Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

October 5, 2019

Automatic News Comment Generation

Filed under: Artificial Intelligence,Natural Language Processing,Social Media — Patrick Durusau @ 3:09 pm

Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation by Ze Yang, Can Xu, Wei Wu, Zhoujun Li.

Abstract: Automatic news comment generation is beneficial for real applications but has not attracted enough attention from the research community. In this paper, we propose a “read-attend-comment” procedure for news comment generation and formalize the procedure with a reading network and a generation network. The reading network comprehends a news article and distills some important points from it, then the generation network creates a comment by attending to the extracted discrete points and the news title. We optimize the model in an end-to-end manner by maximizing a variational lower bound of the true objective using the back-propagation algorithm. Experimental results on two public datasets indicate that our model can significantly outperform existing methods in terms of both automatic evaluation and human judgment.

A tweet said this was a “dangerous” paper, so I had to follow the link.

This research could be abused, but how many news comments have you read lately? The comments made by this approach would have to degrade a lot to approach the average human comment.

Anyone who is interested in abusive and/or inane comments, can scrape comments on Facebook or Twitter, set up a cron file and pop off the next comment for posting. Several orders of magnitude less effort that the approach of this paper.

Wondering, would coherence of comments over a large number of articles be an indicator that a bot is involved?

January 30, 2019

‘Diversity in Faces’ Dataset – Are You Being Treated Unfairly? As A Matter of Fact, Yes.

IBM Research Releases ‘Diversity in Faces’ Dataset to Advance Study of Fairness in Facial Recognition Systems by John R. Smith.

From the post:

Have you ever been treated unfairly? How did it make you feel? Probably not too good. Most people generally agree that a fairer world is a better world, and our AI researchers couldn’t agree more. That’s why we are harnessing the power of science to create AI systems that are more fair and accurate.

Many of our recent advances in AI have produced remarkable capabilities for computers to accomplish increasingly sophisticated and important tasks, like translating speech across languages to bridge communications across cultures, improving complex interactions between people and machines, and automatically recognizing contents of video to assist in safety applications.

Much of the power of AI today comes from the use of data-driven deep learning to train increasingly accurate models by using growing amounts of data. However, the strength of these techniques can also be a weakness. The AI systems learn what they’re taught, and if they are not taught with robust and diverse datasets, accuracy and fairness could be at risk. For that reason, IBM, along with AI developers and the research community, need to be thoughtful about what data we use for training. IBM remains committed to developing AI systems to make the world more fair.

To request access to the DiF dataset, visit our webpage. To learn more about DiF, read our paper, “Diversity in Faces.”

Nice of Smith to we have “ever been treated unfairly?”

Because if not before, certainly now with the limitations on access to the “Diversity in Faces” Dataset.

Step 1

Review the DiF Terms of Use and Privacy Notice.

DOCUMENTS

Terms of use

DiF Privacy Notice

Step 2

Download and complete the questionnaire.

DOCUMENT

DiF Questionnaire (PDF)

Step 3

Email completed questionnaire to IBM Research.

APPLICATION CONTACT

Michele Merler | mimerler@us.ibm.com

Step 4

Further instructions will be provided from IBM Research via email once application is approved.

Check out Terms of Use, 3. IP Rights, 3.2 #5:


Licensee grants to IBM a non-exclusive, irrevocable, unrestricted, worldwide and paid-up right, license and sublicense to: a) include in any product or service any idea, know-how, feedback, concept, technique, invention, discovery or improvement, whether or not patentable, that Licensee provides to IBM, b) use, manufacture and market any such product or service, and c) allow others to do any of the foregoing. (emphasis added)

Treated unfairly? There’s the grasping claw of IBM so familiar across the decades. I suppose we should be thankful it doesn’t include any ideas, concepts, patents, etc., that you develop while in possession of the dataset. From that perspective, the terms of use are downright liberal.

November 21, 2018

Stanford AI Lab (SAIL) Blog (Attn: All Hats)

Filed under: Artificial Intelligence,Hacking,Machine Learning — Patrick Durusau @ 3:45 pm

Stanford AI Lab (SAIL) Blog

From the Hello World post:

We are excited to launch the Stanford AI Lab (SAIL) Blog, where we hope to share our research, high-level discussions on AI and machine learning, and updates with the general public. SAIL has 18 faculty and 16 affiliated faculty, with hundreds of students working in diverse fields that span natural language processing, robotics, computer vision, bioinformatics, and more. Our vision is to make that work accessible to an audience beyond the academic and technical community.

Whether you are a White, Black, Grey, or Customer, hat, start watching the Stanford AI Lab (SAIL) Blog.

Like a Customer hat, AI (artificial intelligence) knows no preset side, only its purpose as set by others.

If that sounds harsh, remember that it has been preset sides that force otherwise decent people (in some cases) to support the starvation of millions in Yemen or the murder of children in Palestine.

Or to say it differently, laws are only advisory opinions on the morality of any given act.

November 15, 2018

Fake ‘Master’ Fingerprints

Filed under: Artificial Intelligence,Security — Patrick Durusau @ 3:20 pm

DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolution by Philip Bontrager et al.

Abstract:

Recent research has demonstrated the vulnerability of fingerprint recognition systems to dictionary attacks based on MasterPrints. MasterPrints are real or synthetic fingerprints that can fortuitously match with a large number of fingerprints thereby undermining the security afforded by fingerprint systems. Previous work by Roy et al. generated synthetic MasterPrints at the feature-level. In this work we generate complete image-level MasterPrints known as DeepMasterPrints, whose attack accuracy is found to be much superior than that of previous methods. The proposed method, referred to as Latent Variable Evolution, is based on training a Generative Adversarial Network on a set of real fingerprint images. Stochastic search in the form of the Covariance Matrix Adaptation Evolution Strategy is then used to search for latent input variables to the generator network that can maximize the number of impostor matches as assessed by a fingerprint recognizer. Experiments convey the efficacy of the proposed method in generating DeepMasterPrints. The underlying method is likely to have broad applications in fingerprint security as well as fingerprint synthesis.

One review of this paper concludes:


At the highest level of security, the researchers note that the master print is “not very good” at spoofing the sensor—the master prints only fooled the sensor less than 1.2 percent of the time.

While this research doesn’t spell the end of fingerprint ID systems, the researchers said it will require the designers of these systems to rethink the tradeoff between convenience and security in the future.

But fingerprint ID systems are only one use case for DeepMasterPrints.

The generated fingerprints, for all intents and purposes, appear to be human fingerprints. If used to intentionally “leave” fingerprints for investigators to discover, there is no immediate “tell” these are artificial fingerprints.

If your goal is to delay or divert authorities for a few hours or even days with “fake” fingerprints, then DeepMasterPrints may be quite useful.

