Logos formal language specification in General Project Discussion

I've been working on a formal language specification for a while, and it finally took a shape I'm satisfied with. If you like, you can take a look at Logos specification in the link below (some twenty minutes read). Logos stands for a (l)anguage (o)f (g)eneral (o)perational (s)emantics. The language represents all of the following:

• Metatheory language formalization
• Theorem prover
• Term rewriting system
• Data computing environment

Want to learn how to train an artificial intelligence model? Ask a friend. in Robotics News

Want to learn how to train an artificial intelligence model? Ask a friend.
25 June 2019, 6:50 pm

The MIT Machine Intelligence Community began with a few friends meeting over pizza to discuss landmark papers in machine learning. Three years later, the undergraduate club boasts 500 members, an active Slack channel, and an impressive lineup of student-led reading groups and workshops meant to demystify machine learning and artificial intelligence (AI) generally. This year, MIC and MIT Quest for Intelligence joined forces to advance their common cause of making AI tools accessible to all.

Starting last fall, the MIT Quest opened its offices to MIC members and extended access to IBM and Google-donated cloud credits, providing a boost of computing power to students previously limited to running their AI models on desktop machines loaded with extra graphics processors. The MIT Quest and MIC are now collaborating on a host of projects, independently and through MIT’s Undergraduate Research Opportunities Program (UROP).

“We heard about their mission to spread machine learning to all undergrads and thought, ‘That’s what we’re trying to do — let’s do it together!” says Joshua Joseph, chief software engineer with the MIT Quest Bridge.

A makerspace for AI

U.S. Army ROTC students Ian Miller and Rishi Shah came to MIC for the free cloud credits, but stayed for the workshop on neural computing sticks. A compute stick allows mobile devices to do image processing on the fly, and when the cadets learned what one could do, they knew their idea for a portable computer vision system would work.

“Without that, we’d have to send images to a central place to do all this computing,” says Miller, a rising junior. “It would have been a logistical headache.”

Built in two months, for $200, their wallet-sized device is designed to plug into a tablet strapped to an Army soldier’s chest and scan the surrounding area for cars and people. With more training, they say, it could learn to spot cellphones and guns. In May, the cadets demo'd their device at MIT’s Soldier Design Competition and were invited by an Army sergeant to visit Fort Devens to continue working on it.

Rose Wang, a rising senior majoring in computer science, was also drawn to MIC by the free cloud credits, and a chance to work on projects with quest and other students. This spring, she used IBM cloud credits to run a reinforcement learning model that’s part of her research with MIT Professor Jonathan How, training robot agents to cooperate on tasks that involve limited communication and information. She recently presented her results at a workshop at the International Conference on Machine Learning.  

“It helped me try out different techniques without worrying about the compute bottleneck and running out of resources,” she says.

Improving AI access at MIT

The MIC has launched several AI projects of its own. The most ambitious is Monkey, a container-based, cloud-native service that would allow MIT undergraduates to log in and train an AI model from anywhere, tracking the training as it progresses and managing the credits allotted to each student. On a Friday afternoon in April, the team gathered in a quest conference room as Michael Silver, a rising senior, sketched out the modules Monkey would need.

As Silver scrawled the words "Docker Image Build Service" on the board, the student assigned to research the module apologized. “I didn’t make much progress on it because I had three midterms!” he said.

The planning continued, with Steven Shriver, a software engineer with the Quest Bridge, interjecting bits of advice. The students had assumed the container service they planned to use, Docker, would be secure. It isn’t.

“Well, I guess we have another task here,” said Silver, adding the word “security” to the white board.

Later, the sketch would be turned into a design document and shared with the two UROP students helping to execute Monkey. The team hopes to launch sometime next year.

“The coding isn’t the difficult part,” says UROP student Amanda Li, a member of MIC Dev-Ops. “It’s the exploring the server side of machine learning — Docker, Google Cloud, and the API. The most important thing I’ve learned is how to efficiently design and pipeline a project as big as this.”

