Neural networks are one of the ways to do machine learning. Machine learning is one of the ways to do artificial intelligence. Support vector machines are an example of non-NN machine learning models. Rule-based programs like early chess computers are an example of non-ML AI. Answer from fan_is_ready on reddit.com
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Google Cloud
cloud.google.com › learn › artificial-intelligence-vs-machine-learning
AI vs. Machine Learning: How Do They Differ? | Google Cloud
Artificial intelligence (AI) and machine learning (ML) are used interchangeably, but they differ with uses, data sets, and more.
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Scribbr
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What is the difference between machine learning and traditional programming?
June 27, 2023 - Traditional programming and machine learning are essentially different approaches to problem-solving, but machine learning is automated.
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July 14, 2025 - In contrast, machine learning enables computers to learn patterns from data and make decisions or predictions, allowing them to handle complex or evolving tasks where manual rule writing is impractical.
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Reddit
reddit.com › r/mlquestions › a "traditional" algorithm vs. machine learning
r/MLQuestions on Reddit: A "Traditional" algorithm vs. Machine Learning
July 1, 2022 -

My Question is: What distinguishes a traditional algorithm from machine learning?

Apologies for the wall of text.

I manage a product with a massive amount of data (1m+ weekly users, 50+ demographic datapoints on each user + user history as well as their interactions with hundreds of customers). At the core of the product is an algorithm that takes a number of inputs (based on trailing historical data) to predict the revenue-optimizing decision.

Recently, our new leadership has begun to call this Data Science and touts this as "Machine Learning". I'm proud of what we've put together and the impact its had on the business, but this feels like the wrong characterization of what is just a semi-complex algorithm with almost all of the calculations occurring in SQL.

This has become a sort of big issue as they've asked me to speak to our "Machine Learning" implementation to customers, investors, and others. I dodged that characterization by instead calling it a "model" or "algorithm" and they took notice and have asked me to embrace the term and update our materials (presentations, roadmap items, etc). Compounding this, they've hired a data scientist who concurs with them that we're using a "predictive machine learning" model. I'm skeptical of his expertise and feel like he should be making an effort to create an actual ML model we can compare against our current model.

The whole thing feels dishonest and misleading. Machine learning feels far outside my depth: I couldn't hold a conversation about it and I have no real clue what a decision forest, neural network, tensors, gradients, or any of the other machine learning terms I see across this sub or elsewhere mean. More details specific to my situation below:

------------------------------------------------------------

The core goal of our data effort is: Based on what we know about a user and what we know about a customer and their provided estimates, what's the optimal revenue-maximizing decision?

There's many calculations that are factored in to accomplish this, for example:

  • We calculate the median average deviation of a customer's proposed vs actual success rate on a rolling basis.

  • We segment our users based on demographic (age/gender/etc) and calculate their success rate relative to the population's average for a success coefficient based on a rolling basis.

  • We run a simple regression between user characteristics and historical success rates for each customer.

  • We factor in historical reconciliation rates from the customer (% of successes that are ultimately rejected by the customer at invoicing) to discount revenue estimations.

  • We determine whether the user's experience should be optimized using a revenue-per-minute or revenue-per-opportunity approach. If we expect them to make a limited number of attempts, we maximum the expected revenue of each interaction. If we expect them to make a larger number of attempts, we optimize for potential revenue per minute. (EPC vs EPM for those in the advertising space)

It gets pretty gnarly, but what we end up with is a huge number of coefficients that inform our user to opportunity matching logic. An example of how this could result in different opportunity rankings for a pair of users could be:

User 1 - Average Attempts per Session 2.1 ( to be ranked by Expected Revenue)

  1. Project A - Potential Revenue $10 | Expected Revenue $2 | Estimated Success Rate 20% | 30 Minutes | Expected Earnings Per Minute $0.06

  2. Project B - Potential Revenue $25 | Expected Revenue $1 | Estimated Success Rate 4% | 10 Minutes | Expected Earnings Per Minute $0.10

  3. Project C - Potential Revenue $1 | Expected Revenue $0.80 | Estimated Success Rate 80% | 5 Minutes | Expected Earnings Per Minute $0.16

  4. Project E - Potential Revenue $10 | Expected Revenue $0.6 | Estimated Success Rate 6% | 4 Minutes | Expected Earnings Per Minute $0.15

User 1 - Average Attempts per Session 6.3 ( to be ranked by Expected Earnings Per Minute)

  1. Project C - Potential Revenue $1 | Expected Revenue $0.90 | Estimated Success Rate 100% |5 Minutes | Expected Earnings Per Minute $0.18

  2. Project D - Potential Revenue $4 | Expected Revenue $0.75 | Estimated Success Rate 18% | 7 Minutes | Expected Earnings Per Minute $0.15

  3. Project E - Potential Revenue $10 | Expected Revenue $0.5 | Estimated Success Rate 5% | 4 Minutes | Expected Earnings Per Minute $0.125

  4. Project B - Potential Revenue $25 | Expected Revenue $0.75 | Estimated Success Rate 3% | 10 Minutes | Expected Earnings Per Minute $0.075

