Thursday, June 30, 2016

The Emerging World of Neural Net Based MT

There has been much in the news lately about the next wave of MT technology driven by a technology called deep learning and neural nets (DNN). I will attempt to provide a brief layman’s overview about what this is, even though I am barely qualified to do this (but if Trump can run for POTUS then surely my trying to do this is less of a stretch). Please feel free to correct me if I have inadvertently made errors here. 

To understand deep learning and neural nets it is useful to first understand what “machine learning” is. Very succinctly stated machine learning is the “Field of study that gives computers the ability to learn without being explicitly programmed” according to Arthur Samuel. 

Machine learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning algorithms iterativelylearn from (training) data by generalizing their experience (“analysis” of the data) into predictive models. These models allow computers to find insights that might be difficult or even impossible for humans to find. In the case of MT the objective is to build predictive models that translate new source data based on “knowledge” it has gathered from translation memory and other natural language data it is shown (trained on). Think of it simply as a branch of statistics and applied computing, designed for and enabled by a world of big data. For example, to some extent SMT is a more flexible and generalized implementation of the older TM technology, which can guess at new sub-segments based on learning obtained from its training experience. 

Machine Learning is the field that studies how to make computers learn on their own (unsupervised), and covers a lot of ground in computing beyond text and MT applications, and most recently has had amazing success with image data. A Machine Learning algorithm is a computer program that teaches computers how to program themselves so that we don’t have to explicitly describe how to perform the task we want to achieve. The information that a Machine Learning algorithm needs in order to write its own program to solve a particular task is a (large) set of known examples e.g. translation memory or radiology images together with resultant diagnoses. 

Machine Learning is a big deal, perhaps even a really big deal. Google CEO, Sundar Pichai, recently laid out the Google corporate mindset: “Machine learning is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in early days, but you will see us — in a systematic way — apply machine learning in all these areas.” Google is all in. Google believes that one day it will be used by all software engineers no matter what the field, and that it will “change humanity.” 

This links to a series of posts that provide the best non-technical overviews I have seen on what Machine Learning is. 

But how is it used, in practice? Very roughly speaking there are three broad concepts that capture most of what goes on under the hood of a machine learning algorithm: feature extraction, which determines what data to use in the model; regularization, which determines how the data are weighted within the model; and cross-validation, which tests the accuracy of the model. Each of these factors helps us identify and separate “signal” (valuable, consistent relationships that we want to learn) from “noise” (random correlations that won’t occur again in the future, that we want to avoid). Every data set has a mix of signal and noise, and skill with these concepts will help you sort through that mix to make better predictions. This is a gross oversimplification and needs much more elaboration than is possible in this post. 

This video with Peter Norvig of Google talking about ML and this one byAndrew Ng of Baidu explaining what Deep Learning is, is worth skimming through (for those who have a deeper interest at least) to get a sense of the larger possibilities that could emerge from machine learning approaches. 

Traditional AI methods of language understanding depended on embedding rules of language into a system, but in the Google SmartReply project, as with all modern machine learning , the system was fed enough data to learn on its own, just as a child would. “I didn’t learn to talk from a linguist, I learned to talk from hearing other people talk,” says Greg Corrado who developed SmartReply at Google. Corrado says that the approach requires a change in mindset for coders, from controlling everything directly to analyzing data, and even new hardware. The company even created its own chip, the Tensor Processing Unit, optimized for its machine-learning library TensorFlow

Getting back to MT, it is useful to first look at how machine learning is used in SMT to better understand the evolution enabled by neural networks. The SMT model is generally made up of two (sometimes more) predictive models learned from “training data” which includes both representative bilingual data and monolingual data in the target language.
  1. SMT Translation Model – Learned from Bilingual Data (Translation Memory)
    1. Probabilistic mapping of equivalencies in source words and phrases with target language words and phrases through the Unsupervised Expected Model (EM) training and word and phrase alignment process.
    2. The Translation Model generates lots of possible translations
  1. Target Language Model – Learned from Monolingual Target Language Data
    1. Probabilistic model of relative fluency and general usage patterns in the target language
    2. The Target Language Model selects the “best” translations from a list of possible candidates
Even though this is essentially a probability maximization exercise (not really a translation), it can do surprisingly well, and translate new source data in the same domain quite accurately. This link provides a relatively simple overview of the learning process in slides, and here is Norvig again, givinga very clear 12 minute overview of how SMT works. Much of what we see today, as phrase based SMT including Moses, is built with this kind of a learning approach. Even with many limitations this is a significant improvement over the older Rule Based MT systems where humans tried to codify and program the language pair. 

