Rescuing Machine Learning with Symbolic AI for Language Understanding

For approaches solely involving advanced machine learning, data scientists can puzzle over techniques like LIME, ICE, and PDP when attempting to determine which specific features, measures, and weights of input data are creating certain outputs. Data lineage is also helpful for explaining AI results with statistical models by enabling organizations to retrace everything that happened to them, from production data to training data. Although these approaches provide insight into machine learning model performance, they’re better for interpretability than they are explainability.

“Neuro-symbolic modeling is one of the most exciting areas in AI right now,” said Brenden Lake, assistant professor of psychology and data science at New York University. His team has been exploring different ways to bridge the gap between the two AI approaches. Publishers can successfully process, categorize and tag more than 1.5 million news articles a day when using expert.ai’s symbolic technology. This makes it significantly easier to identify keywords and topics that readers are most interested in, at scale. Data-centric products can also be built out to create a more engaging and personalized user experience.

AI programming languages

It had the first self-hosting compiler, meaning that the compiler itself was originally written in LISP and then ran interpretively to compile the compiler code. Advancements in sentiment analysis technology are poised to reshape business strategies and customer interactions, offering a glimpse into a transformative future. Successfully navigating accuracy hurdles and ethical considerations in AI sentiment analysis is crucial for its responsible utilization. Balancing continual improvement with ethical standards is key to leveraging its potential for business growth while respecting privacy and societal fairness.

As a result, numerous researchers have focused on creating intelligent machines throughout history. For example, researchers predicted that deep neural networks would eventually be used for autonomous image recognition and natural language processing as early as the 1980s. We’ve been working for decades to gather the data and computing power necessary to realize that goal, but now it is available. Neuro-symbolic models have already beaten cutting-edge deep learning models in areas like image and video reasoning. Furthermore, compared to conventional models, they have achieved good accuracy with substantially less training data.

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“Deep learning in its present state cannot learn logical rules, since its strength comes from analyzing correlations in the data,” he said. Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions. To fill the remaining gaps between the current state of the art and the fundamental goals of AI, Neuro-Symbolic AI (NS) seeks to develop a fundamentally new approach to AI. It specifically aims to balance (and maintain) the advantages of statistical AI (machine learning) with the strengths of symbolic or classical AI (knowledge and reasoning). It aims for revolution rather than development and building new paradigms instead of a superficial synthesis of existing ones.

Read more about Symbolic and use cases here.