AI For Lawyers Waisberg, Noah; Hudek, Alexander
2021/02/08, 2021, 2021-01-12
eBook
Discover how artificial intelligence can improve how your organization practices law with this compelling resource from the creators of one of the world's leading legal AI platforms. AI for Lawyers: ...How Artificial Intelligence is Transforming the Legal Profession explains how artificial intelligence can be used to revolutionize your organization's operations. Noah Waisberg and Dr. Alexander Hudek, a lawyer and a computer science Ph.D. who lead prominent legal AI business Kira Systems, have written an approachable and insightful book that will help you transform how your firm functions. AI for Lawyers explains how artificial intelligence can help your law firm: Win more business and find more clients Better meet and exceed client expectations Find hidden efficiencies Better manage and eliminate risk Increase associate and partner engagement Whether focusing on small or big law, AI for Lawyers is perfect for any lawyer who either feels uneasy about how AI might change law or is looking to capitalize on the evolving practice. With contributions from experts in the fields of e-Discovery, legal research, expert systems, and litigation analytics, it also belongs on the bookshelf of anyone who's interested in the intersection of law and technology.
Das Buch führt nach zwei Jahren Erfahrung mit der Datenschutz-Grundverordnung eine Evaluation aus Verbrauchersicht durch und präsentiert 33 einfache konkrete Vorschläge, ihren Text zu verbessern, um ...ihre Ziele besser zu verwirklichen. Daneben erörtert es konzeptionelle Schwächen der Verordnung und entwickelt Vorschläge für Lösungen, die ihren Schutzauftrag erfüllen. Die Verordnung ist für viele typische Anwendungssituationen viel zu abstrakt und provoziert daher Rechtsunsicherheit und Investitionsstau. Sie wird keiner Herausforderung moderner Informationstechnik gerecht und bewirkt dadurch Schutzlücken. Die wiederkehrenden Evaluationen der Verordnung können dazu beitragen, Mängel zu beseitigen und eine Evolution des EU-Datenschutzrechts zu bewirken.
Utilize R to uncover hidden patterns in your Big Data. Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on ...Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases, Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the marketWho This Book Is For This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows. It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R.What You Will Learn Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platformIn Detail Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O.Style and approach This book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easy-to-follow steps on how to process, transform and analyse large data sets.
The making of road body visualization for the needs of spatial verification is most often used prior to the building of large long-distant objects with a view of establishing the environmental ...influences. In order to work out a proper visualization, many data are needed. These data are acquired with field measurements, digital snapshots, orthophoto snapshots, digitalization and vectorization of the existing data. In the paper, we discuss the visualization methods with the inclusion of the input data, as well as the methods for creating the 3D-model. New methods and approaches for the making of static and dynamic simulations are also presented.
Predstavljena je zasnova in izgradnja relacijske podatkovne baze za funkcionalna živila. To so živila, ki so del vsakodnevne prehrane in pozitivno vplivajo na zdravje ter zmanjšujejo tveganje za ...nastanek nekaterih bolezni. Relacijska podatkovna baza, zasnovana po Fienkelsteinovi metodi, vsebuje entitete, atribute in primarne ključe. Glavne entitete so: biološko aktivne snovi, klasifikacija, lastnosti, živila, fiziološki učinki, bolezni, postopki, zakonodaja, bibliografija in funkcionalna klasifikacija. Normalizirana baza, zgrajena v programu MS Access 2000, temelji na podatkovnem slovarju entitet, atributih, relacijah in entitetnem diagramu. Vsebuje podatke za 35 biološko aktivnih snovi, pridobljene iz 140 primarnih virov (članki, knjige ipd.). Klasifikacijsko drevo temelji na podlagi deskriptorjev iz tezavra FSTA, pridobljenih s pomočjo bibliometrične analize. Predstavljena je s šestimi med seboj povezanimi obrazci: Klasifikacija, Biološko aktivne snovi, Živilo, Bolezni, Zakonodaja, Bibliografija in Iskanje ter podaja odgovore na vprašanja: (1) kaj so funkcionalna živila, (2) katere aktivne snovi vsebujejo, (3) kako delimo biološko aktivne snovi (klasifikacija), (4) katere biološko aktivne snovi znižujejo tveganje za nastanek določenih bolezni, (5) kakšne so fizikalno kemijske lastnosti aktivnih snovi in (6) kakšna je zakonodaja na tem področju. Uporabniška aplikacija je dostopna kot CD-ROM in je namenjena širšemu krogu uporabnikov.
