Look at what clients have stated as their hourly range or project budget and decide if it's reasonable for the work involved. Then look at what they've paid others in the past to get an idea of what they are willing to pay. Apply only if it looks worth it because we now have to buy connects to apply. Expect that most of the time you won't hear back because they got 50+ applicants and won't take the time to contact all of them.
If I know technology which I am using llm for then llm helps me to do it faster. If I am not familiar with technology then llm helps me to learn it faster by showing me win the code that it generates which part of technology is important and how it works in real examples. But I do not think it is helpful and I would say it may be dangerous depending on task you do if you do not know technology and also do not what to learn it and understand how generated code works.
AI is not a bubble and already bring real benefit and will bring even more in future. It will spread more and at some point it will became so common that we will just use it and just talk less about it. For example, same happened with mobile phones, at first everybody was talking about them but now everybody just use them and does not talk much about it.
Apps were a bubble. Cloud was a bubble. WWW was a bubble. Houses were a bubble. Bubble tea was literally bubble.
Things can be beneficial and a bubble at the same time. Useful things in short supply are expensive. It's a bubble when someone buys a thing for expensive in a gamble to sell it off to someone else for more expensive. It's not a bubble when a house seller sells a house to someone who wants to live in one. It's a bubble when they're selling to a seller, who sells to another seller, and so on.
Also I think one oddity with AI is that it came in very cheap. So it's hard to make it a bubble. But I guess grifters are stockpiling the shovels without realizing that everyone has one... hence OP's question.
> “One of the things I love about Sam is every day he’s calling me and saying, ‘I need more, I need more, I need more,’” Nadella told The New York Times in a recent interview.
Links that you have provided are good for learning how deep learning in general and LLMs in particular work. But if you are interested in only building the products based on existing models (like GPT models from OpenAI) you will not need those details of inner work and how those model are created. In that case learn from OpenAI documentation, Azure OpenAI documentation, Azure AI services documentation and etc.
Large Language models are pre-trained by creators on the huge data.
In many cases you do not need to do anything with LLM and you can just use it.
If they were not trained on the data that contains information that you are interested then you can use technique called RAG (Retrieval-Augmented Generation).
You also can do fine-tuning which is kind of training but on small amount of data.
It is not only “just making API calls”. In many cases you need to do mote than that, like RAG, constructing prompt, data pipeline for whole process, etc.
You can't develop statistical software or novel methods without math talent, you can (and it has been done) w/out a math degree.
Many early achievers in math have done solid work prior to being awarded a degree, some have never (for various reasons) earned a degree.
eg:
Mr Malcolm James Hood was a remarkable man. He was a much-loved husband, father and brother, a loyal friend and a respected UWA lecturer. He was an eminent academic, a champion of applied mathematics, a skilled woodworker, a lover of classical music, an avid reader and a lively raconteur, to name just a few of his pursuits and passions.
Born with both great intelligence and a capacity for hard work, Malcolm forged a diverse career, beginning in aerodynamic research and moving into the emerging field of computing, before finally transitioning into lecturing and teaching.
Less well known than Clifford Cocks, locally known for the Hood & Storer Mathematical and statistical tables [1], contributed to S (precursor to R) [2], and known for post war off book work in defense R&D on bomb guidance and statistics (but not by many).
No degree, I don't know the full story, something to do with going straight into classified work and amassing an impressive body of work that was never publicly recognised .. none the less he spent the later part of his career in a math department well regarded by folk with Doctorates.
