Archive for the ‘Artificial Intelligence’ Category

The Complexity of Neurons are Beyond Our Current Imagination

Saturday, February 10th, 2018

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

Saturday, February 10th, 2018

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.

Porn, AI and Open Source Ethics

Thursday, February 8th, 2018

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.

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

Wednesday, January 24th, 2018

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.”

Thursday, January 18th, 2018

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….”

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.

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

Friday, January 12th, 2018

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.

Tutorial on Deep Generative Models (slides and video)

Wednesday, December 27th, 2017

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.

Deep Learning for NLP, advancements and trends in 2017

Sunday, December 24th, 2017

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.

Sunday, December 24th, 2017

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?

IJCAI – Proceedings 1969-2016 Treasure Trove of AI Papers

Tuesday, December 12th, 2017

IJCAI – Proceedings 1969-2016

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]

Tuesday, December 12th, 2017

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!

AlphaZero: Mastering Unambiguous, Low-Dimensional Data

Wednesday, December 6th, 2017

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.

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

Saturday, November 25th, 2017

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?

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

Tuesday, November 14th, 2017

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?

Neuroscience-Inspired Artificial Intelligence

Saturday, August 5th, 2017

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!

Why Learn OpenAI? In a word, Malware!

Tuesday, August 1st, 2017

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)

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?

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

Friday, June 30th, 2017

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.

[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.

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

Saturday, June 17th, 2017

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.

AI Brain Scans

Monday, March 13th, 2017

‘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.

AI Assisted Filtering?

Thursday, February 23rd, 2017

From the post:

A research team tied to Google unveiled a new tool on Thursday that could have a profound effect on how we talk to each other online. It’s called “Perspective,” and it provides a way for news websites and blogs to moderate online discussions with the help of artificial intelligence.

The researchers believe it could turn the tide against trolls on the Internet, and reestablish online comment forums—which many view as cesspools of hatred and stupidity—as a place for honest debate about current events.

The Perspective tool was hatched by artificial intelligence experts at Jigsaw, a subsidiary of Google-holding company Alphabet (GOOGL, -0.04%) that is devoted to policy and ideas. The significance of the tool, pictured below, is that it can decide if an online comment is “toxic” without the aid of human moderators. This means websites—many of which have given up on hosting comments altogether—could now have an affordable way to let their readers debate contentious topics of the day in a civil and respectful forum.

“Imagine trying to have a conversation with your friends about the news you read this morning, but every time you said something, someone shouted in your face, called you a nasty name or accused you of some awful crime,” Jigsaw founder and president Jared Cohen said in a blog post. “We think technology can help.”

I’m intrigued by this, at least to the extent that AI assisted filtering is extended to users. Such that a user can determine what comments they do/don’t see.

I avoid all manner of nonsense on the Internet, in part by there being places I simply don’t go. Not worth the effort to filter all the trash.

But at the same time, I don’t prevent other people, who may have differing definitions of “trash,” from consuming as much of it as they desire.

It’s really sad that Twitter continues to ignore the market potential of filters in favor of its mad-cap pursuit of being an Internet censor.

I have even added Ed Ho, said to be the VP of Engineering at Twitter, to one or more of my tweets suggesting ways Twitter could make money on filters. No response, nada.

It’s either “not invented here,” or Twitter staff spend so much time basking in their own righteousness they can’t be bothered with communications from venal creatures. Hard to say.

Jeff reports this is a work in progress and you can see it from yourself: What if technology could help improve conversations online?.

Check out the code at: https://conversationai.github.io/.

Or even Request API Access! (There no separate link, try: http://www.perspectiveapi.com/.)

Perspective can help with your authoring in real time.

Try setting the sensitivity very low and write/edit until it finally objects. 😉

Especially for Fox news comments. I always leave some profanity or ill comment unsaid. Maybe Perspective can help with that.

AI Podcast: Winning the Cybersecurity Cat and Mouse Game with AI

Wednesday, February 22nd, 2017

AI Podcast: Winning the Cybersecurity Cat and Mouse Game with AI. Brian Caulfield interviews Eli David of Deep Instinct.

From the description:

Cybersecurity is a cat-and-mouse game. And the mouse always has the upper hand. That’s because it’s so easy for new malware to go undetected.

Eli David, an expert in computational intelligence, wants to use AI to change that. He’s CTO of Deep Instinct, a security firm with roots in Israel’s defense industry, that is bringing the GPU-powered deep learning techniques underpinning modern speech and image recognition to the vexing world of cybersecurity.

