Suppose you are working on a machine learning project, for which you want to predict if a set of patients have or not a mortal disease, based on several features on your dataset as blood pressure, heart rate, pulse and others. Sounds like a serious project, for which you'll need to really trust your model and predictions, right? That's why you got hundreds of samples, that your local hospital very gently allowed you to collect, given the importance and the seriousness of the topic. But how do you know if your sample is representative of the whole population? And how can we know how much difference might be reasonable?

We live in a golden age of scientific data, with larger stockpiles of genetic information, medical images and astronomical observations than ever before. Artificial intelligence can pore over these troves to uncover potential new scientific discoveries much quicker than people ever could. But we should not blindly trust AI's scientific insights, argues data scientist Genevera Allen, until these computer programs can better gauge how certain they are in their own results. AI systems that use machine learning -- programs that learn what to do by studying data rather than following explicit instructions -- can be entrusted with some decisions, says Allen, of Rice University in Houston. Namely, AI is reliable for making decisions in areas where humans can easily check their work, like counting craters on the moon or predicting earthquake aftershocks (SN: 12/22/18, p. 25).

Figure 4 (left) shows that the probability of being a hit paper increases gradually with career and team novelty, but expedition novelty rises much more quickly as the strongest predictor. Papers involving the most unexpected publication events or conversations are 3.5 times more likely than random to be hit papers. Figure 4 (left) also shows that career and team novelties are highly correlated, suggesting that successful teams not only have members from multiple disciplines, but also members with diverse backgrounds who "glue" interdisciplinary teams together (also see Figure S3). Successful knowledge expeditions, however, are the most likely path associated with breakthrough discovery. When regressing content and context novelties of a paper separately on the three background novelty measures, we find that expedition novelty has by far the largest effect on context novelty (), but team novelty has the marginal top effect on . 2 3, p 0 0 1 β 2 .

Berkeley Lab researchers Vahe Tshitoyan, Anubhav Jain, Leigh Weston, and John Dagdelen used machine learning to analyze 3.3 million abstracts from materials science papers. Sure, computers can be used to play grandmaster-level chess, but can they make scientific discoveries? Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials.

More detailed analysis would follow from initial discoveries of interesting and significant parameter correlations within complex high-dimensional data. An article was recently published in Nature on "Statistical Errors – p Values, the Gold Standard of Statistical Validity, Are Not as Reliable as Many Scientists Assume" (by Regina Nuzzo, Nature, 506, 150-152, 2014). In this article, Columbia University statistician Andrew Gelman states that instead of doing multiple separate small studies, "researchers would first do small exploratory studies and gather potentially interesting findings without worrying too much about false alarms. Then, on the basis of these results, the authors would decide exactly how they planned to confirm the findings." In other words, a disciplined scientific methodology that includes both exploratory and confirmatory analyses can be documented within an open science framework (e.g., https://osf.io)

We study nonzero-sum hypothesis testing games that arise in the context of adversarial classification, in both the Bayesian as well as the Neyman-Pearson frameworks. We first show that these games admit mixed strategy Nash equilibria, and then we examine some interesting concentration phenomena of these equilibria. Our main results are on the exponential rates of convergence of classification errors at equilibrium, which are analogous to the well-known Chernoff-Stein lemma and Chernoff information that describe the error exponents in the classical binary hypothesis testing problem, but with parameters derived from the adversarial model. The results are validated through numerical experiments.

Hypothesis testing is a critical tool in inferential statistics, for determining what the value of a population parameter could be. We often draw this conclusion based on a sample data analysis. With the advent of data-driven decision making in business, science, technology, social, and political undertakings, the concept of hypothesis testing has become critically important to understand and apply in the right context. There are a plethora of tests, used in statistical analysis, for this purpose. See this excellent article for a comprehensive overview of which test to use in what situation. The basis of hypothesis testing has two attributes: (a) Null Hypothesis and (b) Alternative Hypothesis.

Explorium, a data discovery platform for machine learning models, received a couple of unannounced funding rounds over the last year -- a $3.6 million seed round last September and a $15.5 million Series A round in March. Today, it made both of these rounds public. The seed round was led by Emerge with participation of F2 Capital. The Series A was led by Zeev Ventures with participation from the seed investors. The total raised is $19.1 million.

In recent years there has been an increasing trend in which data scientists and domain experts work together to tackle complex scientific questions. However, such collaborations often face challenges. In this paper, we aim to decipher this collaboration complexity through a semi-structured interview study with 22 interviewees from teams of bio-medical scientists collaborating with data scientists. In the analysis, we adopt the Olsons' four-dimensions framework proposed in Distance Matters to code interview transcripts. Our findings suggest that besides the glitches in the collaboration readiness, technology readiness, and coupling of work dimensions, the tensions that exist in the common ground building process influence the collaboration outcomes, and then persist in the actual collaboration process. In contrast to prior works' general account of building a high level of common ground, the breakdowns of content common ground together with the strengthen of process common ground in this process is more beneficial for scientific discovery. We discuss why that is and what the design suggestions are, and conclude the paper with future directions and limitations.

Recent improvements in whole slide scanning systems, GPU computing, and deep learning make automated slide analysis well-equipped to solve new and challenging analysis tasks. These learning methods are trained on labeled data. This could be anything from annotating many examples of mitosis, labeling tissue types, or categorizing a full slide or set of slides from a particular patient sample. The goal is then to learning a mapping from the input images to the desired output on training data. Then the same model can be applied to unseen data.