Symbolic metaprogram search improves learning efficiency and explains rule learning in humans

Throughout their lives, humans seem to learn a variety of rules for things like applying category labels, following procedures, and explaining causal relationships. These rules are often algorithmically rich but are nonetheless acquired with minimal data and computation. Symbolic models based on program learning successfully explain rule-learning in many domains, but performance degrades quickly as program complexity increases. It remains unclear how to scale symbolic rule-learning methods to model human performance in challenging domains. Here we show that symbolic search over the space of metaprograms—programs that revise programs—dramatically improves learning efficiency. On a behavioral benchmark of 100 algorithmically rich rules, this approach fits human learning more accurately than alternative models while also using orders of magnitude less search. The computation required to match median human performance is consistent with conservative estimates of human thinking time. Our results suggest that metaprogram-like representations may help human learners to efficiently acquire rules

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Child as Hacker

© 2020 The Authors The scope of human learning and development poses a radical challenge for cognitive science. We propose that developmental theories can address this challenge by adopting perspectives from computer science. Many of our best models treat learning as analogous to computer programming because symbolic programs provide the most compelling account of sophisticated mental representations. We specifically propose that children's learning is analogous to a particular style of programming called hacking, making code better along many dimensions through an open-ended set of goals and activities. By contrast to existing theories, which depend primarily on local search and simple metrics, this view highlights the many features of good mental representations and the multiple complementary processes children use to create them.NSF (Grants 1760874, 1122374, 1745302)Eunice Kennedy Shriver National Institute of Child Health & Human Development (Award 1R01HD085996)Office of Naval Research (Grant N00014-18-1-2847)Air Force Office of Scientific Research (Award FA9550-19-1-0269

Learning as Programming: Modeling Efficient Search in Human Concept Learning

Accounts of human and machine concept learning face a fundamental challenge. Some approaches, notably deep learning, frequently achieve human-level performance on specific tasks but lack consistently human-like—i.e. generalizable, composable, and explainable—solutions. Others, including classic symbolic accounts, produce human-like hypotheses but scale poorly. We present a model of learning which is both human-level and human-like. It represents concepts as program-like expressions formed by applying a series of higher-order inferences that iteratively revise preexisting concepts into novel target concepts. Learning seeks the best combination of revisions under a Bayesian score. This model predicts learning behavior in 392 humans over 100 computationally sophisticated concepts more accurately than alternative models (Enumerate, Metagol, RobustFill, Fleet) while using three orders of magnitude less computation. This work shows how humans plausibly construct sophisticated algorithmic representations, a necessity for compelling human-like artificial intelligence

Concept Learning as Coarse-to-Fine Probabilistic Program Induction

Program induction is an appealing model for human concept learning, but faces scaling challenges in searching the massive space of programs. We propose a computational model capturing two key aspects of human concept learning – our ability to judge how promising a vague, partial hypothesis is, and our ability to gradually refine these vague explanations of observations to precise ones. We represent hypotheses as probabilistic programs with randomness in place of unresolved programmatic structure. To model the evaluation of partial hypotheses, we implement a novel algorithm for efficiently computing the likelihood that a probabilistic program produces the observations. With this, we guide a search process whereby high-entropy, coarse programs are iteratively refined to introduce deterministic structure. Preliminary synthesis results on list manipulation and formal grammar learning tasks show improvements in sample efficiency when leveraging likelihood guidance, and a preliminary human study explores how model intermediate hypotheses compare to those of participants

Outcomes of stage I/II follicular lymphoma in the PET era: an international study from the Australian Lymphoma Alliance

Management practices in early-stage (I/II) follicular lymphoma (FL) are variable and include radiation (RT), systemic therapy, or combined modality therapy (CMT). There is a paucity of data regarding maintenance rituximab in this cohort. We conducted an international retrospective study of patients with newly diagnosed early-stage FL staged with positron emission tomography (PET)-computed tomography and bone marrow biopsy. Three hundred sixty-five patients (stage I, n = 221), median age 63 years, treated from 2005-2017 were included, with a median follow-up of 45 months. Management included watchful waiting (WW; n = 85) and active treatment (n = 280). The latter consisted of RT alone (n = 171) or systemic therapy (immunochemotherapy [n = 63] or CMT [n = 46]). Forty-nine systemically treated patients received maintenance rituximab; 72.7% of stage I patients received RT alone, compared to 42.6% with stage II ( < .001). Active therapies yielded comparable overall response rates ( = .87). RT alone and systemic therapy without maintenance rituximab yielded similar progression-free survival (PFS) (hazard ratio [HR], 1.32; 95% confidence interval [CI], 0.77-2.34; = .96). Maintenance rituximab improved PFS (HR, 0.24; 95% CI, 0.095-0.64; = .017). The incidence of transformation was lower with systemic therapy compared to RT or WW (HR, 0.20; 95% CI, 0.070-0.61; = .034). Overall survival was similar among all practices, including WW ( = .40). In the largest comparative assessment of management practices in the modern era, variable practices each resulted in similar excellent outcomes. Randomized studies are required to determine the optimal treatment in early-stage FL

Outcomes of Stages I and II Follicular Lymphoma in the Era of 18F-FDG PET-CT Staging: An International Collaborative Study from the Australian Lymphoma Alliance

Kitty Crowther, Mon Ami Jim, Pastel, 1996. Quelques phrases pour résonner une sublime fable du racontage : Elle déroule son fil sur des galets - six par double page - qui voient s'effectuer la rencontre de Jack, le corbeau, et de Jim, la mouette. Des moments contemplatifs ou plutôt de spauses dans la profondeur des galets nous arrêtent sur une pleine page. Puis l'amour sur un galet s'avoue tête bêche dans un lit. Le village n'accueille pas l'étranger mais quand les histoires que racontent les..