But UK technology startup Opteran is taking a different route, tapping into 600 million years of evolution to unravel and mimic the highly efficient navigational and decision-making abilities of insects.

Spun out from the University of Sheffield in 2020, Opteran – named after the Hymenoptera order of insects, which includes wasps, bees and ants – has now developed a commercially available product: the Opteran Mind – which it claims could help usher in a new era of higher performance autonomy at a fraction of the cost of existing approaches.

Now employing 45 people, the company is growing rapidly, and last month announced a major partnership that will see its technology embedded in advanced warehouse robots developed by German autonomous picking and transportation robot manufacturer Safelog.

But as Opteran CEO David Rajan recently told The Engineer, this is just the beginning, and the company is already exploring and developing further commercial applications in sectors ranging from logistics and automotive, to mining, security and beyond.

The firm has its origins in research carried out at the University of Sheffield’s Department of Computer Science by co-founders Prof James Marshall and Dr Alex Cope.

Over the course of a decade, their group set about advancing the understanding of the structure and function of insect brains, using a host of techniques to figure out exactly how insects see the world, localise themselves in space, navigate over huge distances, and respond to all the uncertainty and chaos of the world around them.

By distilling these capabilities into a series of algorithms, the team arrived at a concept it refers to as “natural intelligence”: a potentially game-changing idea that it claims represents a vastly more sensible and efficient way of solving the autonomy challenge.

“Insects like honeybees have about a million neurons by comparison to around 86 billion in a human being, but the central systems are there,” said Rajan.  “They see the world, localise themselves in space and can even navigate up to 10 kilometers consuming just micro watts of power. If you really want to see state of the art autonomy, don’t go to California…..look at a garden.”

All of these capabilities are now in the process of being embedded in an actual product – the Opteran’ Mind- an edge computing solution composed of a series of insect-inspired algorithms. Crucially – and in stark contrast to most other efforts to crack the autonomy nut  – this technology runs with low-cost cameras and chips and doesn’t require training, infrastructure or connectivity to work.

The technology currently includes algortihms that enable machines to spatially navigate and know where they are in the world. Over the coming months, the team plans to add further capabilities including collision avoidance and – at some point next year –  decision making algorithms that will allow machines to prioritise tasks.

Explaining how Opteran Mind works in practice, the company’s chief product officer, Charlie Rance, contrasted it with the so-called SLAM (simultaneous localisation and mapping) approach to autonomous navigation, which is the most widely used  solution to the problem of robot navigation.

Using SLAM, a machine equipped with advanced cameras and huge amounts of processing power builds a highly accurate map and localises itself on that map at the same time. It’s a sophisticated technique that has been at the heart of autonomous development in recent years, but it’s not without its limitations: It’s expensive, it needs to be trained on huge amounts of data, and the maps it creates can fail or mislocalise if there are sudden, unexpected changes to the environment such as the lights being turned off or something being moved.

Opteran’s approach is fundamentally different. By mimicking the way an insect is able to find its way around using a brain the size of a pinhead and fewer than a million neurons, Opteran’s technology is able to solve this problem with, said Rance “the tiniest amount of compute and the lowest data footprint you can possibly think of.”

Alongside all of this, added Rajan, it’s also inherently better suited to dealing with the unpredictability of the  real word: “Things happen, things change: the weather is horrible, the lighting is bad, in a warehouse everything gets moved around all the time.  We’re operating  as nature can, instead of in this kind of engineered way of using sensors and compute to try and solve 80 per cent of the problem.”

What’s more, unlike existing systems, Opteran’s technology doesn’t need to be trained on vast amounts of data before being deployed. “We’re not gathering data to train a system in a datacenter,” said Rance. “The algorithms are innate, so they know how to move around the world on their own, they can respond to the dynamic variability, they adapt to the world as it’s happening around them.”

The Safelog application is a good illustration of this.  Typically, deploying an autonomous guided vehicle (AGV) into a warehouse takes a fair bit of setting up with operators having to painstakingly scan the entire facility and process huge amounts of data before the robots can be let loose on the warehouse floor. The set up using Opteran’s technology is, said Rance, orders of magnitude less onerous: “All it takes for them to do is to drive one robot at the speed that robot operates and off it goes. We’re essentially creating a solution that allows their AGVs to autonomously map in one shot, so it’s a very quick set up time and you can share that with the rest of your AGVs. We can remove fixed infrastructure, so we’re not using any kind of reflectors or QR codes or anything. And we’re keeping the system at a lower cost.”

Alongside the Safelog deal, the company also has a number of other ongoing commercial applications which, for the time being, are subject to client confidentiality agreements. However, Rajan told The Engineer that these include an application on an indoor security drone for the consumer market, as well as use of the technology in the mining and automotive sectors. Whilst the range of potential application areas is almost unlimited, Rajan stressed that there are some applications that the company is keen to avoid. “We are not in favor of kinetic applications”, he said, “we’ve already turned down projects in the drone industry that would have been kinetic.”

The company is now very consciously harvesting the low-hanging fruit, but its longer-term ambitions are huge: “The real opportunity here is every single machine that moves could have an Opteran mind inside,” said Rajan. “That means all the humanoid robots, all the vacuum cleaners, all the lawn mowers, all the warehouse robots, all the drones. That’s the opportunity in front of us: full general purpose autonomy inside every machine that moves on the planet. We want to be the autonomy company, the company that enables machines to move around the whole world with their own brain. That’s our ambition. We’re building the brain for everybody else’s brawn.”

