Victims of bigots, fascists and misogynists on social media can (and many have) recounted the emotional toll of engaging with them.
How would you like to reduce your emotional toll and consume minutes if not hours of their time?
I thought you might be interested. 😉
Follow the link to DeepPavlov. (Ignore the irony of the name considering the use case I’m outlining.)
From the webpage:
An open source library for building end-to-end dialog systems and training chatbots.
We are in a really early Alfa release. You have to be ready for hard adventures.
An open-source conversational AI library, built on TensorFlow and Keras, and designed for
NLP and dialog systems research
implementation and evaluation of complex conversational systems
Our goal is to provide researchers with:
a framework for implementing and testing their own dialog models with subsequent sharing of that models
set of predefined NLP models / dialog system components (ML/DL/Rule-based) and pipeline templates
benchmarking environment for conversational models and systematized access to relevant datasets
and AI-application developers with:
framework for building conversational software
tools for application integration with adjacent infrastructure (messengers, helpdesk software etc.)
… (emphasis in the original)
Only one component for a social media engagement bot to debate bigots, fascists and misogynists but a very important one. A trained AI can take the emotional strain off of victims/users and at least in some cases, inflict that toll on your opponents.
For OpSec reasons, don’t announce the accounts used by such an AI backed system.
PS: AI ethics debaters. This use of an AI isn’t a meaningful interchange of ideas online. My goals are: reduce the emotional toll on victims, waste the time of their attackers. Disclosing you aren’t hurting someone on the other side (the bot) isn’t a requirement in my view.
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In 2015, Google announced it would release its internal tool for developing artificial intelligence algorithms, TensorFlow, a move that would change the tone of how AI research and development would be conducted around the world. The means to build technology that could have an impact as profound as electricity, to borrow phrasing from Google’s CEO, would be open, accessible, and free to use. The barrier to entry was lowered from a Ph.D to a laptop.
But that also meant TensorFlow’s undeniable power was now out of Google’s control. For a little over two years, academia and Silicon Valley were still the ones making the biggest splashes with the software, but now that equation is changing. The catalyst is deepfakes, an anonymous Reddit user who built around AI software that automatically stitches any image of a face (nearly) seamlessly into a video. And you can probably imagine where this is going: As first reported by Motherboard, the software was being used to put anyone’s face, such as a famous woman or friend on Facebook, on the bodies of porn actresses.
After the first Motherboard story, the user created their own subreddit, which amassed more than 91,000 subscribers. Another Reddit user called deepfakeapp has also released a tool called FakeApp, which allows anyone to download the AI software and use it themselves, given the correct hardware. As of today, Reddit has banned the community, saying it violated the website’s policy on involuntary pornography.
According to FakeApp’s user guide, the software is built on top of TensorFlow. Google employees have pioneered similar work using TensorFlow with slightly different setups and subject matter, training algorithms to generate images from scratch. And there are plenty of potentially fun (if not inane) uses for deepfakes, like putting Nicolas Cage in a bunch of different movies. But let’s be real: 91,000 people were subscribed to deepfakes’ subreddit for the porn.
While much good has come from TensorFlow being open source, like potential cancer detection algorithms, FakeApp represents the dark side of open source. Google (and Microsoft and Amazon and Facebook) have loosed immense technological power on the world with absolutely no recourse. Anyone can download AI software and use it for anything they have the data to create. That means everything from faking political speeches (with help from the cadre of available voice-imitating AI) to generating fake revenge porn. All digital media is a series of ones and zeroes, and artificial intelligence is proving itself proficient at artfully arranging them to generate things that never happened.
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You can imagine the rest or read the rest of Gershgon’s (deep voice): “dark side of open source.”
While you do, remember that Gershgon would have made the same claims about:
Telephones
Photography
Cable television
Internet
etc.
The simplest rejoinder is that the world did not create porn with AI. A tiny subset of the world signed up to see porn created by an even smaller subset of the world.
The next simplest rejoinder is the realization that Gershgon wants a system that dictates ethics to users of open source software. Gershgon should empower an agency to enforce ethics on journalists and check back in a couple of years to report on their experience.
I’m willing to be ahead of time it won’t be a happy report.
Bottom line: Leave the ethics of open source software to the people using such software. May not always have a happy outcome but will always be better than the alternatives.
It has always been the mission of R developers to connect R to the “good stuff”. As John Chambers puts it in his book Extending R:
One of the attractions of R has always been the ability to compute an interesting result quickly. A key motivation for the original S remains as important now: to give easy access to the best computations for understanding data.
From the day it was announced a little over two years ago, it was clear that Google’s TensorFlow platform for Deep Learning is good stuff. This September (see announcment), J.J. Allaire, François Chollet, and the other authors of the keras package delivered on R’s “easy access to the best” mission in a big way. Data scientists can now build very sophisticated Deep Learning models from an R session while maintaining the flow that R users expect. The strategy that made this happen seems to have been straightforward. But, the smooth experience of using the Keras API indicates inspired programming all the way along the chain from TensorFlow to R.
…
The Redditor deepfakes, of AI-Assisted Fake Porn fame mentions Keras as one of his tools. Is that an endorsement?
Rickert’s post is a quick start to Keras and Tensorflow but he does mention:
the MEAP from the forthcoming Manning Book, Deep Learning with R by François Chollet, the creator of Keras, and J.J. Allaire.
I’ve had good luck with Manning books in general so am looking forward to this one as well.
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Dialogue is lacking but this chatbot could be trained to appear to government forces as a live “jihadist” following and conversing with other “jihadists.” Who may themselves be chatbots.
Unlike the expense of pilots for a fleet of drones, a single user could “pilot” a group of chatbots, creating an over-sized impression in cyberspace. The deeper the modeling of human jihadists, the harder it will be to distinguish virtual jihadists.
