All that AI DL training data comes from us

Read a couple of articles the past few weeks that highlighted something that not many of us are aware of, most of the data used to train AI deep learning (DL) models comes from us.

That is through our ignorance or tacit acceptation of licenses for apps that we use every day and for just walking around/interacting with the world.

The article in Atlantic, The AI supply chain runs on ignorance, talks about Ever, a picture sharing app (like Flickr), where users opted in to its facial recognition software to tag people in pictures. Ever also used that (tagged by machine or person) data to train its facial recognition software which it sells to government agencies throughout the world.

The second article, in Engadget , Colorado College students were secretly used to train AI facial recognition (software), talks about a group using a telephoto security camera than was pointed at a high traffic area on campus. The data obtained was used to help train an AI DL model to identify facial characteristics from far away.

The article went on to say that gathering photos from people in public places is not against the law. The study was also cleared by the school. The database was not released until after the students graduated but it did have information about the time and date the photos were taken.

But that’s nothing…

The same thing applies to video sharing and photo animation models, podcasting and text speaking models, blogging and written word generation models, etc. All this data is just lying around the web, freely available for any AI DL data engineer to grab and use to train their models. The article which included the image below talks about a new dataset of millions of webpages.

From an OpenAI paper on better language models showing the accuracy of some AI DL models “trained on a new dataset of millions of webpages called WebText.”

,Google photo search is scanning the web and has access to any photo posted to use for training data. Facebook, IG, and others have millions of photos that people are posting online every day, many of which are tagged, with information identifying people in the photos. I’m sure some where there’s a clause in a license agreement that says your photos, when posted on our app, no longer belong to you alone.

As security cameras become more pervasive, camera data will readily be used to train even more advanced facial recognition models without your say so, approval or even appreciation that it is happening. And this is in the first world, with data privacy and identity security protections paramount, imagine how the rest of the world’s data will be used.

With AI DL models, it’s all about the data. Yes much of it is messy and has to be cleaned up, massaged and sometimes annotated to be useful for DL training. But the origins of that training data are typically not disclosed to the AI data engineers nor the people that created it.

We all thought China would have a lead in AI DL because of their unfettered access to data, but the west has its own way to gain unconstrained access to vast amounts of data. And we are living through it today.

Yes AI DL models have the potential to drastically help the world, humanity and government do good things better. But a dark side to AI DL models also exist to help bad actors, organizations and even some government agencies do evil.

Caveat usor (May the user beware)

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Comments?

Photo Credit(s): “Still Watching You” by jhcrow is licensed under CC BY-NC 2.0 

Computational Photography Homework 1 Results.” by kscottz is licensed under CC BY-NC 2.0 

From Language models are unsupervised multi-task learners OpenAI research paper

Intel’s new DL Boost for DL AI inferencing

I was at a TechFieldDay Extra with Intel Data Centric Innovation Conference last week in San Francisco. It was a lavish affair with many industry analysts in attendance besides the TFDx crew.

At the event Intel announced a number of new products including the availability of their next generation scaleable Xeon processor chips, new Optane DC PM (DIMM) and software, new Ethernet (800) NIC cards, new FPGA line (10nm) and DL (deep learning inferencing) boost functionality.

I was most interested in the DL Boost and Optane DC PM solutions. For this post I focus on DL Boost.

DL Boost for DL inferencing on Xeon

Intel’s DL Boost technology provides a new integer 8 bit precision (INT 8) matrix multiply & summation instruction which can be used to speed up DL inferencing operations. As those who have been following along with my AI-DL-machine learning (ML) blog posts (latest being Learning Machine Learning part 3), probably know, deep learning machine learning that processes data to create a neural network made up with a number of layers and a number of nodes each of which represents a floating point weight used to transform inputs into outputs.

All DL AI projects involve at least two phases: model training and model inferencing (prediction, classification, AI result, etc.). Although both of these activities involve matrix calculations, model training involves a lot more of these compute intensive operations than inferencing. In fact, while training typically is done on GPUs or other special purpose compute hardware (TPU, IPUs, etc.) inferencing can typically be done on standard off the shelf CPUs.

Historically. inferencing used floating point matrix multiplication and summation functionality ,taking input from sensors, logs, photos, etc. and performing the model logic to create an output.

Intel believes (with industry analyst agreement) that over time, 50% or more of the DL AI workload is going to involve inferencing. Hence, the focus on this end of the AI workload, at least for now.

