the first time the Biden administration has buddied up with big business to address an energy issue while simultaneously enacting policies that nullify its own efforts to strengthen the American industrial and energy base.

To the delight of OpenAI, the Biden administration announced that it plans to invest in data center infrastructure projects following the discussion. In response, OpenAI told CNBC that the company “appreciate[s] the White House [for] convening this meeting, as it is a recognition of the priority of infrastructure to create jobs, help guarantee that the benefits of AI are widely distributed, and ensure America will continue to be at the forefront of AI innovation.”

Furthermore, the Biden administration announced the launch of a permitting council to provide increased technical assistance to federal, state, and local authorities handling data center permits, an AI data center engagement team to expand loans, grants, and tax credits, a program to “share resources on repurposing closed coal sites with datacenter developers,” and other measures to address the data center energy issue.

Data centers and fossil fuels

Like much of the rest of the tech industry, AI training heavily relies on data centers, which provide powerful servers and storage capacity for all sorts of ventures. Most of these data centers provide these services by drawing from fossil fuels — the type of energy many globalists and self-styled progressives insist will destroy the world.

The demand for these data centers continues to grow. A Goldman Sachs study showed that data center power demand will grow 160% by 2030. And according to the International Energy Agency, “Electricity consumption from data centres, artificial intelligence (AI) and the cryptocurrency sector could double by 2026.” What’s most concerning is that data centers consumed 460 terawatt-hours of electricity in 2022, and that number could rise to over 1,000 TWh in 2026 — close to Japan’s current electricity consumption.

That’s because more firms are innovating in AI, and AI is becoming more energy-intensive as it develops in complexity. For example, OpenAI’s ChatGPT-4 took 50 times the electricity needed to train GPT-3. Additionally, AI-based programs consume much more energy than basic search engines. A simple ChatGPT prompt response takes nearly 10 times the electricity of a Google search; a ChatGPT prompt response requires 2.9 watt-hours, while a Google search takes 0.3 watt-hours.

Relying on green energy to power these data centers does not make sense, but the Biden administration thinks otherwise.

Cronyism

This isn’t the first time the Biden administration has buddied up with big business to address an energy issue while simultaneously enacting policies that nullify its own efforts to strengthen the American industrial and energy base.

In 2022, a bipartisan coalition in Congress passed the CHIPS and Science Act to boost domestic semiconductor manufacturing. It was also supported and signed by President Joe Biden. The bill was a good start, but with Intel’s recent struggles and shortfalls — and fresh uncertainty surrounding the impact of the highly hyped new TSMC fabrication plant in Arizona — this effort to shore up chip manufacturing is overshadowed by its many flaws. These flaws include woke language, DEI provisions, and, most importantly, climate change and green energy initiatives, even though chip manufacturing is heavily reliant on cheap, reliant, and efficient fossil fuels.

What’s more, after announcing billions of taxpayer dollars would go to “strengthen climate resilience,” the Biden-Harris administration put out a press release detailing its expensive progressive climate agenda to reach net-zero carbon emissions.

“Net-zero emissions pathways require widespread implementation of currently available and cost-effective options for reducing emissions, including the addition of new wind and solar capacity. Reaching net zero will also require rapid expansion of technologies and methods to remove carbon from the atmosphere to balance remaining emissions, as well as the exploration of additional mitigation and transformative adaptation options,” the administration claimed.

As the name of the crisis suggests, the data center energy crisis will only be solved by accessing more reliable energy, not by conspiring with Big Tech to expand its institutional influence and advance the green agenda.

The U.S. has imposed stringent export controls on advanced chips, particularly Nvidia’s powerful H100 chips, aiming to curb technological advancements in China that could have military applications. However, the demand for these chips in China remains high, leading to innovative solutions like those provided by Derek Aw, a former bitcoin miner turned entrepreneur. Aw has facilitated the setup of AI servers equipped with Nvidia chips in locations like Australia, serving Chinese companies remotely.

Aw’s approach involves setting up data centers overseas, like the one in Brisbane, Australia, where over 300 servers were installed to process AI tasks for a Beijing-based company. This method leverages the concept of renting computing power, a practice not new but made more complex by the need for anonymity due to U.S. regulations.

The transactions are often anonymized through smart contracts on blockchain platforms, where identities are concealed by cryptographic keys, and payments are made in cryptocurrencies. This setup allows Chinese companies to operate under the radar, sometimes through subsidiaries in countries like Singapore, further distancing themselves from direct U.S. oversight.

The decentralized GPU model has gained traction, especially after the cryptocurrency mining boom waned, freeing up computing resources worldwide. Platforms like io.net boast of providing unrestricted access to GPU power without the usual customer verification processes, appealing to Chinese entities looking to maintain privacy while accessing technology.

Joseph Tse, formerly with a Shanghai AI startup, highlighted the shift to these decentralized services after traditional cloud providers like Amazon Web Services became inaccessible due to U.S. restrictions. These platforms, while offering a workaround, come with risks like potential data breaches due to the inherent vulnerabilities in blockchain systems.

