Noam Chomsky's problematic article on AI in New York Times

Lately, many within our social circle are sharing an excerpt from Noam Chomsky's article, which was recently published in The New York Times, either as a meme or a significant critique of AI. However, it's quite shocking that Noam Chomsky has chosen to pen an article of this particular nature. 

I apologise, Professor, but your recent article in The New York Times has not cast doubt on the advancements in language processing AI or its foundational constructs. In fact, it has regrettably cast a shadow over the extensive linguistic theories you have diligently cultivated, which have standardised language construction.

Your contributions are far-reaching, particularly the development of the syntactic structure that served as a cornerstone for cognitive science, paving the way for numerous postmodern developments in linguistics. Your introduction of theories such as generative grammar, which dissects sentences into constituent parts using phrase structure rules, and the meticulous application of recursive rules in the creation of Hebrew grammar, as well as the transformation generative grammar breaking down sentences into patterns of relationships among their components—these are all testaments to your commitment to standardising language and its processes. However, these very theories have been dismissed in your effort to support your assumption regarding AI's supposed inability to mitigate language bias through its standardised methods of pattern discovery within language. It is worth noting that AI language processing research heavily relies on these linguistic theories to comprehend the abstract.

Suppose one discredits the potential for bias mitigation in pattern discovery through data analysis. What purpose does Chomsky's hierarchy theory serve? What about the regular grammar, context-free grammar, context-sensitive grammar, and recursively enumerable grammar that you have painstakingly developed for language automation?

Furthermore, could you provide insights into your Descriptive Adequacy Theory, which intricately specifies rules for accounting for all observed data arrangements and, in turn, defines the rules responsible for generating well-formed constructs within the protocol space?

Let us pause here; it is indeed regrettable that in your endeavour to discredit AI language processing, you inadvertently question the fundamental theories you have ardently constructed and imparted over the years as a linguistics scholar.

It's worth noting that linguistic studies, theories, rules, and methods have played an integral role in developing AI data and language processing, underscoring the symbiotic relationship between AI and linguistics.

Should one seek to present a compelling argument against AI, it may be more prudent to explore humanitarian concerns. AI systems are founded on principles of standardisation and a precision-focused approach. In stark contrast, the human mind defies standardisation, echoing Shakespeare's famous words, "To err is human." Furthermore, human imagination, extending beyond the confines of certitude, commences where certainty ceases. Art, representing the abstract musings of the human mind in its quest to comprehend and extrapolate reality, inherently diverges from AI's standardised approach to patterns.

One is still unsure if AI will ever supplant the human mind, just as machines have never entirely replaced human labour. The limitations imposed on the mechanised process were not a consequence of the machinery itself. They were the outcome of decisive interventions through legislation to protect the dignity of labour and human effort by establishing labour rules within the production process. In AI, the trajectory would likely follow a similar path. However, it is crucial not to dismiss the research and academic rigour that underpins AI language processing. To provide a glimpse of the diverse realms of academic research taking place in AI, here are a few key areas. It is essential to acknowledge that these represent just a fraction of the research areas shaping AI, and the field is in a constant state of evolution. 

(the following list is prepared with the help of ai)

Language processing :

1. Multimodal AI: Language processing models are increasingly being combined with computer vision to create more comprehensive models capable of understanding and generating both text and images. This is particularly important in applications like image captioning and visual question-answering.
2. Conversational AI: Chatbots and virtual assistants continue to improve regarding natural language understanding and generation. They are used in customer support, virtual companions, and more.
3. Content Generation: AI generates various types of content, including articles, reports, and creative writing. It has applications in journalism, marketing, and content creation.
4. Translation and Language Localisation: Language processing AI has improved translation quality, enabling real-time translations in various languages. This is valuable for international business, travel, and content localisation.
5. Sentiment Analysis: AI analyses social media and customer feedback for sentiment analysis. This helps businesses understand public opinion about their products and services.
6. Healthcare: In the healthcare sector, AI is used for medical transcription, clinical documentation, and extracting information from medical records. It's also used to assist in diagnosing and monitoring health conditions.
7. Legal and Compliance: AI can review legal documents and contracts for compliance, reducing the time and effort required for legal professionals.
8. Content Recommendations: AI is used to recommend content on various platforms, such as streaming services, e-commerce websites, and news outlets, based on user preferences.
9. Academic Research: AI aids researchers in processing and analysing large volumes of text-based academic literature for insights and trends.
10. Accessibility: Language processing AI makes digital content more accessible to disabled people. This includes speech recognition for those with mobility impairments and text-to-speech for the visually impaired.
11. Education: AI-powered language processing tools are used in online education for grading essays, providing personalised feedback, and assisting with language learning.
12. Academic Conferences: ACL and EMNLP

Data analysis, pattern recognition, and decision-making:

