30 karma · joined June 14, 2015
I was on the fence concerning the essay. I think I understand many of the points made by other respondents: The canonical Star Wars Universe is too black-and-white and inconsistent to use as a backdrop for thinking about engineering ethics.
As a side note regarding the relationship between the Dark Side, the Sith, the Emperor and technology. Actually some of points touched on in the essay (i.e., reliance on technology) are explained in the excellent twenty-five issue Marvel series "Darth Vader."
The biggest difference I remember in the current stackless.py implementation, the move to "continuelets" resulted in the inability to pickle "complex" stackless tasklets. So it becomes more difficult to stop a tasklet in one thread and restart it in another. Maybe the ability to control the recursion depth (including getting rid of it) may also be gone in stackless.py.
By Siri, I am assuming your mean some application that understands speech and carries out commands like answering queries, or carrying out a command? Answering queries is specific domain of NLP. As is developing the acoustic model (roughly the part that links the audio with linguistic units). You will find that there are many sub-problems to tackle. You have to decide on which problems to tackle.
I have very limited experience in this field. If I didn't try out Nuance NLU/MIX at a hackathon a few months ago (and get a lot of help from the Nuance representatives), I would have never taken a stab at writing a voice application.
I am currently building a simple request/response system. Requests of a "HAL, open the pod bay door" nature. As opposed to something more free form and conversational. One has to start somewhere.
2. Voice recognition: same as the above.
As many posters have commented, there are many speech recognition and synthesis systems out there that handle the acoustic model. Some of the system handle languages other than English.
I have tried the Amazon Alexa SDK, Wit.ai and Nuance NLU/MIX. For the most part, they are essentially a client/server model.
1 - At "compile" time, one builds up an acoustic model/language model with samples (at least this is how Nuance works). Or maybe the language model already exists and training through additional use makes it better.
2- At "runtime," a client such as a mobile application interacts with the speech recognition system, via an API. Audio is sent. The speech system parses the audio stream and returns some data structure (or audio if speech synthesis is done). Or in a slight variation, the speech system sends the AST to an end-point for back-end processing. Most of your application will be the back-end that does the meaningful domain specific stuff. For instance, in Alexa, one is developing "Skills"
One of the things I am learning is that there is not a clear cut distinction between what is in the language model and in the back-end. Although the language model allows it, I don't necessarily want to bake in business logic (i.e. Pod bay Door maps onto part number 42). Also the back-end may have to do additional NLP related processing ("Cod bay" is probably "Pod bay").
NLP: I am not aware of any non-English library and don't have much background on the subject.
I found the Stanford course cs224n to be super useful (https://www.coursera.org/course/nlp). There is a book (Speech and Language Processing) associated with the course.
The Peter Norvig paper "How to write a Spell Corrector" (http://norvig.com/spell-correct.html) is very helpful.
Since I am working with Python, I use the book "Natural Language Processing with Python" by Bird, Klein, and Loper and the Python NLTK.
Finally I find that reading the documentation associated with the various speech systems to be very helpful. Vary to suit your needs. For instance, Alexa provides guidelines for U/X like Voice Design Best Practices (https://developer.amazon.com/public/solutions/alexa/alexa-sk...).
3. Web crawling: there are tons of libraries for doing crawling and I have a decent understanding of the subject.
I think crawling is the least of your problems. Look at Week 8, Information Retrieval of the Stanford NLP Course.
Mandela, at this point all I can say is go for it! Look at the speech SDKs out there and pick one, preferably one with a large community. Build something small. See if you can work in a computer language and system you are comfortable with (With Nuance, I could work with mostly with Python and avoid Android and Java).
Have fun!!!
The case study is a learning tool. The required information is a starting point. It is up to the student to use the information "correctly." I recall doing one case study in a TQM course, where I was the only person who bothered to crunch the numbers and create a simple income statement which provided a powerful insight: higher than projected retail prices wasn't killing the company. Rather its return and rework expenses were.
Don`t you think that in the real world, you are faced with many situations where available data is highly ambiguous and unreliable.
