The 2024 Nobel Prize in Physics was awarded jointly to American John Hopfield and British-Canadian Geoffrey Hinton, both hailed as pioneers in the field of artificial intelligence. Their groundbreaking work in machine learning, particularly in the development of "artificial neural networks," laid the foundation for modern AI and its advanced applications.
In the 1980s, John Hopfield made a significant contribution with his "Hopfield Network" model, a type of artificial neural network that revolutionized the approach to AI. This model excelled in pattern storage and retrieval, marking a pivotal shift in the field. Building upon this foundation, Geoffrey Hinton developed "Deep Neural Networks" in the 1990s, taking the concept even further.
Hinton's Deep Neural Networks utilize multiple layers of artificial neurons to identify and learn from complex data patterns. This advancement represented a substantial evolution from the Hopfield model, paving the way for numerous cutting-edge AI applications.
But what exactly are these "Deep Neural Networks" that earned Hopfield and Hinton the Nobel Prize in Physics and sparked a revolution in modern artificial intelligence?
Neural Networks
A neural network serves as the brain of a computer, enabling it to learn and solve problems. It forms the foundation for determining whether a device can make independent decisions or if it's simply executing pre-programmed instructions. These networks utilize deep learning, which is characterized by the ability to process unstructured data – random and diverse information that isn't pre-classified. This capability to handle unstructured data is one of the key features distinguishing deep learning from traditional machine learning.
To illustrate the difference, consider the task of building a model to recognize faces. In machine learning, you would need to feed the system thousands of human face images, classifying them based on various criteria such as color and edges. This process allows the system to learn and eventually recognize human faces independently. In contrast, deep learning employs a deep neural network that automatically learns these features and recognizes faces without the need for manual classification or feature definition.
For instance, if you present a deep learning model with a diverse set of images including humans, animals, landscapes, and tools, it can independently classify and recognize each category through its deep neural networks. But how does this process occur?
A deep neural network mimics the functionality of the human brain. The human brain contains millions of interconnected nerve cells (neurons) that receive, process, and transmit electrical and chemical signals within the body, facilitating learning. Similarly, a deep neural network consists of interconnected groups of artificial neurons arranged in three main layers.
Just as the human brain relies on biological neurons, artificial neural networks depend on computational units called nodes. These nodes are algorithmic programs connected to each other across the three layers, working in concert to solve problems. The structure and function of these layers are as follows:
- The first layer, called the "input layer", is responsible for receiving information from the outside, analyzing it, and classifying it, before passing it to the next layer.
- The second layer, called the "hidden layer", takes the inputs from the first layer, reviews any errors, and corrects them, before passing the information to the third layer.
- The third layer, known as the "output layer", is responsible for making decisions and providing the final result of all the data processing operations performed by the artificial neural network layers.
How Neural Networks Work
The process of analyzing and exchanging information between these layers is carried out through a series of simple calculations (such as addition or multiplication) using what are known as weights. These weights represent the connections between artificial neurons. If the weight is positive, the outputs are transferred to the next layer, but if it is negative, it returns to the neuron within the same layer to detect the error and correct it until a positive weight is obtained. After that, an activation function is applied to determine the outputs that are sent to the next layer until the final decision is made about them.
For example, if you ask an AI program to design a facial image, the neurons in the first layer have learned from the data, and they will draw a facial image and then pass it to the following neurons. These neurons will review the design of the face and assess any errors in it. If there are errors, the weight will be negative, and it will return again, such as designing a face without a nose. This process continues until the weight becomes positive, after which it is passed to the next layer responsible for making the decision and outputting the image in its final form. This process, which occurs in fractions of a second, depends on the computing power of the device used, similar to the mechanism of the human brain.
The main difference is that biological neurons rely on chemical and electrical processes, while artificial neurons are computational units within computer programs. The human brain also has a high ability to adapt and change in response to new experiences, while artificial neural networks rely on improving weights through current data and practices. The human brain is much more complex than artificial neural networks in terms of the number of neurons, the way they interact, and their ability to adapt to new situations and learn instantly.
