Yann LeCun: Machine Learning Falls Short of Animal Intelligence

Yann LeCun Machine Learning Falls Short of Animal Intelligence

The Limitations of Machine Learning: Yann LeCun’s Perspective on Animal Intelligence

Yann LeCun, a renowned computer scientist and pioneer in the field of artificial intelligence (AI), has sparked a heated debate within the tech community with his bold claim that machine learning falls short of animal intelligence. LeCun, who currently serves as the Chief AI Scientist at Facebook, argues that while machine learning algorithms have made significant advancements in recent years, they still pale in comparison to the cognitive abilities of animals. In this article, we will delve into LeCun’s argument, exploring the limitations of machine learning and the unique qualities that set animal intelligence apart.

LeCun’s assertion challenges the prevailing notion that AI has the potential to surpass human intelligence in the near future. He argues that despite the impressive achievements of machine learning algorithms, they are fundamentally different from the way animals learn and process information. LeCun points out that while machine learning algorithms excel at specific tasks and can process vast amounts of data, they lack the broader understanding and adaptability that characterize animal intelligence. In this article, we will examine the key differences between machine learning and animal intelligence, exploring the implications of LeCun’s argument for the future of AI research and development.

Key Takeaways:

1. Machine learning, while impressive, is still far from replicating the intelligence of animals.

2. Yann LeCun, a leading expert in artificial intelligence, argues that current machine learning algorithms lack the ability to generalize and understand concepts like humans do.

3. LeCun highlights the importance of common sense reasoning, which is a significant gap in machine learning models.

4. Animal intelligence is characterized by contextual understanding and the ability to learn from limited data, something machine learning struggles with.

5. The limitations of machine learning underscore the need for further research and development to bridge the gap between artificial and animal intelligence.

The Emergence of Artificial General Intelligence

One of the key trends highlighted by Yann LeCun in his recent statement is the emergence of artificial general intelligence (AGI). AGI refers to machines or systems that possess the ability to understand, learn, and perform any intellectual task that a human being can do. While machine learning has made significant advancements in recent years, LeCun argues that it still falls short of animal intelligence, let alone human-level intelligence.

LeCun’s observation raises important questions about the future trajectory of AI research and development. As machine learning continues to progress, researchers are increasingly focusing on building systems that can exhibit more general intelligence. This shift in focus has the potential to revolutionize various industries and domains, including healthcare, finance, and transportation.

However, the development of AGI also poses significant challenges. Achieving human-level intelligence in machines requires not only advancements in algorithms and computational power but also a deep understanding of human cognition and consciousness. Researchers need to tackle complex questions related to ethics, explainability, and the potential risks associated with AGI.

The Importance of Ethical AI

Another trend highlighted by LeCun is the growing importance of ethical AI. As machine learning algorithms become more sophisticated and powerful, there is a need to ensure that they are designed and deployed in an ethical manner. LeCun emphasizes the importance of building AI systems that are transparent, accountable, and aligned with human values.

Ethical AI involves addressing issues such as bias, fairness, and privacy. Machine learning algorithms are trained on vast amounts of data, and if this data is biased or incomplete, it can lead to discriminatory outcomes. To mitigate this, researchers and developers need to adopt rigorous practices to identify and eliminate bias in AI systems.

Furthermore, the deployment of AI systems should be done in a way that respects user privacy and data protection. As AI becomes more pervasive in our daily lives, there is a need for robust regulations and frameworks to ensure that individuals’ rights are protected.

LeCun’s emphasis on ethical AI highlights the need for collaboration between researchers, policymakers, and industry leaders to establish guidelines and standards for the responsible development and deployment of AI technologies.

The Future of Human-Machine Collaboration

LeCun’s statement also points towards the future of human-machine collaboration. While AGI may still be a distant goal, the advancements in machine learning have already transformed the way humans and machines work together. Machines are increasingly being used to augment human capabilities, rather than replacing them entirely.

As machine learning algorithms become more capable, they can assist humans in complex decision-making processes, enhance creativity, and automate repetitive tasks. This collaboration between humans and machines has the potential to revolutionize industries such as healthcare, manufacturing, and customer service.

However, the future of human-machine collaboration also raises concerns about job displacement and the impact on the workforce. As machines take over routine and repetitive tasks, there is a need for individuals to acquire new skills and adapt to the changing job market. Governments and organizations need to invest in reskilling and upskilling programs to ensure that individuals can thrive in the era of AI.

