What I’m Reading
Shareworthy articles and content syndicated from other sites. These aren’t things I’ve written or necessarily endorse, for the record.
Microsoft to unveil a ‘breakthrough’ in speech recognition
Earlier today, Microsoft Research released a blog post promising that at Interspeech 2011, an event that is underway, the company would unveil a ‘breatkthrough’ in speech recognition.
Importantly, the development does not deal with speech recognition that requires the user to ‘train’ the system, but instead involves “real-time, speaker-independent, automatic speech recognition.” In other words, true recognition of human speech.
Microsoft claims that it has managed to “dramatically improve the potential” of this sort of technology becoming commercially functional. Through the use of deep neural networks, the company has managed to improve the accuracy of ‘on the go’ speech recognition, something that is a near holy grail of technology. How the team managed to execute the breakthrough is exceptionally technical, but we will not summarize it here because it is a topic that requires extensive background knowledge to follow. Microsoft’s blog post has all the information, if you’re curious.
In regards to the results of what Microsoft Research has built, this is the crucial revelation: “The subsequent benchmarks achieved an astonishing word-error rate of 18.5 percent, a 33-percent relative improvement compared with results obtained by a state-of-the-art conventional system.” The company claims that this has “brought fluent speech-to-speech applications much closer to reality.”
That said, this remains very much a research project. The company made that abundantly clear in the discussion of its progress.
This project is not simply an interesting technical problem, but something that Microsoft desperately needs solved. The company is forging ahead with what it calls Natural User Interface integration (think the Kinect, voice to text, and so forth), and so it needs a better voice solution. The company must have its eyes on its Research division, pushing them towards a commercially viable product that can be integrated across the world of its products.
For now, this is one step, albeit an important one.Top Image Credit
Luxemi Is A Rent The Runway For Indian Clothes And Jewelry
Leveraging the success of Rent The Runway’s model, Luxemi is launching as an e-commerce site where customers can both borrow or buy Indian apparel and accessories. The site is targeted at a niche audience of South Asian American women who don’t want to spend the money on an expensive Indian outfit, or that don’t live near a shop that sells traditional Indian wear.
Instead of just offering apparel and accessories for sale, Luxemi maintains two completely separate collections: one consisting of apparel and accessories for sale and the other a “borrow closet” consisting of apparel and accessories available for rental. When borrowing, shoppers will select their reservation date, rental period (4 or 10 days) and their size.
The site’s rental price point starts at $78 for traditional Sarees and Salwars and $28 for jewelry (includes a backup size saree blouse to ensure fit, the Luxe Pack for the clothing including pins, Bindis and a garment bag to keep, and free return shipping). Luxemi, which has been open for only a month, has signed up 4200 memberships, and so far the average order size is over $250.
While this is sure to be a hit amongst Indian-American women, it’s questionable whether Luxemi will be able to create a market outside of this ethnic demographic. But the startup says that they have seen a strong interest outside the South Asian community with 40 percent of orders placed by non-Indian customers.
And Luxemi isn’t the first retail site to cater to the Indian-American shopping community. Exclusively.in is another e-commerce site that caters to the Indian diaspora, but with a flash sales model. The company just raised $16 million from Tiger Global, and is growing fast for a niche e-commerce site.
A test which predicts ability to program before the start of training
Recent
Publications:
We (Saeed
Dehnadi, Richard Bornat) have discovered a test which divides programming
sheep from non-programming goats. This test predicts ability to program
with very high accuracy before the subjects have ever seen a program or
a programming language.
Abstract:
All teachers of programming find that their results display a 'double
hump'. It is as if there are two populations: those who can, and those
who cannot, each with its own independent bell curve. Almost all research
into programming teaching and learning have concentrated on teaching:
change the language, change the application area, use an IDE and work
on motivation. None of it works, and the double hump persists. We have
a test which picks out the population that can program, before the course
begins. We can pick apart the double hump. You probably don't believe
this, but you will after you hear the talk. We don't know exactly how/why
it works, but we have some good theories.
Abstract: An initial cognitive study
of early learning of programming aimed to extract experimental test data
to establish novices' understanding process has been carried out by us.This
empirical study was inspired by the notion that different people bring
different patterns of knowledge in any new learning process, and
demonstrated that how each student tackles the problem in a different way
based on their mental model. The initial study suggests that success in
the first stage of an introductory programming course is predictable, by
noting consistency in use of the mental models which students apply to a
basic programming problem even before they have had any contact with
programming notation, but the consistency/inconsistency measurement was
somewhat subjective. In this paper I present an objective marking method
which hope will lead us to more precise and more finely-graduated
predictions. This method is being trailed in at least one experiment, and
we hope that by the time of the conference I will be able to describe the
results.
Abstract: Learning to program is notoriously dicult. Substantial
failure rates plague introductory programming courses the world over, and have increased rather than
decreased over the years. Despite a great deal of research into teaching methods and student responses,
there have been to date no strong predictors of success in learning to program. Two years ago we appeared
to have discovered an exciting and enigmatic new predictor of success in a first programming course.
We now report that after six experiments, involving more than 500 students at six institutions in three countries,
the predictive effect of our test has failed to live up to that early promise. We discuss the strength of the effects
that have been observed and the reasons for some apparent failures of prediction.
Abstract: A test was designed that apparently examined a student's knowledge of assignment
and sequence before a first course in programming but in fact was designed to capture their reasoning
strategies. An experiment found two distinct populations of students: one could build and
consistently apply a mental model of program execution; the other appeared either unable to build
a model or to apply one consistently. The first group performed very much better in their end-ofcourse
examination than the second in terms of success or failure. The test does not very accurately
predict levels of performance, but by combining the result of six replications of the experiment,
five in UK and one in Australia. We show that consistency does have a strong eect on success in
early learning to program but background programming experience, on the other hand, has little
or no effect.