Diverse Advantages
Neural networks are used in many different industries due to their ability to process large amounts of data and discover hidden patterns. Some common applications include:
1. Medical diagnosis: Neural networks are used to classify medical images and identify diseases with high accuracy.
2. Marketing: It is used to analyze behavioral data on social media, which helps companies improve their advertising campaigns.
3. Financial sector: It is used to predict markets by analyzing historical data of financial instruments. It also plays an important role in predicting energy loads and electricity demand, as well as being used to monitor the quality of industrial processes.
4. Computer vision: Neural network technology enables computers to understand and analyze images and videos in a way similar to humans, and is primarily used in self-driving cars to recognize traffic signs, pedestrians, and other vehicles. It is also used in facial recognition systems to identify individuals and recognize features such as open eyes or glasses. In addition, it helps in tagging images, allowing the identification of commercial logos, clothing, and safety equipment.
5. Speech recognition: Neural networks help computers analyze human speech despite variations in tone and accent. This technology is used in virtual assistants such as Amazon Alexa and also helps in automatically classifying calls in call centers.
6. Natural language processing (NLP): It helps computers understand and analyze written text. This technology is used in chatbots and virtual agents, as well as in organizing and classifying written data automatically. It also helps analyze long business documents such as emails and forms, as well as summarizing long documents and creating articles based on a specific topic.
7. Recommendation and filtering engines: Such as those found in e-commerce platforms, by analyzing user behavior to provide personalized recommendations that suit the customer's needs.
Challenges of Use
Deep neural networks are the real brain of artificial intelligence systems, and despite the many advantages they offer in almost all areas of life, they have many challenges and problems. Some examples include:
1. Need for Huge Amounts of Data: One of the biggest challenges of deep neural networks is that they require huge amounts of training data to achieve high accuracy. Although data is important for training models and improving their performance, it is not always available in all applications or fields, making it difficult to use these models effectively in some scenarios.
2. Consumption of Huge Amounts of Energy: Deep neural networks require high computing power for training and operation, and naturally need large amounts of energy, especially when there are multiple layers with millions or billions of neural connections. Training these models requires powerful devices such as Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs), which are expensive and result in an increase in the carbon footprint due to increased energy consumption.
3. Understanding and Interpreting Results: One of the main problems with deep neural networks is that they are considered a "black box". Although they provide accurate results, understanding how these results were reached remains difficult. This creates problems related to transparency and trust in sensitive applications, such as healthcare or self-driving cars.
4. Overfitting: When a deep neural network is too complex or when it is trained for a long time on a specific dataset, it can suffer from overfitting. This means that the model becomes very good at recognizing patterns in the training data, but fails to generalize to new, unfamiliar data. This reduces the model's accuracy when tested on new data.
5. Bias and Error: Deep neural networks rely heavily on the training data, which means that their performance depends directly on the quality and diversity of this data. If the data is not comprehensive or contains biases, it can lead to wrong or inaccurate decisions when used in different environments or conditions.
6. Susceptibility to Deception: Deep neural networks can be deceived by users; very simple inputs can be modified in a way that makes the model produce completely wrong outputs without realizing it. For example, the ChatGPT application was deceived into providing information about manufacturing bombs.
7. Hallucinations: This happens when the system provides information that does not originally exist or is incorrect without realizing it is making this mistake. This phenomenon is one of the most prominent challenges in the field of artificial intelligence, as it begins to write highly organized and logical information that is not real or does not exist in the first place. This happens because the model learns from patterns and statistics in the data, and does not have a real understanding or awareness of correct or incorrect information.
Despite the challenges, deep neural networks remain one of the most significant developments in the field of artificial intelligence, and their importance continues to grow. As artificial intelligence capabilities increase, and the technological singularity - the stage where AI surpasses human intelligence - draws nearer, the concerns expressed by Hopfield himself become more alarming. He believes there is a lack of deep understanding of how these systems work, describing this development as "worrying." This is because, like nuclear technology and biological engineering, AI can lead to unexpected and dangerous consequences if not fully understood. Therefore, more research on the safety of artificial intelligence is needed to avoid potential risks and ensure responsible development.
