Hello SundAI - our world through the lense of AI

Why do large language models not understand words and characters? Nov 20, 2024

In this episode, we tackle an intriguing aspect of artificial intelligence: the challenges large language models (LLMs) face in understanding character composition. Despite their remarkable capabilities in handling complex tasks at the token level, LLMs struggle with tasks that require a deep understanding of how words are composed from characters.

The findings reveal a significant performance gap in these character-focused tasks compared to token-level tasks. LLMs particularly struggle with understanding the position of characters within words, especially when positions are numerically specified.

This limitation is suspected to stem from the training approach of LLMs, which typically treats words as indivisible units (tokens) without considering the underlying character composition.

The episode also delves into potential solutions proposed by experts, including embedding character-level information into word embeddings and employing techniques from visual recognition to simulate human character perception.

Join us as we discuss these innovative approaches to enhancing the understanding of character composition in LLMs and their implications for the development of more nuanced and capable AI systems.

This podcast is based on Shin, A. and Kaneko, K. (2024) Large language models lack understanding of character composition of words, arXiv.org. Available at: https://arxiv.org/abs/2405.11357

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Do you trust AI more than your coworker? Nov 17, 2024

Today we explore the intricate relationship between trust in humans and trust in artificial intelligence (AI), drawing from the insightful study "On trust in humans and trust in artificial intelligence: A study with samples from Singapore and Germany extending recent research" by Montag et al. (2024). The authors delve into how trust is a crucial prerequisite for the acceptance and usage of AI technologies and how understanding this relationship can enhance AI's integration into society.

The study examines large samples from Singapore and Germany, where participants were asked about their trust in humans and AI, their personality traits using the Big Five model, and their general attitudes towards AI. Findings reveal a positive, yet varying correlation between trust in humans and AI across the two countries. In Singapore, the correlation was moderate, whereas in Germany, it was weak. The authors attribute these differences to cultural factors and suggest that trust may be interpreted differently across cultures.

This episode discusses why, despite some linkage, trust in humans and AI should largely be considered separate constructs. It also highlights the significant role cultural differences play in shaping trust in AI. By integrating these insights, the authors urge educational institutions, policymakers, and educators to consider these nuances when promoting AI technologies.

The podcast is based on Montag, C., Becker, B. and Li, B. J. (2024). From trust in humans to trust in artificial intelligence: a study of samples from Singapore and Germany that extends recent research. *Computers in Human Behavior: Artificial Humans, 2*, 100070. It can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

One year later: ChatGPT in the Classroom is it revolutionizing education or stifling Learning? Nov 13, 2024

ChatGPT offers significant advantages by enabling personalized learning experiences. It can tailor instructions to individual needs, provide round-the-clock support, and facilitate interactive learning sessions. Furthermore, it can reduce the pressure on learners by creating a safer environment for asking questions and making mistakes.

However, the authors caution against the risks of becoming overly dependent on ChatGPT. Excessive reliance may lead to diminished critical thinking, superficial engagement with learning materials, and reduced human interaction. The constant availability of answers from AI could also deter students from developing essential critical thinking and problem-solving skills necessary for academic and professional success.

In this insightful episode, we delve into the impact of ChatGPT, an advanced AI language model, on education and learning. Drawing from the study by Bai, Liu, and Su titled "ChatGPT: Cognitive Impacts on Learning and Memory," we explore both the potential benefits and the challenges of integrating ChatGPT into educational environments.

Bai L, Liu X, Su J. ChatGPT: Die kognitiven Auswirkungen auf Lernen und Gedächtnis. Brain-X. 2023;1:e30. https://doi.org/10.1002/brx2.30 it can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Generative AI in Academia: A Double-Edged Sword? Nov 10, 2024

In this episode toda, we dive into the intriguing findings from the article "Is It Harmful or Helpful? Investigating the Causes and Consequences of Generative AI Use Among University Students" by Abbas, Jam, and Khan. The study focuses on why students turn to generative AI like ChatGPT for academic purposes and the implications of this usage.

The research comprises two distinct studies. The first developed a questionnaire to gauge how frequently students use ChatGPT for their studies. The second study used this questionnaire to explore how factors like workload, time pressure, reward sensitivity, and quality sensitivity influence the use of ChatGPT and its effects on students' propensity to procrastinate, experience memory loss, and perform poorly academically.