The test for any security or counter-security measure isn’t working forever or without fail but only for as long as needful. (For example, encryption that defeats decryption until after an attack has served its purpose. It need not do more than that.)

The Unlearned Lesson Of Amazon’s automated hiring tool

Filed under: Artificial Intelligence,Diversity,Machine Learning — Patrick Durusau @ 1:57 pm

Gender, Race and Power: Outlining a New AI Research Agenda.

From the post:


AI systems — which Google and others are rapidly developing and deploying in sensitive social and political domains — can mirror, amplify, and obscure the very issues of inequality and discrimination that Google workers are protesting against. Over the past year, researchers and journalists have highlighted numerous examples where AI systems exhibited biases, including on the basis of race, class, gender, and sexuality.

We saw a dramatic example of these problems in recent news of Amazon’s automated hiring tool. In order to “learn” to differentiate between “good” and “bad” job candidates, it was trained on a massive corpus of of (sic) data documenting the company’s past hiring decisions. The result was, perhaps unsurprisingly, a hiring tool that discriminated against women, even demoting CVs that contained the word ‘women’ or ‘women’s’. Amazon engineers tried to fix the problem, adjusting the algorithm in the attempt to mitigate its biased preferences, but ultimately scrapped the project, concluding that it was unsalvageable.

From the Amazon automated hiring tool and other examples, the AI Now Institute draws this conclusion:


It’s time for research on gender and race in AI to move beyond considering whether AI systems meet narrow technical definitions of ‘fairness.’ We need to ask deeper, more complex questions: Who is in the room when these technologies are created, and which assumptions and worldviews are embedded in this process? How does our identity shape our experiences of AI systems? In what ways do these systems formalize, classify, and amplify rigid and problematic definitions of gender and race? We share some examples of important studies that tackle these questions below — and we have new research publications coming out to contribute to this literature.

AI New misses the most obvious lesson from the Amazon automated hiring tool experience:

In the face of an AI algorithm that discriminates, we don’t know how to cure its discrimination.

Predicting or curing discrimination from an algorithm alone lies beyond our ken.

The creation of reference datasets for testing AI algorithms, however, enables testing and comparison of algorithms. With concrete results that could be used to reduce discrimination in fact.

Actual hiring and other databases are private for good reasons but wholly artificial reference databases would have no such concerns.

Since we don’t understand discrimination in humans, I caution against a quixotic search for its causes in algorithms. Keep or discard algorithms based on their discrimination in practice. Something we have been shown to be capable of spotting.

PS: Not all discrimination is unethical or immoral. If a position requires a law degree, it is “discrimination” to eliminate all applicants without one, but that’s allowable discrimination.

October 30, 2018

My favorite AI newsletters…

Filed under: Artificial Intelligence,Machine Learning — Patrick Durusau @ 7:10 pm

My favorite AI newsletters, run by people working in the field by Rosie Campbell.

Campbell lists her top five (5) AI newsletters. That’s a manageable number, at least if I discontinue other newsletters that fill my inbox.

Not that my current newsletter subscriptions aren’t valuable, but I’m not the web archive for those mailings and if I lack the time to read them, what’s the point?

It’s not Spring so I need to do some Fall cleaning of my newsletter subscriptions.

Any additions to those suggested by Campbell?

June 7, 2018

Are AI Psychopaths Cost Effective?

Filed under: Artificial Intelligence,Machine Learning,Reddit — Patrick Durusau @ 3:34 pm

Norman, World’s first psychopath AI

From the webpage:


We present you Norman, world’s first psychopath AI. Norman is born from the fact that the data that is used to teach a machine learning algorithm can significantly influence its behavior. So when people talk about AI algorithms being biased and unfair, the culprit is often not the algorithm itself, but the biased data that was fed to it. The same method can see very different things in an image, even sick things, if trained on the wrong (or, the right!) data set. Norman suffered from extended exposure to the darkest corners of Reddit, and represents a case study on the dangers of Artificial Intelligence gone wrong when biased data is used in machine learning algorithms.

Norman is an AI that is trained to perform image captioning; a popular deep learning method of generating a textual description of an image. We trained Norman on image captions from an infamous subreddit (the name is redacted due to its graphic content) that is dedicated to document and observe the disturbing reality of death. Then, we compared Norman’s responses with a standard image captioning neural network (trained on MSCOCO dataset) on Rorschach inkblots; a test that is used to detect underlying thought disorders.

Note: Due to the ethical concerns, we only introduced bias in terms of image captions from the subreddit which are later matched with randomly generated inkblots (therefore, no image of a real person dying was utilized in this experiment).

I have written to the authors to ask for more details about their training process, the “…which are later matched with randomly generated inkblots…” seeming especially opaque to me.

While waiting for that answer, we should ask whether training psychopath AIs is cost effective?

Compare the limited MIT-Norman with the PeopleFuckingDying Reddit with 690,761 readers.

That’s a single Reddit. Many Reddits count psychopaths among their members. To say nothing of Twitter trolls and other social media where psychopaths gather.

New psychopaths appear on social media every day, without the ethics-limited training provided to MIT-Norman. Is this really a cost effective approach to developing psychopaths?

The MIT-Norman project has great graphics but Hitchcock demonstrated over and over again, simple scenes can be packed with heart pounding terror.

May 3, 2018

Not All AI Uses Are Serious: Generating Pusheen with AI

Filed under: Artificial Intelligence,Humor,Machine Learning — Patrick Durusau @ 8:22 pm

Generating Pusheen with AI by Zack Nado.

From the post:

I made a machine learning program that generates new (sometimes novel!) Pusheen pictures!

(I don’t claim any ownership of Pusheen or anything Pusheen related, which is trademarked by Pusheen Corp.) It’s no secret that my girlfriend and I both are huge fans of Pusheen the cat, which many people know from the cute Facebook sticker sets. So, for her birthday I set out to try to create a machine learning program to create cat pictures for her to enjoy! To set some expectations for this post, I only did this for a fun project and didn’t really know what I expected to get out of it given that in all the data available there are really only a handful of unique poses and scenes. Also, you really only need a roughly oval shaped gray blob with eyes to look like Pusheen, so the bar wasn’t that high. That being said I am happy with the outcome and think it produces interesting and (usually) realistic poses and positions.

Given the revolving door at the White House in Washington, DC, you will never be sort of material for generating facial or full body images for posting on social media.

None of them will be as attractive as Pusheen the cat, but, consider your starting point.

Enjoy!

PS: You will be laughing too much to notice you are learning AI skills. Does that suggest a possible approach to an introduction to AI?

April 26, 2018

Ethics and Law in AI ML

Filed under: Artificial Intelligence,Ethics,Machine Learning — Patrick Durusau @ 3:50 pm

Ethics and Law in AI ML

Data Science Renee, author of Women in Data Science (Twitter list with over 1400 members), has created a Twitter list focused on ethics and law in AI/ML.