For example, although speech recognition AI can take a long time to process audio recordings and use reinforcement learning to train a recognition model. But, once trained, you could use that recognition AI model in anything from smart speakers, to speech to text dictation machines, to voice response systems, etc. In all of these the recognition model is passed a voice recording (or voice in real time) and processes these to create a text version of the speech.

But all of this has historically been done in floating point (FP) 32 (bit precision) or FP 16. Google’s TPU is capable of doing this with less precision, but to my knowledge, up to this point, it’s always been floating point.

What is DL Boost

What Intel has done with DL Boost is to create a new X86 instruction which can perform an integer (INT) 8 (bit precision) matrix multiplication and summation with less cycles than what it took before. Intel believes if customers were to modify their trained AI neural network models to move from FP 32 (or 16) to INT 8, they could perform inferencing much faster on Xeon Cascade Lake CPUs, than they could before and not have to rely on GPUs for this activity at all.

Yes, this does require hand optimization of trained AI neural network. Some of this may be automated, but not all. Intel claims the precision loss, if done properly, is less than a few percent and it’s impact on AI inferencing correctness is negligible at best.

At the moment, for all the DL modeling I have done, i have never looked at the trained model’s weights leaving this to TensorFlow/Keras to manage for me. But I’m not creating production level DL AI systems (yet). So, I don’t know what it would take to modify my AI models to use INT 8 nor what level of degradation in correctness would ensue. But I also don’t have Cascade Lake Xeon CPUs available.

Some potential problems here:

  1. Manual activity to hand tune the INT 8 neural network is not going to be that popular, except for those organizations where inferencing requires GPUs.
  2. Most production DL AI models, undergo some form of personalization for a user or implementation instance which would require a further FP to INT conversion for each user/implementation.
  3. Most production DL AI models also undergo periodic retraining to fine tune the model with the latest data that has been accumulated. This would also require further FP to INT conversion after each training cycle.

In the end, there’s an advantage for production AI inferencing, for models that don’t require substantial retraining/personalization as they don’t change that often. And there’s a definite cost advantage to using DL Boost INT 8, for those AI inferencing that must use GPUs today to perform in real time or under other performance constraints.

But hand converting neural networks, reminds me of creating assembly code for modules that can impact performance. This is normally reserved for only a select modules or functionality that executud a lot. However, DL models are much more monolithic and by definition, less modular. Identifying which models (or model layers) within a production DL AI solution that are performance sensitive and hand optimizing them to work on CPUs rather than GPUs, seems like a hard task.

It would be better from my perspective to create a single FP 16 matrix multiplication instruction. Alternatively, create some software that would automatically convert any DL AI model (or model layer) from FP to INT 8. That way DL Boost optimization would be just another step in the model training process and could be automatically generated to see if A) it loses too much sensitivity and B) if it’s worthwhile using CPU inferencing.

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Comments?

Decoding deep learning

Read an article in Qaunta Magazine (A new approach to understanding how machines think) about what Google’s team has been doing to interpret deep learning models. They have created TCAV (Testing with Content Activation Vectors), a software tool that can be used to interrogate deep learning models to determine how sensitive they are to features of interest.

Essentially, TCAV provides a way to exercise a deep learning model using a select set of test data (that isolates a feature of interest) and to determine how sensitive the deep learning model is to that feature.

What TCAV can do for DL models

For example, in my experiments with deep learning, I’ve trained a model to predict popularity of a (RayOnStorage) blog post based on its title. But, I was also intending to do the same based on content attributes such as, text length, heading count, image count, link count, etc. In the end, I was hoping to come up with some idea of the popularity of a post based on these attributes. But in reality what I wanted to know was how each of these parameters (or features) impacted blog post (predicted and actual) popularity.

With TCAV, you essentially select training examples such as posts that have or show the parameter of interest (e.g. blog posts with a high number of images). Once you have your example set you use TCAV to feed in the samples to the model and it generates a number between 0 and 1, that tells you how sensitive the model is to the feature in the training set.

So in the example from my blog above, it might show that the blog popularity prediction DL model has a 0.2 sensitivity to the number of images in a post. In the example shown in the graphic the base model interprets images and TCAV is used to determine how important stripes are to interpreting an image has a zebra in it.

How TCAV actually works

The use of TCAV is a bit technical but essentially it feeds the example data set into the model as well as some random set of data without the feature of interest and isolates the model’s (neural net node) activation deltas between random data and example data.

