Fast and easy tech‪!‬ Tech Discourse

With the new Exposure Notification Express, which forms the second phase of Exposure Notification System, the system removes one of the key barriers to adoption that led to a slow start to the software. Once users update iOS 13.7, users can now enable COVID-19 Exposure Notifications directly in the Settings app on their iPhone. This new process is called Exposure Notifications Express. The process is as simple as enabling user preferences in any other application and just takes a few taps, including agreeing to the public health agency terms and conditions of service. Additionally, users will also be able to opt-in to receive a push notification when their local public health authority adopts Exposure Notifications Express.

The philosophy of the microservices architecture is: “Do one thing and do it well." Services might run within the same process, but they should be independently deployable and easy to replace. They can be implemented using different programming languages, databases, and software environment. The services are small and fine-grained to perform a single function. They embrace automation of testing and deployment, continuous delivery software development process, failure and faults, similar to anti-fragile systems. Each service is elastic, resilient, composable, minimal, and complete.

Classic machine learning algorithms involve techniques such as decision trees and association rule learning, including market basket analysis (ie, customers who bought Y also bought Z). Deep learning, a subset of machine learning that includes neural networks, attempts to model brain architecture through the use of multiple, overlaying models.

Classic examples of Machine learning include Virtual Personal Assistants like Siri, Alexa, Google, Predictions while Commuting, Videos Surveillance, Social Media Services from personalizing your news feed to better ads targeting, Face recognition, Email Spam and Malware Filtering, Online Customer Support including chat bots, Search Engine Result Refining, Product Recommendations based on previous purchase trends, and Online Fraud Detection including efforts to curb money laundering.

Removing bias from AI is the result of deliberate, calculated and thought-out human endeavors, and certainly not an unintended byproduct of certain data analysis. Companies employing any or all five forms of AI — computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning — must realize that any output they hope to derive are only as good as the data on which the applications are trained.

The Exposure Notification System or ENS allows public health authorities to develop apps that augment manual contact tracing efforts while preserving the privacy of their citizens. As of today, public health authorities have used ENS to launch in 16 countries and regions across Africa, Asia, Europe, North America and South America, with more apps currently under development.

Apple likely made the change to use Exposure Notification service without the need for a contact tracing app to quietly encourage more public health departments to use its service. Only a handful of states in the United States and a few countries worldwide are using Google and Apple’s mobile technology. Most health departments still use an old fashioned contact tracing method that relies on in-person interviews and phone calls to locate those individuals who came in contact with an infected person.

All content platforms and social media companies must keep the content flowing because that is the business model: Content captures attention, provides viewership and generates data (users’ statistics). Content is the starting and the end point of consumers’ journeys on social media. A video, an information post, a tweet, blog post, picture, public service advisories, are all types of content. The platforms then sell that attention (read: viewership), enriched by that data (read: customized ads). But how do you deal with the objectionable, disgusting, pornographic, illegal, or otherwise verboten content uploaded alongside legitimate content?

How do Facebook and other tech and social media companies ensure integrity of content on their networks? And how do these companies work to curb misinformation on their platforms about the Coronavirus pandemic or the 2020 elections or any other global or regional event. We have seen state and non-state sponsored actors with nefarious intent take advantage of lax content posting norms.

Whenever there is a talk of infusing technology, automation and intelligence in to tasks or processes, the solutions proposed fall in to two broad buckets. The first is the conventional use of the term Artificial intelligence, machine learning, deep learning and cognitive learning, which typically work outside human intervention and are largely driven by some kind of an algorithm to achieve its goal. The handling of regular transactions as well as any exceptions are defined within the AI program and the machine doing the job in case of touch and feel jobs like manufacturing or the algorithm producing the result in case of purely online work. The second category is where Artificial Intelligence is applied to work in conjunction with the humans and human intervention and interaction is a critical part of the successful completion of the job. This second type is known as Augmented intelligence or Augmented reality.

Another way of looking at what happens with augmented and artificial intelligence is the degree to which algorithms and the programs running the physical machines are expected to make decisions on their own. Is the decision making assisted by humans, controlled by humans or completely out of control of humans?