Silver knew he wanted to be an AI engineer in 2016, when the computer program AlphaGo defeated the world’s reigning Go champion. As a senior at Boston University Academy, Silver worked on natural language processing in the lab of MIT Professor Boris Katz, and has continued to work with Katz since coming to MIT. Seeking more coding experience, he left HackMIT, where he had been co-director, to join MIC Dev-Ops.

“A lot of students read about machine learning models, but have no idea how to train one,” he says. “Even if you know how to train one, you’d need to save up a few thousand dollars to buy the GPUs to do it. MIC lets students interested in machine learning reach that next level.”

Conceived by MIC members, a second project is focused on making AI research papers posted on arXiv easier to explore. Nearly 14,000 academic papers are uploaded each month to the site, and although papers are tagged by field, drilling into subtopics can be overwhelming.

Wang, for one, grew frustrated while doing a basic literature search on reinforcement learning. “You have a ton of data and no effective way of representing it to the user,” she says. “It would have been useful to see the papers in a larger context, and to explore by number of citations or their relevance to each other.”

A third MIC project focuses on crawling MIT’s hundreds of listservs for AI-related talks and events to populate a Google calendar. The tool will be closely patterned after an app Silver helped build during MIT’s Independent Activities Period in January. Called Dormsp.am, the app classifies listserv emails sent to MIT undergraduates and plugs them into a calendar-email client. Students can then search for events by day or by a color-coded topic, such as tech, food, or jobs. Once Dormsp.am launches, Silver will adapt it to search for and post AI-related events at MIT to an MIC calendar.

Silver says the team spent extra time on the user interface, taking a page from MIT Professor Daniel Jackson’s Software Studio class. “This is an app that can live or die on its usability, so the front end is really important,” he says.  

Wang is now collaborating with Moin Nadeem, MIC’s outgoing president, to build the visualization tool. It’s exactly the kind of hands-on experience MIC was intended to provide, says Nadeem, a rising senior. “Students learn fundamental concepts in class but don’t know how to implement them,” he says. “I’m trying to build what freshman me would have liked to have had: a community of people excited to do interesting stuff with machine learning.”

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.

What thought next in Human Experience and Psychology

When you're in a state of mind, what should be the next state? I can see 4 cases.

* Association

   ocean
      makes you think
   blue
      makes you think
   DeepBlue
      makes you think
   Ai
      makes you think
   AiDreams

* Utility

   it can cause pleasure or satisfaction
      it matters, think about it
   it can cause pain or be uncomfortable
      it matters, think about it
   it's neutral
      it's irrelevant, no need to focus on this

* Plan

   I'm starting a compound activity
      go down and do first step
   I'm in the middle of an activity
      do next step
   the current activity is over
      go up and start next activity

* Surprise

   standard predicted stuff happened
      why bother
   something unexpected happened
      analysis needed, could be important



===== am I missing something?

XKCD Comic : Motivated Reasoning Olympics in XKCD Comic

Motivated Reasoning Olympics
24 June 2019, 5:00 am



Source: xkcd.com

Using solenoids for a plotter instead of other mechanical methods in General Hardware Talk

If your movement on your plotting machine was purely magnetic, wouldn't it be more accurate than say with belts, cogs, and worms?

My machine planned is going to brush up against a nichrome wire to get its absolute position - but other than that, if it was just purely arriving at the destination via a solenoid it would have no other machine in the chain to get to the position.

So, can you make solenoids that go over a space of 50cm?   but even if that's not the case,  getting 10cm would already be enough if you were going to be making a ram chip or cpu with it.

Electronic Longevity in General AI Discussion

Anything we make breaks down. The more complicated it is the faster this happens. There is a water wheel that has been turning since 1854 on the Isle of Man, a light bulb has been glowing since 1901 in Livermore California. Those are relatively simple constructs. When you start talking about cars the time frame begins to shrink. Desktop computers, laptops, cellphones, down down down, the more compact and complex our machines and electronics get, the more strings entropy gets to pull, and the sooner things begin to unravel. A functional AGI that is also a body, (as opposed to an unwieldy government complex), will be the epitome of cramped complexity. How can we get them to have life spans past a few months?

