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- ScholarSphere Newsletter #15
ScholarSphere Newsletter #15
Where AI meets Academia
Welcome to 15th edition of ScholarSphere
"Peace is its own reward." Mahatma Gandhi
Welcome to our AI Newsletter—your ultimate guide to the rapidly changing world of AI in academia. If you haven't joined us yet, now's your chance! Click that button, subscribe with your email, and get ready for an exciting journey through all things AI in the academic realm!
In today's search of AI, we'll see...
Deep Dive into AI: Expand Your Knowledge
A Comparative Study between Motivated Learning and Reinforcement Learning
By James T. Graham, et al. (2015)
Fig. 1 ML agent interacting with the environment.
This paper explores advanced reinforcement learning (RL) techniques and compares them to motivated learning (ML). The study emphasizes the limitations of traditional RL in dynamic environments, particularly when resources are scarce or competing. In contrast, ML, developed by Starzyk (2012), introduces an internal reward system that motivates agents to act effectively under changing conditions. The paper describes a black box scenario to evaluate learning efficiency, where ML outperformed all tested RL algorithms in resource-sharing and task coordination tasks.
The introduction provides a background on RL, highlighting its dominance in machine learning for over two decades. RL relies on cumulative rewards to learn proper behavior, assuming sufficient resources are available, which is often not the case in dynamic environments. The need for effective task coordination and resource allocation is emphasized, with existing RL approaches being limited by stationary operating conditions. The paper proposes that ML, through its internal reward system, effectively addresses these challenges, allowing for better performance in dynamic environments.
Section II discusses advances in RL, including modern RL techniques like approximate value iteration, policy iteration, and policy search. These methods address the limitations of classical RL by approximating value functions and action policies, essential for real-world problems with infinite state spaces. The section also covers intrinsic motivation concepts such as artificial curiosity and active learning, which improve learning efficiency by focusing exploration on areas of greater uncertainty. ML extends these concepts by developing specific goal-oriented motivations, enhancing performance in dynamic environments.
Fig. 14. Comparison of Reinforcement Learning algorithms' average reward performance to Motivated Learning with SRate = 8.0.
The paper's experimental section compares ML with several RL algorithms, including Q-learning, SARSA(λ), hierarchical RL (HRL), Dyna-Q+, Explauto, TD-FALCON, and Neural Fitted Q (NFQ). Using a black box scenario, the study demonstrates that ML consistently achieves higher performance in maintaining resources and overall reward. The results show that while RL algorithms struggle under increasing pressure and resource constraints, ML maintains near-perfect performance until extreme conditions. The conclusion highlights ML's superiority in complex environments, particularly in discovering relationships between resources and managing them effectively.
Graham, et al. (2015) A Comparative Study between Motivated Learning and Reinforcement Learning
In summary, the paper argues that ML offers a robust solution for resource sharing and task coordination in dynamic environments, outperforming traditional RL methods. The study's findings suggest that incorporating internal motivations and goal management into learning algorithms can significantly enhance their adaptability and efficiency. Future work includes extending the black box scenario to incorporate cooperative and competitive actions by other agents, further validating ML's effectiveness in more complex settings. The research was supported by grants from The National Science Centre and the National Science Foundation.
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Mastering AI: Prompt Perfection
Research Shows Tree Of Thought Prompting Better Than Chain Of Thought
By searchenginejournal.com
Main Points:
A new prompting technique called Tree-of-Thought (ToT) has emerged as a game-changer for large language models (LLMs). Compared to the traditional Chain-of-Thought (CoT) prompting, ToT unlocks superior reasoning abilities in LLMs. CoT prompting guides LLMs through a linear sequence of steps, limiting their ability to explore alternative solutions or adapt to unforeseen situations. ToT, on the other hand, empowers LLMs with a branching structure. This allows them to consider various possibilities, revisit previous steps if needed, and ultimately arrive at more robust solutions, especially in complex tasks.
Study Findings:
Researchers conducted a study to compare the effectiveness of ToT and CoT prompting. LLMs were tasked with complex reasoning problems, such as jailbreaking a fictional device following a series of steps. LLMs equipped with ToT prompting significantly outperformed their CoT counterparts. They demonstrated a deeper understanding of the required steps, the ability to adapt to changes in the scenario, and ultimately achieved a higher success rate in completing the task.
Benefits of ToT Prompting:
The branching structure of ToT prompting offers significant advantages over the linear approach of CoT. In complex tasks, real-world situations rarely unfold in a perfectly predictable manner. ToT prompting allows LLMs to explore alternative approaches, backtrack and refine their reasoning if necessary, and ultimately arrive at a solution that considers various factors and contingencies. This flexibility makes ToT prompting a powerful tool for tackling intricate problems that require critical thinking and adaptability.
Real-World Applications:
The success of ToT prompting opens doors for its application in various real-world scenarios. Chatbots and virtual assistants could leverage ToT prompting to handle more nuanced user queries and provide more comprehensive responses. Additionally, ToT prompting holds immense potential in tasks demanding creative thinking. Imagine an LLM generating different storylines for a narrative or crafting content in diverse creative formats – the possibilities are vast. By allowing LLMs to explore a "tree" of ideas rather than a single "chain," ToT prompting unlocks a new level of sophistication and adaptability in these powerful language models.
For reading the full article and practical examples, click here.
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Cutting-Edge AI Insights for Academia
By Turkasz, Gencraft ,
Prompt: “The image depicts a magical morning scene, capturing the essence of H. Gábor Erzsébet's poem "Kisrigó" in Stephen Gammell's unique and evocative style. A small blackbird awakens, with indigo-tinted feathers and bright brown eyes, perched on a branch against the early morning sky. The bird sings with all its might, causing the branch to bend slightly under the force of its song. Dew drops glisten on the grass, and the first golden rays of the sun bathe the landscape. Flowers stretch and bask in the morning light. Silhouettes of people, symbolizing human presence, begin to wake in the distance, touched by the sunbeams. The central element is the blackbird's freedom and flight, soaring above the clouds, expressing the poet's longing to be as free as the bird.”
InnovationNewsNetwork:
Minister outlines new approach to UK science and tech at G7:
The Science Minister declared UK science and technology “open for business” as he met counterparts at the G7 Science and Technology Ministerial in Italy.
Gaper.io:
Quantum Decisions: The Fusion of AI and Quantum Computing
Image Credit to Gaper.io
Springer Conference proceedings © 2022: Artificial Intelligence Applications and Innovations 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part II (Download)
Spotlight on AI Tools for Academic Excellence
Lingolette : Language Teaching Machine, focusing on improving spoken and written fluency through real-time conversations
Logoai : An AI-powered logo maker and brand building platform that helps businesses create professional logos, design matching identities, and automate brand promotion with on-brand social media content.
IdeaApe : Advanced and user-friendly AI market research tool
Saner.ai : Stay on top of work and life with peace of mind One-stop AI Productivity app for ADHD Drowning in information? Forgetting tasks? Our intuitive app, with AI grounded in your knowledge, simplifies your life by consolidating notes, searches, and to-dos into one place.
Thryve: Your 24/7 AI powered companion for emotional, wellbeing & fitness support. Start your journey to a healthier, happier you today. And by the way - Thryve speaks 50+ languages!
For finding more selected AI apps, please subscribe to ScholarSphere Newsletter Series
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