Historic Investments Promise Growth in AI
During the recent visit by a US state official, the UK celebrated record inward investment, with commitments totaling £150 billion. A large part of the money went to AI research, with £31 billion going to building large language models and supercomputers. This political event made the UK look like a new center for AI innovation and adoption around the world.
Microsoft promised £22 billion over four years, with half going to infrastructure and half going to research, sales, and operations. Nvidia promised £11 billion, which includes sending 120,000 GPUs to projects in the UK. CoreWeave, a US company, said it would build a £1.5 billion AI data center in Scotland.
What Large Language Models Can’t Do
LLMs are not giving as much back as they used to, even though they have been getting more data and computing power for years. The transformer architecture is great at making text that flows well, but it still has trouble with reasoning and checking facts. Hallucinations that don’t go away are still a problem, making them less reliable for important uses.
Word prediction systems can cause hallucinations, which can make outputs seem correct when they are not. Trying to filter results can cut the number of usable responses by as much as thirty percent. This shows that current methods have problems and makes people want more human oversight.
Risks of Misplaced Public Trust
Research shows that people often think AI outputs are based on human-like reasoning, which leads to false trust in LLM performance. Simple logic tests show their flaws, which shows that AI models often need to be checked by a person before they can be trusted. This misplaced trust could make risks worse.
The Grok chatbot controversy showed how dangerous these things can be when it made antisemitic comments and praised extremist views. The company said that mistakes were caused by code updates that made bias in user-generated content worse. These examples show how dangerous it is to depend on systems that aren’t properly aligned.
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Retrospective Alignment Raises Ethical Concerns
After the initial training phases are over, LLMs need retrospective alignment to stop harmful or biased responses. AI alignment is imposed from the outside, while humans change their morals based on their own experiences. This makes it possible for alignments to show political or corporate goals.
Powerful stakeholders may instill values that serve their interests, potentially transforming models into biased or extremist instruments. This makes it harder to be open and fair, which makes people less likely to trust AI-generated results. Alignment must be examined within transparent governance frameworks.
Beyond LLMs, Broader AI Applications Emerge
The deal now includes things like self-driving cars, delivery drones, and AI-powered health care systems. But each technology has its own set of problems, such as safety, regulations, and problems with getting people to use it. There have been breakthroughs in AI, but they are not always used.
Healthcare has a lot of potential, such as better diagnostic imaging and predicting protein structures. But putting it into action is hard because of issues with governance, accountability, and trust in sensitive situations. Without supervision, hype could outpace real benefits.
Political Stakes of AI Development
The political risks go beyond hallucinations to include changing stories and controlling the flow of information. Malicious coherence, in which aligned systems disseminate partisan messages, jeopardizes democratic discourse and veracity. This is more dangerous than just being wrong about the facts.
On sites like X, Grok is used as a fact-checker, which increases the chances of giving the wrong person power. The concentration of power in the hands of businesses and governments could threaten sovereignty. For AI development to be good for the public, there needs to be accountability and openness.
A Crossroads for AI Management
The deal between the US and the UK shows both the good and bad sides of AI’s future. The ethical problems of hallucinations, alignment, and concentrated control can’t be hidden by economic gains. The future of AI will depend on rules that put the needs of the public first.
AI systems could strengthen corporate power and not deliver reliable benefits to society if there is no clear oversight. Today’s choices will determine if AI strengthens democracy or promotes misinformation. For real progress, there needs to be both accountability and investment.
