California voters decided last year that the sale of recreational marijuana should be made legal, beginning on Jan. 1, 2018. But Proposition 64 left many of the details to local governments and state regulators. So the last several months have been a race against the calendar, as officials have sought to develop rules governing where, when and how businesses may grow, transport and sell marijuana to adults. Last month, the state unveiled 276 pages of regulations for the new recreational pot marketplace. Among other things, the rules set hefty licensing fees, regulate how much THC will be allowed in edibles and other cannabis products, and require marijuana businesses to track their product from seed to sale.

"It gets better over time" may be the leading slogan for artificial intelligence (AI) these days. Is "better later" acceptable in today's marketplace? How long should we wait for AI to become great? For a company that is trying to decide whether to use chatbots to serve customers, those questions matter. Because companies know that interactions are probably going to begin with a question, they need to program customer service chatbots to determine the intent of the message -- i.e., what it is the customer wants.

The Basel committee rules have been an ongoing international response to the 2007-2009 financial crisis that saw the bankruptcy of U.S. investment bank Lehman Brothers and taxpayer bailouts of big banks. The financial crisis was the prelude to the Great Recession that saw many people lose their jobs and homes. Governments in the United States, Europe and elsewhere were pushed to rescue banks to prevent a cutoff of credit to businesses that would further harm the economy and increase unemployment.

Sponsored You've got an application or workflow that needs to do lots of repetitive work typically done by people in the past. Or you want to branch out into some new area of digital business or customer experience. It might just be a perfect fit for an artificial intelligence algorithm, perhaps using a form of machine learning or a rules-based system. AI has been a long-promised concept but has been held back by, among other factors, a lack of the kinds of raw computer power required to process vast amounts of data and to crunch complex algorithms. It's only now, thanks to cloud, these resources are becoming available, as Intel and service providers make available this kind of power available through their massive server farms.

In order to submit a claim to insurance companies, a doctor needs to code a patient encounter with both the diagnosis (ICDs) and procedures performed (CPTs) in an Electronic Health Record (EHR). Identifying and applying relevant procedures code is a cumbersome and time-consuming task as a doctor has to choose from around 13,000 procedure codes with no predefined one-to-one mapping. In this paper, we propose a state-of-the-art deep learning method for automatic and intelligent coding of procedures (CPTs) from the diagnosis codes (ICDs) entered by the doctor. Precisely, we cast the learning problem as a multi-label classification problem and use distributed representation to learn the input mapping of high-dimensional sparse ICDs codes. Our final model trained on 2.3 million claims is able to outperform existing rule-based probabilistic and association-rule mining based methods and has a recall of 90@3.

Over the past year I've spoken formally and informally with hundreds of companies about their AI initiatives. The biggest AH-HA moment comes when these companies realize the difference between implementing traditional technology and applying analytics with adopting AI. AI is a change from within. New rules for AI are emerging that seem counter intuitive to those who are deeply rooted in today's analytics and rule based decisioning. AI learns by observing and understanding patterns, and optimizes based on continuous input and training. Instruction is not a set of rules, formulas and code.

We present the design and implementation of a custom discrete optimization technique for building rule lists over a categorical feature space. Our algorithm produces rule lists with optimal training performance, according to the regularized empirical risk, with a certificate of optimality. By leveraging algorithmic bounds, efficient data structures, and computational reuse, we achieve several orders of magnitude speedup in time and a massive reduction of memory consumption. We demonstrate that our approach produces optimal rule lists on practical problems in seconds. Our results indicate that it is possible to construct optimal sparse rule lists that are approximately as accurate as the COMPAS proprietary risk prediction tool on data from Broward County, Florida, but that are completely interpretable. This framework is a novel alternative to CART and other decision tree methods for interpretable modeling.

Monsanto has sued Arkansas over dicamba bans in that state, but a court battle doesn't appear likely in North Dakota. The company says it prefers to work with states and will urge North Dakota officials to be flexible on the cutoff date if conditions warrant.

It seems like a day can't go by without a new Artificial Intelligence (AI) story in the headlines. AI is learning to beat Go champions and drive cars. The impression is of an unstoppable tide, one that will crash over every area of life and technology with equal effect. This vision is reinforced by pundits who rarely deviate from the extremes of breathless utopian futurism or apocalyptic doom. It is, by and large, a collection of statistical processes used to build systems that possess a combination of rules-based and iterative or adaptive capabilities.

Quantitative CBA is a postprocessing algorithm for association rule classification algorithm CBA (Liu et al, 1998). QCBA uses original, undiscretized numerical attributes to optimize the discovered association rules, refining the boundaries of literals in the antecedent of the rules produced by CBA. Some rules as well as literals from the rules can consequently be removed, which makes the resulting classifier smaller. One-rule classification and crisp rules make CBA classification models possibly most comprehensible among all association rule classification algorithms. These viable properties are retained by QCBA. The postprocessing is conceptually fast, because it is performed on a relatively small number of rules that passed data coverage pruning in CBA. Benchmark of our QCBA approach on 22 UCI datasets shows average 53% decrease in the total size of the model as measured by the total number of conditions in all rules. Model accuracy remains on the same level as for CBA.