When discussions of ethics for data scientists come up, remember that many players, corporations, governments, military organizations, spy agencies, abide by no code of ethics or laws. Adjust your ethics expectations accordingly.

February 26, 2018

Forbes Vouches For Public Data Sources

Filed under: Artificial Intelligence,BigData — Patrick Durusau @ 8:48 pm

For Forbes readers, a demonstration with one of Bernard Marr’s Big Data And AI: 30 Amazing (And Free) Public Data Sources For 2018 (Forbes, Feb. 26, 2018), adds a ring of authenticity to your data. Marr and by extension, Forbes has vouched for these data sets.

Beats the hell out of opera, medieval boys choirs, or irises for your demonstration. 😉

These data sets show up everywhere but a reprint from Forbes to leave with your (hopefully) future client, sets your data set from others.

Tip: As interesting as it is, I’d skip the CERN Open Data unless you are presenting to physicists. Yes? Hint: Pick something relevant to your audience.

February 22, 2018

Deep Voice – The Empire Grows Steadily Less Secure

Filed under: Artificial Intelligence,Cybersecurity — Patrick Durusau @ 5:17 pm

Baidu AI Can Clone Your Voice in Seconds

From the post:

Baidu’s research arm announced yesterday that its 2017 text-to-speech (TTS) system Deep Voice has learned how to imitate a person’s voice using a mere three seconds of voice sample data.

The technique, known as voice cloning, could be used to personalize virtual assistants such as Apple’s Siri, Google Assistant, Amazon Alexa; and Baidu’s Mandarin virtual assistant platform DuerOS, which supports 50 million devices in China with human-machine conversational interfaces.

In healthcare, voice cloning has helped patients who lost their voices by building a duplicate. Voice cloning may even find traction in the entertainment industry and in social media as a tool for satirists.

Baidu researchers implemented two approaches: speaker adaption and speaker encoding. Both deliver good performance with minimal audio input data, and can be integrated into a multi-speaker generative model in the Deep Voice system with speaker embeddings without degrading quality.

See the post for links to three-second voice clips and other details.

Concerns?


The recent breakthroughs in synthesizing human voices have also raised concerns. AI could potentially downgrade voice identity in real life or with security systems. For example voice technology could be used maliciously against a public figure by creating false statements in their voice. A BBC reporter’s test with his twin brother also demonstrated the capacity for voice mimicking to fool voiceprint security systems.

That’s a concern? 😉

I think cloned voices of battlefield military commanders, cloned politician voices with sex partners, or “known” voices badgering help desk staff into giving up utility plant or other access, those are “concerns.” Or “encouragements,” depending on your interests in such systems.

February 10, 2018

The Complexity of Neurons are Beyond Our Current Imagination

Filed under: Artificial Intelligence — Patrick Durusau @ 9:28 pm

The Complexity of Neurons are Beyond Our Current Imagination by Carlos E. Perez.

From the post:

One of the biggest misconceptions around is the idea that Deep Learning or Artificial Neural Networks (ANN) mimic biological neurons. At best, ANN mimic a cartoonish version of a 1957 model of a neuron. Neurons in Deep Learning are essentially mathematical functions that perform a similarity function of its inputs against internal weights. The closer a match is made, the more likely an action is performed (i.e. not sending a signal to zero). There are exceptions to this model (see: Autoregressive networks) however it is general enough to include the perceptron, convolution networks and RNNs.

Jeff Hawkins of Numenta has always lamented that a more biologically-inspired approach is needed. So, in his research on building cognitive machinery, he has architected system that more mimic the structure of the neo-cortex. Numenta’s model of a neuron is considerably more elaborate than the Deep Learning model of a neuron:

I rather like the line “ANN mimic a cartoonish version of a 1957 model of a neuron.”

You need not worry about the MIT Intelligence Quest replicating neurons anytime soon.

In part because no one really knows how neurons work or how much more we need to learn to replicate them.

The AI crowd could train a neural network to recognize people and to fire weapons at them. Qualifies as destruction of humanity by an AI but if we are really that stupid, perhaps its time to make space for others.

MIT Intelligence Quest

Filed under: Artificial Intelligence,Machine Learning — Patrick Durusau @ 8:36 pm

MIT Intelligence Quest

From the webpage:

The MIT Intelligence Quest will advance the science and engineering of both human and machine intelligence. Launched on February 1, 2018, MIT IQ seeks to discover the foundations of human intelligence and drive the development of technological tools that can positively influence virtually every aspect of society.

The Institute’s culture of collaboration will encourage life scientists, computer scientists, social scientists, and engineers to join forces to investigate the societal implications of their work as they pursue hard problems lying beyond the current horizon of intelligence research. By uniting diverse fields and capitalizing on what they can teach each other, we seek to answer the deepest questions about intelligence.

We are setting out to answer two big questions: 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?

Drawing on MIT’s deep strengths and signature values, culture, and history, MIT IQ promises to make important contributions to understanding the nature of intelligence, and to harnessing it to make a better world.

The most refreshing aspect of the MIT Intelligence Quest page is that it ends a contact form.

That’s right, a contact form.

Unlike the ill-fated EU brain project that had pre-chosen approaches and had a roadmap for replicating a human brain. Are they still consuming funds with meetings, hotel rooms, etc.?

You know my mis-givings about creating intelligence in the absence of understanding our own.

On the other hand, mimicking how human intelligence works in bounded situations is a far more tractable problem.

Not too tractable but tractable enough to yield useful results.

February 8, 2018

Porn, AI and Open Source Ethics

Filed under: Artificial Intelligence,Deep Learning,Open Source,Porn,TensorFlow — Patrick Durusau @ 4:18 pm

Google Gave the World Powerful AI Tools, and the World Made Porn With Them by Dave Gershgorn.

From the post:

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.

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:

  1. Telephones
  2. Photography
  3. Cable television
  4. Internet
  5. 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.

January 24, 2018

‘Learning to Rank’ (No Unique Feature Name Fail – Update)

Filed under: Artificial Intelligence,ElasticSearch,Ranking,Searching — Patrick Durusau @ 8:02 pm

Elasticsearch ‘Learning to Rank’ Released, Bringing Open Source AI to Search Teams

From the post:

Search experts at OpenSource Connections, the Wikimedia Foundation, and Snagajob, deliver open source cognitive search capabilities to the Elasticsearch community. The open source Learning to Rank plugin allows organizations to control search relevance ranking with machine learning. The plugin is currently delivering search results at Wikipedia and Snagajob, providing significant search quality improvements over legacy solutions.

