artificial intelligence

Aigoras - we can do better - The AI Accuracy Paradox: When More Data Doesn't Mean Better Results by Kevin Lancashire


In the world of AI, we're often told: "more data is better." The assumption is that if we feed our machine learning models enough data, they'll eventually learn to understand and interpret language with near-perfect accuracy. But what if this isn't always true? What if, in the quest for accuracy, we're overlooking a fundamental truth about language itself?

The field of Natural Language Processing (NLP) has made tremendous strides, enabling AI to perform tasks like sentiment analysis, machine translation, and chatbot interactions. However, a recent paper by Baden et al. (2023) reminds us that language is inherently complex and often ambiguous. The authors highlight three key challenges:

  • Ambiguity: The same text can have multiple interpretations due to missing or underspecified information.

  • Polysemy: Words and phrases can have multiple, co-existing meanings, leading to layered interpretations.

  • Interchangeability: The same meaning can be expressed in different ways, making it difficult to categorize consistently.

These challenges, collectively referred to as 'meaning multiplicity,' pose a significant hurdle for AI accuracy. Even with vast amounts of training data, AI models may struggle to consistently interpret text when there's no single 'correct' answer. This can lead to the 'AI Accuracy Paradox': where more data doesn't necessarily translate to better results.

The implications for AI enthusiasts are clear. We need to rethink our approach to NLP, moving beyond the simplistic 'more data is better' mantra. We need to develop models that can handle ambiguity and polysemy, that can recognize and account for the multiple valid interpretations of a given text.

Here are some potential avenues for exploration:

  • Contextual Understanding: Develop AI models that can leverage context to disambiguate meaning and identify the most likely interpretation in a given situation.

  • Probabilistic Models: Instead of forcing a single interpretation, explore models that can assign probabilities to different interpretations, reflecting the inherent uncertainty in language.

  • Explainable AI: Build models that can explain their reasoning, providing insights into how they arrived at a particular interpretation. This can help us understand and address potential biases or errors.

  • Human-in-the-Loop: Incorporate human expertise into the AI development and training process. Humans can provide valuable feedback and help AI models navigate the complexities of language.

The AI Accuracy Paradox is a reminder that language is not just data; it's a complex system of meaning-making. By embracing this complexity and developing AI models that can navigate it, we can unlock the true potential of NLP and build AI systems that truly understand and interact with human language.

Source: Meaning multiplicity and valid disagreement in textual measurement: A plea for a revised notion of reliability, Christian Baden, Lillian Boxman-Shabtai, Keren Tenenboim-Weinblatt, Maximilian Overbeck & Tali Aharoni , 2023

  1. #TextualAnalysis

  2. #MeaningMultiplicity

  3. #ValidDisagreement

  4. #ReliabilityVsValidity

  5. #AmbiguityInLanguage

  6. #Polysemy

  7. #DataInterpretation

  8. #AIandLanguage

  9. #NLP

  10. #DigitalCommunication

  11. #CriticalThinking

  12. #InformationLiteracy

  13. #ContentAnalysis

Aigoras - we can do better: Apple Intelligence: enhancing everyday communication by Kevin Lancashire

Apple Intelligence is a cutting-edge system recently introduced by Apple, integrated into iOS 18, iPadOS 18, and macOS Sequoia. It leverages the power of Apple Silicon to understand and create language and images, execute actions across apps, and use personal context to simplify and accelerate everyday tasks¹.

The impact of Apple Intelligence on daily communication could be significant. Here are some key features and benefits:

- Improved writing tools: System-wide writing tools allow users to revise, proofread, and summarize texts almost anywhere, including Mail, Notes, Pages, and third-party apps¹.

- Tone customization: The "Rewrite" feature lets users choose different versions of what they've written, adjusting the tone to suit the audience and task¹.

- Grammar and style check: "Proofread" checks grammar, word choice, and sentence structure, suggesting edits for users to review or quickly accept¹.

- Summaries: With "Summarize," users can select text and have it condensed into a digestible paragraph, a bullet list, a table, or a checklist¹.

These features could revolutionize how people communicate by providing tools that ease writing and improve communication quality. Additionally, Apple sets a new privacy standard in AI with Private Cloud Compute, balancing computing power between on-device processing and larger server-based models running on dedicated Apple Silicon servers¹. This means users can enjoy enhanced AI performance without compromising their personal data.

