Much like automated machinery, it could in theory free the workers to do more important, valuable work and leave the menial stuff for the machine/AI. In theory this should make everyone richer as the companies can produce stuff cheaper and so more of the profits can go to worker salaries.

Unfortunately what happens is that the extra productivity doesn’t go to the workers, but just let’s the owners of the companies take more of the money with fewer expenses. Usually rather firing the human worker rather than giving them a more useful position.

So yea I’m not sure myself tbh

Most email spam detection and antimalware use ML. There are use cases in medicine with trying to predict whether someone has a condition early

Medical use is absolutely revolutionary. From GP’s consultations to reading tests results, radios, AI is already better than humans and will be getting better and better.

Computers are exceptionally good at storing large amount of data, and with ML they are great at taking a lot of input and inferring a result from that. This is essentially diagnosing in a nutshell.

AI is a very broad topic. Unless you only want to talk about Large Language Models (like ChatGPT) or AI Image Generators (Midjourney) there are a lot of uses for AI that you seem to not be considering.

It’s great for upscaling old videos: (this would fall under image generating AI since it can be used for colorizing, improving details, and adding in additional frames) so that you end up with something like: https://www.youtube.com/watch?v=hZ1OgQL9_Cw

It’s useful for scanning an image for text and being able to copy it out (OCR).

It’s excellent if you’re deaf, or sitting in a lobby with a muted live broadcast and want to see what is being said with closed captions (Speech to Text).

Flying your own drone with object detection/avoidance.

There’s a lot more, but basically, it’s great at taking mundane tasks where you’re stuck doing the same (or similar) thing over, and over, and over again, and automating it.

AI has some interesting use cases, but should not be trusted 100%.

Like github copilot ( or any “code copilot”):

• Good for repeating stuff but with minor changes
• Can help with common easy coding errors

• Code quality can take a big hit
• For coding beginners, it can lead to a deficit of real understanding of your code
  ( and because of that could lead to bugs, security backdoors… )

Like translations ( code or language ):

• Good translation of the common/big languages ( english, german…)
• Can extend a brief summary to a big wall of text ( and back )

• If wrong translated it can lead to that someone else understands it wrong and it misses the point
• It removes the “human” part. It can be most of the time depending on the context easily identified.

Like classification of text/Images for moderation:

• Help for identify bad faith text / images

• False Positives can be annoying to deal with.

But dont do anything that is IMPORTANT with AI, only use it for fun or if you know if the code/text the AI wrote is correct!

They are the greatest gift to solo-brainstorming that I’ve ever encountered.

_ /\ _

AI is a revolution in learning.

An interesting point that I saw about a trail on one of the small, London Tube stations:

• most of the features involved a human who could come and assist or review the footage. The AI being able to flag wheelchair users was good because the station doesn’t have wheelchair access with assistance.

• when they tried to make a heuristic for automatically flagging aggressing people, they found that people with the arms up tend to be aggressive. This flagging system led to the unexpected feature that if a Transport For London (TFL) staff member needed assistance (i.e. if medical assistance was necessary, or if someone was being aggressive towards them, the TFL staff member could put their arms up to bring the attention onto them.

That last one especially seems neat. It seems like the kind of use case where AI has the most power when it’s used as a tool to augment human systems, rather than taking humans out of stuff.

Our software uses ML to detect tax fraud and since tax offices are usually understaffed they can now go after more cases. So yes?

Lots of boring applications that are beneficial in focused use cases.

Computer vision is great for optical character recognition, think scanning documents to digitize them, depositing checks from your phone, etc. Also some good computer vision use cases for scanning plants to see what they are, facial recognition for labeling the photos in your phone etc…

Also some decent opportunities in medical research with protein analysis for development of medicine, and (again) computer vision to detect cancerous cells, read X-rays and MRIs.

Today all the hype is about generative AI with content creation which is enabled with Transformer technology, but it’s basically just version 2 (or maybe more) of Recurrent Neural Networks, or RNNs. Back in 2015 I remember this essay, The Unreasonable Effectiveness of RNNs being just as novel and exciting as ChatGPT.

