Self-supervised learning (SSL) is an approach to machine learning allows machine learning algorithms to use observed inputs to predict unknown inputs.
An important goal for self-supervised learning is to programmatically change unsupervised learning models into supervised learning models by developing pre-training deep learning systems that can learn to fill in missing information.
BERT (Bidirectional Encoder Representations from Transformers) and Q Learning are perhaps two of the most well-known applications of self-supervised learning in AI. During the pre-training phase, each system is shown a short example of text in which some of the words are missing. The systems are then trained how to extract supervisory signals from the input data in order to predict the missing words accurately.
SSL and has played an important role in the development of natural language processing (NLP). It is one of several approaches being studied to reduce the need for massive amounts of data to train AI learning algorithms.
During the Association for the Advancement of Artificial Intelligence (AAAI) 2020 conference, the French computer scientist, Yann LeCun, said that self-supervised learning is what would take AI and deep learning systems to the next level.
Because self-supervised learning uses previously-learned information to predict data patterns and upcoming events, effectively becoming smarter, it is not limited by human capabilities. Its independence allows it to be highly scalable, growing exponentially into pattern prediction and recognition skills, along with advanced decision-making capabilities.
Due to the lack of human assistance throughout the learning process, this approach requires powerful and complex machine learning algorithms along with high computational power. They need to be able to handle massive amounts of data of various types and be able to catalog and categorize them flexibly and effectively.
Proper encoding of all training items is key to a successful self-supervised learning approach. The more detailed and data-rich each study item is, the more information the AI system can successfully extract from them. As a result, the system will have a better chance at classifying items and input correctly in relation to other items during and after concluding training.
In general, AI systems that are designed using self-supervised learning are not used to directly solve a problem in the data it was first presented with. With this approach, the system creates clusters of data points that show a set of similarities or share patterns, while being as different as possible from other clusters. As a result, the AI system would provide information on how it represented the objects it analyzed. The representation it figured out, or the simple neural network, would come in handy in solving similar object-oriented tasks in the future.
While not immaculate, self-supervised machine learning can open many doors when it comes to developing AI systems and deep learning. Some benefits that are unique to self-supervised learning include:
- Scalability
– Without self-supervised learning, building strong prediction and
categorization models would be inefficient and time-consuming.
Alternatively, AI systems that rely on self-supervised learning can
automate sets of complex tasks as long as they have adequate
computational power, knowledge, and time.
- Efficient Problem Solving
– No longer needing to rely on the preconceived notions of the human
brain with labeled data, AI systems on their own can find the best route
to solving a problem, from filling gaps in images to statistical
predictions and object categorization.
- Improving Computer Vision
– Self-supervised learning lets AI systems train themselves similar to
how a human brain grows to recognize its surrounding environment. It
ensures the system does not get stuck or waste computational power
looking for similarities between what it is seeing and already labeled
training items.
- Recreating Human Intelligence – Similarly to improving computer vision, AI systems that rely on self-supervised learning not only have the potential to grow to near-human levels of intelligence but can also help neuroscientists understand how the human brain works.
With all its benefits, the self-supervised machine learning approach has limitations that prevent it from wide-spread use. For one, it requires enormous computational power that is hard to come by for smaller projects and amateur developers. Additionally, self-supervised learning, by default, is highly sensitive. Small inaccuracies in the items used to train it or how they were encoded could yield highly inaccurate results that are near-impossible to fix or ‘debug’ individually.
Additionally, work is being done to with images, specifically using the SimCLR framework, and advances with natural language processing (NLP) are impacting the field of self-supervised learning in ways that are exciting to those in the industry, with great benefits to consumers and end-users.
Still,
with all of its limitations and relative infancy, self-supervised
learning is what many computer scientists hope for the future.
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