“It’s exactly like Tom and Jerry, the cat and the mouse, with the difference being that, in this case, Jerry the mouse always has the upper hand,” David said in a conversation on the AI Podcast with host Michael Copeland. He notes that more than 1 million new pieces of malware are created every day.

Interesting take on detection of closely similar malware using deep learning.

Directed in part at detecting smallish modifications that evade current malware detection techniques.

OK, but who is working on using deep learning to discover flaws in software code?

The Rise of the Weaponized AI Propaganda Machine

Tuesday, February 14th, 2017

The Rise of the Weaponized AI Propaganda Machine by Berit Anderson and Brett Horvath.

From the post:

“This is a propaganda machine. It’s targeting people individually to recruit them to an idea. It’s a level of social engineering that I’ve never seen before. They’re capturing people and then keeping them on an emotional leash and never letting them go,” said professor Jonathan Albright.

Albright, an assistant professor and data scientist at Elon University, started digging into fake news sites after Donald Trump was elected president. Through extensive research and interviews with Albright and other key experts in the field, including Samuel Woolley, Head of Research at Oxford University’s Computational Propaganda Project, and Martin Moore, Director of the Centre for the Study of Media, Communication and Power at Kings College, it became clear to Scout that this phenomenon was about much more than just a few fake news stories. It was a piece of a much bigger and darker puzzle — a Weaponized AI Propaganda Machine being used to manipulate our opinions and behavior to advance specific political agendas.

By leveraging automated emotional manipulation alongside swarms of bots, Facebook dark posts, A/B testing, and fake news networks, a company called Cambridge Analytica has activated an invisible machine that preys on the personalities of individual voters to create large shifts in public opinion. Many of these technologies have been used individually to some effect before, but together they make up a nearly impenetrable voter manipulation machine that is quickly becoming the new deciding factor in elections around the world.

Before you get too panicked, remember the techniques attributed to Cambridge Analytica were in use in the 1960 Kennedy presidential campaign. And have been in use since then by marketeers for every known variety of product, including politicians.

It’s hard to know if Anderson and Horvath are trying to drum up more business for Cambridge Analytica or if they are genuinely concerned for the political process.

Granting that Cambridge Analytica has more data than was available in the 1960’s but many people, not just Cambridge Analytica have labored on manipulation of public opinion since then.

If people were as easy to sway, politically speaking, as Anderson and Horvath posit, then why is there any political diversity at all? Shouldn’t we all be marching in lock step by now?

Oh, it’s a fun read so long as you don’t take it too seriously.

Besides, if a “weaponized AI propaganda machine” is that dangerous, isn’t the best defense a good offense?

I’m all for cranking up a “demonized AI propaganda machine” if you have the funding.

Yes?

2017/18 – When you can’t believe your eyes

Friday, December 23rd, 2016

From the post:

Smile Vector is a Twitter bot that can make any celebrity smile. It scrapes the web for pictures of faces, and then it morphs their expressions using a deep-learning-powered neural network. Its results aren’t perfect, but they’re created completely automatically, and it’s just a small hint of what’s to come as artificial intelligence opens a new world of image, audio, and video fakery. Imagine a version of Photoshop that can edit an image as easily as you can edit a Word document — will we ever trust our own eyes again?

“I definitely think that this will be a quantum step forward,” Tom White, the creator of Smile Vector, tells The Verge. “Not only in our ability to manipulate images but really their prevalence in our society.” White says he created his bot in order to be “provocative,” and to show people what’s happening with AI in this space. “I don’t think many people outside the machine learning community knew this was even possible,” says White, a lecturer in creative coding at Victoria University School of design. “You can imagine an Instagram-like filter that just says ‘more smile’ or ‘less smile,’ and suddenly that’s in everyone’s pocket and everyone can use it.”

Vincent reviews a number of exciting advances this year and concludes:

AI researchers involved in this fields are already getting a firsthand experience of the coming media environment. “I currently exist in a world of reality vertigo,” says Clune. “People send me real images and I start to wonder if they look fake. And when they send me fake images I assume they’re real because the quality is so good. Increasingly, I think, we won’t know the difference between the real and the fake. It’s up to people to try and educate themselves.”

An image sent to you may appear to be very convincing, but like the general in War Games, you have to ask does it make any sense?

Verification, subject identity in my terminology, requires more than an image. What do we know about the area? Or the people (if any) in the image? Where were they supposed to be today? And many other questions that depend upon the image and its contents.

Unless you are using a subject-identity based technology, where are you going to store that additional information? Or express your concerns about authenticity?

