{"id":3992,"date":"2020-11-29T02:02:07","date_gmt":"2020-11-29T02:02:07","guid":{"rendered":"https:\/\/techclot.com\/index.php\/2020\/11\/29\/opteran-technologies-receives-2-8m-to-develop-an-ai-honeybee-brain\/"},"modified":"2020-11-29T02:02:07","modified_gmt":"2020-11-29T02:02:07","slug":"opteran-technologies-receives-2-8m-to-develop-an-ai-honeybee-brain","status":"publish","type":"post","link":"https:\/\/techclot.com\/index.php\/2020\/11\/29\/opteran-technologies-receives-2-8m-to-develop-an-ai-honeybee-brain\/","title":{"rendered":"Opteran Technologies receives $2.8M to develop an AI honeybee brain"},"content":{"rendered":"

Opteran Technologies receives $2.8M to develop an AI honeybee brain<\/a><\/p>\n

<\/p>\n

\"Opteran<\/a><\/div>\n

<\/p>\n

At its core, artificial intelligence (AI) is a technology that lets computers think like a human. This is accomplished by creating artificial neuron pathways for data processing and memory. While most of today\u2019s AI systems try to replicate the human brain, none are anywhere close to doing so.<\/p>\n

James Marshall, a professor of computer science<\/a> at the University of Sheffield is baffled by this trend. He believes that the future of AI needs to start with smaller ideas\u2014literally.<\/p>\n

His startup, Opteran Technologies<\/a>, has just raised $2.8 million to build AI systems modeled on honeybee brains. The team argues that this is a more logical starting point for the technology and could yield more immediate results.<\/p>\n

\nAdvertisement<\/span><\/p>\n
\n \"Manage<\/a>\n<\/div>\n<\/div>\n

Marshall told Digital Trends<\/a>, \u201cIf you\u2019re going to start building a model of any brain on the planet, why on Earth would you start with the most complicated one?\u201d<\/p>\n

Thinking Small<\/h2>\n

In the tech world, big ideas are usually what earn the biggest payday<\/a>. However, it\u2019s clear that thinking on a large scale is slowing down the process of AI development. Marshall and his team believe that focusing on smaller, more attainable goals is the key to eventual large-scale success.<\/p>\n

That\u2019s an interesting theory considering the fact that many of today\u2019s most sophisticated AI systems already have more artificial neurons than an average honeybee. Even so, the general intelligence of those programs is nowhere close to that of a bee.<\/p>\n

Despite the fact that many people look down on insects, honeybees can be incredibly smart. Marshall notes that bees are, \u201cconsummate visual navigators, [adept at] long-distance navigation, with very sophisticated learning abilities.\u201d<\/p>\n

He adds, \u201cThey\u2019re much more than the simple kind of reactive automata that people often think insects are. Individually, they\u2019re very clever.\u201d<\/p>\n

That isn\u2019t just a hypothesis. Data has already proven that honeybees are good problem solvers. Researchers from the University of London found that bees solved the traveling salesman problem<\/a> faster than the world\u2019s top supercomputers. The test requires the bee or computer to find the shortest route between flowers that are discovered in a random order.<\/p>\n

Ultimately, this means that honeybee-based artificial intelligence systems aren\u2019t a token achievement. David Rajan, CEO of Opteran, says, \u201cBuilding a honeybee brain in silicon could therefore help develop sophisticated navigation tools that could be lightweight, ultra-low-powered, and orders of magnitude more efficient than the deep learning approaches.\u201d<\/p>\n

Different Approach<\/h2>\n

Most of today\u2019s deep-learning systems focus on an approach pioneered by the brain\u2019s visual cortex<\/a>. In other words, the system uses cameras to identify something and then processes that information into a useful database that it can refer back to later.<\/p>\n

The brain\u2014as a whole\u2014doesn\u2019t think like that. Marshall notes, \u201cWhen you look at a complete brain, it is highly structured. You have different brain regions that do different things, that are internally structured in different ways, with well-defined connections between them.\u201d<\/p>\n

In that sense, Opteran\u2019s honeybee-inspired AI<\/a> is different from other types on the market. Rather than trying to create a new type of thinking, it is designed to think naturally\u2014just like a biological brain.<\/p>\n

Marshall says, \u201cHaving a million neurons and however many synapses isn\u2019t the end of the story; it\u2019s about how you connect them together.\u201d<\/p>\n

He adds, \u201cIt\u2019s also about the kind of information processing that\u2019s done at the neuron level, because there\u2019s more than one kind of neuron in the real brain, although there\u2019s often only one neuron type in a deep net.\u201d<\/p>\n

