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Recent articles

"Biological computing" explained. ​ Let's start with a definition. ​ A biological computer differs from a conventional one as it uses parts of living organisms to perform computations. ​ Using biologically derived molecules to perform tasks similar to those done by regular computers might initially seem outlandish, but read on for some simple science. ​ The word "computer" originates from the Latin word "computare," meaning "to calculate" (it evolved from initially describing people who performed calculations to then describe devices). ​ DNA holds a huge amount of data in a small space; much like our disks. ​ Proteins, the essential macromolecules that perform most of the work within cells, carry out instructions and make things happen - just like the semiconductor chips that run our devices. ​ Biological computers differ from the machines of today in three noteworthy aspects: what they are made from, how they do processing, and the amount of energy that they use: ​ ​1) Conventional computers are made from silicon and metal, while biological computers use organic molecules like proteins. ​ ​2) Traditional computers use electrical signals to process information, whereas biological computers use chemical reaction. ​ ​3) As biological computers use natural processes, these require less energy compared to the electrical circuits in standard PCs - so they can be more energy efficient. ​ Given the link to and reliance upon the natural world, in the future this could be the gateway to bring the current limitations and inconsistent fallibility of Artificial Intelligence (AI) closer to our own general human intelligence. ​ Two years ago, researchers from The Johns Hopkins University and scientists at Cortical Labs suggested that the answer to less hallucinagenic AI was organoids - computers created with human brain cells. ​ The team has now developed what they label Synthetic Biological Intelligence (SBI) by integrating digital computing with neural cultures developed through synthetic biology methods. ​ In short, they have built the first deployable biological computer - available now to order. ​ Hundreds of thousands of real tiny neurons are cultivated inside a nutrient-rich solution; each is roughly the size of an ant brain. ​ Grown across a silicon chip these send and receive electrical impulses into the neural structure through a combination of hard silicon and soft tissue, and are said to be "self-programming" & "infinitely flexible". ​ Cortical previously made a big splash in 2022 after teaching human brain cells in a petri dish how to play the classic video game "Pong". ​ Whether the team can progress from the simple ball-and-two-paddles of one of the earliest arcade games...to quickly making correct contextual decisions in unpredictable environments remains to be seen. ​ Biological computing offers much potential, but it remains early days for this frontier technology as it scales and integrates.

In the future we might well change from Caveat Emptor (Latin for "Let the buyer beware") to a default of Caveat Venditor ("Let the seller beware"). ​ The principle of Caveat Emptor is that the buyer alone is responsible for checking the quality and suitability of goods before a purchase is made. ​ This can be disadvantageous to the buyer due to information asymmetry (when sellers potentially have more knowledge than buyers). ​ Caveat Venditor flips this, pushing responsibility onto the seller to be sure that their products and services function as advertised. ​ The catalyst will be due to what we see, hear, and consume in advertising and other forms of content being increasingly generated autonomously by Artificial Intelligence (AI). ​ This is a crux of two damaging factors which intersect: difficulty in being able to tell what is actually real (or true), as well as a disinclination to verify due to reducing attention spans. ​ Let's take the example of a short video, 3m 11sec long, which was produced with only one single aspect which is real - the other 99% is generated. ​ View it here: https://lnkd.in/gJBhHyDM ​ It is stunningly realistic - yet was created in around three weeks, using the Veo 2 text-to-video system from Deepmind (part of Google). ​ Done for real, this sort of footage would likely have cost hundreds of thousands of dollars - requiring a huge team across many locations to shoot, followed by editing and post-production. ​ As artistic processes become more accessible and automated, we will enter a realm of true personalised entertainment - at any given time, you can order bespoke content freshly cooked-up to your taste, just like a meal. ​ Then - how will we know what is real...or perhaps we simply don't care? ​ Thus, the accountability for what is created being both labelled as such (plus not masquerading as true) needs to be clearly sitting with producers of these tools and the custom content from them; Caveat Venditor! ​ ​NB: no AI was used in the creation of this thought leadership piece, save for the accompanying image which took no less than 38 iterations to produce something containing the words "Caveat Emptor Caveat Venditor" pseudo-clearly...

Show Me The Honey! ​ 32,000 honey bees were equipped with what look like tiny QR codes on their backs thanks to a collaboration between engineers and entomologists at Penn State University. ​ Not the latest in insect fashion, but part of research to gain deeper insight into the mechanics of bee behaviour, hive dynamics, and foraging trips. ​ It is a long-standing mystery as to how how far bees travel from their hives to collect pollen and nectar. ​ The tags allow scientists to quietly and noninvasively observe the bees without disturbing their natural habitats. ​ Observing six distinct colonies split in three separate locations over several weeks, researchers learnt that while most trips outside of the hive last mere minutes, a small minority of bees can spend more than two hours away. ​ Though they are estimated to only live for four to six weeks on average, honey bees are capable of flying long distances when they need to (up to 10 kilometers from their hive). ​ The 2mm square codes (pictured) are known as fiducial tags; whilst they resemble a 2D-barcode such as a "Quick Response" (QR) code, fiducial tags differ significantly in detectability speed and data payload. ​ Whilst QR codes can carry comparatively high amounts of information, they do demand high-resolution and precise alignment for detection. ​ Fiducial tags can be automatically detected under conditions of low resolution, poor illumination, and random rotation. ​ Furthermore, multiple tags can be detected simultaneously in a blocked, blurred, or cluttered image. ​ Specifically, the type of fiducial tags attached to the bees are what are known as an AprilTag, using a system designed by the APRIL (Autonomy, Perception, Robotics, Interfaces, and Learning) robotics laboratory at the University of Michigan. ​ A camera at the hive entrance automatically scans the image and the AprilTag detection software computes the position, orientation, and identity of the tag (which is designed to be recognisable at any angle). ​ The low-cost system is a break with conventional entomology field work in which researchers visually observe bees for limited periods, enabling far more comprehensive and expanded observations thanks to more data. ​ As one of the authors of the study states: “In field biology, we usually just look at things with our eyes, but the number of observations we can make as humans will never scale up to what a machine can do”. ​ As our environments evolve due to climate change, leveraging technology to enhance the depth and scale of insight into natural world patterns might give us early warning of issues occurring in the future. ​ Bee collaboration, plus being copied via biomimetics was previously explored here: https://lnkd.in/e-CSBTAn

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