Four Experiments in Handwriting with a Neural Network

Tuesday, December 6th, 2016

While the handwriting experiments are compelling and entertaining, the author’s have a more profound goal for this activity:

The black box reputation of machine learning models is well deserved, but we believe part of that reputation has been born from the programming context into which they have been locked into. The experience of having an easily inspectable model available in the same programming context as the interactive visualization environment (here, javascript) proved to be very productive for prototyping and exploring new ideas for this post.

As we are able to move them more and more into the same programming context that user interface work is done, we believe we will see richer modes of human-ai interactions flourish. This could have a marked impact on debugging and building models, for sure, but also in how the models are used. Machine learning research typically seeks to mimic and substitute humans, and increasingly it’s able to. What seems less explored is using machine learning to augment humans. This sort of complicated human-machine interaction is best explored when the full capabilities of the model are available in the user interface context.

Setting up a search alert for future work from these authors!

None/Some/All … Are Suicide Bombers & Probabilistic Programming Languages

Tuesday, November 8th, 2016

The Design and Implementation of Probabilistic Programming Languages by Noah D. Goodman and Andreas Stuhlmüller.

Abstract:

Probabilistic programming languages (PPLs) unify techniques for the formal description of computation and for the representation and use of uncertain knowledge. PPLs have seen recent interest from the artificial intelligence, programming languages, cognitive science, and natural languages communities. This book explains how to implement PPLs by lightweight embedding into a host language. We illustrate this by designing and implementing WebPPL, a small PPL embedded in Javascript. We show how to implement several algorithms for universal probabilistic inference, including priority-based enumeration with caching, particle filtering, and Markov chain Monte Carlo. We use program transformations to expose the information required by these algorithms, including continuations and stack addresses. We illustrate these ideas with examples drawn from semantic parsing, natural language pragmatics, and procedural graphics.

If you want to sharpen the discussion of probabilistic programming languages, substitute in the pragmatics example:

‘none/some/all of the children are suicide bombers’,

The substitution raises the issue of how “certainty” can/should vary depending upon the gravity of results.

Who is a nice person?, has low stakes.

Who is a suicide bomber?, has high stakes.

“Why Should I Trust You?”…

Tuesday, August 23rd, 2016

Abstract:

Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.

In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.

For a quick overview consider: Introduction to Local Interpretable Model-Agnostic Explanations (LIME) (blog post).

Or what originally sent me in this direction: Trusting Machine Learning Models with LIME at Data Skeptic, a podcast described as:

Machine learning models are often criticized for being black boxes. If a human cannot determine why the model arrives at the decision it made, there’s good cause for skepticism. Classic inspection approaches to model interpretability are only useful for simple models, which are likely to only cover simple problems.

The LIME project seeks to help us trust machine learning models. At a high level, it takes advantage of local fidelity. For a given example, a separate model trained on neighbors of the example are likely to reveal the relevant features in the local input space to reveal details about why the model arrives at it’s conclusion.

Data Science Renee finds deeply interesting material such as this on a regular basis and should follow her account on Twitter.

I do have one caveat on a quick read of these materials. The authors say in the paper, under 4. Submodular Pick For Explaining Models:

Even though explanations of multiple instances can be insightful, these instances need to be selected judiciously, since users may not have the time to examine a large number of explanations. We represent the time/patience that humans have by a budget B that denotes the number of explanations they are willing to look at in order to understand a model. Given a set of instances X, we define the pick step as the task of selecting B instances for the user to inspect.

The pick step is not dependent on the existence of explanations – one of the main purpose of tools like Modeltracker [1] and others [11] is to assist users in selecting instances themselves, and examining the raw data and predictions. However, since looking at raw data is not enough to understand predictions and get insights, the pick step should take into account the explanations that accompany each prediction. Moreover, this method should pick a diverse, representative set of explanations to show the user – i.e. non-redundant explanations that represent how the model behaves globally.

The “judicious” selection of instances, in models of any degree of sophistication, based upon large data sets seems problematic.

The focus on the “non-redundant coverage intuition” is interesting but based on the assumption that changes in factors don’t lead to “redundant explanations.” In the cases presented that’s true, but I lack confidence that will be true in every case.

Still, a very important area of research and an effort that is worth tracking.

What’s the Difference Between Artificial Intelligence, Machine Learning, and Deep Learning?

Friday, August 19th, 2016

From the post:

Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to.

For example, when Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things.

The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both.

If you are confused by the mix of artificial intelligence, machine learning, and deep learning, floating around, Copeland will set you straight.

It’s a fun read and one you can recommend to non-technical friends.

When AI’s Take The Fifth – Sign Of Intelligence?

