This is the second'I, Lawyer' podcast Artificial Lawyer/TromansConsulting has done with Sweden's leading legal tech writer, Fredrik Svärd, who runs the super-informative, Legaltech.se In this approximately 30 minutes chat we knock around a few subjects, such as where legal AI as an industry has got to; how the use of algorithms does not always mean there is any AI involved; why AI may be the answer to removing bias rather than the cause of it, and much, much more. We also give a special shout out to Lexpo, which is now just around the corner and will take place in Amsterdam 8 9 May 2017. Many thanks to Fred for organising and producing the podcast, which is below on Soundcloud.

Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data. For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The same classification algorithm used to recognize handwritten numbers could also be used to classify emails into spam and not-spam without changing a line of code.

Use this easy-to-understand, downloadable infographic overview of machine learning basics to identify the popular algorithms used to answer common machine learning questions. Algorithm examples help the machine learning beginner understand which algorithms to use and what they are used for. Azure Machine Learning Studio comes with a large number of machine learning algorithms that you can use to solve predictive analytics problems. The downloadable infographic below demonstrates how the four types of machine learning algorithms - regression, anomaly detection, clustering, and classification - can be used to answer your machine learning questions. Get the most out of the infographic by downloading it - the PDF has links to examples of each algorithm.

This paper is concerned with a class of algorithms that perform exhaustive search on propositional knowledge bases. We show that each of these algorithms defines and generates a propositional language. Specifically, we show that the trace of a search can be interpreted as a combinational circuit, and a search algorithm then defines a propositional language consisting of circuits that are generated across all possible executions of the algorithm. In particular, we show that several versions of exhaustive DPLL search correspond to such well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF. By thus mapping search algorithms to propositional languages, we provide a uniform and practical framework in which successful search techniques can be harnessed for compilation of knowledge into various languages of interest, and a new methodology whereby the power and limitations of search algorithms can be understood by looking up the tractability and succinctness of the corresponding propositional languages.

Ghosh, P., Sharma, A., Chakrabarti, P.P., Dasgupta, P.

The proposed algorithms use a best first search technique and report the solutions using an implicit representation ordered by cost. In this paper, we present two versions of the search algorithm -- (a) an initial version of the best first search algorithm, ASG, which may present one solution more than once while generating the ordered solutions, and (b) another version, LASG, which avoids the construction of the duplicate solutions. The actual solutions can be reconstructed quickly from the implicit compact representation used. We have applied the methods on a few test domains, some of them are synthetic while the others are based on well known problems including the search space of the 5-peg Tower of Hanoi problem, the matrix-chain multiplication problem and the problem of finding secondary structure of RNA. Experimental results show the efficacy of the proposed algorithms over the existing approach. Our proposed algorithms have potential use in various domains ranging from knowledge based frameworks to service composition, where the AND/OR structure is widely used for representing problems.