At industry events, such as one in Singapore, companies are openly marketing these decentralized GPU services, indicating a growing market for such solutions among Chinese developers who need less intensive computing power for smaller AI applications. However, for large-scale AI training, like that required for models similar to ChatGPT, these decentralized setups fall short, prompting efforts to create larger, more centralized computing clusters.

Edge Matrix Computing (EMC) is one example of a company expanding its network to include thousands of GPUs, including those under U.S. export controls, for more robust AI training capabilities. EMC and similar ventures are exploring bulk purchasing of chips like Nvidia’s H100, aiming to reduce costs for intensive AI computing.

The U.S. government, aware of these developments, is tightening its oversight. Senator John Kennedy has expressed concerns over the effectiveness of current export controls, urging stricter measures. The Commerce Department, in response, has been monitoring and attempting to clamp down on these illicit procurement networks.

Meanwhile, entrepreneurs like Aw continue to expand their operations, planning new clusters with the latest chip technologies, legally operating under the guise of foreign subsidiaries. This cat-and-mouse game between regulatory enforcement and technological circumvention highlights the complex landscape of international tech trade and the ongoing battle over technological supremacy.

Article generated from corporate media reports.

We continually hear from globalists at the World Economic Forum and other elitist institutions that AI is the catalyst for the “4^th Industrial Revolution” – A technological singularity that will supposedly change every aspect of our society forever. I keep waiting for the moment that AI does something significant in terms of advancing human knowledge or making our lives better. The moment never comes. In fact, the globalists keep moving the goalposts for what AI really is.

I would note that WEF zealots like Yuval Harari talk about AI like it is the rise of an all powerful deity (I discuss the globalist worship of AI in my article ‘Artificial Intelligence: A Secular Look At The Digital Antichrist’). Yet, Harari has also recently downplayed AI as a sentient intelligence. He argues that it doesn’t need to achieve self awareness or consciousness in order to be considered a super being or living entity. He even suggests that the popular image of a Terminator-like AI with individual agency and desire is not a legitimate expectation.

In other words, AI as it stands today is nothing more than a mindless algorithm, and thus, it is not AI.  But, if every aspect of our world is engineered around digital infrastructures and the populace is taught to put blind faith in the “infallibility” of algorithms then eventually become the robot gods the globalists so desperately desire.  That is to say, AI dominance is only possible if everyone BELIEVES that AI is legitimate.  Harari essentially admits to this agenda in the speech above.

The allure of AI for average people is the pie-in-the-sky promise of freedom from worry or responsibility. As with all narcissists, the global elite love to future-fake and buy popular conformity now on false promises of rewards that will never come.

Yes, algorithms are currently used to help laymen do things they could not do before, such as build websites, edit essays, cheat on college exams, create bad artwork and video content, etc. Useful applications are few and far between. For example, the claim that AI is “revolutionizing” medical diagnosis and treatment is far-fetched.  The US, the nation that arguably has the most access to AI tools, is also suffering from declining life expectancy.  We know it’s not covid because the virus has a 99.8% average survival rate.  You would think that if AI is so powerful in its ability to identify and treat ailments the average American would be living longer.

There is no evidence of a single benefit to AI on a broader social scale. At most, it looks like it will be good at taking jobs away from web developers and McDonald’s drive-thru employees.  The globalist notion that AI is going to create a robotic renaissance of art, music, literature and scientific discovery is utter nonsense.  AI has proven to be nothing more than a tool of mediocre convenience, but that’s actually why it’s so dangerous.

I suspect the WEF has changed its ideas about what AI should be because it’s not living up to the delusional aspirations they originally had for it. They’ve been waiting for a piece of software to come to life and start giving them insights into the mechanics of the universe and they’re starting to realize that’s never going to happen. Instead, the elitists are shifting their focus increasingly into the melding of the human world and the digital world. They want to fabricate the necessity of AI because human dependency on the technology serves the purposes of centralization.

But what would this actually look like? Well, it requires that the population continues to get dumber while AI becomes more integral to society.

For example, it is widely accepted at this point that a college education is no indication of intelligence or skill. There are millions of graduates entering the workforce today that display an unsettling level of incompetence. This is partially because college educators are less capable, ideologically biased and the average curriculum has degraded. But, also, we need to start accounting for the number of kids coasting their way through school using ChatGPT and other cheat boxes.

They don’t need to learn anything, the algorithm and their cell phone camera does it all for them. This trend is disturbing because human beings have a tendency to take the easiest path in every aspect of survival. Most people stopped learning how to grow food because industrial farming does it for us. They stopped learning how to hunt because there are slaughterhouses and refrigerated trucks.  Many Zennials today are incapable of cooking for themselves because they can get takeout to their door anytime they want. They barely talk on the phone or create physical communities anymore because texting and social media have become the intermediaries in human interaction.

Yes, everything is “easier”, but that does not mean anything is better.

My great fear – The future that I see coming down the road, is one in which human beings no longer bother to think. AI might be seen as the ultimate accumulation of human knowledge; a massive library or digital brain that does all the searching and thinking for you. Why learn anything when AI “knows everything.” Except, this is a lie..