Wednesday, July 6th, 2016

Taking the fifth amendment in Turing’s imitation game by Kevin Warwick and Huma Shahb.

Abstract:

In this paper, we look at a specific issue with practical Turing tests, namely the right of the machine to remain silent during interrogation. In particular, we consider the possibility of a machine passing the Turing test simply by not saying anything. We include a number of transcripts from practical Turing tests in which silence has actually occurred on the part of a hidden entity. Each of the transcripts considered here resulted in a judge being unable to make the ‘right identification’, i.e., they could not say for certain which hidden entity was the machine.

A delightful read about something never seen in media interviews: silence of the person being interviewed.

Of the interviews I watch, which is thankfully a small number, most people would seem more intelligent by being silent more often.

I take author’s results as a mark in favor of Fish’s interpretative communities because “interpretation” of silence falls squarely on the shoulders of the questioner.

If you don’t know the name Kevin Warwick, you should.

As of today, footnote 1 correctly points to the Fifth Amendment text at Cornell but mis-quotes it. In relevant part the Fifth Amendment reads, “…nor shall be compelled in any criminal case to be a witness against himself….”

…possibly biased? Try always biased.

Friday, June 24th, 2016

From the post:

Much has been made of the tech industry’s lack of women engineers and executives. But there’s a unique problem with homogeneity in AI. To teach computers about the world, researchers have to gather massive data sets of almost everything. To learn to identify flowers, you need to feed a computer tens of thousands of photos of flowers so that when it sees a photograph of a daffodil in poor light, it can draw on its experience and work out what it’s seeing.

If these data sets aren’t sufficiently broad, then companies can create AIs with biases. Speech recognition software with a data set that only contains people speaking in proper, stilted British English will have a hard time understanding the slang and diction of someone from an inner city in America. If everyone teaching computers to act like humans are men, then the machines will have a view of the world that’s narrow by default and, through the curation of data sets, possibly biased.

“I call it a sea of dudes,” said Margaret Mitchell, a researcher at Microsoft. Mitchell works on computer vision and language problems, and is a founding member—and only female researcher—of Microsoft’s “cognition” group. She estimates she’s worked with around 10 or so women over the past five years, and hundreds of men. “I do absolutely believe that gender has an effect on the types of questions that we ask,” she said. “You’re putting yourself in a position of myopia.”

Margaret Mitchell makes a pragmatic case for diversity int the workplace, at least if you want to avoid male biased AI.

Not that a diverse workplace results in an “unbiased” AI, it will be a biased AI that isn’t solely male biased.

It isn’t possible to escape bias because some person or persons has to score “correct” answers for an AI. The scoring process imparts to the AI being trained, the biases of its judge of correctness.

Unless someone wants to contend there are potential human judges without biases, I don’t see a way around imparting biases to AIs.

By being sensitive to evidence of biases, we can in some cases choose the biases we want an AI to possess, but an AI possessing no biases at all, isn’t possible.

AIs are, after all, our creations so it is only fair that they be made in our image, biases and all.

Bots, Won’t You Hide Me?

Thursday, June 23rd, 2016

Emerging Trends in Social Network Analysis of Terrorism and Counterterrorism, How Police Are Scanning All Of Twitter To Detect Terrorist Threats, Violent Extremism in the Digital Age: How to Detect and Meet the Threat, Online Surveillance: …ISIS and beyond [Social Media “chaff”] are just a small sampling of posts on the detection of “terrorists” on social media.

The last one is my post illustrating how “terrorist” at one time = “anti-Vietnam war,” “civil rights,” and “gay rights.” Due to the public nature of social media, avoiding government surveillance isn’t possible.

I stole the title, Bots, Won’t You Hide Me? from Ben Bova’s short story, Stars, Won’t You Hide Me?. It’s not very long and if you like science fiction, you will enjoy it.

Bova took verses in the short story from Sinner Man, a traditional African spiritual, which was recorded by a number of artists.

All of that is a very round about way to introduce you to a new Twitter account: ConvJournalism:

All you need to know about Conversational Journalism, (journalistic) bots and #convcomm by @martinhoffmann.

Surveillance of groups on social media isn’t going to succeed, The White House Asked Social Media Companies to Look for Terrorists. Here’s Why They’d #Fail by Jenna McLaughlin bots can play an important role in assisting in that failure.

Imagine not only having bots that realistically mimic the chatter of actual human users but who follow, unfollow, etc., and engage in apparent conspiracies, with other bots. Entirely without human direction or very little.

Follow ConvJournalism and promote bot research/development that helps all of us hide. (I’d rather have the bots say yes than Satan.)