Some of the most obvious problems include the following:
  • Since this is a word based approach it is not as effective with character based languages (CJK) because of imperfect segmentation and tokenization issues.
  • It has a very limited sense of context and is often quite mindlessly literal.
    It is not especially effective with language combinations that have varying morphology, non-contiguous phrases and syntactic transformations.
  • It has limited success in scarce data scenarios and more data does not always drive improvement.
Deep Learning with Neural Networks to the rescue
Neural nets are modeled on the way biological brains learn. When you attempt a new task, a certain set of neurons will fire. You observe the results, and in subsequent trials your brain uses feedback to adjust which neurons get activated. Over time, the connections between some pairs of neurons grow stronger and other links weaken, laying the foundation of a memory. A neural net (DNN) essentially replicates this process in code. 

Neural Nets are a specific implementation of a Machine Learning algorithm. In a way Neural Nets allow one to extract “more knowledge” from the training data set and access deeper levels of “understanding” from the reference data. Neural networks are complex, computationally intensive and hard to tune as machines may see multiple layers of patterns that don’t always make sense to a human, yet can be surprisingly effective in building prediction models of astonishing accuracy. 

Again, Andrew Ng explains why this technology that has been around for 20 years, has reached its perfect storm moment. Basically because the availability of really big data + high performance computing + evidence of successful prediction in image processing suggest that it can, and could work, in solving complex problems in many other areas where standard programming approaches would be impractical. 

Deep Neural Nets can have a large number of hidden layers and are able to extract much deeper related features from the data. Recently, deep neural networks have performed particularly well for image recognition problems . Deep neural networks have become extremely popular in more recent years due to their unparalleled success in image and voice recognition problems. Neural Nets have been successful with sequence recognition problems (gesture, speech, ATM handwritten check text recognition), medical diagnosis, financial trading systems, visualization and e-mail spam filtering. 

Just as with dirty data SMT, one of the biggest reasons why neural networks may not work is because people do not properly pre process the data being fed into the neural network. Data normalization, removal of redundant information, and outlier removal should all be performed to improve the probability of good neural network performance. There are a variety of DNN techniques that solve different kinds of deep learning problems. An understanding of how these different approaches perform better under different constraints and different evaluation criteria is underway in the research community as we speak. 

In particular, Neural Networks excel in cases where the strategy is not known ahead of time and instead must be discovered. In cases where this is NOT true and all that must be learned are the parameters for that strategy, there are algorithms that can find good solutions a whole lot faster and with fewer resources. 

So in the MT context, the rationale behind using the neural network-based training is about discovering the hidden “reasons” behind the translation of the text in the training set; essentially, the machine may be “writing rules of linguistic relationship” automatically, and producing a more flexible engine by extracting more useful ”knowledge” from the existing training data. Note that these features are not necessarily the same features human linguists would use (parts of speech, morphology, syntax, transitivity, etc...) But these hidden layers have solved problems of great difficulty in image recognition and there is reason to believe that they can do this with NLP as well. Of course, this is easier said than done but that’s the basic reasoning and there is much research underway with Google, Facebook, Microsoft and Baidu (the big four) leading the way. 

One DNN breakthrough example is WORD2VEC. Its creators have shown how it can recognize the similarities among words (e.g., the countries in Europe) as well as how they’re related to other words (e.g., countries and capitals). It’s able to decipher analogical relationships (e.g., short is to shortest as big is to biggest), word classes (e.g., carnivore and cormorant both relate to animals) and “linguistic regularities” (e.g., “vector(‘king’) – vector(‘man’) + vector(‘woman’) is close to vector(‘queen’)). Kaggle’s Howard calls Google’s word2vec the “crown jewel” of natural language processing. “It’s the English language compressed down to a list of numbers,” he said. The real benefit of this will take years to unfold as more researchers experiment with it to try and solve new NLP problems. 

From the data scientist’s perspective, MT aims to find for the source language sentence presented to it, the most probable target language sentence that shares the most similar meaning. Essentially, MT from the data scientists perspective is a sequence-to-sequence prediction task. 