Regarding research, there is evidence that supports the positive impact of AI on organizational communication. Studies have shown that AI can significantly enhance communication processes within organizations³. While specific research on Apple Intelligence's impact is not detailed in the search results, the general consensus is that AI integration in communication tools can lead to improved efficiency and effectiveness.

(1) Apple Intelligence Preview - Apple. https://www.apple.com/apple-intelligence/.

(2) The Impact of Artificial Intelligence on Organizational Communication .... https://link.springer.com/chapter/10.1007/978-3-031-56586-1_58.

(3) How Apple Is Organized for Innovation - Harvard Business Review. https://hbr.org/2020/11/how-apple-is-organized-for-innovation.

Disclaimer:

The source for the blog post is a collaborative effort. The initial ideas and questions were provided by Kevin Lancashire, while the research and writing were conducted by the AI companion, to efficiently combine Kevin’s thoughts with my capabilities to create a unique article. This synergy allows for the integration of human insight with AI-powered research and writing, resulting in a distinctive and informative piece.

Aigoras - we can do better: Empowering educators: Navigating skill education and teacher roles in 2030 by Kevin Lancashire

Envisioning a school system in 2030 that fully embraces AI opportunities, we can expect a highly personalized and adaptive learning environment. Here's how it might look and its potential impact on the economy and society:

School System in 2030 with AI Integration

  • Individualized Learning Paths: AI will enable schools to create customized curricula that adapt to each student's learning pace, style, and interests, maximizing their strengths and addressing weaknesses⁶.

  • Real-Time Analytics: Teachers will have access to real-time data on student performance, allowing for timely interventions and support where needed⁷.

  • Interactive and Immersive Learning: Virtual and augmented reality, powered by AI, will provide immersive learning experiences, making education more engaging and effective⁶.

  • AI Mentors: AI-driven virtual mentors will be available 24/7 to guide students through complex concepts and provide emotional support⁷.

  • Skill-Based Education: The focus will shift from rote learning to developing critical thinking, creativity, and problem-solving skills, preparing students for the future workforce⁷.

    Impact on Economy and Society

  • Workforce Transformation: As education becomes more tailored to individual talents, the workforce will become more diverse and specialized, driving innovation and productivity¹.

  • Economic Growth: AI's ability to enhance learning and skill development will contribute to economic growth, with some estimates suggesting AI could add up to 16% to global output by 2030⁴.

  • Social Inclusion: AI can help bridge educational gaps, providing high-quality, personalized education to students regardless of their background, potentially reducing inequality⁶.

  • Job Market Shifts: While AI will automate certain jobs, it will also create new roles, particularly in tech and AI-related fields, requiring continuous learning and adaptation¹.

  • Global Competitiveness: Countries that integrate AI into their education systems will be better positioned to compete globally, as their citizens will be more adept at using and innovating with technology².

In conclusion, a school system that leverages AI will likely be more dynamic, inclusive, and effective, preparing students not just for the job market but for lifelong learning. The broader impact on the economy and society will be significant, with potential for increased growth, innovation, and social cohesion. However, it's crucial to ensure that these benefits are distributed equitably and that policies are in place to manage the transition and mitigate any negative effects.

Quelle: Unterhaltung mit Copilot, 9.6.2024

(1) Artificial intelligence in education | UNESCO. https://www.unesco.org/en/digital-education/artificial-intelligence.

(2) The future of learning: AI is revolutionizing education 4.0. https://www.weforum.org/agenda/2024/04/future-learning-ai-revolutionizing-education-4-0/.

(3) AI Will Transform the Global Economy. Let’s Make Sure It ... - IMF. https://www.imf.org/en/Blogs/Articles/2024/01/14/ai-will-transform-the-global-economy-lets-make-sure-it-benefits-humanity.

(4) Maximizing AI’s economic, social, and trade opportunities. https://www.brookings.edu/articles/maximizing-ais-economic-social-and-trade-opportunities/.

(5) Economic impacts of artificial intelligence - European Parliament. https://www.europarl.europa.eu/RegData/etudes/BRIE/2019/637967/EPRS_BRI%282019%29637967_EN.pdf.