1. Explainable AI (XAI): One of the critical advancements in analytical models is the push for transparency and interpretability. XAI techniques aim to make AI models more understandable and explain their decisions, which is crucial in healthcare, finance, and law.
2. Federated Learning: This approach enables analytical models to be trained across decentralised data sources while keeping the data localised. It's essential for privacy-sensitive applications like healthcare and finance.
3. AutoML: Automated Machine Learning (AutoML) tools are becoming more sophisticated, allowing non-experts to create, train, and deploy analytical models without in-depth machine learning knowledge. This democratises AI and expands its use.
4. Graph Analytics: With the growth of network data, graph analytics has gained prominence. It's used in social network analysis, recommendation systems, and fraud detection.
5. Reinforcement Learning: In analytical models, reinforcement learning is used for optimisation problems, such as supply chain management and autonomous systems like self-driving cars and robotics.
6. Anomaly Detection: Improved analytical models for anomaly detection are used in various applications, from cybersecurity to predictive maintenance in industrial equipment.
7. Natural Language Processing (NLP): Analytical models integrate NLP for text analysis, sentiment analysis, and information extraction from unstructured data sources.
8. Time Series Analysis: There are ongoing developments in time series analysis for forecasting, resource planning, and trend analysis in various domains.
9. AI in Finance: Analytical models in finance are evolving for risk assessment, fraud detection, algorithmic trading, and customer service.
10. AI in Healthcare: In the healthcare sector, analytical models are used for medical imaging, disease diagnosis, patient management, and drug discovery.
11. AI in Supply Chain and Logistics: AI is increasingly used for optimising supply chains and logistics operations, including demand forecasting, route optimisation, and inventory management.
12. AI in Energy and Sustainability: Analytical models optimise energy consumption, monitor environmental data, and improve sustainability practices.
13. AI in Marketing: AI analytics are used for customer segmentation, personalised marketing, and recommendation systems, improving the effectiveness of marketing campaigns.
14. AI Ethics and Fairness: There is growing attention to ensuring that analytical models are ethical and fair, addressing issues related to bias and discrimination.

Cultural expression processing :

1. Generative Art: AI has been used to create generative art, producing paintings, music compositions, and even poetry. The technology often relies on neural networks to generate original pieces inspired by different artistic styles or cultural contexts.
2. Language and Literature: AI is used to analyse and generate literary works, helping authors and researchers explore new narratives, genres, and styles. Chatbots and AI-driven virtual authors are also being developed.
3. Music and Creativity: AI is used in music composition, generating melodies and harmonies in various genres. This is particularly useful for assisting musicians, scoring films, and creating background music for video games.
4. Design and Fashion: AI tools can assist designers by generating fashion designs, offering recommendations, and predicting trends. Virtual try-on applications use AI to enhance the online shopping experience.
5. Cultural Preservation: AI is helping in the preservation and restoration of cultural heritage. This includes the repair of the damaged artwork and the digitisation of historical texts and artefacts.
6. Language Translation and Localisation: AI-powered language translation tools have improved significantly, making it easier to translate cultural expressions like literature, films, and music.
7. Recommendation Systems: AI-driven recommendation systems, such as those used by streaming platforms, suggest culturally relevant content to users based on their preferences and viewing history.
8. Digital Museums and Galleries: AI technology creates immersive digital experiences in museums and art galleries, enhancing visitor engagement and education.
9. Creative Collaboration: AI tools facilitate collaboration between human creators and AI systems. Artists, writers, and musicians are experimenting with AI as a creative partner.
10. Cultural Understanding and Interpretation: AI can analyse and interpret artistic expressions, providing insights into the meaning and significance of art, literature, and music within different cultural contexts.
11. Personalised Content: AI-driven platforms offer personalised content experiences based on individual preferences, allowing users to explore and engage with their preferred cultural expressions.
12. Digital Storytelling: AI-driven chatbots and narrative generation tools create interactive and immersive digital storytelling experiences.

Governance and ethics:

1. Ethical AI Frameworks: Organisations and governments have been developing ethical frameworks and guidelines for AI development. These frameworks focus on responsible data usage, fairness, transparency, and accountability in AI systems.
2. Data Privacy Regulations: Implementing data privacy regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States has forced organisations to pay more attention to data governance and ensure data purity.
3. AI Ethics Committees: Some companies have established AI ethics committees or boards responsible for reviewing and ensuring the ethical use of AI technologies, including data governance and purity.
4. Data Quality Tools: There has been a growing emphasis on data quality tools and platforms that help organisations clean, validate, and maintain high-quality data. These tools are essential for ensuring data purity.
5. Data Anonymisation Techniques: To protect sensitive information, AI developers use advanced data anonymisation techniques to minimise the risk of data breaches while preserving data utility.
6. Bias Mitigation: Researchers and organisations are developing algorithms and strategies to reduce bias in AI systems. This includes identifying and addressing discrimination in training data and algorithms.
7. Explainable AI (XAI): Developments in XAI are making it easier to understand how AI models make decisions. This helps ensure that AI decisions are justifiable and unbiased.
8. Blockchain for Data Governance: Some organisations are exploring using blockchain technology to improve data governance and enhance data purity, ensuring that data remains tamper-proof and transparent.
9. Data Catalogs and Metadata Management: The development of data catalogues and metadata management solutions makes it easier to discover, understand, and manage data assets, which is crucial for data governance.
10. AI Auditing and Compliance Tools: AI auditing tools are being developed to monitor AI system behaviour, assess compliance with regulations, and identify issues related to data governance and purity.
11. AI in Regulatory Compliance: AI systems help organisations comply with data governance and purity regulations by automating compliance tasks, data monitoring, and reporting.
12. Data Stewardship and Data Ownership: Organisations are establishing clear roles and responsibilities for data stewardship and data ownership to ensure accountability for data governance.
13. Collaboration and Knowledge Sharing: Initiatives to foster cooperation and knowledge sharing in the AI community, such as AI ethics conferences and research collaborations, promote best practices in data governance and purity.