Indirect DNN application designs new features with DNNs in the framework of standard SMT systems, which consist of multiple sub-models (such as optimal translation candidate selection and more fluent and natural language models). For example, DNNs can be leveraged to represent the source language context’s semantics and better predict translation candidates. (The two columns shown to the right in the graphic above reflect an indirect implementation.)
The indirect application of DNNs in SMT aims to solve difficult problems in an SMT system with more accurate context modeling and syntactic/semantic representation e.g. Word Alignment in SMT which have two disadvantages: 1) The current process can’t capture the similarity between words, and 2) contextual information surrounding the word isn’t fully explored. Traditionally, translation rule selection is usually performed according to co-occurrence statistics in the bilingual training data rather than by exploring the larger context and its semantics. DNNs help to improve the process to consider context and semantics more effectively. 

Language Model – The most popular language model is the count-based n-gram model described by Norvig and by the charts above. One big issue here is that data sparseness becomes severe as n grows. Using RecurrentNN ( a type of DNN) allows a better solution than the standard count-based n-gram model. All the history words available are applied to predict the next word instead of just n-1. This allows an SMT model to have a much better sense of context. The table below shows one view on how different types of DNNs can help address common SMT problems. 

Statistical machine translation difficulties and their corresponding deep neural network solutions.
Word alignment                                           FNN, RecurrentNN
Translation rule selection                            FNN, RAE, CNN
Reordering and structure prediction             RAE, RecurrentNN, RecursiveNN
Language model                                          FNN, RecurrentNN
Joint translation prediction `                       FNN, RecurrentNN, CNN
However, indirect application of DNNs makes the SMT system much more complicated and difficult to deploy.
Direct application (NMT) regards MT as a sequence-to-sequence prediction task and, without using any information from standard MT systems, designs two deep neural networks—an encoder, which learns continuous representations of source language sentences, and a decoder, which generates the target language sentence with source sentence representation. (Yes I really cannot figure out a way to say this in a more intelligible way.)
In contrast, direct application is simple in terms of model architecture: a network encodes the source sentence and another network decodes to the target sentence. Translation quality is improving, but this new MT architecture is far from perfect. There’s still an open question of how to efficiently cover more of the vocabulary, how to make use of the target large-scale monolingual data to improve fluency, and how to utilize more syntactic/semantic knowledge in addition to what is possible learn from source sentences. 

How do DNNs improve translation quality?
For example, several algorithms can be applied to calculate the similarity between phrases or sentences. But they also capture much more contextual information than standard SMT systems, and data sparseness isn’t as big a problem. For example, the RecurrentNN can utilize all the history information in text that comes before the currently predicted target word; this is impossible with standard SMT systems.

Can DNNs lead to a big breakthrough?
  • There have been recent breakthroughs but NMT is computationally much more complex. Because the network structure is complicated, and normalization over the entire vocabulary is usually required, DNN training is a time-consuming task. Training a standard SMT system on millions of sentence pairs only requires a day or two, whereas training a similar NMT system can take several weeks, even with powerful GPUs.
  • Currently it is hard to understand and pinpoint why it is better or worse than SMT – i.e. error analysis is problematic but experimentation is underway at the big four listed above.
  • Limited reasoning and remembering capabilities and suffers with rare words and long sentences.
  • A straightforward Moses-like toolkit that fosters more experimentation is desperately needed but will take at least a year or two to become widely available.
  • NMT produces much more natural sounding translations than SMT claim Facebook, Google and Microsoft.
  • Better ability to handle idiom and metaphor as the Facebook team is claiming.
Purely neural machine translation (NMT) is the new MT paradigm. The standard SMT system consists of several sub-components that are separately optimized and normally implemented in a production pipeline. In contrast, NMT employs only one neural network that’s trained to maximize the conditional likelihood on the bilingual training data. The basic architecture includes two networks: one encodes the variable-length source sentence into a real-valued vector, and the other decodes the vector into a variable length target sentence. 

Experiments report similar or superior performance in English-to-French translation compared to the standard phrase-based SMT system. The MT network architecture is simple, but it has many shortcomings. For example, it restricts tens of thousands of vocabulary words for both languages to make it workable in real applications, meaning that many unknown words appear. Furthermore, this architecture can’t make use of the target large-scale monolingual data. Attempts to solve the vocabulary problem are heuristic, e.g. they use a dictionary in the post-processor to translate the unknown words.