Learning to Rank lets organizations:

  • Directly optimize sales, conversions and user satisfaction in search
  • Personalize search for users
  • Drive deeper insights from a knowledge base
  • Customize ranking down for complex nuance
  • Avoid the sticker shock & lock-in of a proprietary "cognitive search" product

“Our mission is to empower search teams. This plugin gives teams deep control of ranking, allowing machine learning models to be directly deployed to the search engine for relevance ranking” said Doug Turnbull, author of Relevant Search and CTO, OpenSource Connections.

I need to work through all the documentation and examples but:

Feature Names are Unique

Because some model training libraries refer to features by name, Elasticsearch LTR enforces unique names for each features. In the example above, we could not add a new user_rating feature without creating an error.

is a warning of what you (and I) are likely to find.

Really? Someone involved in the design thought globally unique feature names was a good idea? Or at a minimum didn’t realize it is a very bad idea?

Scope anyone? Either in the programming or topic map sense?

Despite the unique feature name fail, I’m sure ‘Learning to Rank’ will be useful. But not as useful as it could have been.

Doug Turnbull (https://twitter.com/softwaredoug) advises that features are scoped by feature stores, so the correct prose would read: “…LTR enforces unique names for each feature within a feature store.”

No fail, just bad writing.

January 18, 2018

For Some Definition of “Read” and “Answer” – MS Clickbait

Filed under: Artificial Intelligence,Machine Learning,Microsoft — Patrick Durusau @ 11:37 am

Microsoft creates AI that can read a document and answer questions about it as well as a person by Allison Linn.

From the post:

It’s a major milestone in the push to have search engines such as Bing and intelligent assistants such as Cortana interact with people and provide information in more natural ways, much like people communicate with each other.

A team at Microsoft Research Asia reached the human parity milestone using the Stanford Question Answering Dataset, known among researchers as SQuAD. It’s a machine reading comprehension dataset that is made up of questions about a set of Wikipedia articles.

According to the SQuAD leaderboard, on Jan. 3, Microsoft submitted a model that reached the score of 82.650 on the exact match portion. The human performance on the same set of questions and answers is 82.304. On Jan. 5, researchers with the Chinese e-commerce company Alibaba submitted a score of 82.440, also about the same as a human.

With machine reading comprehension, researchers say computers also would be able to quickly parse through information found in books and documents and provide people with the information they need most in an easily understandable way.

That would let drivers more easily find the answer they need in a dense car manual, saving time and effort in tense or difficult situations.

These tools also could let doctors, lawyers and other experts more quickly get through the drudgery of things like reading through large documents for specific medical findings or rarified legal precedent. The technology would augment their work and leave them with more time to apply the knowledge to focus on treating patients or formulating legal opinions.

Wait, wait! If you read the details about SQuAD, you realize how far Microsoft (or anyone else) is from “…reading through large documents for specific medical findings or rarified legal precedent….”

What is the SQuAD test?

Stanford Question Answering Dataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets.

Not to take anything away from Microsoft Research Asia or the creators of SQuAD, but “…the answer to every question is a segment of text, or span, from the corresponding reading passage.” is a long way from synthesizing an answer from a long legal document.

The first hurdle is asking a question that can be scored against every “…segment of text, or span…” such that a relevant snippet of text can be found.

The second hurdle is the process of scoring snippets of text in order to retrieve the most useful one. That’s a mechanical process, not one that depends on the semantics of the underlying question or text.

There are other hurdles but those two suffice to show there is no “reading and answering questions” in the same sense we would apply to any human reader.

Click-bait headlines don’t serve the cause of advocating more AI research. On the contrary, a close reading of alleged progress leads to disappointment.

January 12, 2018

Secrets to Searching for Video Footage (AI Assistance In Your Future?)

Filed under: Artificial Intelligence,Deep Learning,Journalism,News,Reporting,Searching — Patrick Durusau @ 11:24 am

Secrets to Searching for Video Footage by Aric Toler.

From the post:

Much of Bellingcat’s work requires intense research into particular events, which includes finding every possible photograph, video and witness account that will help inform our analysis. Perhaps most notably, we exhaustively researched the events surrounding the shoot down of Malaysian Airlines Flight 17 (MH17) over eastern Ukraine.

The photographs and videos taken near the crash in eastern Ukraine were not particularly difficult to find, as they were widely publicized. However, locating over a dozen photographs and videos of the Russian convoy transporting the Buk anti-aircraft missile launcher that shot down MH17 three weeks before the tragedy was much harder, and required both intense investigation on social networks and some creative thinking.

Most of these videos were shared on Russian-language social networks and YouTube, and did not involve another type of video that is much more important today than it was in 2014 — live streaming. Bellingcat has also made an effort to compile all user-generated videos of the events in Charlottesville on August 12, 2017, providing a database of livestreamed videos on platforms like Periscope, Ustream and Facebook Live, along with footage uploaded after the protest onto platforms like Twitter and YouTube.

Verifying videos is important, as detailed in this Bellingcat guide, but first you have to find them. This guide will provide advice and some tips on how to gather as much video as possible on a particular event, whether it is videos from witnesses of a natural disaster or a terrorist attack. For most examples in this guide, we will assume that the event is a large protest or demonstration, but the same advice is applicable to other events.

I was amused by this description of Snapchat and Instagram:


Snapchat and Instagram are two very common sources for videos, but also two of the most difficult platforms to trawl for clips. Neither has an intuitive search interface that easily allows researchers to sort through and collect videos.

I’m certain that’s true but a trained AI could sort out videos obtained by overly broad requests. As I’m fond of pointing out, not 100% accuracy but you can’t get that with humans either.

Augment your searching with a tireless AI. For best results, add or consult a librarian as well.

PS: I have other concerns at the moment but a subset of the Bellingcat Charlottesville database would make a nice training basis for an AI, which could then be loosed on Instagram and other sources to discover more videos. The usual stumbling block for AI projects being human curated material, which Bellingcat has already supplied.

December 27, 2017

Tutorial on Deep Generative Models (slides and video)

Filed under: Artificial Intelligence,Deep Learning,Machine Learning — Patrick Durusau @ 10:55 am

Slides for: Tutorial on Deep Generative Models by Shakir Mohamed and Danilo Rezende.

Abstract:

This tutorial will be a review of recent advances in deep generative models. Generative models have a long history at UAI and recent methods have combined the generality of probabilistic reasoning with the scalability of deep learning to develop learning algorithms that have been applied to a wide variety of problems giving state-of-the-art results in image generation, text-to-speech synthesis, and image captioning, amongst many others. Advances in deep generative models are at the forefront of deep learning research because of the promise they offer for allowing data-efficient learning, and for model-based reinforcement learning. At the end of this tutorial, audience member will have a full understanding of the latest advances in generative modelling covering three of the active types of models: Markov models, latent variable models and implicit models, and how these models can be scaled to high dimensional data. The tutorial will expose many questions that remain in this area, and for which thereremains a great deal of opportunity from members of the UAI community.

