Digital life in 2025

I came across a report entitled "Digital life in 2025", that was produced in Elon University. 

Some of the predictions are:

1. Information sharing over the Internet will be so effortlessly interwoven into daily life that it will become invisible, flowing like electricity, often through machine intermediaries.
2.  The spread of the Internet will enhance global connectivity that fosters more planetary relationships and less ignorance.
3. The Internet of Things, artificial intelligence, and big data will make people more aware of their world and their own behavior. 
4. Augmented reality and wearable devices will be implemented to monitor and give quick feedback on daily life, especially tied to personal health.
5. Political awareness and action will be facilitated and more peaceful change and public uprisings like the Arab Spring will emerge. 
6.  The spread of the ‘Ubernet’ will diminish the meaning of borders, and new ‘nations’ of those with shared interests may emerge and exist beyond the capacity of current nation-states to control. 
7.  The Internet will become “the Internets” as access, systems, and principles are renegotiated
8. An Internet-enabled revolution in education will spread more opportunities, with less money spent on real estate and teachers.
9.  Dangerous divides between haves and have-nots may expand, resulting in resentment and possible violence.
10.   Abuses and abusers will ‘evolve and scale.’ Human nature isn’t changing; there’s laziness, bullying, stalking, stupidity, pornography, dirty tricks, crime, and those who practice them have new capacity to make life miserable for others.
11.  Pressured by these changes, governments and corporations will try to assert power -- and at times succeed – as they invoke security and cultural norms. 
12.  People will continue – sometimes grudgingly -- to make tradeoffs favoring convenience and perceived immediate gains over privacy; and privacy will be something only the upscale will enjoy.
13.  Humans and their current organizations may not respond quickly enough to challenges presented by complex networks.
14.  Most people are not yet noticing the profound changes today’s communications networks are already bringing about; these networks will be even more disruptive in the future. 
15.  Foresight and accurate predictions can make a difference; ‘The best way to predict the future is to invent it.’

I like the last one.  I think that the rest are also thought provoking.   Details about each of these predictions can be found in the full report - enjoy!

On latent data

I came across a post by EwanD from Microsoft entitled: “Latent Data” – the secret sauce of the Internet of Things. Since I am interested in both secret sauces and IoT, I was curious to understand what is this sauce. 

It seems that the term latent data refers to data that is typically not available, and also data that does not have any meaning on its own, and need to be aggregated, or joined with other data to be useful.  

Indeed IoT brings to the picture a lot of data that has not been available previously, and in my terminology, much of this data is about event that occur.   Sometimes the raw events are of interest, sometimes the interest is on derived events that are aggregation, transformation, or function that involve multiple events, and possibly also historical data and state information.   Note that when latent data becomes available it is not latent anymore,  and also that latent is a relative term, some piece of data can be available to somebody, and concealed from somebody else.     From this post one can learn what Microsoft sees its role in the IoT era, what I understood is that the role is twofold: both provider of OS for embedded systems, and as a cloud provider.   I am now trying to understand roles of different players in the IoT world, looking for sponsors for my recent activity.  

Big data - are we making a big mistake

I came across an interesting article by Tim Harford in FT Magazine.   This article in in line of several posts I have made on this Blog, which express some skeptics on the ability of merely looking at statistical correlation in the past to create "big insights". Harford brings some examples for that and concludes that there are some naive believes around the big data hypes.   I'll keep writing more insights about this topic. 

The Technological Empowerment Institute -- first exposure

I have written a month ago about moving on, I still need to post  a summary of my IBM time, but it will have to wait as I am quite busy in my new role.  The role is an attempt to establish (from scratch) an applied research institute called "The Technological  Empowerment Institute (TEI)".   This is a first of a series of posts about the institute's plan.    

The slide below shows the idea in a nutshell:

The domain that we are looking at is in general, exploiting Internet of Things for societal purposes. 

