How AI is changing the way humans interact with machines

The past 12 months have seen the global digital paradigm evolve tremendously, especially regarding how humans interact with machines. In fact, the space has undergone such a radical transformation that people of all ages are now fast becoming conversant with artificial intelligence (AI) models, most popularly OpenAI’s ChatGPT. 

The primary driving force behind this revolution has been the advancements made in natural language processing (NLP) and conversational AI. NLP is a subfield of AI that focuses on the interaction between computers and humans using everyday language and speech patterns. The ultimate objective of NLP is to read, decipher, understand and make sense of human language in a way that is understandable and easy to digest for users.

To elaborate, it combines computational linguistics — i.e., rule-based modeling of human language — with other fields, such as machine learning, statistics and deep learning. As a result, NLP systems allow machines to understand, interpret, generate, and respond to human language in a meaningful and contextually appropriate way.

Moreover, NLP involves several key tasks and techniques, including part-of-speech tagging, named entity recognition, sentiment analysis, machine translation and topic extraction. These tasks help machines understand and generate human language-type responses. For example, part-of-speech tagging involves identifying the grammatical group of a given word, while named entity recognition involves identifying individuals, companies or locations in a text.

NLP redefining communication frontiers

Even though AI-enabled tech has only recently started becoming part of the digital mainstream, it has profoundly influenced many people for the better part of the last decade. Companions like Amazon’s Alexa, Google’s Assistant and Apple’s Siri have woven themselves into the fabric of our everyday lives, assisting us with everything from jotting down reminders to orchestrating our smart homes.

The magic behind these helpers is a potent mix of NLP and AI, enabling them to comprehend and react to human speech. That said, the scope of NLP and AI has now expanded into several other sectors. For example, within customer service, chatbots now enable companies to provide automated customer service with immediate responses to customer inquiries.

With the ability to juggle multiple customer interactions simultaneously, these automated chatbots have already slashed wait times.

Language translation is another frontier where NLP and AI have made remarkable progress. Translation apps can now interpret text and speech in real time, dismantling language barriers and fostering cross-cultural communication.

A paper in The Lancet notes that these translation capabilities have the potential to redefine the health sector. Researchers believe these systems can be deployed in countries with insufficient health providers, allowing doctors and medical professionals from abroad to deliver live clinical risk assessments.

Sentiment analysis, another application of NLP, is also being employed to decipher the emotional undertones behind words, making responses from platforms like Google Bard, ChatGPT and Jasper.ai even more human-like.

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Thanks to their growing prowess, these technologies can be integrated into social media monitoring systems, market research analysis and customer service delivery. By scrutinizing customer feedback, reviews and social media chatter, businesses can glean valuable insights into how their customers feel about their products or services.

Lastly, AI and NLP have ventured into the realm of content generation. AI-powered systems can now craft human-like text, churning out everything from news articles to poetry, helping create website content, generating personalized emails and whipping up marketing copy.

The future of AI and NLP 

Looking toward the horizon, many experts believe the future of AI and NLP to be quite exciting. Dimitry Mihaylov, co-founder and chief science officer for AI-based medical diagnosis platform Acoustery, told Cointelegraph that the integration of multimodal input, including images, audio, and video data, will be the next significant step in AI and NLP, adding:

“This will enable more comprehensive and accurate translations, considering visual and auditory cues alongside textual information. Sentiment analysis is another focus of AI experts, and that would allow a more precise and nuanced understanding of emotions and opinions expressed in text. Of course, all companies and researchers will work on enabling real-time capabilities, so most human interpreters, I am afraid, will start losing their jobs.”

Similarly, Alex Newman, protocol designer at Human Protocol, a platform offering decentralized data labeling services for AI projects, believes that NLP and AI are on the verge of significantly increasing individual productivity, which is crucial given the anticipated shrinkage of the workforce due to AI automation. 

Newman sees sentiment analysis as a key driver, with a more sophisticated interpretation of data taking place through neural networks and deep learning systems. He also envisions the open-sourcing of data platforms to better cater to those languages that have traditionally been under-served by translation services.

Megan Skye, a technical content editor for Astar Network — an AI-based multichain decentralized application layer on Polkadot — sees the sky as the limit for innovation in AI and NLP, particularly with AI’s ability to self-assemble new iterations of itself and extend its own functionality, adding:

“AI and NLP-based sentiment analysis is likely already happening on platforms like YouTube and Facebook that use a knowledge graph, and could be extended to the blockchain. For example, if a new domain-specific AI is configured to accept freshly indexed blocks as a stream of source input data, and we had access to or developed an algorithm for blockchain-based sentiment analysis.”

Scott Dykstra, chief technical officer for AI-based data repository Space and Time, sees the future of NLP at the intersection of edge and cloud computing. He told Cointelegraph that in the near to mid-term, most smartphones would likely come with an embedded large-language model that will work in conjunction with a massive foundational model in the cloud. “This setup will allow for a lightweight AI assistant in your pocket and heavyweight AI in the data center,” he added.

The road ahead is paved with challenges

While the future of AI and NLP is promising, it is not without its challenges. For example, Mihaylov points out that AI and NLP models rely heavily on large volumes of high-quality data for training and performance.

However, due to various data privacy laws, acquiring labeled or domain-specific data can be challenging in some industries. Furthermore, different industries have unique vocabularies, terminologies and contextual variations that require very specific models. “The shortage of qualified professionals to develop these models presents a significant barrier,” he opined.

Skye echoes this sentiment, noting that while AI systems can potentially operate autonomously in almost any industry, the logistics of integration, modification of workflows, and education present significant challenges. Furthermore, AI and NLP systems require regular maintenance, especially when the quality of answers and a low probability of error are important.

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Lastly, Newman believes that the problem of access to new data sources pertinent to each industry looking to use these technologies will become more and more apparent with each passing year, adding:

“There’s plenty of data out there; it’s just not always accessible, fresh or sufficiently prepared for machine training. Without data that reflects the particulars of an industry, its language, rules, systems, and specifics, AI won’t be able to appreciate any context and operate effectively.”

Therefore, as more and more people continue to gravitate toward the use of the aforementioned technologies, it will be interesting to see how the existing digital paradigm continues to evolve and mature, especially given the rapid rate at which the use of AI seems to be seeping into various industries.