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Writing at the intersection of AI, philosophy, and the cognitive sciences | | Get my articles for free:


Definitions, Results, Hype, Problems, Critiques, & Counter-Critiques.

In May 2020, Open AI published a groundbreaking paper titled Language Models Are Few-Shot Learners. They presented GPT-3, a language model that holds the record for being the largest neural network ever created with 175 billion parameters. It’s an order of magnitude larger than the largest previous language models. GPT-3 was trained with almost all available data from the Internet, and showed amazing performance in various NLP (natural language processing) tasks, including translation, question-answering, and cloze tasks, even surpassing state-of-the-art models.

One of the most powerful features of GPT-3 is that it can perform new tasks (tasks it has never…


I worked for 3 years at an AI company. Now I’ve decided to leave the industry indefinitely.

3 years ago the words Artificial Intelligence evoked powerful sensations in me. Entering that world felt like taking a step into the mysteries and secrets of the future. I was mind-blown by the promises of intelligent machines, capable of solving tasks forever reserved to us. I was deep-diving into the amazement of the mind through the familiar passages of technology.

I had just finished my bachelor’s in aerospace engineering and wanted to leap towards AI. It was late 2017 when I met the great Geoffrey Hinton and Andrew Ng. …


And it’s open-source!

We may be closer than we think from human-like conversational artificial intelligence.

In April 2020, Facebook released and open-sourced the — at the time — largest chatbot ever created; BlenderBot. Although GPT-3 overshadowed it, BlenderBot’s abilities were nothing short compared to OpenAI’s superstar. Facebook AI team opted for combining the super-successful trend of large transformer-based language models — BlenderBot had 9.4 billion parameters, a record at the time — with other techniques to improve performance.

Their ultimate goal was to create a chatbot capable of “asking and answering a wide range of questions, displaying knowledge, and being empathetic, personable, engaging…


Is the AI startup sector for you?

Working at an AI company is different. Being the first employee is magical.

Since I can remember, I’ve loved math and physics. I got the best grades in high school and always felt in my element. As everyone around me expected, I ended up getting a degree in engineering in 2017. Sadly, after five long years on a bumpy road of doubts and failures, I fell out of love. I wanted a change and that summer I had an epiphany: Why not combine what I did best, with what I wanted to do next? Artificial intelligence was my answer.



AI could be dangerous if we don’t do it right.

A general artificial intelligence may be far in the future, but we have reasons to be extremely careful.

For some years now, important public figures have raised concerns about the potential dangers of AI. The discourse revolves around the idea of superintelligent AI freeing itself from our control. Some skeptics argue that the scenario of AI “enslaving” us is so distantly dystopic that it isn’t worth considering. For instance, Gary Marcus ridiculed it saying that “it’s as if people in the fourteenth century were worrying about traffic accidents, when good hygiene might have been a whole lot more helpful.”



Media coverage often portrays AI as more intelligent than it is.

AI systems seem so intelligent because they give more exposure to achievements that reflect it. Reality tells us otherwise.

Every time there’s a notable breakthrough in AI, we only hear how intelligent and skilled the systems are getting. In 2012 Hinton’s team got 63% top-1 accuracy on the ImageNet challenge. A few years later, a system topped human performance by achieving a striking +90% top-1 accuracy. The news: “AI can recognize objects better than humans.” Well, no. When they tested this exact model on a real-world object dataset its performance dropped 40–45%.

Last year people went nuts over GPT-3. The…


Hard work and effort sometimes aren’t enough.

We like to believe hard work trumps everything. It’s false.

Whatever we want to achieve professionally, several factors always affect the outcomes. We love the idea of “hard work = success” because effort is a controllable factor that recognizes our merit. However, elements outside our control also influence our lives. I could train hard to play at the NBA, but at 5'11 it doesn’t matter how much I try, the odds are strongly against me.

Hard work can compensate for the lack of other factors, but only to an extent. Believing anyone could be anything just by changing the direction…


The time will come for it as it happened to symbolic AI.

Machine learning and deep learning will slowly lose their status until they get relegated to what they truly are; fancy statistical techniques.

AI has been dominated by connectionist AI — neural network-based AI — for at least two decades. From recognizing handwritten digits to mastering language, breakthroughs have been occurring one after the other. AI has been advancing so fast, the world hasn’t been able to keep pace. …


GitHub, Microsoft, and OpenAI have reached a new milestone.

When OpenAI released GPT-3 last year, people got surprised by its ability to generate code from natural language prompts. Sharif Shameem and others excitedly shared their discoveries and soon the hype — and the worry — went through the roof. But GPT-3 was nowhere near being a great programmer. It’s a notable feat that it could understand an English text and transform it into a chunk of code, but it performed mediocrely.

OpenAI and Microsoft (now backing their projects financially) saw a very promising commercial product in GPT-3’s coding abilities and soon started to develop another language model; a programmer…


The past, the present, and the future of AI.

AI has become intertwined with every aspect of our lives. For the last 60 years, countless scientists and philosophers have worked hard to advance the field to what it is today. Throughout the decades, several perspectives, approaches, and paradigms have guided AI research, and very wise people have expressed their thoughts and insights about its great quest: Conquering intelligence.

These insights have come to us in the form of cryptic, yet appealing phrases whose underlying meaning often escape us. We’re left there, nodding to a beautiful simplification of a complex soup of thought. But it takes expertise and years of…

Alberto Romero

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