Google is threatening to pull out of China because of attacks on its servers aimed at Chinese human rights activists:
“These attacks and the surveillance they have uncovered — combined with the attempts over the past year to further limit free speech on the web — have led us to conclude that we should review the feasibility of our business operations in China. We have decided we are no longer willing to continue censoring our results on Google.cn, and so over the next few weeks we will be discussing with the Chinese government the basis on which we could operate an unfiltered search engine within the law, if at all. We recognize that this may well mean having to shut down Google.cn, and potentially our offices in China.”
(Via The Lowy Interpreter)
A new report by senior US intelligence officers recommends sweeping changes to intelligence-gathering practices in Afghanistan. The two most interesting recommendations:
The first point interests me because it suggests that problem-solving doesn’t always scale through specialisation, as tends to be assumed in academia: when the flow of information is constricted, a geographically-organised hierarchy of generalists may be more effective than a taxonomically-organised hierarchy of specialists.
The second point bears more directly on mobblog’s research interests (though I’m not suggesting we should design communication systems for the US military): manual aggregation and curation of information are still necessary, even when that information is in digital form. More surprisingly, the oldest method of aggregation – sneakernet – remains the most reliable.
The issues discussed in the report might seem specific to the chaotic and poorly connected environment of Afghanistan, but I want to argue that the fundamental problem – finding relevant information in a shifting sea of circumstances, practices, organisational structures and data formats – exists everywhere, and is not solved by better connectivity, nor by making everything digital.
David Weinberger has suggested that in the digital realm, tags will replace taxonomies and it will no longer be necessary to separate the organisation of information from its retrieval; but while the notion of a ‘hierarchy of generalists’ does cast doubt on the usefulness of a priori taxonomies, the recommendation of manual data collection and curation is directly opposed to Weinberger’s ‘tag soup’ approach.
Does this simply reflect a lack of tools (or, God help us, standards), or is the complexity of real-world information as irreducible to tags as it is to taxonomies? Anyone who’s used Google Images will recognise the difficulty of applying tags to non-textual data; assuming the sea never stops shifting, will the extraction of relevant knowledge from information always be a matter of – well – intelligence?
“The Greater London Authority is currently in the process of scoping London’s DataStore. Initially we propose to release as much GLA data as possible and to encourage other public agencies in London to do the same and we’d like your help!” (past event + @londondatastore + blog post)
(MSNBC)
Having a positive attitude, even striving for cheerful, in the workplace isn’t always easy. The ability to be positive is an essential leadership skill and responsibility.
nice user study (CHI’09)
a mobile location sharing system. In our study, (n = 56), one group was given feedback in the form of a history of location requests, and a second group was given no feedback at all – feedback allays privacy concerns
The third ACM RecSys conference started today in New York; unfortunately I could not make it. However, a number of people who I follow on Twitter are there (@xamat, @danielequercia, @barrysmyth)… and are tweeting away as the conference unfolds. You can follow the stream of #recsys09 tweets here. Although I’m sure that there are many details that do not make it into the 140 character-long tweets, they provide a real time snapshot of what is going on in the conference.
For example, the first keynote has just ended. Francisco Martin, Founder/CEO Strands, gave a talk about the “Top 10 Lessons Learned Developing, Deploying, and Operating Real-World Recommender Systems.” Here’s the twitter summary (note: copy/pasted and lightly edited to merge similar tweets).
Lesson 1 – Make sure a recommender is really needed! Do you have lots of recommendable items? Many diverse customers?… also think Return-on-Invesment… a more sophisticated recommender may not deliver a better ROI.
Lesson 2 – Make sure the recommendations make strategic sense. Is the best recommendation for the customer also the best for the business? What is the difference between a good and useful recommendation? Good recommendations vs useful recs; Obvious recommendations may not be useful; risky recs may deliver better long-term value
Lesson 3 - Choose the right partner! Select the right rec vendor vs hire some #recsys09 students. If you are a big company the best you can do is to organize a contest
Lesson 4 – Forget about cold-start problems (!) …. just be creative. The internet has the data you need (somewhere…)
Lesson 5 – Get the right balance between data and algorithms. 70% of the success of a #recsys is on the data, the other 30% on the algorithm
Lesson 6 – Finding correlated items is easy but deciding what, how, and when to present to the user is hard… or dont just recommend for the sake of it. Remember user attention is a scarce and valuable resource. Use it wisely! … dont make a recommendations to a customer who is just about to pay for items at the checkout! User interface should get at least 50% of your attention.
Lesson 7 – Dont’s waste time computing nearest neighbours (use social connections)… just mine the social graph. Might miss useful connections??