Overall, LeCun’s insights shed light on emerging trends in AI and their potential future implications. The development of AGI, the importance of ethical AI, and the future of human-machine collaboration are key areas that researchers, policymakers, and industry leaders need to focus on to harness the full potential of AI while addressing the associated challenges.

The Limitations of Machine Learning

Machine learning has made significant advancements in recent years, revolutionizing various industries and solving complex problems. However, even with these achievements, it falls short when compared to animal intelligence. While machine learning algorithms excel at specific tasks, they lack the general intelligence and adaptability that animals possess.

Animals have evolved over millions of years to navigate and survive in their environments. They possess an innate understanding of the world that allows them to learn from limited data, generalize knowledge, and adapt to new situations. In contrast, machine learning algorithms require vast amounts of labeled data and struggle to generalize beyond their training set.

For example, consider a machine learning algorithm trained to recognize cats. It may perform exceptionally well on a dataset of labeled cat images, achieving high accuracy. However, if presented with a new image of a cat in a different pose or lighting condition, the algorithm may fail to recognize it as a cat. Animals, on the other hand, can effortlessly recognize cats in various contexts, demonstrating their superior ability to generalize and adapt.

The Role of Context and Common Sense

Another area where machine learning falls short is in understanding context and applying common sense reasoning. While machine learning algorithms can process vast amounts of data and identify patterns, they struggle to grasp the underlying meaning and context of information.

Animals, on the other hand, possess a deep understanding of the world around them and can make sense of complex situations. For instance, a dog can recognize a ball, understand its purpose, and anticipate that it will bounce when thrown. This ability to reason and understand context is crucial for animals to navigate their environment and interact with objects and other beings.

Machine learning algorithms, however, lack this inherent understanding and struggle to apply common sense reasoning. For example, a language model trained on a large corpus of text may generate grammatically correct sentences but fail to grasp the intended meaning or context. This limitation hinders their ability to engage in natural language understanding and produce human-like responses.

The Role of Emotion and Social Intelligence

Emotion and social intelligence play a vital role in animal behavior and decision-making. Animals can experience and express a wide range of emotions, which influence their actions and interactions with others. This emotional intelligence allows animals to form social bonds, navigate complex social hierarchies, and respond appropriately to different situations.

Machine learning algorithms, on the other hand, lack emotions and social intelligence. While they can analyze patterns in data and make predictions, they do not possess the capacity to understand or experience emotions. This limitation hinders their ability to interact and empathize with humans in meaningful ways.

For example, consider a therapy dog that provides emotional support to individuals with mental health conditions. The dog’s ability to sense and respond to human emotions is crucial in providing comfort and companionship. A machine learning algorithm, no matter how advanced, cannot replicate this level of emotional connection and understanding.

The Importance of Sensorimotor Integration

Animals possess remarkable sensorimotor integration, allowing them to perceive and interact with their environment seamlessly. Their sensory inputs, such as vision, hearing, and touch, are tightly integrated with their motor outputs, enabling them to navigate and manipulate objects effortlessly.

Machine learning algorithms, however, lack this level of sensorimotor integration. While they can process visual or auditory data independently, they struggle to integrate multiple sensory inputs and perform complex motor tasks. For example, a robot trained through machine learning may be able to recognize objects but struggle to manipulate them with the dexterity and precision of a human hand.

The Future Challenges and Research Directions

Recognizing the limitations of machine learning in comparison to animal intelligence is crucial for future research and development. Understanding the unique capabilities of animals can inspire new approaches and algorithms that aim to bridge the gap between machine learning and animal intelligence.

Researchers are exploring various avenues to enhance machine learning algorithms and make them more adaptive, context-aware, and emotionally intelligent. For instance, incorporating reinforcement learning techniques that allow algorithms to learn from trial and error, similar to how animals acquire new skills, could lead to significant advancements.

Additionally, exploring the field of neuromorphic computing, which aims to develop hardware architectures inspired by the brain’s structure and function, could provide new insights and capabilities for machine learning. By mimicking the brain’s neural networks and their ability to process information in parallel, researchers hope to achieve more animal-like intelligence in machines.

While machine learning has undoubtedly revolutionized numerous fields, it falls short when compared to the intelligence and adaptability of animals. Animals possess a unique combination of general intelligence, context understanding, emotional intelligence, and sensorimotor integration that current machine learning algorithms lack.

