To be more specific, The traditional chatbot framework consists of 3 components:
NLU (1.intent classification 2. entity recognition)
Dialogue Management (1. DST 2. Dialogue Policy)
NLG.
I am just confused that If I use a deep learning model(seq2seq, lstm, transformer, attention, bert…) to train a chatbot, Is it cover all those 3 components? If so, could you explain more specifically how it related to those 3 parts? If not, how can I combine them?
For example, I have built a closed-domain chatbot, but it is only task-oriented which cannot handle the other part like greeting… And it can’t handle the problem of Coreference Resolution (it seems doesn't have Dialogue Management).
It seems like your question can be split into two smaller questions:
What is the difference between machine learning and deep learning?
How does deep learning factor into each of the three components of chatbot frameworks?
For #1, deep learning is an example of machine learning. Think of your task as a graphing problem. You transform your data so it has an n-dimensional representation on a plot. The goal of the algorithm is to create a function that represents a line drawn on the plot that (ideally) cleanly separates the points from one another. Each sector of the graph represents whatever output you want (be it a class/label, related words, etc). Basic machine learning creates a line on a 'linearly separable' problem (i.e. it's easy to draw a line that cleanly separates the categories). Deep learning enables you to tackle problems where the line might not be so clean by creating a really, really, really complex function. To do this, you need to be able to introduce multiple dimensions to the mapping function (which is what deep learning does). This is a very surface-level look at what deep learning does, but that should be enough to handle the first part of your question.
For #2, a good quick answer for you is that deep learning can be a part of each component of the chatbot framework depending on how complex your task is. If it's easy, then classical machine learning might be good enough to solve your problem. If it's hard, then you can begin to look into deep learning solutions.
Since it sounds like you want the chatbot to go a bit beyond simple input-output matching and handle complicated semantics like coreference resolution, your task seems sufficiently difficult and a good candidate for a deep learning solution. I wouldn't worry so much about identifying a specific solution for each of the chatbot framework steps because the tasks involved in each of those steps blend into one another with deep learning (e.g. a deep learning solution wouldn't need to classify intent and then manage dialogue, it would simply learn from hundreds of thousands of similar situations and apply a variation of the most similar response).
I would recommend handling the problem as a translation problem - but instead of translating from one language to another, you're translating from the input query to the output response. Translation frequently needs to resolve coreference and solutions people have used to solve that might be an ideal course of action for you.
Here are some excellent resources to read up on in order to frame your problem and how to solve it:
Google's Neural Machine Translation
Fine Tuning Tasks with BERT
There is always a trade-off between using traditional machine learning models and using deep learning models.
Deep learning models require large data to train and there will be an increase in training time & testing time. But it will give better results.
Traditional ML models work well with fewer data with moderate performance comparatively. The inference time is also less.
For Chatbots, latency matters a lot. And the latency depends on the application/domain.
If the domain is banking or finance, people are okay with waiting for a few seconds but they are not okay with wrong results. On the other hand in the entertainment domain, you need to deliver the results at the earliest.
The decision depends on the application domain + the data size you are having + the expected precision.
RASA is something worth looking into.
Using Python, I'm trying to predict if an individual is eligible for a loan or not. So, my output will be either a 1 or 0.
I want to use a Reinforcement Learning approach to learn and experiment. But, I haven't found any useful resources on how to use RL in classification problems.
My question is, is to suitable to use RL or is it too complex for my problem and it's not used in similar real-world problems?
If the answer is yes, how can I apply RL in classification problems?
I have been reading so many articles on Machine Learning and Data mining from the past few weeks. Articles like the difference between ML and DM, similarities, etc. etc. But I still have one question, it may look like a silly question,
How to determine, when should we use ML algorithms and when should we use DM?
Because I have performed some practicals of DM using weka on Time Series Analysis(future population prediction, sales prediction), text mining using R/python, etc. Same can be done using ML algorithms also, like future population prediction using Linear regression.
So how to determine, that, for a given problem ML is best suitable or Dm is best suitable.
Thanks in advance.
Probably the closest thing to the quite arbitrary and meaningless separation of ML and DM is unsupervised methods vs. supervised learning.
Choose ML if you have training data for your target function.
Choose DM when you need to explore your data.
I want to program a robot which will sense obstacles and learn whether to cross over them or bypass around them.
Since my project, must be realized in week and a half period, I must use an online learning algorithm (GA or such would take a lot time to test because robot needs to try to cross over the obstacle in order to determine is it possible to cross).
I'm really new to online learning so I don't really know which online learning algorithm to use.
It would be a great help if someone could recommend me a few algorithms that would be the best for my problem and some link with examples wouldn't hurt.
Thanks!
