As technology becomes more advanced, those who design, use and are otherwise affected by it want to know that it will perform correctly, and understand why it does what it does, and how to use it appropriately. In essence they want to be able to trust the systems that are being designed. In this survey we present assurances that are the method by which users can understand how to trust autonomous systems. Trust between humans and autonomy is reviewed, and the implications for the design of assurances are highlighted. A survey of existing research related to assurances is presented. Much of the surveyed research originates from fields such as interpretable, comprehensible, transparent, and explainable machine learning, as well as human-computer interaction, human-robot interaction, and e-commerce. Several key ideas are extracted from this work in order to refine the definition of assurances. The design of assurances is found to be highly dependent not only on the capabilities of the autonomous system, but on the characteristics of the human user, and the appropriate trust-related behaviors. Several directions for future research are identified and discussed.

We used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted exploration of specific, user-constrained associations. Redescription mining enabled finding specific constructs of clinical and biological attributes that describe many groups of subjects of different size, homogeneity and levels of cognitive impairment. We confirmed some previously known findings. However, in some instances, as with the attributes: testosterone, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis. Applying redescription mining on ADNI data resulted with the discovery of one largely unknown attribute: the Pregnancy-Associated Protein-A (PAPP-A), which we found highly associated with cognitive impairment in AD. Statistically significant correlations (p <= 0.01) were found between PAPP-A and various different clinical tests. The high importance of this finding lies in the fact that PAPP-A is a metalloproteinase, known to cleave insulin-like growth factor binding proteins. Since it also shares similar substrates with A Disintegrin and the Metalloproteinase family of enzymes that act as {\alpha}-secretase to physiologically cleave amyloid precursor protein (APP) in the non-amyloidogenic pathway, it could be directly involved in the metabolism of APP very early during the disease course. Therefore, further studies should investigate the role of PAPP-A in the development of AD more thoroughly.

Create and deploy custom annotation models without writing a single line of code. With our new IBM Watson Knowledge Studio free plan, developers can create custom annotator components and five machine learning projects using 5GBs of storage. And there's no time restriction to do so. Use both machine learning and rule-based approaches to create custom language models using a cloud-based application. The rule-based approach (currently experimental) gets results fast, while the machine learning approach helps the model scale.

Education Department officials opened formal negotiations on Monday to rewrite federal rules meant to protect students from fraud by colleges and universities. The talks with university representative and student advocates are taking place as the department faces criticism for delaying consideration of tens of thousands of loan forgiveness claims from students who say they were defrauded by for-profit colleges. The 1994 rule, known as borrower defense, allowed loan forgiveness if it was determined that the college had deceived them. But the rule was rarely used until the demise of Corinthian and ITT Tech for-profit chains several years ago, when thousands of students flooded the department with requests to cancel their loans. In 2016, the Obama administration passed revisions to the rule, which clarified the process and added protections for students.

"Today, AI augments what we do, but in the future you'll see decisions made by (AI) entities," said Bernt Wahl, executive director of the Brain Machine Consortium. Wahl argues a logical progression of technological advances will result in smarter, more proactive AI systems. "With the web we created all these search engines and collected all this information," said Wahl, adding that systems like IBM's Watson now help determine whether all that information being collected is accurate. "In the future we'll have a'wisdom engine' that can take the knowledge we know is accurate and make decisions based on that,' he said. Jack Berkowitz, vice president of products and data science for Oracle's Adaptive Intelligence effort said AI has proven useful in helping companies filter the massive amounts of new data they're accumulating. "We call our program adaptive intelligence because it's about learning and adaptation and keeping pace," said Berkowitz. "Companies that try to keep up using the kind of rules-based systems we've had for years won't be able to.

This reminds me a bit of what /u/sixwings used to say. I think the idea was that (most) neural networks were still basically just rule-based systems and that they all used supervised learning (even the reinforcement/unsupervised learning ones). I will also note that often the network's inputs and outputs are symbolic in the sense that we associate them with local and (somewhat) interpretable meanings (although this is a bit more debatable for things like pixels). Under all of this lies a question of what "a GOFAI approach" is. Neural networks have certainly been around for a very long time, so someone could say they're old(-fashioned), good and AI...

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science: Stitcher, TuneIn, iTunes, SoundCloud, RSS. In this episode of the O'Reilly Data Show, I spoke with Fang Yu, co-founder and CTO of DataVisor. We discussed her days as a researcher at Microsoft, the application of data science and distributed computing to security, and hiring and training data scientists and engineers for the security domain. DataVisor is a startup that uses data science and big data to detect fraud and malicious users across many different application domains in the U.S. and China. Founded by security researchers from Microsoft, the startup has developed large-scale unsupervised algorithms on top of Apache Spark, to (as Yu notes in our chat) "predict attack vectors early among billions of users and trillions of events."

The deputy director of a large county human services agency, she's been wrestling all week with staff turnover and media coverage about long wait times for services. Heading home on Friday evening, she worries that she might spend the rest of her career playing defense at work. After a Saturday morning of chauffeuring her kids to soccer games and music lessons, Natalie collapses on the couch. She relaxes to music from one of her favorite radio stations, wondering how Pandora always manages to serve up exactly the songs that fit her mood. After she's had a chance to unwind, Siri gives her the week's top headlines, reminds her that her niece's graduation is coming up, recommends a gift for the niece, and, when Natalie confirms the choice, places an order. Later, Natalie's fitness band reminds her that it's time to head to the gym for a session with her trainer. On the way to the gym, Waze alerts her to an accident ahead and automatically routes her around it.

Under new rules issued Oct. 7 by the Department of Health and Human Services, employers and universities are allowed to cite religious or moral objections in order to end birth control coverage that was available under President Barack Obama's Affordable Care Act.

WASHINGTON/TAIPEI – New security measures including stricter passenger screening take effect on Thursday on all U.S.-bound flights to comply with government requirements designed to avoid an in-cabin ban on laptops, airlines said. Airlines contacted by Reuters said the new measures could include short security interviews with passengers at check-in or the boarding gate, sparking concerns over flight delays and extended processing time. They will affect 325,000 airline passengers on about 2,000 commercial flights arriving daily in the United States, on 180 airlines from 280 airports in 105 countries. The United States announced the new rules in June to end its restrictions on carry-on electronic devices on planes coming from 10 airports in eight countries in the Middle East and North Africa in response to unspecified security threats. Those restrictions were lifted in July, but the Trump administration said it could reimpose measures on a case by case basis if airlines and airports did not boost security.