If the raw data is available, the pair claim, their algorithm can identify causal relations between variables as well as a clinical study could. Instead of looking for causes by running a fresh randomized controlled trial, the software may be able do this using existing data. Lee admits that people will need convincing and hopes that the algorithm will at least be used initially to complement trials, perhaps by highlighting potential causal links for study. Yet he notes that official bodies such as the US Food and Drug Administration already approve new drugs on the basis of trials that show correlation only. "The way in which drugs go through randomized controlled trials is less convincing than using these algorithms," he says.
This paper is an examination of several well-known applications of artificial intelligence in music generation. The algorithms in EMI, GenJam, WolframTones, and Swarm Music are examined in pursuit of ad hoc modifications. Based on these programs, it is clear that ad hoc modifications occur in most algorithmic music programs. We must keep this in mind when generalizing about computational creativity based on these programs.
Finding the longest common subsequence of multiple strings is a classical computer science problem and has many applications in the areas of bioinformatics and computational genomics. In this paper, we present a new sequential algorithm for the general case of MLCS problem, and its parallel realization. The algorithm is based on the dominant point approach and employs a fast divide-and-conquer technique to compute the dominant points. When applied to find a MLCS of 3 strings, our general algorithm is shown to exhibit the same performance as the best existing MLCS algorithm by Hakata and Imai, designed specifically for the case of 3 strings. Moreover, we show that for a general case of more than 3 strings, the algorithm is significantly faster than the best existing sequential approaches, reaching up to 2-3 orders of magnitude faster on the large-size problems. Finally, we propose a parallel implementation of the algorithm. Evaluating the parallel algorithm on a benchmark set of both random and biological sequences reveals a near-linear speed-up with respect to the sequential algorithm.
So the day after the study came out, actually, New York regulators, the Department of Financial Services and the Department of Health sent a letter to the company saying they were investigating this algorithm and that the company had to show that the way the algorithm worked wasn't in violation of anti-discrimination laws in New York. So that investigation is pending. One encouraging thing is that when the researchers did the study, they actually reached back to Optum and let them know about the discrepancy in the data. And the company was glad to be told about it. And I'm told that they're working on a fix.