Lesson 8 – Dont wait to scale
Lesson 9 – Choose the right feedback mechanism. Stars vs thumbs …. the YouTube problem. More research on implicit and other feedback mechanisms is needed. The perfect rating system is no rating system! … focus on the interface. Seems to me this is one of the gaps in current research… algorithms > data > interface
Lesson 10 – Measure Everything! … business control and analytics is a big opportunity here.
Keynote Takeaway – Think about application context; Focus on interface as much as algs; Be creative with startup data. … the UI needs to get the lion’s share of the effort (50%) compared to algorithms (5%) , knowledge (20%), analytics (25%)
Mobile phones offer more radical possibilities than ‘PC + internet’ in terms of bringing information into the real spatial environment, argues The City Project – which means architects and urban planners need to start engaging with the way space is experienced and manipulated through mobile software. Map-tagging and location-tracking could help planners to understand how space is used, reducing the tension between the ideal space of architecture and the real space of inhabitation.
So if the prophets of user-generated-everything need to learn that space matters, do those who dream of clean, Cartesian space also need to learn that use matters? No doubt – but to reduce location-aware software to a feedback channel from users to developers (in either sense), or to see it as another element in an architectural programme, would be to miss its truly radical potential, which would lie – if sufficiently open platforms could be developed – in enabling the unplanned, disorganised and ever-changing use of space, without architects.
Chris Aderson said that the future of business is free. We are still waiting for this revolution (email Chris for a detailed revolutionary plan, he’ll be happy to answer).
Now comes Clay Shirky with the next revolution: “innovation can happen everywhere”, he said yesterday (1:39′ of his talk). OK, let’s buy few more copies of Shirky’s books and wait for the next Microsoft or Google coming from Tanzania. Meanwhile, let me tell you why I think speeches on web 2.0 revolutions are motivational infoporn.
The Engaging Data conference went very very well. Thanks to Caitlin and Francisca for their fantastic job! Few notes I’ve put quickly together:
Day 1
Interestingly, they find spikes or dips with respect to a baseline level of actitivity (normal level of activity in a specific area)
Great stuff!!!
Day 2
I was busy having meetings with my lab’s sponsors and followed just the panel session. Surprisinlgy, during the session, David Lazer pointed out that i’m the most central node in the twitter networks of the conference participants in win (pic done using NodeXL) and engaging data (pic). wow!:-)
New mobile network to launch in the UK, built on and by its users. Radical new model? It’s called giffgaff. Youtube video.
New O2 service: ” customers will be encouraged to get directly involved in the business and be rewarded with various perks…. The service is a SIM-only service that gives customers cheaper calls if they refer friends and family, write blogs about it, and regularly post on its online forum. Customers who are actively involved will be given rebates twice a year and they will be given a few options on what to do with it: they will be able to use it for mobile calls and texts, take it as cash or even make a charitable donation.”
The netflix prize winners have been announced, as well as the next $1 million competition. From here:
“The new challenge focuses on predicting the movie preferences of people who rarely or never rate the movies they rent. This will be deduced from more than 100 million data points, including information about renters’ ages, genders, ZIP codes, genre ratings and previously chosen movies.
Instead of a single $1 million prize, this new challenge will be split into one $500,000 award to the team judged to be leading after six months and an additional $500,000 to the team in the lead at the 18-month mark, when the contest is wrapped up.”
Interestingly, our previous discussion on the viability of the winner’s results has now an answer. From here:
The team’s 10 percent achievement will not be immediately incorporated into Netflix.com, said Neil Hunt, chief product officer.
“There are several hundred algorithms that contribute to he overall 10 percent improvement – all blended together,” Hunt said. “In order to make the computation feasible to generate the kinds of volumes of predictions that we needed for a real system – we’ve selected just a small number – two or three of those algorithms for direct implementation.”
this science paper is very interesting. however, as pointed out here, it shows two major problems:
1) The paper more or less ignores the effects of technical safeguards built into modern smartphones operating systems.
2) the paper mentions that the reason why there hasn’t been more mobile outbreaks is that no smartphone operating system is dominating enough. Then in the next paragraph it mentions that Symbian has, oh, 65% market share of all smartphones.
Abstract
We model the mobility of mobile phone users to study the fundamental spreading patterns characterizing a mobile virus outbreak. We find that while Bluetooth viruses can reach all susceptible handsets with time, they spread slowly due to human mobility, offering ample opportunities to deploy antiviral software. In contrast, viruses utilizing multimedia messaging services could infect all users
in hours, but currently a phase transition on the underlying call graph limits them to only a small fraction of the susceptible users. These results explain the lack of a major mobile virus breakout so far and predict that once a mobile operating system´s market share reaches the phase transition point, viruses will pose a serious threat to mobile communications.
