It's an interesting time to be making a case for philosophy in science. On the one hand, some scientists working on ideas such as string theory or the multiverse--ideas that reach far beyond our current means to test them--are forced to make a philosophical defense of research that can't rely on traditional hypothesis testing. On the other hand, some physicists, such as Richard Feynman and Stephen Hawking, were notoriously dismissive of the value of the philosophy of science. Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. That value is asserted with gentle but firm assurance by Michela Massimi, the recent recipient of the Wilkins-Bernal-Medawar Medal, an award given annually by the UK's Royal Society. Massimi's prize speech, delivered earlier this week, defended both science and the philosophy of science from accusations of irrelevance.

The challenge of controlling and protecting data is a big one, but the bigger question is how to make people more productive with corporate information while maintaining standards of compliance and governance for broad access and use in the age of digital business. Applying AI and Natural Language Processing within the various stages of data analytics is a key way to democratize data and build in safeguards for broad use. Below is a Q&A with Ayush Parashar, a Co-Founder and Vice President of Engineering with Unifi Software. Often the quest for security can eclipse data usability. How can applying AI be used to both discover data and ensure information is not being seen or used by those that shouldn't have access to certain kinds of data?

We develop a novel computationally efficient and general framework for robust hypothesis testing. The new framework features a new way to construct uncertainty sets under the null and the alternative distributions, which are sets centered around the empirical distribution defined via Wasserstein metric, thus our approach is data-driven and free of distributional assumptions. We develop a convex safe approximation of the minimax formulation and show that such approximation renders a nearly-optimal detector among the family of all possible tests. By exploiting the structure of the least favorable distribution, we also develop a tractable reformulation of such approximation, with complexity independent of the dimension of observation space and can be nearly sample-size-independent in general. Real-data example using human activity data demonstrated the excellent performance of the new robust detector.

We don't have to wait for the patient to get sick and present themselves anymore. Now, we can intervene before they end up in the ED or go to see the specialist. Illness is usually detectable to an algorithm before it is detectible to a patient. The fact that we wait long enough for someone to acknowledge that they should get some help is an artifact of the traditional notions we have about healthcare.

Unleash the power of your hidden, undocumented data with automatic discovery with Io-Tahoe. Io-Tahoe is recognized by Gartner as a smart data discovery and modern information stewardship solution. Big Data equals big need for robust Data Governance.

The U.S. Senate and House hearings last week on Facebook's use of data and foreign interference in the U.S. election raised important challenges concerning data privacy, security, ethics, transparency, and responsibility. They also illuminated what could become a vast chasm between traditional privacy and security laws and regulations and rapidly evolving internet-related business models and activities. To help close this gap, technologists need to seriously reevaluate their relationship with government. Here are four ways to start. Help to increase tech literacy in Washington.

Organizations around the world -- and not just those in the EU but also those consuming goods and services from Europe -- need to prepare now for the EU GDPR. If you're providing electronic goods or services to anyone who's in Europe, be they a citizen, a temporary resident, or even if they're passing through a European airport for half an hour, potentially, GDPR may apply, and you need to comply with that. It may also apply to anyone in the world, anywhere, if you are profiling or doing analytics on them. You have limited time and increasing pressure to get ready by 25 May 2018. GDPR practical data actions and accelerators from IBM can help your organization on its journey to compliance.

Determining how certain properties are related to other properties is fundamental to scientific discovery. As data collection rates accelerate, it is becoming increasingly difficult yet ever more important to determine whether one property of data (e.g., cloud density) is related to another (e.g., grass wetness). Only if two properties are related are further investigations into the geometry of the relationship warranted. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes in real data scenarios, and do not address how they are related. Our key insight is that one can adaptively restrict the analysis to the "jointly local" observations---that is, one can estimate the scales with the most informative neighbors for determining the existence and geometry of a relationship. "Multiscale Graph Correlation" (MGC) is a framework that extends global procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship. In several real data applications, MGC uniquely detects the presence and reveals the geometry of the relationships.

Io-Tahoe, a machine learning-driven smart data discovery company recently announced the launch of its smart data discovery platform at the Gartner Data & Analytics 2018 Summit, where it will showcase the product. The new version includes the addition of Data Catalog, a new feature designed to allow data owners and stewards to use a machine learning-based smart catalog to create, maintain and search business rules. Also, it would help define policies and provide governance workflow functionality. It reportedly enables a business user to govern the rules and define policies for critical data elements. It allows data-driven enterprises to enhance information about data automatically, regardless of the underlying technology and build a data catalog.

Io-Tahoe today announced the general availability of its smart data discovery solution, with the addition of a new machine-learning Data Catalog feature that enables the creation of business rules and policy definition.