@T: ALWAYS THE TWAIN

I read this IBM news release and it’s definitely worth going over (and clicking through its various links) because at its crux it reveals how current Semantic Web pioneers’ attempts to build their version of a Global Brain with AI are likely to fail and won’t result in tools as smart or as proxy to the human brain as they’d like and think.

It’s notable that IBM differentiates between the need to simulate:

·      perception

·      multi-lateral processing

·      emotions

·      synaptronics

·      an alternative to the von Neumann bottleneck of purely processing words backwards and forwards

The lead of the IBM research project, Dharmendra Modha, notes:

“The vision for the anticipated DARPA SyNAPSE program is the enabling of electronic neuromorphic machine technology that is scalable to biological levels.  Programmable machines are limited not only by their computational capacity, but also by an architecture requiring (human-derived) algorithms to both describe and process information from their environment.  In contrast, biological neural systems (e.g., brains) autonomously process information in complex environments by automatically learning relevant and probabilistically stable features and associations.  Since real world systems are always many body problems with infinite combinatorial complexity, neuromorphic electronic machines would be preferable in a host of applications—but useful and practical implementations do not yet exist.”

For me, in certain ways, whilst the frameworks of the Semantic Web Stack are potentially enormously useful for sifting through all the billions of documents already online and interconnecting them with each other, that still doesn’t quite mean that the end-result will be a Global Brain whereby computers will be able to solve the world’s most complex issues: climate change, disease eradication, global democracy, universal education and poverty reduction.  

It will be a better-organized encyclopedia with annotations by multiple editors and library stewards is all. It focusses on ontologies and social graphs but not on multi-sensory discern of interpretation or synapses.

For a genuine Global Brain the IBM Synapse team’s aims are worth following.

Mohda also notes:

Synapses are junctions between neurons. In mouse and rat brains, there are roughly 10,000 times more synapses in the brain than neurons. Strength/efficacy/efficiency of synapses is subject to change (plasticity) as the animal interacts with the environment, and these synaptic junctions are hyothesized to encode our individual experience. The computation, communication, memory, power, and space requirements for representing brain in software or hardware seem to scale with the number of synapses. Thus, brain is much less a neural network, and more correctly, a synaptic network.

This is a very valid observation. I’ve worked previously with neural networks and AI models in asset allocation software. The results the system generated still had to be sanity-checked by humans — even though the algorithms were derived from them in the first place.

There is other contiguous and multi-lateral information that resides in human synapses and are not yet programmable into neural net, AI or SemWeb solutions.

IBM’s attempts to simulate these synapses is a “must follow”.

Perhaps the natural life cycle stage of the Web after the Semantic Web is the Synaptic Web, the Smart Solutions Web and the Seer Web BEFORE the Singularity?

Dynamic rating systems are typically built with Ajax, JSON, php and an SQL database. For the Media Sensors it’s helpful to be able to track who’s designated a rating (so incorporate some type of log-in for each rating or recognition of cookie information), allow only one rating per user on any particular item on a site and to facilitate analysis of that rating on an ongoing and dynamic basis. Elements of semantic search querying will also need to be included.

Ultimately, the Media Sensors solution will be integrated with comments and rss feeds in an intuitive manner as shown by this example using a 5-star rating solution.

There are lots of ways to include comments in rating systems and what would be interesting is to investigate whether it’s possible to modularize comment panels so they can be propagated elsewhere to similar content.

In any case, I spent this weekend creating SQL databases. Here’s an example of an extremely simple one with its query:

//declare the SQL statement that will query the database
$query = “SELECT id, name, year “;
$query .= “FROM cars “;
$query .= “WHERE name=’BMW’”;

with its generated result:

It isn’t only the front-end UI / applet that needs to be user-friendly and simple. The back-end or database also has to be highly functional and enable accurate surfacing of previously input user-generated content so that it’s searchable, semantic, can be analyzed and serves to also calibrate 360-2020 insights on users’ tastes, preferences and perceptions — in conjunction with the front-end Media Sensors.

It’s shaping up, as they say………………….