SYMBOLIC AI: THE KEY TO THE THINKING MACHINE

摘要： Even as many enterprises are just starting to dip their toes into the AI pool with rudimentary machine learning (ML) and deep learning (DL) models, a new form of the technology known as symbolic AI is emerging from the lab that has the potential to upend both the way AI functions and how it relates to its human overseers.

▲圖片來源：leackstat.com

Even as many enterprises are just starting to dip their toes into the AI pool with rudimentary machine learning (ML) and deep learning (DL) models, a new form of the technology known as symbolic AI is emerging from the lab that has the potential to upend both the way AI functions and how it relates to its human overseers.

Symbolic AI’s adherents say it more closely follows the logic of biological intelligence because it analyzes symbols, not just data, to arrive at more intuitive, knowledge-based conclusions. It’s most commonly used in linguistics models such as natural language processing (NLP) and natural language understanding (NLU), but it is quickly finding its way into ML and other types of AI where it can bring much-needed visibility into algorithmic processes.

What’s old is new

The technology actually dates back to the 1950s, says expert.ai’s Luca Scagliarini, but was considered old-fashioned by the 1990s when demand for procedural knowledge of sensory and motor processes was all the rage. Now that AI is tasked with higher-order systems and data management, the capability to engage in logical thinking and knowledge representation is cool again.

One of the keys to symbolic AI’s success is the way it functions within a rules-based environment. Typical AI models tend to drift from their original intent as new data influences changes in the algorithm. Scagliarini says the rules of symbolic AI resist drift, so models can be created much faster and with far less data to begin with, and then require less retraining once they enter production environments.

Because they are bound by rules, however, symbolic algorithms cannot improve themselves over time, which is, after all, one of the key value propositions that AI brings to the table, says Jans Aasman, CEO of knowledge graph solutions provider Franz Inc. This is why symbolic AI is being integrated into ML, DL, and other forms of rules-free AI to create hybrid environments that provide the best of both worlds: full machine intelligence with logic-based brains that improve with each application.

This, in turn, enables AI to be trained using multiple techniques, including semantic inferencing and both supervised and unsupervised learning, which will ultimately create AI systems that can reason, learn, and engage in natural language question-and-answer interactions with humans. Already, this technology is finding its way into such complex tasks as fraud analysis, supply chain optimization, and sociological research.

Problem solver

This creates a crucial turning point for the enterprise, says Analytics Week’s Jelani Harper. Data fabric developers like Stardog are working to combine both logical and statistical AI to analyze categorical data; that is, data that has been categorized in order of importance to the enterprise. Symbolic AI plays the crucial role of interpreting the rules governing this data and making a reasoned determination of its accuracy. Ultimately this will allow organizations to apply multiple forms of AI to solve virtually any and all situations it faces in the digital realm – essentially using one AI to overcome the deficiencies of another.

For organizations looking forward to the day they can interact with AI just like a person, symbolic AI is how it will happen, says tech journalist Surya Maddula. After all, we humans developed reason by first learning the rules of how things interrelate, then applying those rules to other situations – pretty much the way symbolic AI is trained. Integrating this form of cognitive reasoning within deep neural networks creates what researchers are calling neuro-symbolic AI, which will learn and mature using the same basic rules-oriented framework that we do.

While this may be unnerving to some, it must be remembered that symbolic AI still only works with numbers, just in a different way. By creating a more human-like thinking machine, organizations will be able to democratize the technology across the workforce so it can be applied to the real-world situations we face every day.

It certainly won’t be able to solve all our problems, but it will relieve us of the most annoying ones.

轉貼自Source： leackstat.com

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