AI doesn’t know everything; it only knows what its programmers want it to know. It only gives you the information its programmers want you to have. The globalists understand this and they can taste the power that they will have should AI become paramount as an educational platform.  They see it as a way to trick people into abandoning personal development and individual thought.

Look at it this way: If everyone in the world starts turning to AI for answers to all their questions, then everyone in the world will be given the same exact answers and will come to the same exact conclusions. All AI has to do is actively censor any information that contradicts the official narrative.

We got a taste of this Orwellian condition during the covid pandemic when Big Tech companies like Google used algorithms to bury any and all data that proved covid was not the threat that government officials said it was. You could not go on YouTube for at least three years and look up alternative information on covid or the vaccines. The algorithm forced everyone to sift through a long list of establishment sources, many of them promoting blatant lies about masking, social distancing, the covid death rate and vaccine safety.

The powers-that-be don’t even need to directly censor or remove information they don’t like. All they have to do is let the algorithm dictate the search results and bury the truth on Page 10,000 where no one will look.

How would the effect the average person?  Say AI is programmed to dictate scientific discourse.  What if AI says that man-made climate change is an undeniable reality and the “science is settled” while never presenting the mountain of counter-evidence to the contrary?  No one will look up the real data because AI will make it impossible to find.  Everyone will assume AI is telling them all there is to know about the subject, but it gets worse than that…

Many readers might remember a few months back when Google AI system “Gemini” was programmed to force DEI onto its users. Whenever a person asked the AI to create a historical image the algorithm made everyone black or brown and often female. Depictions of white men were suspiciously rare despite historical accuracy. This meant endless images of black and brown Highlanders in Scotland, black Founding Fathers in America, female Catholic Popes, Asian knights in Medieval Europe, and hilariously, even black Nazis in WWII Germany.

AI developers often claim that once an AI is created they don’t really control what it does and how it grows. The Gemini incident proves this is a lie. AI can definitely be controlled, or at least molded by coding to promote whatever propaganda the programmers want it to promote. There is no such thing as an autonomous AI; there is always an agenda.

To summarize, globalists want the proliferation of AI because they know that people are lazy and will use the system as a stand-in for individual research. If this happens on a large scale then AI could be used to rewrite every aspect of history, corrupt the very roots of science and mathematics and turn the population into a drooling hive mind; a buzzing froth of braindead drones consuming every proclamation of the algorithm as if it is sacrosanct.

In this way, Yuval Harari is right. AI does not need to become sentient or wield an army of killer robots to do great harm to humanity. All it has to do is be convenient enough that we no longer care to think for ourselves.  Like the “Great and Powerful” OZ hiding behind a digital curtain, you assume you’re gaining knowledge from a wizard when you’re really being manipulated by globalist snake oil salesmen.

If you would like to support the work that Alt-Market does while also receiving content on advanced tactics for defeating the globalist agenda, subscribe to our exclusive newsletter The Wild Bunch Dispatch.  Learn more about it HERE.

(Mercola)—In the video above, computer scientist and AI researcher Lex Fridman interviews Aravind Srinivas, CEO of Perplexity, an AI-powered “answer engine.” Unlike typical search engines, which require you to sort through pages of results to find the information you need, Perplexity provides a real-time answer to your query.

One of the pitfalls of current AI technologies like ChatGPT is the tendency to hallucinate or fabricate information on occasion. To minimize this risk, you can ask it to provide source links and verify the accuracy of the information given. However, Perplexity addresses this issue from the start, and while it can still hallucinate, it has a factual grounding.

“[Perplexity] aims to revolutionize how we humans get answers to questions on the internet. It combines search and large language models, LLMs, in a way that produces answers where every part of the answer has a citation to human-created sources on the web,” Fridman says. “This significantly reduces LLM hallucinations, and makes it much easier and more reliable to use for research, and general curiosity-driven late night rabbit hole explorations that I often engage in.”¹

Part Search Engine, Part Question-Answering Platform

Fridman describes Perplexity as part search engine — a software system designed to search for information on the internet — and part LLM. LLM is a type of artificial intelligence system trained on vast amounts of text data to understand and generate human-like text. LLMs can perform various language-related tasks such as answering questions, generating content, translating languages and more.

Unlike standard search engines that provide links, Perplexity attempts to directly answer queries. Srinivas explains:²

“Perplexity is best described as an answer engine. You ask it a question, you get an answer. Except the difference is, all the answers are backed by sources. This is like how an academic writes a paper. Now, that referencing part, the sourcing part is where the search engine part comes in. You combine traditional search, extract results relevant to the query the user asked. You read those links, extract the relevant paragraphs, feed it into an LLM …

That LLM takes the relevant paragraphs, looks at the query, and comes up with a well-formatted answer with appropriate footnotes to every sentence it says, because it’s been instructed to do so, it’s been instructed with that one particular instruction, given a bunch of links and paragraphs, write a concise answer for the user, with the appropriate citation.