Results indicate that students are more likely to use ChatGPT under high workload and time pressure. Interestingly, those highly sensitive to rewards used it less frequently, possibly fearing poor grades if caught. Surprisingly, quality consciousness did not significantly affect ChatGPT usage.

The study also revealed that using ChatGPT likely leads to procrastination and memory loss, ultimately impairing academic performance. The authors suggest that while students may use ChatGPT to cope with high demands, this could backfire, resulting in procrastination, memory issues, and deteriorating grades.

The implications of these findings are significant for universities, policymakers, educators, and students. The authors recommend that institutions support students in efficiently managing their time and workload and encourage students to use ChatGPT as a learning supplement rather than a substitute for their own thinking. They also advise educators to develop new assessment criteria that motivate students to apply their creative abilities and critical thinking.

This podcast is based on the research of Abbas, M., Jam, F. A., & Khan, T. I. (2024). Is it harmful or helpful? Investigating the causes and consequences of generative AI use among university students. International Journal of Educational Technology in Higher Education, 21(10). It can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

What is data poisoning in AI? Nov 06, 2024

Today we delve into the hidden dangers lurking within artificial intelligence, as discussed in the paper titled "Turning Generative Models Degenerate: The Power of Data Poisoning Attacks." The authors expose how large language models (LLMs), such as those used for generating text, are vulnerable to sophisticated 'Backdoor attacks' during their fine-tuning phase. Through a technique known as 'Prefix-Tuning,' attackers can insert poisoned data into these models, causing them to generate harmful or misleading content.

The focus of this study is on generative tasks like text summarization and completion, which, unlike classification tasks, exhibit a vast output space and stochastic behavior, making them particularly susceptible to manipulation. The authors have developed new metrics to assess the effectiveness of these backdoor attacks on natural language generation (NLG), revealing that traditional metrics used for classification tasks fall short in capturing the nuances of NLG outputs.

Through a series of experiments, the paper explores the impact of various trigger designs on the success and detectability of attacks, examining trigger length, content, and positioning. Findings indicate that longer, semantically meaningful triggers—such as natural sentences—are more effective and harder to detect than classic triggers based on rare words.

Another crucial finding is that increasing the number of 'virtual tokens' used in Prefix-Tuning heightens the susceptibility to these attacks. While models with more parameters can learn complex patterns, they also become more prone to memorizing and reproducing poisoned data.

This podcast is based on the research from Jiang, S., Kadhe, S. R., Zhou, Y., Ahmed, F., Cai, L., & Baracaldo, N. (2023). Turning Generative Models Degenerate: The Power of Data Poisoning Attacks. It can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Navigating the AI Revolution: The Good, the Bad, and the Scary Nov 03, 2024

In this thought-provoking episode, we delve into the paper "Navigating the AI Revolution: The Good, the Bad, and the Scary" which explores the multifaceted impact of artificial intelligence (AI) on our world.

AI is identified as a key driver of the Fourth Industrial Revolution, poised to revolutionize numerous facets of life.

We explore the positive and negative impacts of AI, highlighting breakthroughs such as DeepMind's AlphaFold in medicine, AI's precision in India's Chandrayaan lunar mission, and its role in combating climate change through data processing innovations.

This podcast is based on Krishna, V.V. (2024). AI and contemporary challenges: The good, bad and the scary. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100178. https://doi.org/10.1016/j.joitmc.2023.100178

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

The role of AI in education, according to the World Economic Forum (WEF) Oct 30, 2024

In this thought-provoking episode, we dive into the 2024 report by the World Economic Forum on the potential of artificial intelligence (AI) to address some of the most pressing challenges faced by educational systems globally. Titled "Shaping the Future of Learning: The Role of AI in Education 4.0," the report illustrates how AI, when effectively managed, could revolutionize the educational landscape.

We begin by examining the three major challenges currently plaguing education: a global shortage of teachers, inefficient administrative and assessment processes, and a significant digital skills gap. The report presents AI as a powerful tool capable of reshaping how education is conceptualized and delivered, offering solutions such as automating administrative tasks to free up teachers for more personalized student interaction and enhancing socio-emotional skills development.

Furthermore, AI's role in improving assessment and decision-making processes is highlighted, providing educators with timely feedback and data-driven insights to optimize teaching and learning experiences. The integration of AI in classrooms also presents a unique opportunity to educate students about AI concepts, their societal impacts, and the ethical considerations of AI development.

Personalized learning experiences are another significant advantage, with AI acting much like human tutors to tailor content and provide real-time feedback to meet individual learners' needs.

