can I embed an entity in an intent,
eg
'what can a #DVD do'
entities for DVD have been set up a digital video disc, digital versatile disc etc.
Based on your example, create one intent with how to ask about features for your #DVD, create one Entity #DVD with your values: digital versatile disc, digital video disc, and with synonymous.
One good Idea is to create one condition with the Intent alone and ask about "What DVD? You can choice: digital versatile disc, digital video disc". And in the next flow, you will create some nodes with your response. Like:
#DVD:(digital versatile disc)
#DVD:(digital video disc)
And After, create one flow with 2 conditions. Because if user have the intent and set the entitie in the same question, Watson will recognize. Check:
#DVDWants AND #DVD:digital versatile disc
Check examples:
Check Watson recognize:
Related
I built a convolution neural network for image classification that works successfully with large amount of data for each class, but I want to implements it with specific database with limited amount of data available for each class (e.g. may be 1, 2, 3). The accuracy of the same model will be very low in stead of I used data augmentation, batch normalization,and drop out. How can I raise the system accuracy with low amount of data available, is there some model specialized for this case, or any other addition to my system or editing to my image in order to get height evaluated accuracy system. Can anyone please help me, I'm confusing. Thanks...
If you didn't do test with small amount of data you should try it, conv net can work well even with limited amount of data, it's depend how "hard" classification task is.
few option I see with small amount of data:
transfer learning (from you'r network trained with big data base, or for a more real world condition, from DCNN trained by google or other big one, since if you take weight from ur own CNN u'll never know if you could have achieve those performances with just small data base)
If there is some research about ur classification task, find which feature ingeniering people do and apply it. Then try different classifier on extracted feature like SVM,randomforest... Look at ensemble learning and stacking model which are curetly used a lot
ps: for what I know there are 2 option to classify image. Automatic feature extraction which are done by neural network and "manual" feature extraction which can be identified by having a deep knowledge in the field, as a data scientist AND as a profesionnal of the field.
When you have extractd you'r feature you can use different classifier, most of the people which extract feature with conv net use their neural network as classifier
Trying to create a set of entities & intents for things of the ilk "describe <something>" or "tell me about <something>" or "list instances of <something>". The <something>s are not known in advance. Consequently, I cannot exhaustively list the possible values for the entity.
My impression from (albeit very little) use and from the documentation is that the conversation API isn't good at this type of thing. Experience thus far says that it will recognize things that match the examples given for some entity, but I haven't seen that it can generalize to something like
describe #target
show me instances of #target
without knowing the set of values for #target.
What am I missing?
Based on your example, you can combine the Intents and Entities for your purpose.
It's a good practice, I think.
Like Daniel said, you can create one intent with examples for asking about something, like these examples within your #describeAbout:
Describe about
Can you please describe
Can you please explain about
List instances of
etc...
And create one entity like #typesDescribe, with yours values. Like this values:
Paper
Love
Fruits
After Watson training your examples, with your Dialog, create one flow with the condition
if #describeAbout AND #typesDescribe:Paper
Response:
#typesDescribe (Will show the value: Paper) is a thin material produced by pressing together moist fibres of cellulose pulp derived from wood, rags or grasses, and drying them into flexible sheets.
And, usually, if your confidence about Intent and Entity are small, you can add one more condition for your Intent with the confidence level that you want. Check.
Obs.: You can create one Intent alone, with condition #describeAbout, and the response will ask for your user "You can know what?", and create one flow with various #typesDescribe:value, for example.
Which services are you talking about? NLC is able to do this, and so is Conversation, by using wildcards. Either one of these can be trained to recognize intents with wildcard values in their training data. Just use an asterisk ( like this - "*" ) for the wildcard.
You don't have to train Conversation with every possible utterance, it learns from it's training data. So if you provided the service a series of utterances like "describe apples", "describe oranges", "describe fireflies", and "describe astrophysics", and then associated all of them with an intent of "#provide_description", then the Conversation service would indicte this intent for requests like "describe math".
Please also try to use real utterances for your training. I am not sure that your users will speak in two word sentences all of the time. Provide enough training data for each intent so the service is able to learn the various different ways people express the same intents.
" In the late 1990s, a Dutch electronics technician named Romke Jan Berhnard Sloot announced the development of the Sloot Digital Coding System, a revolutionary advance in data transmission that, he claimed, could reduce a feature-length movie down to a filesize of just 8KB. The decoding algorithm was 370MB, and apparently Sloot demonstrated this to Philips execs, dazzling them by playing 16 movies at the same time from a 64KB chip. After getting a bunch of investors, he mysteriously died on September 11, 1999"
it's possible or just a story
There are two views on the story of the Sloot Digital Coding System. They are incompatible: In one view it is impossible, in the other it is possible.