However in spite of these issues, as Chris Wendt at Microsoft says: “Neural networks bring up the quality of languages with largely differing sentence structure, say English<>Japanese, up to the quality level of languages with similar sentence structure, say English<>Spanish. I have looked at a lot of Japanese to English output: Finally actually understandable." 
Jeff Dean at Google is excited about his own team’s effort to push things forward with NMT. “This is a model that uses only neural nets to do end-to-end language translation,” he says. “You train on pairs of sentences in one language or another that mean the same thing. French to English say. You feed in English sentences one word at a time, boom, boom, boom… and then you feed in a special ‘end of English’ token. All of a sudden, the model starts spitting out French.” Dean shows a head-to-head comparison between the neural model and Google’s current system — and his deep learning newcomer one is superior in picking up nuances in diction that are key to conveying meaning. “I think it’s indicative that if we scale this up, it’s going to do pretty powerful things,” says Dean. 

Alan Packer at FaceBook said they believe neural networks can learn “the underlying semantic meaning of the language,” so what is produced are translations “that sound more like they came from a person.” He said neural network-based MT can also learn idiomatic expressions and metaphors, and “rather than do a literal translation, find the cultural equivalent in another language.” Machine Learning is deeply embedded into the FaceBook system infrastructure and we should expect many new breakthroughs.

Tayou is the only language industry MT vendor so far who seems to have experimented with NMT thus far, and they have mixed results, that are presented here. These early results are useful to understand the challenges with early NMT, but these experiments are not useful to conclusively conclude on the real the potential of NMT to outperform SMT or not. The development tools will get better and the same experiments described here could yield different outcomes in future, as tools improve.

So while there are indeed challenges to getting NMT launched in a broad and pervasive way, there are many reasons to march forward. We see the largest Internet players including Microsoft, Google, FaceBook and Baidu are all working with DNNs and all have NMT initiatives in motion. Microsoft has already deployed pure neural networks on mobile translation apps for Android and iOS. Of course with a very small and limited vocabulary but this will only grow and evolve. 

I doubt very much that phrase based SMT is going away to quietly die in the short term (LT 5 years). But as supercomputing access becomes commonplace, and as NMT fleshes out with more comprehensive support tools, the same way that SMT did (which takes some years), we could see a gradual transition and evolution to this new kind of MT. 

There is enough actual evidence of success with NMT to generate real excitement, and I expect we will see a super-Moses-like kit to build NMT systems appear within the next 12 months. This will foster more experimentation and possibly discover new pathways to better automated translation . All this only points to improving MT, albeit gradually, and while MT is a truly difficult engineering problem, the best minds in the world are far from finished on what is possible to improve MT using machine intelligence technology. The emergence of NMT also points to the high likelihood of obsolescence for those who like to keep everything on-premise or the desktop. The best MT solutions will likely happen in the cloud and will be unlikely to be possible at all on the desktop.

MT naysayers should be aware, that the mindset that most of these MT researchers have in place, can be encapsulated by the following statement originally made by Thomas Edison: "I have not failed. I've just found 10,000 ways that won't work." It will indeed get better.


  1. In TAUS Review #3 (April 2015,, I wrote that, "The evolution in artificial intelligence and machine learning consists of deep neural networks (DNNs,) biologically inspired computing paradigms designed like the human brain, enabling computers to learn through observation.
    At the beginning of the last decade, building DNN-based systems proved hard, and many researchers turned to other solutions with more near-term promises. Now, thanks to big data, new DNN-based models can learn as they go and build larger and more complex bodies of knowledge from and about the dataset they are trained on. Machine translation is a promising research field for the application of DNNs."
    Neural networks have been around for twenty years or more, but they have become exploitable only recently. The investment required are huge and DNN MT is not going to be accessible to laymen for a long time.
    This makes the foolishness of the marketing fanfaronade of "translation big data" just another milestone in the irrelevance of translation and the associated industry.
    Translation is going to be a narrower and narrower niche for higlhy specialized poorly paid workers, whose survival will depend on their ability to exploit generalist tools, as DDN MT will be much more than special tools.