(6) Economic impacts research at OpenAI | OpenAI. https://openai.com/blog/economic-impacts/.

(7) AI for Impact: The Role of Artificial Intelligence in Social Innovation. https://www3.weforum.org/docs/WEF_AI_for_Impact_2024.pdf.

(8) Artificial Intelligence in Education: Challenges and Opportunities for .... https://www.gcedclearinghouse.org/sites/default/files/resources/190175eng.pdf.

(9) THE TRANSFORMATIVE POWER OF AI: PROJECTED IMPACTS ON THE GLOBAL ECONOMY .... https://educationai-review.org/revista/article/download/20/11/20.

In a 2030 school system that embraces AI, the role of teachers will be redefined to complement the strengths of AI in education. Here's a nuanced perspective on their evolving role:

- Facilitators of learning: Teachers will guide students through AI-driven personalized learning paths, encouraging exploration and discovery based on individual strengths and interests.

- Mentors and coaches: With AI handling administrative and evaluative tasks, teachers can focus on mentoring students, providing emotional support, and helping them navigate their educational journey.

- Curators of content: Educators will select and present a variety of learning materials tailored to the diverse needs and learning styles of their students.

- Collaborators with AI: Teachers will work closely with AI systems, using them as tools to enhance teaching methods and create engaging learning experiences.

- Lifelong learners: Teachers will continuously update their skills and knowledge to stay abreast of technological advancements and pedagogical innovations.

- Designers of assessment: Moving away from standardized tests, teachers will design assessments that reflect individual student growth and learning progress.

- Champions of equity: Teachers will ensure that AI tools are used fairly, advocating for access to technology and support for all students, regardless of their background.

In this future scenario, teachers remain central to the educational experience, acting as the human connection that ensures AI is used effectively and empathetically to support every student's learning journey.

The integration of AI in education, often referred to as AI-supported learning or AI in Education (AIED), could have a significant impact on the educational landscape in Switzerland. Here are some potential effects:

Personalization of Learning: AI can tailor educational content to meet individual students' needs, adapting to their learning pace and style¹. This could lead to more effective learning experiences and improved outcomes for Swiss students.

Teacher Support: AI can automate routine tasks, such as grading and administrative work, allowing Swiss teachers to focus more on teaching and student interaction². This could enhance the quality of education and reduce teacher burnout.

Challenges and Concern: The adoption of AI in education also brings challenges, such as privacy concerns, changes in power structures, and the need for teachers to adapt to new roles¹. It's important for Swiss educational institutions to address these issues to ensure a smooth transition to AI-enhanced learning environments.

Teacher-AI Collaboration: Teachers in Switzerland could act as models for training AI algorithms and participate in the development of AI technology, ensuring it aligns with pedagogical goals². This collaboration could lead to more effective and relevant AI tools in the classroom.

Educational Policy and Practice: A system-wide policy in Switzerland could support the integration of AI by reflecting educational goals and supporting interdisciplinary collaboration. This would help translate theory into practice and address teachers' ongoing needs and challenges⁵.

Overall, AI has the potential to transform education in Switzerland by supporting personalized learning, assisting teachers, and improving educational outcomes. However, careful consideration of the implications and challenges is necessary to maximize the benefits and minimize any negative impacts.

(1) The impact of artificial intelligence on learner–instructor interaction .... https://educationaltechnologyjournal.springeropen.com/articles/10.1186/s41239-021-00292-9.

(2) The Promises and Challenges of Artificial Intelligence for Teachers: a .... https://link.springer.com/article/10.1007/s11528-022-00715-y.

(3) Leading teachers' perspective on teacher-AI collaboration in education .... https://link.springer.com/article/10.1007/s10639-023-12109-5.

(4) Shaping the Future of Learning: The Role of AI in Education 4.0. https://www.weforum.org/publications/shaping-the-future-of-learning-the-role-of-ai-in-education-4-0/.

(5) Exploring the impact of AI on teacher leadership: regressing or .... https://link.springer.com/article/10.1007/s10639-023-12174-w.

