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Category: robotics/AI – Page 2,025

AI-Designed ‘Living Robots’ Crawl, Heal Themselves

Biological organisms have certain useful attributes that synthetic robots do not, such as the abilities to heal, adapt to new situations, and reproduce. Yet molding biological tissues into robots or tools has been exceptionally difficult to do: Experimental techniques, such as altering a genome to make a microbe perform a specific task, are hard to control and not scalable.

Now, a team of scientists at the University of Vermont and Tufts University in Massachusetts has used a supercomputer to design novel lifeforms with specific functions, then built those organisms out of frog cells.

The new, AI-designed biological bots crawl around a petri dish and heal themselves. Surprisingly, the biobots also spontaneously self-organize and clear their dish of small trash pellets.

Software detects backdoor attacks on facial recognition

As the U.S. Army increasingly uses facial and object recognition to train artificial intelligent systems to identify threats, the need to protect its systems from cyberattacks becomes essential.

An Army project conducted by researchers at Duke University and led by electrical and computer engineering faculty members Dr. Helen Li and Dr. Yiran Chen, made significant progress toward mitigating these types of attacks. Two members of the Duke team, Yukun Yang and Ximing Qiao, recently took first prize in the Defense category of the CSAW ‘19 HackML competition.

“Object recognition is a key component of future intelligent systems, and the Army must safeguard these systems from cyberattacks,” said MaryAnne Fields, program manager for intelligent systems at the Army Research Office. “This work will lay the foundations for recognizing and mitigating backdoor attacks in which the data used to train the system is subtly altered to give incorrect answers. Safeguarding object recognition systems will ensure that future Soldiers will have confidence in the intelligent systems they use.”

Hidden Computational Power Found in the Arms of Neurons

The information-processing capabilities of the brain are often reported to reside in the trillions of connections that wire its neurons together. But over the past few decades, mounting research has quietly shifted some of the attention to individual neurons, which seem to shoulder much more computational responsibility than once seemed imaginable.

The latest in a long line of evidence comes from scientists’ discovery of a new type of electrical signal in the upper layers of the human cortex. Laboratory and modeling studies have already shown that tiny compartments in the dendritic arms of cortical neurons can each perform complicated operations in mathematical logic. But now it seems that individual dendritic compartments can also perform a particular computation — “exclusive OR” — that mathematical theorists had previously categorized as unsolvable by single-neuron systems.

“I believe that we’re just scratching the surface of what these neurons are really doing,” said Albert Gidon, a postdoctoral fellow at Humboldt University of Berlin and the first author of the paper that presented these findings in Science earlier this month.

The discovery marks a growing need for studies of the nervous system to consider the implications of individual neurons as extensive information processors. “Brains may be far more complicated than we think,” said Konrad Kording, a computational neuroscientist at the University of Pennsylvania, who did not participate in the recent work. It may also prompt some computer scientists to reappraise strategies for artificial neural networks, which have traditionally been built based on a view of neurons as simple, unintelligent switches.

The Limitations of Dumb Neurons

In the 1940s and ’50s, a picture began to dominate neuroscience: that of the “dumb” neuron, a simple integrator, a point in a network that merely summed up its inputs. Branched extensions of the cell, called dendrites, would receive thousands of signals from neighboring neurons — some excitatory, some inhibitory. In the body of the neuron, all those signals would be weighted and tallied, and if the total exceeded some threshold, the neuron fired a series of electrical pulses (action potentials) that directed the stimulation of adjacent neurons.

At around the same time, researchers realized that a single neuron could also function as a logic gate, akin to those in digital circuits (although it still isn’t clear how much the brain really computes this way when processing information). A neuron was effectively an AND gate, for instance, if it fired only after receiving some sufficient number of inputs.

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Ferroelectric Semiconductors Could Mix Memory and Logic

FSJs (Ferroelectric Semiconductor Junction) in neuromorphic chips.

Engineers at Purdue University and at Georgia Tech have constructed the first devices from a new kind of two-dimensional material that combines memory-retaining properties and semiconductor properties. The engineers used a newly discovered ferroelectric semiconductor, alpha indium selenide, in two applications: as the basis of a type of transistor that stores memory as the amount of amplification it produces; and in a two-terminal device that could act as a component in future brain-inspired computers. The latter device was unveiled last month at the IEEE International Electron Devices Meeting in San Francisco.

Ferroelectric materials become polarized in an electric field and retain that polarization even after the field has been removed. Ferroelectric RAM cells in commercial memory chips use the former ability to store data in a capacitor-like structure. Recently, researchers have been trying to coax more tricks from these ferroelectric materials by bringing them into the transistor structure itself or by building other types of devices from them.

In particular, they’ve been embedding ferroelectric materials into a transistor’s gate dielectric, the thin layer that separates the electrode responsible for turning the transistor on and off from the channel through which current flows. Researchers have also been seeking a ferroelectric equivalent of the memristors, or resistive RAM, two-terminal devices that store data as resistance. Such devices, called ferroelectric tunnel junctions, are particularly attractive because they could be made into a very dense memory configuration called a cross-bar array. Many researchers working on neuromorphic- and low-power AI chips use memristors to act as the neural synapses in their networks. But so far, ferroelectric tunnel junction memories have been a problem.

Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs

A lack of tools to precisely control gene expression has limited our ability to evaluate relationships between expression levels and phenotypes. Here, we describe an approach to titrate expression of human genes using CRISPR interference and series of single-guide RNAs (sgRNAs) with systematically modulated activities. We used large-scale measurements across multiple cell models to characterize activities of sgRNAs containing mismatches to their target sites and derived rules governing mismatched sgRNA activity using deep learning. These rules enabled us to synthesize a compact sgRNA library to titrate expression of ~2,400 genes essential for robust cell growth and to construct an in silico sgRNA library spanning the human genome. Staging cells along a continuum of gene expression levels combined with single-cell RNA-seq readout revealed sharp transitions in cellular behaviors at gene-specific expression thresholds. Our work provides a general tool to control gene expression, with applications ranging from tuning biochemical pathways to identifying suppressors for diseases of dysregulated gene expression.

Sexy robot influencers are taking over Instagram — and coming for your jobs

In reality, Shudu, who has 196,000 followers on Instagram, is more painting than person. She’s a 3D digital animation made by an Englishman named Cameron-James Wilson, who bills his creation as “the world’s first digital supermodel.”

Influencers, beware: hot bots are coming for your jobs. Shudu is representative of a growing crop of beautiful and highly realistic avatars on social media, created for the sole purpose of gaining followers and making money. And it’s working — these otherworldly beauties are landing lucrative partnerships with the biggest names in fashion, such as Balmain, Calvin Klein and Dior. Social-media-savvy celebrities are embracing them as well; Kim Kardashian, Bella Hadid and Zendaya have all appeared in photos and videos with their digital counterparts. Even top modeling agencies, including IMG and Lipps, have signed on to manage the most popular bots.

“Over the past few years, this has really taken off,” Wilson, who runs the virtual-influencer company the Diigitals, tells The Post. Wilson, 30, now controls the careers of seven robot models who, like human influencers, post sponsored content on social media for money. “My company has grown massively. This is a really lucrative industry.”