The post Reverse engineering the insect brain appeared first on News Agency nabakhabar.

Whether you turn on the news or attempt to unwind with a Netflix series, a lot of the messages in today’s world are negative. It’s easy to become immune to the heaviness, and you may not realize how much its constant drum beat impacts you. That’s because the human brain is wired to focus on the negative. It’s tied to survival.

The post I tried a 2-week negativity fast. Here’s how it went appeared first on News Agency nabakhabar.

Not so fast, said UNLV anthropologist Brian Villmoare and Liverpool John Moores University scientist Mark Grabowski.

In a new paper published last week in Frontiers in Ecology and Evolution, the UNLV-led team analyzed the dataset that the research group from last year’s study used and dismissed their findings.

“We were struck by the implications of a substantial reduction in modern human brain size roughly 3,000 years ago, during an era of many important innovations and historical events—the appearance of Egypt’s the New Kingdom, the development of Chinese script, the Trojan War, and the emergence of the Olmec civilization, among many others,” Villmoare said.

“We re-examined the dataset from DeSilva et al. and found that human brain size has not changed in 30,000 years, and probably not in 300,000 years,” Villmoare said. “In fact, based on this dataset, we can identify no reduction in brain size in modern humans over any time period since the origins of our species.”

Key takeaways

The UNLV research team questioned several of the hypotheses that DeSilva et. al gleaned from a dataset of nearly 1,000 early human fossil and museum specimens, including:

• The UNLV team says the rise of agriculture and complex societies occurred at different times around the globe—meaning there should be variation in the timing of skull changes seen in different populations. However, DeSilva’s dataset sampled only 23 crania from the timeframe critical to the brain shrinkage hypothesis and lumped together specimens from locations including England, China, Mali, and Algeria.
• The dataset is heavily skewed because more than half of the 987 skulls examined represent only the last 100 years of a 9.8-million-year span of time—and therefore don’t give scientists a good idea of how much cranial size has changed over time.
• Multiple hypotheses on causes of reduction in modern human brain size need to be reassessed if human brains haven’t actually changed in size since the arrival of our species.

The post No, the human brain did not shrink 3,000 years ago: research appeared first on News Agency nabakhabar.

In recent years, technological advances have allowed neuroscientists to study the diverse cell populations in the brain more in-depth. While this has shed light on the function of some cell populations and their molecular composition, the relationship between mature cell populations and progenitor cells is still poorly understood.

Researchers at Karolinska Institute, KTH Royal Institute of Technology and Stockholm University have recently carried out a study aimed at better understanding the clonal relations between cells in the mouse brain. Their findings, published in Nature Neuroscience, were collected using a new approach they developed that combines single-cell and spatial transcriptomics with clonal barcoding, two different methods used to conduct neuroscience studies.

“Our lab studies the potential of neural stem cells to generate a wide variety of cell types, which is important to understand normal brain development and could be exploited to regenerate lost cells in neurological diseases,” Michael Ratz, one of the researchers who carried out the study, told Medical Xpress.

To better understand the potential of stem cells as generators of different cell populations, the researchers used an approach known as “fate mapping” or “clonal tracking.” This is a powerful technique that allows scientists to identify the “progeny” of a single cell and to reconstruct its developmental history (i.e., ancestry).

“These methods have led to fundamental insights about tissue development and have been used for many years, but they are limited in their ability to study many cells at the same time due to their reliance on microscopy which can only distinguish a few colors (usually <5) at once,” Ratz said. “We wanted to establish an approach that matches the complexity of the nervous system, by converting the problem of fate mapping into a form that can be read out by modern high-throughput sequencing methods.”

Past studies have found that cells in the mammalian brain can differ significantly in shape, function and spatial location. However, traditional methods to study the brain did not allow researchers to gather extensive information about individual cells.

An interesting metaphor that is sometimes used to describe this previous lack of insight about individual cells is the smoothie vs. fruit salad analogy. Essentially, previously neuroscientists were only able to observe large sections of brain tissues as a single, homogenous mixture (i.e., resembling a smoothie). This prevented them from learning more about specific cells in these sections of brain tissue (i.e., individual fruits inside the smoothie).

“In recent years, technological advances allowed us to look at individual cells at the mRNA level, to study the cellular composition of brain tissue and how cells that malfunction can lead to disease,” Ratz explained. “One such method is single-cell transcriptomics, where individual cells are extracted from tissue and the sequences of thousands of mRNA molecules present in a single cell are sequenced while spatial transcriptomics achieves the same, but with intact tissue sections which preserves critical information about an individual cells’ location in a tissue.”

In their study, Ratz and his colleagues used genetic barcodes to characterize individual stem (progenitor) cells in the mouse brain. As these barcodes are inherited by daughter cells during brain development, they allowed them to study the clonal relationships between the cells and derive their phenotype profiles.

Subsequently, the researchers generated barcoding reagents and characterized the performance of these reagents in vivo (i.e., in the brain tissue of live mice). Their results could be very valuable for the neuroscientific community and could soon inform new studies investigating cell clonal relations more in-depth. In the future, their experimental approach could also be used to conduct other studies examining specific cell populations or the relations between progenitor and daughter cells.

“We used this technology to discover unique clonal features of microglia, the brain’s immune cells that play an important role in neurodegenerative diseases, such as the large groups of cells an individual precursor cell forms and their widespread migration across large brain areas,” Ratz added. “The tool we developed sheds new light on the molecular profiles underlying those cellular behaviors during normal development and could be adapted to study such microglia features in neurological diseases.”

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