We expect our doctors to be demi-gods – flawless, tireless, always right. But they are only human. Increasingly, they are stretched thin, working long hours, under immense pressure, and often with limited resources. Of course, better conditions would help, including more staff and improved systems. But even in the best-funded clinics with the most committed professionals, standards can still fall short; doctors, like the rest of us, are working with stone age minds.

Right now, most big AI labs have a team figuring out ways that rogue AIs might escape supervision, or secretly collude with each other against humans. But there's a more mundane way we could lose control of civilisation: we might simply become obsolete. This wouldn't require any hidden plots – if AI and robotics keep improving, it's what happens by default. Well, AI developers are firmly on track to build better replacements for humans in almost every role we play: not just economically as workers and decision-makers, but culturally as artists and creators, and even socially as friends and romantic companions. What place will humans have when AI can do everything we do, only better?

This was a fireside chat like no other as Ilya Sutskever, OpenAI's co-founder and chief scientist, and Jensen Huang, NVIDIA's president, sat down to discuss their shared passion for AI at the NVIDIA GTC. As a mere spectator, it was impossible not to be drawn into their conversation -- a discussion that felt like two long-time friends catching up on their new-found AI toys, alongside deep discussion around the limitations of human learning, limitations of GPT-4, and more. But what made it even more remarkable was the eerie sensation that these two giants of the AI world were almost talking to each other like an AI chatbot, exchanging ideas and insights with a level of precision and fluidity that left the layman in awe. Interestingly, the session was recorded a day after the official launch of GPT-4. The easy calm on Sutskever's face was clearly visible throughout the conversation.

San Francisco's board of supervisors recently voted to let their police deploy robots equipped with lethal explosives – before backtracking several weeks later. In America, the vote sparked a fierce debate on the militarisation of the police, but it raises fundamental questions for us all about the role of robots and AI in fighting crime, how policing decisions are made and, indeed, the very purpose of our criminal justice systems. In the UK, officers operate under the principle of "policing by consent" rather than by force. But according to the 2020 Crime Survey for England and Wales, public confidence in the police has fallen from 62% in 2017 to 55%. One recent poll asked Londoners if the Met was institutionally sexist and racist.

Elon Musk thinks you don't exist. But it's nothing personal: he thinks he doesn't exist either. Instead we are just immaterial software constructs running on a gigantic alien computer simulation. Musk has stated that the odds are billions to one that we are actually living in "base reality", ie the physical universe. At the end of last year, he responded to a tweet about the anniversary of the crude tennis video game Pong (1972) by writing: "49 years later, games are photo-realistic 3D worlds. What does that trend continuing imply about our reality?"

How should you spend your time? For centuries, people have relied on their gut instincts to figure out the answers to these life-changing questions. Now, though, there is a better way. We are living through a data explosion, as vast amounts of information about all aspects of human behaviour have become more and more accessible. We can use this big data to help determine the best course to chart.

There's a children's toy, called the See'n Say, which haunts the memories of many people born since 1965. It's a bulky plastic disc with a central arrow that rotates around pictures of barnyard creatures, like a clock, if time were measured in roosters and pigs. There's a cord you can pull to make the toy play recorded messages. The See'n Say is an input/output device, a very simple one. Put in your choice of a picture, and it will put out a matching sound.

Ever since Garry Kasparov lost his second chess match against IBM's Deep Blue in 1997, the writing has been on the wall for humanity. Or so some like to think. Advances in artificial intelligence will lead – by some estimates, in only a few decades – to the development of superintelligent, sentient machines. Movies from The Terminator to The Matrix have portrayed this prospect as rather undesirable. But is this anything more than yet another sci-fi "Project Fear"?

Artificial intelligence (AI) is paving the way towards the fourth industrial revolution with the fire domain (Fire 4.0). As a matter of fact, the next few years will be elemental to how this technology will shape our academia, practice, and entrepreneurship. Despite the growing interest between fire research groups, AI remains absent of our curriculum, and we continue to lack a methodical framework to adopt, apply and create AI solutions suitable for our problems. The above is also true for parallel engineering domains (i.e., civil/mechanical engineering), and in order to negate the notion of history repeats itself (e.g., look at the continued debate with regard to modernizing standardized fire testing, etc.), it is the motivation behind this letter to the Editor to demystify some of the big ideas behind AI to jump-start prolific and strategic discussions on the front of AI & Fire. In addition, this letter intends to explain some of the most fundamental concepts and clear common misconceptions specific to the adoption of AI in fire engineering. This short letter is a companion to the Smart Systems in Fire Engineering special issue sponsored by Fire Technology. An in-depth review of AI algorithms [1] and success stories to the proper implementations of such algorithms can be found in the aforenoted special issue and collection of papers. This letter comprises two sections. The first section outlines big ideas pertaining to AI, and answers some of the burning questions with regard to the merit of adopting AI in our domain. The second section presents a set of rules or technical recommendations an AI user may deem helpful to practice whenever AI is used as an investigation methodology. The presented set of rules are complementary to the big ideas.

My 21-year-old goddaughter, a second-year undergraduate, mentioned in passing that she watches video lectures offline at twice the normal speed. Struck by this, I asked some other students I know. Many now routinely accelerate their lectures when learning offline – often by 1.5 times, sometimes by more. Speed learning is not for everyone, but there are whole Reddit threads where students discuss how odd it will be to return to the lecture theatre. One contributor wrote: "Normal speed now sounds like drunk speed."