TCAV uses a machine learning model to interrogate another machine learning model of the sensitivity to a characteristic feature vs a random feature set. The paper goes into much more detail than this if interested, but you train this new model to predict the sensitivity of the old model to the feature of interest. In the end, TCAV comes up with a single number determining that sensitivity

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TCAV is available as an open source tool (see GitHub TCAV project page) and works with Google TensorFlow frameworks. TCAV was originally developed to work with image classification models but can work with other models as well.

If your running TensorFlow already, adding TCAV appears easy enough (checkout the readme page for the project for more info). On the TCAV project page, there’s a Jupyter notebook (Run TCAV in the GitHub directory) available that explains it in more detail.

Can’t wait to try it out on my blog popularity prediction model.

Comments?

Photo Credit(s): From Neural Networks, Multiple Outputs from caesar harda (Flickr)

From the Testing with Content Activation Vectors paper

From The face of a robot with human-like features, Penn State

Learning machine learning – part 3

Image of the cover of the book Deep Learning with Python

Decided to take the plunge and purchase the Deep Learning with Python book and see what it has to offer. In prior posts (see Learning machine learning – part 1 & part 2) we were working with the cloud tutorials. This Part one is based on the book

It has a great introduction into deep learning which is a subset of machine learning. After what I know today, the Microsoft Azure session was more on traditional (statistical) machine learning and not deep learning.

 

Installing deep learning

In order to use the book, you need access to Keras, Python, Jupyter and a Keras backend (TensorFlow, Microsoft CNTK or Theanno).

I decided not to use any cloud solutions and rather install Python, Jupiter, TensorFlow and Keras on my MacBook. Although it probably would have been much easier (and more costly) to use any cloud solution.

I followed the directions on the installing TensorFlow website for the PIP install (you have to install a “virtual environment” and “PIP” first). The MacBook didn’t have a NVIDIA GPU so I needed to install the CPU version of TensorFlow.

But I had the hardest time running any of the book examples. Whenever I changed any command cell in a Jupyter notebook with Keras functionality in them (like adding a space to the end of an “import Keras” command line), it would throw a (module not found) error.

After days of web searching for what path is used for Jupyter notebook-iPython/Python imports (sys.path and PYTHONPATH) and where I should be importing Keras from (it’s not “~/ .keras”), I got nowhere closer to running anything.

I finally saw that I could directly install Keras (again, when I installed Tensorflow, it installed Keras as well) into my VENV. After I did that, everything worked. (I probably have one too many Keras environments, but who cares).

Finally getting the environment correct, I could now execute any command cells in a Jupyter notebook (with Keras functionality properly, well most of them anyways).

Jupyter notebooks for dummies…

It took me a while to figure out that the way you run a Jupyter notebook server is by issuing the command “jupyter notebook” (nowhere in the command’s help file, but can be found in Jupyter tutorials). That’s when I started to see the problems in the installation section above with my Keras installation.

Understanding Jupyter notebooks is non-trivial. Yes, I know it’s an interactive code and documentation environment. It’s sort of like BASIC on steroids with WORD functionality built in/escapeable into at any time.

First thing to understand is that when you open up a jupyter notebook, you haven’t executed anything yet. YES there are output lines in the notebook you just opened but NO, they aren’t from executing them under your client-server environment.

The output lines you see in the notebook is output from someone else’s execution run. So while they may look like they worked fine but they haven’t executed in your installation environment yet..

Also, when executing Jupyter notebook command cells, pay special attention to the In [?]: that’s shown to the right of every command cell.

When the ‘?’ is a number, like In:[12] that tells you what sequence (12th in the sequence) that (multi-line command cell) has been executed in and when the ‘?’ a “*”, like In[*], it says that the Jupyter notebook server is executing that command cell. 

Some command cells generate Out [?]: lines and others do not. So can’t use this to tell if something’s been executed or not. The only way to tell if some command cell has been executed is by seeing the In [n]: integer as n be incremented from the last command cell you executed. Of course you can execute command cells out of sequence if you wish.

Jupyter notebook coding/executing was weird as one who is more used to C, Java, and other coding languages and IDEs. A video tutorial on Jupiter notebooks would probably have helped here, but I couldn’t find one.

Running the examples

You can download all of the books current examples from the book’s website.

The book suggests you add model layers, subtract model layers and change the parameters of the number of nodes in a model as examples for you to try at home.

In general, doing so (once the environment was setup properly) seemed to work as desired. Adding layers didn’t seem to change the accuracy of the models, if anything it degraded it, and deleting layers didn’t help either. Ditto for adding or reducing node counts within a layer.

