Help Mr. Wiginnie recognize these pictures! in General AI Discussion

Mr. Wiginnie has scrambled his photos once again. He accidentally got tipsy one night and went on a rampage in photo editors. Unfortunately he didn't name them properly. Now we need to help him figure out which are the originals.

Get to know the originals, stare at them for minutes. When viewing the edits, make sure to not tip your head or edit them, keep your head up straight.

Below is 8 photos. Four are the originals. Four are the edits. For all four of the them, you need to give your opinion on which original is which edit. If you have an ANN, you may also use it to help you.

13 Comments | Started December 13, 2018, 04:43:49 pm


Fake news and now...Fake meat!! in General Chat

Meat and potatoes...a once accepted diet for many, is soon to be replaced with meat and potatoes...but using a somewhat different kind of meat.

This "meat" is lab-grown meat taken from cells of a real animal (chicken or beef) and cultured in a laboratory. Yum...can't you just taste it?

You soon will. Eventually, prices will drop, supply will increase and demand will increase until suddenly it becomes commonplace.

The wheels are already in motion, like it or not.


Wonder....does this mean the Vegans of the world will no longer have issues with the slaughter of animals if "no animals were harmed during the production of this meat" labels appear on the product?

Started Today at 01:29:27 pm




14 Comments | Started December 09, 2018, 03:24:07 am


SIDIS in General Project Discussion

Hi guys,

I'm currently developing an AI.  I made a web page with some screen captures from the project which can be found here : http://joebecker.webivore.com/sidisweb/SIDIS.html

I'm interested in hearing your thoughts.


35 Comments | Started November 09, 2018, 04:48:48 pm


3Q: Aleksander Madry on building trustworthy artificial intelligence in Robotics News

3Q: Aleksander Madry on building trustworthy artificial intelligence
14 December 2018, 8:40 pm

Machine learning algorithms now underlie much of the software we use, helping to personalize our news feeds and finish our thoughts before we’re done typing. But as artificial intelligence becomes further embedded in daily life, expectations have risen. Before autonomous systems fully gain our confidence, we need to know they are reliable in most situations and can withstand outside interference; in engineering terms, that they are robust. We also need to understand the reasoning behind their decisions; that they are interpretable.

Aleksander Madry, an associate professor of computer science at MIT and a lead faculty member of the Computer Science and Artificial Intelligence Lab (CSAIL)’s Trustworthy AI initiative, compares AI to a sharp knife, a useful but potentially-hazardous tool that society must learn to weild properly. Madry recently spoke at MIT’s Symposium on Robust, Interpretable AI, an event co-sponsored by the MIT Quest for Intelligence and CSAIL, and held Nov. 20 in Singleton Auditorium. The symposium was designed to showcase new MIT work in the area of building guarantees into AI, which has almost become a branch of machine learning in its own right. Six faculty members spoke about their research, 40 students presented posters, and Madry opened the symposium with a talk the aptly titled, “Robustness and Interpretability.” We spoke with Madry, a leader in this emerging field, about some of the key ideas raised during the event.

Q: AI owes much of its recent progress to deep learning, a branch of machine learning that has significantly improved the ability of algorithms to pick out patterns in text, images and sounds, giving us automated assistants like Siri and Alexa, among other things. But deep learning systems remain vulnerable in surprising ways: stumbling when they encounter slightly unfamiliar examples in the real world or when a malicious attacker feeds it subtly-altered images. How are you and others trying to make AI more robust?

A: Until recently, AI researchers focused simply on getting machine-learning algorithms to accomplish basic tasks. Achieving even average-case performance was a major challenge. Now that performance has improved, attention has shifted to the next hurdle: improving the worst-case performance. Most of my research is focused on meeting this challenge. Specifically, I work on developing next-generation machine-learning systems that will be reliable and secure enough for mission-critical applications like self-driving cars and software that filters malicious content. We’re currently building tools to train object-recognition systems to identify what’s happening in a scene or picture, even if the images fed to the model have been manipulated. We are also studying the limits of systems that offer security and reliability guarantees. How much reliability and security can we build into machine-learning models, and what other features might we need to sacrifice to get there?

