A theory of FIML

FIML is both a practice and a theory. The practice  is roughly described here and in other posts on this website.

The theory states (also roughly) that successful practice of FIML will:

  • Greatly improve communication between participating partners
  • Greatly reduce or eliminate mistaken interpretations (neuroses) between partners
  • Give partners insights into the dynamic structures of their personalities
  • Lead to much greater appreciation of the dynamic linguistic/communicative nature of the personality

These results are achieved because:

  • FIML practice is based on real data agreed upon by both partners
  • FIML practice stops neurotic responses before they get out of control
  • FIML practice allows both partners to understand each other’s neuroses while eliminating them
  • FIML practice establishes a shared objective standard between partners
  • This standard can be checked, confirmed, changed, or upgraded as often as is needed

FIML practice will also:

  • Show partners how their personalities function while alone and together
  • Lead to a much greater appreciation of how mistaken interpretations that occur at discreet times can and often do lead to (or reveal) ongoing mistaken interpretations (neuroses)

FIML practice eliminates neuroses because it shows individuals, through real data, that their (neurotic) interpretation(s) of their partner are mistaken. This reduction of neurosis between partners probably will be generalizable to other situations and people, thus resulting a less neurotic individual overall.

Neurosis is defined here to mean a mistaken interpretation or an ongoing mistaken interpretation.

The theory of FIML can be falsified or shown to be wrong by having a reasonably large number of suitable people learn FIML practice, do it and fail to gain the aforementioned results.

FIML practice will not be suitable for everyone. It requires that partners have a strong interest in each other; a strong sense of caring for each other; an interest in language and communication; the ability to see themselves objectively; the ability to view their use of language objectively; fairly good self-control; enough time to do the practice regularly.

Wolfram’s ‘computational irreducibility’ explains FIML perfectly

[In mathematics, a ‘computation’ is the process of performing mathematical operations on one or more inputs to produce a desired output. A problem in analyzing human psychology arises when we understand that human psychology cannot be reduced computationally. The ‘computational irreducibility’ of human psychology does not mean, however, that there is no way to probe it and understand it. In the following essay, I show how FIML practice can greatly enhance our understanding of our own psychologies and, by extension, the psychologies of others.

Rather than rely on tautological data extractions or vague theories about human psychology, FIML focuses on small interpersonal exchanges that can be objectively agreed upon by at least two people. These small exchanges correspond to what Wolfram calls ‘specific little pieces of computational reducibility’. When we repeatedly view our psychologies from the point of view of specific little pieces of computational reducibility, we begin amassing a profoundly telling collection of very good data that shows how we really think, speak, and act.]

FIML is a method of inquiry that deals with the computational irreducibility of humans. It does this by isolating small incidents and asking questions about them. These small incidents are the “little pieces of computational reducibility” that Stephan Wolfram remarks on at 45:34 in this video. Here is the full quote:

One of the necessary consequences of computational irreducibility is within a computationally irreducible system there will always be an infinite number of specific little pieces of computational reducibility that you can find.

45:34 in this video

This is exactly what FIML practice does again and again—it finds “specific little pieces of computational reducibility” and learns all it can about them.

In FIML practice, two humans in real-time, real-world situations agree to isolate and focus on one “specific little piece of computational reducibility” and from that gain a deeper understanding of the whole “computationally irreducible system”, which is them.

When two humans do this hundreds of times, their grasp and appreciation of the “computationally irreducible system” which is them, both together and individually, increases dramatically. This growing grasp and understanding of their shared computationally irreducible system upgrades or replaces most previously learned cognitive categories about their lives, or psychologies, or how they think about themselves or other humans.

By focusing on many small bits of communicative information, FIML partners improve all aspects of their human minds.

I do not believe any computer will ever be able to do FIML. Robots and brain scans may help with it but they will not be able to replace it. In the not too distant future, FIML may be the only profound thing humans will both need to and be able to do on their own without the use of AI. To understand ourselves deeply and enjoy being human, we will have to do FIML. In this sense, FIML may be our most important human answer to the AI civilization growing around us. ABN

It’s possible an AI-induced financial catastrophe could happen as early as this year

One AI, called Truth Terminal, has recently made the news by becoming the first AI millionaire by promoting crypto currencies it was gifted. While not fully autonomous yet, it’s quite likely by later this year, some AI agents—not dissimilar from viruses—will be able to independently wander the internet, causing significant change in the real world.

AI could have huge ramifications for the financial world. Let’s examine one wild scenario—which I call the AI Monetary Hegemony—something that could possibly already happen in 2025.

A fully autonomous AI agent is programmed to go on to the internet and create cryptocurrency wallets, then create crypto currencies, then endlessly create millions of similar versions of itself that want to trade that crypto.

Now let’s assume all these AIs are programmed to try to indefinitely increase the value of their crypto, something they accomplish in similar ways humans do—by promotion and then trading their cryptos for higher values. Additionally, the autonomous AIs open their crypto to be traded with humans, creating a functioning market on the blockchain for all.

