As data becomes more abundant, machine learning has emerged as a prominent force, resulting in more intricate models being developed. Moreover, the swift improvement in connectivity and computing power has instigated a significant transformation in the realm of artificial intelligence.
Day by day, several new technologies are being created, ranging from prediction algorithms and image/speech recognition systems to recommendation tools and many others. It may be surprising to realize how many machine learning techniques are currently influencing our everyday lives.
While Artificial Intelligence (AI) is widely recognized, it is also recognized that AI systems can make errors and omissions just like human beings. For instance, neural networks can experience sudden memory loss which may result in disastrous outcomes.
What Exactly is Meant by a Neural Network?
In the domain of Machine Learning, Neural Networks have gained significant recognition, particularly in the prediction-making realm. This is owing to their capacity to imitate the way in which humans learn and process new information, thus being named as such.
Although the math behind it may be complicated, the essential idea is uncomplicated. Visualize a system of equations, or routes, that connect with one another in a manner that might be compared to the neural pathways in the brain.
Data initiates a neural network, much like how the brain reacts when encountering a certain stimulus. Based on the nature of the information being analyzed, specific pathways are either activated or hindered.
Towards the conclusion of this process, an output node generates a prediction or some other novel piece of information. As an illustration, upon seeing something with four legs, the processing of that data triggers brain activity which enables you to draw the conclusion that the animal in question is a dog and not a cat.
The capacity to differentiate between a dog and a cat, much like a human being, must be based on certain principles. For a Neural Network to gain knowledge, it needs to have access to relevant data. During the training process, pre-established datasets such as a group of pictures labelled as either ‘cat’ or ‘dog’ are utilized to facilitate the system’s ability to categorize new data.
After the training phase, the outcomes were assessed with the help of a distinct dataset. The time has come to test the validity of our predictions. Imagine this as an examination at the end of a class lesson about animals. If the network proves capable of making precise predictions with a high level of accuracy, deployment becomes feasible.
Reasons for Memory Loss in Artificial Neural Networks
Upon grasping the distinctions between cats and dogs, we move on to examining other animals and plants. The ability to learn and adjust with the passage of time is termed ‘plasticity’.
Despite certain benefits associated with Neural Networks, using them in this field comes with various crucial limitations. McCloskey and Cohen undertook an influential research study where Neural Networks were trained using 17 puzzles which involved the number ‘one’ (i.e. 1+9=10).
Following the validation of the output, they proceeded to evaluate the system’s capability to solve a new series of puzzles which focussed on the number 2. As predicted, the system accomplished the puzzles related to number 2 successfully, but it had forgotten the ones related to number 1.
This occurrence can be concisely elucidated; during the training phase, the neural network creates pathways between nodes in a dynamic manner. These connections are established by utilizing the input data.
With the introduction of new data, the algorithm has the ability to produce fresh connections. However, this can result in an escalation of the margin of error, eventually causing the machine to deviate from its original training objective. This occurrence is recognized as Catastrophic Forgetting or Catastrophic Interference.
How Prevalent is Catastrophic Forgetting?
Absolutely, present-day Neural Networks are commonly trained using the process of directed learning. To circumvent any complications that may emerge from utilizing raw data, engineers meticulously pick the data that will be fed into the network.
With the progress of Machine Learning, our agents will soon be capable of self-learning and consistently enhancing their capacities. Consequently, neural networks will be able to learn from novel data without any active involvement from human beings.
One of the significant hazards correlated with self-learning is the likelihood of catastrophic interference if the system is subjected to data that is considerably different from its original training data. This is primarily because there is no explicit recognition of the type of data being utilized by the network for learning.
Opting to abstain from autonomous networks may seem like the most appropriate course of action, however, it must not be disregarded. We should bear in mind the preceding research that was discussed; even though the resultant duties may not have diverged significantly from the original ones, they still led to interference.
Catastrophic Interference has the potential to occur even when utilizing the same datasets. It cannot be ascertained with complete confidence beforehand whether or not it will happen. The intermediate layers of a neural network (commonly referred to as ‘hidden layers’) remain somewhat enigmatic, so it is not viable to foresee the effect of the data on their stability.
Can Catastrophic Interference Be Prevented?
Despite the perpetually looming possibility of Catastrophic Forgetting, it is not an insuperable hindrance. Through meticulous planning, several risk alleviation schemes, including Node Sharpening and Latent Learning, can be utilized to curtail the risk of interference.
From a strategic standpoint, it is sensible to create a replica of a network before initiating its retraining so that there is a backup plan in case the original one fails.
Employing the technique of comprehensive data aggregation for training a new neural network is another prevalent method. This issue strictly arises in sequential learning, where the assimilation of novel information may conflict with the network’s existing knowledge.
The Journey Ahead Is Lengthy…
Researchers in the realm of machine learning are dedicating efforts to tackle the problem of Catastrophic Forgetting, along with other challenges. Even though we are presently at the nascent phase of artificial intelligence development, the potential of this technology is prodigious. Deciphering intelligence, regardless of it being artificial or natural, has always been intricate, but we are making considerable strides.
Machine learning is an extremely captivating field, owing to its pragmatic applications as well as the profound philosophical implications it entails. As an instance, Works aimed to fabricate a robot that is imperceptible from a human being.
Prior to determining whether this is factual, we must first tackle a seemingly straightforward inquiry. For centuries, philosophers have been intrigued by the notion of humanity. What we see in artificial intelligence is a reflection of humanity in a mirror.