We’re still burdened with this comment from the first paragraph, though.

Within a few dozen minutes of training my first baby model (with rather arbitrarily-chosen hyperparameters) started to generate very nice looking descriptions of images that were on the edge of making sense.

This will likely be a very difficult chasm to cross, because there is a lot more to human knowledge than thinking of the next letter in a word or the next word in a sentence. We have knowledge domains where, as an individual we may be brilliant, and others where we may be ignorant. Generative AI is trying to become a genius in all areas at once, and finds itself borrowing “knowledge” from Shakespearean literature to answer questions about modern philosophy because the order of the words in the sentences is roughly similar given a noun it used 200 words ago.

Enter Tiny Language Models. Using the technology from large language models, but hyper focused to write children’s stories appears to have progress with specialization, and could allow generative AI to stay focused and stop sounding incoherent when the details matter.

This is relatively full circle in my opinion, RNNs were designed to solve one problem well, then they unexpectedly generalized well, and the hunt was on for the premier generalized model. That hunt advanced the technology by enormous amounts, and now that technology is being used in Tiny Models, which is again looking to solve specific use cases extraordinarily well.

Still very TBD to see what use cases can be identified that add value, but recent advancements to seem ripe to transition gen AI from a novelty to something truly game changing.

Machine learning is important in healthcare and it’s going to get better and better. If you train an algorithm on two sets of data where one is a collection of normal scans and the other from patients with an abnormality, it’s often more accurate than a medical professional in sorting new scans.

As for the fancy chatbot side of things, I suspect it’s only going to lead to a bunch of middle management dickheads believing they can lay off staff until the inevitable happens and it blows up in their faces.

There are lots of things that are very hard to program, but people can do very easily. For example, play Go or recognize that an animal is a bird.

Machine learning/ai makes it competitively simple to make computers do some of these things, but at the cost of efficiency and speed at runtime. This is true if computers vs people as well, a human brain is much slower, less efficient, and less accurate than a calculator.

Machine learning/AI is exciting because it enables computers to quickly be trained to do tasks that were impossible or would have required years of dedicated effort. The tech world is excited about it because whole new enterprises and areas of tech may spring up, big markets that were previously out of reach.

Downsides:

• AI uses a lot more electricity. Especially for things that computers can already do, using AI is very inefficient.

• Limited control. You train an ai model to do a task, but you don’t have direct control over how it thinks. If chatgpt gives a wrong answer, they can’t just trace the program and figure out why. It takes serious effort to figure out how chatgpt answers simple questions, so figuring out how it gets complex answers or why an answer is wrong is nearly impossible at this point. This also applies to unwanted behaviors,if you had a really good history chatbot who happened to turn out racist, you can’t just turn that off. You end up having to retrain the model, or secretly add “make sure your answer isn’t racist” to every submitted prompt.

Someone I know recently published in Nature Communications an enormous study where they used machine learning to pattern match peptides that are clinically significant/bioactive (don’t forget, the vast amount of peptides are currently believed to be degradation products).

Using mass spectrometry, they effectively shoot a sawed off shotgun at a wall then using machine learning to detect pellets that may have interesting effects. This opens up for new understanding in the role peptides play in the translational game as well as a potential for a huge amount of new treatments for a vast swathe of diseases.

The legal industry is going to get turned on its head when AI can read, comment, and write contracts.

Maybe you only do an “odd bit” of mundane writing and the image/music generation is a gimmick, but a lot of the modern world is mundane and pays people lots of money for mundane work. E.g. think of those internal corporate videos which require a script, stock photography and footage, basic corporate music following a 4 chord progression, a voiceover, all edited into a video.

Steve Taylor is most famous for being the voiceover for Kurzgesagt videos, but more generally he’s a voiceover artist that features in lot of these boring corporate videos. This type of content has such high demand there is an entire industry dedicated towards it, which seems well suited to AI.

https://youtu.be/vDb2h1-7LA0

This does raise further ethical/economical issues though, as most people in these creative industries actually require income from this boring work to get by.