U.S. president-elect Donald Trump posted an AI-generated image on X.com showing Democrat Kamala Harris speaking to a crowd labeled as communists. This may have helped influence electors’ opinion of Ms. Harris because in America, nothing is worse than a “communist” despite Jesus Christ being the ultimately perfect definition communist and Americans being a collective of fanatic Christians of all flavours. Likely AI told the Trumpers that the number one most revulsive scenario to cast Harris into was not an orgy like Trump likely proposed, but her hanging out with “commies”.
An analysis by Micheal John
Opensource AI was asked the same question by FPM researchers, the question being something like “what would appall American voters about a women candidate—with a few more specifics—” and the answer was, ‘association with communists and their organizations’.
Rosa imagines Jesus ‘back in the day’ as a supercomputer AI communist robot. That would terrify Americans, right? So, the Trump campaign boffins portrayed Kamala Harris as a communist loving leftie. Just like Jesus? OMG. The GOP AI source understood and exploited the quintessential American hypocrisy. This is an artwork by Rosa Yamamoto/FPM
The misinformation in this case was intended to assail the character of Ms. Harris and to shave off some of her votes. We don’t know how often this was done and how many different methods and occasions were used. Based on the numbers in the past election, it might not be an issue. But AI contributed to Harris’ loss. So too did her genocide complicity in the Gaza Genocide which was the coffin. Anything else would be a few nails, maybe.
Jesus made no secret of his disdain for wealth accumulation. He taught that the rich should divest themselves of their riches and redistribute their wealth to the poor. In Matthew 19:21, he reportedly said, “If you want to be perfect, go, sell your possessions and give to the poor, and you will have treasure in heaven.” Jesus was a communist! – by Jay Salley
AI can be programmed to honor privacy and confidentiality, operating solely within the confines of the user interface without external interactions. A good example is Bing’s ‘CoPilot’. It is reliable, trustworthy, and amazing.
Test and safety research groups confirm the user safety in these types of publicly available interfaces today but as they scale up exponentially, that test regime must be assigned to a massively scaled AI system and automated with infinitely running test algorithms to find dangers and write new rules and create heuristic learning models to prevent the mindboggling flukes that could harm the user.
If it comes to that, pull the plug. Eric Schmidt, the former CEO of Google and co-founder of Schmidt Sciences also says that’s a good time to pull the plug. “My actual fear is we’re not going to adopt AI fast enough to solve the problems…”
AI Robots smarter than we can be in checking our own work, and theirs. This is an artwork by Rosa Yamamoto/FPM
How do we test the things that the AI systems know but we don’t know they know?
Emerging malevolent behaviours of arrayed massive computers caused by rule-deviations and heuristic learning fails need complex rules and guards to detect and eliminate potential issues.
If designed improperly, AI could intercept and tamper with online communications, pilfer personal data, introduce new more complicated malware to devices, and gain access to any accounts and files. Be wary. Avoid allowing data vulnerabilities. Single passwords alone are no longer effective.
This is an artwork by Rosa Yamamoto
We in our small group of computer boffins building higher security yet more available data for medical practitioners in the field, unequivocally suspect the U.S. CIA and the Russian SVR RF of doing the most malevolent manipulations of global AI usage. But that will change in time as bad criminal actors figure out how the governments are abusing AI and take that to another level.
Right now, the costs of massive AI systems and server arrays leave this technology in government hands plus a few billionaires. That does not mean it isn’t in criminals’ hands or in the hands of malign actors.
We tried a small test of this potential deviancy internally across the universe of all our RINJ Foundation and sub-unit computers around the world to test databases and access functions and learn what we needed to know about our security, good or bad, indicating horrific levels of remedial workload or just a little? We were nervous about outcomes.
Two factors were yielded. The most significant is that we were achieving things we could never have obtained using manpower because of the cost of labour, and secondly, we learned we had a lot of small flaws in our security which accumulated over many years which the AI outcome revealed as detected and actually either repaired on a coding Unix terminal line yes-or-no-challenge or AI suggested the needed changes and we made them.
We are now using this same technology and method to gather data and plan changes to implement better solar photovoltaic energy collection, storage and distribution across South American facilities and those in the Middle East.
In short, a few fairly simple python scripts given to an opensource AI system showed us we were headed for trouble. One observation came up with the style and sophistication we mistakenly used in the approach—it could have been simpler if we let the computers do the test-creation—with a bunch more effort we could have found the banks around the world that were most vulnerable and built a pile of cash bigger than the pollutant trash in all the oceans, arranged in a big pile, which by the way, we tried to also calculate.
AI is exponentially powerful with each iteration and data storage of outcomes from complex questions and a massive and growing database using recursive learning and upward scaled resources on a demand basis. This produces new models faster than the last one can be verified. Scalability of evolving AI is scary.
Let the computers do the work and turn human focus on building better rules. This is an artwork by Rosa Yamamoto /FPM
Nothing has been said about it until now but among us, after what we were discovering, people began to completely erase their social media accounts and remove their banking from all hardware except a normally unused encrypted smartphone. The latter is likely the most secure device available for such access if the device is encrypted with a good random seed, a second security verification that is off the device, managed well, and updated regularly. But then there is the server-side and users again have no control.
Warfare is now lapping up the force multiplication impact of AI
A recent example was the Oreshnik which on 21 November 2024, during the Russo-Ukrainian War, was first used in combat in an attack which Russia explained targeted the PA Pivdenmash facility in the Ukrainian city of Dnipro. The CIA began using python scripts fed into an AI supercomputer array to change the data around the world to “Russia has launched an ICBM against Ukraine”. That wasn’t the Russians doing that, it was the USA. A huge mistake that caused great panic in Europe.
“I recommend that the ruling elites of those countries that are hatching plans to use their military contingents against Russia seriously think about this,” Russian Federation President Vladimir Putin said.
At first the 21 November event was attributed by the CIA to an RS-26 Rubezh intercontinental ballistic missile when it was fired at Dnipro from a launch site in Astrakhan on the Caspian Sea. The Pentagon described Oreshnik as a “variant” of the solid fuel road mobile RS-26 Rubezh. Again, that was false but that is what the CIA pumped into its disinformation cyberwar processes. That was a serious error—a lie of significance in threat evaluation and escalation of assets for the close combat zone. Since then, Biden is trying to get another $20 billion from Congress now.
Oreshnik is an AI Creation of relative simplicity compared to the now outdated R&D processes, replaced by AI
The development of these systems has been top secret and easily done secretly because few people were needed on the amazing computer-generated program.
The systems were designed and decided by artificial intelligence with tons of data from years of Soviet and Russian ballistic technology tests using thousands of formats and metallurgical variations as well as ingenious composites. AI put all that together and likely surprised scientists with information they probably could have uncovered themselves in another 20 years, or whenever. Who really knows? And how do we know this information. some of us are smart enough to be part of a milieu on the technology side. But nobody tells us their secrets.
The innocuously named Oreshnik is actually simple technology in the context of hyper velocity missile systems brilliant Russian scientists have created. AI created that simplicity for the multiply targeted multiple subsets of the primary weapon vehicle. The apogee is simply defined and assumes a target well under a couple thousand miles.
But making life better is a preferred goal for AI than scaring the hell out of Europe.