{"id":128609,"date":"2024-08-11T15:40:57","date_gmt":"2024-08-11T19:40:57","guid":{"rendered":"https:\/\/www.shortform.com\/blog\/?p=128609"},"modified":"2026-04-28T11:56:29","modified_gmt":"2026-04-28T15:56:29","slug":"machine-learning-limitations","status":"publish","type":"post","link":"https:\/\/www.shortform.com\/blog\/machine-learning-limitations\/","title":{"rendered":"Machine Learning Limitations: 3 Ways Narrow AI Is Unreliable"},"content":{"rendered":"\n

What are the limitations of artificial intelligence? Why does AI sometimes makes mistakes that seem obvious to humans?<\/p>\n\n\n\n

In their book Rebooting AI<\/em>, Gary Marcus and Ernest Davis explore the challenges facing AI systems. They discuss three key machine learning limitations: issues with training data, language processing<\/a> problems, and difficulties in perceiving the physical world.<\/p>\n\n\n\n

Read on to discover why AI isn’t as smart as you might think and what needs to change for machines to truly understand our world.<\/p>\n\n\n\n\n\n\n\n

Machine Learning Limitations<\/h2>\n\n\n\n

So far, AIs have been successful when they\u2019ve been designed to do a single task<\/a>. Their problem is reliability\u2014even if they work most of the time, we never know when they\u2019ll make nonsensical mistakes that no human ever would. Marcus and Davis blame AI\u2019s problems on fundamental design deficiencies. They discuss three machine learning limitations: the nature of the data it\u2019s trained on, problems with how computers process language, and the way that machines perceive the physical world.<\/p>\n\n\n\n

Limitation #1: Data<\/h3>\n\n\n\n

Davis and Marcus\u2019s main objection to training neural networks using large amounts of data is that, when this strategy is employed to the exclusion of every other programming tool<\/em>, it\u2019s hard to correct for a system\u2019s dependence<\/a> on statistical correlation instead of logic and reason. Because of this, neural networks can\u2019t be debugged in the way that human-written software can, and they\u2019re easily fooled when presented with data that don\u2019t match what they\u2019re trained on.<\/p>\n\n\n\n

AI Hallucinations<\/h4>\n\n\n\n

When neural networks are solely trained on input data rather than programmed by hand, it\u2019s impossible to say exactly why <\/em>the system produces a particular result from any given input. For example, suppose an airport computer trained to identify approaching aircraft mistakes a flight of geese for a Boeing 747. In AI development, this kind of mismatch error is referred to as a \u201challucination,\u201d and under the wrong circumstances\u2014such as in an airport control tower\u2014the disruptions an AI hallucination<\/a> might cause range from costly to catastrophic.<\/p>\n\n\n\n

Marcus and Davis write that, when AI hallucinations occur, it\u2019s impossible to identify where the errors take place<\/strong> in the maze of computations inside a neural network. This makes traditional debugging impossible, so software engineers have to \u201cretrain\u201d that specific error out of the system, such as by giving the airport computer\u2019s AI thousands of photos of birds in flight that are clearly labeled as \u201cnot airplanes.\u201d Davis and Marcus argue that this solution does nothing to fix the systemic issues that cause hallucinations in the first place.<\/p>\n\n\n\n

Hallucinations and Big Data<\/h4>\n\n\n\n

AI hallucinations aren\u2019t hard to produce, as anyone who\u2019s used ChatGPT<\/a> can attest. In many cases, AIs hallucinate when presented with information in an unusual context<\/strong> that\u2019s not similar to what\u2019s included in the system\u2019s training data. Consider the popular YouTube video of a cat dressed as a shark riding a Roomba<\/a>. No matter that the image is bizarre, a human has no difficulty identifying what they\u2019re looking at, whereas an AI tasked with the same assignment would offer a completely wrong answer. Davis and Marcus argue that this matters when pattern recognition is used in critical situations, such as in self-driving cars. If the AI scanning the road ahead sees an unusual object in its path, the system could hallucinate with disastrous results. <\/p>\n\n\n\n

Hallucinations illustrate a difference between human and machine cognition\u2014we can make decisions based on minimal information, whereas machine learning requires huge datasets to function. Marcus and Davis point out that, if AI is to interact with the real world\u2019s infinite variables and possibilities, there isn\u2019t a big enough dataset in existence to train an AI for every situation.<\/strong> Since AIs don\u2019t understand what their data mean, only how those data correlate, AIs will perpetuate and amplify human biases that are buried in their input information. There\u2019s a further danger that AI will magnify its own hallucinations as erroneous computer-generated information becomes part of the global set of data used to train future AI.<\/p>\n\n\n\n

Limitation #2: Language<\/h3>\n\n\n\n

A great deal of AI research has focused on systems that can analyze and respond to human language. While the development of language interfaces has been a vast benefit to human society, Davis and Marcus insist that the current machine language learning models leave much to be desired. They highlight how language systems based entirely on statistical correlations can fail at even the simplest of tasks and why the ambiguity of natural speech is an insurmountable barrier for the current AI paradigm.<\/p>\n\n\n\n

It\u2019s easy to imagine that, when we talk to Siri or Alexa, or phrase a search engine request as an actual question, that the computer understands what we\u2019re asking; but Marcus and Davis remind us that AIs have no idea that words stand for things in the real world. <\/strong>Instead, AIs merely compare what you say to a huge database of preexisting text to determine the most likely response. For simple questions, this tends to work\u2014but if your phrasing of a request doesn\u2019t match the AI\u2019s database, the odds of it not responding correctly increase. For instance, if you ask Google \u201cHow long has Elon Musk been alive?<\/a>\u201d it will tell you the date of his birth but not his age unless that data is spelled out online in something close to the way you asked it.<\/p>\n\n\n\n

