1,024 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
Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology
The great behavioral heterogeneity observed between individuals with the same
psychiatric disorder and even within one individual over time complicates both
clinical practice and biomedical research. However, modern technologies are an
exciting opportunity to improve behavioral characterization. Existing
psychiatry methods that are qualitative or unscalable, such as patient surveys
or clinical interviews, can now be collected at a greater capacity and analyzed
to produce new quantitative measures. Furthermore, recent capabilities for
continuous collection of passive sensor streams, such as phone GPS or
smartwatch accelerometer, open avenues of novel questioning that were
previously entirely unrealistic. Their temporally dense nature enables a
cohesive study of real-time neural and behavioral signals.
To develop comprehensive neurobiological models of psychiatric disease, it
will be critical to first develop strong methods for behavioral quantification.
There is huge potential in what can theoretically be captured by current
technologies, but this in itself presents a large computational challenge --
one that will necessitate new data processing tools, new machine learning
techniques, and ultimately a shift in how interdisciplinary work is conducted.
In my thesis, I detail research projects that take different perspectives on
digital psychiatry, subsequently tying ideas together with a concluding
discussion on the future of the field. I also provide software infrastructure
where relevant, with extensive documentation.
Major contributions include scientific arguments and proof of concept results
for daily free-form audio journals as an underappreciated psychiatry research
datatype, as well as novel stability theorems and pilot empirical success for a
proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
Transmuting values in artificial intelligence: investigating the motivations and contextual constraints shaping the ethics of artificial intelligence practitioners
Advances in Artificial Intelligence (AI) research and development have seen AI applied in
various high-stakes domains such as healthcare and welfare. Furthermore, portrayals of AI are
often characterised by narratives of perpetual progress and sleek optimisation, obscuring the
intricate interactions of materiality and socio-political decision-making inherently embedded
within wider systems of design and development. The resulting ethical and social concerns
have prompted proposal of numerous frameworks, tools and guidelines for the ethical design
and development of AI. However, translating these proposals into practice has proven
challenging, and there is a paucity of research into the practical contexts shaping the ethico-onto-epistemology of AI practice. In this thesis I illustrate these contexts via the accounts of
24 AI practitioners, complemented by ethnographic observations from an industry research
lab, examining the values which motivate practitioners, the constraints which shape their
practice, and their approaches to ethics.
Weaving through these discussions of practice, values, and the nature of
responsibility, I examine how ambiguities pervade practice and shape the realities of ethical
reflection and engagement at all stages of development. My findings uncover practitioner
motivations linked with interconnected intellectual and moral values, how these related to
intellectual conduct and culture within the field, and how practitioner heuristics for ethical
decision-making are often relational and character-based in nature. This realization of values
in practice is tempered by numerous constraints including hardware limitations, epistemic
cultures, and ethical knowledge.
Drawing upon the Ethics of Ambiguity, I discuss how the
uncertainty, ambiguity and unequal access to resources shaping AI practice necessitate a
process-focused ethics which pivots away from solutions, towards critical contextual
reflexivity and awareness of how contexts impact realisation of values. To this end, I
demonstrate how The Ethics of Ambiguity can offer a path forward for ethical AI practice.
This vision of AI practice embraces ambiguities rather than attempting to segment and
sideline them, focusing on how practitioner decisions (and their eventual outputs) impact
othersâ freedoms while acknowledging the multiplicity of values across socio- and geo-political contexts
Beyond Quantity: Research with Subsymbolic AI
How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately
Development of the eVOLVER continuous culture platform for high-throughput phage-assisted continuous evolution
Continuous directed evolution (CDE) has emerged as a powerful paradigm for generating biomolecules toward radically altered or even new functions capable of addressing unmet needs in medicine, biotechnology, and synthetic biology. Indeed, CDE methods, such as Phage-Assisted Continuous Evolution (PACE), can theoretically enable extensive speed, scale, and depth in an evolutionary search. However, the technical limitations of implementing PACE (and other CDE techniques) have restricted what can be practically achieved with PACE alone. Here, I combine the continuous hypermutation and selection advantages of PACE with the automated, scalable, and customizable eVOLVER continuous culture platform to create ePACE. ePACE overcomes many of the design and operational challenges of traditional PACE to facilitate parallel, automated, and continuous evolution of biomolecules directly on the benchtop. ePACE is applied to the evolution of Nme2Cas9 to target single-nucleotide-pyrimidine PAM sequences via high-throughput parallel evolution against individual PAM sequences, resulting in four highly-active Cas9 variants
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