There’s a bunch of datasets that comes with Keras install used in the examples. Many examples have a first step where you modify this data so as to be more amenable to deep learning modeling.

For example, there’s a IMDB dataset that has film reviews. The film reviews are text files. But deep learning doesn’t work on text strings so you need to convert the text files into lists of integers. You do this by looking up each word in a word dictionary and substituting the index for each word in the review, generating an array (list) of integers.

This is all done through the NumPy package. It’s worth the time to become familiar with Python and probably NumPy. I took the verbal Python tutorial (but did nothing to learn NumPy).

Another example is a real estate prediction model that has 13 different parameters across 500 or so neighborhoods. The parameters are all different, some are distances, some %s, some pricing differences, etc. In order to perform deep learning on them, the example normalizes all of them, using distance from mean, in units of standard deviation.

There are other examples of data transformations as well. It seems that transforming your data into something amenable to deep learning is one part of the magic of deep learning.

Back to the book

Getting through chapter 3 of the book i- fairly straightforward when everything is set up properly. I found a iPad app (Juno) that could be used to connect to the Jupyter Server and it seemed to work once I found the proper command to use to start Jupyter (jupyter notebook –ip=”*”) and the proper Jupyter configuration parameters to use.

The examples are pretty self-documenting so you should be able to try out any of them on your own. The book adds great explanations on machine learning, deep learning and and the overall flow of how to approach a deep learning project.

Once you finish chapter 4 of the book you have all the tools one needs to tackle any deep learning project that you want to attack. You may need to read up on how to transform your data and you will probably be using one of the modeling techniques in one of the examples but it seems easy enough.

The rest of the book’s chapters (which I have yet to complete) deal with deep learning in practice and it’s in these chapters that you can learn some of the art of deep learning data science and model science..

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I ended up having fun with Jupyter notebooks, once I got them running with the iPad client in one hand and the book in the other. At the end of chapter 4, I startedto see some applications to my consulting business that might be interesting to model.

Using the Mac CPU was fast enough for the examples but I may have to tear down the crypto mine and use it as an AI server for my home network if I plan to tackle something with more data.

Wish me luck…

Learning Machine Learning – part 2

In Learning Machine Learning – part 1, we covered AWS and GCP tutorials on machine learning within each of their clouds. In part 2 we cover Microsoft tutorial(s) on machine learning in Azure.

I found Machine Learning Jump Start in Microsoft Visual Academy with instructors, Buck Woody and Seayoung Rhee. This is a series of 4 video tutorials on Azure ML Studio. ML studio seems similar to AWS SageMaker as it’s a framework to perform machine learning.

Azure (and probably AWS & GCP) have a number of methods to perform machine learning. ML studio happens to be the one that I found but there are many others worth examining.

Azure’s ML Studio tutorial videos were a better than AWS but not as good as GCP IMHO for learning machine learning.  There are four videos in the series. I watched  the first (~45 minutes), the second  (~45 minutes) and most of the third (only 25 of ~45 minutes).

Video 1 Concepts and setting up a ML Studio account

In the first video, the instructors took a long time to get going and then when  got someplace interesting, it was all play acting (human as a machine learner) to teach concepts.

The tutorials do distinguish between Supervised learning and Unsupervised learning. Both of which can apply to prediction or classification types of problems or outcomes. These are discussed as classic machine learning characteristics.

 

In the last 1/3 of the first video they discuss Azure ML Studio. It provides a common place to work and collaborate across team members. It also provides a graphical approach to machine learning. ML Studio also supports a programmable API, but I never got to that section in my viewing.

Some Azure ML Studio strengths:

  1. It provides industry recognized  data sets and data science algorithms that can be used as a black box, such as recommendation engines.
  2. It allows you to publish and consume machine learning solutions.

On the Azure portal there’s a machine learning studio icon (it’s now buried under the 100+ services link, in the AI + Machine Learning section). You use this to create a new ML studio workspace.

Inside a workspace you can use Azure ML studio services.  In the workspace you can review all your experiments (these are algorithms or predictive models being worked). 

In the Experiments page you can create a new experiment which is sort of a graphical workflow of the machine learning task.

There you will find a list of Azure sample data sets and sample algorithms that can be used in your experiment. The first video didn’t go into much detail on any of this other than showing you how to get started and create a ML studio workspace.

Video 2 how to use ML studio

Video 2 takes your ML studio workspace and runs a rudimentary experiment with it. In this video they walk you through selecting a data set, selecting algorithms to use and how to connect them into a machine learning workflow.