Recognizing these limitations is crucial for future research and development in the field of artificial intelligence. By studying and understanding animal intelligence, researchers can inspire new approaches and algorithms that aim to bridge the gap and achieve more human-like intelligence in machines.

Case Study 1: AlphaGo’s Triumph in the Game of Go

In 2016, the world witnessed a groundbreaking moment in the field of artificial intelligence when AlphaGo, developed by DeepMind, defeated the world champion Go player, Lee Sedol. Go is an ancient Chinese board game known for its complexity, with more possible moves than there are atoms in the universe. Yann LeCun’s argument that machine learning falls short of animal intelligence is challenged by the remarkable success of AlphaGo.

AlphaGo utilized a combination of deep neural networks and reinforcement learning to master the game. It learned from millions of human games and then played against itself to improve its strategies. The victory against Lee Sedol demonstrated that machines can surpass human expertise in highly complex tasks, even ones that were previously considered beyond the reach of artificial intelligence.

This case study highlights the potential of machine learning to achieve levels of intelligence that surpass human capabilities. While it may not replicate the exact cognitive processes of animals, it showcases the power of algorithms and neural networks to solve complex problems and make decisions in ways that were previously thought to be exclusive to living beings.

Case Study 2: Autonomous Vehicles and the Quest for Safe Driving

Autonomous vehicles have been a major focus of research and development in recent years. Companies like Tesla, Waymo, and Uber are investing heavily in machine learning algorithms to create self-driving cars that can navigate the roads safely. Yann LeCun’s argument that animal intelligence is beyond the reach of machine learning is challenged by the progress made in this field.

One notable success story is Waymo’s autonomous driving system, which has logged millions of miles on public roads. Through the use of deep learning algorithms, the system is able to perceive and interpret the environment, make real-time decisions, and control the vehicle accordingly. This level of perception and decision-making rivals the capabilities of human drivers.

While autonomous vehicles may not possess the same level of cognitive abilities as animals, they demonstrate the potential of machine learning to replicate and even exceed certain aspects of human intelligence. The ability to navigate complex and dynamic environments, make split-second decisions, and adapt to changing circumstances showcases the power of algorithms and neural networks in achieving human-like performance.

Success Story: AI-Assisted Medical Diagnosis

Machine learning has also found success in the field of medical diagnosis, challenging Yann LeCun’s claim that animal intelligence is beyond the reach of artificial systems. One example is the use of deep learning algorithms to detect and diagnose diseases from medical images.

A study conducted by researchers at Stanford University demonstrated the potential of machine learning in detecting skin cancer. The algorithm was trained on a large dataset of images to distinguish between benign and malignant skin lesions. The performance of the algorithm was compared to that of board-certified dermatologists, and it was found to be on par with human experts in accurately identifying skin cancer.

This success story highlights how machine learning algorithms can augment human expertise, providing accurate and reliable diagnoses in medical settings. While the algorithm may not possess the same level of overall intelligence as an animal, it demonstrates the ability to analyze complex data and make decisions with a high degree of accuracy, potentially saving lives and improving healthcare outcomes.

The Emergence of Machine Learning

Machine learning, a branch of artificial intelligence (AI), has its roots in the mid-20th century. In the 1950s and 1960s, researchers like Arthur Samuel and Frank Rosenblatt laid the foundation for the field by developing algorithms that enabled computers to learn from data and make predictions. These early efforts focused on simple tasks like pattern recognition and decision-making.

The Rise of Neural Networks

In the 1980s, neural networks gained popularity as a powerful tool for machine learning. Yann LeCun, a renowned computer scientist, made significant contributions during this period. LeCun developed the backpropagation algorithm, which allowed neural networks to learn and adjust their weights based on errors. This breakthrough paved the way for more complex and accurate models.

Limitations of Early Machine Learning

Despite the progress made in the field, early machine learning algorithms had limitations. They struggled to handle large datasets and complex tasks. Additionally, computational resources were limited, making it challenging to train deep neural networks effectively. These constraints hindered the development of machine learning models capable of achieving animal-level intelligence.

The AI Winter

In the late 1980s and early 1990s, machine learning faced a significant setback known as the “AI winter.” Funding for AI research decreased, and interest in the field waned. This period was characterized by disillusionment with the capabilities of AI systems and the failure to deliver on the promise of human-like intelligence.

