Hinton & Nowlan’s computational Baldwin effect revisit: Are we happy with it? Akira Imada

In their seminal paper published in 1987, Hinton & Nowlan showed us an elegant experiment which might be called an evolution with the Baldwin effect in computers which searches for only one object located in a huge search space. The object was called a-needle-in-a-haystack. Hinton & Nowlan evolved a population of candidates of the solution in the same way as a standard evolutionary search. What made it unique was an exploitation of individual’s lifetime-learning. Since then we have had afair amount of proposals of how we reach the needle more efficiently. The issue, however, is still open to debate. We try to repeat their experiment and take a consideration on it.Naujai peržiūrimas Hintono ir Nowlano skaičiuojamasis Baldwino efektas: ar tai mus tenkina?Akira Imada  SantraukaSavo užuomazginiame straipsnyje, publikuotame 1987 m., Hintonas ir Nowlanas pademonstravo elegantišką eksperimentą, kurį galima vadinti evoliucija su Baldwino efektu kompiuteriuose, kuri ieško vieno objekto milžiniškoje paieškos erdvėje. Šis objektas buvo pavadintas adata šieno kupetoje. Hintonas ir Nowlanas išvystė visą populiaciją sprendimo kandidatų analogiškų standartinei evoliucinei paieškai. Unikalu buvo tai, kad panaudotas individo mokymasis visą gyvenimą. Nuo to laiko pateikta pakankamai daug siūlymų, kaip efektyviau pasiekti ieškomąją adatą. Tačiau šis klausimas išlieka atviras diskusijoms. Šiame straipsnyje pakartojamas ir apsvarstomas Hintono ir Nowlano eksperimentas

Tiny Classifier Circuits: Evolving Accelerators for Tabular Data

A typical machine learning (ML) development cycle for edge computing is to maximise the performance during model training and then minimise the memory/area footprint of the trained model for deployment on edge devices targeting CPUs, GPUs, microcontrollers, or custom hardware accelerators. This paper proposes a methodology for automatically generating predictor circuits for classification of tabular data with comparable prediction performance to conventional ML techniques while using substantially fewer hardware resources and power. The proposed methodology uses an evolutionary algorithm to search over the space of logic gates and automatically generates a classifier circuit with maximised training prediction accuracy. Classifier circuits are so tiny (i.e., consisting of no more than 300 logic gates) that they are called "Tiny Classifier" circuits, and can efficiently be implemented in ASIC or on an FPGA. We empirically evaluate the automatic Tiny Classifier circuit generation methodology or "Auto Tiny Classifiers" on a wide range of tabular datasets, and compare it against conventional ML techniques such as Amazon's AutoGluon, Google's TabNet and a neural search over Multi-Layer Perceptrons. Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline. When synthesised as a Silicon chip, Tiny Classifiers use 8-18x less area and 4-8x less power. When implemented as an ultra-low cost chip on a flexible substrate (i.e., FlexIC), they occupy 10-75x less area and consume 13-75x less power compared to the most hardware-efficient ML baseline. On an FPGA, Tiny Classifiers consume 3-11x fewer resources.Comment: 14 pages, 16 figure

Redundant Representations in Evolutionary Computation

Redundanz , Evolutionary programmin

Pregnant Women Inmates: Evaluating Their Rights and Identifying Opportunities for Improvements in Their Treatment

Pregnant women incarcerated at the time of our nation\u27s founding faced the prospect of giving birth in their cells alone and a considerable likelihood that their infants would die. This is somewhat unsurprising. At this time infant mortality rates were high. Given the pace of advances in the treatment of pregnant women since that time, one might expect that the experience of pregnant women incarcerated in today\u27s correctional facilities would have improved as it has for their peers on the outside. That, however, would be an unrealistic assumption. In addition to facing decidedly substandard environments in some facilities - inappropriate accommodations, widespread exposure to disease and unsanitary conditions, among other challenges - pregnant women sometimes still risk the possibility of giving birth without assistance. Such was the case of Louwanna Yeager. Ms. Yeager, upon going into labor in May 1987, was informed by guards that she would have to wait because no medical staff members were available to help her. The birthing process is not one amenable to being put on hold and, as such, Ms. Yeager gave birth three hours later on a thin mat outside of the door of the clinic in the jail. Ms. Yeager\u27s horrifying experience and those of her peers at the Kern County Jail led to a lawsuit that changed conditions for pregnant and post-partum women at the facility. Pregnant women incarcerated in correctional facilities that have been the subject of litigation have seen an improvement in the conditions they experience. However, most of these facilities would not have made these changes without the threat of litigation. Thus, those pregnant women incarcerated in facilities that have evaded legal scrutiny may still face conditions not much improved than those endured by Ms. Yeager and others like her. This article illustrates the challenges faced by pregnant women incarcerated in correctional facilities, their rights, and ways in which change for these women can be effected as well as programs that have provided clear improvements for their care. The treatment of pregnant inmates merits special attention - especially in the competition for scarce correctional resources - because of the particular complications for these women and their infants which can result from improper care