Ultimately, Opteran\u2019s approach uses a variety of neuron types to create more efficient processing. That gives it an advantage in the power arena as it requires fewer computing resources<\/a> than typical deep-learning tools.<\/p>\n

This makes it easier for researchers to access the technology and start using it \u201cout of the box.\u201d<\/p>\n

The latter is something that Opteran is focused on. It has promised that no additional training will be required for those who want to use the system. As an added bonus, the honeybee-inspired AI has transparent rules.<\/p>\n

This addresses a major issue with AI in general, which is that researchers often don\u2019t know how the system makes decisions<\/a>.<\/p>\n

Ready to Roll<\/h2>\n

Thanks to its \u201cstart small\u201d mindset, Opteran is already getting close to rolling out its first commercial tool. The startup hopes to have a product on the market within the next 18 months. Its first applications will focus on things like obstacle avoidance and autonomous decision making.<\/p>\n

Paired with Opteran See, a 360-degree camera, the algorithms could become a key component for applications like autonomous driving. With a huge push by a number of companies in that space right now, Opteran is poised to get in on the success.<\/p>\n

That being said, there are still plenty of challenges for the startup to face. For one, there are a lot of things about the brain that researchers don\u2019t know. The knowledge gap makes it difficult to reverse engineer certain traits.<\/p>\n

Fortunately, Opteran doesn\u2019t think this will be a problem. Marshall says, \u201cWhat we really care about commercially is behavior, the competency of the system. As a business, we\u2019re not fixated on saying we\u2019re confident we\u2019ve reproduced the way the honeybee works. [Instead, we want to say] we are confident that we have reproduced a system which is behaviorally robust, and which seems to us to behave as if it was a honeybee acting like a honeybee.\u201d<\/p>\n

Fans of Alan Turing might find that statement eerily familiar. It is a reference back to Turing\u2019s definition of AI<\/a> being something that can fool a human into thinking they are talking to another human.<\/p>\n

Although there may not be a Turing test for bee-based AI systems, it will be interesting to see where this technology goes in the future. Indeed, Opteran\u2019s approach could become a mainstream part of the AI sector. If more companies start thinking at a smaller level, perhaps it will one day be possible to create an AI that thinks like the human brain.<\/p>\n<\/p>\n

Published at Sat, 28 Nov 2020 22:41:15 +0000<\/p>\n

Opteran Technologies receives $2.8M to develop an AI honeybee brain<\/a><\/p>\n

<\/p>\n

\"Opteran<\/a><\/div>\n

<\/p>\n

At its core, artificial intelligence (AI) is a technology that lets computers think like a human. This is accomplished by creating artificial neuron pathways for data processing and memory. While most of today\u2019s AI systems try to replicate the human brain, none are anywhere close to doing so.<\/p>\n

James Marshall, a professor of computer science<\/a> at the University of Sheffield is baffled by this trend. He believes that the future of AI needs to start with smaller ideas\u2014literally.<\/p>\n

His startup, Opteran Technologies<\/a>, has just raised $2.8 million to build AI systems modeled on honeybee brains. The team argues that this is a more logical starting point for the technology and could yield more immediate results.<\/p>\n

\nAdvertisement<\/span><\/p>\n
\n \"Manage<\/a>\n<\/div>\n<\/div>\n

Marshall told Digital Trends<\/a>, \u201cIf you\u2019re going to start building a model of any brain on the planet, why on Earth would you start with the most complicated one?\u201d<\/p>\n

Thinking Small<\/h2>\n

In the tech world, big ideas are usually what earn the biggest payday<\/a>. However, it\u2019s clear that thinking on a large scale is slowing down the process of AI development. Marshall and his team believe that focusing on smaller, more attainable goals is the key to eventual large-scale success.<\/p>\n

That\u2019s an interesting theory considering the fact that many of today\u2019s most sophisticated AI systems already have more artificial neurons than an average honeybee. Even so, the general intelligence of those programs is nowhere close to that of a bee.<\/p>\n

Despite the fact that many people look down on insects, honeybees can be incredibly smart. Marshall notes that bees are, \u201cconsummate visual navigators, [adept at] long-distance navigation, with very sophisticated learning abilities.\u201d<\/p>\n

He adds, \u201cThey\u2019re much more than the simple kind of reactive automata that people often think insects are. Individually, they\u2019re very clever.\u201d<\/p>\n

That isn\u2019t just a hypothesis. Data has already proven that honeybees are good problem solvers. Researchers from the University of London found that bees solved the traveling salesman problem<\/a> faster than the world\u2019s top supercomputers. The test requires the bee or computer to find the shortest route between flowers that are discovered in a random order.<\/p>\n