My colleague Luca Daniel, who also spoke, is working on an important aspect of this problem: developing a way to measure the resilience of a deep learning system in key situations. Decisions made by deep learning systems have major consequences, and thus it’s essential that end-users be able to measure the reliability of each of the model’s outputs. Another way to make a system more robust is during the training process. In her talk, “Robustness in GANs and in Black-box Optimization,” Stefanie Jegelka showed how the learner in a generative adversarial network, or GAN, can be made to withstand manipulations to its input, leading to much better performance.

Q: The neural networks that power deep learning seem to learn almost effortlessly: Feed them enough data and they can outperform humans at many tasks. And yet, we’ve also seen how easily they can fail, with at least three widely publicized cases of self-driving cars crashing and killing someone. AI applications in health care are not yet under the same level of scrutiny but the stakes are just as high. David Sontag focused his talk on the often life-or-death consequences when an AI system lacks robustness. What are some of the red flags when training an AI on patient medical records and other observational data?

A: This goes back to the nature of guarantees and the underlying assumptions that we build into our models. We often assume that our training datasets are representative of the real-world data we test our models on — an assumption that tends to be too optimistic. Sontag gave two examples of flawed assumptions baked into the training process that could lead an AI to give the wrong diagnosis or recommend a harmful treatment. The first focused on a massive database of patient X-rays released last year by the National Institutes of Health. The dataset was expected to bring big improvements to the automated diagnosis of lung disease until a skeptical radiologist took a closer look and found widespread errors in the scans’ diagnostic labels. An AI trained on chest scans with a lot of incorrect labels is going to have a hard time generating accurate diagnoses.

A second problem Sontag cited is the failure to correct for gaps and irregularities in the data due to system glitches or changes in how hospitals and health care providers report patient data. For example, a major disaster could limit the amount of data available for emergency room patients. If a machine-learning model failed to take that shift into account its predictions would not be very reliable.

Q: You’ve covered some of the techniques for making AI more reliable and secure. What about interpretability? What makes neural networks so hard to interpret, and how are engineers developing ways to peer beneath the hood?

A: Understanding neural-network predictions is notoriously difficult. Each prediction arises from a web of decisions made by hundreds to thousands of individual nodes. We are trying to develop new methods to make this process more transparent. In the field of computer vision one of the pioneers is Antonio Torralba, director of The Quest. In his talk, he demonstrated a new tool developed in his lab that highlights the features that a neural network is focusing on as it interprets a scene. The tool lets you identify the nodes in the network responsible for recognizing, say, a door, from a set of windows or a stand of trees. Visualizing the object-recognition process allows software developers to get a more fine-grained understanding of how the network learns.

Another way to achieve interpretability is to precisely define the properties that make the model understandable, and then train the model to find that type of solution. Tommi Jaakkola showed in his talk, “Interpretability and Functional Transparency,” that models can be trained to be linear or have other desired qualities locally while maintaining the network’s overall flexibility. Explanations are needed at different levels of resolution much as they are in interpreting physical phenomena. Of course, there’s a cost to building guarantees into machine-learning systems — this is a theme that carried through all the talks. But those guarantees are necessary and not insurmountable. The beauty of human intelligence is that while we can’t perform most tasks perfectly, as a machine might, we have the ability and flexibility to learn in a remarkable range of environments.

Source: MIT News - CSAIL - Robotics - Computer Science and Artificial Intelligence Laboratory (CSAIL) - Robots - Artificial intelligence

Reprinted with permission of MIT News : MIT News homepage

Use the link at the top of the story to get to the original article.

Started December 15, 2018, 12:00:58 pm


XKCD Comic : arXiv in XKCD Comic

14 December 2018, 5:00 am

Both arXiv and archive.org are invaluable projects which, if they didn't exist, we would dismiss as obviously ridiculous and unworkable.

Source: xkcd.com

Started December 15, 2018, 12:00:57 pm


Anyone wants to learn logic? in General AI Discussion

Here is Stanford Introduction to Logic, an online course on symbolic logic. I believe it covers the most interesting knowledge of logic in general. Enjoy :)

73 Comments | Started December 07, 2018, 01:32:40 pm


Borsuk in AI Programming

I have a new project. My former project was Perkun, an experimental AI language based on my own optimization algorithm supporting hidden variables. My new project is Borsuk, you can download it from:


It is not finished yet, but I wanted to discuss it here. The problem with Perkun is that it assumes all hidden variables to be not independent (the most general assumption but also very costly in terms of memory and computational power). Borsuk will assume that most hidden variables are independent. This will allow it to have hundreds, possibly thousands of hidden variables.