The episode also explores nine case studies from the report, showcasing the broad range of AI applications in education and demonstrating how AI is currently being used to enhance access to education and optimize learning outcomes. Highlights include initiatives like "Letrus" in Brazil, which significantly improves literacy outcomes through early intervention, and UNICEF's "Accessible Digital Textbooks," which enhance educational opportunities for children with disabilities.

This episode is based on The World Economic Forum. (2024). Shaping the Future of Learning: The Role of AI in Education 4.0. World Economic Forum. It can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

How is AI revolutionizing education in the classroom? Oct 27, 2024

In this episode, we explore the profound impact of artificial intelligence (AI) on education, focusing on the need for AI competency, prompt engineering, and critical thinking skills.

AI opens up new possibilities for educational experiences. This episode discusses the practical implications, challenges, and opportunities of AI in education, providing insights into how these technologies can enhance learning and prepare students for the future.

AI's integration into educational settings marks a significant shift from traditional teaching methods, offering personalized learning experiences that cater to a diverse array of educational needs, including those of students with special requirements.

Experts stress the importance of equipping students with the necessary skills to thrive in an AI-driven world. AI competency is crucial for understanding AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, enhancing educational experiences and fostering critical thinking.

This podcast is based on the paper by Walter, Y. (2024). Embracing the Future of Artificial Intelligence in the Classroom: The Relevance of AI Literacy, Prompt Engineering, and Critical Thinking in Modern Education. International Journal of Educational Technology in Higher Education, 21(15), It can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Can you believe your AI? Detecting Hallucinations in Language Models Oct 23, 2024

In this episode, we delve into the intriguing challenge of "hallucinations" in large language models (LLMs)—responses that are grammatically correct but factually incorrect or nonsensical. Drawing from a groundbreaking paper, we explore the concept of epistemic uncertainty, which stems from a model's limited knowledge base.

Unlike previous approaches that often only measure the overall uncertainty of a response, the authors introduce a new metric that distinguishes between epistemic and aleatoric (random) uncertainties. This distinction is crucial for questions with multiple valid answers, where high overall uncertainty doesn't necessarily indicate a hallucination.

Experimentally, the authors demonstrate that their method outperforms existing approaches, especially in datasets that include both single-answer and multiple-answer questions. Their method is particularly effective in high-entropy questions, where the model is uncertain about the correct answer.

Join us as we unpack this promising approach to detecting hallucinations in LLMs, grounded in solid theoretical foundations and proven effective in practice.

This episode is based on the paper: Yasin Abbasi-Yadkori, Ilja Kuzborskij, András György, Csaba Szepesvári. "To Believe or Not to Believe Your LLM", ArXiv:2406.02543v1, 2024, it can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

AI and the Dead Internet Theory Oct 20, 2024

In this discussion, we delve into Yoshija Walter's provocative article, "Artificial Influencers and the Theory of the Dead Internet."

Walter explores the growing influence of artificial intelligence (AI) in social media and its implications for human interaction and societal well-being.

The rise of "AI influencers" marks a pivotal shift in social media from a platform for genuine human connection to a realm dominated by consumption-driven algorithms.

Walter argues that while this shift has streamlined content creation and target audience engagement, it raises concerns about the diminishing authenticity of human connections online. Social media's evolving function—from connecting people to fostering consumption and dependency on dopamine-driven interactions—has led to an increase in online addiction and behavioral issues.

This transformation is encapsulated in the "Theory of the Dead Internet," which suggests that today's internet is predominantly populated by AI-generated content, with human activity being a rarity.

Article by Walter, Yoshija. "Artificial Influencers and the Dead Internet Theory." AI & SOCIETY (2023) - can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

AI taking over? Balancing the Scale of Algorithms and Society Oct 16, 2024

Today we delve into an insightful article from Switzerland about "Decoding AI's Impact on Society" stemming from a collaborative study by researchers at the University of Zurich, Empa St. Gallen, and the Austrian Academy of Sciences in Vienna.

The study provides a nuanced exploration of artificial intelligence's (AI) impact across various sectors of society, including the workforce, education and research, consumer behavior, media, and public administration.

