Chatbots offer the promise of frictionless access to goods, services and information, but creating effective bots can be deceptively tricky. The flip side of the opportunity to interact with users in a seamless, natural way is that user expectations can be prohibitively high. Bots need to be smart and provide greater convenience than apps -- a very effective UI paradigm tailored for today's mobile devices that has been carefully refined for more than a decade. The good news is that the belief that bots must master human language or replace apps to succeed is false. Bots will engage with consumers in new ways that combine the strengths of humans and machines to allow both structured and unstructured information to be exchanged naturally and efficiently.

Visual Analytics and Data Discovery allow analysis of big data sets to find insights and valuable information. This is much more than just classical Business Intelligence (BI). See this article for more details and motivation: "Using Visual Analytics to Make Better Decisions: the Death Pill Exa...". Let's take a look at important characteristics to choose the right tool for your use cases. Several tools are available on the market for Visual Analytics and Data Discovery.

Can a planet be alive? Lynn Margulis, a giant of late 20th-century biology, who had an incandescent intellect that veered toward the unorthodox, thought so. She and chemist James Lovelock together theorized that life must be a planet-altering phenomenon and the distinction between the "living" and "nonliving" parts of Earth is not as clear-cut as we think. Many members of the scientific community derided their theory, called the Gaia hypothesis, as pseudoscience, and questioned their scientific integrity. But now Margulis and Lovelock may have their revenge. Recent scientific discoveries are giving us reason to take this hypothesis more seriously. At its core is an insight about the relationship between planets and life that has changed our understanding of both, and is shaping how we look for life on other worlds.

Several institutions are embroiled in a legal dispute over the foundational patent rights to CRISPR-Cas9 gene-editing technology, and it may take years for their competing claims to be resolved (1–4). But even before ownership of the patents is finalized, the institutions behind CRISPR have wasted no time capitalizing on the huge market for this groundbreaking technology by entering into a series of license agreements with commercial enterprises (see the figure). With respect to the potentially lucrative market for human therapeutics and treatments, each of the key CRISPR patent holders has granted exclusive rights to a spinoff or "surrogate" company formed by the institution and one of its principal researchers (5, 6). Although this model, in which a university effectively outsources the licensing and commercialization of a valuable patent portfolio to a private company, is not uncommon in the world of university technology transfer, we suggest it could rapidly bottleneck the use of CRISPR technology to discover and develop useful human therapeutics.

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.