I was thinking the same, a generalist looks like an even way to divest ones talents. It's a good perspective that often is talked about in effective altruism, through 80,000 hours, the question is, how should one spend their career time. There are some institutions that seem to celebrate generalist(typically problem driven organisations, philanthropy, venture capital ), while some celebrate specialists (scale driven companies)
That is indeed my question. I want to remain a generalist, mainly due to the fact that its a big part of my character and specialising often doesn't consider whether the technology will be viable in a few years. Generalising means that if a technology is no longer used, they can move into something they already have a general insight into. Also, it looks like there was once a time for example, early 90s when technology was very new, being a generalist web developer would be something valuable, or during the renaissance being a philosopher & mathematician was celebrated, but now it would be hard to balance those. It looks like being in the bleeding edge space would be rewarding for a generalist, because they are always looking for wide-range people.
This is super interesting, I think this is brilliant way to visualise the ML space, ofcourse there are some topics like topological data science that are not mentioned there like scikit-TDA. But so far its fascinating. AI Bots can help generate code aside visuals (Alphacode style)
If you are interested in Elixir, supabase is hiring and they are comfortable with unconventional hires, meaning your hobbyist approach could be a plus for them https://github.com/supabase/realtime
I would argue that computational complexity helps you branch out on many problems and fields. In terms of thinking models I would say basic understanding of theories like Moore's Law, Pareto Principle, Uncertainty principle
and Matrix Multiplication go a long way.
I guess, since i never wrote an academic paper, i do not know if it will be up to par with the methodologies, I thought every research kind of needs an advisor to offer direction, otherwise you can get rejected without knowing the reason.
Recently there was an article from Cornell about using arbitrary physical systems for training neural nets, i think a hybrid of a physical system would be more interesting in leading to breakthrough
https://news.cornell.edu/stories/2022/01/physical-systems-pe...
Obviously we cannot ignore the fast that Alex was just 19 when he started Scale, and he is able to talk with world leading experts on AI. That doesn't seem like an easy job. Although Scale earns its money from human workers who clean and annotate data
I made a short list of problems or areas in AI that I struggle to find answers on or simply understand. And I wonder if some of us also have similar issues about AI in general. The field of machine learning is growing so fast to keep track of everything evolving.
1. What is the computational complexity class of problems that humans can solve but artificial intelligence cannot?
2. Can neural nets always solve problems better than social nets (collective of people)?
3. Is there an Ecological Approach to Computer Visual Perception? JJ Gibson proposed a treatise of vision as related with objects around, so is an agent affected by its environment (similar to the concept of phenotropic computing by Jaron Lanier)
4. Does the presence of an agent affect the environment and model in Reinforcement Learning?
4.1 What is the causality of having an agent present in the environment, does its presence affect the reward system. In quantum computing each measurement affects the environment.
The early internet was preposed to have so called micro-paywalls by Ted Nelson, somehow the pioneers didn't really push for this paradigm. It would have created a generally sanity preserving web.
7 months ago, I asked natfriedman the same question, of which he responded:
"We think that software development is entering its third wave of productivity change. The first was the creation of tools like compilers, debuggers, garbage collectors, and languages that made developers more productive. The second was open source where a global community of developers came together to build on each other's work. The third revolution will be the use of AI in coding.
The problems we spend our days solving may change. But there will always be problems for humans to solve."
Thats a good meditation. I think the winters were more driven by research dichotomy, for example Marvin Minsky's critique of the perceptron really slowed the research by 10 years. Advances made thus far have too much commercial relevance that companies invested dont look like they are gonna stop soon.
But its a valid point. Looks like there is more upside being in subsets of computing like quantum computing, web3, metaverse etc than being a regular front-end engineer
I like this descriptions better, considering that companies like Anthropic are working specifically on Alignment and AI Safety. Being that the team actually spun out of Deep Mind, it is interesting.
Anyone interested in building a software solution around data harvesting with data dignity can hit me up. I am working on a solution that focuses on users benefitting from this kind of transactions.
This seems to coincide with the new update on OpenAI's GPT-3 ability to reference links from searching, A new version of GPT-3 that can use a web browser to more accurately answer questions: https://t.co/bzaaP9XnZm
I simply think the company that is going to increase mass adoption is Facebook, through Oculus and The Vision Lab, Facebook has positioned Oculus such that you can only log in with FB Auth, this is kind of intentional, and perhaps we can think of the Metaverse as the next killer app.
Since the AR glasses are coming from Apple soon that is going to make AR a house-hold product like the smart-speakers and thus increase appetite for VR. VR needs to be reduced in hardware and portability to be relevant to everyday users. I think this question is interesting but not easy to answer, if you asked in 2018, when will NFT's reach mass adoption, people might have said, not until crypto itself reaches that scale, however we saw this year, they became a household name, whether bubble or not. I think once people find utility of VR except gaming, then they will take it seriously, if you could make money in VR as a remote worker for example. It's not so predictable, I think network effects can play interesting roles, and they are often black swans. If I were to stretch my imagination, it would be something like 2025, purporting that hardware would have been reduced and developed further.
I appreciate this insight, as a proponent of progress studies. It is indeed a pragmatic view of what the industry will be or should be. I believe the thing that would be also appreciated would be a pair security auditor. Most vulnerabilities in software can be avoided early on in development , I
believe this could be a great addition to Github's Security Lab securitylab.github.com/
This is obviously controversial, since we are thinking about how this could displace a large portion of developers. How do you see Copilot being more augmentative than disruptive to the developer ecosystem? Also, how you see it different from regular code completion tools like tabnine.