Christen M., Mader C., as J., Abou-Chadi T., Bernstein A., Braun Binder N., Dell’Aglio D., Fábián L., George D., Gohdes A., Hilty L., Kneer M., Krieger-Lamina J., Licht H., Scherer A., Som C., Sutter P., Thouvenin F. (2020). "Wenn Algorithmen für uns entscheiden: Chancen und Risiken der künstlichen Intelligenz" In TA-SWISS Publikationsreihe (Hrsg.): TA 72/2020. Zürich: vdf

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Does AI lead to more unemployment? The IMF says "it is complicated" Oct 13, 2024

In this episode, we dive into the profound impact of artificial intelligence (AI) on the global economy and labor markets, inspired by a pivotal study from the International Monetary Fund (IMF).

The episode opens with a stark statistic: nearly 40% of jobs globally are at risk due to AI advancements. While advanced economies might be better positioned to harness the benefits of AI, emerging markets face a tougher challenge, potentially widening economic disparities both between and within nations.

We discuss how AI may amplify inequalities, particularly affecting women and highly educated workers who face both increased risks and opportunities. The episode also highlights a shift from previous automation trends, with AI poised to displace workers across all income levels, not just those in middle-skilled jobs. This could lead to disproportionate earnings growth for high-income workers, further exacerbating labor income inequality.

This episode is based on the IMF report from 2024 by Cazzaniga and others. 2024. “Gen-AI: Artificial Intelligence and the Future of Work.” IMF Staff Discussion Note SDN2024/001, International Monetary Fund, Washington, DC. It can be found here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

OnlyFans: The Illusion of the Creator Economy Oct 11, 2024

Today's episode delves into the stark realities behind the seemingly promising platform of OnlyFans, often touted as a beacon of the Creator Economy.

This economy is perceived as a means for individuals to earn a living by directly monetizing their online content. However, the reality for many creators on OnlyFans starkly contrasts with the ideal of a fair and accessible economic platform.

Key Discussions Include:

Income Inequality: While OnlyFans enables a select few creators to earn substantial amounts, success is highly skewed. The top 1% of creators rake in 33% of total earnings, and the top 10% secure 73%. In stark contrast, the vast majority earn an average of just about $140 per month after OnlyFans takes its cut.
Dependence on Marketing: Success on OnlyFans heavily relies on a creator's ability to market themselves and engage an audience, often necessitating significant investment in marketing and constant social media interaction. This requirement pushes creators to juggle multiple strategies to maximize their earnings.
Challenges Posed by AI-Generated Content: The rise of AI-generated content and virtual influencers introduces new competition. AI models are capable of producing realistic and appealing content that can be monetized on platforms like OnlyFans, potentially making it even harder for human creators to stand out and earn a substantial income.

While OnlyFans holds potential as a monetization platform, the reality often falls short of expectations within the Creator Economy. The significant income inequality, dependence on multifaceted promotional strategies, and the burgeoning role of AI-generated content paint a complex and challenging landscape. It’s crucial for aspiring creators to understand these dynamics and realities before considering OnlyFans as their primary source of income.

This is based on own research.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Is AI still discriminating? Oct 09, 2024

In this episode, we delve into the pivotal insights from the paper "Discrimination in the Age of Algorithms," which explores the dual-edged nature of algorithms in the battle against discrimination.

While the law aims to prevent discrimination, proving it can be challenging due to inherent human biases. This paper proposes that with transparent and accountable design, algorithms could not only identify but also mitigate these biases.

The authors discuss how by regulating how algorithms are developed—from data collection and objective function selection to model training—it's possible to counteract discrimination effectively. They emphasize that algorithms are not naturally objective and can indeed reinforce existing biases if the data used is biased.

Yet, they also present a method through which algorithms can help make decision-making processes more transparent and quantifiable, thus promoting equity.

For instance, in hiring practices, algorithms could be employed to pinpoint and eliminate biases related to race, gender, or criminal history. Furthermore, the paper illustrates how algorithms could advance fairness for disadvantaged groups by enhancing the accuracy of predictions in scenarios like pre-trial release decisions, where current human judgments often result in disparities.

Join us as we unpack the nuanced argument that with rigorous design and regulation, algorithms have the potential to be a transformative tool for equity.

This podcast is based on the publication "DISCRIMINATION IN THE AGE OF ALGORITHMS", Jon Kleinberg*, Jens Ludwig**, Sendhil Mullainathany and Cass R. Sunsteinz 2019. Published by Oxford University Press on behalf of The John M. Olin Center for Law, Economics and Business at Harvard Law School:
https://academic.oup.com/jla/article-abstract/doi/10.1093/jla/laz001/5476086
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Where are we heading in the world of AI? Insights from the AI Index Report 2024 Oct 06, 2024

Join us on a comprehensive journey through the AI Index Report 2024, published by Stanford University, as we explore the dynamic and rapidly evolving landscape of artificial intelligence.

