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The Misadventures of AI:
The Funny Fails and Fixes

The Misadventures of AI:
The Funny Fails and Fixes

Published on: 11 Jan 2024 11 min read

Artificial Intelligence (AI) continues to be a hot topic with each passing day – mainly with its rapid development and remarkable achievements in various industries. However, like any emerging technology, AI has its not-so-successful results, leading to some pretty funny and sometimes confusing situations. Let’s talk about the lighter and funnier side of AI by delving into some of the comical mistakes and genius fixes that add witty touches to the world of artificial intelligence.

The Misadventures of AI: The Funny Fails and Fixes

Key Areas in the Application of AI 

First, let’s clarify what artificial intelligence is. It is the development of computer systems to perform tasks that normally require human intelligence such as visual perception, speech recognition, decision making and language translation. 

It has multiple applications across different industries, such as: 

Healthcare

Artificial intelligence is transforming healthcare, especially when it comes to diagnosis and treatment. AI-driven algorithms now provide more accurate reading and interpretation of medical images, enabling early detection of health problems.

AI also helps analyze huge databases of drugs, which speeds up the identification of the right ones for a particular treatment and predicts potential side effects. 

A good example is Google Health, which outperforms radiologists in detecting breast cancer from mammograms. The accuracy of AI accelerates treatment-critical decision-making by adapting treatment plans based on various analyses and patient data. 

Autonomous vehicles

The combination of innovative AI software, sophisticated sensors and real-time data is a true revolution in transportation.

Companies like Tesla and Waymo are leaders in developing autonomous systems that manage to navigate the roads without human intervention. This technology promises to improve road safety, reduce congestion and usher in a new era of intelligent and autonomous transport. 

Finance

Artificial intelligence is also playing a big role in the financial industry, especially through algorithmic trading and customer service through AI chatbots.

Institutions use AI algorithms that quickly and accurately make decisions, optimize investment strategies, and execute high-volume transactions at a speed unattainable by human traders. 

The effectiveness of AI also manifests itself in the improvement of user experience, through faster service for routine inquiries and reduced waiting times. Thanks to this, employees can focus on more complex tasks. 

Law and legal services

When reviewing legal documents, AI once again excels at quickly analyzing huge volumes of information, improving accuracy and reducing the time it takes to complete monotonous tasks.

In addition, AI enables the prediction of litigation outcomes by analyzing data from historical cases and legal precedents. This helps lawyers in decision-making, which increases the efficiency and effectiveness of legal processes. 

Entertainment

In the entertainment business, artificial intelligence has proven to be a huge creative force, contributing to the generation of all kinds of content – music, text, images and even animation.

A very good example can also be shown in video games, where the development of AI has improved the behavior of NPCs (non-player characters). Compared to previous years, AI-controlled characters display more complex and realistic responses and interactions, greatly enhancing gamers’ experience.

Programming

Despite initial concerns that AI will replace programmers, it’s helping them tremendously throughout the development process. AI-driven tools and algorithms automatically generate code snippets, which significantly reduce typing and enable software professionals to streamline the process itself and improve their productivity and efficiency.

By analyzing patterns, identifying redundancies and suggesting improvements, AI also helps create more stable and efficient software. 

Known AI Failures 

Microsoft Tay (2016)

Tay is a Twitter bot created by Microsoft with the intention of learning by interacting with users on the platform. Within 24 hours of its release, Tay began issuing a barrage of racist, sexist and other offensive tweets. 

Aftermath: This incident publicly exposed the vulnerability of AI systems to manipulation and the need for better safety and monitoring measures.

Amazon’s AI Recruitment Tool (2018)

Amazon built a resume reviewer for job candidates that uses natural language processing (NLP) and machine learning (ML). It subsequently had to be scrapped because it showed a bias towards women. 

Aftermath: Amazon shuts down the program and the incident exposes the possibility of bias in AI. This puts a focus on the importance of unbiased data in AI from then on.

Google Photos (2015)

Google’s image recognition algorithms faced a blow when AI labeled photos of people from certain ethnic groups as “gorillas”. 

Aftermath: Increasing the need for more extensive and diverse data in AI training to avoid such mistakes in the future. 

