The last 12 months have seen the global digital paradigm shift tremendously, especially with regards to how humans interact with machines. In fact, the space has undergone such a drastic transformation that people of all ages are quickly becoming familiar with artificial intelligence (AI) models, the most popular being OpenAI’s ChatGPT.
The main driver of this revolution has been advances 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 goal of NLP is to read, decipher, understand and make sense of human language in a way that is understandable and easy for users to digest.
To elaborate, it combines computational linguistics – that is, rule-based modeling of human language – with other fields, such as machine learning, statistics and deep learning. As a result, NLP systems enable machines to understand, interpret, generate, and respond to human language in a meaningful and context-appropriate way.
Additionally, 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-like responses. For example, part-of-speech tagging involves identifying the grammatical grouping of a given word, while named entity recognition involves identifying individuals, companies, or locations in a text.
NLP redefines the boundaries of communication
Even though AI-based technology has only recently begun to enter the digital mainstream, it has profoundly influenced many people for most of the past decade. Companions like Amazon’s Alexa, Google’s Assistant, and Apple’s Siri have woven themselves into the fabric of our daily lives, helping us with everything from writing reminders to orchestrating our smart homes.
The magic behind these aids is a powerful blend of NLP and AI, allowing them to understand and react to human speech. That said, the reach of NLP and AI has now expanded to several other industries. For example, within customer service, chatbots now allow businesses 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 reduced 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, breaking down language barriers and promoting cross-cultural communication.
A paper in The Lancet notes that these translation capabilities have the potential to redefine the healthcare industry. Researchers believe these systems can be deployed in countries with insufficient healthcare providers, allowing doctors and healthcare professionals from overseas to provide live clinical risk assessments.
Sentiment analysis, another application of NLP, is also used to decipher emotional nuances behind the words, making responses from platforms like Google Bard, ChatGPT and Jasper.ai even more human.
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With their growing prowess, these technologies can be incorporated into social media monitoring systems, market research analysis, and customer service delivery. By looking at customer comments, reviews, and social media conversations, businesses can glean valuable insights into how their customers feel about their product or service.
Finally, AI and NLP have ventured into the realm of content generation. AI-powered systems can now arts and crafts human-like text, producing everything from news articles to poetry, helping to create website content, generating personalized emails and whipping up marketing copy.
The future of AI and NLP
Looking towards the horizon, many experts think the future of AI and NLP is quite exciting. Dimitry Mihaylov, co-founder and chief scientific officer of AI-based medical diagnostic platform Acoustery, told Cointelegraph that the integration of multimodal input, including images, audio and video data, will be the next big step in AI and NLP, adding:
“This will allow for more complete and accurate translations, taking into account visual and auditory cues alongside textual information. Sentiment analysis is another focus of AI experts, and this would allow for a more accurate and nuanced understanding of the emotions and opinions expressed in text. Of course, all companies and all researchers will be working on enabling real-time capabilities, so most human performers, I’m 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 poised to dramatically increase individual productivity. , which is crucial given the anticipated reduction in the workforce due to AI. automating.
Newman sees sentiment analysis as a key driver, with more sophisticated interpretation of data through neural networks and deep learning systems. It also envisions open source data platforms to better cater to languages that have traditionally been underserved by translation services.
Megan Skye, technical content editor for Astar Network – an AI-powered multi-channel decentralized application layer on Polkadot – sees the sky as the limit for innovation in AI and NLP, especially with the ability to AI to self-assemble new iterations of itself and extend its own functionality, adding:
“AI and NLP-based sentiment analysis is probably already underway on platforms like YouTube and Facebook that use a knowledge graph, and could be extended to blockchain. For example, if a new AI specific to a domain is configured to accept freshly indexed blocks as a source input data stream, and that we have accessed or developed an algorithm for blockchain-based sentiment analysis.
Scott Dykstra, CTO of AI-powered data repository Space and Time, sees the future of NLP at the intersection of edge computing and the cloud. He told Cointelegraph that in the short to medium term, most smartphones will likely come with a built-in large-tongue model that will work in conjunction with a massive base cloud model. “This setup will allow for a lightweight AI assistant in your pocket and heavy AI in the data center,” he added.
The road ahead is paved with challenges
Although the future of AI and NLP is bright, it is not without 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 difficult in some industries. Additionally, different industries have unique vocabularies, terminologies, and contextual variations that require very specific patterns. “The shortage of qualified professionals to develop these models presents a significant barrier,” he said.
Skye echoes that sentiment, noting that while AI systems can potentially operate autonomously in almost any industry, the logistics of integration, changing workflows, and education present significant challenges. Moreover, AI and NLP systems require regular maintenance, especially when the quality of responses and a low probability of error are important.
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Finally, Newman believes that the problem of accessing new sources of relevant data for every industry seeking to use these technologies will become more apparent year on year, adding:
“There’s a lot of data out there; it’s just not always accessible, fresh, or prepared enough for on-machine training. Without data that reflects the particularities of an industry, its language, its rules, its systems and its specificities, AI will not be able to appreciate any context and operate effectively.
Therefore, as more and more people continue to turn to using the aforementioned technologies, it will be interesting to see how the existing digital paradigm continues to evolve and mature, especially given the rapid pace at which the use of AI seems to be seeping in. in various industries.