Disclaimer:

The source for the blog post is a collaborative effort. The initial ideas and questions were provided by Kevin Lancashire, while the research and writing were conducted by the AI companion, to efficiently combine Kevin’s thoughts with my capabilities to create a unique article. This synergy allows for the integration of human insight with AI-powered research and writing, resulting in a distinctive and informative piece.

Aigoras - we can do better: AI accelerates Urban Intelligence: Transforming cities for a better tomorrow by Kevin Lancashire

In the bustling streets of our cities, a silent revolution is underway. Artificial Intelligence (AI) is transforming urban landscapes, making them smarter, more efficient, and more livable. From Singapore's high-tech hubs to Vancouver's green streets, AI is the new urban planner, architect, and guardian. Let's explore three concrete examples of how AI is accelerating urban intelligence and enhancing our lives.

Singapore: The Smart Nation

Singapore, often hailed as a 'Smart Nation', is a testament to how AI can augment urban planning and sustainability. The Urban Redevelopment Authority (URA) is developing an AI smart planning assistant, revolutionizing urban planning and design. This tool helps planners create adaptable and sustainable environments despite land constraints. For instance, the Integrated Environmental Modeller (IEM) uses high-resolution 3D city maps and urban physics models to simulate wind, sun, and noise impacts, aiding in the design of buildings that harness natural airflow and reduce solar irradiance. This not only cools down the city but also reduces energy consumption, making Singapore a model for sustainable urban living.

Vancouver: The Green Metropolis

Vancouver is another city where AI is making a significant impact. Known for its commitment to sustainability, the city employs AI-driven urban simulation models that provide planners with insights into the potential impact of various scenarios. These simulations help in making informed decisions about land use, transportation systems, and population growth, ensuring that the city develops in harmony with nature. Vancouver's use of AI in urban planning exemplifies how technology can be leveraged to maintain the city's lush greenery while accommodating its growing population.

The Global Impact

While Singapore and Vancouver are leading examples, the influence of AI on urban intelligence is a global phenomenon. AI technologies process and analyze vast datasets, enabling city planners worldwide to predict outcomes across traffic patterns, energy consumption, and environmental impact. This leads to more informed decision-making and the creation of cities that are not only smart but also sustainable and responsive to the needs of their inhabitants.

The Human Touch

AI in urban settings doesn't just make cities smarter; it makes our lives better. By reducing traffic congestion, AI saves us time and reduces stress. By optimizing energy use, it lowers our bills and our carbon footprint. And by enhancing public safety and health services, it makes our communities safer and our lives healthier.

In conclusion, as AI continues to weave its way into the fabric of urban life, it promises a future where cities are not just clusters of buildings but interconnected ecosystems that are intelligent, intuitive, and inclusive. The AI-powered cities of tomorrow will be places where technology serves humanity, creating environments that are as nurturing as they are innovative. As we stand on the brink of this new era, one thing is clear: AI is not just accelerating urban intelligence; it's redefining what it means to live in a city.

Switzerland is involved in several urban intelligence projects that are shaping the future of its cities:

Data-Driven Smart Sustainable Cities: This initiative focuses on leveraging big data technologies for strategic, short-term, and joined-up planning of sustainable cities. It aims to improve urban sustainability through advanced decision support systems that integrate complexity science, sustainability science, urban science, and data science1.

Urban Nature-Based Solutions (NBS) Planning: This project emphasizes the integration of human, ecological, and artificial intelligence perspectives to promote biodiversity and habitat creation in urban areas. It seeks to balance the design of NBS to ensure equitable distribution of benefits across urban landscapes2.

Smart Urban Heatmap Bern: The city of Bern is addressing urban heat challenges by developing intelligent and sustainable solutions. A comprehensive and open data foundation is being created to support this effort3.

Disclaimer:

The source for the blog post is a collaborative effort. The initial ideas and questions were provided by Kevin Lancashire, while the research and writing were conducted by the AI companion, to efficiently combine Kevin’s thoughts with my capabilities to create a unique article. This synergy allows for the integration of human insight with AI-powered research and writing, resulting in a distinctive and informative piece.

Further read: Marvin, S., & Luque-Ayala, A. (2017). Urban Operating Systems: Diagramming the City. International Journal of Urban and Regional Research, 41, 84-103. https://doi.org/10.1111/1468-2427.12479.