This plan sounds beneficial for all parties, even if people decry that the AI created-crypto currencies are essentially just Ponzi schemes. But they’re not Ponzi schemes because there is an endless supply of AIs always newly appearing to buy and trade more crypto.

It doesn’t take a genius to realize the AIs endlessly replicating and acting like this could quickly amass far more digital wealth than all humanity possesses.

link

Trump Likely to Unveil ‘Stargate’ – Domestic Infrastructure via AI Data Centers

According to leaked media reports [CBS HERE] President Trump is going to unveil a private sector launch of $500 billion to create data processing centers for a national Artificial Intelligence (AI) network. The network will be known as “Stargate.”

SoftBank Chief Executive Officer Masayoshi Son, OpenAI head Sam Altman and Oracle co-founder Larry Ellison are expected to be at the White House for the announcement.

The initial launch will consist of $100 billion for the first Stargate (AI processing center) in Texas. Other projects in other states will come online in the future.

link

The inevitable arriving more or less as predicted. Only DARPA can stop or slow technology and it can only do that by hiding it and owning all the researchers. AI is out in the open. KOBK absolutely 100% demands USA continue developing it. Not saying I like it. Just saying this is how power is wielded in this world. Altman and Ellison are both Jewish. Son is Japanese. The only defense we the plebs have is free speech and as many of us as possible being on more or less the same page. I do not believe that is near enough but it is all we have. ABN

The CDC, Palantir and the AI-Healthcare Revolution

The CDC’s Center for Forecasting and Outbreak Analytics (CFA) has partnered with the CIA-linked Palantir to cement the public-private model of invasive surveillance in “public health,” all while pushing the U.S. national security state and Silicon Valley even closer together.

The Pentagon and Silicon Valley are in the midst of cultivating an even closer relationship as the Department of Defense (DoD) and Big Tech companies seek to jointly transform the American healthcare system into one that is “artificial intelligence (AI)-driven.” The alleged advantages of such a system, espoused by the Army itself, Big Tech and Pharma executives as well as intelligence officers, would be unleashed by the rapidly developing power of so-called “predictive medicine,” or “a branch of medicine that aims to identify patients at risk of developing a disease, thereby enabling either prevention or early treatment of that disease.”

This will apparently be achieved via mass interagency data sharing between the DoD, the Department of Health and Human Services (HHS) and the private sector. In other words, the military and intelligence communities, as well as the public and private sector elements of the US healthcare system, are working closely with Big Tech to “predict” diseases and treat them before they occur (and even before symptoms are felt) for the purported purpose of improving civilian and military healthcare.

This cross-sector team plans to deliver this transformation of the healthcare system by first utilizing and sharing the DoD’s healthcare dataset, which is the most “comprehensive…in the world.” It seems, however, based on the programs that already utilize this predictive approach and the necessity for “machine learning” in the development of AI technology, that this partnership would also massively expand the breadth of this healthcare dataset through an array of technologies, methods and sources.

Yet, if the actors and institutions involved in lobbying for and implementing this system indicate anything, it appears that another—if not primary—purpose of this push towards a predictive AI-healthcare infrastructure is the resurrection of a Defense Advanced Research Projects Agency (DARPA)-managed and Central Intelligence Agency (CIA)-supported program that Congress officially “shelved” decades ago. That program, Total Information Awareness (TIA), was a post 9/11 “pre-crime” operation which sought to use mass surveillance to stop terrorists before they committed any crimes through collaborative data mining efforts between the public and private sector.

link

Unique AI case, first in the country — North Carolina man used AI songs and bots to steal $10M in streaming royalties, feds say

In the first case of its kind in the country, a Charlotte-area man is charged with using AI to manipulate music streaming platforms to siphon off over $10 million in royalties, federal authorities said Wednesday.

Michael Smith, 52, of Cornelius, was arrested Wednesday and is charged with wire fraud conspiracy, wire fraud and money laundering conspiracy, the U.S. Attorney for the Southern District of New York said in a news release.

Each charge carries up to 20 years in prison if convicted. Smith’s complex scheme used automated programs, or “bots,” to stream songs created with artificial intelligence, according to an indictment released Wednesday by the U.S. Attorney’s office and the FBI’s New York Field Office.

He then used the bot accounts to stream those songs billions of times across multiple streaming platforms to boost his royalties to over $10 million, according to the indictment.

link

Walmart AI reorganizes product catalog 100x faster than humans

CEO Doug McMillon said on the post-earnings call with analysts that Walmart was finding “tangible ways” to leverage generative artificial intelligence to improve customer, member and employee experiences.

One area where Walmart is using generative AI is its product catalog, where it’s used multiple large language models to create or improve over 850 million pieces of data in the catalog.

“Without the use of generative AI, this work would have required nearly 100 times the current head count to complete in the same amount of time and for associates picking online orders, showing them high-quality images of product packages helps them quickly find what they’re looking for,” McMillon said, according to an AlphaSense transcript.

McMillon added that customers and members will get more help shopping on Walmart’s website and app with a new AI shopping assistant that will provide shopping advice and ideas.

link