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The whole thing feels dishonest and misleading. Welcome to the industry :p Here are some of my thoughts: "Machine learning" and "data science" are both incredibly poorly defined, very broad terms meaning often depends a lot on the context in which these terms are being used. I'm personally of the opinion that a better phrase for most of what we call "Machine learning" is "statistical learning" and subsumes basically all of predictive analytics. In other words, if you're model is just a simple linear regression: I still think it's ok to call that "machine learning", although I think it's also fair to maybe characterize that as somewhat dishonest since the phrase obviously conveys a notion of technical sophistication. increasingly, the phrase "Machine Learning" is becoming analogous with "Deep Learning," but this absolutely is not correct. The vast majority of applied ML in industry uses techniques like GLMs and tree ensembles. Ultimately, this is all marketing speak anyway and people will use the language that maximizes hype for whatever they're trying to pitch to you. Speaking from my own diverse experience as someone who's worked as a data professional in a wide diversity of organizations (including two FAANGS), you'd be surprised how unsophisticated a lot of real world "data science" is. Most, even. most data scientist roles are at organizations that are undergoing a "data transformation" to become more "data driven". This is code for "historically, we made most of our business decisions based on intuition and maybe sticking a finger in the wind. the consequence of this is that data scientists get buried in "low hanging fruit" opportunities because there's so much opportunity for low-effort improvements by minimally attending to data, the data teams and the organization at large are heavily incentivized to leverage simple, unsophisticated solutions so they can tackle more problems quickly rather than heavily optimizing solutions to narrow problems. the value of sophistication is really a function of scale when I talk about "low hanging fruit", I'm talking along the lines of: domain expertise gets you say 50% of the available value for some opportunity, data-informed busienss rules takes that up 70%, simple modeling takes that up to 80-85%. So by tackling the low hanging fruit, you've captured close to 70% of the available additional value with very little effort because that last 15% optimization isn't low hanging fruit, we're going to quickly encounter diminishing returns. Every additional percentage point of optimization is going to come with exponentially more effort. This is the reason the bulk of data scientists are employed by huge companies like FAANGs: the scale of their business is large enough that an incremental improvement of a fraction of a percent can mean millions of dollars in revenue or savings. conversely, if your organization is not operating at that scale, it's not unlikely that it'll will cost your company more to invest in optimizing some solution than the value they would get from that solution. And even after putting in that investment, it's still a huge risk. Every application of predictive analytics is essentially a kind of experiment, and with every experiment, there's a possibility that the tested hypothesis is wrong and will be rejected, i.e. the model doesn't do anything of value. When data scientists are given the freedom to do their best work, they are and need to be a huge cost center for whatever organization they operate in. Otherwise, you're asking them to essentially be be data-savvy business consultants who are perpetually chasing low hanging fruit, which is exactly the position most industry data scientists find themselves in. Because of these scaling effects, most of the work that actually does require sophistication will end up getting subsumed by engineering teams if you are a data scientist working in isolation, your ability to operate on large data sets and deploy complex models is limited. This type of work requires engineering support, which means the further away your data scientist is in the org tree from the closest engineer, the more their hands will be tied with respect to the amount of sophistication they can apply to anything that will be deployed. deep learning and data science tools are rapidly becoming staples of undergraduate CS curricula, which means more engineers are equipped to identify and act on opportunities to apply ML without engaging with a data scientist. this creates a kind of feedback loop that further isolates data scientists from the engineering resources they need, often relegating them to being a kind of "ad hoc analytics monkey" for leadership. the "value function" the solution here is optimizing is probably more multi-faceted than you realize specifically, even if your data scientist has all of the engineering resources they could want to deploy the most sophisticated SOTA solution to your orgs problem, there might be good reasons why they wouldn't want to. the ultimate goal of these sorts of projects is almost always to drive some kind of behavior. This often means that it's more important for the outputs of a model to be interpretable than predictively accurate. additionally, the data scientist is ultimately subject to the demands of their customer: the business stakeholder. This unfortunately means that sometimes they will be relegated to approaches whose mechanism can be understood by the stakeholder, especially if it's a new relationship and the data scientist is still building trust in the org. It can even mean the scientist will be required by their client to incorporate features in the model that don't carry any predictive signal at all. This creates an even heavier bias away from sophistication than the whole "low hanging fruit" or "I'm my own data engineer" thing. First and foremost, the data scientist is doing work for their customer and they need to make their customer happy as best they can. TL;DR: From what you've shared, I see nothing inappropriate about describing this work at least as "data science". Calling it "ML" carries a weak implication that deep learning or something similarly sophisticated is being used, but even if that's not the case: the fact that they're forming predictions of any kind by performing computations on historical data I think makes it appropriate.
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What distinguishes a traditional algorithm from machine learning? Traditional algorithm: All of reward is processed by the human engineer. Machine learning: The engineer delegates processing some of the reward to the machine itself.
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iSchool
ischool.syracuse.edu › home › articles
Deep Learning vs Machine Learning: Key Differences
September 22, 2025 - Machine learning (ML) is a subset of AI that is primarily focused on enabling computers to learn from data with minimal human intervention. In traditional programming, a human writes explicit rules for the computer to follow, but in machine learning, the computer learns the rules from examples.
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institutedata.com › us › blog › machine-learning-vs-traditional-programming-choosing-the-right-approach-for-your-projects
Machine Learning vs Traditional Programming: Choosing the Right Approach for Your Projects | Institute of Data
May 23, 2023 - The main difference between machine learning and traditional programming is that while the latter depends entirely on user input, the former can utilize algorithms that enable it to learn from its environment and experience, allowing the computer ...
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September 19, 2025 - While both approaches can solve ... A major difference is deep learning algorithms perform automatic feature extraction, while traditional machine learning relies on manual feature engineering....
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August 15, 2024 - The machine learning model’s lack of explicit rule programming is the primary distinction. It gains knowledge of the patterns and rules from the data itself. This makes it feasible for machine learning to address far more complicated issues than traditional programming alone can handle.
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quixl.ai › home › deep learning vs. traditional machine learning: choosing the right approach for edtech applications
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pmf-research.eu › home › deep learning: differences with machine learning and traditional ai
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