Luis García de la Fuente

Hopefully, sooner or later, people will realise that the current question-answer approach of custom GPTs and LLMs is not exactly the way to achieve AGI (artificial general intelligence).

Asking a machine questions and getting answers has more to do with the traditional database approach than with real intelligence. In fact, the whole “prompt engineering” thing is the exact opposite of AGI.…

Many experts proclaim that we’re on the verge of creating artificial intelligence that will allow machines to take over everything. Others seem terrified by the possibility of humans becoming obsolete.

The media and public discourse are filled with both utopian and dystopian visions of AI, often presenting it as an imminent revolution that will dramatically reshape society.…

AI in the Workplace: Boosting Productivity or Just Hype?

Upwork Study Finds Employee Workloads Rising Despite Increased C-Suite Investment in Artificial Intelligence

A new study from Upwork shows that people have very different ideas about AI. While 96% of executives think AI makes them more productive, 77% of workers think it has made their jobs harder and less efficient.…

Many are still selling the idea of AI as some kind of hybrid between a conscious encyclopaedia and an electronic Leonardo da Vinci.

These toxic ideas of AI taking care of almost everything and firing or killing everyone have burdened the AI industry for several years due to failed expectations.

There won’t be AGI (artificial general intelligence) any time soon.…

AI is often touted as the next big productivity booster, with Workday predicting it could add £119 billion a year to the UK economy.

The potential lies in saving significant time for managers and employees by handling repetitive tasks, which could boost engagement.

However, overcoming issues of trust, governance and a lack of AI education are critical to realising this potential.…

An LLM may need billions of tagged documents to learn to speak. Humans learn differently – a baby needs far fewer interactions to learn a language. And yet AI often trains faster for specific tasks. Why does this happen?

AI excels at well-defined, iterative computations within clear boundaries. This results in different learning processes:

• AI can discover and extract knowledge from large, homogeneous data sets (low entropy data).

Experts and the media claim that we are on the brink of artificial intelligence (AI), where machines will start to think like humans and take care of everything. Let’s see why this is nonsense.

First, consciousness and meaning are not computable processes (Penrose). This is why computers are very good at saying what happened, but not at saying why it happened, creating new rules, or designing experiments to validate hypotheses.…

The problem with AI today is not that we don’t know how to implement it. The problem is that, for the most part, we don’t have the domain-specific data to train it on (legally). This is where companies like Google and Facebook have an advantage. At least for consumer data.

The technology is not the advantage. The data is. The tech is available to anyone who knows high school level Python and can rent cloud servers, but only the big guys have the data.…