A New Society, a new education!

To get there though we’ll have to confront a number of hurdles:

We need to gather the data. Emerging, massively distributed and networked sensors will be the equivalent of human sensory transducers like rods and cones. The rise of the Internet of Things also means that every device will be able to contribute its own data stream to a collective understanding of the current state of the world.

Much of the content of big data these days is exhaust – data originally collected for transactional or other purposes, for which mining and analysis are afterthoughts, and whose characteristics are often ill-suited to further analysis. This will certainly change, as data collection matures into a process explicitly designed to improve our peceptual and decision-making capabilities.

We need the processing power to interpret the data While it has become fashionable to note how cheap compute cycles have become, it’s certainly not the case that we can process billions or trillions of input streams in real time –especially when we need to find patterns that are distributed across many noisy and possibly contradictory sensor inputs (i.e., we can’t just process each stream in isolation). We may need to develop new processor technologies to handle these kind of astronomically parallel and heterogeneous inputs.

We need the algorithms. To actually make sense of the data and decide what actions and responses to take, we have to figure out how to extract high-level patterns and concepts from the raw inputs. There is an ongoing debate over the right approach: Most researchers will say that we need something more “brain-like” than current systems, but there are many different (and opposing) theories about which aspects of our brain’s computational architecture are actually important. My own bet is on probabilistic programming methods, which are closely aligned with an emerging body of theory that views the brain as a Bayesian inference and decision engine.