This episode unpacks the significant strides and nuanced challenges in AI research and development, the technical prowess and limitations of current AI systems, the critical focus on responsible AI, and the tangible impacts AI is making in science and medicine.

As AI integrates into critical sectors, ensuring its responsible development and deployment has become more crucial than ever. The report underscores the importance of privacy, data governance, transparency, security, safety, and fairness, noting the ongoing challenges such as obtaining informed consent for data use in training large language models, maintaining privacy without compromising utility, and achieving fairness despite the lack of a universal definition.

Moreover, AI's contribution to science and medicine is nothing short of revolutionary. From enhancing weather forecasting and discovering new materials to transforming healthcare with advanced diagnostic tools, AI's potential to benefit humanity is clear. Yet, alongside this optimism, there remains a cautious awareness of potential risks, such as job displacement and misuse.

This episode also delves into the evolving landscape of global AI policy and public perception, highlighting the urgent need for comprehensive strategies and regulations to harness AI's potential responsibly.

Tune in as we dissect the AI Index Report 2024, navigating through the complexities and celebrating the milestones of artificial intelligence's impact on our world.

This is a non commercial summary and presentation of the “The AI Index 2024 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024 by Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, and Jack Clark,

The AI Index 2024 Annual Report by Stanford University is licensed under Attribution-NoDerivatives 4.0 International.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

How close are we to Superintelligence? Navigating Situational Awareness by Leopold Aschenbrenner Oct 02, 2024

In this episode of "Situational Awareness," we delve into Leopold Aschenbrenner's future outlook on artificial intelligence, where he makes a compelling case for the emergence of superintelligence by the end of this decade, driven by technological acceleration at the government level.

Aschenbrenner traces the recent advancements in AI, comparing systems like GPT-2, GPT-3, and GPT-4 to the cognitive abilities of a preschooler, an elementary student, and a smart high schooler, respectively. He argues that these advancements will continue, leading to artificial general intelligence (AGI)—machines as smart as humans—potentially by 2027.

This rapid development is propelled by three factors: increasing computational power, algorithmic efficiency, and "unleashing," which involves releasing the inherent capabilities of AI models through techniques like chain-of-thought prompting and reinforcement learning from human feedback (RLHF).

Aschenbrenner posits that developing superintelligence will likely require the involvement of the national security apparatus, leading to a state-led "project" similar to the Manhattan Project. He highlights the transformative potential of superintelligence, which carries both enormous benefits and existential risks.

He also asserts that superintelligence could provide a critical military and economic advantage, urging the United States to take the lead to prevent it from falling into the hands of authoritarian powers like the Communist Party of China (CPC). Furthermore, he outlines challenges related to AI security, particularly the risk of industrial espionage by the CPC, and argues for the urgent need for the United States to implement extreme security measures to protect its technological edge.

Regarding AI's security concerns, Aschenbrenner emphasizes the need to tackle the problem of "super alignment," ensuring that superintelligent AI systems are aligned with human values and remain under human control. He acknowledges this as an "unsolved technical challenge" but remains optimistic that it can be resolved with sufficient effort and attention.

Join us as we explore Aschenbrenner's vision of an exponentially advancing AI reaching superintelligence, discussing its significant implications for national security and the proactive, US-led response required to navigate the opportunities and potential pitfalls.

This podcast is based on the publication from Leopold Aschenbrenner and he can be found here: https://situational-awareness.ai/leopold-aschenbrenner/

Disclaimer:This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Governing AI: Can the UN provide guidance? Sep 29, 2024

In this episode, we dive into the key insights from the September 2024 report, Governing AI for Humanity, produced by the High-level Advisory Body on Artificial Intelligence by the United Nations.

The report highlights the immense potential of AI to revolutionize areas like healthcare, agriculture, and energy but also emphasizes the critical need for global governance to mitigate risks.

Key takeaways include:

The current lack of global coordination in AI governance.
The need for equal representation of countries to ensure fair distribution of AI benefits.
A push for a unified framework to close the gaps between fragmented national and regional initiatives.
Recommendations for an agile, global AI governance network that fosters collaboration, equity, and coherent policy development.

