This is an image of the Zaamenkomst panel: one of the best remaining exemplars of rock art from the San people of Southern Africa. As soon as you see it, you are inevitably herded, like the eland in the scene, through a series of thoughts. Does it have a meaning? Why are the eland running? What do the white lines coming from the mouths of the humans and animals signify? What event is unfolding in this scene?

Essentially good hypotheses lead decision-makers like you to new and better ways to achieve your business goals. When you need to make decisions such as how much you should spend on advertising or what effect a price increase will have your customer base, it's easy to make wild assumptions or get lost in analysis paralysis. A business hypothesis solves this problem, because, at the start, it's based on some foundational information. In all of science, hypotheses are grounded in theory. Theory tells you what you can generally expect from a certain line of inquiry.

Owing to the data deluge and the Cambrian explosion of machine learning techniques over the past decade, one might have expected the transformation of marketing strategy into a predominantly quantitative discipline by now. The fact that it hasn't happened yet, and the observation that marketing is still influenced by a lot of qualitative inputs can be ascribed to two reasons, in my opinion. The first and principal reason continues to be institutional inertia. Second, there is a significant communication and knowledge gap between data scientists and marketers, owing to their relative lack of familiarity with the other side's perspectives and paradigms. The successful marketer of the next decade is someone who is conversant with management theories of Kotler[1] as well as machine learning advances by Hinton[2]/LeCun[3]/ Ng[4].

A more historical-cognitive approach was the aim of the work on BACON, which rediscovered various scientific laws by finding patterns in numerical data (Langley, Simon, Bradshaw & Zytkow, 1987). Simon's early work on finding patterns in sequences (Simon & Kotovsky, 1963) was extended in BACON to heuristic search for patterns in numerical data. The most creative of BACON's abilities was the decomposition of relational data to conjecture intrinsic properties in one or more of the objects engaging in the relations. This step went beyond curve-fitting and was based on the metaphysical assumption that an entity's relational properties are caused by its intrinsic properties. In addition to the data-driven tasks modeled in BACON, the group also investigated theory-driven discovery in STAHL.

This volume explores abduction (inference to explanatory hypotheses), an important but neglected topic in scientific reasoning. My aim is to inte grate philosophical, cognitive, and computational issues, while also discuss ing some cases of reasoning in science and medicine. The main thesis is that abduction is a significant kind of scientific reasoning, helpful in delineating the first principles of a new theory of science. The status of abduction is very controversial. When dealing with abduc tive reasoning misinterpretations and equivocations are common.

Airbnb is going open house on open source with a pair of new projects that double down on all that traveling data moving in. Plug-and-play machine learning data models that can instantly analyze information and provide intelligent insights? Yep, Microsoft has'em, it says. Unveiled at the company's OpenAir engineering summit in San Francisco on Thursday, the vacation rental wunderkind announced it was open sourcing two of its homegrown data mining products: Airflow and Aerosolve. Airflow is a workflow management platform built for authoring, scheduling and monitor data pipelines at scale in a timely manner -- an absolute necessity for a burgeoning global travel service that has exploded in the last few years.

Over the past decade, most of my discovery research has focused on a new framework, inductive process modeling, that combines background knowledge in the form of generic processes with time-series data to construct explanatory models stated as sets of differential equations. The basic approach carries out exhaustive search through a space of model structures followed by gradient descent through the parameter space for each candidate structure. Later work extended the framework to use constraints among processes to guide search through the structure space and even to induce constraints to discriminate between successful and unsuccessful structures.

The mission of the Center for Discovery Science and Health Informatics is to research computational methods to improve healthcare cost, quality, safety and effectiveness. Specifically, it conducts basic and applied research on developing computational theories, analytic methods, and software applications that support decision making and discovery of knowledge from healthcare data. This includes data mining, artificial intelligence and other knowledge discovery methods and tools tailored towards the meaningful use of health data, health services research, evidenced based practice, and decision support for a variety of health system stakeholders and end-users (clinicians, managers, researchers, policy makers, and consumers) from all sectors of the health system.

Science is an activity of human societies. It is our belief that computer-based scientific discovery must support strong integration into existing the social environment of human scientific communities. The discovered knowledge must add to and build on existing science. We believe that the ability to incorporate background knowledge and re-use learned knowledge together with the comprehensibility of the hypotheses, have marked out ILP as a particularly effective approach for scientific knowledge discovery.

Important dual aspects of this research program are to contribute to both basic computer science (creativity as an ill-understood phenomenon) and scientific applications. The collaborative application of current tools, as well as explorations of new tasks, are welcome.