What is impossible?
To store every possible movie down to a file size of just 8KB. This boils down to the Pigeonhole principle.
A key of a limited length (whether it is a kilobyte or a terabyte) can
only store a limited number of codes, and therefore can only
distinguish a finite number of movies. However, the actual number of
possible movies is infinite. For, suppose it were finite; in that case
there would be a movie that is the longest. By just adding one extra
image to the movie, I would have created a longer movie, which I
didn't have before. Ergo, the number of possible movies is infinite.
Ergo, any key of limited length cannot distinguish every possible
movie.
The SDCS is only possible if keys are allowed to become infinite, or
the data store is allowed to become infinite (if the data store
already contains all movies ever made, a key consisting of a number
can be used to select the movie you want to see -- however, in that
case it is impossible to have keys for movies that have not been made
yet at the time the data store was constructed). This would, of
course, make the idea useless.
Pieter Spronck
What is possible?
To store or load a finite amount of feature-length movies on a device and be able to unlock them with a 8KB key.
Then it is not so about compression, but encoding / databases / data transmission. This is a change in distribution model: Why ship software/data at a later time over telephone or DVD, when you can pre-store it during fabrication, or pipe it all at once at intervals. This model is pretty close to how phones come with pre-loaded apps, or how some games allow you to unlock new game elements by entering a key.
The Sloot patents never claim feature-length movie -> 8KB data compression. They claim an 8x compression rate.
It is not about compression. Everyone is mistaken about that. The principle can be compared with a concept as Adobe-postscript, where sender and receiver know what kind of data recipes can be transferred, without the data itself actually being sent.
- Roel Pieper
In this view SDCS is a primitive form of DRM, that would reduce the band-with of getting access to a certain piece of pre-stored data to an 8KB key.
Imagine storing that month's popular movies by bringing your device to your local video store. Then when you want to see an available movie, you just call for your key, or buy a chipcard at the gas station. Now we have enough band-width for streaming Netflix, but back in the late 90s we were on dial-up and there was a billion dollar data transmission industry (DVD's, CD's, Video tapes, floppies, hard disks).
Was playing 16 movies at once possible?
This is unverified. Though many investors claim to have seen the demonstration. These people worked for respected companies like Philips, Oracle, Endemol, 'Kleiner, Perkins, Caufield and Byers'. I'd say it is not impossible, but await more verification.
A very interesting concept. Conceptually, the Sloot encoding premise seems to be that the "receiver" would have a heavy data rich (DRM-Like) program, capable of a large pre-programmed capabilities ready and able to execute complex programming tasks with minimal data instruction.
I am not a programmer, however, at present, current data transfer challenges exist where there seems to be more focus on the "transmission" of the of data (dense and voluminous), versus the capability of the receiving program/hardware. Whereas with Sloot, the emphasis is on the pre-loading of such data (with hardware/software that has much higher capabilities built-in). I hope I'm not saying the obvious here.
As an example, using sound files for simplicity, rather than sending a complex sound file containing say an Mp3 of Vivaldi – The Four Seasons, the coding just instructs the receiver the "musical notes" of the composition, where the system is pre-programmed to play the notes. Obviously there is more to it than that, however, the concept makes perfect sense. In other words rather than transmitting "Vivaldi" data rich signal, send simpler instructions to a "Vivaldi" trained receiver. Don't send the composer, send the instructions to a composer already there.
Yes, movies can contain billions of instructional data under the current system (and that of 1999), however, can beefing up the abilities, the pre-progammed functions, of the receiver achieve what Sloot had figured out?
Currently, the data stream seems to be carrying the load, where instead the receiver should be, as suggested by Sloot. So, does it make more sense to send the music composer by train to the concert hall across the country, or to send the music notes to another composer who is already there? This not to be confused with pre-loaded movies being "unlocked", rather that the movie player has infinite abilities that simple coding can instruct within an order of magnitude greater ability.
Just some random thoughts from a layman.
I want to develop a small musical library. My objective is to add an idea of suggestions for users :
A user adds musics into the application, he is not connected at all, it's anonymous.
When a user open or close the application, we send his library to our database, to collect (only) new music tracks information.
When a user click on suggestions, i want to check the database and to compare his library with the database. I want to find the music that users like him, who listen the same music as him, listen to.
My idea was to create a link between two musics who defined to percentage of users who got those two musics. If this percentage is high, we can suggest the second one to the users who listen the first one.