Yes. However I think you are misunderstanding me. I have a graduate background in computer science and management. I've played games. However I can't recall many times I was bored in an MBA class. All other things being equal, I don't believe MBA courses, or any course, needs gamification to keep students interested. That is not the selling point. I think more about when Alan Kay talked about the rise of "Skeptical Man" and his ability to create powerful simulations and perform what-ifs and see things from multiple points-of-view. Perhaps Y-Combinator is the wrong place to argue about pedagogy. You probably have a good product. Just the rhetoric is off-putting. Immersive long running games are definitely a part of the future of business education*. Throw in some form of integration with an ERP, you have some really powerful stuff going on....
I have taken MBA courses. For the most part, I found the classroom to be highly engaging, especially when case studies are being presented. I suspect, I, like many people that have gone through graduate management programmes, will disagree with your fundamental premise. Mind you, since my concentration was operations, I have both played simulations (and loved them) and had to develop simulations. So I am not unfamiliar to games and simulations as learning tools. However its only one of many tools. Perhaps you should change the tone of your proposal since I think it would alienate the audience that would most likely buy your product.
"My primary competition is non-consumption. Firms (especially fast-paced tech firms) will just bootstrap a solution using MS Visio or some other simple software application with no business intelligence behind the drawings. In other cases, firms spend a lot of money on consultants or on robust software and a team to learn how to use it. They often get lost in the process of process improvement with very little impact on the bottom line.
A comment about title: don't equate largest with best, or most profitable (also this hotel chain does not seem to exist yet).
However you probably don't want a person in a wheelchair delivering one's pizzas ....
Tom, again I would recommend you do some computer simulations and simple break-even cost analysis. Is the problem of pizza delivery really the human not being available, or being idle (is this really a capacity and demand management problem)? What stops a Zippy unit from not being available as it slowly heads back to base? From what I see, Zippy being a motorized land vehicle, suffers all the problems of a car, without any of its benefits. What makes drones appealing to say an Amazon, is the combination of flying that probably easier and safer than driving, and the value of the payload.
In general, last mile problems are tough. Also you have to ask yourself how does Zippy enhance the customer experience?
Cheers, Andrew
"Having small items delivered to homes by robots gives us near zero cost per delivery, "
You would still have variable costs like fuel, which would be incurred on a delivery by delivery basis. Then how high is Zippy's fixed costs?
I suspect that Zippy could only work in certain very densely populated areas, where users are willing to make concessions. In a suburb, Zippy because it using a sidewalk, would be too slow, but could do front door delivery. For an apartment building, Zippy could only go to the lobby's front door, hence requiring a concession from the customer that they may not want to make. Also how far can Zippy operate from the restaurant in question (it has to travel back)? What is its payload?
When I come to think about it, Zippy would be analogous to a person in a wheelchair making deliveries.
I suspect one could use computer simulations and some what-ifs on a spreadsheet to figure out Zippy's feasibility. I would wager that a person in car is simply more flexible, cost-effective and provides a more satisfying experience to the customer.
I read the article. I have seen similar arguments made by Nicholas Carr in "Peak Code" (http://www.roughtype.com/?p=5594) Perhaps learning to code and universal code literacy (for now, lets not argue exactly what literacy means) may, or may not save individual jobs. I sense as software development as a discipline matures, at the level of the firm, software developer wages as percent of a software project's budget, may shrink. At a macro level, software development as percent of GNP may rise but the software developer employment may look like the numbers involved in manufacturing, or farming. The point I feel Ruskoff misses is that coding literacy may not save an individual's job. Or having a sizable chunk of the U.S economy (or any sophisticated modern economy's) GDP coming from software development. However a critical mass of the population programming, like universal literacy, will most likely translate into vast productivity gains and ability to create new opportunities. An example. The idea behind "software carpentry"http://software-carpentry.org) is about teaching scientists just enough programming, so they can write tools be productive at their main activities, instead of doing things by hand, or hiring programmers. As a whole, research immensely benefits. It is easy to extend these ideas to the populate in general (and yes, I, like chef Gusteau, believe anyone can program). When I come to think about it, maybe this is what the BBC Microbit is about. I feel fields like home automation would explode once a critical mass of the population are code literate. To get back to Ruskoff's analogy, book publishing would be a much smaller industry if universal literacy did not create the supply of writers and demand in the form of readers.
A second choice would not be so much code, but an algorithm: Thompson constructions for regular expressions.