The magic is all of this working together in one single orchestrated product, and that’s what we built Perplexity for.”

Srinivas, who previously was an AI researcher at DeepMind, Google and OpenAI, says he views Perplexity as a discovery engine that feeds curiosity:³

“The journey doesn’t end once you get an answer. In my opinion, the journey begins after you get an answer. You see related questions at the bottom, suggested questions to ask. Why? Because maybe the answer was not good enough, or the answer was good enough, but you probably want to dig deeper and ask more. That’s why in the search bar, we say where knowledge begins, because there’s no end to knowledge. You can only expand and grow.”

Breakthroughs in AI

Please understand that while Perplexity is not perfect and still exhibits some bias, particularly regarding COVID-19 information, it significantly outperforms Google in almost every other search query. The AI-driven technology behind Perplexity provides more accurate, comprehensive, and nuanced results, making it a superior choice for general searches. Its advanced algorithms ensure that users receive the most relevant and insightful information, setting it apart from traditional search engines.

Srinivas describes several ways Perplexity embraces state-of-the-art advances in machine learning, along with general innovation. This includes retrieval augmented generation (RAG), an advanced technique in natural language processing (NLP) that combines the capabilities of LLMs with information retrieval systems to produce more accurate and contextually relevant responses.

This approach is particularly useful for tasks that require precise and up-to-date information, such as question answering, summarization and dialogue systems. In short, RAG involves the search aspect of the query, but Perplexity goes beyond this. Srinivas says:⁴

“The principle in Perplexity is you’re not supposed to say anything that you don’t retrieve, which is even more powerful than RAG because RAG just says, ‘OK, use this additional context and write an answer.’ But we say, ‘Don’t use anything more than that too.’ That way we ensure a factual grounding. And if you don’t have enough information from documents you retrieve, just say, ‘We don’t have enough search resource to give you a good answer.’”

They’re also using chain of thought reasoning, which takes NLP tasks up a notch in terms of performance. Chain of thought reasoning in AI refers to the ability of a language model to generate logical, step-by-step explanations or sequences of thoughts that lead to a conclusion or answer. This approach enhances the model’s performance on complex reasoning tasks by encouraging it to articulate the intermediate steps in its reasoning process. Srinivas explains:⁵

“Chain of thought is this very simple idea where, instead of just training on prompt and completion, what if you could force the model to go through a reasoning step where it comes up with an explanation, and then arrives at an answer?

Almost like the intermediate steps before arriving at the final answer. And by forcing models to go through that reasoning pathway, you’re ensuring that they don’t overfit on extraneous patterns, and can answer new questions they’ve not seen before.”

The Beginning of Real Reasoning Breakthroughs

Whether or not AI is fundamentally capable of higher-level reasoning, akin to human cognitive processes, remains to be seen. Reaching that point, however, relies in part on applying more inference compute, which in AI refers to the computational resources and processes involved in running an AI model to make predictions or decisions based on new data.

This stage is distinct from the training phase, which involves building and optimizing the model. Broken down, inference is the process by which an AI model applies learned patterns to new data to generate predictions, classifications or other outputs. For example, using AI to classify images or predict stock prices.

The compute aspect, meanwhile, refers to the computational power required to perform inference. It involves hardware, software frameworks and algorithms optimized for efficient computation. Srinivas says:⁶

“Can you have a conversation with an AI where it feels like you talked to Einstein or Feynman? Where you ask them a hard question, they’re like, I don’t know. And then after a week they did a lot of research … and come back and just blow your mind.

I think if we can achieve that amount of inference compute, where it leads to a dramatically better answer as you apply more inference compute, I think that will be the beginning of real reasoning breakthroughs … It’s possible. We haven’t cracked it, but nothing says we cannot ever crack it.”

Curiosity Is a Key Part of What Separates Humans From AI

Part of cracking this code involves teaching AI how to mimic natural human curiosity. “What makes humans special though, is our curiosity,” Srinivas explains. “Even if AIs cracked this, it’s us still asking them to go explore something. And one thing that I feel like AIs haven’t cracked yet is being naturally curious and coming up with interesting questions to understand the world and going and digging deeper about them.”⁷

Beyond this, there’s a lot of controversy and fear around artificial general intelligence (AGI), which refers to a type of AI that possesses the ability to understand, learn and apply knowledge across a wide range of tasks at a level comparable to human intelligence.

Srinivas says he doesn’t think we need to worry about “AIs going rogue and taking over the world,” but there is an issue of who controls the compute on which AGI runs. “It’s less about access to a model’s weights. It’s more access to compute that is putting the world in more concentration of power and few individuals. Because not everyone’s going to be able to afford this much amount of compute to answer the hardest questions.”

A sign of higher intelligence in AI, Srinivas says, is becoming capable of creating new knowledge and providing truth to questions we don’t know the answers to — and helping us understand why it’s the truth.

“Can you build an AI that’s like Galileo or Copernicus where it questions our current understanding and comes up with a new position, which will be contrarian and misunderstood, but might end up being true? … And the answer should be so mind-blowing that you never even expected it.”⁸

What’s the Future of Search and AI?

