A common experience: Many brilliant scientists cannot grasp elementary philosophical distinctions. Last night I could not get a colleague to understand the difference between the "hard" (sentience, subjectivity, experience) and "easy" (reportability, information access) senses of "consciousness." Nor the difference between a definition of consciousness and various explanations of consciousness. Hypothesis: scientists tend to equate rigorous thinking with mechanistic explanation, and don't recognize that abstract concepts requires sharp analysis as well.
Thrilled to launch Project Genie, an experimental prototype of the world's most advanced world model. Create entire playable worlds to explore in real-time just from a simple text prompt - kind of mindblowing really! Available to Ultra subs in the US for now - have fun exploring!
A fly buzzes inside a virtual reality flight simulator, which scientists are using to investigate the inner workings of the insect brain.
Learn more during #InsectWeek: https://t.co/HvapQsJYn5 @NewsfromScience
Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.” Statements discouraging people from learning to code are harmful!
In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment.
As coding becomes easier, more people should code, not fewer!
Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step.
I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively.
One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that.
When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result.
Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools are continuing to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do.
[Original text: https://t.co/HdI3Jb9HmF ]
The psychological study of the mind was crucial to the creation of AI – and will remain an essential part of the technology's future. @Swinburne https://t.co/67rzu6Vlwu
Cognitive Maps for a Non-Euclidean Environment: Path Integration and Spatial Memory on a Sphere - Misun Kim, Christian F. Doeller, 2024 https://t.co/OsrARbn9sI
@Mark_Butler_MP social work, nursing, and Ed to receive $319 per week for placements. Clin Psych students have longer placements - why are they missing out? Makes no sense.
Experimental results presented in our Nature comp. sci. paper stuggest that ChatGPT (and other LLMs) can engage in both System 1 and System 2 thinking. ChatGPT's intuitions are more accurate than those of humans. https://t.co/KmHJg86O4P