Creating an ML Studio experiment is almost like flowcharting your workflow. You select the data you want and drop it into the workflow. Next select an extraction engine you want to use and drop it into the work flow and connect it to the data. Then. you identify what you want to do with the data (like training) and drop that algorithm into the workflow and so on.  In the end you have defined a sequence of actions to perform on data.

In their example, dataset they use a user movie ratings dataset. They connect this to a bayesian learning model and to a IMDB database to extract movie titles. The tutorial experiment is a movie recommendation engine.Although it wasn’t a neural net many of the same techniques apply.

 

ML Studio uses an intuitive graphical approach to defining a machine learning workflow.

Video 3 publishing your ML Studio web service

Video 3 shows you how to publish (on Azure’s Marketplace) the recommendation engine created in video 2 as an OData web service.

I stopped watching the 3rd video after about 25 minutes as it was setting up various aspect of the OData web service to be deployed on Azure marketplace.

Using Azure ML studio seemed pretty straightforward. But it was much more data science/data analytics activity than neural network training.

The Azure MVA ML Studio tutorial was created in 2014 so some of the concepts are a bit dated but most still apply.

Looking today on the Azure Portal, I was still able to find the ML studio workspaces under one of the 10 AI + Machine Learning services.  Again I would have to say the GCP tutorial was a better fit for what I wanted which was how do I create a neural  net and get it trained.

Other ML approaches under Azure

There are other Azure approaches to machine learning and tutorials that support them. For example, there’s a quick start tutorial to understand how to use Python and Jupyter notebooks under Azure, which is probably closer to the neural net training in GCP.

I found myself skipping ahead a lot in video 1 as it was mainly about concepts and not much technical detail. Video 2 was a good intro into ML studio and Video 3 showed you how to publish a ML studio web service in Azure but it was more details than I wanted to know. I never got to video 4, which probably talked about ML Studio’s programable API.

If I had to do it over again, I probably would have viewed the quick start tutorial with Python and Jupyter notebooks, which sounded more like the GCP tutorials in the part 1 post.

On the other hand, Azure ML Studio tutorials supplied a good complement to the GCP tutorial, as a different (more graphical) way to do ML. It would probably be worthwhile to view before taking the AWS Sagemaker tutorials as it’s a bit higher level and quicker introduction into the workflow of AI and machine learning.

Comments?

Picture credit(s): Screen shots of Videos 1, 2 and 3 in the MVA series, (c) Microsoft 

Learning machine learning – part 1

Saw an article this past week from AWS Re:Invent that they just released their Machine Learning curriculum and materials  free to the public. Google (Cloud Platform and elsewhere) TensorFlow,  (Facebook’s) PyTorch, and Microsoft Azure CNTK frameworks  education is also available and has been for awhile now.

My money is on PyTorch and Tensorflow as being the two frameworks most likely to succeed. However all the above use many open source facilities and there seems to be a lot of cross breeding across them. Both AWS ML solutions and Microsoft CNTK offer PyTorch and TensorFlow frameworks/APIs as one option among many others.  

AWS Machine Learning

I spent about an hour plus looking over the AWS SageMaker tutorial videos in the developer section of AWS machine learning curriculum. Signing up was fairly easy but I already had an AWS login. You also had to enroll/register for the course on your AWS login  but once that was through, you could access courses.

In the comments on the AWS blog post there were a number of entries indicating broken links and other problems but I didn’t have any issues. Then again, I didn’t start at the beginning, only looked at over one series of courses, and was using the websites one week after they were announced at Re:Invent.

Amazon SageMaker is an overarching framework that can be used to perform machine learning on AWS, all the way from gathering, analyzing and modifying the dataset(s), to training the model, to creating a inference engine available as an endpoint that can be used to perform the inferencing.

Amazon also has special purpose API based tools that allow customers to embed intelligence (inferencing) directly into their application, without needing to perform the ML training. These include:

  • Amazon Recognition which provides image (facial and other tagging) recognition services
  • Amazon Polly which provides text to speech services in multilple languages, and
  • Amazon Lex which provides speech recognition technology (used by Alexa) and together with Polly helps embed conversational interfaces into customer applications.

TensorFlow Machine Learning

In the past I looked over the TensorFlow tutorials and recently rechecked them out. I found them much easier to follow this time.

 

The Google IO 2018 video on TensorFlowGetting Started With TensorFlow High Level APIs, takes you through a brief introduction to the Colab(oratory),  a GCP solution that uses TensorFlow and how to use Tensorflow Keras, tf.data and TensorFlow Eager Execution to create machine learning models and perform machine learning.

