This paper presents two studies that use a machine learning expert system (MLES). One focuses on a system to advise to United States federal judges for regarding consistent federal criminal sentencing, based on both the federal sentencing guidelines and offender characteristics. The other study aims to develop a system that could prospectively assist the U.S. Patent and Trademark Office automate their patentability assessment process. Both studies use a machine learning-trained rule-fact expert system network to accept input variables for training and presentation and output a scaled variable that represents the system recommendation (e.g., the sentence length or the patentability assessment). This paper presents and compares the rule-fact networks that have been developed for these projects. It explains the decision-making process underlying the structures used for both networks and the pre-processing of data that was needed and performed. It also, through comparing the two systems, discusses how different methods can be used with the MLES system.

This article elaborates on how machine learning (ML) can leverage the solution of a contemporary problem related to the security of maritime domains. The worldwide ``Illegal, Unreported, and Unregulated'' (IUU) fishing incidents have led to serious environmental and economic consequences which involve drastic changes in our ecosystems in addition to financial losses caused by the depletion of natural resources. The Fisheries and Aquatic Department (FAD) of the United Nation's Food and Agriculture Organization (FAO) issued a report which indicated that the annual losses due to IUU fishing reached $25 Billion. This imposes negative impacts on the future-biodiversity of the marine ecosystem and domestic Gross National Product (GNP). Hence, robust interception mechanisms are increasingly needed for detecting and pursuing the unrelenting illegal fishing incidents in maritime territories. This article addresses the problem of coordinating the motion of a fleet of marine vessels (pursuers) to catch an IUU vessel while still in local waters. The problem is formulated as a pursuer-evader problem that is tackled within an ML framework. One or more pursuers, such as law enforcement vessels, intercept an evader (i.e., the illegal fishing ship) using an online reinforcement learning mechanism that is based on a value iteration process. It employs real-time navigation measurements of the evader ship as well as those of the pursuing vessels and returns back model-free interception strategies.

Science is a catalyst for human progress. But a publishing monopoly and funding monopsony have inhibited research. We intend to improve incentives in science by developing smart research contracts. These will collectively reward scientific activities, including proposals, papers, replications, datasets, analyses, annotations, editorials, and more. Peer-to-peer review networks will be designed to help evaluate proposals and publications. Long term, these smart contracts help accelerate research by minimizing science friction, ensuring science quality, and maximizing science variance. Email bits@atoms.org or follow @atoms_org to help us build a flourishing research economy. Papers are the fundamental asset of the research economy: they serve as proof of work that valuable research has been completed. Funding agencies and research institutions evaluate scientists based on their publications. Principal investigators (PIs) attract prospective students and collaborators via papers. Investors and companies use scientific literature to conduct due diligence on research ranging from basic discoveries to clinical studies. Thus, the evaluation and dissemination of papers are vital to this research economy. Publishers are the sole arbiters of papers today. They assign a value -- denominated in "prestige" -- by accepting a paper into the appropriate journal based on selectivity and domain. To evaluate papers, journals typically outsource it to two or three PIs, who often outsource it further to their students. Reviewers are unpaid for this peer review work, as it is an expected part of their scientific duties. Peer review is believed to be necessary because of the industrialization of science. Research papers and proposals have become too specialized and too numerous, making it difficult to assess merit prima facie. As a result, scientific incentives have become distorted in two major ways: prestige capture and reviewer misalignment. Over half of all research papers in 2013 were published by five companies, who have used their centuries of brand equity to build an economic moat. This results in prestige capture, which akin to regulatory capture, causes public and scientific interest to be directed towards the regulators of prestige.

The numbers are often ugly. The competition is fierce … and resources are stretched drum tight. This is the reality of a modern legal marketer and not much rosier on the law firm business development side. A recent business development-focused survey produced by LexisNexis found that marketing and business development were the hardest-hit elements of law firm business. With that being said, differentiation occurred when comparing high growth/ high performing firms with no-growth firms.

To enable the reusability of massive scientific datasets by humans and machines, researchers aim to create scientific datasets that adhere to the principles of findability, accessibility, interoperability, and reusability (FAIR) for data and artificial intelligence (AI) models. This article provides a domain-agnostic, step-by-step assessment guide to evaluate whether or not a given dataset meets each FAIR principle. We then demonstrate how to use this guide to evaluate the FAIRness of an open simulated dataset produced by the CMS Collaboration at the CERN Large Hadron Collider. This dataset consists of Higgs boson decays and quark and gluon background, and is available through the CERN Open Data Portal. We also use other available tools to assess the FAIRness of this dataset, and incorporate feedback from members of the FAIR community to validate our results. This article is accompanied by a Jupyter notebook to facilitate an understanding and exploration of the dataset, including visualization of its elements. This study marks the first in a planned series of articles that will guide scientists in the creation and quantification of FAIRness in high energy particle physics datasets and AI models.

This work licensed under a Creative Commons Attribution 4.0 license. That means you can use it any way you like, including commercially, provided that you attribute it to me, Cory Doctorow, and include a link to pluralistic.net.

Activision Blizzard Inc. said an executive named in a gender-bias lawsuit filed against the company last month by California regulators is leaving the videogame company. J. Allen Brack is immediately stepping down from his role as president of Blizzard Entertainment, the unit behind hit franchises such as World of Warcraft and Overwatch, the company said Tuesday. Two company veterans, Jen Oneal and Mike Ybarra, were named co-leaders of the unit, which it acquired in 2008. "It became clear to J. Allen Brack and Activision Blizzard leadership that Blizzard Entertainment needs a new direction and leadership given the critical work ahead in terms of workplace culture, game development, and innovation," the company said in a statement. Mr. Brack didn't immediately respond to a request for comment.

Do you live in a Smart Home? Can you control your lighting, heating, and electronic devices with nothing more than a smartphone? It's no secret that demand for smart homes and devices has gone from novelty to necessity. Artificial Intelligence (AI) assistants like Siri and Alexa have exploded in popularity and have been accepted into millions of homes. Many consumers have come to not only accept the help of these devices but have come to rely upon them. Can you even begin to imagine life today without social media, smartphones, or GPS systems?

An EPFL Master's student has shown that artificial intelligence (AI) can be used to further automate the process of land-use classification in Switzerland, especially for rare and complicated land categories that until now have been classified manually. A stretch of land in Valais Canton served as the sample for her research. Switzerland regularly maps land use in the country in order to better track urbanization, monitor soil permeability and combat urban sprawl. Surveyors take aerial photos of the land every three years, but the survey itself is published only every six years because classifying the images into over 70 different categories is still done mostly by hand. To help speed up the process, the Swiss Federal Statistical Office (FSO) is evaluating the potential of AI with the Arealstatistik Deep Learning (ADELE) project.

The Australian Federal Court recently handed down its first-instance judgement in Thaler v Commissioner of Patents [2021] FCA 879 where the central issue considered was whether an artificial intelligence (AI) system could be an'inventor' for the purposes of the Australian Patents Act 1990 (Act) and its corresponding regulations. The Court found that an AI system can be an inventor – where'inventor' may be construed broadly to include a'person or thing that invents'1. This decision puts Australia in the spotlight as a favourable country to patent AI-created inventions – for now. Given the subject-matter and controversy generated by this decision, an appeal to the Full Federal Court is almost certain. This Federal Court decision is an appeal from a Patent Office hearing where the Office rejected Australian patent application no. Interestingly, the objection to inventorship was initially raised in a formalities objection issued within a few weeks after the application was filed, and not during examination which would be years later under normal circumstances.