Deep sledding on the latest developments in deep generative models (August 2017 presentation) that ends with a bibliography starting on slide 84 of 96.

Depending on how much time has passed since the tutorial, try searching the topics as they are covered, keep a bibliography of your finds and compare it to that of the authors.

December 24, 2017

Deep Learning for NLP, advancements and trends in 2017

Filed under: Artificial Intelligence,Deep Learning,Natural Language Processing — Patrick Durusau @ 5:57 pm

Deep Learning for NLP, advancements and trends in 2017 by Javier Couto.

If you didn’t get enough books as presents, Couto solves your reading shortage rather nicely:

Over the past few years, Deep Learning (DL) architectures and algorithms have made impressive advances in fields such as image recognition and speech processing.

Their application to Natural Language Processing (NLP) was less impressive at first, but has now proven to make significant contributions, yielding state-of-the-art results for some common NLP tasks. Named entity recognition (NER), part of speech (POS) tagging or sentiment analysis are some of the problems where neural network models have outperformed traditional approaches. The progress in machine translation is perhaps the most remarkable among all.

In this article I will go through some advancements for NLP in 2017 that rely on DL techniques. I do not pretend to be exhaustive: it would simply be impossible given the vast amount of scientific papers, frameworks and tools available. I just want to share with you some of the works that I liked the most this year. I think 2017 has been a great year for our field. The use of DL in NLP keeps widening, yielding amazing results in some cases, and all signs point to the fact that this trend will not stop.

After skimming this post, I suggest you make a fresh pot of coffee before starting to read and chase the references. It will take several days/pots to finish so it’s best to begin now.

Adversarial Learning Market Opportunity

The Pentagon’s New Artificial Intelligence Is Already Hunting Terrorists by Marcus Weisgerber.

From the post:

Earlier this month at an undisclosed location in the Middle East, computers using special algorithms helped intelligence analysts identify objects in a video feed from a small ScanEagle drone over the battlefield.

A few days into the trials, the computer identified objects – people, cars, types of building – correctly about 60 percent of the time. Just over a week on the job – and a handful of on-the-fly software updates later – the machine’s accuracy improved to around 80 percent. Next month, when its creators send the technology back to war with more software and hardware updates, they believe it will become even more accurate.

It’s an early win for a small team of just 12 people who started working on the project in April. Over the next year, they plan to expand the project to help automate the analysis of video feeds coming from large drones – and that’s just the beginning.

“What we’re setting the stage for is a future of human-machine teaming,” said Air Force Lt. Gen. John N.T. “Jack” Shanahan, director for defense intelligence for warfighter support, the Pentagon general who is overseeing the effort. Shanahan believes the concept will revolutionize the way the military fights.

So you will recognize Air Force Lt. Gen. John N.T. “Jack” Shanahan (Nvidia conference):

From the Nvidia conference:

Don’t change the culture. Unleash the culture.

That was the message one young officer gave Lt. General John “Jack” Shanahan — the Pentagon’s director for defense for warfighter support — who is hustling to put artificial intelligence and machine learning to work for the U.S. Defense Department.

Highlighting the growing role AI is playing in security, intelligence and defense, Shanahan spoke Wednesday during a keynote address about his team’s use of GPU-driven deep learning at our GPU Technology Conference in Washington.

Shanahan leads Project Maven, an effort launched in April to put machine learning and AI to work, starting with efforts to turn the countless hours of aerial video surveillance collected by the U.S. military into actionable intelligence.

There are at least two market opportunity for adversarial learning. The most obvious one is testing a competitor’s algorithm so it performs less well than yours on “… people, cars, types of building….”

The less obvious market requires US sales of AI-enabled weapon systems to its client states. Client states have an interest in verifying the quality of AI-enabled weapon systems, not to mention non-client states who will be interested in defeating such systems.

For any of those markets, weaponizing adversarial learning and developing a reputation for the same can’t start too soon. Is your anti-AI research department hiring?

December 12, 2017

IJCAI – Proceedings 1969-2016 Treasure Trove of AI Papers

Filed under: Artificial Intelligence,Machine Learning — Patrick Durusau @ 8:44 pm

IJCAI – Proceedings 1969-2016

From the about page:

International Joint Conferences on Artificial Intelligence is a non-profit corporation founded in California, in 1969 for scientific and educational purposes, including dissemination of information on Artificial Intelligence at conferences in which cutting-edge scientific results are presented and through dissemination of materials presented at these meetings in form of Proceedings, books, video recordings, and other educational materials. IJCAI conferences present premier international gatherings of AI researchers and practitioners. IJCAI conferences were held biennially in odd-numbered years since 1969. They are sponsored jointly by International Joint Conferences on Artificial Intelligence Organization (IJCAI), and the national AI societie(s) of the host nation(s).

While looking for a paper on automatic concept formulation for Jack Park, I found this archive of prior International Joint Conferences on Artificial Intelligence proceedings.

The latest proceedings, 2016, runs six volumes and approximately 4276 pages.

Enjoy!

AI-Assisted Fake Porn Is Here… [Endless Possibilities]

Filed under: Artificial Intelligence,Government,Politics,Porn — Patrick Durusau @ 5:06 pm

AI-Assisted Fake Porn Is Here and We’re All Fucked by Samantha Cole.

From the post:

Someone used an algorithm to paste the face of ‘Wonder Woman’ star Gal Gadot onto a porn video, and the implications are terrifying.

There’s a video of Gal Gadot having sex with her stepbrother on the internet. But it’s not really Gadot’s body, and it’s barely her own face. It’s an approximation, face-swapped to look like she’s performing in an existing incest-themed porn video.

The video was created with a machine learning algorithm, using easily accessible materials and open-source code that anyone with a working knowledge of deep learning algorithms could put together.

It’s not going to fool anyone who looks closely. Sometimes the face doesn’t track correctly and there’s an uncanny valley effect at play, but at a glance it seems believable. It’s especially striking considering that it’s allegedly the work of one person—a Redditor who goes by the name ‘deepfakes’—not a big special effects studio that can digitally recreate a young Princess Leia in Rogue One using CGI. Instead, deepfakes uses open-source machine learning tools like TensorFlow, which Google makes freely available to researchers, graduate students, and anyone with an interest in machine learning.
… (emphasis in original)

Posts and tweets lamenting “fake porn” abound but where others see terrifying implications, I see boundless potential.