 Keras on TensorFlow seems to be the easiest approach to  use machine learning technologies. The video spends most of the time discussing a Colab Keras code element,  ~9 lines, that loads a image classification dataset, defines a 1 level (one standard layer and one output layer), trains it, validates it and uses it to perform  inferencing.

The video also touches a bit on tf.data and TensorFlow Eager Execution but the main portion discusses the 9 line TensorFlow Keras machine learning example.

Both Colab and AWS Sagemaker use and discuss Jupyter Notebooks. These appear to be an open source approach to documenting and creating a workflow and executing Python code automatically.

GCP Colab is essentially a GCP-Google Drive based Jupyter notebook execution engine. With Colab you create a Jupyter notebook on google drive and interactively execute it under Colab. You can download your Juyiter notebook files and essentially execute them anywhere else that supports TensorFlow (that supports TensorFlow v1.7 or above, with Keras API support).

In the video, the Google IO   instructors (Josh Gordon and Lawrence Moroney) walk you through building a model to recognize handwritten digits and outputs a classification (0..9) of what the handwritten digit represents.

It uses a standard labeled handwriting to digits labeled data set, called the MNIST database of handwritten digits that’s already been broken up into a training set and a validation set. Josh calls this the “Hello World” of machine learning.

The instructor in the video walks you through the (Jupyter Notebook – Eager Execution-Keras) code that inputs the data set (line 2), builds a 1 level (really two layer, one neural net layer and one output layer) neural network model (lines 3-6), trains the model (line 7), tests/validates the model (line 8) and then uses it to perform an inference (line 9).

Josh spends a little time discussing neural networks and model optimizations and some of the other parameters used in the code above. He has a few visualizations of what this all means but for the most part, the code uses a simple way to build a neural net model and some standard optimization techniques for the network.

He then goes on to discuss tf.data which is an API that can be used to create machine learning datasets and provide this data to the neural net for training or inferencing.  Apparently tf.data has a number of nifty features that allow you to take raw data and transform it into something that can be used to feed neural nets. For example, separating the data into batches, shuffling (randomizing) the batches of data, pre-fetching it so as to not starve the GPU matrix multipliers, etc.

Then it goes into how machine learning is different than regular coding. And show how TensorFlow Eager Execution is really just like Python execution. They go through another example (larger) of machine learning, this one distinguishes between cats and dogs. While they use an open source Python IDE ,  PyCharm, to test and walk through their TF Eager Execution code, setting breakpoints and examining data along the way.

At the end of the video they show a link to a Google crash course on TensorFlow machine learning and they refer to a book Deep Learning with Python by Francois Chollet. They also mention a browser version of TensorFlow which uses Java Script and  your browser to develop, train and perform inferences using TensorFlow Keras machine learning.

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Never got around to Microsoft’s Azure training other than previewing some websites but plan to look over that soon.

I would have to say that the Google IO session on using TensorFlow high level APIs was a lot more enjoyable (~40 minutes) than the AWS multiple tutorial videos (>>40 minutes) that I watched to learn about SageMaker.

Not a fair comparison as one was a Google IO intro session on TensorFlow high level APIs and the other was a series of actual training videos on Amazon SageMaker and the AWS services you can use to take advantage of it.

But the GCP session left me thinking I can handle learning more and using machine learning (via TensorFlow, Keras, Eager Execution, & tf.data) to actually do something while the SageMaker sessions left me thinking, how much AWS facilities and AWS infrastructure services,  I would need to understand and use to ever get to actually developing a machine learning model.

I suppose one was more of an (AWS SageMaker) infrastructure tutorial  and the other was more of an intro into machine learning using TensorFlow wherever you wanted to execute it.

I think I’m almost ready to start creating and feeding a TensorFlow model with my handwriting and seeing if it can properly interpret it into searchable text. If it can do that, I would be a happy camper

Comments…

Photo credits: 

Screenshos from AWS Sagemaker series of tutorial video 1, 2, 3, 4 & 5, you may need a signin to view them

Screenshots from the Getting Started with TensorFlow High Level APIs YouTube video 

IBM using PCM to implement better AI – round 6

Saw a recent article that discussed IBM’s research into new computing architectures that are inspired by brain computational techniques (see A new brain inspired architecture … ). The article reports on research done by IBM R&D into using Phase Change Memory (PCM) technology to implement various versions of computer architectures for AI (see Tutorial: Brain inspired computation using PCM, in the AIP Journal of Applied Physics).