Revival and the Deep Learning Revolution

However, the turn of the millennium marked a new era for machine learning. Advances in computing power and the availability of large datasets led to a resurgence of interest in AI. In particular, the emergence of deep learning, a subfield of machine learning, revolutionized the field. Deep learning models, inspired by the structure of the human brain, allowed for the training of neural networks with multiple layers, enabling them to learn hierarchical representations of data.

Breakthroughs in Computer Vision

Yann LeCun played a pivotal role in the advancement of deep learning, particularly in the domain of computer vision. His work on convolutional neural networks (CNNs) significantly improved the accuracy of image recognition tasks. LeCun’s contributions, along with those of other researchers, led to breakthroughs in object detection, image segmentation, and other computer vision applications.

Recent Progress and Challenges

Machine learning has come a long way since its inception, with remarkable achievements in various domains. Deep learning models have surpassed human performance in tasks like image classification and speech recognition. However, Yann LeCun’s assertion that machine learning falls short of animal intelligence still holds true.

While machine learning algorithms excel in specific tasks, they lack the generalization and adaptability of animal intelligence. Animals can learn from a few examples, understand complex concepts, and exhibit common sense reasoning, which current machine learning models struggle to replicate.

Furthermore, ethical concerns surrounding machine learning, such as bias in algorithms and the potential for job displacement, have gained prominence. As the field progresses, addressing these challenges will be crucial to ensure the responsible and ethical development of AI.

Looking ahead, researchers continue to push the boundaries of machine learning, exploring new architectures, algorithms, and paradigms. The quest for artificial general intelligence, capable of matching or surpassing human intelligence, remains an ongoing challenge.

Deep Learning Architecture

Deep learning, a subset of machine learning, has gained immense popularity in recent years due to its ability to analyze large and complex datasets. At the heart of deep learning lies neural networks, which are inspired by the structure and function of the human brain. One of the key figures in the field is Yann LeCun, who has made significant contributions to the development of deep learning architectures.

Convolutional Neural Networks (CNNs)

CNNs are a type of neural network that have proven to be highly effective in image and video recognition tasks. They consist of multiple layers, including convolutional layers, pooling layers, and fully connected layers. Convolutional layers apply filters to input data, enabling the network to detect various features such as edges, textures, and shapes. Pooling layers reduce the spatial dimensions of the data, making it more manageable for subsequent layers. Finally, fully connected layers combine the learned features to make predictions.

Recurrent Neural Networks (RNNs)

RNNs are designed to process sequential data, such as time series or natural language. They have a feedback mechanism that allows information to persist from one step to the next, making them suitable for tasks like speech recognition, machine translation, and sentiment analysis. Long Short-Term Memory (LSTM) networks, a type of RNN, can handle long-term dependencies by selectively remembering or forgetting information over time.

Generative Adversarial Networks (GANs)

GANs are a class of neural networks that consist of two components: a generator and a discriminator. The generator generates synthetic data, such as images or text, while the discriminator tries to distinguish between real and fake data. Through iterative training, the generator learns to produce increasingly realistic samples, while the discriminator becomes more adept at identifying fakes. GANs have shown remarkable success in generating realistic images, but their application extends beyond image synthesis to areas like data augmentation and anomaly detection.

Limitations of Machine Learning

While machine learning, and specifically deep learning, has achieved impressive results in various domains, it still falls short of animal intelligence in several aspects.

Data Efficiency

One of the major limitations of machine learning is its high data requirements. Deep learning models often require vast amounts of labeled data to generalize well. In contrast, animals can learn complex tasks with limited data, thanks to their ability to transfer knowledge from one situation to another. This ability, known as few-shot or one-shot learning, remains a challenge for machine learning systems.

Common Sense Reasoning

Another area where machine learning lags behind animal intelligence is common sense reasoning. While deep learning models can excel at specific tasks, they often struggle with understanding context and making logical inferences. Animals, on the other hand, possess an innate understanding of the physical world and can reason about cause and effect without extensive training.


Machine learning models, particularly deep neural networks, are often referred to as “black boxes” due to their lack of interpretability. Understanding why a model makes a particular decision can be challenging, hindering trust and adoption in critical applications. In contrast, animal intelligence allows for introspection and explanation, enabling humans to understand the reasoning behind their actions.