We’re already seeing AI tools like Perplexity, which are exponentially superior to existing search engines. In the future, however, Srinivas says the goal isn’t about building a better search tool but building a platform for knowledge:⁹

“If you zoom out, before even the internet, it’s always been about transmission of knowledge. That’s a bigger thing than search … So, we imagine a future where the entry point for a question doesn’t need to just be from the search bar. The entry point for a question can be you listening or reading a page, listening to a page being read out to you, and you got curious about one element of it and you just asked a follow-up question to it.

That’s why I’m saying it’s very important to understand your mission is not about changing the search. Your mission is about making people smarter and delivering knowledge. And the way to do that can start from anywhere. It can start from you reading a page. It can start from you listening to an article … It’s just a journey. There’s no end to it.”

Keep in mind that Perplexity and other AI tools are not a replacement for your own critical thinking; rather, they serve as an aid to enhance your creativity. It’s vital to keep this in mind and remember that AI is an adjunct to, not a substitute for, your intellectual and creative capacities.

While precautions need to be taken, including not sharing personal or confidential information, this is not about replacing human action but enhancing it, allowing individuals to focus on aspects of their work that require uniquely human attributes like empathy, strategic thinking, creativity and curiosity. Srinivas explains:¹⁰

“So, I think curiosity makes humans special and we want to cater to that. That’s the mission of the company, and we harness the power of AI and all these frontier models to serve that. And I believe in a world where even if we have even more capable cutting-edge AIs, human curiosity is not going anywhere and it’s going to make humans even more special.

With all the additional power, they’re going to feel even more empowered, even more curious, even more knowledgeable in truth-seeking and it’s going to lead to the beginning of infinity.”

• ¹ LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 0:28
• ² LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 2:05
• ³ LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 7:14
• ⁴ LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:56:44
• ⁵ LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:16:04
• ^6, 7 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:23:53
• ⁸ LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:34
• ⁹ LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 2:34
• ¹⁰ LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 2:50

In conversation with the State Department’s chief data and AI officer Matthew Graviss, he cited a number of initiatives – such as the UN’s Sustainable Development Goals and Washington’s Enterprise AI Strategy as the foundations for the ultimate goal – using AI to “advance our foreign policy.”

The second part of the push to equip the State Department with AI tools is to – “strengthen this institution.”

According to Blinken, his department is a leader within the government when it comes to testing and “harnessing” the technology. Some reports speak about this as combating whatever happens to be considered foreign disinformation.

And while on the subject of meddling, the Washington Times says AI tests are “part of an ambitious media monitoring and analysis project that spans the globe.”

As sinister as that may sound, packaging the message as the need to combat (only) “foreign disinformation” certainly makes the policy more palatable at home, where the department’s past activity features in congressional probes into government-orchestrated online censorship.

This scrutiny is presented as something hindering the Department of State’s “anti-disinformation” work – while the tools now in development are quite openly described as a possible different means “to pursue the same goals.”

Blinken’s remarks reveal how the technology is used seemingly innocuously as a (translation and summarization) tool “in multilateral organizations;” but then he praised the ability of AI-powered tools to make mass surveillance (“monitoring”) cover a much larger number of media, making its scope and scale “vast,” as the report put it.

And also – combat “disinformation” – which Blinken quite dramatically refers to as “one of the poisons in the international system today.”

“We have one program that we’re using that is able to basically ingest a million articles every day from around the world — to be able to do that in a couple hundred countries in over a hundred languages — and then immediately translate, synthesize and give you a clear picture of what’s happening in the information space immediately,” the secretary is quoted as saying.

But given the scale of the operation, and the shortcomings of the current limitations of AI – those in the know might wish Blinken good luck with the accuracy and reliability of getting that “immediate, clear picture.”

However, when the “AI weapon” is pointed at online platforms as a means of identifying and censoring “disfavored” speech, it is objectively more likely to be efficient.

And the State Department is no stranger to such – strange given its mission – activities: after all, it is the home of the investigated-by-Congress and highly controversial Global Engagement Center.

(End of the American Dream)—When I first started researching this, I could hardly believe that it was true. A company in Switzerland known as “Final Spark” has constructed a bizarre hybrid biocomputer that combines lab-grown miniature human brains with conventional electronic circuits.

This approach saves an extraordinary amount of energy compared to normal computers, but there is a big problem.  The lab-grown miniature human brains keep wearing out and dying, and so scientists have to keep growing new ones to replace them.

Stem cells that are derived from human skin tissue are used to create the 16 spherical brain “organoids” that the system depends upon.  I realize that this sounds like something straight out of a really bad science fiction movie, but it is actually happening.

Scientists at Final Spark are calling their hybrid computer “the Neuroplatform”, and it is being reported that it only uses “a fraction of the energy required to power a traditional set up”…

Swiss tech startup FinalSpark is now selling access to biocomputers that combine up to four tiny lab-grown human brains with silicon chips.