Spoiler: The nay-sayers are on the wrong side of history – The Erotic Engine: How Pornography has Powered Mass Communication, from Gutenberg to Google Paperback by Patchen Barss.

or,


“The industry has convincingly demonstrated that consumers are willing to shop online and are willing to use credit cards to make purchases,” said Frederick Lane in “Obscene Profits: The Entrepreneurs of Pornography in the Cyber Age.” “In the process, the porn industry has served as a model for a variety of online sales mechanisms, including monthly site fees, the provision of extensive free material as a lure to site visitors, and the concept of upselling (selling related services to people once they have joined a site). In myriad ways, large and small, the porn industry has blazed a commercial path that other industries are hastening to follow.”
… (PORN: The Hidden Engine That Drives Innovation In Tech)

Enough time remains before the 2018 mid-terms for you to learn the technology used by ‘deepfakes’ to produce campaign imagery.

Paul Ryan, current Speaker of the House, isn’t going to (voluntarily) participate in a video where he steals food from children or steps on their hands as they grab for bread crusts in the street.

The same techniques that produce fake porn could be used to produce viral videos of those very scenes and more.

Some people, well-intentioned no doubt, will protest that isn’t informing the electorate and debating the issues. For them I have only one question: Why do you like losing so much?

I would wager one good viral video against 100,000 pages of position papers, unread by anyone other than the tiresome drones who produce them.

If you insist on total authenticity, then take Ryan film clips on why medical care can’t be provided for children and run it split-screen with close up death rattles of dying children. 100% truthful. See how that plays in your local TV market.

Follow ‘deepfakes’ on Reddit and start experimenting today!

December 6, 2017

AlphaZero: Mastering Unambiguous, Low-Dimensional Data

Filed under: Ambiguity,Artificial Intelligence,High Dimensionality,Machine Learning — Patrick Durusau @ 8:57 pm

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm by David Silver, et al.

Abstract:

The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved within 24 hours a superhuman level of play in the games of chess and shogi (Japanese chess) as well as Go, and convincingly defeated a world-champion program in each case.

The achievements by the AlphaZero team and their algorithm merit joyous celebration.

Joyous celebration recognizing AlphaZero masters unambiguous, low-dimensional data governed by deterministic rules that define the outcomes for any state, more quickly and completely than any human.

Chess, Shogi and Go appear complex to humans due to the large number of potential outcomes. But every outcome is the result of the application of deterministic rules to unambiguous, low-dimensional data. Something that AlphaZero excels at doing.

What hasn’t been shown is equivalent performance on ambiguous, high-dimensional data, governed by partially (if that) known rules, for a limited set of sub-cases. For those cases, well, you need a human being.

That’s not to take anything away from the AlphaZero team, but to recognize the strengths of AlphaZero and to avoid its application where it is weak.

November 25, 2017

23 Deep Learning Papers To Get You Started — Part 1 (Reading Slowly)

Filed under: Artificial Intelligence,Deep Learning,Machine Learning — Patrick Durusau @ 9:36 pm

23 Deep Learning Papers To Get You Started — Part 1 by Rupak Kr. Thakur.

Deep Learning has probably been the single-most discussed topic in the academia and industry in recent times. Today, it is no longer exclusive to an elite group of scientists. Its widespread applications warrants that people from all disciplines have an understanding of the underlying concepts, so as to be able to better apply these techniques in their field of work. As a result of which, MOOCs, certifications and bootcamps have flourished. People have generally preferred the hands-on learning experiences. However, there is a considerable population who still give in to the charm of learning the subject the traditional way — through research papers.

Reading research papers can be pretty time-consuming, especially since there are hordes of publications available nowadays, as Andrew Ng said at an AI conference, recently, along with encouraging people to use the existing research output to build truly transformative solutions across industries.

In this series of blog posts, I’ll try to condense the learnings from some really important papers into 15–20 min reads, without missing out on any key formulas or explanations. The blog posts are written, keeping in mind the people, who want to learn basic concepts and applications of deep learning, but can’t spend too much time scouring through the vast literature available. Each part of the blog will broadly cater to a theme and will introduce related key papers, along with suggesting some great papers for additional reading.

In the first part, we’ll explore papers related to CNNs — an important network architecture in deep learning. Let’s get started!

The start of what promises to be a great series on deep learning!

While the posts will extract the concepts and important points of the papers, I suggest you download the papers and map the summaries back to the papers themselves.

It will be good practice on reading original research, not to mention re-enforcing what you have learned from the posts.

In my reading, I will be looking for ways to influence deep learning towards one answer or another.

Whatever they may say about “facts” in public, no sane client asks for advice without an opinion on the range of acceptable answers.

Imagine you found ISIS content on Twitter has no measurable impact on ISIS recruiting. Would any intelligence agency would ask you for deep learning services again?

November 14, 2017

Hackers! 90% of Federal IT Managers Aiming for Their Own Feet!

Filed under: Artificial Intelligence,Cybersecurity,Government,Machine Learning,Security — Patrick Durusau @ 2:58 pm

The Federal Cyber AI IQ Test November 14, 2017 reports:


Most Powerful Applications:

  • 90% of Feds say AI could help prepare agencies for real-world cyber attack scenarios and 87% say it would improve the efficiency of the Federal cyber security workforce
  • 91% say their agency could utilize AI to monitor human activity and deter insider threats, including detecting suspicious elements and large amounts of data being downloaded, and analyzing risky user behavior
  • (emphasis in original)

One sure conclusion from this report, 90% of Feds don’t know AIs mistake turtles for rifles, 90% of the time. The adversarial example literature is full of such cases and getting more robust by the day.

The trap federal IT managers have fallen into is a familiar one. To solve an entirely human problem, a shortage of qualified labor, they want mechanize the required task, even if it means a lower qualify end result. Human problems are solved poorly, if at all, by mechanized solutions.

Opposed by lowest common denominator AI systems, hackers will be all but running the mints as cybersecurity AI systems spread across the federal government. “Ghost” federal installations will appear on agency records for confirmation of FedEx/UPS shipments. The possibilities are endless.

If you are a state or local government or even a federal IT manager, letting hackers run wild isn’t a foregone conclusion.

You could pattern your compensation packages after West Coast start-ups, along with similar perks. Expensive but do you want an OMB type data leak on your record?

August 5, 2017

Neuroscience-Inspired Artificial Intelligence

Neuroscience-Inspired Artificial Intelligence by Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, and Matthew Botvinick.

Abstract:

The fields of neuroscience and artificial intelligence (AI) have a long and intertwined history. In more recent times, however, communication and collaboration between the two fields has become less commonplace. In this article, we argue that better understanding biological brains could play a vital role in building intelligent machines. We survey historical interactions between the AI and neuroscience fields and emphasize current advances in AI that have been inspired by the study of neural computation in humans and other animals. We conclude by highlighting shared themes that may be key for advancing future research in both fields.

