Page type: Article / Wiki · Category: Computer science / Artificial intelligence

Limitations of Current AI Systems

Current AI systems, especially learned models, fail in patterned ways: they can be confident, fluent, and wrong. This wiki page lists limitations without theater.

Overview

Current AI systems, especially learned models, fail in patterned ways: they can be confident, fluent, and wrong.

This wiki page lists limitations without theatre and without timelines for “general intelligence,” which are speculation, not wiki facts.

Definition

Distribution shift: the world is not the training file. Generated text that is fluent but false. Metrics that do not match the real decision. Compute and data costs that papers sometimes omit.

Evaluation can be expensive. Skipping it does not make the system robust; it makes the claim cheap.

This is a catalog of limitations, not a manifesto against research.

Why the distinction matters

If you skip shift, you will ship a demo. If you skip factuality checks on generation, you will ship a fluent error.

If you skip cost, you will surprise the person who pays the bill.

Core pieces

A training distribution.
A deployment distribution that may differ.
A metric that may not match harm.

A human process around the model that may be missing.

If a tutorial skips these pieces and jumps to a demo, you are watching a product, not reading a definition.

Worked intuition

A captioning model trained on stock photos meets a medical image and still produces a caption. Fluency is not a licence.

That is why out-of-scope lines on a model card are not etiquette. They are engineering.

Common confusions

Announcing a timeline to “human-level” as if it were a measured quantity.

Treating a jailbreak as the only safety topic.
Hiding energy and labeling labor.

Using one English benchmark as “language.”

Limits

Some tasks should not be automated with today’s systems. That is a limitation of the systems, and sometimes a feature of the world.

Dual use is real. A wiki page cannot hold it all; it can refuse to pretend it is zero.

Practical checks

Name shift you already know about.
Measure fluent error if you generate text.

Publish cost at least approximately.
Refuse uses that the card marks out of scope.

What a careful page refuses

It refuses fake precision, fake timelines, and vendor adjectives that are not part of the definition.

Dual use is real. A wiki page cannot hold it all; it can refuse to pretend it is zero.

See also: evaluation metrics, model cards, data leakage.

Glossary

Shift: deployment unlike training.

Overconfidence: scores that do not match error rates.
Out of scope: uses the builders will not stand behind.

How to use this wiki page

Read the definition, then the confusions, then the checks. The FAQ is last on purpose: it should not replace the definition.

If you cite this page, cite the limitation that matches your use, not only the first sentence.

FAQ

Will these limits vanish next year?

Some will ease. This page does not sell a date.

Is this anti-AI?

It is anti-bluff.

Can I still use a model?

Yes, on a task you have tested, with a process around it.

Why this page exists in the collection

Limitations of Current AI Systems sits in a Article / Wiki slot with category Computer science / Artificial intelligence. That pairing is not decoration: readers should be able to tell a research note from a listing, and a home page from a wiki overview, before they quote a sentence out of context.

The one-line job of the page is this: Wiki-style catalog of limitations: distribution shift, hallucinations in generation, and cost of evaluation.

If you only remember one constraint, remember the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

The page is written for computer science readers who will either teach from it, cite it, or use it as a map. It is not written as a press release and it does not invent measurements that were not collected.

Scope and non-scope, stated slowly

In scope: the practice and documents around Computer science, Artificial intelligence, limitations, robustness. Out of scope: ranking offices, promising outcomes, or turning a classroom into a market.

A useful test is whether a sentence still holds if you remove adjectives. “A training distribution.” is the kind of object this page is willing to talk about because it can be pointed at.

Another object on the table is “A deployment distribution that may differ.”. If your question is actually about something else—private casework, live filings, clinical advice, or product pricing—stop and go to a qualified channel.

Non-scope also includes gossip about named minors, unnamed “secret” datasets, and any request to hide a limitation because it makes the story less tidy.

Walking through the checklist in full sentences

Item 1. A training distribution. Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 2. A deployment distribution that may differ. Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 3. A metric that may not match harm. Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 4. A human process around the model that may be missing. Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 5. Announcing a timeline to “human-level” as if it were a measured quantity. Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 6. Treating a jailbreak as the only safety topic. Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 7. Hiding energy and labeling labor. Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

Item 8. Using one English benchmark as “language.” Treat this as something you could put on a table in a meeting about Limitations of Current AI Systems. If you cannot point to an artifact, a date, or a named owner for it, it is not yet evidence; it is a wish. Write the missing piece before you scale the idea across a year of computer science work.

A longer narrative of the problem

People usually meet Limitations of Current AI Systems as a short slogan. The slogan travels faster than the log. Then a team is surprised when a term ends and the only remaining trace is a folder of unused files.

