"The problem of giving rules for producing true scientific statements has been replaced by the problem of finding efficient heuristic rules for culling the reasonable candidates for an explanation from an appropriate set of possible candidates [and finding methods for constructing the candidates]."
– B. Buchanan, quoted in Lindley Darden. Recent Work in Computational Scientific Discovery.
Although not all researchers agree on the exact bounds of scientific discovery, theory formation is clearly at the core of the domain. Relevant AI research done in scientific discovery includes Kocabas (1992); Karp (1989); Prager, Belanger, and De Mori (1989); Kulkarni and Simon (1988); and Langley et al. (1987). I consider model-based discovery to be a diagnosis and design problem. More precisely, modelbased-theory refinement can be seen as a four-step process: (1) gather data, (2) compare the data to model-based predictions, (3) identify the sources of discrepancies between the predictions and the field data, and (4) fix these discrepancies by modifying the model. The first three steps are traditionally addressed by diagnosis systems, but the fourth step requires design techniques.
Knowledge discovery programs in the biological sciences require flexibility in the use of symbolic data and semantic information. Because of the volume of nonnumeric, as well as numeric, data, the programs must be able to explore a large space of possibly interesting relationships to discover those that are novel and interesting. Thus, the framework for the discovery program must facilitate proposing and selecting the next task to perform and performing the selected tasks. The framework we describe, called the agenda-and justificationbased framework, has several properties that are desirable in semiautonomous discovery systems: It provides a mechanism for estimating the plausibility of tasks, it uses heuristics to propose and perform tasks, and it facilitates the encoding of general discovery strategies and the use of background knowledge. The complexity of the data and the underlying mechanisms argue for providing computer assistance to biologists.
A fundamental question in AI is what mechanisms suffice for computer programs to make scientific discoveries. My Ph.D. thesis (Valdés-Pérez 1990e) addresses this question by automating the following scientific task to a significant extent: Given observed data about a particular chemical reaction, discover the underlying set of reaction steps from starting materials to products, that is, elucidate the reaction pathway. My scientific contribution is to describe and interpret the design of a system that forms plausible explanatory hypotheses about dynamic processes in science and that proposes unseen entities in a manner justified by simplicity. Some byproducts of the thesis are several novel contributions to chemistry knowledge in addition to scientific tools of immediate use. Chapter 1 surveys previous work in machine discovery, focusing on work that involved assembling an extensive amount of knowledge particular to a domain.
I describe a formalism for organizing knowledge into such functional categories and some of its implementations. In this formalism, descriptive scientific knowledge is classified into seven categories. The categorization formalism allows complex propositions to be analyzed into their simple constituents; in turn, these constituents can be maintained in their categories. They can then be combined using a simple transformation function to form complex constructs such as frames and schemata. To demonstrate its viability, I implemented this knowledge organization in four computational models of scientific reasoning and discovery that operate in astronomy and particle physics, incorporating various methods of learning and theory revision.
Baba Vanga, a blind Bulgarian mystic who died in 1996, is said to have predicted a huge scientific discovery for 2018: a new form of energy on the planet Venus. With no planned missions to Venus this year, her prediction is not expected to come to fruition. More than 20 years after her death, people are waiting to see if Baba Vanga's prophecies for 2018 will come to pass. They reportedly include the Venus discovery, as well as China passing the United States in economic power, although it is unclear where these statements are coming from. Baba Vanga, whose real name was Vangelia Gushterova, was blinded as a child during a tornado.
In our last article, Lifecycle mapping: uncovering rich, predictive data sources, we discussed the importance of mapping out your customer lifecycle to better understand where your most predictive customer data is hiding. Lifecycle mapping is the first step to using artificial intelligence (AI) to optimize your customer lifecycle marketing initiatives. Now, we'll pose some questions to help identify your predictive customer attributes and lifecycle events, pinpoint where that data is located, and recognize patterns to predict outcomes for future prospects, leads, and customers. Data discovery is the second stage in the customer lifecycle optimization (CLO) process. The primary task of this stage is to expand on your lifecycle map to identify authoritative data sources that establish progress.
NASA has called a press conference to reveal a breakthrough discovery from its alien-hunting Kepler telescope. The discovery was driven by Google's machine-learning artificial intelligence software. The announcement will be live-streamed on NASA's website, according to a press release. It will take place Thursday, December 14, at 1 p.m. EST. NASA's Kepler space telescope has been searching for habitable planets since 2009.
I started my career as a MIS professional and then made my way into Business Intelligence (BI) followed by Business Analytics, Statistical modeling and more recently machine learning. Each of these transition has required me to do a change in mind set on how to look at the data. But, one instance sticks out in all these transitions. This was when I was working as a BI professional creating management dashboards and reports. Due to some internal structural changes in the Organization I was working with, our team had to start reporting to a team of Business Analysts (BA).
According to a new press release, "MicroStrategy Incorporated, a leading worldwide provider of enterprise analytics and mobility software, today announced the general availability of MicroStrategy 10.10, the newest feature release to the company's MicroStrategy 10 platform. This feature release empowers business teams to confidently embrace an enterprise-wide, data-driven culture by introducing two exciting products -- a completely redesigned and more powerful MicroStrategy Desktop and the new MicroStrategy Workstation. 'We are incredibly excited to release MicroStrategy 10.10, which empowers business teams to confidently author, promote and certify analytics content, operationalize dossiers, and deliver the agility a business needs, along with the governance that IT requires,' said Tim Lang, Senior Executive Vice President and Chief Technology Officer, MicroStrategy Incorporated. 'The latest capabilities in MicroStrategy 10.10 are part of MicroStrategy's commitment to deliver the next generation of enterprise analytics to our customers so they can discover growth opportunities, solve complex business problems, and drive real results'."
Scientists have discovered a 75-million-year-old fossil resembling the'duck-dinosaur.' The creature had "killer claws" that could tear prey to shreds. A link has been posted to your Facebook feed. Scientists have discovered a 75-million-year-old fossil resembling the'duck-dinosaur.' The creature had "killer claws" that could tear prey to shreds.