296 research outputs found
Modern meat: the next generation of meat from cells
Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community.
The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World.
The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia
Pathway to Future Symbiotic Creativity
This report presents a comprehensive view of our vision on the development
path of the human-machine symbiotic art creation. We propose a classification
of the creative system with a hierarchy of 5 classes, showing the pathway of
creativity evolving from a mimic-human artist (Turing Artists) to a Machine
artist in its own right. We begin with an overview of the limitations of the
Turing Artists then focus on the top two-level systems, Machine Artists,
emphasizing machine-human communication in art creation. In art creation, it is
necessary for machines to understand humans' mental states, including desires,
appreciation, and emotions, humans also need to understand machines' creative
capabilities and limitations. The rapid development of immersive environment
and further evolution into the new concept of metaverse enable symbiotic art
creation through unprecedented flexibility of bi-directional communication
between artists and art manifestation environments. By examining the latest
sensor and XR technologies, we illustrate the novel way for art data collection
to constitute the base of a new form of human-machine bidirectional
communication and understanding in art creation. Based on such communication
and understanding mechanisms, we propose a novel framework for building future
Machine artists, which comes with the philosophy that a human-compatible AI
system should be based on the "human-in-the-loop" principle rather than the
traditional "end-to-end" dogma. By proposing a new form of inverse
reinforcement learning model, we outline the platform design of machine
artists, demonstrate its functions and showcase some examples of technologies
we have developed. We also provide a systematic exposition of the ecosystem for
AI-based symbiotic art form and community with an economic model built on NFT
technology. Ethical issues for the development of machine artists are also
discussed
Multimodal connectivity of the human basal forebrain
The cholinergic innervation of the cortex originates from neurons in the basal forebrain (BF) and plays a crucial role in cognitive processing. However, it is unclear how the organization of BF cholinergic neurons in the human brain is related to their functional and structural integration with the cortex. To address this, we have used high-resolution 7 Tesla diffusion and resting-state functional MRI to examine multimodal forebrain cholinergic connectivity with the neocortex in humans. Discrete parcellation analyses revealed that structural and functional parcellation broadly differentiated the anteromedial from posterolateral nuclei of BF. Next, we used gradient estimation to capture more fine-grained connectivity profile of the BF-cortical projectome and found moving from anteromedial to posterolateral BF, structural and functional gradients became progressively detethered, with the most pronounced dissimilarity localized in the nucleus basalis of Meynert (NbM). Additionally, functional but not structural connectivity with the BF grew stronger at shorter geodesic distances, with weakly myelinated transmodal cortical areas most strongly expressing this divergence. Moreover, [18F] FEOBV PET imaging was used to demonstrate that these transmodal cortical areas are also among the most densely innervated regions. This intrinsic BF cholinergic connectivity map of cortex was compared with meta-analytic connectivity map of cholinergic modulation on attention, demonstrating that patterns of brain activity evoked by directed attention are altered by pharmacological activation of acetylcholine (ACh) compared to placebo and these patterns spatially overlap with the intrinsic BF cholinergic connectivity map. Altogether, our findings provide new insights into how cholinergic signaling is organized in the human brain
Neuromorphic Computing between Reality and Future Needs
Neuromorphic computing is a one of computer engineering methods that to model their elements as the human brain and nervous system. Many sciences as biology, mathematics, electronic engineering, computer science and physics have been integrated to construct artificial neural systems. In this chapter, the basics of Neuromorphic computing together with existing systems having the materials, devices, and circuits. The last part includes algorithms and applications in some fields
Internet and Biometric Web Based Business Management Decision Support
Internet and Biometric Web Based Business Management Decision Support
MICROBE
MOOC material prepared under
IO1/A5 Development of the MICROBE personalized MOOCs content and teaching materials
Prepared by:
A. Kaklauskas, A. Banaitis, I. Ubarte
Vilnius Gediminas Technical University, Lithuania
Project No: 2020-1-LT01-KA203-07810
The 26th Annual Boston University Undergraduate Research (UROP) Abstracts
The file is available to be viewed by anyone in the BU community. To view the file, click on "Login" or the Person icon top-right with your BU Kerberos password. You will then be able to see an option to View.Abstracts for the 2023 UROP Symposium, held at Boston University on October 20, 2023 at GSU Metcalf Ballroom. Cover and logo design by Morgan Danna. Booklet compiled by Molly Power
Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications
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