The longer story is operational. Someone has to name the text, the hour, the owner, and the thing students or readers will produce. Without that, Computer science, Artificial intelligence, limitations, robustness becomes wallpaper.

Consider a week in which A training distribution. is supposed to happen, but A deployment distribution that may differ. is competing for the same hour. The honest publication names the collision instead of adding a new poster.

Consider also the quiet failure: the work is done, but nobody can find it next month because the filename is “final-final-v3”. Documentation is part of the method, not an afterthought for Limitations of Current AI Systems.

None of this requires a new brand of software. It requires a calendar, a named artifact, and a sentence about what will not be claimed. That is the tone of this page.

Worked scenario A: a careful trial

A small team decides to trial one idea from Limitations of Current AI Systems for four weeks, not a year. They write the question in one sentence copied from the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

Week 1 is setup: they identify the artifact that will count as “done.” It should be as concrete as A training distribution.. They also write the exclusion: they will not claim effects they did not measure.

Week 2 is the first real run. They expect friction around A deployment distribution that may differ.. They log what was skipped and why, in language a substitute colleague could understand.

Week 3 is a repair week. They drop one extra ambition so A metric that may not match harm. can actually finish. Repair is not failure; it is the method.

Week 4 is a write-up of two pages: what happened, what they will keep, what they will not repeat. They cite this page as a map, not as proof.

Worked scenario B: the over-scoped version that fails

A different team announces Limitations of Current AI Systems as a whole-institution priority in the same week they have reports, a public event, and a system migration. Nothing is named as the single artifact.

They create a dashboard. The dashboard cannot answer whether A training distribution. occurred. It can only show that a file was uploaded.

By week six the original lead—Page type: Article / Wiki · Category: Computer science / Artificial intelligence—is no longer mentioned in meetings. People mention “the initiative.” Initiatives do not leave notebooks.

The recovery is embarrassing and simple: shrink back to one unit, one owner, one collected task, and the limits already written on this page.

A twelve-week implementation sketch

Week 1: Name the question Limitations of Current AI Systems is actually asking.

Week 2: Inventory current documents related to Computer science, Artificial intelligence, limitations, robustness.
Week 3: Pick one artifact as concrete as: A training distribution..
Week 4: Write the non-claims in language copied from this page’s limits.

Week 5: Run a tiny version that still includes A deployment distribution that may differ..
Week 6: Log skips; do not hide them in a highlight reel.

Week 7: Repair the calendar so A metric that may not match harm. can finish.
Week 8: Share a two-page note with a colleague who was not in the room.
Week 9: Decide whether to stop, continue, or redesign.

Week 10: If continuing, freeze the definition of “done” for the next month.
Week 11: Check that citations still point at dated sources, not at rumours.

Week 12: Retire leftover files that contradict the lead: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

This calendar is a sketch for Limitations of Current AI Systems, not a contract. If a public deadline in computer science collides with a week, move the week—do not pretend both happened.

If you skip logging, you are back to slogans. The sketch exists to make skipping visible.

Documentation pack

A one-sentence question taken from Limitations of Current AI Systems.
The dated lead as published: Page type: Article / Wiki · Category: Computer science / Artificial intelligence

A list of in-scope objects, starting with A training distribution..
A list of out-of-scope requests (advice, rankings, invented rates).

Names of owners for A deployment distribution that may differ. and a substitute if they are away.
A filename convention that includes a date.
A citation line that includes limits.

Links to sibling pages in Computer science.
A retirement note for superseded files.

A short glossary so newcomers do not invent synonyms.

If the pack cannot fit in a folder a new colleague can open in five minutes, it is too baroque for Limitations of Current AI Systems.

Pretty templates are optional. Dates and owners are not.

Error catalog

Publishing identifiable information that the method said to remove.
Quoting Limitations of Current AI Systems as if it measured an outcome it explicitly refused to measure.

Scaling across all of computer science before a four-week trial exists.
Mixing page type Article / Wiki with a different genre in the same citation.

Asking the page to do casework, medical advice, or live filings.
Hiding the collision between A deployment distribution that may differ. and a hard calendar event.
Letting an undated PDF outrank the dated page.

Inventing a percentage because a meeting wanted a percentage.
Treating A training distribution. as optional theatre while keeping the slogan.
Citing an unofficial look-alike domain as the primary source.

Each error is recoverable if you name it early. It is expensive if it becomes the public story of the work.

The cheapest prevention for Limitations of Current AI Systems is to reread the non-claims before you present.

Glossary for this page

Limitations of Current AI Systems — the document you are reading, with page type Article / Wiki and category Computer science / Artificial intelligence.
Artifact — a thing you could hold up, such as: A training distribution.

