Can AI predict Donald Trump? Is that a risk? What can be done about it?

November 22, 2024

Can AI predict what Donald Trump will say or do? Is that a risk to our National Security? What can be done about it?

Yes, of course, absolutely, no doubt about it. AI transformer based language learning models can easily predict what Trump will say or do in any given situation. That is a risk to National Security as everything else related to Donald Trump and to our Constitutional framework of assigning so much power to one human being. There is not much to do about it other than continue increasing the use of AI in all societal aspects including governance and decision-making. 

In the future, the use of AI may be institutionalized use across government. Governmental decisions could be made by committees informed by AI rather than by a single individual. Future Constitutional amendments may replace the President with the Presidents (plural). The Presidential Committee (PC) may be composed of a minimum of three decision-makers: a man, a woman, and an AI chatbot. All three could be democratically elected by the people. Their executive decisions will most likely be of higher quality than the decisions of just one individual. 

In the meantime, another alternative is for Presidents to delegate more actions to committees that include AI input into their decision-making. Another approach would be to delegate some decisions completely to AI chatbots. Yet another alternative would be to randomize certain decisions such as by using lottery systems and games of chance.

To Trump or Not to Trump: What would Jesus do?

In the Christian world, we are often invited to reflect on what would Jesus do in any given situation. We use our knowledge of the gospel to predict what would Jesus say or do. We know the stories of what JC said and did in a different array of situations. We use that knowledge to identify behavioral patterns and extrapolate them into predictions. The more we know about what JC said and did, the easier and potentially more accurate the prediction becomes. The same applies to every human, including of course the President of the United States. 

A President like Donald Trump has received significantly more contemporaneous media coverage (both traditional and social) than Jesus Christ ever did. It is easier for AI to learn all the data that is publicly available about Trump to identify his mental patterns and cognitive proclivities. Well trained AI transformer chatbots can make super accurate predictions about what Trump would say or do in any given situation. 

Learning from Mistakes

AI chatbots using language learning models using the transformer AI architecture can easily predict the thoughts, words, and actions of President Trump. The predictions are not 100% accurate. However, every mistake makes the AI predictive power better. The mistake becomes a lesson learned in the gigantic data set. 

Unlike humans, AI machines are not concerned about making mistakes, simply learning from them. Humans are afraid of the consequences of mistakes, which oftentimes interfere with the ability to recognize mistakes, admit them, and learn from them. Trump, for example, for many reasons (personality, liability, business, political, criminal consequences, etc) never admits to any mistake. Any wrongdoing is denied; every decision made was always great. That is part of what AI can easily spot as a pattern to make predictions about Trump's behavior and decision-making process. Trump is readable and predictable.  

Alternatives to Trump's Predictability

Alternatives to Trump's predictability (and potential vulnerability) include delegating some decisions to others. These others can be other humans, AI chatbots, and random lottery systems. Combining those delegation systems can lead to decisions that are not only less predictable than Trump's own, but potentially of significantly higher quality than Trump's decisions. Of course, he would never concur with that, which again showcases how easily predictable our President is. 

Some things become better with age; some simply deteriorate

As Trump becomes older and more senile in his cognitive and decision-making processes he may become even more predictable for AI machines. Humans tend to obviate the obvious. AI machines identify the patterns and connect the dots absent human cognitive shortfalls. AI can process all data known about Trump, all data about white males in their 80s, baby boomers, New Yorkers, felons, racists, misogynists, narcissists, fathers, grandfathers, leaders, salespeople, etc, etc. to complete the profile of Trump. 

For sure, AI is more knowledgeable than Trump. Is it also smarter? Maybe not yet.

Everyone knows that AI chatbots like ChatGPT are significantly more knowledgeable than President Trump, or that any other human for that matter. However, they are not necessarily smarter, at least not yet. There's a distinction between being knowledgeable and being smart. If you are smart, you know the difference. Being knowledgeable entails having lots of data "savings" in your brain just like having lots of money in the bank or other resources at your disposal. Being smart entails having the wisdom and ability to use those resources wisely. Some people have more resources than others yet are not necessarily smarter in their decision-making.  

AI Smart Supremacy is a Matter of Time

It's undisputed that AI chatbots are more knowledgeable than humans. The jury is still out on whether AI is already smarter than a human. It seems to be a matter of time for AI to become smarter than humanity. It seems to be a question of when rather than of "if". AI will be become smarter than humanity. This is simply a matter of processing capacity, which AI can continue increasing and expanding through digital technology while humans continue trapped by evolutionary biology. 

Transforming the Future

AI will transform our future in many ways. The future has not been created yet. We, all of us in this creative matrix or "creatix" that we call the universe, are creating the future right now as we move in spacetime. Every little action counts. There's no predetermined fate. You are not watching a prescripted movie. We are filming it as we go. 