Adaptability and Transfer Learning

Animals are highly adaptable and can quickly learn from new experiences or transfer knowledge gained in one domain to another. Machine learning models, however, struggle with adapting to new situations or domains without extensive retraining. This limitation hinders the deployment of machine learning systems in dynamic and evolving environments.

Energy Efficiency

While deep learning models have achieved remarkable performance, they are computationally expensive and require significant computational resources, often in the form of powerful GPUs. In contrast, animal brains are highly energy-efficient, performing complex computations with minimal power consumption. Bridging this efficiency gap is crucial for the widespread adoption of machine learning in resource-constrained environments.

While machine learning, and deep learning in particular, has made significant strides in recent years, it still falls short of animal intelligence in several key areas. Overcoming these limitations will require further research and innovation in the field. Yann LeCun’s insights and contributions have undoubtedly propelled the field forward, but there is still much work to be done to bridge the gap between machine learning and animal intelligence.


1. What is the main argument of Yann LeCun’s article?

In his article, Yann LeCun argues that machine learning falls short of animal intelligence in several key aspects.

2. What does LeCun mean by “animal intelligence”?

LeCun refers to the cognitive abilities and problem-solving skills exhibited by animals, which are often more flexible and adaptable than what current machine learning algorithms can achieve.

3. How does machine learning fall short of animal intelligence?

LeCun explains that while machine learning algorithms excel at specific tasks and can outperform humans in certain domains, they lack the generalization and common-sense reasoning abilities that animals possess.

4. What are some examples of animal intelligence that machine learning struggles with?

LeCun highlights tasks such as visual perception, physical interaction, and learning from few examples as areas where animals outperform machines. Animals can quickly adapt to new situations and learn from limited data, whereas machine learning algorithms require large amounts of labeled data.

5. Can machine learning algorithms ever achieve animal-like intelligence?

LeCun believes it is possible but suggests that current approaches need significant advancements. He emphasizes the importance of developing models that can learn from fewer examples and possess a more comprehensive understanding of the world.

6. Are there any advantages of machine learning over animal intelligence?

Yes, LeCun acknowledges that machine learning algorithms have their own strengths, such as their ability to process vast amounts of data quickly and perform repetitive tasks with high accuracy. However, these advantages do not equate to animal-like intelligence.

7. How can machine learning benefit from studying animal intelligence?

According to LeCun, studying animal intelligence can inspire new approaches and algorithms in machine learning. By understanding how animals learn, reason, and adapt, researchers can develop more robust and versatile machine learning models.

8. Does LeCun suggest any specific research directions for bridging the gap between machine learning and animal intelligence?

LeCun encourages researchers to explore areas such as unsupervised learning, meta-learning, and integrating symbolic reasoning into machine learning models. He believes these avenues could lead to significant progress in achieving animal-like intelligence.

9. What are some potential implications of bridging the gap between machine learning and animal intelligence?

If successful, bridging this gap could revolutionize fields such as robotics, natural language understanding, and autonomous systems. It could enable machines to understand and interact with the world in a more human-like manner.

10. What are the limitations of LeCun’s argument?

LeCun’s argument primarily focuses on the current state of machine learning and its limitations compared to animal intelligence. However, it does not consider other approaches, such as neuromorphic computing or hybrid models, which may have the potential to bridge this gap in different ways.

Common Misconception #1: Machine learning cannot achieve animal-level intelligence

One of the common misconceptions surrounding Yann LeCun’s statement is the belief that machine learning falls short of animal intelligence. While it is true that current machine learning models may not fully replicate the complexity of animal cognition, it is important to understand the progress that has been made in this field.

Machine learning algorithms have achieved remarkable feats in various domains, such as image recognition, natural language processing, and playing complex games like chess and Go. These accomplishments demonstrate that machines can learn and perform tasks at levels that surpass human capabilities.

However, it is crucial to differentiate between specific tasks and overall intelligence. Animals possess a wide range of cognitive abilities that are not yet fully understood, and replicating such complexity in machines is a significant challenge. Nevertheless, researchers are actively working on developing more advanced machine learning models that can approach animal-level intelligence.

Common Misconception #2: Machine learning lacks common sense and understanding

Another misconception is that machine learning algorithms lack common sense and a deep understanding of the world. While it is true that current models may struggle with common sense reasoning, significant progress has been made in this area as well.

