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ACTIVE LEARNING "uses abductive logic programming to generate valid hypotheses that explain the observations; and uses these hypotheses to determine the next most informative experiment." https://lnkd.in/dUdRjJH View in LinkedIn

CHANGING MANY VARIABLES TOGETHER. "In most of science, you try to keep everything constant except for one variable. You turn one knob at a time, and see how the system responds. That’s wonderful for linear systems...But most biology is complex and non-linear. Emergent behaviors are very hard to understand unless you turn many knobs at a time, and we can’t figure out which knobs to turn. So we’re going to let Eureqa pick them.” https://lnkd.in/dEC3j_P View in LinkedIn

ACTIVE LEARNING. "The Robot Scientist makes use of an iterative approach to experimentation, where knowledge acquired from a previous iteration is used to guide the next experimentation step. This is a process known as Active Learning, where the learner can plan its own agenda, i.e. decide how best to improve its knowledge base and how to go about acquiring this information." https://lnkd.in/dUdRjJH View in LinkedIn

SCRAPYARD MINING. "This knowledge will initially correspond to the goals of the scientific task, but increasingly the internet repositories that are often constructed to store the data have become the focus of less directed scientific study, where "hidden" knowledge not originally anticipated by the goals of the scientific task may be found. However, this "scrapyard" approach is partly a result of overexperimentation where many unnecessary experiments were conducted along with the potentially informative ones." https://lnkd.in/dUdRjJH View in LinkedIn

BIOINFORMATICS DATA MINING. "It has become typical practice in Bioinformatics to separate the data collection or experimentation process and the understanding process, where large numbers of experiments are conducted and then specially designed data mining tools are used to identify correlations in the data that might represent hitherto undiscovered scientific knowledge." https://lnkd.in/dUdRjJH View in LinkedIn

COMPLEXITY AND INFORMATION. "Around 1950 mathematicians and computer scientists began studying artificial self replicating systems in order to gain a deeper understanding of complex systems and the fundamental information processing principles involved in self replication." https://lnkd.in/dAx3RM7 View in LinkedIn

EPIGENETIC RESPONSES. "Pargellis envisages chips with simple instruction sets, whose programs dynamically mutate in response to environmental stimuli." https://lnkd.in/dJPTA3e View in LinkedIn

SIMPLIFYING EFFICIENCY. "Pargellis found that on average, a self-replicating sequence of instructions would spontaneously arise after about 400 generations. Initially, such 'self-reps' were often unnecessarily complex, and contained portions of code which performed no useful task, or were skipped over. This is directly analagous to the 'introns' and 'exons' found in real biological genes, where useful genetic information is interspersed with patches of nonsense." https://lnkd.in/dJPTA3e View in LinkedIn

SIMPLICITY AND ABUNDANCE. "One reason for Amoeba's fecundity is that its life-forms have a simple structure. Pergellis has carefully designed his instruction set such that it is possible to create a self-replicating cell with only 5 instructions." https://lnkd.in/dJPTA3e View in LinkedIn

FINITE SIZE RANGE. "Proteins exist in a finite range of sizes with a limited amino acid alphabet. We find that these restrictions set an intrinsic limit to designing protein interfaces that can bind selectively to target proteins, while remaining relatively repulsive to all other protein interfaces." https://lnkd.in/eTyvTV7 View in LinkedIn