Technology is ‘The Other’ with whom Humans Engage the most

The Contemporary Media Environment (CME) is the current situation of the widespread connected world of computing, which features the pervasive presence of technology in an increasingly rich information environment between and amongst human and machine entities.

One aspect of the CME is the increasing emergence of technology as ‘the other’ in the human-technology relation. Humans are now in a wholly new conceptualization and interaction with technology, and also information, where non-human entities are the primary other party in the majority of interactions (Floridi 2014). Technology is ‘the other’ with whom humans are engaging the most.

The theme of the ‘technology other’ has often been explored in film, with the increasing trend of humans and technology being portrayed in full partnership, for example in Big Hero 6 (2014), Her (2013), and Robot & Frank (2012).

Another way that the CME is manifesting the technology other is through embodiment, and in an escalation in the forms and types of human interaction. The technology other is no longer conceived narrowly as Amazon and Netflix recommendations, but instead as a fully-embodied agent. An example of this is robotic personal assistants for home and work like Robotbase’s Personal Robot, MIT’s JIBO, and Amazon’s Echo. Likewise artificial companions, for a variety of functional interaction with humans, may be the next innovation.

A sense of embodiment might also be perceived with advanced voice assistants like Apple’s Siri, Google Now, and Microsoft’s Cortana; they are a new kind of object-person.

Even beyond technology-as-other is technology-as-partner: the best ‘worker’ for many contemporary jobs in the automation economy, perhaps soon to be the machine economy, is a human and a machine in collaboration (Cowen 2013, Carr 2014).

MOOCs The Platform: Education, Vocational Training, and More

MOOCs (massive online open courses) reinvented education in the mode of global accessibility, even faster than blogs and ebooks reshaped the publishing industry. Now in place as a concept and an infrastructure, ‘MOOCs as a platform’ can be used for other purposes, most proximately vocation and training. Already much of MOOC content is an educational-vocational hybrid of learning new things like knowledge and skills for the digital economy in the form of bootcamps and code academies for software programming, web services, mobile applications, and big data science.

MOOCs are a resilience tool for being able to quickly retrain large numbers of individuals that may be displaced in economic shifts such as the increasing automation of the economy (i.e.; self-driving vehicles, machine intelligence supplanting knowledge-worker jobs). More generally MOOCS as a concept category are concerned with ‘in-habbing’ - habilitating anyone into any situation - and ultimately the next-generation of the Internet that facilitates massive online collaboration and social connectivity.

A fun science fiction idea could be artificial intelligence waking up grâce à contemporary digital environments like MOOCs, YouTube (image recognition), and high-frequency trading networks. As a MOOC instructor, the new Turing Test would be determining if your online student is a machine or a person; that is to the degree this question still matters.

Turning Big Data into Smart Data

A key contemporary trend is big data - the creation and manipulation of large complex data sets that must be stored and managed in the cloud as they are too unwieldy for local computers. Big data creation is currently on the order of zettabytes (10007 bytes) per year, in roughly equal amounts by four segments: individuals (photos, video), companies (transaction monitoring), governments (surveillance (e.g.; the new Utah Data Center)), and scientific research (astronomical observations).

Big data fanfare abounds, we continuously hear announcements like more data was created last year than in the entire history of humanity, and that data creation is on a two year-doubling cycle. Better cheap fast storage has been the historical answer to supporting the ever-growing capacity to generate data, however this is not necessarily the best solution. Already much collected data is thrown away (e.g.; CCTV footage, real-time surgery video, and genome sequencing data) without saving anything. Much of stored data remains unused, and not cleaned up into a form that is human-usable since this is costly and challenging (de-duplication a primary example).

Turning big data into smart data means moving away from data fundamentalism, the idea that data must be collected, and that data collection in itself is an ends rather than a means. Advancement comes from smart data, not more data; being able to cleanly extract and use salient aspects of data (e.g.; the ‘diffs,’ for example identifying relevant genomic polymorphisms from the whole genome sequence), not just generate and discard or mindlessly store.