  2. For those interested this is a very good technical definition of Neural Net MT

  3. Roee Aharoni

    Found that as another great resource, albeit more technical:

  4. Menno Mafait Kirti, scientists make fundamental mistakes when it comes to ANN/DNN. For example, neurons are not essential to intelligence, in the same way as feathers and flapping wings are not essential to aviation.

    • Another artificial meaning: The so-called “learning” of ANN / DNN reveals a fundamental problem: Once a pattern (ANN) or problem (DNN) is “learned”, it isn’t able to accept another a pattern or problem. So, it is limited to recognize one type of pattern / to solve one type of problem. It proves that ANN / DNN is nothing but engineering (a specific solution to a specific problem), while a science delivers a generic solution.

    So, I am sure that the use of DNN in MT is just a hype. Ask any (human) translator: They had enough of the empty promises of this field.

    Isn't it about time to define intelligence in a natural way, and to discover the relationship between natural intelligence and natural language?

    Did you know that we only know very little of the logic of language? That our understanding of the logic of language is limited to verb “is/are”?

    I defy anyone to beat the simplest results of my natural language reasoner in a generic way:

    Besides that the word “learning” of DNN is ambiguous:

    • The natural meaning of learning: Animals and humans are able to gain knowledge, as well as learn from mistakes. However, scientists being unable to define intelligence in a natural way, the field of AI and knowledge technology has no foundation in nature. Therefore, no technique in this field is able to implement either of these natural learning capabilities;

    • An artificial meaning: The so-called “learning” of Evolutionary Algorithms just means: optimizing or adapting, which is similar to the PID Controller ( in a central heating system, which optimizes the burning time to avoid undershoot and overshoot;

    1. I agree that the patterns that are learned are always a reduction of any real learning or form of intelligence. The progress with MT is incremental and a long way from learning with words even though the patterns identified with images are quite impressive.

    2. Kirti, we don't have to feed a child thousands of pictures of a cat before the child is able to recognize a cat. Only one example is sufficient. And when the child sees another cat, it will point to the animal and just ask “Cat?”, in order to get a confirmation that their pattern recognition was successful.

      I would say, Neural Net Driven MT is not intelligent, but just a copycat :)

    3. Menno actually that is not true .. If you have spent time with a baby you see that it takes time and repeated exposure to pictures, shapes and sounds before the notion of cat is established . Initially they may sometimes think a pig is a cat or dog. I think it is more accurate to say Neural Nets are a very crude model of this assimilation and learning process. NMT is just another attempt to extract knowledge from a lot of data using models that are more sophisticated but still far from the possibilities of a human brain.

  5. Kirti, children are able to recognize thousands of different objects, if not more, while an ANN is limited to recognize only one type of object. And Deep NN is limited to a few types of objects

  6. As I said it is a very crude model of what a baby can do

    1. Kirti, isn't this field lacking a natural definition of intelligence? Isn't this field lacking a foundation in nature? Don't scientists take the word "artificial" too literally?

    2. Menno I wonder if having a definition of any kind is the answer. Can you define life? And if you could what value would the words really have? The description is not the described. Much of what exists in nature is hard to define so all these technologies can only be a reduction or a skeletal model of anything that happens in nature. or life, or intelligence.

    3. "Isn't it about time to define intelligence in a natural way"
      It depends.
      Automatizing some (rather domain specific) task does not necessarily help understanding how it is done by humans. Automatization and research of the mind do not naturally go hand-in-hand. The two has different goals, although marketing is trying to convince us that automatic solutions are "clever" and "intelligent". Actually, psychology, philosophy, cognitive/formal/functional/...etc semantics are presenting models of the mind and understanding, but these are not much of use for the industry.

      "Kids and Cats"
      10 years ago someone from the Oxford baby lab explained their research using exactly the example of cats - and lions. Babies can only tell the difference between cats and lions after a certain age. The lab tried to create a computational model of this visual/semantic learning, documented by human experiments.

      Btw, I agree, the field is full of overused buzzwords. Everything is "deep" these days...

    4. Kirti, the use of a definition of intelligence:

      • I have defined intelligence in a natural way (;
      • Through this definition, I have discovered a relationship between natural intelligence and natural language (;
      • I am implementing these laws of nature in software;
      • And I defy anyone to beat the simplest results of my natural language reasoner in a generic way:

      So, I have defined intelligence, and discovered a relationship between intelligence and language, of which scientists are still unaware. Scientists are therefore unable to implement even the simplest results of my software in a generic way (as described in my challenge).

