Engineers 3D print smart objects with ’embodied logic

Even without a brain or a nervous system, the Venus flytrap appears to make sophisticated decisions about when to snap shut on potential prey, as well as to open when it has accidentally caught something it can’t eat.

Researchers at the University of Pennsylvania’s School of Engineering and Applied Science have taken inspiration from these sorts of systems. Using stimuli-responsive materials and geometric principles, they have designed structures that have “embodied logic.” Through their physical and chemical makeup alone, they are able to determine which of multiple possible responses to make in response to their environment.

Despite having no motors, batteries, circuits or processors of any kind, they can switch between multiple configurations in response to pre-determined environmental cues, such as humidity or oil-based chemicals.

Using multi-material 3D printers, the researchers can make these active structures with nested if/then logic gates, and can control the timing of each gate, allowing for complicated mechanical behaviors in response to simple changes in the environment. For example, by utilizing these principles an aquatic pollution-monitoring device could be designed to open and collect a sample only in the presence of an oil-based chemical and when the temperature is over a certain threshold.

The Penn Engineers published an open access study outlining their approach in the journal Nature Communications.

The study was led by Jordan Raney, assistant professor in Penn Engineering’s Department of Mechanical Engineering and Applied Mechanics, and Yijie Jiang, a postdoctoral researcher in his lab. Lucia Korpas, a graduate student in Raney’s lab, also contributed to the study.

Raney’s lab is interested in structures that are bistable, meaning they can hold one of two configurations indefinitely. It is also interested in responsive materials, which can change their shape under the correct circumstances.

These abilities aren’t intrinsically related to one another, but “embodied logic” draws on both.

“Bistability is determined by geometry, whereas responsiveness comes out of the material’s chemical properties,” Raney says. “Our approach uses multi-material 3D printing to bridge across these separate fields so that we can harness material responsiveness to change our structures’ geometric parameters in just the right ways.”

In previous work, Raney and colleagues had demonstrated how to 3D print bistable lattices of angled silicone beams. When pressed together, the beams stay locked in a buckled configuration, but can be easily pulled back into their expanded form.

This bistable behavior depends almost entirely on the angle of the beams and the ratio between their width and length,” Raney says. “Compressing the lattice stores elastic energy in the material. If we could controllably use the environment to alter the geometry of the beams, the structure would stop being bistable and would necessarily release its stored strain energy. You’d have an actuator that doesn’t need electronics to determine if and when actuation should occur.”

Shape-changing materials are common, but fine-grained control over their transformation is harder to achieve.

“Lots of materials absorb water and expand, for example, but they expand in all directions. That doesn’t help us, because it means the ratio between the beams’ width and length stays the same,” Raney says. “We needed a way to restrict expansion to one direction only.”

The researchers’ solution was to infuse their 3D-printed structures with glass or cellulose fibers, running in parallel to the length of the beams. Like carbon fiber, this inelastic skeleton prevents the beams from elongating, but allows the space between the fibers to expand, increasing the beams’ width.

With this geometric control in place, more sophisticated shape-changing responses can be achieved by altering the material the beams are made of. The researchers made active structures using silicone, which absorbs oil, and hydrogels, which absorb water. Heat- and light-sensitive materials could also be incorporated, and materials responsive to even more specific stimuli could be designed.

Largest known prime number discovered

The Great Internet Mersenne Prime Search (GIMPS) has discovered the largest known prime number, 277,232,917-1, having 23,249,425 digits. A computer volunteered by Jonathan Pace made the find on December 26, 2017. Jonathan is one of thousands of volunteers using free GIMPS software.

The new prime number, also known as M77232917, is calculated by multiplying together 77,232,917 twos, and then subtracting one. It is nearly one million digits larger than the previous record prime number, in a special class of extremely rare prime numbers known as Mersenne primes. It is only the 50th known Mersenne prime ever discovered, each increasingly difficult to find. Mersenne primes were named for the French monk Marin Mersenne, who studied these numbers more than 350 years ago. GIMPS, founded in 1996, has discovered the last 16 Mersenne primes. Volunteers download a free program to search for these primes, with a cash award offered to anyone lucky enough to find a new prime. Prof. Chris Caldwell maintains an authoritative web site on the largest known primes, and has an excellent history of Mersenne primes.

The primality proof took six days of non-stop computing on a PC with an Intel i5-6600 CPU. To prove there were no errors in the prime discovery process, the new prime was independently verified using four different programs on four different hardware configurations.