As you may recall, we have been reporting on IBM Research into different computing architectures to support AI processing for quite awhile now, (see: Parts 1, 2, 3, 4, & 5). In our last post, More power efficient deep learning through IBM and PCM, we reported on a unique hybrid PCM-silicon solution to deep learning computation.

Readers should also be familiar with PCM as well as it’s been discussed at length in a number of our posts (see The end of NAND is near, maybe; The future of data storage is MRAM; and New chip architectures with CPU, storage & sensors …). MRAM, ReRAM and current 3D XPoint seem to be all different forms of PCM (I think).

In the current research, IBM discusses three different approaches to support AI  utilizing PCM devices. All three approaches stem from the physical characteristics of PCM.

(Some) PCM physics

FIG. 2. (a) Phase-change memory is based on the rapid and reversible phase transition of certain types of materials between crystalline and amorphous phases by the application of suitable electrical pulses. (b) Transmission electron micrograph of a mushroom-type PCM device in a RESET state. It can be seen that the bottom electrode is blocked by the amorphous phase.

It turns out that PCM devices have many  characteristics that lend themselves to be useful for specialized computation. PCM devices crystalize and melt in order to change state. The properties associated with melting and crystallization of the PCM media cell can be used to support unique forms of computation. Some of these PCM characteristics include::

  • Analog, not digital memory – PCM devices are, at the core, an analog memory device. We mean that they don’t record just a 0 or 1 (actually resistant or conductive) state, but rather a continuum of values between those two.
  • PCM devices have an accumulation capability –   each PCM cell actually  accumulates a level of activation. This means that one cell can be more or less likely to change state depending on prior activity.
  • PCM devices are noisy – PCM cells arenot perfect recorders of state chang signals  but rather have a well known, random noise which impacts the state level attained, that can be used to introduce randomness into processing.

The other major advantage of PCM devices is that they take a lot less power than a GPU-CPU to work.

Three ways to use PCM for AI learning

FIG. 4. “In-memory computing,” computation is performed in place by exploiting the physical attributes of memory devices organized as a “computational memory” unit. For example, if data A is stored in a computational memory unit and if we would like to perform f(A), then it is not required to bring A to the processing unit. This saves energy and time that would have to be spent in the case of conventional computing system and memory unit. Adapted from Ref. 19.

The Applied Physics article describes three ways to use PCM devices in AI learning. These three include:

  1. Computational storage – which uses the analog capabilities of PCM to perform  arithmetic and learning computations. In a sort of combined compute and storage device.
  2. AI co-processor – which uses PCM devices, in an “all PCM nodes connected to all other PCM nodes” operation that could be used to perform neural network learning. In an AI co-processor there would be multiple all connected PCM modules, each emulating a neural network layer.
  3. Spiking neural networks –  which uses PCM activation accumulation characteristics & inherent randomness to mimic, biological spiking neuron activation.

FIG. 11.
A proposed chip architecture for a co-processor for deep learning based on PCM arrays.28

It’s the last approach that intrigues me.

Spiking neural nets (SNN)

FIG. 12. (a) Schematic illustration of a synaptic connection and the corresponding pre- and post-synaptic neurons. The synaptic connection strengthens or weakens based on the spike activity of these neurons; a process referred to as synaptic plasticity. (b) A well-known plasticity mechanism is spike-time-dependent plasticity (STDP), leading to weight changes that depend on the relative timing between the pre- and post-synaptic neuronal spike activities. Adapted from Ref. 31.

Biological neurons accumulate charge from all input (connected) neurons and when they reach some input threshold, generate an output signal or spike. This spike is then used to start the process with another neuron up stream from it

Biological neurons also exhibit randomness in their threshold-spiking process.

Emulating spiking neurons, n today’s neural nets, takes computation.  Also randomness takes more.

But with PCM SNN, both the spiking process and its randomness, comes from device physics. Using PCM to create SNN seems a logical progression.

PCM as storage, as memory, as compute or all the above

In the storage business, we look at Optane (see our 3D Xpoint post) SSDs as blazingly fast storage. Intel has also announced that they will use 3D Xpoint in a memory form factor which should provide sadly slower, but larger memory devices.

But using PCM for compute, is a radical departure from the von Neumann computer architectures we know and love today. HPE has been discussing another new computing architecture with their memristor technology, but only in prototype form.

It seems IBM, is also prototyping hardware done this path.

Welcome to the next computing revolution.