Facebook AI Language (2017)

Researchers at Facebook’s AI lab found that their chatbots began to deviate from their assigned script and began to communicate in a new language that they created without any human input. 

Aftermath: While not a dangerous occurrence, it is troubling that AI could potentially operate beyond human control or understanding. 

UBER Self-Driving Car (2018)

An Uber self-driving car hit and killed a pedestrian in Arizona. The software failed to correctly identify a human being and take the necessary actions to prevent the collision.

Aftermath: The incident led to a general dismissal of the industry for a long time and skepticism towards secondary attempts.  

Apple Maps (2015)

When Apple Maps was first released, there were tons of bugs and inaccuracies – from misplacing landmarks on the map to guiding people to the wrong places.

Aftermath: Most iOS users still use Google Maps to this day. 

Predictive models of Covid-19

In the early stages of the pandemic, numerous AI models tried to predict the spread of the virus. Most of them were well off target due to the lack of quality data and the complexity of epidemiological modeling.

Aftermath: Skepticism towards the reliability of AI predictions in complex real-world scenarios. 

Hilarious AI Misinterpretations 

Autocorrect errors

The most common situations are when the typing autocorrect function interprets what we wrote inaccurately.

For example, after someone writes “let’s eat, grandma!”, it can be corrected to “let’s eat grandma!”, giving a cannibalistic touch to a simple prompt.

Literal interpretations of virtual assistants

Virtual assistants are useful in the household with the multitude of tasks they can perform, but sometimes their interpretations of commands can lead to funny or unexpected results. For example, when a user asks, “How do I cook a turkey?”, they might get a response like “Cook it until it’s done.” Thus, it will be deprived of the expected culinary guidelines or sample recipe. Similarly, in a smart home, the user can state “It’s quite hot in here.” and the assistant can respond with a fact about the heat or simply confirm his statement with “Yes, the temperature is currently 26 degrees.”, without taking the initiative to adjust the thermostat. And finally, in the realm of humor, when virtual assistants are faced with a nonsensical string of words like “She sells seashells by the seashore.” Some of the assistants can sense the teasing and respond with a witty remark, but others may take the command literally and react with confusion or an error message. 

Lost in Translation

You are surely aware of online translators and their reliability. In most cases they work well, but they can create comical situations when they translate idioms literally. For example, there is a French phrase which means “when the chickens have teeth”, that reminds us of “when pigs fly”, but until recently it was translated with its literal meaning by AI, which confused people.

Image recognition

In image recognition, artificial intelligence has made significant progress, but not without its comedic mistakes. These often manifest themselves in AI detecting hairless men as “babies”, likening a dragon fruit to a “luxury mouse”, or a pug dog to bread. In some cases, there are more serious consequences – like falsely identifying individuals leading to invalid police arrests. All these examples show the complexity that AI faces when deciphering the visual world. 

GPS navigation mishaps

Artificial intelligence errors in GPS navigations can not only give you a headache, but also lead you to unexpected and humorous destinations. Systems can misinterpret addresses or names, sending you on an unwanted adventure. For example, instead of Paris, France, you might get a route to Paris, Texas. 

Challenges in NLP and ML  

Having looked at some of the more interesting mishaps, it’s also important to explain why they happened – they can usually be traced back to a few key challenges in natural language processing (NLP) and machine learning (ML). One significant obstacle is the lack of common sense in AI, as it misses the nuanced understanding that we humans acquire through years of life experience. Additionally, AI struggles with literal interpretation, unable to grasp idioms, metaphors, and common expressions, sometimes leading to comical and incorrect interpretations. Ambiguity in language further complicates matters when words and phrases have identical meanings, making it difficult for artificial intelligence to recognize context. Sarcasm, humor, and variations in tone are also a problem, as are homonyms.

What’s more, unconventional and creative uses of language considered normal to humans can be confusing to AI, highlighting the complex hurdles researchers face in refining NLP and machine learning models to better understand the complexities of human expression. 

Improvements to AI Systems

AI developers encounter multiple challenges in the dynamic world of artificial intelligence. The main problems are related to the limitations of computing power, solved by the development of specialized neural network chips and more efficient algorithms that allow AI systems to run on less powerful machines. 