  • Aaron Blosser verified it using Prime95 on an Intel Xeon server in 37 hours.
  • David Stanfill verified it using gpuOwL on an AMD RX Vega 64 GPU in 34 hours.
  • Andreas Höglund verified the prime using CUDALucas running on NVidia Titan Black GPU in 73 hours.
  • Ernst Mayer also verified it using his own program Mlucas on 32-core Xeon server in 82 hours. Andreas Höglund also confirmed using Mlucas running on an Amazon AWS instance in 65 hours.

Jonathan Pace is a 51-year old Electrical Engineer living in Germantown, Tennessee. Perseverance has finally paid off for Jon — he has been hunting for big primes with GIMPS for over 14 years. The discovery is eligible for a $3,000 GIMPS research discovery award.

GIMPS Prime95 client software was developed by founder George Woltman. Scott Kurowski wrote the PrimeNet system software that coordinates GIMPS’ computers. Aaron Blosser is now the system administrator, upgrading and maintaining PrimeNet as needed. Volunteers have a chance to earn research discovery awards of $3,000 or $50,000 if their computer discovers a new Mersenne prime. GIMPS’ next major goal is to win the $150,000 award administered by the Electronic Frontier Foundation offered for finding a 100 million digit prime number.

Credit for this prime goes not only to Jonathan Pace for running the Prime95 software, Woltman for writing the software, Kurowski and Blosser for their work on the Primenet server, but also the thousands of GIMPS volunteers that sifted through millions of non-prime candidates. In recognition of all the above people, official credit for this discovery goes to “J. Pace, G. Woltman, S. Kurowski, A. Blosser, et al.”

The Great Internet Mersenne Prime Search (GIMPS) was formed in January 1996 by George Woltman to discover new world record size Mersenne primes. In 1997 Scott Kurowski enabled GIMPS to automatically harness the power of thousands of ordinary computers to search for these “needles in a haystack.” Most GIMPS members join the search for the thrill of possibly discovering a record-setting, rare, and historic new Mersenne prime. The search for more Mersenne primes is already under way. There may be smaller, as yet undiscovered Mersenne primes, and there almost certainly are larger Mersenne primes waiting to be found. Anyone with a reasonably powerful PC can join GIMPS and become a big prime hunter, and possibly earn a cash research discovery award.

Turning deep-learning AI loose on software development

Computer scientists at Rice University have created a deep-learning, software-coding application that can help human programmers navigate the growing multitude of often-undocumented application programming interfaces, or APIs.

Known as Bayou, the Rice application was created through an initiative funded by the Defense Advanced Research Projects Agency aimed at extracting knowledge from online source code repositories like GitHub. A paper on Bayou will be presented May 1 in Vancouver, British Columbia, at the Sixth International Conference on Learning Representations, a premier outlet for deep learning research. Users can try it out at askbayou.com.

Designing applications that can program computers is a long-sought grail of the branch of computer science called artificial intelligence (AI).

“People have tried for 60 years to build systems that can write code, but the problem is that these methods aren’t that good with ambiguity,” said Bayou co-creator Swarat Chaudhuri, associate professor of computer science at Rice. “You usually need to give a lot of details about what the target program does, and writing down these details can be as much work as just writing the code.

“Bayou is a considerable improvement,” he said. “A developer can give Bayou a very small amount of information — just a few keywords or prompts, really — and Bayou will try to read the programmer’s mind and predict the program they want.”

Chaudhuri said Bayou trained itself by studying millions of lines of human-written Java code. “It’s basically studied everything on GitHub, and it draws on that to write its own code.”

Bayou co-creator Chris Jermaine, a professor of computer science who co-directs Rice’s Intelligent Software Systems Laboratory with Chaudhuri, said Bayou is particularly useful for synthesizing examples of code for specific software APIs.

“Programming today is very different than it was 30 or 40 years ago,” Jermaine said. “Computers today are in our pockets, on our wrists and in billions of home appliances, vehicles and other devices. The days when a programmer could write code from scratch are long gone.”

Bayou architect Vijay Murali, a research scientist at the lab, said, “Modern software development is all about APls. These are system-specific rules, tools, definitions and protocols that allow a piece of code to interact with a specific operating system, database, hardware platform or another software system. There are hundreds of APIs, and navigating them is very difficult for developers. They spend lots of time at question-answer sites like Stack Overflow asking other developers for help.”

