What is the future of NLP in AI?
Learn from the community’s knowledge. Experts are adding insights into this AI-powered collaborative article, and you could too.
This is a new type of article that we started with the help of AI, and experts are taking it forward by sharing their thoughts directly into each section.
If you’d like to contribute, request an invite by liking or reacting to this article. Learn more
— The LinkedIn Team
Natural language processing (NLP) is a branch of artificial intelligence (AI) that deals with the interaction between humans and machines using natural language. It enables computers to understand, analyze, generate, and manipulate human language in various forms and contexts. NLP has many applications in various domains, such as search engines, chatbots, voice assistants, social media analysis, machine translation, sentiment analysis, text summarization, and more. But what is the future of NLP in AI? How will it evolve and impact our lives in the coming years? In this article, we will explore some of the trends, challenges, and opportunities of NLP in AI.
One of the most significant trends in NLP in recent years is the development and use of pre-trained language models, such as BERT, GPT-3, and T5. These models are trained on large amounts of text data from various sources, such as books, websites, news articles, and social media posts. They learn to capture the general patterns and structures of natural language, such as syntax, semantics, and pragmatics. They can then be fine-tuned or adapted to specific tasks or domains, such as question answering, text classification, or text generation. Pre-trained language models have achieved remarkable results on many NLP benchmarks and tasks, surpassing human performance in some cases. They have also enabled the creation of new and innovative applications, such as natural language generation, conversational AI, and text summarization.
-
Samantha Glover
📈“𝐈 𝐮𝐬𝐞 𝐝𝐚𝐭𝐚 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬.” CIO & AI Consultant, Research Scientist, Mathematician, Data Science, ISO - Business Loan Broker
A pre-trained language model is a ML model previously trained to understand patterns and context within language. It can be fine-tuned on specific tasks, such as translation, answering questions, and text generation, without having to start from scratch. The future of NLP in AI as it relates to pre-trained language models presents an expansive landscape that already exists and continues to evolve. For one pre-trained models serve as foundation models which, as mentioned they can be fine tuned for a wide range of NLP tasks. This is a game changer because it reduces the barrier to entry for organizations to leverage NLP. The future looks bright with a foundation to experiment and innovate in the field of NLP through pre-trained models.:)
11
-
Venkatesh S.
Security Operations Manager @ Core42 | Security & AI Expert
Pre-trained models will only get better, there are ways the technology could improve such as chain of thought and logic aspects understanding NLU is still in early stages. New architecture like T5, BERT has to be developed for capturing context, pre-training has to be improved in terms of efficiency.
5
Another important trend in NLP is the expansion of multilingual and cross-lingual capabilities. Multilingual NLP refers to the ability to process and generate natural language in multiple languages, while cross-lingual NLP refers to the ability to transfer knowledge and skills from one language to another. Multilingual and cross-lingual NLP are essential for enabling global communication, information access, and cultural diversity. They also pose many challenges and opportunities for NLP research and development. For example, how can we leverage the similarities and differences between languages to improve NLP models and systems? How can we deal with low-resource languages that have limited data and resources? How can we ensure the quality, accuracy, and fairness of multilingual and cross-lingual NLP?
-
Samantha Glover
📈“𝐈 𝐮𝐬𝐞 𝐝𝐚𝐭𝐚 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬.” CIO & AI Consultant, Research Scientist, Mathematician, Data Science, ISO - Business Loan Broker
Multilingual and Cross Lingual NLP in my opinion will bring about great advancements in global communications. We can probably expect a significant impact on various use cases as well as sectors. Multilingual NLP is a tool we can use to break down language barriers across the globe. We already have some applications that do this. With real-time translation services in the future that will be more efficient, accurate and ubiquitous than what we currently have. This may improve sectors such as tourism where anyone can travel anywhere with the ability to understand language in real time. This can be helpful for students, and workers who want to travel abroad in addition to the government or any area where communication is required.