The mission is to help developing areas, first in the Israeli periphery, in this case, the concentration will be on the northern part of Israel, and developing countries over the world.  

The idea is to create partnership with:

1. Multidisciplinary researchers dealing with technology, the human aspect of creating and consuming smart systems (a topic that anybody  following this blog realized I have been focused in the last couple of years), and the domain oriented research (agriculture, gerontology, healthcare and more). The affiliated researchers will be international.

2. Partnership with high-tech companies for using their platforms and products for the implementation projects (see below).

3. Using students projects and internship program to carry out concrete implementation projects that fit the institute's mission.

4. Partnership with academic institutes in developing countries to collaborate on the above.

In the next series of posts I'll write about each of these items.  I am now spending much of my time in creating all these partnership -- a big challenge, and also fun. 

More from the Big Data workshop -- crowd wisdom vs. expert wisdom

Yesterday I spent all day in the second day of the Technion Computer Engineering center workshop on big data.  There were a few interesting talks, and the organizers promised to put the slides of all talks on their website (eventually).   I chose to write about an interesting talk given by Tova Milo, from Tel-Aviv University.  Tova talked about her work on crowd wisdom,  and also presented a video in which a contestant in a TV show who did not know an answer used the option of - ask the audience, and followed the audience to the wrong result, and out.   The talk discussed some means of knowledge acquisition, how to phrase questions.   The examples she gave were: what to do when I have headache,  and I am looking for a place to go for children attraction in NYC and a nearby restaurant which is children friendly.  

I asked her whether in the case of constant headache it is not better to ask an expert physician, and her answer was that people trust the crowd wisdom more than they trust their physicians, well I think it is a function of who the person is, and who the physician is.  When we planned the trip to New Zealand ,    We could use crowd wisdom, there is a lot of material on the web, of course, but we chose to go to an expert travel advisor and ask for a trip plan (including all travel arrangements), it certainly saved us time, but if one has enough time,  getting advices from the crowd is useful.   I wonder if somebody researched the trade-offs between expert wisdom and crowd wisdom, and classified the cases in which each should be used. 

My talk in the Technion Big Data workshop

Yesterday, I gave a talk in the Technion Computer Engineering Big Data days --  the talk dealt with three topics:  why  the Internet of things did not happen yet,  very brief introduction to "The Event Model", and a new introduction of the Technological Empowerment Institute.  I'll write more about the institute soon.

The talk is on slideshare - enjoy.

Latency might be subjective

Last week I have been in a short visit to England, served as external examiner in a PhD exam (called by the locals: "Viva"). The exam was strange in the sense that it was the first PhD exam I have ever attended where the advisor was not present (actually he was not invited, I guess it is a matter of culture).   The dissertation that was submitted by Jenny Li dealt with performance metrics and benchmark framework for event processing system.  One of the ideas raised in this dissertation is that the measured latency may be subjective in what it means.   It took me some time to understand the idea, so I'll explain it through example.  

Let's assume that the pattern that we are looking for is a sequence of four events, of types E1, E2, E3, E4.

The two common metrics associated with latency are:

Measurement starts at E4, since the consumer expects to see results only when the last event that closes the loop occurs.

Measurement start at any event occurrence, and ends when the system finishes processing the individual event, which can be merely storing it in the buffer.

Note that the first metric is biased towards eager evaluation, doing the minimal work at the end, and preparing sub-patterns, while the second metric is more balanced. 

The proposed metric is -- start measuring in the event most significant to the consumer, and end at the end of the processing of the pattern.   Let's say that the most significant event to the consumer is E2, then the latency starts at the occurrence of E2, and ends either when there is a match of a pattern, or when the event E2 can be discarded since there is no match.   This can be applicable in cases that all events in the sequence typically happen (e.g. when they are time series events) in relatively fixed differences.   This is an interesting metric, we asked the student to define definite criteria for when it is an applicable metric and when it is not.