Murali said developers can now begin asking some of those questions at Bayou, which will give an immediate answer.

“That immediate feedback could solve the problem right away, and if it doesn’t, Bayou’s example code should lead to a more informed question for their human peers,” Murali said.

Jermaine said the team’s primary goal is to get developers to try to extend Bayou, which has been released under a permissive open-source license.

“The more information we have about what people want from a system like Bayou, the better we can make it,” he said. “We want as many people to use it as we can get.” Bayou is based on a method called neural sketch learning, which trains an artificial neural network to recognize high-level patterns in hundreds of thousands of Java programs. It does this by creating a “sketch” for each program it reads and then associating this sketch with the “intent” that lies behind the program.

When a user asks Bayou questions, the system makes a judgment call about what program it’s being asked to write. It then creates sketches for several of the most likely candidate programs the user might want.

“Based on that guess, a separate part of Bayou, a module that understands the low-level details of Java and can do automatic logical reasoning, is going to generate four or five different chunks of code,” Jermaine said. “It’s going to present those to the user like hits on a web search. ‘This one is most likely the correct answer, but here are three more that could be what you’re looking for.'”

Breakthrough in construction of computers for mimicking human brain

A computer built to mimic the brain’s neural networks produces similar results to that of the best brain-simulation supercomputer software currently used for neural-signaling research, finds a new study published in the open-access journal Frontiers in Neuroscience. Tested for accuracy, speed and energy efficiency, this custom-built computer named SpiNNaker, has the potential to overcome the speed and power consumption problems of conventional supercomputers. The aim is to advance our knowledge of neural processing in the brain, to include learning and disorders such as epilepsy and Alzheimer’s disease.

“SpiNNaker can support detailed biological models of the cortex — the outer layer of the brain that receives and processes information from the senses — delivering results very similar to those from an equivalent supercomputer software simulation,” says Dr. Sacha van Albada, lead author of this study and leader of the Theoretical Neuroanatomy group at the Jülich Research Centre, Germany. “The ability to run large-scale detailed neural networks quickly and at low power consumption will advance robotics research and facilitate studies on learning and brain disorders.”

The human brain is extremely complex, comprising 100 billion interconnected brain cells. We understand how individual neurons and their components behave and communicate with each other and on the larger scale, which areas of the brain are used for sensory perception, action and cognition. However, we know less about the translation of neural activity into behavior, such as turning thought into muscle movement.

Supercomputer software has helped by simulating the exchange of signals between neurons, but even the best software run on the fastest supercomputers to date can only simulate 1% of the human brain.

“It is presently unclear which computer architecture is best suited to study whole-brain networks efficiently. The European Human Brain Project and Jülich Research Centre have performed extensive research to identify the best strategy for this highly complex problem. Today’s supercomputers require several minutes to simulate one second of real time, so studies on processes like learning, which take hours and days in real time are currently out of reach.” explains Professor Markus Diesmann, co-author, head of the Computational and Systems Neuroscience department at the Jülich Research Centre.

He continues, “There is a huge gap between the energy consumption of the brain and today’s supercomputers. Neuromorphic (brain-inspired) computing allows us to investigate how close we can get to the energy efficiency of the brain using electronics.”

Developed over the past 15 years and based on the structure and function of the human brain, SpiNNaker — part of the Neuromorphic Computing Platform of the Human Brain Project — is a custom-built computer composed of half a million of simple computing elements controlled by its own software. The researchers compared the accuracy, speed and energy efficiency of SpiNNaker with that of NEST — a specialist supercomputer software currently in use for brain neuron-signaling research.

“The simulations run on NEST and SpiNNaker showed very similar results,” reports Steve Furber, co-author and Professor of Computer Engineering at the University of Manchester, UK. “This is the first time such a detailed simulation of the cortex has been run on SpiNNaker, or on any neuromorphic platform. SpiNNaker comprises 600 circuit boards incorporating over 500,000 small processors in total. The simulation described in this study used just six boards — 1% of the total capability of the machine. The findings from our research will improve the software to reduce this to a single board.”

Van Albada shares her future aspirations for SpiNNaker, “We hope for increasingly large real-time simulations with these neuromorphic computing systems. In the Human Brain Project, we already work with neuroroboticists who hope to use them for robotic control.”