8
-
Vaibhav Kulshrestha
Lead AI Engineer @ Slytek, Inc. | AI | Robotics | DevOps
- An illustrative example is the development of AI-powered translation tools that can effortlessly switch between languages, making international travel, business, and cultural exchanges smoother than ever. - However, this trend also brings forth challenges like handling low-resource languages with limited data and ensuring the quality and fairness of multilingual and cross-lingual NLP systems. - Embracing these challenges and opportunities is key to shaping the promising future of NLP in AI. #NLP #AI #MultilingualNLP #CrosslingualNLP #ArtificialIntelligence #NaturalLanguageProcessing
2
A third major trend in NLP is the increasing demand for explainable and trustworthy NLP. Explainable NLP refers to the ability to provide transparent and understandable explanations for the decisions and outputs of NLP models and systems. Trustworthy NLP refers to the ability to ensure the reliability, security, privacy, and ethics of NLP models and systems. Explainable and trustworthy NLP are crucial for building user confidence, accountability, and responsibility in NLP applications, especially in sensitive and high-stakes domains, such as healthcare, education, law, and finance. They also raise many questions and challenges for NLP research and development. For example, how can we measure and improve the explainability and trustworthiness of NLP models and systems? How can we balance the trade-off between performance and explainability? How can we address the issues of bias, fairness, and social impact of NLP?
-
Umaid Asim
CEO at SensViz | Building human-centric AI applications that truly understands and empowers you | Helping businesses and individuals leverage AI | Entrepreneur | Top AI & Data Science Voice
Making NLP explainable and trustworthy is key. It's like, if NLP tools can tell us 'how' they got an answer, we'd trust them more, right? Especially, when they help in serious stuff like health or legal advice. So, the future is not just about getting the right answers from NLP, but also understanding 'how' it got there. This way, we can use NLP more responsibly and really make the most out of it in different areas.
10
-
Ali A.
Explainable and trustworthy NLP has impact : 1.Transparency is essential for regulatory compliance, especially in fields like healthcare or finance. 2. Accountability: Explainable NLP models, makes it easier to trace errors to their source. This improves model performance and fairness 3.Bias Mitigation: They allow for the identification and mitigation of biases in NLP. Developers can take steps to reduce biases and ensure fairer outcomes 4.Trustworthiness: These models instill trust in end-users, as they can see the reasoning behind a model's output.Sensitive applications : medical diagnosis & legal decision-making. 5. Compliance: In highly regulated industries : healthcare or finance, explainable NLP models make it easier to comply.
5
One of the main challenges for NLP in AI is the quality and diversity of data. Data is the fuel for NLP models and systems, but it is often noisy, incomplete, inconsistent, or biased. Data quality and diversity affect the performance, robustness, and generalization of NLP models and systems. For example, how can we ensure that the data we use for training and testing NLP models and systems is representative, relevant, and reliable? How can we handle the data heterogeneity, complexity, and dynamics in different domains and scenarios? How can we reduce the data sparsity, scarcity, and imbalance in some languages and tasks?
-
Vaibhav Kulshrestha
Lead AI Engineer @ Slytek, Inc. | AI | Robotics | DevOps
- NLP's applications span search engines, chatbots, voice assistants, and much more. - However, as we look ahead, one significant challenge lies in data quality and diversity. - For instance, when developing NLP models, ensuring data represents a wide range of scenarios, domains, and languages is crucial. - Data often comes with noise, incompleteness, and bias, affecting the models' performance and adaptability. - Overcoming these challenges will be instrumental in unleashing NLP's full potential in AI. #NLP #AI #ArtificialIntelligence #FutureOfAI #DataQuality #NLPChallenges #NaturalLanguageProcessing
1
-
Athul Mathew Konoor
📌 Data Scientist | 5.4 K+ LinkedIn 🚀 | AI/ML Engineer @ Sun Mobility (🔋EV Startup) | Ex- AI Scientist @ Data POEM (Marketing Startup)
Similar to having LLM's (Large Language Models), there should be efforts to have LLD's(Large Language Datasets), where collaborators can add, modify, rate, and evaluate the consistency of the data. This will to an extent curb bias, inconsistency and reliability issues. Models trained on these kinds of community-managed datasets (quality datasets-peer reviewed) can be a game changer for the models to generalise better and be robust enough.
1
Another key challenge for NLP in AI is the computational efficiency and scalability. NLP models and systems are becoming more complex, sophisticated, and resource-intensive. They require large amounts of data, computing power, and memory to achieve high performance and functionality. Computational efficiency and scalability affect the feasibility, cost, and sustainability of NLP models and systems. For example, how can we optimize the design, implementation, and deployment of NLP models and systems? How can we reduce the computational complexity, latency, and energy consumption of NLP models and systems? How can we leverage the advances in hardware, software, and cloud technologies to support NLP models and systems?
