The promise of AI is that it will deliver digital transformation and improve productivity and efficiency across businesses. For many of our customers, IBM Watson has already helped deliver on this promise – by enriching customer interactions, accelerating research and discovery, empowering employees, and mitigating risk. The next step for businesses is to make AI ubiquitous by operationalizing their workflows across the full AI lifecycle. IBM is committed to delivering these fundamental, end-to-end AI capabilities and giving enterprises everything they need. For example, consider the critical step of understanding and preparing data for productive and speedy use in analytical tools, machine learning and deep learning.

Astroinformatics is primarily focused on developing the tools, methods, and applications of computational science, data science, and statistics for research and education in data-oriented astronomy.[1] Early efforts in this direction included data discovery, metadata standards development, data modeling, astronomical data dictionary development, data access, information retrieval,[3] data integration, and data mining[4] in the astronomical Virtual Observatory initiatives.[5][6][7] Further development of the field, along with astronomy community endorsement, was presented to the National Research Council (United States) in 2009 in the Astroinformatics "State of the Profession" Position Paper for the 2010 Astronomy and Astrophysics Decadal Survey.[8] That position paper provided the basis for the subsequent more detailed exposition of the field in the Informatics Journal paper Astroinformatics: Data-Oriented Astronomy Research and Education.[1] Astroinformatics as a distinct field of research was inspired by work in the fields of Bioinformatics and Geoinformatics, and through the eScience work[9] of Jim Gray (computer scientist) at Microsoft Research, whose legacy was remembered and continued through the Jim Gray eScience Awards.[10]

The 18th-century Irish philosopher Bishop George Berkeley concluded that, since all we know of the universe is what our senses convey to us, things in the world exist only to the extent that we perceive them. They have no material reality, but are phenomena in and of our minds, or the mind of God. Samuel Johnson famously countered this philosophy by kicking a large stone and saying, "I refute it thus!" Two hundred years later, while American campuses roiled with protests against the Vietnam War, the philosopher, historian, and physicist Thomas Kuhn met with a grad student at Princeton's legendary Institute for Advanced Study to discuss the student's paper. The professor and student disagreed on some fundamental ideas, and the conversation grew heated.

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.