Humanity only has 100 years left on Earth... in General Chat

...before Doomsday!

So sayeth Stephen Hawking.
He thinks climate change, asteroid strikes, epidemics, and overpopulation will be our downfall.

Could he be correct in this hypothesis?

https://futurism.com/stephen-hawking-humanity-only-has-100-years-left-on-earth-before-doomsday

Thoughts on graphs & XML in AI Programming

Hi,

I wanted to share my current point of view about symbolic Ai: how to manipulate it, and where it could live. I'm just talking about my own approach of course, nothing more.

I work with Javascript. It's cheap, fast enough, and it's everywhere. And, the ecosystem is just huge.

Whatever I explore, I always go back to my "consnet" idea, which is a network of cons cell (sort of like lisp's cons cells). The "brain" of the system, would be a directed graph. This graph would be modified step by step, by a rule engine. Rules would be stored in the very graph they modify. My best luck would be Cytoscape, a Js library for graph visualization& manipulation.

Then. This is a brains' world, but it wouldn't be nothing without a body. An Ai wouldn't learn anything without a teacher, like a parent, who lives in the same world, and who has approx the same body. During the 80's, MUDs were a popular type of text-based multiplayer "online" video-games. That would be the "physical world" intelligent agents and teachers live in. The problem is that natural language, traditionally used in MUDs, is not appropriate for a baby Ai, since it would be hard to "decrypt". The solution I see here, is to use XML descriptions instead of natural language descriptions.

So the world is a set of XML "rooms", containing XML "objects", that XML "bodies" (Ai's and teacher's bodies) can manipulate.

With such a setting, the teacher can actually teach things to Ai, about how this world works. There can be a need for food for example, how to find it, how to cook it, whatever you want. The bodies can "talk", in natural language, to each others. So the Ai can learn how to talk, how to express desires, ...etc.

Then one day, you can connect this XML world to the real world, either through sensors or to through the internet, or both.

That's how I see things currently.

XKCD Comic : Stack in XKCD Comic

Stack
21 June 2019, 5:00 am



Source: xkcd.com

Spotting objects amid clutter in Robotics News

Spotting objects amid clutter
20 June 2019, 4:59 am

A new MIT-developed technique enables robots to quickly identify objects hidden in a three-dimensional cloud of data, reminiscent of how some people can make sense of a densely patterned “Magic Eye” image if they observe it in just the right way.

Robots typically “see” their environment through sensors that collect and translate a visual scene into a matrix of dots. Think of the world of, well, “The Matrix,” except that the 1s and 0s seen by the fictional character Neo are replaced by dots — lots of dots — whose patterns and densities outline the objects in a particular scene.

Conventional techniques that try to pick out objects from such clouds of dots, or point clouds, can do so with either speed or accuracy, but not both.

With their new technique, the researchers say a robot can accurately pick out an object, such as a small animal, that is otherwise obscured within a dense cloud of dots, within seconds of receiving the visual data. The team says the technique can be used to improve a host of situations in which machine perception must be both speedy and accurate, including driverless cars and robotic assistants in the factory and the home.

“The surprising thing about this work is, if I ask you to find a bunny in this cloud of thousands of points, there’s no way you could do that,” says Luca Carlone, assistant professor of aeronautics and astronautics and a member of MIT’s Laboratory for Information and Decision Systems (LIDS). “But our algorithm is able to see the object through all this clutter. So we’re getting to a level of superhuman performance in localizing objects.”

Carlone and graduate student Heng Yang will present details of the technique later this month at the Robotics: Science and Systems conference in Germany.

“Failing without knowing”

Robots currently attempt to identify objects in a point cloud by comparing a template object — a 3-D dot representation of an object, such as a rabbit — with a point cloud representation of the real world that may contain that object. The template image includes “features,” or collections of dots that indicate characteristic curvatures or angles of that object, such the bunny’s ear or tail. Existing algorithms first extract similar features from the real-life point cloud, then attempt to match those features and the template’s features, and ultimately rotate and align the features to the template to determine if the point cloud contains the object in question.

