A key goal of many organizations using artificial intelligence (AI) systems is for them to mirror human language and intelligence; However, mimicking human language and mastering its unique complexity remains one of the greatest challenges facing AI.
According to IBM’s Global AI Adoption Index, almost one in three IT professionals say their company is now using AI, and 43% reporting say their company has accelerated AI deployment due to the pandemic. As more companies implement AI systems, the limitations of the technology are also being recognized, including the amount of data required to train machine learning (ML) algorithms and the flexibility of those algorithms to understand human language.
Today, many artificial intelligence applications in customer service use ML algorithms, which have proven to be essential in the face of ever-changing consumer behavior. ML algorithms have the ability to process information and automate conversations, increasing companies’ ability to speak to their customers anytime, anywhere. As companies move from high-frequency one-way communication to two-way conversations, these algorithms will play an important role in the customer journey. However, a deeper understanding of human language is crucial when companies are trying to improve their interactions with customers.
I believe that when artificial intelligence systems gain a deeper understanding beyond traditional means of data analysis, they will outperform human performance on language tasks. Organizations on a global scale. Thanks to self-supervised learning, ML techniques now have the power to change that.
What Is Self-Supervised Learning?
As babies, we mostly get to know the world through observation and trial and error, which paves the way for us to develop common sense and the ability to learn complex tasks like driving a car. Some examples of a specific task and machine learning algorithms can’t? Self-directed learning can help here.
The technique generally involves taking an input record and hiding part of it. The self-supervised learning algorithm needs to analyze the visible data, which allows it to predict the remaining hidden data. As a result, this process creates the tags that enable the system to learn. This opens up a great opportunity to make better use of unlabeled data and to help companies optimize data processes. With self-supervised learning, there isn’t a need for a person to manually review and flag extreme amounts of data. Self-Supervised Learning creates a data-efficient artificial intelligence system that can analyze and process data without human intervention, eliminating the need for “full supervision”.
Our brains, and certainly the brains of young children, are constantly trying to make sense of the world by predicting what will happen next; When the prediction does not correspond to reality, we are surprised and learn. In a similar fashion, ML Algorithms learn to fill in the gaps through semi-supervised learning. ML algorithms trained with self-supervised learning appear to pick up general human cues and be able to outperform human performance on language tasks.
Breakthroughs in Self-Supervised Learning: How Will This Revolutionize Deep Learning?
Self-supervised approaches to learning have enabled significant advances in natural language processing (NLP), which empowers computers to understand, write, and speak languages like humans. The real breakthrough in NLP came when Google introduced the BERT model in 2018. The architecture typically used for machine translation let you learn the meaning of a word in relation to its context in a sentence. NLP keeps breaking records in understanding human language: In the last two years there have been more advances in NLP than in the last four decades, these artificial intelligence algorithms now surpass human performance in understanding the subject of a text and finding the answer to it random question and do it in 100+ languages at the same time. With the advent of mobile messaging, more companies are turning to chatbots and virtual assistants to answer customer questions in real time and increase engagement.
Deep learning algorithms, a subset of machine learning, have evolved to recognize faces with the same, if not better, precision than humans. However, it took until 2015 to develop an algorithm that could recognize faces with a precision comparable to humans. Facebook’s DeepFace is 97.4% accurate, just under 97.5% of human performance, and the FBI’s facial recognition algorithm is only 85% accurate, which means it stays wrong more than one in seven cases. Although deep learning is a critical aspect of artificial intelligence systems and has made significant advances in recent years, large amounts of data are required to produce useful results. Most importantly, it enables AI systems to act more humanely and understand language without intervention. Reaching this milestone opens up endless possibilities in the world of machine learning. It’s only a matter of time.