I need some help to find documentation about that type of database, without any user idea. I have to compare a user library with a big list of music. I've found that it's item-based recommendation. Am I in a good way ?
Whether a user listens to a particular song or has it in his/her library can be misleading. Lots of times, sample music will come with an operating system or music player and the user just doesn't care enough to remove it, or lots of times it can be hard for a machine to determine the difference between music and other sounds. Or maybe somebody has some music they downloaded because it seemed interesting on paper or came on an album that they liked as a whole, but they actually ended up not liking that song, but again didn't delete it.
One time I set Windows Media player to shuffle all the music on my computer, and to my surprise, I heard punch sound effects, music I had never heard before (from artists I had never heard of, in genres I didn't listen to), and even Windows click sounds that confused me as I wasn't clicking anything.
I say all that to point out that you might want to put more thought into it than which users appear to listen to the same music. Maybe you could have users rate the songs they listen to, and compare not only the songs in their libraries but their ratings of the songs. If two users have all the same songs but one user hates all the songs that the other likes and vice-versa, they really don't have similar tastes.
I would define a UDF that compares two users' tastes by taking each song user 1 has and ignoring it if user 2 doesn't, but subtracting the absolute value of the difference of their ratings from the maximum rating if it does, then adds all these values together.
Then I would run this UDF for each pair of one user to another and pick the top few, then suggest the songs that they have highly-rated.
This will take a long time, particularly if you have a large number of users, so what you can also do is make a Suggestors table that stores each user's most similar users, and update (that is, truncate and then rebuild) it via the above process daily, weekly, monthly, whatever fits your situation. The suggestions feature (when used by the user) would then only need to check the user's suggestors' high-rated songs, which would take substantially less time but would keep things fairly up to date with additions and changes to users' libraries.
There are two courses: "AI" and "AI in Games" both 15 students for 15 weeks.
I want to keep them motivated and creative.
I know I want some kind of competition (obvious for the latter course).
Maybe something like Marathon Match or ICFP.
I will need good visualization, so it would be great if it already exist.
One idea was to write AI for "Battle of Wesnoth", but I guess it's to diverse / boring.
Another game of Go. But that's too hard.
What are your ideas?
It will be work in groups of 3 students for 15 weeks.
MIT hosts a competition called BattleCode.
BattleCode, is a real-time strategy
game. Two teams of robots roam the
screen managing resources and
attacking each other with different
kinds of weapons. However, in
BattleCode each robot functions
autonomously; under the hood it runs a
Java virtual machine loaded up with
its team's player program. Robots in
the game communicate by radio and must
work together to accomplish their
goals.
Teams of one to four students enter
are given the BattleCode software and
a specification of the game rules.
Each team develops a player program,
which will be run by each of their
robots during BattleCode matches.
Contestants often use artificial
intelligence, pathfinding, distributed
algorithms, and/or network
communications to write their player.
At the final tournaments, the
autonomous players are pitted against
each other in a dramatic head-to-head
tournament. The final rounds of the
MIT tournament are played out in front
of a live audience, with the top teams
receiving cash prizes.
(source: mit.edu)
BattleCode in action.
You essentially are given the BattleCode software from MIT and your students can program the AI for their robots. They have a test suite so you can practice running your autonomous bots on your own in a practice arena. Towards the end of the semester they can enter in MIT's Open Tournament, where they compete with their software AI robots against schools all over the nation. Up to $40,000 is given away in cash and prizes as well as bragging rights for winning.
If you are looking to teach them about AI, Pathfinding, Swarm Intelligence, etc. I can't think of a more fun way.
May the best AI bot win!
Wii gesture recognition using hidden markov models.
I wouldn't count out Go. It's computationally hard for Go AI to compete with top human players, but the simple rules of Go (compared to Chess) make it a relatively easy game to write AI for. Your students' programs only need to compete against each other, not against Dan level human players. See An Introduction to the Computer Go Field and Associated Internet Resources for a lot of Go programming resources.
I think it's a good idea to select a theme both challenging enough that it can't be completely solved, yet allows the user to see the value of it in the real world and not so much a toy problem. My suggestion would thus be:
Word segmentation problem (e.g. convert "iamaboy" to "i'am a boy")
Word sense disambiguation (e.g. "The apple is nice to eat" - The apple is a fruit or a company?)
Optical character recognition
What I just list down is some of the more basic stuff of natural language processing. If your students is much more technically inclined, you can probably take it to the next level and let them tackle the problem of machine translation.
Empire, it's addictive as whatever and there are open source D versions (1 and 2) and a not quite free c++ version .