From Fiction to Science. From Science to Science Fiction

Human civilization began with fiction, then added science, and is now actively pursuing science fiction. We rose to the top of the animal food chain leveraging our imagination into motivation for collaboration, We invented language, our greatest invention of all time. With language, we created images representing what exists and imaginations of things that do not exist outside our imagination. God and money are the top two human creations after language. We became victims of our creations such as when humans get obsessed with gods (or god) and with money (or power). Eventually we developed science (measured observation) as a way of distinguish between what is imaginary and what is real independent of our imagination. We are now using the technologies (tools and methods) developed from science to create the things that we imagined (e.g. metallic horses like cars; flying birds like planes; computers; AI; etc). Within the next centuries we will be creating almost immortal demigods, which we may call AI Sapiens. Ultimately, we can create an almost omniscient, omnipotent, and virtually omnipresent singularity as the God that many imagine rules humanity and Earth. 

The Need for New Constitutions or New Constitutional Amendments

Coincidentally, President Trump may be the kick starting soon the "need" to amend our Constitution. The "National Emergency" over illegal immigration will most likely be used to test the waters about Constitutional amendments redefining citizenship and restructuring the balance of powers. As AI can easily predict, Constitutional amendments proposed by or endorsed by Trump would be geared at making him stay in power for life. It is impossible to predict what amendments will actually succeed. Nonetheless, it is conceivable to think that sooner or later, both here in the US and around the world, constitutional amendments or new constitutions altogether will begin to address the issues of AI. Needless to say, our Founding Fathers never saw AI coming. It's here. It's here to stay. Nations that begin adding AI capabilities to their constitutions and structures of government will fare better in the long run. Governments (national and local; big or small) that begin enacting laws and regulations requiring AI consultation and the use of AI in decision-making will fare better. You bet. AI transformers and language models are revolutionizing human societies. They're just the beginning of the AI Revolution. 

What is a Transformer in AI?

A transformer is a type of deep learning model architecture introduced in the 2017 paper "Attention Is All You Need". It has revolutionized natural language processing (NLP) and other fields of AI. Key Features of a Transformer:

Self-Attention Mechanism: The transformer uses self-attention to weigh the importance of different words or tokens in a sequence, allowing it to focus on contextually relevant information. For example, in the sentence "The cat sat on the mat," the word "cat" is more relevant to "sat" than "the."

Encoder-Decoder Architecture: The encoder processes the input data (e.g., text) and creates a contextual representation. The decoder generates output (e.g., translated text or next words in a sentence).

Parallel Processing: Transformers process entire sequences simultaneously, making them much faster and more efficient.

Transformers are the foundation of many modern AI models, including GPT (used by ChatGPT), BERT, and T5, powering applications like translation, summarization, and question-answering.

Attention is All You Need

The 2017 paper "Attention Is All You Need" introduced the Transformer model. The paper proposed a programming architecture that would rely on "self-attention mechanisms" to process data. The attention approach entails giving more attention (more importance) to different parts of input sequence regardless of their order. Input is processed in parallel rather than sequentially. 

The model learns to put more emphasis or attention on data has been identified as having more importance or relevant weight. That mimics how the human brain processes language, for example. We focus on what is important in any given context. The Transformer model effectively handled long sequences by focusing on all words in a sequence simultaneously in parallel neural processing. The model had amazing results in tasks like machine translation, outperforming traditional models on benchmarks like language translation.

The Transformer architecture became the foundation of almost all modern natural language processing (NLP) models such as GPT, T5, and Transformer-XL. Transformers were then extended to fields like image processing (e.g., Vision Transformers) and protein folding (e.g., AlphaFold).

In sum, the "Attention Is All You Need" paper introduced a paradigm shift in AI by proving that the "attention" model (relevance-weighted parallel processing) outperforms traditional models of sequential processing without assigned weights of relevance. Its influence has reshaped AI research and applications across diverse domains.

The paper "Attention Is All You Need" was authored by a team of researchers from Google Brain and Google Research. The authors are: Ashish Vaswani; Noam Shazeer; Niki Parmar; Jakob Uszkoreit; Llion Jones; Aidan N. Gomez; Łukasz Kaiser; and Illia Polosukhin. The paper was presented at the 31st Conference on Neural Information Processing Systems (NeurIPS 2017).

What is a Language Model in AI?

A language model (LM) is an AI system designed to understand, generate, and work with human language. LMs can predict the next word, phrase, or sentence based on the context provided. Language models are trained on vast amounts of text data from books, websites, and other sources to learn grammar, context, meaning, and relationships between words.

Earlier models were merely statistical, applying probabilities of word sequences. Modern models using  deep learning, especially transformers, learn the relevant importance of data in different data sets (context) and can provide more accurate and nuanced predictions.

Models are used for text completion, translation, text generation, contextual analysis, and answering  questions by both predicting what a human answer would be and creating human-like alternatives based on contextual data patterns. 

In summary, transformers are the backbone technology, while language models are specific applications of this technology in understanding and generating human language.

The AI Revolution is Just Beginning 

More to come about it here. You better stay tuned to Creatix if you don't want to miss out.

Now you know it. 

Live well. Die better. Enjoy. Remember that life is not a problem to be solved. The solution would be death. Life is an opportunity to embrace while it lasts. 

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