    5. Gabor, as long as scientists fail to define intelligence in a natural way, AI is engineering rather than science. It is programmed intelligence rather than an artificial implementation of natural intelligence. It is a "flight simulator" rather than an "airplane".

      A flight simulator moves pixels on the screen – and the cones of the speakers – but it will not leave the room.

      Actually, AI scientists made a fundamental mistake 60 years ago:

      Intelligence and language are natural phenomena. Natural phenomena obey laws of nature. And laws of nature are investigated using fundamental science. However, the field of AI and knowledge technology is researched using cognitive science.

      I am basically redoing the field of AI and knowledge technology largely from scratch, using fundamental science (algebra) instead of cognitive science (simulation of behavior).

  7. I just caught up on some reading. The official NMT reports of experiments presented at MT Marathon of the Americas ( Did I read these details wrong?:

    # Corpus size: 30,000–100,000 words in the vocabulary
    # Create engine: 1 to 3 weeks
    # Translation production: 100,000–500,000 sentences/day (referred to as "fast")
    # Hardware: NVIDIA Titan X and amuNN GPU (i.e. used to make supercomputers)

    Compare to SMT (Slate/Moses):

    # Corpus size: 1 million words
    # Create engine: 8-10 hours (overnight)
    # Translation production: 3-5 million sentences per day
    # Hardware: Intel i7

    We know hardware always gets faster and algorithms are optimized. Ten years ago, SMT required 1-3 weeks to train an SMT engine. True to form, hardware got faster and academic researchers refined the algorithms.

    The same will happen over time with NMT. With Moore's law, maybe it could even take less than the 10 years it took SMT. For now, NMT is still very much experimental. It's funny how these details in the reports are so easily overlooked.

    1. Tom - I am not sure what your point is. I think it is VERY clear to anybody who has looked at NMT that it requires some serious computing horsepower to play. It is unlikely to be a desktop tool in the next 10 years. So what?

      There may not even be a desktop in 10 years. There is much more going on with production NMT at Facebook, Microsoft, Google and now Systran. Take a look at the output samples if you think that this is still 10 years away.

      Many of these resource issues will be addressed and it probably will take years before the average MT practitioner. This technology is very likely a cloud-only option but this is not an issue for the kinds of people who are already using it.

    2. This is a better source for understanding what the state of leading edge MT systems are today at least at the University level NMT

  8. Many readers rely on reviews without reverting to the original academic studies or first-hand experience. The reviews I've seen failed to convey the computational realities. Social media discussions are popping up in response to those reviews (not studies) speculating that NMT will replace humans. The hype cycle has begun. My point is to defuse the hype early in the game so readers keep their feet on the ground.

    I answer the social media questions by giving some perspective. It took us 10 years (2006 to 2016) to learn that Big Data cloud-based SMT deployments, on average, deliver less than 10% correct translations ( I continue by saying early NMT research shows a promising 26% improvement over SMT's baseline( Again, keeping things in perspective, that's a 26% improvement over a 10% base. NMT is promising and research will continue. I don't recommend anyone abandon their SMT systems (regardless of the deployment technology) in favor of NMT systems. In this, I think you and I agree.

    Re desktop? I'm not omniscient nor omnipotent. If you know what a "desktop of the future" looks like, you're better than I. Successful technologies naturally migrate from expert systems to commodity deployments. Speech recognition software once ran only on specialized DSP co-processor boards in call center servers. Now it runs on your Android/iPhone. Today, researchers experiment with NMT using the same NVIDIA GPU's that deliver virtual reality experience to gamers. I won't pidgin-hole future use cases into any particular deployment technology.

    SMT has reached commodity status. Re NMT "very likely a cloud-only option," we and others already have neural hybrid Moses running on Posix desktops, but the business decision to migrate to Windows desktops is undecided. In the near- to mid-term business forecast, adaptive technologies, fixed adaptations with personalized translation engines and dynamic adaptations with predictive adaptation, here and much more exciting.

    By the time NMT achieves commodity status -- and it will -- the cloud *as we know it* might not exist and (to quote R.E.M.) "I feel fine."

  9. Really nice info? keep up the work...