Take a look at the file examples/example2_fantasy.borsuk . If you run it with borsuk (which you have to build first) you will obtain a file content of examples/example2_fantasy.txt. It contains among others 387 hidden variables generated from the following code:

hidden variable has_(A:person)_(X:activity)_(B:person):boolean;
hidden variable does_(A:person)_like_(B:person):boolean;
hidden variable is_(A:person)_afraid_of_(B:person):boolean;
hidden variable (A:person)_is_in_(X:town):boolean;
hidden variable has_(A:person)_told_(B:person)_that_(C:person)_has_(X:activity)_(D:person):boolean;

These are "templates" of the hidden variables to be generated. The last one for example generates all tuples (A,B,C,X,D) satisfying the condition A is a person, B is a person, C is a person, X is an activity, D is a person. It then generates variables like:

hidden variable has_pregor_told_dorban_that_pregor_has_attacked_me:{none,false,true};

In short - I plan to use the same algorithm but allow many more hidden variables than in Perkun.

Borsuk requires the SWI Prolog (the devel packages) to be built.

6 Comments | Started December 12, 2018, 12:01:26 am


ChatbotML on Twitch in Home Made Robots


NOTE:  The chatbot has increased control of the video, with the ability to accurately pause and allow extra reading time.  

ChatbotML, is turning out to be a fun way to discover how chatbots work on Twitch.  This early prototype can read and write to the Twitch Stream Chat.  It can lip sync text to speech, and render a video response on the fly.   Currently it is being developed as an alpha version made available for testing.

If you are on twitch, please have a look:  https://www.twitch.tv/chatbotml

« Edit Notes:  This post has been slightly edited.  A video been updated in the post.

17 Comments | Started October 10, 2018, 01:11:36 am


XKCD Comic : FDR in XKCD Comic

12 December 2018, 5:00 am

June 21st, 365, the date of the big Mediterranean earthquake and tsunami, lived in infamy for a few centuries before fading. Maybe the trick is a catchy rhyme; the '5th of November' thing is still going strong over 400 years later.

Source: xkcd.com

Started December 13, 2018, 12:00:08 pm
Mortal Engines

Mortal Engines in Robots in Movies

Mortal Engines is a 2018 post-apocalyptic adventure film directed by Christian Rivers and with a screenplay by Fran WalshPhilippa Boyens and Peter Jackson, based on the novel of the same name by Philip Reeve.

Tom (Robert Sheehan) is a young Londoner who has only ever lived inside his travelling hometown, and his feet have never touched grass, mud or land. His first taste of the outside comes quite abruptly: Tom gets in the way of an attempt by the masked Hester (Hera Hilmar) to kill Thaddeus Valentine (Hugo Weaving), a powerful man she blames for her mother’s murder, and both Hester and Tom end up thrown out of the moving "traction" city, to fend for themselves.

Stars Stephen Lang as Shrike, the last of an undead battalion of soldiers known as Stalkers, who were war casualties re-animated with machine parts, and Hester's guardian.

Dec 08, 2018, 18:50:44 pm
Alita: Battle Angel

Alita: Battle Angel in Robots in Movies

Alita: Battle Angel is an upcoming American cyberpunk action film based on Yukito Kishiro's manga Battle Angel Alita. Produced by James Cameron and Jon Landau, the film is directed by Robert Rodriguez from a screenplay by Cameron and Laeta Kalogridis.

Visionary filmmakers James Cameron (AVATAR) and Robert Rodriguez (SIN CITY) create a groundbreaking new heroine in ALITA: BATTLE ANGEL, an action-packed story of hope, love and empowerment. Set several centuries in the future, the abandoned Alita (Rosa Salazar) is found in the scrapyard of Iron City by Ido (Christoph Waltz), a compassionate cyber-doctor who takes the unconscious cyborg Alita to his clinic. When Alita awakens she has no memory of who she is, nor does she have any recognition of the world she finds herself in. Everything is new to Alita, every experience a first.