This new bioprocessing platform, called the Neuroplatform, uses small versions of human brains to do computer work instead of silicon chips. The company says it can fit 16 of these mini-brains onto the Neuroplatform and use a fraction of the energy required to power a traditional set up.

The platform, currently adopted by nine institutions, integrates hardware, software and biology to construct a processing system that is energy-efficient and high-performing.

This “breakthrough” is being hailed as a way to save a gigantic amount of energy. But what about the lab-grown human brains that are being enslaved to run the Neuroplatform?

Each of the 16 mini-brains is made up of approximately 10,000 living neurons, and they are kept alive by a “microfluidics system that supplies water and nutrients for the cells”…

Rather than merely integrating biological concepts into computing, FinalSpark’s online platform ‘taps’ into spherical clusters of lab-grown human brain cells called organoids. A total of 16 organoids are housed within four arrays that connect to eight electrodes each and a microfluidics system that supplies water and nutrients for the cells.

The approach, known as wetware computing, in this case harnesses researchers’ abilities to culture organoids in the lab, a fairly new technology that allows scientists to study what are essentially mini replicas of individual organs.

During their short lives, the mini-brains are literally trained to perform certain tasks using a reward and punishment system…

Researchers do this by training the organoids through a reward system. The organoids are rewarded with dopamine, the neurotransmitter responsible for pleasure (and addiction).

Meanwhile, as “punishment,” the organoids are exposed to chaotic stimuli, such as irregular electrical activity.

If the enslaved mini-brains do what they are supposed to do, they are rewarded with lots of pleasure.

If the enslaved mini-brains do not do what they are supposed to do, they are hit with lots of “irregular electrical activity”.

In other words, these miniature human brains are tortured until they learn to obey. Reading that should literally make you sick.

What these scientists are doing is so incredibly evil. Final Spark claims that the miniature human brains use “a million times less power than their silicon counterparts”…

Swiss technology firm Final Spark has successfully launched Neuroplatform, the world’s first bioprocessing platform where human brain organoids (lab-grown miniaturized versions of organs) perform computational tasks instead of silicon chips.

The first such facility hosts the processing prowess of 16 brain organoids, which the company claims uses a million times less power than their silicon counterparts.

Final Spark hopes that their new “technology” will become the primary energy source for the AI revolution.

Because at this point training AI models uses a colossal amount of conventional energy…

According to Final Spark’s estimates, training the popular large language model GPT-3 that powered ChatGPT in its initial days alone consumed 10 GWh of energy. This is a whopping 6,000 times more energy than an average European city consumes in an entire year.

Replacing silicon chips with bioprocessors could lead to drastic energy savings. Final Spark allows research labs to experience the power of biological processors on the Neuroplatform.

To a lot of people out there, this is going to sound really great.

Final Spark insists that the processor that it has created will use a million times less energy compared to a normal silicon chip.

There is just one enormous problem.

The mini-brains keep dying and must be regularly replaced.

At first they would die “in just a few hours”, but now they are apparently living for up to 100 days…

Final Spark faced many challenges in its early years since the organoids would die in just a few hours. The company has worked on this shortcoming and improved its MEA systems to ensure that organoids live for 100 days.

These “organoids” are literally worked to death.

They are hooked up to electrodes and worked until they can work no more…

Final Spark has made working these varied components possible through an innovative setup called Multi-Electrode Arrays (MEAs), where the three-dimensional masses of brain tissue are placed.

Each MEA has four brain organoids that interface with eight electrodes. These electrodes perform the dual role of stimulating the organoids and recording the data they process.

Data transfer is done through digital analog converters with a 16-bit resolution and a 30 kHz frequency. A microfluidic system provides life support for the MEAs, and cameras can monitor their overall operation.

Have you ever seen “The Matrix”? I was reminded of that film as I researched all of this.

Just like in that movie, human energy powers the entire system. And just like in that movie, those that power the system are enslaved.

The creators of “the Neuroplatform” insist that this is perfectly okay because the mini-brains are not sentient beings.

Whether that is true or not, what they are doing is still very wrong.

Creating miniature human brains and using them to power a computer may be a way to save a lot of energy, but it also perfectly illustrates how far our society has fallen.

We are crossing lines that should never be crossed, and eventually we will pay a very great price for the crimes that our scientists are committing.

Michael’s new book entitled “Chaos” is available in paperback and for the Kindle on Amazon.com, and you can subscribe to his Substack newsletter at michaeltsnyder.substack.com.

Obviously, this was in the wake of new AI technologies like ChatGPT and others. This is also not a new issue. In 2017, the Wall Street Journal also published “Protecting Against AI’s Existential Threat.” Of course, AI has been impressively more developed recently, bringing the usual reactions—assumptions that this technology will totally change everything, amused interest, reasonable concerns (e.g., students cheating), and the typical hand-wringing.

All this was in response to a recent statement from the Center for AI Safety, who posted an open letter with the following warning: “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.” As these claims were made, even by creators of the technologies, I had a sneaking suspicion that I already knew what these AI companies wanted: cronyism. Not long after that, my suspicions were confirmed by the inevitable call for that one, vague, seemingly magical word that everybody seems to demand in situations like these. That word is “regulation.”