Extremely rich article with nearly four (4) pages of citations.

Reading this paper closely and chasing the citations is a non-trivial task but you will be prepared understand and/or participate in the next big neuroscience/AI breakthrough.

Enjoy!

August 1, 2017

Why Learn OpenAI? In a word, Malware!

Filed under: Artificial Intelligence,Cybersecurity,Malware — Patrick Durusau @ 6:46 pm

OpenAI framework used to create undetectable malware by Anthony Spadafora.

Spadafora reports on Endgame‘s malware generating software, Malware Env for OpenAI Gym.

From the Github page:

This is a malware manipulation environment for OpenAI’s gym. OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. This makes it possible to write agents that learn to manipulate PE files (e.g., malware) to achieve some objective (e.g., bypass AV) based on a reward provided by taking specific manipulation actions.
… (highlight in original)

Introducing OpenAI is a good starting place to learn more about OpenAI.

The value of the OpenAI philosophy:

We believe AI should be an extension of individual human wills and, in the spirit of liberty, as broadly and evenly distributed as possible. The outcome of this venture is uncertain and the work is difficult, but we believe the goal and the structure are right. We hope this is what matters most to the best in the field.

will vary depending upon your objectives.

From my perspective, it’s better for my AI to decide to reach out or stay its hand, as opposed to relying upon ethical behavior of another AI.

You?

June 30, 2017

Mistaken Location of Creativity in “Machine Creativity Beats Some Modern Art”

Filed under: Art,Artificial Intelligence,Machine Learning — Patrick Durusau @ 12:13 pm

Machine Creativity Beats Some Modern Art

From the post:

Creativity is one of the great challenges for machine intelligence. There is no shortage of evidence showing how machines can match and even outperform humans in vast areas of endeavor, such as face and object recognition, doodling, image synthesis, language translation, a vast variety of games such as chess and Go, and so on. But when it comes to creativity, the machines lag well behind.

Not through lack of effort. For example, machines have learned to recognize artistic style, separate it from the content of an image, and then apply it to other images. That makes it possible to convert any photograph into the style of Van Gogh’s Starry Night, for instance. But while this and other work provides important insight into the nature of artistic style, it doesn’t count as creativity. So the challenge remains to find ways of exploiting machine intelligence for creative purposes.

Today, we get some insight into progress in this area thanks to the work of Ahmed Elgammal at the Art & AI Laboratory at Rutgers University in New Jersey, along with colleagues at Facebook’s AI labs and elsewhere.
… (emphasis in original)

This summary of CAN: Creative Adversarial Networks, Generating “Art” by Learning About Styles and Deviating from Style Norms by Ahmed Elgammal, Bingchen Liu, Mohamed Elhoseiny, Marian Mazzone, repeats a mistake made by the authors, that is the misplacement of creativity.

Creativity, indeed, even art itself, is easily argued to reside in the viewer (reader) and not the creator at all.

To illustrate, I quote a long passage from Stanley Fish’s How to Recognize a Poem When You See One below but a quick summary/reminder goes like this:

Fish was teaching back to back classes in the same classroom and for the first class, wrote a list of authors on the blackboard. After the first class ended but before the second class, a poetry class, arrived, he enclosed the list of authors in a rectangle and wrote a page number, as though the list was from a book. When the second class arrived, he asked them to interpret the “poem” that was on the board. Which they proceeded to do. Where would you locate creativity in that situation?

The longer and better written start of the story (by Fish):

[1] Last time I sketched out an argument by which meanings are the property neither of fixed and stable texts nor of free and independent readers but of interpretive communities that are responsible both for the shape of a reader’s activities and for the texts those activities produce. In this lecture I propose to extend that argument so as to account not only for the meanings a poem might be said to have but for the fact of its being recognized as a poem in the first place. And once again I would like to begin with an anecdote.

[2] In the summer of 1971 I was teaching two courses under the joint auspices of the Linguistic Institute of America and the English Department of the State University of New York at Buffalo. I taught these courses in the morning and in the same room. At 9:30 I would meet a group of students who were interested in the relationship between linguistics and literary criticism. Our nominal subject was stylistics but our concerns were finally theoretical and extended to the presuppositions and assumptions which underlie both linguistic and literary practice. At 11:00 these students were replaced by another group whose concerns were exclusively literary and were in fact confined to English religious poetry of the seventeenth century. These students had been learning how to identify Christian symbols and how to recognize typological patterns and how to move from the observation of these symbols and patterns to the specification of a poetic intention that was usually didactic or homiletic. On the day I am thinking about, the only connection between the two classes was an assignment given to the first which was still on the blackboard at the beginning of the second. It read:

Jacobs-Rosenbaum
Levin
Thorne
Hayes
Ohman (?)

[3] I am sure that many of you will already have recognized the names on this list, but for the sake of the record, allow me to identify them. Roderick Jacobs and Peter Rosenbaum are two linguists who have coauthored a number of textbooks and coedited a number of anthologies. Samuel Levin is a linguist who was one of the first to apply the operations of transformational grammar to literary texts. J. P. Thorne is a linguist at Edinburgh who, like Levin, was attempting to extend the rules of transformational grammar to the notorious ir-regularities of poetic language. Curtis Hayes is a linguist who was then using transformational grammar in order to establish an objective basis for his intuitive impression that the language of Gibbon’s Decline and Fall of the Roman Empire is more complex than the language of Hemingway’s novels. And Richard Ohmann is the literary critic who, more than any other, was responsible for introducing the vocabulary of transformational grammar to the literary community. Ohmann’s name was spelled as you see it here because I could not remember whether it contained one or two n’s. In other words, the question mark in parenthesis signified nothing more than a faulty memory and a desire on my part to appear scrupulous. The fact that the names appeared in a list that was arranged vertically, and that Levin, Thorne, and Hayes formed a column that was more or less centered in relation to the paired names of Jacobs and Rosenbaum, was similarly accidental and was evidence only of a certain compulsiveness if, indeed, it was evidence of anything at all.