As she learns to navigate her new life and the treacherous streets of Iron City, Ido tries to shield Alita from her mysterious past while her street-smart new friend, Hugo (Keean Johnson), offers instead to help trigger her memories. A growing affection develops between the two until deadly forces come after Alita and threaten her newfound relationships. It is then that Alita discovers she has extraordinary fighting abilities that could be used to save the friends and family she’s grown to love.

Determined to uncover the truth behind her origin, Alita sets out on a journey that will lead her to take on the injustices of this dark, corrupt world, and discover that one young woman can change the world in which she lives.

Scheduled to be released on February 14, 2019

Nov 16, 2018, 18:25:25 pm
The Beyond

The Beyond in Robots in Movies

A team of robotically-advanced astronauts travel through a new wormhole, but the mission returns early, sparking questions about what was discovered.

Nov 12, 2018, 22:38:18 pm
Mitsuku wins Loebner Prize 2018!

Mitsuku wins Loebner Prize 2018! in Articles

The Loebner Prize 2018 was held in Bletchley Park, England on September 8th this year and Mitsuku won it for a 4th time to equal the record number of wins. Only 2 other people (Joseph Weintraub and Bruce Wilcox) have achieved this. In this blog, I’ll explain more about the event, the day itself and a few personal thoughts about the future of the contest.

Sep 17, 2018, 19:10:51 pm
Automata (Series)

Automata (Series) in Robots on TV

In an alternate 1930's Prohibition-era New York City, it's not liquor that is outlawed but the future production of highly sentient robots known as automatons. Automata follows former NYPD detective turned private eye Sam Regal and his incredibly smart automaton partner, Carl Swangee. Together, they work to solve the case and understand each other in this dystopian America.

Sep 08, 2018, 00:16:22 am
Steve Worswick (Mitsuku) on BBC Radio 4

Steve Worswick (Mitsuku) on BBC Radio 4 in Other

Steve Worswick: "I appeared on BBC Radio 4 in August in a feature about chatbots. Leeds Beckett University were using one to offer places to students."

Sep 06, 2018, 23:50:39 pm

Extinction in Robots in Movies

Extinction is a 2018 American science fiction thriller film directed by Ben Young and written by Spenser Cohen, Eric Heisserer and Brad Kane. The film stars Lizzy Caplan, Michael Peña, Mike Colter, Lilly Aspell, Emma Booth, Israel Broussard, and Lex Shrapnel. It was released on Netflix on July 27, 2018.

Peter, an engineer, has recurring nightmares in which he and his family suffer through violent, alien invasion-like confrontations with an unknown enemy. As the nightmares become more stressful, they take a toll on his family, too.

Sep 06, 2018, 23:42:51 pm

Tau in Robots in Movies

Tau is a 2018 science fiction thriller film, directed by Federico D'Alessandro, from a screenplay by Noga Landau. It stars Maika Monroe, Ed Skrein and Gary Oldman.

It was released on June 29, 2018, by Netflix.

Julia is a loner who makes money as a thief in seedy nightclubs. One night, she is abducted from her home and wakes up restrained and gagged in a dark prison inside of a home with two other people, each with an implant in the back of their necks. As "subject 3," she endures a series of torturous psychological sessions by a shadowy figure in a lab. One night, she steals a pair of scissors and destroys the lab in an escape attempt, but she is stopped and the other two subjects are killed by a robot in the house, Aries, run by an artificial intelligence, Tau.

Alex, the technology executive who owns the house, reveals the implant is collecting her neural activity as she completes puzzles, and subjects her to more tests, because he is using the data to develop more advanced A.I. with a big project deadline in a few days.

Sep 06, 2018, 23:30:00 pm
Bot Development Frameworks - Getting Started

Bot Development Frameworks - Getting Started in Articles

What Are Bot Frameworks ?

Simply explained, a bot framework is where bots are built and where their behavior is defined. Developing and targeting so many messaging platforms and SDKs for chatbot development can be overwhelming. Bot development frameworks abstract away much of the manual work that's involved in building chatbots. A bot development framework consists of a Bot Builder SDK, Bot Connector, Developer Portal, and Bot Directory. There’s also an emulator that you can use to test the developed bot.

Mar 23, 2018, 20:00:23 pm