I think you could start with A* (A-Star)
It's simple and robust, and widely used.
There are some nice tutorials on the web like this http://www.raywenderlich.com/4946/introduction-to-a-pathfinding
Online algorithm is just the one that can collect new data and update a model incrementally without re-training with full dataset (i.e. it may be used in online service that works all the time). What you are probably looking for is reinforcement learning.
RL itself is not a method, but rather general approach to the problem. Many concrete methods may be used with it. Neural networks have been proved to do well in this field (useful course). See, for example, this paper.
However, to create real robot being able to bypass obstacles you will need much then just knowing about neural networks. You will need to set up sensors carefully, preprocess data from them, work out your model and collect a dataset. Not sure it's possible to even learn it all in a week and a half.
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What is machine learning ?
What does machine learning code do ?
When we say that the machine learns, does it modify the code of itself or it modifies history (database) which will contain the experience of code for given set of inputs?
What is a machine learning ?
Essentially, it is a method of teaching computers to make and improve predictions or behaviors based on some data. What is this "data"? Well, that depends entirely on the problem. It could be readings from a robot's sensors as it learns to walk, or the correct output of a program for certain input.
Another way to think about machine learning is that it is "pattern recognition" - the act of teaching a program to react to or recognize patterns.
What does machine learning code do ?
Depends on the type of machine learning you're talking about. Machine learning is a huge field, with hundreds of different algorithms for solving myriad different problems - see Wikipedia for more information; specifically, look under Algorithm Types.
When we say machine learns, does it modify the code of itself or it modifies history (Data Base) which will contain the experience of code for given set of inputs ?
Once again, it depends.
One example of code actually being modified is Genetic Programming, where you essentially evolve a program to complete a task (of course, the program doesn't modify itself - but it does modify another computer program).
Neural networks, on the other hand, modify their parameters automatically in response to prepared stimuli and expected response. This allows them to produce many behaviors (theoretically, they can produce any behavior because they can approximate any function to an arbitrary precision, given enough time).
I should note that your use of the term "database" implies that machine learning algorithms work by "remembering" information, events, or experiences. This is not necessarily (or even often!) the case.
Neural networks, which I already mentioned, only keep the current "state" of the approximation, which is updated as learning occurs. Rather than remembering what happened and how to react to it, neural networks build a sort of "model" of their "world." The model tells them how to react to certain inputs, even if the inputs are something that it has never seen before.
This last ability - the ability to react to inputs that have never been seen before - is one of the core tenets of many machine learning algorithms. Imagine trying to teach a computer driver to navigate highways in traffic. Using your "database" metaphor, you would have to teach the computer exactly what to do in millions of possible situations. An effective machine learning algorithm would (hopefully!) be able to learn similarities between different states and react to them similarly.
The similarities between states can be anything - even things we might think of as "mundane" can really trip up a computer! For example, let's say that the computer driver learned that when a car in front of it slowed down, it had to slow down to. For a human, replacing the car with a motorcycle doesn't change anything - we recognize that the motorcycle is also a vehicle. For a machine learning algorithm, this can actually be surprisingly difficult! A database would have to store information separately about the case where a car is in front and where a motorcycle is in front. A machine learning algorithm, on the other hand, would "learn" from the car example and be able to generalize to the motorcycle example automatically.
Machine learning is a field of computer science, probability theory, and optimization theory which allows complex tasks to be solved for which a logical/procedural approach would not be possible or feasible.
There are several different categories of machine learning, including (but not limited to):
Supervised learning
Reinforcement learning
Supervised Learning
In supervised learning, you have some really complex function (mapping) from inputs to outputs, you have lots of examples of input/output pairs, but you don't know what that complicated function is. A supervised learning algorithm makes it possible, given a large data set of input/output pairs, to predict the output value for some new input value that you may not have seen before. The basic method is that you break the data set down into a training set and a test set. You have some model with an associated error function which you try to minimize over the training set, and then you make sure that your solution works on the test set. Once you have repeated this with different machine learning algorithms and/or parameters until the model performs reasonably well on the test set, then you can attempt to use the result on new inputs. Note that in this case, the program does not change, only the model (data) is changed. Although one could, theoretically, output a different program, but that is not done in practice, as far as I am aware. An example of supervised learning would be the digit recognition system used by the post office, where it maps the pixels to labels in the set 0...9, using a large set of pictures of digits that were labeled by hand as being in 0...9.