A Deficiency of Meaning<\/h4>\n\n\n\n

Davis and Marcus say that, though progress has been made in teaching computers to differentiate parts of speech and basic sentence structure, AIs are unable to compute the meaning <\/em>of a sentence from the meaning of its parts.<\/strong> As an example, ask a search engine to \u201cfind the nearest department store that isn\u2019t Macy\u2019s.<\/a>\u201d What you\u2019re likely to get is a list of all the Macy\u2019s department stores in your area, clearly showing that the search engine doesn\u2019t understand how the word \u201cisn\u2019t\u201d relates to the rest of the sentence.<\/p>\n\n\n\n

An even greater difficulty arises from the inherent ambiguity of natural language. Many words have multiple meanings, and sentences take many grammatical forms. However, Marcus and Davis illustrate that what\u2019s most perplexing to AI are the unspoken assumptions behind every human question or statement. Given their limitations, no modern AI can read between the lines. <\/strong>Every human conversation rests on a shared understanding of the world that both parties take for granted, such as the patterns of everyday life or the basic laws of physics that constrain how we behave. Since AI language<\/a> models are limited to words alone, they can\u2019t understand the larger reality that words reflect.<\/p>\n\n\n\n

Limitation #3: Awareness<\/h3>\n\n\n\n

While language-processing AI ignores the wider world, mapping and modeling objects in space is of primary concern in the world of robotics. Since robots and the AIs that run them function in the physical realm beyond the safety of mere circuit pathways, they must observe and adapt to their physical surroundings. Davis and Marcus describe how both traditional programming and machine learning are inadequate for this task, and they explore why future AIs will need a broader set of capabilities than we currently have in development if they\u2019re to safely navigate the real world.<\/p>\n\n\n\n

Robots are hardly anything new\u2014we\u2019ve had robotic machinery in factories for decades, not to mention the robotic space probes we\u2019ve sent to other planets. However, all our robots are limited in function and the range of environments in which they operate.<\/strong> Before neural networks<\/a>, these machines were either directly controlled or programmed by humans to behave in certain ways under specific conditions to carry out their tasks and avoid a limited number of foreseeable obstacles.\u00a0<\/p>\n\n\n\n

Real-World Complexity<\/h4>\n\n\n\n

With machine learning\u2019s growing aptitude for pattern recognition, neural network AIs operating in the real world are getting better at classifying the objects that they see. However, statistics-based machine learning systems still can\u2019t grasp how real-world objects relate to each other,<\/strong> nor can AIs fathom how their environment can change from moment to moment. Researchers have made progress on the latter issue using video game simulations, but Marcus and Davis argue that an AI-driven robot in the real world could never have time to simulate every possible course of action. If someone steps in front of your self-driving car, you don\u2019t want the car to waste too much time simulating the best way to avoid them.<\/p>\n\n\n\n

Davis and Marcus believe that an AI capable of safely interacting with the physical world must have a human-level understanding<\/strong> of how the world actually works. It must have a solid awareness of its surroundings, the ability to plan a course of action on its own, and the mental flexibility<\/a> to adjust its behavior on the fly as things change. In short, any AI whose actions and decisions are going to have real-world consequences beyond the limited scope of one task needs to be strong AI<\/em>, not the narrow AIs we\u2019re developing now.<\/p>\n\n\n\n

Exercise: Reflect on the Impact of AI in Your Life<\/h3>\n\n\n\n

AI is becoming omnipresent in our lives, including smartphone tools, search engines, automated product recommendations, and the chatbots that now write much of the news. However, Marcus and Davis argue that AI could be doing much more and that we overestimate what AI does because we don\u2019t really understand its inner workings. Think about how you use AI at present and how that might shift in the future.<\/p>\n\n\n\n

    \n
  1. What AI application do you use the most often, such as a search engine or the map app on your phone? Do you feel that it gives the best results that it can? In what way have you ever found its results frustrating?<\/li>\n\n\n\n
  2. What experience have you had with AI errors and hallucinations? Given Marcus and Davis\u2019s description of AI\u2019s faults, what would you guess caused any errors you experienced?<\/li>\n\n\n\n
  3. What\u2019s one thing that you wish <\/em>AI could do? If AI could develop that skill by learning to think more like a human being, do you feel that would be a good thing for society? Why or why not?<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    What are the limitations of artificial intelligence? Why does AI sometimes makes mistakes that seem obvious to humans? In their book Rebooting AI, Gary Marcus and Ernest Davis explore the challenges facing AI systems. They discuss three key machine learning limitations: issues with training data, language processing problems, and difficulties in perceiving the physical world. Read on to discover why AI isn’t as smart as you might think and what needs to change for machines to truly understand our world.<\/p>\n","protected":false},"author":9,"featured_media":128615,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[34,160],"tags":[1570],"class_list":["post-128609","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-communication","category-science","tag-rebooting-ai","","tg-column-two"],"yoast_head":"\nMachine Learning Limitations: 3 Ways Narrow AI Is Unreliable - Shortform Books<\/title>\n<meta name=\"description\" content=\"A few things need to change for machines to truly understand our world and work reliably. 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