what would we do without europe seriously
593 karma · joined October 15, 2012
It is possible, those of us who have been nerds from an early age develop with body skeleto-muscular structure that is more forgiving to this lifestyle.
So, it is "Your salary depends on your ability to convince others that you can not be replaced." basically.
Everyone is talking about "that other developer who has less experience, they will be the one to be replaced". The more I hear, the more suspicious it gets to me. Like everyone is so sure of themselves.
the same field who has the most imposter syndrome rants, also has most people who think they can't be replaced by someone with less experience equipped with AI.
Everyone is talking about the junior, mid-tier programmer who will replaced, but not themselves. It just sounds like willful ignorance.
I wonder who is to blame, is this the current state of scientific studies? or they are actually considering these limitations in the papers, but it is ignored by public? then, is it the science communication to the public that's failing?
I feel shy even bringing up such topics among friends, because it is so far out of the common understanding that it will sound like flat-earth conspiracy to most.
People don't like to hear anything that goes like "we don't know some things at all". There is a comfort in knowing somewhere, some scientist are %100 accurate and know it all.
Most people, including myself until recently, don't realise that most of our astrophysics/space science is very limited to our immediate close promixity in the cosmological scale.
Correct me if I am wrong but we are only able to do science around the other stars in the Milky way (e.g. exoplanet observations)
Beyond Milky way, all the light from the stars are collapsed into a single blob along with the rests of the stars in their home galaxy.
Nobody knows if average consumer will be able to afford to pay for GPT-5 or whatever the next thing is.
It may not in the best interest of the companies to give access to you, if it proves valuable beyond measure.
Asked a question, got an answer, thanked or explored more -> GOOD
Asked a question, got an answer left -> OKAY
Asked a question, got an answer, corrected -> NEGATIVE
Asked a question, got an answer, got confrontational -> VERY NEGATIVE
> The article is about the use of large language models (LLMs) in the legal profession. The author argues that LLMs have the potential to revolutionize the way lawyers practice law, by automating many of the tasks that are currently done manually. For example, LLMs could be used to generate legal documents, research case law, and identify relevant precedents. The author also argues that LLMs could be used to improve the quality of legal services, by providing lawyers with access to a vast amount of information and expertise. However, the author also acknowledges that there are some challenges that need to be addressed before LLMs can be widely adopted in the legal profession. These challenges include the need for LLMs to be trained on large amounts of data, the need for LLMs to be able to understand the nuances of legal language, and the need for LLMs to be able to generate accurate and reliable results. Despite these challenges, the author believes that LLMs have the potential to make a significant impact on the legal profession, and that they are likely to become increasingly important in the years to come.
Here are some of the benefits of using LLMs in the legal profession:
LLMs can automate many of the tasks that are currently done manually, freeing up lawyers to focus on more complex work. LLMs can access and process vast amounts of information, which can help lawyers to make better decisions. LLMs can identify patterns and trends that would be difficult for humans to see, which can help lawyers to spot potential problems. LLMs can generate creative solutions to legal problems, which can help lawyers to find new and innovative ways to represent their clients. Here are some of the challenges that need to be addressed before LLMs can be widely adopted in the legal profession:
LLMs need to be trained on large amounts of data, which can be expensive and time-consuming. LLMs need to be able to understand the nuances of legal language, which can be difficult for computers to do. LLMs need to be able to generate accurate and reliable results, which can be a challenge for complex legal problems. Overall, the author believes that LLMs have the potential to make a significant impact on the legal profession, and that they are likely to become increasingly important in the years to come.
Life is complicated. You are not omnipotent. These are very basic absolute truths.
You need to actively recognise this, and make it a part of your core mentality, and mentality drives the sentiment. Sentiments drive emotions. Emotions control how you react.
I personally created some content-creating bots with GPT-4, and it succeeded to a level that I don't trust anything I see online anymore. It does a better job than me, which doesn't say much because I am an engineer not a content creator. But still, I could get same results as one with a script that I made GPT-4 write itself.
...Yes, I am losing sleep over GPT-4's performance. If you are not losing sleep over it yet, you haven't really given it a genuine try yet.
Something was lost in the internet culture. Flash was the language of web art. I don't know what is the new language for that anymore.
If anyone knows, please do tell me. WebGL? Any WebGL-powered framework? What is it?
Not only that, but their business model is completely unclear, which is the scariest part for me, as an individual developer.
Let's go one step forward, they use our texts online including this very comment perhaps. They have a software that can analyse all our texts at scale. But we don't get even the API access to this thing.
It is the most self-serving software ever released, consuming the human creativity both the content and the future relevance of it.
Think about it, how many millions of articles are posted online produced by OpenAI's GPTs to date... Good luck clearing out the training data for GPT-5.
True human content will get gradually scarce. We steer it for sure for our posts, but it is still GPTs that do the heavy lifting.
OpenAI's own classifier fails to detect GPT-4 generated text at the moment.
Found it! : https://medium.com/swlh/bicameral-mind-humanoid-robot-with-g...