The older adult population will grow exponentially in the coming years with more baby boomers reaching retirement age. Yet, our online communities are not well supported for engaging them online. Older adults with internet access struggle to see the value of engaging in certain online communities. Offline seniors face many barriers to internet use (e.g. cost, access). Despite these barriers, there are social, financial, and health benefits to engaging online, specifically for older adults.
Emerging intelligent bump-in-the-wire network adapters, iNICs, are increasingly deployed for accelerating custom network functions in large-scale data center and cloud environments such as Microsoft Azure. However, their potential to speed up network-intensive server applications remains largely unexplored due to the lack of appropriate programming models and OS abstractions.
For more than a decade, a grand challenge posed to computer researchers has been to understand, and eventually replicate, the way the brain computes – “reverse engineer the brain”, so to speak. Despite its universally recognized importance, computer researchers have made little forward progress. In fact, theoretical neuroscientists have assumed leadership in architecting plausible computing models and consequently have taken significant first steps toward solving the problem.
In this talk, Venkat will focus on fundamental concepts baked in all traditional systems software that aren't very well suited to exploit the economics of the cloud. Almost all systems software that run in the cloud today were originally built for on-premises data center installations and have simply been ported to work on cloud VMs. This talk will explore properties that truly cloud-native software should have, some of the advances that is ongoing and open challenges that still remain.
Abstract: In machine learning often a tradeoff must be made between accuracy and intelligibility: the most accurate models usually are not very intelligible (e.g., deep nets, boosted trees, and random forests), and the most intelligible models usually are less accurate (e.g., linear or logistic regression). This tradeoff often limits the accuracy of models that can be safely deployed in mission-critical applications such as healthcare where being able to understand, validate, edit, and ultimately trust a learned model is important.