Photo & Caption Credit(s): Photo and caption from Figure 2 in AIP Journal of Applied Physics article

Photo and caption from Figure 4 in AIP Journal of Applied Physics article

Photo and caption from Figure 11 in AIP Journal of Applied Physics article

Photo and caption from Figure 12 in AIP Journal of Applied Physics article

 

 

AI reaches a crossroads

There’s been a lot of talk on the extendability of current AI this past week and it appears that while we may have a good deal of runway left on the machine learning/deep learning/pattern recognition, there’s something ahead that we don’t understand.

Let’s start with MIT IQ (Intelligence Quest),  which is essentially a moon shot project to understand and replicate human intelligence. The Quest is attempting to answer “How does human intelligence work, in engineering terms? And how can we use that deep grasp of human intelligence to build wiser and more useful machines, to the benefit of society?“.

Where’s HAL?

The problem with AI’s deep learning today is that it’s fine for pattern recognition, but it doesn’t appear to develop any basic understanding of the world beyond recognition.

Some AI scientists concede that there’s more to human/mamalian intelligence than just pattern recognition expertise, while others’ disagree. MIT IQ is trying to determine, what’s beyond pattern recognition.

There’s a great article in Wired about the limits of deep learning,  Greedy, Brittle, Opaque and Shallow: the Downsides to Deep Learning. The article says deep learning is greedy because it needs lots of data (training sets) to work, it’s brittle because step one inch beyond what’s it’s been trained  to do and it falls down, and it’s opaque because there’s no way to understand how it came to label something the way it did. Deep learning is great for pattern recognition of known patterns but outside of that, there must be more to intelligence.

The limited steps using unsupervised learning don’t show a lot of hope, yet

“Pattern recognition” all the way down…

There’s a case to be made that all mammalian intelligence is based on hierarchies of pattern recognition capabilities.

That is, at a bottom level  human intelligence consists of pattern recognition, such as vision, hearing, touch, balance, taste, etc. systems which are just sophisticated pattern recognition algorithms that label what we are hearing as Bethovan’s Ninth Symphony, tasting as grandma’s pasta sauce, and seeing as the Grand Canyon.

Then, at the next level there’s another pattern recognition(-like) system that takes all these labels and somehow recognizes this scene as danger, romance, school,  etc.

Then, at the next level, human intelligence just looks up what to do in this scene.  Almost as if we have a defined list of action templates that are what we do when we are in danger (fight or flight), in romance (kiss, cuddle or ?), in school (answer, study, view, hide, …), etc.  Almost like a simple lookup table with procedural logic behind each entry

One question for this view is how are these action templates defined and  how many are there. If, as it seems, there’s almost an infinite number of them, how are they selected (some finer level of granularity in scene labeling – romance but only flirting …).

No, it’s not …

But to other scientists, there appears to be more than just pattern recognition(-like) algorithms and lookup and act algorithms, going on inside our brains.

For example, once I interpret a scene surrounding me as in danger, romance, school, etc.,  I believe I start to generate possible action lists which I could take in this domain, and then somehow I select the one to do which makes the most sense in this situation or rather gets me closer to my current goal (whatever that is) in this situation.

This is beyond just procedural logic and involves some sort of memory system, action generative system, goal generative/recollection system, weighing of possible action scripts, etc.

And what to make of the brain’s seemingly infinite capability to explain itself…

Baby intelligence

Most babies understand their parents language(s) and learn to crawl within months after birth. But they haven’t listened to thousands of hours of people talking or crawled thousands of miles.  And yet, deep learning requires even more learning sets in order to label language properly or  learning how to crawl on four appendages. And of course, understanding language and speaking it are two different capabilities. Ditto for crawling and walking.

How does a baby learn to recognize these patterns without TB of data and millions of reinforcements (“Smile for Mommy”, say “Daddy”). And what to make of the, seemingly impossible to contain wanderlust, of any baby given free reign of an area.

These questions are just scratching the surface in what it really means to engineer human intelligence.

~~~~

MIT IQ is one attempt to try to answer the question that: assuming we understand how to pattern recognition can be made to work well on today’s computers what else do we need to do to build a more general purpose intelligence.

There are obvious ethical questions on whether we want to engineer a human level of intelligence (see my Existential risks… post). Our main concern is what it does (to humanity) once we achieve it.

But assuming we can somehow contain it for the benefit of humanity, we ought to take another look at just what it entails.

 

Photo Credits:  Tech trends for 2017: more AI …., the Next Silicon Valley website. 

HAL from 2001 a Space Odyssey 

Design software test labeling… 

Exploration in toddlers…, Science Daily website