> Is it fair to say that AI and ML are synonymous now in 2023? Or are there people who are still actively working on non-ML techniques for building AI?

AI means "I am a lay person or a media person talking to lay people".

ML means "I know what I'm talking about".

AGI means "I know what I'm talking about but I don't know what it is or how to build it".

The term 'AI' followed a previous hype-then-disappointment curve and got a bad name. Researchers restricted themselves to "things that worked" and called it Machine Learning to imply that we are teaching models and they are learning which is obviously true, rather than implying "this thing is intelligent" which it probably isn't.

Side topic: humans keep moving the bar on what counts as intelligent. It used to be "can play chess" and it then was "can play Go and hold a conversation" and then it was "can draw and show creativity". Humans will keep moving the bar on the definition of intelligence for as long as (humanly) possible.

Ex-software freelancer here.

> First thing is that as a freelancer you're not part of "the team". This can be good or bad for you, I think it's fantastic.

Agreed, but with a few caveats:

• You always need a plan for your contract to end, including early. (Never happened to me, but I always planned for it).

• Companies will eventually try to treat you like staff: assuming you'll always be there and they can tell you what to do rather than asking if you'll do something. At this point you need to start telling them about the break from them you are about to be taking.

> In my experience most small companies won't have use for you. For one, you'll be more expensive than their employed staff, but they also want to keep that know how in house.

Disagree here: small companies struggle to get a wide enough set of skills, and they also have projects that need finishing without expanding their committed outgoings.

There are two major downsides to freelancing:

• Location. If you are not in a very big tech city you will have to frequently relocate, or work primarily from home (in which case you are competing with very, very cheap people).

• Skills. Companies do not give you time to learn the next big thing. You are expected to turn a profit for them from day 1. If they are going to be investing in their staff learning new things, it will be with staff they expect to stick around.

> Another thing to keep in mind: Do not go into this for the money. If you factor everything in: Vacation, sick days, hardware, licenses, pension/retirement (rule of thumb: 30% of your net income) etc. it doesn't come out that far apart.

Agreed.... depends how much you value flexibility and time to work on your own projects.

As regards finding work: agencies are essential at first, tell everyone you meet you are a freelance software guy (keep it vague: they'll probe if they need someone), friends and contacts works great but not at first, try to find a "social technology hub" in your city. These are clubs that are frequented by people who work at the big tech places and socialise, this might be a hackerspace or an exercise club. They are not always easy to find.

And you trust the governments of the world to make and impose these decisions on us? Because they have such a good track record so far?

> So, why this techology and equipment still open to everyone, without any regulations or limitations?

Mostly AI has been used for good rather than harm. Thus far it is a net good, so you are trying to outlaw a good thing because of an unproven future possibility of a bad thing. But you're ok with biotech? Nanomaterials? Chip research?

But let's suppose that you in the (let's guess) US decide to outlaw AI.

The US now falls behind Europe and China in developing and understanding AI technologies, with no way to get back in the game.

Ok, so let's say Europe agrees with you, then what?

So let's say even China agrees with you....... so now the only people developing AI are the underground illegal communities, and the mainstream researchers no longer even know what AI is.

> So, why this techology and equipment still open to everyone, without any regulations or limitations?

What, exactly, do you propose as an alternative? How do you keep it in universities (who publish in public journals)? And should you?

> How's that, a few seconds of Clip of The fruit was more than enough for my brain to easily identify it from meters away but a ML would need so many of the same clips.

Short answer: we don't know.

Long answer: you have much more compute available than most classification projects. Billions of neurons, thousands of synapses each, and a neuron is a lot more capable than a transistor. Even if we organised enough compute to be human capable, we don't know how to organise it to be human capable. You have an exceptional one-shot classification capability because you are extremely good at classifying objects (especially natural food for obvious reasons), partly because you are so good at recognising what something is not.

You are also cheating a little: you know that dragon fruit is a fruit, and you are in a mall. You also know what all the other common fruits are, so your brain sees an item and instantly you know it's in a mall therefore it is probably food, and it's not something you recognise as normal. So you are now comparing "unknown thing" against "list of rare things that don't get seen much". Less satisfyingly you might have gotten lucky.