But the point cloud data that streams into a robot’s sensor invariably includes errors, in the form of dots that are in the wrong position or incorrectly spaced, which can significantly confuse the process of feature extraction and matching. As a consequence, robots can make a huge number of wrong associations, or what researchers call “outliers” between point clouds, and ultimately misidentify objects or miss them entirely.

Carlone says state-of-the-art algorithms are able to sift the bad associations from the good once features have been matched, but they do so in “exponential time,” meaning that even a cluster of processing-heavy computers, sifting through dense point cloud data with existing algorithms, would not be able to solve the problem in a reasonable time. Such techniques, while accurate, are impractical for analyzing larger, real-life datasets containing dense point clouds.

Other algorithms that can quickly identify features and associations do so hastily, creating a huge number of outliers or misdetections in the process, without being aware of these errors.

“That’s terrible if this is running on a self-driving car, or any safety-critical application,” Carlone says. “Failing without knowing you’re failing is the worst thing an algorithm can do.”

A relaxed view

Yang and Carlone instead devised a technique that prunes away outliers in “polynomial time,” meaning that it can do so quickly, even for increasingly dense clouds of dots. The technique can thus quickly and accurately identify objects hidden in cluttered scenes.

The MIT-developed technique quickly and smoothly matches objects to those hidden in dense point clouds (left), versus existing techniques (right) that produce incorrect, disjointed matches. Gif: Courtesy of the researchers

The researchers first used conventional techniques to extract features of a template object from a point cloud. They then developed a three-step process to match the size, position, and orientation of the object in a point cloud with the template object, while simultaneously identifying good from bad feature associations.

The team developed an “adaptive voting scheme” algorithm to prune outliers and match an object’s size and position. For size, the algorithm makes associations between template and point cloud features, then compares the relative distance between features in a template and corresponding features in the point cloud. If, say, the distance between two features in the point cloud is five times that of the corresponding points in the template, the algorithm assigns a “vote” to the hypothesis that the object is five times larger than the template object.

The algorithm does this for every feature association. Then, the algorithm selects those associations that fall under the size hypothesis with the most votes, and identifies those as the correct associations, while pruning away the others.  In this way, the technique simultaneously reveals the correct associations and the relative size of the object represented by those associations. The same process is used to determine the object’s position.  

The researchers developed a separate algorithm for rotation, which finds the orientation of the template object in three-dimensional space.

To do this is an incredibly tricky computational task. Imagine holding a mug and trying to tilt it just so, to match a blurry image of something that might be that same mug. There are any number of angles you could tilt that mug, and each of those angles has a certain likelihood of matching the blurry image.

Existing techniques handle this problem by considering each possible tilt or rotation of the object as a “cost” — the lower the cost, the more likely that that rotation creates an accurate match between features. Each rotation and associated cost is represented in a topographic map of sorts, made up of multiple hills and valleys, with lower elevations associated with lower cost.

But Carlone says this can easily confuse an algorithm, especially if there are multiple valleys and no discernible lowest point representing the true, exact match between a particular rotation of an object and the object in a point cloud. Instead, the team developed a “convex relaxation” algorithm that simplifies the topographic map, with one single valley representing the optimal rotation. In this way, the algorithm is able to quickly identify the rotation that defines the orientation of the object in the point cloud.

With their approach, the team was able to quickly and accurately identify three different objects — a bunny, a dragon, and a Buddha — hidden in point clouds of increasing density. They were also able to identify objects in real-life scenes, including a living room, in which the algorithm quickly was able to spot a cereal box and a baseball hat.

Carlone says that because the approach is able to work in “polynomial time,” it can be easily scaled up to analyze even denser point clouds, resembling the complexity of sensor data for driverless cars, for example.

“Navigation, collaborative manufacturing, domestic robots, search and rescue, and self-driving cars is where we hope to make an impact,” Carlone says.

This research was supported in part by the Army Research Laboratory, the Office of Naval Research, and the Google Daydream Research Program.

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.