[4] In the time between the two classes I made only one change. I drew a frame around the assignment and wrote on the top of that frame “p. 43.” When the members of the second class filed in I told them that what they saw on the blackboard was a religious poem of the kind they had been studying and I asked them to interpret it. Immediately they began to perform in a manner that, for reasons which will become clear, was more or less predictable. The first student to speak pointed out that the poem was probably a hieroglyph, although he was not sure whether it was in the shape of a cross or an altar. This question was set aside as the other students, following his lead, began to concentrate on individual words, interrupting each other with suggestions that came so quickly that they seemed spontaneous. The first line of the poem (the very order of events assumed the already constituted status of the object) received the most attention: Jacobs was explicated as a reference to Jacob’s ladder, traditionally allegorized as a figure for the Christian ascent to heaven. In this poem, however, or so my students told me, the means of ascent is not a ladder but a tree, a rose tree or rosenbaum. This was seen to be an obvious reference to the Virgin Mary who was often characterized as a rose without thorns, itself an emblem of the immaculate conception. At this point the poem appeared to the students to be operating in the familiar manner of an iconographic riddle. It at once posed the question, “How is it that a man can climb to heaven by means of a rose tree?” and directed the reader to the inevitable answer: by the fruit of that tree, the fruit of Mary’s womb, Jesus. Once this interpretation was established it received support from, and conferred significance on, the word “thorne,” which could only be an allusion to the crown of thorns, a symbol of the trial suffered by Jesus and of the price he paid to save us all. It was only a short step (really no step at all) from this insight to the recognition of Levin as a double reference, first to the tribe of Levi, of whose priestly function Christ was the fulfillment, and second to the unleavened bread carried by the children of Israel on their exodus from Egypt, the place of sin, and in response to the call of Moses, perhaps the most familiar of the old testament types of Christ. The final word of the poem was given at least three complementary readings: it could be “omen,” especially since so much of the poem is concerned with foreshadowing and prophecy; it could be Oh Man, since it is mans story as it intersects with the divine plan that is the poem’s subject; and it could, of course, be simply “amen,” the proper conclusion to a poem celebrating the love and mercy shown by a God who gave his only begotten son so that we may live.

[5] In addition to specifying significances for the words of the poem and relating those significances to one another, the students began to discern larger structural patterns. It was noted that of the six names in the poem three–Jacobs, Rosenbaum, and Levin–are Hebrew, two–Thorne and Hayes–are Christian, and one–Ohman–is ambiguous, the ambiguity being marked in the poem itself (as the phrase goes) by the question mark in parenthesis. This division was seen as a reflection of the basic distinction between the old dis-pensation and the new, the law of sin and the law of love. That distinction, however, is blurred and finally dissolved by the typological perspective which invests the old testament events and heroes with new testament meanings. The structure of the poem, my students concluded, is therefore a double one, establishing and undermining its basic pattern (Hebrew vs. Christian) at the same time. In this context there is finally no pressure to resolve the ambiguity of Ohman since the two possible readings–the name is Hebrew, the name is Christian–are both authorized by the reconciling presence in the poem of Jesus Christ. Finally, I must report that one student took to counting letters and found, to no one’s surprise, that the most prominent letters in the poem were S, O, N.

The account by Fish isn’t long and is highly recommended if you are interested in this issue.

If readers/viewers interpret images as art, is the “creativity” of the process that brought it into being even meaningful? Or does polling of viewers measure their appreciation of an image as art, without regard to the process that created it? Exactly what are we measuring when polling such viewers?

By Fish’s account, such a poll tells us a great deal about the viewers but nothing about the creator of the art.

FYI, that same lesson applies to column headers, metadata keys, and indeed, data itself. Which means the “meaning” of what you wrote may be obvious to you, but not to anyone else.

Topic maps can increase your odds of being understood or discovering the understanding captured by others.

June 17, 2017

If You Don’t Think “Working For The Man” Is All That Weird

Filed under: Artificial Intelligence,Finance Services,Jobs,Machine Learning — Patrick Durusau @ 2:29 pm

J.P.Morgan’s massive guide to machine learning and big data jobs in finance by Sara Butcher.

From the post:

Financial services jobs go in and out of fashion. In 2001 equity research for internet companies was all the rage. In 2006, structuring collateralised debt obligations (CDOs) was the thing. In 2010, credit traders were popular. In 2014, compliance professionals were it. In 2017, it’s all about machine learning and big data. If you can get in here, your future in finance will be assured.

J.P. Morgan’s quantitative investing and derivatives strategy team, led Marko Kolanovic and Rajesh T. Krishnamachari, has just issued the most comprehensive report ever on big data and machine learning in financial services.

Titled, ‘Big Data and AI Strategies’ and subheaded, ‘Machine Learning and Alternative Data Approach to Investing’, the report says that machine learning will become crucial to the future functioning of markets. Analysts, portfolio managers, traders and chief investment officers all need to become familiar with machine learning techniques. If they don’t they’ll be left behind: traditional data sources like quarterly earnings and GDP figures will become increasingly irrelevant as managers using newer datasets and methods will be able to predict them in advance and to trade ahead of their release.

At 280 pages, the report is too long to cover in detail, but we’ve pulled out the most salient points for you below.

How important is Sarah’s post and the report by J.P. Morgan?

Let put it this way: Sarah’s post is the first business type post I have saved as a complete webpage so I can clean it up and print without all the clutter. This year. Perhaps last year as well. It’s that important.

Sarah’s post is a quick guide to the languages, talents and tools you will need to start “working for the man.”

It that catches your interest, then Sarah’s post is pure gold.

Enjoy!

PS: I’m still working on a link for the full 280 page report. The switchboard is down for the weekend so I will be following up with J.P. Morgan on Monday next.

March 13, 2017

AI Brain Scans

Filed under: Artificial Intelligence,Graphs,Neural Networks,Visualization — Patrick Durusau @ 3:19 pm

‘AI brain scans’ reveal what happens inside machine learning


The ResNet architecture is used for building deep neural networks for computer vision and image recognition. The image shown here is the forward (inference) pass of the ResNet 50 layer network used to classify images after being trained using the Graphcore neural network graph library

Credit Graphcore / Matt Fyles

The image is great eye candy, but if you want to see images annotated with information, check out: Inside an AI ‘brain’ – What does machine learning look like? (Graphcore)

From the product overview:

Poplar™ is a scalable graph programming framework targeting Intelligent Processing Unit (IPU) accelerated servers and IPU accelerated server clusters, designed to meet the growing needs of both advanced research teams and commercial deployment in the enterprise. It’s not a new language, it’s a C++ framework which abstracts the graph-based machine learning development process from the underlying graph processing IPU hardware.

Poplar includes a comprehensive, open source set of Poplar graph libraries for machine learning. In essence, this means existing user applications written in standard machine learning frameworks, like Tensorflow and MXNet, will work out of the box on an IPU. It will also be a natural basis for future machine intelligence programming paradigms which extend beyond tensor-centric deep learning. Poplar has a full set of debugging and analysis tools to help tune performance and a C++ and Python interface for application development if required.

The IPU-Appliance for the Cloud is due out in 2017. I have looked at Graphcore but came up dry on the Poplar graph libraries and/or an emulator for the IPU.

Perhaps those will both appear later in 2017.

Optimized hardware for graph calculations sounds promising but rapidly processing nodes that may or may not represent the same subject seems like a defect waiting to make itself known.

Many approaches rapidly process uncertain big data but being no more ignorant than your competition is hardly a selling point.

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