The Exploration and Analysis of Mancala from an AI Perspective

Through the study of popular games such as Chess and Go, countless artificial intelligence (AI) research has been conducted in an attempt to create algorithms equipped for adversarial search problems. However, there are still a plethora of avenues that offer insight into further development. Mancala is traditionally a two-player board game that originated in the East and offers a unique opponent-based playing experience. This thesis not only attempts to create a competitive AI algorithm for mancala games by analyzing the performance of several different algorithms on this classic board game, but it also attempts to extract applications that may have relevance to other “game-solving” AI problems

Multimodaalisen äänitietoaineiston luominen joukkoistamisen avulla

Creating large multimodal datasets for machine learning tasks can be difficult. Annotating large amounts of data for the dataset is costly and time consuming if done by finding and hiring participants. This thesis outlines a method for gathering multimodal annotations with the crowdsourcing platform Amazon Mechanical Turk (AMT). Specifically, the method in this thesis is made for annotating audio files with five captions and subjective scores for description accuracy and fluency for each caption. The durations of the audio files used in this thesis are uniformly distributed from 15 to 30 seconds. The method divides the whole annotation task into three separate tasks, namely audio description, description editing and description scoring. The editing and scoring tasks were introduced to attempt to fix errors from the previous tasks. The inputs for the audio description task are the audio files that are to be annotated. The inputs for the description editing task are the descriptions from the audio description task, and the inputs for the description scoring task are the descriptions from the previous tasks. Each audio file is described five times, each description is edited once, and each set of descriptions is scored three times. At the end of the process there are ten descriptions for each audio file and three scores for accuracy and fluency for each description. The scores are used to sort the descriptions and the top five descriptions are used as the final captions for the files. This thesis creates an audio captioning dataset using this method for 5,000 audio files

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations