Fler och fler industrier vänder blickarna mot A.I. (artificiell intelligens) för att undersöka om och hur det kan användas för att effektivisera olika processer. Men för att träna upp en A.I. krävs oftast stora mängder data där man kan behöva förbereda väldigt mycket manuellt innan man ens kan påbörja träningsprocessen. SCA Skog AB ser dock många fördelar med att göra A.I. till en naturlig del av sin digitaliseringsprocess, där man bland annat är intresserad utav visuella bedömningar av träd. Dataset för visuella bedömningar kan se ut på olika sätt, men i detta fall var det relevant att skapa dataset i form av konturer för trädstammar. Med hjälp av en A.I. som skulle kunna visuellt segmentera och klassificera träd så skulle man öppna upp för många nya möjligheter inom skogsindustrin. Under detta projekt har jag undersökt hur man skulle kunna automatisera processen för skapandet av dataset i  skogsmiljöer för just visuella bedömningar. Som ett resultat av att försöka uppnå detta, så fick jag experimentera med bildbaserade punktmoln som på olika sätt tillät projektet att avancera framåt. Ur dessa punktmoln kunde jag sedan segmentera träden för att i nästa process skapa konturer längs alla träd med hjälp av utvunnen data ur segmenteringen. Jag tittade först och främst på hur man automatiskt skulle kunna skapa konturer för alla träd i bildsekvensen, för att sedan låta en användare gå in och finjustera konturerna. I resultatet kan man sedan tydligt se skillnaden i tidsåtgång för att använda programmet och inte. Programmet kan skapa och uppdatera pixel-masker snabbare än vad jag manuellt kunde utföra samma arbete, där jag dock hade önskat på en mer markant skillnad i tidsåtgång jämfört med den rent manuella insatsen. Under projektets gång så kunde jag identifiera några större problem som förhindrade detta, där man med lämplig utrustning skulle kunna uppnå ett mycket bättre resultat än vad som gjordes under detta projekt. Resultaten talar ändå för att det kan vara lönt att undersöka metoden mer ingående. / More and more industries are turning their eyes towards A.I. (artificial intelligence) and its rapid development, in hope of utilizing it to remove labor intense operations. But large amounts of manually processed data is often required before starting the learning process, which can be a huge problem to deal with. SCA Skog AB is still very curious in how they could use A.I. in forestry, where visual inspection of trees is of particular interest. There are many visual problems that modern A.I. can solve, where in this case it’s a matter of finding contours of trees and classify them. If this would be possible, a lot of interesting opportunities would open up to be experimented with. During this project I’ve examined the possibility of reducing the time it takes to manually create datasets of forest environments for this particular visual problem. As a result of trying to achieve this, I had to examine image-based point clouds and their properties to find out how they could be used in this process. From the SfM-point cloud I was able to segment all visible trees with an segmentation algorithm and isolate these points to extract the 2D→3Dconnection. I could then use that connection to create pixel masks and apply it to the image sequence to paint out all the contours of the segmented trees. A method to automatically update these pixel masks in terms of adding and removal was also implemented, where any update would propagate through the image sequence and reduce the time for manual adjustment. From testing the program, it’s clear that time could be saved doing various kinds of contour updating-operations. The program could by itself create pixel masks that then could be updated in a way that a lot of need for manual updating was reduced, though the result in terms of time saved was not as substantial as one would have hoped for. Issues with the point cloud caused some major  problems due to it’s low precision. Using better equipment for image gathering would most likely be the best way to improve the results of this project. The result still tells us that this method is worth researching further.

Collectible Card Games (CCG) are one of the most popular types of games in both digital and physical space. Despite their popularity, there is a great deal of room for exploration into the application of artificial intelligence in order to enhance CCG gameplay and development. This paper presents Fifth Aeon a novel and open source CCG built to run in browsers and two A.I applications built upon Fifth Aeon. The first application is an artificial intelligence competition run on the Fifth Aeon game. The second is an automatic balancing system capable of helping a designer create new cards that do not upset the balance of an existing collectible card game. The submissions to the A.I competition include one that plays substantially better than the existing Fifth Aeon A.I with a higher winrate across multiple game formats. The balancer system also demonstrates an ability to automatically balance several types of cards against a wide variety of parameters. These results help pave the way to cheaper CCG development with more compelling A.I opponents.

This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future. / Detta examensarbete, utfört på Ericsson AB, ämnar att undersöka huruvida maskininlärningstekniker kan användas för att klassificera dumpfiler för mer effektiv problemidentifiering. Projektet fokuserar på övervakad inlärning och då speciellt Bayesiansk klassificering. Arbetet visar att ett program som utnyttjar Bayesiansk klassificering kan uppnå en noggrannhet väl över slumpen. Arbetet indikerar att maskininlärningstekniker mycket väl kan komma att bli användbara alternativ till mänsklig klassificering av dumpfiler i en nära framtid.

Parkinson’s disease(PD) is a neurodegenerative disease that mainly affects the motor system. These symptoms can be treated temporarily with medicine,but it’s difficult to determine the right dosage. The goal was to use sensorsthat could measure the symptoms of PD and thus be able to give an objec-tive rating of the disease as a basis to determine the correct medicine dosage.Coincidentally with A.I and modern devices we can make it more exciting byusing mobile device games to generate the bulk of the information for whatdoctors need to give out correct medication guidelines without guidance fromthe engineers. Processing past research papers and data sets of use is im-portant to use adequate methodology and A.I tools to generate the expectedresult. Innately using a system that could measure Accelerometer-data andeasily log this data into MatLab to be processed. The system would also needto be used in similar ways by multiple patients so that the results could becompared to each other. The patients that would use this system would alsoneed to willingly use this multiple times a day, so it could not be tiresome touse it at a daily basis. Main programming language used was MATLAB and with its’ internal intelligent system tools namely ML toolbox, you can gen-erate machine learning system. The smartphone solution satisfied all of theprerequisites and would prove to be a viable choice with its strength in its ac-cessibility and ease of use. The group with imitated symptoms while playingthe game gave similar results as preceding research papers that was measuredon real PD patients, so from the results this solution has the possibility tobe used by patients and neurologists to asses the correct PD treatment withmedicine. Despite sudden impedance from the hospital and current Covid-19situation this is a field that can be further studied in the future.

Is data material? Is it feasible to recycle information and bring it into the physical world? How do a thousand dental surgeries look when they take shape and become part of an object? Big data is the oil of the twenty-first century; we have an abundance of it. There are several possible implications of data collecting, and we are currently waiting to discover the environmental cost of some. In spite of this, relatively little of the data is really used. Unintentional Artefacts, is a degree project which attempts to look at data from a different perspective and invite everyone to look beyond the facts. There is a world of potential forms hidden in information. The methodology is illustrated through the conceptual design of three sneakers. Each shoe is transformed into a new shape by recycled information, originating from root canals, library trips, and recreational fishing.

This paper develops a possible explanation for a facet of visual processing inspired by the biological brain's mechanisms for information gathering. The primary focus is on how humans observe elements in their environment and reconstruct visual information within the brain. Drawing on insights from diverse studies, personal research, and biological evidence, the study posits that the human brain captures high-level feature information from objects rather than replicating exact visual details, as is the case in digital systems. Subsequently, the brain can either reconstruct the original object using its specific features or generate an entirely new object by combining features from different objects, a process referred to as "Imagination." Central to this process is the "Imagination Core," a dedicated unit housing a modified diffusion model. This model allows high-level features of an object to be employed for tasks like recreating the original object or forming entirely new objects from existing features. The experimental simulation, conducted with an Artificial Neural Network (ANN) incorporating a Convolutional Neural Network (CNN) for high-level feature extraction within the Information Processing Network and a Diffusion Network for generating new information in the Imagination Core, demonstrated the ability to create novel images based solely on high-level features extracted from previously learned images. This experimental outcome substantiates the theory that human learning and storage of visual information occur through high-level features, enabling us to recall events accurately, and these details are instrumental in our imaginative processes. / Master of Science / This study takes inspiration from how our brains process visual information to explore how we see and imagine things. Think of it like a digital camera, but instead of saving every tiny detail, our brains capture the main features of what we see. These features are then used to recreate images or even form entirely new ones through a process called "Imagination." It is like when you remember something from the past – your brain does not store every little detail but retains enough to help you recall events and create new ideas. In our study, we created a special unit called the "Imagination Core," using a modified diffusion model, to simulate how this process works. We trained an Artificial Neural Network (ANN) with a Convolutional Neural Network (CNN) to extract the main features of objects and a Diffusion Network to generate new information in the Imagination Core. The exciting part? We were able to make the computer generate new images it had never seen before, only using details it learned from previous images. This supports the idea that, like our brains, focusing on important details helps us remember things and fuels our ability to imagine new things.

Begreppet ”fan edits” betecknar filmer som klipps om av fans, vilka är missnöjda med hur en adaption för vita duken som gjorts. I min uppsats vill jag påvisa dels hur samspelet mellan fans och filmmakare/filmbolag sett och ser ut, dels försöka klargöra varför copyright/fair use är så knepigt att applicera på området.

In car racing simulation games, finishing first is the main goal. To reach that goal, it is required to go around a racing track, competing against other cars aiming for the same goal. Implementing a bot for doing so may have its difficulties, although using a technique called multi-agent systems combined with artificial potential field, let- ting the agents take care of subtasks like keeping the car on the track, minimize how much the car turns in a curvature and basics in navigation around the track, has showed that artificial potential fields very well fit the problem of driving a car in simulated car racing in a competitive way. / Mobiltelefon: 0707422666

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在機器學習與深度學習技術帶動第三次人工智慧熱潮，特別是與機器人、大數據、3D列印等結合，「人工智慧」成為各大科技企業重點發展技術，無論是透過成立研究小組或是併購的方式，在2016年即有40多家人工智慧技術公司被併購，同時這些技術也被應用在各式產品與服務中。此外不同產業中也陸續引進人工智慧，從事需要耗時費力的基礎工作，節省成本，也引發人類被取代的恐慌。世界各國除著力投資發展人工智慧之外，也重視人工智慧為社會及經濟產生的影響與現行法制的衝擊。 人工智慧技術也應用於創作與發明過程中，且在機器學習技術下，人類僅需輸入指示與限制，人工智慧完成內容創作所產出作品，與人類創作成果並無二致。或是像是神燈精靈一般，人類只要以人工智慧可理解的方式定義問題、要件分析、功能設計，人工智慧即能完成最重要的物理設計，解決人類之問題，其產出物可能是符合產業利用性、新穎性與進步性等專利要件。這些人工智慧創作與發明物是否符合我國現行著作權法與專利法之規定而受到保護，將是本文探討重點，本文將以文獻研究以及比較法的方式深入研究。 智慧財產權制度設立的主旨是為保護人類精神活動成果，以人類為創作主體為前提，人工智慧參與創作之成果對於現行制度而言自然有所扞格。然而人工智慧創作力對於未來創作與發明而言，都是有所助益，可豐富文化的多樣性並加快技術的發展。而日本知識產權戰略本部也於2016年四月時也將針對人工智慧創作物之法律保護，擬修訂智慧財產權法。如我國未來亦研擬將人工智慧創作物納入法律保護，本文參考日本立法相關討論以及美國學者之見解，提出立法時應考量的權利歸屬以及衍生的相關問題。 / Machine Learning and Deep Learning are leading the new artificial intelligence era, especially when integrated with technologies of robot, big data, and 3D printing. As A.I. gradually became the one of the most popular technologies, corporate giants, such as Google, IBM, Facebook, and Apple, have been setting up research labs and acquiring A.I. startups to improve the quality of their services and products. Meanwhile, through using their service and products, our daily life is filled with A.I. Moreover, in many different industries, companies are using A.I. to reduce their cost by replacing labors from time-consuming jobs. Governments not only invest in the development of A.I. technology, but also response to the impact A.I. brings to the society, economic and Law. Artificial creativity is a new way for creation and invention. With machine learning, human only need to input the indication and limitation for A.I. to generate the outcome which is almost the same as what human can do. A.I. is also being described as a “genie in the machine”. Human input the description of their problems, functional analysis, and functional design, then A.I. will do the physical design to solve the problems and generate inventions which are useful, novelty and un-obviousness. Whether these creations and inventions are copyrightable and patentable is the core of this essay. Intellectual property system is aimed to protect the result from human’s mind activity, so the author or inventor must be human beings. When A.I. is not just a creation tool, but a creative subject, that’s where a conflict occurs. However, A.I. creativity is beneficial to human creative and inventing activity, because it can quicken the progress of technologies and improve culture diversity. Legislators in Japan are planning to protect A.I. creation through modification of intellectual property law. If we also expect to protect A.I. creation and invention in Taiwan in the future, with Japan legislative discussion and America scholars’ theories, this essay might offer some useful indications on the right attribution and other derivation problems.