Arguing a similar point soon after the announcement of this “existential threat,” another writer from the Wall Street Journal perceptively wrote an article titled, “AI is the Technocratic Elite’s New Excuse for a Power Grab.” That said, while there is certainly a penchant for the technocratic elite to form and expand bureaucracies in order to regulate, it is worth mentioning that very often these very companies are themselves the biggest proponents of government regulation of their industries.

Why would private firms ever want cumbersome government regulation? The answer is cronyism. Cronyism, crony capitalism, “crapitalism,” corporatism, managed capitalism, a “mixed” economy, or fascism are different titles for the same concept. Whatever the designation, it is a public-private partnership that employs the powers of the state to grant special legal privilege to non-State entities for their mutual benefit at the expense of the consumer/taxpayer.

Private businesses and government are often not enemies but very comfortable friends. More than that, their working together is always in the name of protecting the consumer/taxpayer. Depending on their ideology, people might see themselves teaming up with business against the government or teaming up with government against business, but they rarely recognize that there is a third option—government and business teaming up against the consumer/taxpayer.

By inviting “regulation,” private firms can use the legal apparatus of the government to limit competition, raise barriers to entry for competitors, restrict output for higher prices, and shift the costs of “health and safety” standards to the taxpayers. These are options that businesses and industries would never have save an alliance with the state. The taxpayers will pay the government, via bureaucratic agencies, to inspect and maintain certain standards for businesses, which removes the costs of these standards from the business, subsidizes their operations, and places them on the taxpayer. Oddly enough, this regulative bureaucratic apparatus can simultaneously be burdensome by hampering the productive aspects of a business, assistive in providing for costs to businesses and industries that they would otherwise have to bear, and incompetent in the goal of protecting consumers.

A costly regulatory burden for competitors, an attempt to capture the market, movement toward monopolization, the government as enforcer, and getting the consumer/taxpayer to pay for bureaucracies to write more regulations and inspect standards: What business wouldn’t at least be tempted?

Cronyism has a long history. In fact, monopoly used to mean exclusive legal grants of government privilege to certain companies, not arbitrary standards like firm size, number of firms, or market share. Cronyism has also had a long history in the United States, especially since the Progressive era (ca. 1890–1920). We are often told that the Progressive era involved the government intervening to stop monopoly when the truth is precisely the opposite—the government intervened with a new bureaucratic technocracy, usually at the request of private firms, to stifle competition and impose monopoly.

This took place in all sorts of areas, such as meatpacking, insurance, textiles, money and banking, etc. It turned out, according to G. Edward Griffin, that use of the word “reform” would be enough: “The American people are suckers for the word ‘reform.’ You just put that into any corrupt piece of legislation, call it ‘reform’ and people say ‘Oh, I’m all for “reform,”’ and so they vote for it or accept it.”

Furthermore, while the tendency in America was toward competition, this was unacceptable to many businesses who invited and embraced new “regulations” and “reforms.” Writes historian Gabriel Kolko, “Ironically, contrary to the consensus of historians, it was not the existence of monopoly that caused the federal government to intervene in the economy, but the lack of it.” The simple solution was, “Monopoly could be put over in the name of opposition to monopoly!” The same word was used, but the content could be the opposite. Government, itself a monopoly, was to monopolize industries because, if it didn’t, monopolies might result!Many vaguely call for “regulation” because they may not know what else to ask for whenever they see a problem, but often businesses call for it to use the governmental apparatus to their benefit at the expense of the consumer/taxpayer.

As the new and growing AI industry develops, it should be no surprise that they want “regulation.” It is probably not that they really believe that the AI technology they produce is a literal threat to human existence, but that, by leaning into the scaremongering and the statist non sequitur, they can form a cozy relationship with the government first and legally limit competition to their benefit. The American people are suckers for “reform.”

Speaking at the Hill & Valley Forum’s gathering of top technology and government officials in Washington this week, Mulchandani’s made it clear that the agency is aggressively pursuing advancements in artificial intelligence (AI) to bolster both offensive and defensive capabilities, the Washington Times reports.

“We’re looking at transforming every single part of what the agency does,” he stated, underscoring the depth of the CIA’s commitment to integrating AI into its core operations. The agency’s push includes the development of large language models, sophisticated algorithms that are the backbone of generative AI tools, aiming to enhance everything from field operations to analytical and support functions.

This strategic pivot comes as geopolitical rivalry with China is intensifying. The CCP has repeatedly expressed its ambition to dominate the AI sphere, which would present profound challenges and implications for global power dynamics. Mulchandani emphasized the need to rethink the concept of this competition as a “race,” suggesting that viewing it as having a definitive end is a misstep. “This is an infinite race. This is not going to stop. It’s going to keep on going,” he explained, framing the scenario as a continuous struggle for technological superiority.

The implications of this shift are profound. If the deployment of these new tools escalates to warfare, it will test America’s position in the technology stakes, a scenario Mulchandani hopes will never materialize. He predicts the next major conflict will be “primarily a software war,” driven by AI, changing the nature of warfare from hardware-dependent to software-driven.

The concerns are not just theoretical. At Stanford’s Hoover Institution, Herbert Lin of the Stanford Emerging Technology Review highlighted the shift in global tech leadership, with the U.S. losing its primacy in certain key areas like AI. Lin pointed out the critical need for a robust talent pipeline and a strategic vision, especially in fields like biotechnology, to maintain competitiveness.

Moreover, the CIA is particularly wary of AI-driven Ubiquitous Technical Surveillance (UTS), which threatens the secrecy of U.S. intelligence operations. In response, the agency is engaged in foundational infrastructure work, which Mulchandani described as the “sewer and plumbing work” necessary to navigate the AI revolution. This involves constant adaptation to rapid technological changes, ensuring that the CIA remains agile in its tech tactics.

“We talk about UTS, which is basically something that’s really, really killing us out in the field in terms of competitively, you know, biometrics, video cameras,” he said. “Well, how do we turn it around [and continue] those operations in the face of this much AI being thrown at us is another big area that they’re looking at. So directorate by directorate, we’re rethinking, reshaping every part of what CIA needs to do in the face of using it and deploying it.”

The urgency of these initiatives is echoed in the broader governmental plea for collaboration from Silicon Valley. House Speaker Mike Johnson’s call to technologists and venture capitalists at the forum to guide and assist the government underscores the critical role of public-private partnerships in navigating the technological labyrinth.

As the U.S. and China continue their relentless pursuit of technological dominance, the narrative is clear: this is not a sprint with a finish line but a marathon without end, defining the future of global power, security, and technological innovation.

Big Mike Begs

No, not that Big Mike… House Speaker Mike Johnson (R?-LA), who implored the technologists and venture capitalists at the forum to help the government wherever they can.

“There are not many industries, not many leaders and experts, who we just openly plead for your counsel, but I am doing that here today,” said Johnson. “Because a lot of the people who are of goodwill here, who want to do the right thing, could use some of your guidance along the way to make sure that we don’t step on any land mines that we don’t see. You have a much better vision, I think, on a lot of that than we do.”

The artificial intelligence (AI) division of Microsoft in Asia has been working on the program by compiling real single images of people, real audio, and in many cases various control signals such as the movements of people’s faces as they talk. Using all this data, Microsoft Research Asia is generating moving images of fake people that could someday replace actual newscasters and podcasters – at least those with so little personality and soul that robots could basically do their job.

“Our premiere model, VASA-1, is capable of not only producing lip movements that are exquisitely synchronized with the audio but also capturing a large spectrum of facial nuances and natural head motions that contribute to the perception of authenticity and liveliness,” the research team wrote in a paper about these latest developments.

“The core innovations include a holistic facial dynamics and head movement generation model that works in a face latent space, and the development of such an expressive and disentangled face latent space using videos. Through extensive experiments including evaluation on a set of new metrics, we show that our method significantly outperforms previous methods along various dimensions comprehensively.”

(Related: Microsoft says that female video game characters need to be fatter and uglier to better reflect society and “avoid stereotypes.”)

High-quality deepfakes

The methods used by Microsoft Research Asia to develop these sort-of human-like deepfakes produce high-quality video coupled with realistic facial and head dynamics. Such video can be generated online at 512×512 with up to 40 frames per second (FPS) and negligible starting latency.

In layman’s terms, the technology is so believable that many people would probably fall for it and think these are real people on their screens. Only the most discerning will be able to tell that something is not quite right with what they are seeing.

“It paves the way for real-time engagements with lifelike avatars that emulate human conversational behaviors,” Microsoft Research Asia proudly claims.

If you are interested in seeing a few examples of these creepy AI moving and speaking images, you can do so at Microsoft.com.

“Our method is capable of not only producing precious lip-audio synchronization, but also generating a large spectrum of expressive facial nuances and natural head motions,” the company says.

“It can handle arbitrary-length [sic] audio and stably output seamless talking face videos.”

The purpose of the research is to unleash an entire society or army of virtual AI avatars, Microsoft says, but don’t worry: it’s all “aiming for positive applications,” the company insists.

“It is not intended to create content that is used to mislead or deceive,” reads a disclaimer on the site. “However, like other related content generation techniques, it could still potentially be misused for impersonating humans.”

“We are opposed to any behavior to create misleading or harmful contents of real persons, and are interested in applying our technique for advancing forgery detection. Currently, the videos generated by this method still contain identifiable artifacts, and the numerical analysis shows that there’s still a gap to achieve the authenticity of real videos.”

The alleged positive use cases for such technology read like a parody, with Microsoft claiming that it can create “educational equity” while “improving accessibility for individuals with communication challenges, offering companionship or therapeutic support to those in need …”

The powers that be are trying to make humans obsolete by unleashing human-impersonating AI and other tech-based abominations. Learn more at Transhumanism.news.

Sources for this article include:

• Microsoft.com
• NaturalNews.com