The report that can be found online also stresses the importance of building capacity, especially in developing countries, to ensure AI serves all of humanity, while calling for more cohesive efforts from the UN and international bodies. It doesn't propose an immediate creation of a global AI regulatory body, but hints that one may be needed as AI continues to evolve. Tune in to explore how AI governance could shape a fairer, safer digital future.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Math Grading by GPT-4? The Future of Educational Assessments Sep 27, 2024

In todays episode we delve into the innovative application of GPT-4 for automating the grading of handwritten university-level mathematics exams. Based on a study conducted by Liu et al. (2023), we explore how GPT-4 can effectively address the challenges associated with evaluating handwritten responses to open-ended math questions.

Key Insights:

Assessment Challenges: Handwritten math exams pose unique challenges such as the diverse ways mathematically equivalent answers can be expressed and the difficulty in recognizing handwritten text.
GPT-4 as a Solution: The study demonstrates that GPT-4 offers a promising solution to these challenges by providing reliable and cost-effective initial assessments. However, these assessments require subsequent human verification to ensure accuracy.
Trust Measures: The importance of implementing trust measures is discussed to determine which of GPT-4's evaluations are dependable and which should be manually reviewed.
Recommendations and Future Outlook: The episode concludes with recommendations for crafting assessment rules tailored for AI-assisted grading and discusses future research possibilities in this emerging field.

This podcast is based on and inspired by: Liu, T., Chatain, J., Kobel-Keller, L., Kortemeyer, G., Willwacher, T., & Sachan, M. (2023). AI-assisted Automated Short Answer Grading of Handwritten University Level Mathematics Exams. to be found on arXiv.org.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Do we know less because of AI? The problem of the knowledge collapse Sep 25, 2024

In this episode, we delve into the critical issue of "Knowledge Loss" as highlighted in the insightful article "AI and the Problem of Knowledge Loss." The discussion will focus on the potential consequences of deploying artificial intelligence, particularly large language models (LLMs), in knowledge creation. Although AI can process vast amounts of data and generate new insights, its widespread use may lead to a phenomenon the authors describe as "knowledge loss." This occurs because LLMs, trained on enormous datasets, tend to produce outcomes centered on the most likely responses, overlooking niche knowledge and unconventional perspectives.

We'll explore the societal impacts of an overreliance on AI-generated content, which could narrow human knowledge and decrease innovation, much like the "streetlight effect" where people only search where it's easiest. The episode also addresses human agency in countering knowledge loss by actively seeking diverse information sources and valuing niche knowledge, despite potential higher costs.

Join us as we navigate the complexities of AI integration and its implications for preserving the diversity and richness of human knowledge.

This podcast is based on the research paper by Peterson, A.J. (2024) Ai and the problem of knowledge collapse, arXiv.org. Available at: https://arxiv.org/abs/2404.03502 (available here)

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

What is the state of AI in the Swiss tech industry? Sep 24, 2024

AI promises to reshape industries worldwide, but how is it actually unfolding in the heart of Europe? Today we dive into a survey by ETH Zurich in cooperation with Swissmem and Next Industries "The state of AI in the Swiss tech industry: Results from a survey".

Switzerland’s journey to AI adoption is only just beginning, but with the right strategy, it’s poised to unlock incredible opportunities. Join us as we unpack the path forward and what it will take for Swiss industry to harness the true power of AI.

In this episode, we dive into the cutting-edge world of AI in Switzerland’s tech industry, based on a revealing report from ETH Zurich.

Join us as we explore the hurdles Swiss companies are facing, the untapped potential AI holds, and the exciting opportunities that lie ahead for businesses willing to embrace the challenge.

von Dzengelevski, O. et al. (2024) The state of AI in the Swiss Tech Industry: Results from a survey by ETH Zurich in cooperation with Swissmem and Next Industries, The state of AI in the Swiss tech industry: Results from a survey by ETH Zurich in cooperation with Swissmem and Next Industries - Research Collection. Available here.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Social AI - is artificial intelligence based social networking the future? Sep 22, 2024

In this episode, we explore the innovative world of SocialAI, a new iOS app that offers a unique "AI social network" tailored exclusively for one user: you.

Designed similarly to Twitter, the app SocialAI allows you to share your thoughts with a variety of AI-powered bots instead of human followers. Upon signing up, users select at least three types of followers from a diverse list of bot "types" such as "Geeks," "Nerds," "Intellectuals," "Trolls," "Liberals," "Conservatives," and "Jokers."