Concerns about data scarcity and data quality are mitigated by creating synthetic datasets where real ones are lacking or sensitive.  

To reduce bias and achieve greater honesty in AI work, tools and methodologies are applied to detect such problems, while developers adhere to ethical norms. 

Also, a huge priority is ensuring the safety and sustainability of AI. Organizations like OpenAI and DeepMind are dedicating teams to study long-term safety through extensive testing on huge data sets. 

And last but not least, the accessibility of AI is being improved through open-source initiatives, making tools and frameworks widely available and partially free. As technology advances, the field remains dynamic, with an ongoing commitment to overcoming these obstacles and a focus on maximizing the benefits of AI to society. 

Success born out of adversity

The journey taken in overcoming the challenges in artificial intelligence has led to significant achievements and distinctive successes in various industries. In speech recognition, advances in computing power and algorithms, exemplified by models such as GPT for language processing, have not only met but surpassed human performance. In healthcare, IBM’s Watson Health and Google’s DeepMind Health demonstrate the successful applications of AI, contributing to medical advances. The robustness of decision-making in autonomous cars has greatly advanced thanks to AI, while finance companies, such as PayPal, are using AI to detect fraudulent transactions with remarkable accuracy. In the energy sector, AI optimizes consumption and improves system maintenance. This has been demonstrated by Google’s use of DeepMind AI to reduce their data center cooling energy consumption by up to 40%. Creative applications of AI extend to the creation of art and literature. It also plays a crucial role in environmental protection by monitoring deforestation, forecasting forest fires and tracking wildlife. These various examples highlight the continuous development and improvement of AI technology, demonstrating its versatile and impactful contributions in numerous fields. 

The Future of AI

The good… 

The future of artificial intelligence has enormous potential for success, affecting almost every part in the field of technology and business. One promising aspect lies in personalization, where AI can revolutionize services and plans ranging from education to healthcare solutions tailored to individual needs. Automation, another important element, promises increased efficiency and productivity in various industries by streamlining and automating tasks. AI’s potential for economic growth is highlighted by its ability to model and simulate solutions to complex problems, such as climate change.

Furthermore, the future of AI offers a burst of creativity by aiding creative processes. AI tools can generate art, compose music and write content, demonstrating their ability to amplify human creativity and innovation. As AI continues to develop, its potential impact on various aspects of our lives seems limitless, promising a future where technology becomes an even more integral part of our daily lives.

…and the bad and the ugly. 

While the future of artificial intelligence is promising, there are potential risks that require our attention. One serious concern is the effect AI may have on employment; automation, while increasing efficiency, has the potential to displace jobs, introducing complex dynamics with positive and negative consequences. Abuse of AI, although not addressed here, is a risk that requires vigilance to prevent unethical applications.

Another critical issue is bias and discrimination in AI systems, highlighting the importance of thorough configuration and oversight to ensure fairness. Also, security vulnerabilities are a constant risk, as AI systems can be susceptible to attacks and data poisoning, necessitating robust cybersecurity measures. Finally, an emerging issue is the reliance on AI. Overdevelopment can lead to a reliance on automated systems at the expense of human expertise and critical thinking skills, highlighting the need for a balanced approach to integrating AI into our evolving technological world. 

Directions for AI research and development 

The advancement of the field depends significantly on the direction of current research and development. Explainable AI (XAI) stands out as a key focus, offering tools to understand and interpret AI model responses. This not only facilitates debugging and performance improvement, but also helps make the behavior of models understandable to others, promoting transparency and accountability. Human-AI collaboration represents another major path, emphasizing the exploration of ways in which AI can enhance human tasks rather than replace them.

This collaborative approach spans processes from decision-making to creative endeavors, unlocking the potential for synergistic partnerships between humans and AI. Safety and control are an important topic with a focus on developing various methods to ensure that AI systems work as intended, mitigating unintended consequences and enhancing reliability.

The future of AI research and development is marked by these key directions, reflecting a commitment to responsible and collaborative progress in the field. 

About the Author:

Positive, communicative and dedicated front-end developer with 5 years of experience with different projects. Hard-working and striving for excellence. With a passion for React.

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