I say “jihadists” for headline effect. You could create interacting chatbots for right/left wing hate groups, gun owners, churches, etc., in short, anyone seeking to dilute surveillance.
(Unlike the ACLU or EFF, I don’t concede there are any legitimate reasons for government surveillance. The dangers of government surveillance far exceed any possible crime it could prevent. Government surveillance is the question. The answer is NO.)
Tensorflow is a powerful open-source software library for machine learning developed by researchers at Google Brain. It has many pre-built functions to ease the task of building different neural networks. Tensorflow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.
This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.
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This is an experimental tensorflow implementation of synthesizing images from captions using Skip Thought Vectors. The images are synthesized using the GAN-CLS Algorithm from the paper Generative Adversarial Text-to-Image Synthesis. This implementation is built on top of the excellent DCGAN in Tensorflow. The following is the model architecture. The blue bars represent the Skip Thought Vectors for the captions.
Six lines of Python is all it takes to write your first machine learning program! In this episode, we’ll briefly introduce what machine learning is and why it’s important. Then, we’ll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.
Hundreds of different big data and analytics products and services fight for your attention as it’s one of the most fertile areas of innovation in our industry. And it’s no wonder; the most amazing consumer experiences are driven by insights derived from information. This is an area where Google Cloud Platform has invested almost two decades of engineering, and today at GCP NEXT we’re announcing some of the latest results of that work. This next round of innovation builds on our portfolio of data management and analytics capabilities by adding new products and services in multiples key areas:
Machine Learning:
We’re on a journey to create applications that can see, hear and understand the world around them. Today we’ve taken a major stride forward with the announcement of a new product family: Cloud Machine Learning. Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications. It provides access to the same technologies that power Google Now, Google Photos and voice recognition in Google Search as easy to use REST APIs. It enables you to build powerful Machine Learning models on your data using the open-source TensorFlow machine learning library:
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Big Data and Analytics:
Doing big data the cloud way means being more productive when building applications, with faster and better insights, without having to worry about the underlying infrastructure. To further this mission, we recently announced the general availability of Cloud Dataproc, our managed Apache Hadoop and Apache Spark service, and we’re adding new services and capabilities today:
…
Open Source:
Our Cloud Machine Learning offering leverages Google’s cutting edge machine learning and data processing technologies, some of which we’ve recently open sourced:
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What, if anything, do you see as a serious omission in this version of the Google-verse?
Suggestions?
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Just a couple of years ago, you couldn’t talk to the Google app through the noise of a city sidewalk, or read a sign in Russian using Google Translate, or instantly find pictures of your Labradoodle in Google Photos. Our apps just weren’t smart enough. But in a short amount of time they’ve gotten much, much smarter. Now, thanks to machine learning, you can do all those things pretty easily, and a lot more. But even with all the progress we’ve made with machine learning, it could still work much better.
So we’ve built an entirely new machine learning system, which we call “TensorFlow.” TensorFlow is faster, smarter, and more flexible than our old system, so it can be adapted much more easily to new products and research. It’s a highly scalable machine learning system—it can run on a single smartphone or across thousands of computers in datacenters. We use TensorFlow for everything from speech recognition in the Google app, to Smart Reply in Inbox, to search in Google Photos. It allows us to build and train neural nets up to five times faster than our first-generation system, so we can use it to improve our products much more quickly.

We’ve seen firsthand what TensorFlow can do, and we think it could make an even bigger impact outside Google. So today we’re also open-sourcing TensorFlow. We hope this will let the machine learning community—everyone from academic researchers, to engineers, to hobbyists—exchange ideas much more quickly, through working code rather than just research papers. And that, in turn, will accelerate research on machine learning, in the end making technology work better for everyone. Bonus: TensorFlow is for more than just machine learning. It may be useful wherever researchers are trying to make sense of very complex data—everything from protein folding to crunching astronomy data.
Machine learning is still in its infancy—computers today still can’t do what a 4-year-old can do effortlessly, like knowing the name of a dinosaur after seeing only a couple examples, or understanding that “I saw the Grand Canyon flying to Chicago” doesn’t mean the canyon is hurtling over the city. We have a lot of work ahead of us. But with TensorFlow we’ve got a good start, and we can all be in it together.
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
TensorFlow [1] is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
This will show up in the GitHub search link but I wanted to call it out for the high quality use of benchmarks and the discussion that follows. (I am sure there are other high quality discussions but I haven’t seen and therefore have not captured them.)
7. TensorFlow: Second Generation Deep Learning System by Jeff Dean. (approximately 45 minutes)
In November 2007, Google laid the groundwork to dominate the mobile market by releasing Android, an open source operating system for phones. Eight years later to the month, Android has an an 80 percent market share, and Google is using the same trick—this time with artificial intelligence.
Today Google is announcing TensorFlow, its open source platform for machine learning, giving anyone a computer and internet connection (and casual background in deep learning algorithms) access to one of the most powerful machine learning platforms ever created. More than 50 Google products have adopted TensorFlow to harness deep learning (machine learning using deep neural networks) as a tool, from identifying you and your friends in the Photos app to refining its core search engine. Google has become a machine learning company. Now they’re taking what makes their services special, and giving it to the world. (emphasis in original)
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TensorFlow came out today, and like the rest of the ML world, I buried myself with it. I have never been more excited about a new open source code. There are actionable tutorials etc. on the home page that’s worth checking out, but I wanted to know if this was yet another computational graph framework — we already have Theano and CGT (CGT is fast; Theano is most popular).
In the time it took to collect these resources on TensorFlow, I am certain more resources have appeared but hopefully these will continue to be fundamental resources for everyone interested in TensorFlow.
