Live cockroaches with a controlling backpack receiving signals for a remote and interacting with their antennae to steer their movements

I have published, some years ago and again few months ago, the news of scientists trying (and succeeding) to interact with cockroaches by sending signals to their antennas and therefore forcing them to move in a certain direction. The research has two objectives: a better understanding of how the brain works (and yes, neurones in a cockroach brain are exactly like the ones in our brain…) and exploiting the capability of a cockroach to move under rubbles to find victims of an earthquake buried under a collapsed building.

I have to say that I felt a bit of sympathy for the cockroach but the goals seemed to me to be worth the inconvenience (pain?) brought to the animal.

But now, I run into a project seeking funds on Kickstarter that aims at transforming a roach into a sort of micro-machine remotely controlled by a cell phone. The proposers are claiming that the cyber-roach will help kids understanding neuroscience.

The idea is to sell a kit containing a chip (as the one shown in the figure above) and some needles that can be inserted in the antennae of a roach (or in its legs) and wirelessly connected to a smartphone.

The kit comes with an app through which it is possible to interact with the insect and steer its movement as if it were a micro-machine.

Controlling a roach with an app on your smart phone.

Needles in its legs can detect the spikes created by brain commands and are relayed to the smart phone and displayed on the screen so that one can see what is going on as the animals move around.

According to the proposers the kids using this device show a better grasp of neurology and that demonstrate the usefulness of their product.

They are also showing a video clip on the Kickstarter web site as they advertise their idea and ask for funding. You can see it through the link I included. I decided not to include it here because I really don’t like using a roach as if it were a micro-machine. My sympathy is all to the roach, none to the proposers.

Nevertheless, I have to admit that we are more and more in the understanding of how our brain works at a “mechanical” level and we are learning how to interact with it, with tools that are easier and easier to use and affordable. This is going to create a nightmare in the next years. Will the wedding ring eventually contain some sort of interface letting our partner to influence our life at a “mechanical level”, would it open up a window on our thoughts? The scary aspect is that this is no more science fiction, just science….

The Mantis Shrimp has the best eyes in Nature, as far as we can tell… Credits: Wikipedia

Ever wondered what your dog actually sees or what a bee sees? The short answer is that we really don’t know because seeing is a very complex activity. You cannot even be sure if what you are seeing is what your fellow passenger on a train is seeing. You are both, most likely, getting the same signals from the eyes (assuming your eyes acuity is comparable) but the way the brain processes those signal can vary significantly. As an example, it is certain that in the middle of a desert what you and a Tuareg are seeing may be the same in terms of signals created by the eye (actually the latest studies have shown that it may not be the case since the brain influence the eyes in their photo detection and processing) but for sure it is quite different in terms of perception. What may look to you as an endless and uniform expanse can be reach in telling tales to the Tuareg. And the same goes if you were in Greenland comparing vision with an Eskimo…. Of course, also the reverse is true. An Eskimo or a Tuareg in the middle of Milan would miss most of the details that makes that part of MIlan unique to your eyes… (have you ever notice that your wife can see things like “the curtains have to be washed” that you are absolutely blind to…?)

The point is that what we see depend as much on what our eyes capture as on what kind of processing goes on in our brain (and that depends on our experiences and the way our brain has evolved through our life).

Now, if it is difficult to say what your fellow really sees just imagine how difficult it is to say what a dog or a bee see. We do know that a dog vision (or a bee vision) is different from ours because its eye create different signals (a dog cannot distinguish colours the way that we do because it lacks the kind of receptors that we have, check on dog vision; a bee can see something more and something less than our eyes can see, check  on “The world through the eyes of a bee“).

A dog, can actually see through its nose…. in the sense that its brain can mix visual and olfactory signals to create a “vision of the world” that for us is completely impossible (and therefore “absurd”). As an example a dog “can see” that a person was on a couch half a hour ago, or a hour ago getting hints from the remnants of the odour… It is like saying that a dog has a 4D vision, both spatial and temporal!

The artificial eye (a) and its curvature represented on a sphere (b). In (c) the eye of a trilobite, now extinct, and in (d) the eye of a fruit fly. Credits: EPFL

No wonder, with so many interpretation of the word “seeing” that scientists are studying Nature and look into what can be replicated by technology. Apparently, one of the best sensors for picking up visual information is the compound eye of the Mantis Shrimp…

Researchers at the EPFL have come up with a compound eye (and related processing chip/software) that mimics the kind of vision of a fruit fly. The fruit fly can detect movement in a 180° spherical space and it is very effective in that. It cannot see the way we see (it cannot see an apple or a flower, but for that it uses odour detection) but its eye are perfect for navigation in flight. And this is what the artificial eye developed at EPFL can do.