Reinforcement Learning
In reinforcement learning, the program is responsible for making decisions, and it periodically receives some sort of award/utility for its actions. However, unlike in the supervised learning case, the results are not immediate; the algorithm could prescribe a large sequence of actions and only receive feedback at the very end. In reinforcement learning, the goal is to build up a good model such that the algorithm will generate the sequence of decisions that lead to the highest long term utility/reward. A good example of reinforcement learning is teaching a robot how to navigate by giving a negative penalty whenever its bump sensor detects that it has bumped into an object. If coded correctly, it is possible for the robot to eventually correlate its range finder sensor data with its bumper sensor data and the directions that sends to the wheels, and ultimately choose a form of navigation that results in it not bumping into objects.
More Info
If you are interested in learning more, I strongly recommend that you read Pattern Recognition and Machine Learning by Christopher M. Bishop or take a machine learning course. You may also be interested in reading, for free, the lecture notes from CIS 520: Machine Learning at Penn.
Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. Read more on Wikipedia
Machine learning code records "facts" or approximations in some sort of storage, and with the algorithms calculates different probabilities.
The code itself will not be modified when a machine learns, only the database of what "it knows".
Machine learning is a methodology to create a model based on sample data and use the model to make a prediction or strategy. It belongs to artificial intelligence.
Machine learning is simply a generic term to define a variety of learning algorithms that produce a quasi learning from examples (unlabeled/labeled). The actual accuracy/error is entirely determined by the quality of training/test data you provide to your learning algorithm. This can be measured using a convergence rate. The reason you provide examples is because you want the learning algorithm of your choice to be able to informatively by guidance make generalization. The algorithms can be classed into two main areas supervised learning(classification) and unsupervised learning(clustering) techniques. It is extremely important that you make an informed decision on how you plan on separating your training and test data sets as well as the quality that you provide to your learning algorithm. When you providing data sets you want to also be aware of things like over fitting and maintaining a sense of healthy bias in your examples. The algorithm then basically learns wrote to wrote on the basis of generalization it achieves from the data you have provided to it both for training and then for testing in process you try to get your learning algorithm to produce new examples on basis of your targeted training. In clustering there is very little informative guidance the algorithm basically tries to produce through measures of patterns between data to build related sets of clusters e.g kmeans/knearest neighbor.
some good books:
Introduction to ML (Nilsson/Stanford),
Gaussian Process for ML,
Introduction to ML (Alpaydin),
Information Theory Inference and Learning Algorithms (very useful book),
Machine Learning (Mitchell),
Pattern Recognition and Machine Learning (standard ML course book at Edinburgh and various Unis but relatively a heavy reading with math),
Data Mining and Practical Machine Learning with Weka (work through the theory using weka and practice in Java)
Reinforcement Learning there is a free book online you can read:
http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html
IR, IE, Recommenders, and Text/Data/Web Mining in general use alot of Machine Learning principles. You can even apply Metaheuristic/Global Optimization Techniques here to further automate your learning processes. e.g apply an evolutionary technique like GA (genetic algorithm) to optimize your neural network based approach (which may use some learning algorithm). You can approach it purely in form of a probablistic machine learning approach for example bayesian learning. Most of these algorithms all have a very heavy use of statistics. Concepts of convergence and generalization are important to many of these learning algorithms.
Machine learning is the study in computing science of making algorithms that are able to classify information they haven't seen before, by learning patterns from training on similar information. There are all sorts of kinds of "learners" in this sense. Neural networks, Bayesian networks, decision trees, k-clustering algorithms, hidden markov models and support vector machines are examples.
Based on the learner, they each learn in different ways. Some learners produce human-understandable frameworks (e.g. decision trees), and some are generally inscrutable (e.g. neural networks).
Learners are all essentially data-driven, meaning they save their state as data to be reused later. They aren't self-modifying as such, at least in general.
I think one of the coolest definitions of machine learning that I've read is from this book by Tom Mitchell. Easy to remember and intuitive.
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E
Shamelessly ripped from Wikipedia: Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.
Quite simply, machine learning code accomplishes a machine learning task. That can be a number of things from interpreting sensor data to a genetic algorithm.
I would say it depends. No, modifying code is not normal, but is not outside the realm of possibility. I would also not say that machine learning always modifies a history. Sometimes we have no history to build off of. Sometime we simply want to react to the environment, but not actually learn from our past experiences.
Basically, machine learning is a very wide-open discipline that contains many methods and algorithms that make it impossible for there to be 1 answer to your 3rd question.
Machine learning is a term that is taken from the real world of a person, and applied on something that can't actually learn - a machine.
To add to the other answers - machine learning will not (usually) change the code, but it might change it's execution path and decision based on previous data or new gathered data and hence the "learning" effect.
there are many ways to "teach" a machine - you give weights to many parameter of an algorithm, and then have the machine solve it for many cases, each time you give her a feedback about the answer and the machine adjusts the weights according to how close the machine answer was to your answer or according to the score you gave it's answer, or according to some results test algorithm.
This is one way of learning and there are many more...