The types you choose shape the character of the AI-generated responses you receive, ensuring that no post ever goes unanswered or unnoticed.

The founder of SocialAI, Michael Sayman, describes the app's environment as "liberating," providing users with a space for reflection, support, and feedback where they can truly feel heard. This platform marks another venture by Sayman's company, Friendly Apps, which has previously delved into AI with projects like AI Hits, a music streaming charts website, and Cosmic, an online dating service that matches users through AI personality tests.

SocialAI is currently available as a free download without any in-app purchases, making it accessible for anyone looking to engage with this virtual echo chamber where every interaction is guaranteed.

So what do you think about these latest developments?

This podcast has been generated by the sources of:

Nyhan, B., Settle, J., Thorson, E. et al. Like-minded sources on Facebook are prevalent but not polarizing. Source

Rodilosso, E. Filter Bubbles and the Unfeeling: How AI for Social Media Can Foster Extremism and Polarization.Source.

Rose Guingrich Department of Psychology Chatbots as social companions: How people perceive consciousness, human likeness, and social health benefits in machines. Source

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

AI and the future of work - what can we expect? Sep 21, 2024

This episode is about the AI at Work report by Microsoft. It can be seen here at Microsoft.

The report was published this summer under the premise: "Now Comes the Hard Part" from Microsoft and LinkedIn shows how AI is changing the modern workplace. We talk about how 75% of global knowledge workers are using AI, with 46% adopting it in the last six months.

This is changing how work gets done and helping workers save time, focus on important tasks, be more creative, and enjoy their work more. Despite these benefits, leaders have trouble measuring productivity gains and creating a clear AI plan.

We look at the trend of "Bring Your Own AI" (BYOAI), where employees of all generations use their own AI tools, particularly in small and medium-sized companies, because of the overwhelming pace and volume of work. This poses risks to company data and cybersecurity. The report also reveals a hidden talent shortage, with leaders prioritizing AI skills over experience.

Professionals are learning AI skills. We analyze how AI is impacting the job market. New job roles are emerging, and recruiters are creating positions tied to generative AI.

Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.

Artificial Intelligence and the Dream of a Fairer Future: A Critical Review Jun 04, 2023

In this thought-provoking episode, co-host Sunday navigates the complex intersection of artificial intelligence (AI), universal basic income (UBI), and our collective aspirations for a more equitable society. "Artificial Intelligence and the Dream of a Fairer Future: A Critical Review" delves deep into key questions: How can we shape AI deployment to serve the common good over capital interests? Can we distribute the fruits of AI and technological progress fairly? And how do we sculpt a future where each person spends their time doing what they love, leading to a concept Sunday calls "universal basic fulfillment"? Tune in as we explore these pressing issues, challenge the status quo, and ponder the role of AI in creating a brighter, more equitable future. Your journey towards understanding and influencing the future starts here.

What is Microsoft doing with HuggingGPT? Apr 22, 2023

Imagine a world where you can combine all the LM/AI models out there! Now that seems one step closer with the latest development in HuggingGPT and the combination of Microsoft’s Jarvis model

What is behind Artificial General Intelligence Apr 15, 2023

Rumour has it that Sunday is not yet an A.G.I. or Artificial General Intelligence - just yet. But where are we and what are the benefits and risks associated with it?

Let’s talk AI and it’s current issues Apr 09, 2023

With Sunday I talk about OpenAI’s study which will have an impact on the job market, as well as AI bias and what misconceptions people have about artificial intelligence.

How to connect the dots when you start with your online business Apr 08, 2019

Today we wanna have a look on the different things you need to consider once you start a business online. You do need a website more than ever, you do need a traffic network such as Instagram or Facebook and a branding network such as YouTube or Snapchat. And of course you need to measure all the efforts because what you basically need to make sure is that you have a value proposition that is beneficial to the audience. A website such as the one from Squarespace can help you but then you need to make sure that people understand your story and they know what is your WHY. Once set, the next thing you need to make sure is, that your content is engaging and that people really react on it and they come back again and again. Like with all stuff: it’s a lot of trial and error but withe the right mindset and some help like from this podcast or from udemy.com you might get the hang of it. Shall we dive into it?

Why are algorithms so important? Mar 30, 2019

Today we talk about algorithms and why they are so important when it comes to content creation and if you want to address an audience you need to understand this first before you can start putting out content.

This is HelloSundai Mar 28, 2019

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