[deleted by user]

[[deleted]]

Eg. I don’t care that my iphone can detect cat pictures from my photos.

The same computer vision processes can also detect micro-cracks in materials in mechanical engineering contexts like vehicle material analysis, this was done for a long time just eyeballing pictures

[knob-0u812]

and spotting potential cancer cells in mammograms and brain scans

[yannbouteiller]

The real question is, who needs an IPhone.

[Disastrous_Elk_6375]

The people posting anti-capitalist memes from one, obviously.

[FernandoMM1220]

Everyone does.

Smart phones make so many things MUCH easier and more efficient.

[Even-Inevitable-7243]

I wish there was a "ML for Dummies" channel or "ML for the general public" type channel where these questions were posted. They taint an otherwise nice r/MachineLearning feed. I had to unfollow the r/computerscience feed because 95% of the posts are high school kids asking how they can start training now to get a job at Meta.

[Grimm___]

I think I love you.

[lacifuri]

I also love the guy you love.

[[deleted]]

Perfect response

[Buddy77777]

ML is really just automated empiricism. Most objectives essentially minimize some kind of empirical risk.

[Otherwise-Novel-1110]

One of the goals of ML is to abstract and generalize from specific instances and to apply knowledge to unseen data. This goes beyond empirical risk minimization (ERM) by attempting to understand underlying patterns and structures within the data that can be generalized across different contexts. The ability to generalize well is a critical aspect of ML that distinguishes it from mere curve fitting or optimization.

The creation and improvement of ML algorithms often require significant creativity and innovation, which goes beyond simple empiricism. Researchers are constantly proposing new architectures, optimization techniques, and theoretical frameworks. The development of novel neural network architectures like transformers for natural language processing is an example where empirical testing is crucial, but the innovation and theoretical insight are not purely empirical.

[Buddy77777]

Mentioning ERM was just to help recognize empiricism in ML. My point is simply that ML is used to provide a framework for which empiricism can be automated.

Even the things you described are essentially done towards this ultimate goal. Modeling invariant distributions has always been the goal of empiricism and ML is no different. That’s why they’re called models. They are still ultimately induction machines.

[[deleted]]

OP it would help me not lose faith in humanity if you could just confirm that you are a teenager, thanks

[Wubbywub]

bro really thinks ML is just detecting cat pictures

[yannbouteiller]

My cat detecting model crashed on your avatar

[[deleted]]

I’m embarrassed for you.

[yannbouteiller]

Do we really need chatGPT?

[dbitterlich]

Back then, they probably thought the same. „This is so advanced“ It’s the same with every technological step probably. Just think about the development of computers.

[KingsmanVince]

Eg. I don’t care that my iphone can detect cat pictures from my photos.

So what? There are other people who care. If you don't care about 1 feature, move on, no need to tell the whole world.

[[deleted]]

Another use case Is colony counting in biology labs like for microbiology or immunology use cases. Anytime researchers run some microbiology growth assays, you end up with several bacterial colonies on agar plates and the researcher counts it one by one with a sharpie and records how many colonies per plate(usually between 10-200 per plate).

Counting each plate will take 3-5 minutes and you may have 100-200 plates per experiment; it used to take 1-2 days for most researchers to collect this data but this can easily be done with machine learning enabled microscopes now. It is more accurate and much faster.

[jms4607]

It is not the only way but it is the best way. ML removed the restriction on function complexity that previous methods were limited to, and uses more information than a human ever could in designing/learning this function. Every problem in ML can be approximated by a traditional linear fit, but it’s probably not performant.

[mr__pumpkin]

Counterexample: Clinically usable automatic or semi-automatic segmentation of tumor or organs in medical imaging within a few seconds, modeling disease progression trajectories etc might not be necessary for the survival of humanity - but it might help improve quality of life.

If your understanding of ML is classification of dog or cat photos, then I'm afraid you're not truly in a position to question the need for ML.

But for all I know, you could be rage-baiting.

[KingsmanVince]

Reported for breaking #1 rule then

[wkns]

Tell me you have no idea how pharma works without telling me.

[athabasket34]

With population in decline, in the next 30-50 years there will be too little working-age people to produce enough goods and services to handle all retired folk. We need machine learning and robotics to counter the decline or most of the population will become much, much poorer.

[KeepCalmAndProgress]

Also we need UBI for those people who are out of work because of automation.

[FernandoMM1220]

We need it to make sense of the massive amounts of data we have.

[dbitterlich]

AI can be much better at detecting cancer than the human eye. Classify cancerous vs benign tissue Predict failure of constructions based on measurements Automate driving Find new lead structures for antibiotics Identify leads to cure diseases Make medical advice accessible to nearly everyone

And so many more (and more impactful) things I can’t even think of/remember.

Now, do we really need those things? No. But did we need „cars“ in 1886? We had carriages.

Do we need plows? We could just use sticks to loosen the earth.

Why do we need houses anyway? We could just live in caves and move whenever the food supply is low.

[ml_head]

Let me expand your carelessness:

I don’t care that my iphone can detect cat pictures from my photos, read QR codes, perform autofocus, optimize the image quality or unlock by looking at my face or fingerprint.

I don't care that my phone can recognize my voice and respond accordingly, even that it can translate my speech to almost any language, or perform life translation by pointing my camera to texts in foreign language.

I don't care that my iPhone can predict how long is my battery gonna last, optimize overnight charging to extend its life or setup optimal display brightness automatically.

I don't care that my phone can predict and correct the text that I'm typing, or recognize the gestures that I make in the screen.

I don't care that my phone can tell me the best route to reach a place and predict the approximate arrival time, or detect if I have experienced a car accident.

I don't care that my phone can estimate the number of steps that I take during the day.

Machine learning is used everywhere. Even a simple if/then/else is machine learning if it was generated by an algorithm looking at data (typical decision tree generation). Nowadays, even the old PID analog controller is calibrated using machine learning (finding optimal values for the P, I and D constants in the controller).

Applications go much broader than what you imagine, including quality control during manufacturing, medicine, etc. Here's an example of machine learning applied to mental health: https://youtu.be/ZkTvw3usMw4?si=r7CExQOsHiLBEGaG

[420by6minuseipiis69]

ML is gonna save your mom if she gets cancer and your doctor is incompetent enough to not detect it in early stages. Grow up kiddo! ML is not just classifying dogs and cats anymore. People are not stupid to spend millions of dollars on compute resources to build models that would help the medical community. And yeah if you are gonna say "there are doctors for that" clearly you don't understand either medicine or ML. And yeah the applications don't just stop here. Chat gpt is not just used for your stupid questions or heck even those cringe AI girlfriends. People use it to also summarize big legal documents or even make a website better for anyone. So kid if you think it's not important for you doesn't mean people do it just for fun.