Humanity’s constant quest to improve living conditions has led to the creation of machines that can perform complex tasks automatically. From the construction of the first calculating machines to the rise of electronic computers during World War II, we have made significant progress in process automation. However, we face a crucial challenge: how to address problems that cannot be solved by algorithms?
In this context, the history of neural networks becomes a fascinating tale of ingenuity and discovery. Charles Babbage was one of the pioneers in the search for machines that could solve mathematical problems, although his initial efforts did not bear fruit. During the Second World War, with the availability of electronic instruments, the first advances began to be seen. In 1946, the first electronic computer, ENIAC, was built, marking a milestone in the history of computing.
In recent years, artificial intelligence (AI) has become an increasingly present technology in the health sector, and virtual assistants are one of its most prominent applications. These computer programs can be of great help to both patients and doctors.
Electronic computers allowed the implementation of algorithms to solve a wide variety of problems that were previously cumbersome to address. However, a significant limitation became evident: how to address problems that cannot be broken down into algorithms, such as classifying objects by common characteristics?
The answer to this challenge lies in human learning and experience. Humans are capable of solving complex situations based on their accumulated experience. Artificial intelligence (AI) emerged as an attempt to understand and emulate aspects of human intelligence in machines. Neural networks, in particular, were inspired by the human ability to memorize and associate facts.
Neural networks, at their core, are artificial and simplified models of the human brain, which is the most perfect example of a system that acquires knowledge through experience. Neural networks mimic the basic function of biological neurons in communication and information processing. They are a new way of processing information and data.
The nucleus of a biological neuron involves receiving inputs, processing them, and, when a threshold is reached, transmitting a signal down the axon. Subsequent research demonstrated that these processes are related to electrochemical events. Human memory, learning, and decision-making are related to the activity of interconnected neurons in the brain.
Learning is essential to solve problems that are initially unsolvable. Neural networks share this ability to learn and improve their functioning. They are made up of processing units that exchange information and are used to recognize patterns in a variety of data, from images to time sequences such as financial trends.
The history of neural networks spans decades of key research and development. From the initial theories of Warren McCulloch and Walter Pitts in 1943 to advances in learning and network architectures, the evolution of neural networks has been impressive.
Alan Turing, Dartmouth Congress, Donald Hebb, Frank Rosenblatt, Karl Lashley and many others influenced the development of neural network theory. Each of these milestones contributed to the resurgence of neural networks in the 1980s, and since then, they have become a vital tool in fields such as image processing, machine learning, and artificial intelligence.
Key aspects of neural networks:
- A New Form of Computing, Inspired by Biological Models: Neural networks are inspired by the functioning of the human nervous system and other biological systems. Like neurons in the brain, elements in a neural network communicate with each other to process information and perform specific tasks. This biological inspiration allows them to learn, adapt, and operate similarly to the human mind.
- Mathematical Model Composed of Procedural Elements:From a mathematical perspective, a neural network can be seen as a model composed of a large number of interconnected procedural elements. Each of these elements, known as artificial neurons or nodes, performs calculations based on the inputs it receives and transmits them to other neurons through weighted connections. From a mathematical perspective, a neural network can be viewed as a model composed of a large number of interconnected procedural elements. Each of these elements, known as artificial neurons or nodes, performs calculations based on the inputs it receives and transmits them to other neurons through weighted connections.
- Highly Interconnected Computing System: Neural networks are highly interconnected parallel computing systems. This means that many nodes work simultaneously to process information. Its hierarchical and massively parallel structure is essential to its ability to perform complex tasks.
Advantages of Neural Networks:
Artificial neural networks offer a number of advantages due to their ability to learn, adapt and process information in a similar way to the human mind. Here are some of the most significant advantages:
- Adaptive Learning: One of the most attractive features of neural networks is their adaptive learning capacity. These networks can learn to carry out specific tasks through a training process with illustrative examples. As they are presented with more information, they adjust their connections to improve their performance. This eliminates the need to develop detailed models or specific algorithms, as neural networks can learn from experience.
- Auto-organization: Neural networks use their adaptive learning capacity to self-organize the information they receive during learning and operation. While learning refers to the modification of each procedural element (neuron), self-organization involves the modification of the entire network to meet a specific goal. For example, a neural network can create its own characteristic representation of information that allows it to recognize input patterns.
- Error Tolerance Neural networks are inherently fault tolerant. Unlike traditional computing systems that can lose functionality due to a small memory error, neural networks can continue functioning even if some of their neurons are damaged. This tolerance is due to the distribution of information in the connections between neurons and the redundancy in these connections.
- Real-time Operations: The parallel processing capacity of neural networks makes them suitable for operating in real time. They often require initial training and minimal tuning to operate efficiently in real-time applications.
- Easy Insertion into Existing Technology: Neural networks can be trained to perform specific tasks and subsequently integrated in a modular way into existing systems. With the right computational tools, a network can be trained, verified, and ported to a low-cost hardware implementation. This flexibility makes it easy to integrate neural networks into applications in a wide range of fields.
Neural Computing vs. Digital:
It is important to highlight that neural networks differ from digital computing in several aspects:
- Non-Algorithmic: Neural networks do not follow algorithms in the traditional sense. Instead of relying on a fixed sequence of instructions, they learn from data and adapt dynamically.
- Not Based on Binary Logic: Unlike the binary logic on which digital computing is based, neural networks operate with continuous and weighted values, allowing them to perform more flexible calculations suitable for perception and learning tasks.
- Non-Bistable: While the elements of digital computing are bistable (0 or 1), the neurons and connections in neural networks are continuous and change gradually over time. This better reflects the analog nature of information in the brain.
In summary, artificial neural networks are highly adaptable systems inspired by the human brain. Their versatility and ability to simulate brain processes make them a powerful tool in the field of artificial intelligence and machine learning.
About AlgoNew
At AlgoNew, we add intelligence to your digital interactions so you can deliver a personalized and efficient experience to your customers. How do we do it? Through a combination of intelligent decision management, natural language processing, and advanced analytics.
We use algorithms to help you make informed decisions in real-time and improve the efficiency of your processes. In other words, we make sure that every action you take is based on relevant data and artificial intelligence, resulting in faster and more accurate decision-making.
Conversation management, on the other hand, refers to how you interact with your customers through digital platforms such as chatbots or virtual assistants. We use natural language processing technology to understand and respond to customer requests effectively and naturally. This means your customers can interact with digital systems in the same way they would with a human, which enhances the user experience.
Finally, we use advanced data analytics to gain valuable insights from your digital interactions. We analyze the data generated from your interactions to identify patterns and trends that can help you improve your business. This can include things like identifying common problems your customers have and how to solve them efficiently or identifying areas for improvement in your business processes.
This combination of intelligence that we offer at AlgoNew can help you significantly improve your digital interactions with customers. It helps you make informed, data-driven decisions, interact with them effectively and naturally, and gain valuable insights into your business processes.
All leads to a better customer experience and greater business efficiency!