Communication, articulation and expression :

1. Conversational AI: Conversational AI systems, including chatbots and virtual assistants, have improved their ability to engage in more context-aware and natural conversations. These systems can handle multi-turn dialogues, offer personalised responses, and provide better user experiences.
2. Natural Language Understanding (NLU): AI models have advanced in understanding the nuances of human language, including slang, idioms, and regional dialects. This allows AI systems to comprehend user queries more accurately.
3. Multimodal AI: Integrating language with other modalities like images and videos is becoming more prevalent. AI systems can describe visual content and generate text-based descriptions for multimedia data.
4. Emotion Recognition and Sentiment Analysis: AI is better at recognising and understanding human emotions from text and speech, which is essential for personalised and emotionally intelligent interactions.
5. Voice Assistants: Voice-based AI assistants, such as Siri, Google Assistant, and Alexa, improve their ability to understand and respond to natural voice commands, making them more user-friendly.
6. Text Summarisation and Generation: AI models have made significant progress in summarising long texts and generating coherent and contextually relevant text, which is beneficial for content creation and knowledge extraction.
7. Content Recommendation: AI-driven recommendation systems are becoming more accurate in suggesting content, products, and services based on user's preferences and behaviours.
8. Language Translation: Machine translation has improved, making it easier for people to communicate across different languages and access content in their preferred language.
9. Speech Synthesis: Text-to-speech (TTS) technology has advanced, producing more natural-sounding and expressive synthesised speech.
10. Creative Writing Assistance: AI assists writers by suggesting ideas, helping with plot development, and providing grammar and style recommendations.
11. Cultural and Regional Adaptation: AI systems are trained to adapt their language and expressions to specific cultural and regional contexts, making interactions more relatable and respectful of cultural differences.
12. Accessibility: AI is being used to improve accessibility for individuals with disabilities, including speech recognition for those with mobility impairments and text-to-speech for the visually impaired.
13. Voice Cloning and Personalisation: AI enables voice cloning for personalised voice assistants and applications, making interactions more individualised and engaging.
14. Ethical and Bias Mitigation: Efforts are being made to ensure that AI communication is honest and unbiased, with developments in responsible AI to reduce harmful or discriminatory language in AI systems.

Ethics and moral comprehension:

1. Ethical AI Frameworks: Organisations and researchers are developing ethical frameworks and guidelines for AI development. These frameworks promote responsible AI behaviour and address ethical considerations.
2. Ethical Decision-Making Models: AI systems are designed with ethical decision-making models, enabling them to assess moral dilemmas and make choices that align with established ethical principles.
3. Explainable AI (XAI): XAI is gaining importance to make AI's decision-making processes transparent and understandable. This is critical for identifying and addressing ethical biases in AI systems.
4. Bias Mitigation: Researchers are developing algorithms and strategies to reduce bias in AI systems, particularly concerning gender, race, and other sensitive attributes. Addressing discrimination is a fundamental aspect of ethical AI.
5. Value Alignment: AI developers focus on aligning AI systems with human values and ethics. This involves training AI models to understand and prioritise moral values in their actions.
6. Moral Philosophy Integration: AI systems incorporate moral philosophy to better understand and navigate complex ethical dilemmas. They can use established ethical theories to make decisions.
7. Ethical Chatbots and Virtual Assistants: Chatbots and virtual assistants are being designed to provide honest guidance and adhere to ethical guidelines, especially in contexts where moral decisions are involved.
8. Moral Reasoning and Explanation: AI is trained to provide moral reasoning and explanations for its decisions, helping users understand why a particular ethical choice was made.
9. Cross-Cultural Ethics: AI systems are becoming more adaptable to cross-cultural ethical considerations, recognising that ethics can vary across different societies and cultures.
10. AI Ethics Committees and Boards: Some organisations have established AI ethics committees or boards responsible for reviewing AI system behaviour, addressing ethical concerns, and ensuring compliance with ethical guidelines.
11. Fairness and Accountability: Efforts are being made to hold AI systems accountable for their actions and to ensure that they operate fairly and justly.
12. Legal and Regulatory Compliance: Ethical AI development includes adherence to legal and regulatory requirements, such as data protection and privacy laws.
13. Education and Training: Training data for AI models is sourced from diverse and ethical sources, and AI practitioners receive education on ethical considerations.
14. Ethical AI Auditing: There's a growing focus on auditing AI systems for ethical compliance to identify and rectify any ethical issues.
15. Public Awareness and Engagement: Ethical AI initiatives aim to raise public awareness about AI ethics and involve the public in discussions about the moral and ethical aspects of AI development and deployment.