-
Brian L. Keith
Data, AI & Cloud Leader | Azure Cloud | I help government leaders to digitally transform the way they operate and deliver services.
Reducing the computational complexity, latency, and energy consumption of NLP models and systems is a complex task that requires a combination of techniques. We need to start designing hardware and software components together to optimize performance and energy efficiency.
3
-
Athul Mathew Konoor
📌 Data Scientist | 5.4 K+ LinkedIn 🚀 | AI/ML Engineer @ Sun Mobility (🔋EV Startup) | Ex- AI Scientist @ Data POEM (Marketing Startup)
"ChatGPT drinks 500ml of water for every conversation" - Indian Express. This is something a lot of us have heard recently. In a world where generative AI is being pushed as a solution to everything, we have to be concerned about the impact it will have on our future & and ecosystem. Higher computational efficiency and effective use of resources are a big must if we are to scale up sensibly in the world of AI and have to keep having the flexibility of using it.
2
One of the exciting opportunities for NLP in AI is the interdisciplinary and multimodal integration. NLP can benefit from and contribute to other disciplines and modalities, such as computer vision, speech processing, cognitive science, linguistics, psychology, sociology, and more. Interdisciplinary and multimodal integration can enhance the understanding, generation, and interaction of natural language in various contexts and situations. For example, how can we combine NLP with computer vision to process and generate natural language from images and videos? How can we combine NLP with speech processing to process and generate natural language from speech and audio? How can we use NLP to model and analyze the cognitive, linguistic, and social aspects of human communication?
-
Heena Purohit
LinkedIn Top AI Voice | Building Next-Gen AI Products @ IBM | 3x Top 10 Women in AI Award Recipient | Keynote Speaker | Startup Advisor | Responsible AI Advocate
Multimodal NLP unlocks a new frontier of reimagined use cases. By combining diverse data modalities, we’re moving to more intuitive AI tools that better understand the world around us. The early traction is in the consumer space. E.g., ChatGPT Plus users can now move from text-only to conversational experiences grounded in image context. And we now have AI accessibility tools that can better support users with disabilities. But the enterprise potential is massive, too. Think: - Medical diagnostics tools that combine both images and patient data - Marketing analytics tools that use performance and image/video data - More natural learning tools in education The possibilities are endless!
14
-
Vaibhav Kulshrestha
Lead AI Engineer @ Slytek, Inc. | AI | Robotics | DevOps
- In the coming years, NLP is set to evolve in remarkable ways, transforming our daily lives. - One promising opportunity is interdisciplinary and multimodal NLP, where NLP integrates with various fields and modalities. - For instance, combining NLP with computer vision allows machines to extract insights from images and videos, bridging the gap between visual and textual data. - This integration can revolutionize areas like content summarization, accessibility for the visually impaired, and automated image captioning. - The future of NLP is a journey of innovation and collaboration across disciplines. #NLP #AI #ArtificialIntelligence #FutureTech #MultimodalNLP #InterdisciplinaryIntegration #NaturalLanguageProcessing
1
-
Samantha Glover
📈“𝐈 𝐮𝐬𝐞 𝐝𝐚𝐭𝐚 𝐭𝐨 𝐬𝐨𝐥𝐯𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬.” CIO & AI Consultant, Research Scientist, Mathematician, Data Science, ISO - Business Loan Broker
Future communications will be advanced AI Technologies that involve NLP applications (conversational AI). With more research, we can create sustainable infrastructure around ethics. Ethics is a blurred line… We should not only call upon AI leaders but regular everyday people to give feedback. Race, religion, politics, war, love, etc… All of these topics and more don’t really have edges nor borders. Don't forget that AI was and is a philosophy. Cognitive technologies are based on human learning, perception and action. There are many perceptions that behold the world that we live in. I hope that we will be able to work together to create ethical models that will support and adapt to the many minds that make up our world.:)
12
-
Kaushik Shakkari
Senior Data Scientist: Making search & extraction easy across messy unstructured data | Mentor and Blogger: Bridging the gap between academia and industry; helping budding students & professionals transition into AI
Some of the trends which were not mentioned and I see them coming are: 1. Explainable AI: Large Language Models are complex and I see there going to be more push from customers to make these models explainable 2. Fine-tuning: Even though new heavy models come into market, I see efforts for fine-tuning will keep increasing 3. Ethics and Compliance: Todays models still have a lot of bias where these are not applied to certain industries or use cases. I see there will be many efforts to reduce these bias going forward.
10