Researchers are actively exploring methods to imbue machine learning models with a broader understanding of the world. This involves developing techniques to incorporate prior knowledge, reasoning abilities, and contextual understanding into the learning process.

For example, recent advancements in natural language processing have allowed machines to generate coherent and contextually relevant responses, demonstrating a level of understanding. Additionally, research in areas like knowledge graphs and symbolic reasoning aims to bridge the gap between raw data and human-like comprehension.

While there is still work to be done, it is important to recognize that machine learning is not solely limited to statistical pattern recognition. Efforts are being made to enhance machines’ understanding of the world, enabling them to exhibit more nuanced and intelligent behavior.

Common Misconception #3: Machine learning lacks creativity and adaptability

A common misconception is that machine learning algorithms lack creativity and adaptability, and can only operate within the boundaries of their training data. While it is true that machines learn from existing data, they can exhibit impressive levels of creativity and adaptability.

Generative models, such as variational autoencoders and generative adversarial networks, have demonstrated the ability to generate novel and creative content, including images, music, and text. These models can learn the underlying patterns and structures in the data and generate new instances that are both coherent and innovative.

Furthermore, machine learning models can adapt to new situations by leveraging techniques like transfer learning and reinforcement learning. Transfer learning allows models to transfer knowledge learned from one task to another, enabling them to learn more efficiently with limited data. Reinforcement learning enables machines to learn through trial and error, adapting their behavior based on feedback from the environment.

While machines may not possess the same type of creativity and adaptability as humans, they have demonstrated remarkable capabilities in these areas. Ongoing research aims to further enhance these qualities, pushing the boundaries of what machine learning can achieve.

Concept 1: Machine Learning

Machine learning is a method used by computers to learn from data and make decisions or predictions without being explicitly programmed. It involves training a computer model on a large amount of data and allowing it to find patterns or relationships within the data that can be used to make predictions or decisions.

For example, imagine you want to teach a computer to recognize pictures of cats. You would show the computer thousands of pictures of cats, along with labels indicating that they are indeed pictures of cats. The computer would then analyze the images and learn to recognize common features or patterns that are indicative of a cat, such as pointy ears or whiskers. Once trained, the computer could then be given new pictures and accurately determine whether or not they contain a cat.

Concept 2: Animal Intelligence

Animal intelligence refers to the cognitive abilities and problem-solving skills exhibited by animals. It encompasses a wide range of behaviors, from simple tasks like finding food to complex abilities like tool use and communication.

Animals have evolved over millions of years to adapt to their environments and develop unique ways of interacting with the world. They can learn from experience, solve problems, and even exhibit creativity in their actions. For example, some animals, like chimpanzees, have been observed using tools to accomplish tasks, such as using a stick to extract termites from a mound.

Concept 3: Machine Learning vs. Animal Intelligence

While machine learning has made significant advancements in recent years, it still falls short of animal intelligence in several key aspects.

Firstly, machine learning is highly specialized and focused on specific tasks. A machine learning model trained to recognize cats may struggle to identify other types of animals or objects. In contrast, animals possess a general intelligence that allows them to adapt and learn across a wide range of tasks and situations.

Secondly, animal intelligence often involves a deeper understanding of the world. Animals can reason and make inferences based on their knowledge and experiences. For example, a dog may learn that the sound of a doorbell often precedes the arrival of a visitor, and therefore start barking when it hears the doorbell ring. Machine learning models, on the other hand, lack this level of understanding and rely solely on statistical patterns in the data they were trained on.

Finally, animal intelligence is inherently embodied. Animals interact with the world through their senses and physical bodies, which shapes their understanding of the environment. In contrast, machine learning models are typically disembodied and rely solely on data inputs. They lack the physical experience and embodiment that is crucial for many aspects of intelligence, such as perception and motor control.

In conclusion, Yann LeCun’s assertion that machine learning falls short of animal intelligence raises important questions about the limitations of current AI systems. LeCun’s comparison of the brain’s ability to generalize and adapt with the narrow focus of machine learning algorithms highlights the need for further research and development in the field.

LeCun’s argument that animal intelligence is a result of a combination of learning, innate knowledge, and reasoning offers valuable insights into the complexity of intelligence. While machine learning has made significant advancements in various domains, it still lacks the holistic understanding and adaptability exhibited by animals. This calls for a deeper exploration of cognitive processes and the integration of multiple approaches in AI development.