Ultimately, this means that honeybee-based artificial intelligence systems aren\u2019t a token achievement. David Rajan, CEO of Opteran, says, \u201cBuilding a honeybee brain in silicon could therefore help develop sophisticated navigation tools that could be lightweight, ultra-low-powered, and orders of magnitude more efficient than the deep learning approaches.\u201d<\/p>\n

Different Approach<\/h2>\n

Most of today\u2019s deep-learning systems focus on an approach pioneered by the brain\u2019s visual cortex<\/a>. In other words, the system uses cameras to identify something and then processes that information into a useful database that it can refer back to later.<\/p>\n

The brain\u2014as a whole\u2014doesn\u2019t think like that. Marshall notes, \u201cWhen you look at a complete brain, it is highly structured. You have different brain regions that do different things, that are internally structured in different ways, with well-defined connections between them.\u201d<\/p>\n

In that sense, Opteran\u2019s honeybee-inspired AI<\/a> is different from other types on the market. Rather than trying to create a new type of thinking, it is designed to think naturally\u2014just like a biological brain.<\/p>\n

Marshall says, \u201cHaving a million neurons and however many synapses isn\u2019t the end of the story; it\u2019s about how you connect them together.\u201d<\/p>\n

He adds, \u201cIt\u2019s also about the kind of information processing that\u2019s done at the neuron level, because there\u2019s more than one kind of neuron in the real brain, although there\u2019s often only one neuron type in a deep net.\u201d<\/p>\n

Ultimately, Opteran\u2019s approach uses a variety of neuron types to create more efficient processing. That gives it an advantage in the power arena as it requires fewer computing resources<\/a> than typical deep-learning tools.<\/p>\n

This makes it easier for researchers to access the technology and start using it \u201cout of the box.\u201d<\/p>\n

The latter is something that Opteran is focused on. It has promised that no additional training will be required for those who want to use the system. As an added bonus, the honeybee-inspired AI has transparent rules.<\/p>\n

This addresses a major issue with AI in general, which is that researchers often don\u2019t know how the system makes decisions<\/a>.<\/p>\n

Ready to Roll<\/h2>\n

Thanks to its \u201cstart small\u201d mindset, Opteran is already getting close to rolling out its first commercial tool. The startup hopes to have a product on the market within the next 18 months. Its first applications will focus on things like obstacle avoidance and autonomous decision making.<\/p>\n

Paired with Opteran See, a 360-degree camera, the algorithms could become a key component for applications like autonomous driving. With a huge push by a number of companies in that space right now, Opteran is poised to get in on the success.<\/p>\n

That being said, there are still plenty of challenges for the startup to face. For one, there are a lot of things about the brain that researchers don\u2019t know. The knowledge gap makes it difficult to reverse engineer certain traits.<\/p>\n

Fortunately, Opteran doesn\u2019t think this will be a problem. Marshall says, \u201cWhat we really care about commercially is behavior, the competency of the system. As a business, we\u2019re not fixated on saying we\u2019re confident we\u2019ve reproduced the way the honeybee works. [Instead, we want to say] we are confident that we have reproduced a system which is behaviorally robust, and which seems to us to behave as if it was a honeybee acting like a honeybee.\u201d<\/p>\n

Fans of Alan Turing might find that statement eerily familiar. It is a reference back to Turing\u2019s definition of AI<\/a> being something that can fool a human into thinking they are talking to another human.<\/p>\n

Although there may not be a Turing test for bee-based AI systems, it will be interesting to see where this technology goes in the future. Indeed, Opteran\u2019s approach could become a mainstream part of the AI sector. If more companies start thinking at a smaller level, perhaps it will one day be possible to create an AI that thinks like the human brain.<\/p>\n<\/p>\n

Published at Sat, 28 Nov 2020 22:41:15 +0000<\/p>\n","protected":false},"excerpt":{"rendered":"

Opteran Technologies receives $2.8M to develop an AI honeybee brain At its core, artificial intelligence…<\/p>\n","protected":false},"author":3,"featured_media":3990,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3],"tags":[],"class_list":["post-3992","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/techclot.com\/wp-content\/uploads\/2020\/11\/EgOydk.jpg?fit=696%2C475&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p3orZX-12o","jetpack-related-posts":[],"_links":{"self":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/3992","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/comments?post=3992"}],"version-history":[{"count":0,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/posts\/3992\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media\/3990"}],"wp:attachment":[{"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/media?parent=3992"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/categories?post=3992"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/techclot.com\/index.php\/wp-json\/wp\/v2\/tags?post=3992"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}