Lead — the opening claim: Page type: Article / Wiki · Category: Computer science / Artificial intelligence
Limit — a sentence that forbids a nicer claim than the method can carry.
Computer science — the home section of this page, not a licence to speak for every office in the world.

Date — the difference between a publication and a rumour.
Owner — the person who can change A deployment distribution that may differ. without a mystery committee.

Sibling page — another title in the same section, listed below when available.

Reader checklist before you cite or adopt

Can you state the job of Limitations of Current AI Systems without adjectives?

Can you point at A training distribution. in a real folder or classroom?
Is every number (if any) sourced, or did you add none because none were collected?
Does the citation include the limit that belongs with Computer science, Artificial intelligence, limitations, robustness?

Would a substitute colleague know what “done” looks like next week?
Have you avoided promising a ranking, a cure, or a guaranteed placement?

Is the page type still honestly Article / Wiki?
Is the category still honestly Computer science / Artificial intelligence?

If you fail two checks, do not cite yet. Fix the file or shrink the claim.

This checklist is part of Limitations of Current AI Systems, not a generic poster.

What “good enough” looks like without fake scores

Good enough for Limitations of Current AI Systems is a dated artifact, a named owner, and a next step that survived contact with a calendar.

It is not a launch photograph. It is not a dashboard that cannot answer whether A training distribution. happened.

It is certainly not a claim that Computer science, Artificial intelligence, limitations, robustness has been “solved.” Solved is a word this collection tries not to use.

If you need a number, collect one that matches the question, then publish the instrument. Until then, write in sentences.

Teaching notes

If you teach Limitations of Current AI Systems, give students a primary object first: a form, a lab page, a syllabus line, a model card, a gazette. Then give them this page as a map of how to talk about that object.

A good thirty-minute seminar: (1) read the lead, (2) mark the non-claims, (3) try to apply A training distribution. to a public document you did not write.

Do not ask students to harvest private data. Do not ask them to impersonate an office. Do not ask them to produce a rate you would not defend.

Assessment can be a two-page memo that cites this page and one official source, with the date of capture written on the first line. That is enough to see whether computer science literacy is happening.

For information officers and editors

If you maintain public pages in computer science, steal the habits, not the adjectives: date, owner, next step, non-claim.

Limitations of Current AI Systems will age. Put a review month on it. If you cannot review it, do not let it remain the featured link.

When legal, medical, or emergency readers arrive, your first job is to send them to a qualified channel. Education pages that pretend to be those channels cause harm.

When you quote Limitations of Current AI Systems in a newsletter, quote a limit next to the attractive sentence. Attractive sentences travel; limits do not, unless you chain them.

Notes on wiki genre

A wiki overview defines, distinguishes, and lists failure modes. It does not sell a library or a timeline to imaginary general intelligence.

Limitations of Current AI Systems should be cited for the distinction it draws, not as proof that a product works.

If a tutorial skips evaluation and jumps to a demo, it is not this page.

Update the glossary if a word starts meaning three things in your course. Do not pretend the field is settled.

Related pages in this collection

Neural Networks (Basics) — Wiki-style basics of neural networks as layered functions with learned weights, not as brains.
Overfitting and Regularization — Wiki article on overfitting: fitting the training sample too closely, and regularization as a family of restraints.

Data Leakage — Wiki article on data leakage: test information entering training, including target leakage in features.
Training and Inference — Wiki article distinguishing training (fitting weights) from inference (using a fixed model).

Rule-Based Systems and Statistical AI — Wiki article contrasting rule-based systems with statistical (learned) methods in AI history and practice.

These titles share the Computer science section with Limitations of Current AI Systems. They are not duplicates. Read the page type before you mix citations.

If a sibling contradicts this page, prefer the dated limits on each page rather than blending them into a mash-up claim.

Plain-language recap

Limitations of Current AI Systems is a Article / Wiki page in Computer science / Artificial intelligence. Its job is: Wiki-style catalog of limitations: distribution shift, hallucinations in generation, and cost of evaluation.

Do the concrete thing (A training distribution.). Write down what you will not claim. Date the file. Name an owner for A deployment distribution that may differ..

Do not invent rates. Do not use this page as a clinic, a court, or a marketplace. Do not strip the limits off the attractive sentences.

If you do only that, the collection has done enough work for one reading.

Versioning and review

When you locally adapt Limitations of Current AI Systems, keep a version line: date, editor, what changed, what did not.

A change to the lead is a new document. A change to an example can be a minor note.

Review at least when the surrounding computer science calendar jumps (new term, new statute text, new dataset version).

If nobody is named to review it, the page is already on its way to becoming folklore.