The artificial compound eye (sensors) is actually more performant than the one of the fruit fly since it can process signals at 300 Hz vs the 100 Hz processing speed of a fruit fly. However, it is difficult to say if the processing made by the chip (and software) is as efficient as the one made by the 5,000 neurones of the fruit fly and hence we cannot tell who has got the best “vision”.

Researchers are planning to use this artificial eyes in mobile robots to provide them with navigation clues.

The work is being done in the context of a European Research project, CurvACE.

It would seem a moot question. Of course in order to cooperate people, or things, would require intelligence, of some sort. Look at swarms or at flocks; the hundreds (of thousands sometimes) of animals are able to coordinate their flight and aim at a given spot indicated (?) by some sort of sentinel or guide.

Well, actually scientists have discovered that there is not such a thing as a guide, nor a communications across the group. Quite simply there is no single intelligence at all, the intelligent behaviour is something that is emerging from the group and it is perceived by an external observer, not by any single entity within the group.

It might seem unbelievable, but that’s what it is!

A robot developed at Sheffield. Hundreds of them create an intelligent global behaviour

The trick is based on very simple rules upon which each member of the community bases its behaviour. All together these micro behaviours result in an emergent behaviour that appears to be the result of an intelligent -and sentient- being. This does not just happen with flocks and swarms, it is happening right now in your body. Individual cells are busy at doing things (making proteins, releasing fluids, closing or opening membrane to sodium and potassium ions…) and there you are, reading this post and thinking, intelligently, about it.

Well, in a way this is what scientists at the University of Sheffield and at the Sheffield Hallam University are trying now to replicate to create intelligent behaviour. Rather than setting up an intelligence to control what should be done they are studying what set of very very simple rules can be assigned to nano robots in such a way that once there are many of them this result in an intelligent behaviour.

Their idea of the future is one where there will be thousands of these nano-robots in the home, at the office, in hospitals and so one. Each one very cheap to manufacture, able to perform simple tasks. And able to cluster in swarms that will be able to carry out much complex assignments without having been “programmed” for that. Their global behaviour will be an emergent property of the set.

I feel this is really the way to the future, mimicking billion of years of evolution in natural ecosystems. And this will apply to the future of networks too and will bring us to the age of “semantic networks”. More on this to follow…

I have always been amazed by insects, by the way such tiny “things” seems to be able to live in a complex world and take smart decisions. And once I look at gregarious insects, such as ants and bees, the amazement grows.

Locust, grasshoppers, are somewhere in between loners and gregarious. As long as there are a few of them they behave like loners but once their density reaches a thresholds (it can vary from species to species, usually the thresholds is around 100 – 200 per square meter) they start becoming gregarious and their density rapidly increases (to reach thousands per square meters). At that point their behaviour changes and a group “soul” emerges.

Scientists have studied the various aspects of this transition and of the rules that take over once the transition occur leading to the emergence of the “group’s soul”. A good book I read recently is “Insect Outbreaks Revisited”.

A group of researchers at the University of Lincoln have studied the way locusts can manage to move into tightly packed swarms (both on the ground where as soon as the thresholds is reached they stop their random walking and start to march in columns and in flight) without bumping into each others.

The set of rules they apply are straightforward:

1. move in the same direction as your neighbour

2. stay as close as possible to the neighbour you follow

3. don’t bump into it

but what interested the researchers was how the locust could apply those rules -coded in their neural system-, based on the visual stimuli they get.

These insects have a very simplified neural system (at least if we compare it with the one of mammalian) and yet they are very effective in navigation and avoidance. Exactly what we would like to have in a swarm of … cars!

The researchers have been able to replicate the neural machinery the locust uses when moving in a swarm in a robot and voila!, the robots, shown in the photo, has acquired the capability to move in a complex environment avoiding moving obstacles. In a way it has become able to predict where other moving objects are heading and to avoid their path.

The next step is to see how this kind of behaviour can be applied to cars to make them aware and responsive to their environment. Although cars in a urban environment may look as extremely packed they are much less so than locust in a swarm and if they can only get as smart as a locust is, well we could be safer in our every day commute!

I love this technology evolution learning from Nature, and I do expect to see much more of this during this and next decade!

We know of Google effort in developing self driving cars. Actually they are already sending these autonomous cars to pick up their important guest at their hotel and drive them to the Mountain View headquarter to feel the trill of driving in a driverless car. The Google car is using GPS to know where it is and a laser and a video camera to detect obstacles.

The iPad interfacing the driver-passenger with the car-robot

Now researchers at the Oxford University, UK, have shown their version of a driver-less car, that is actually using a completely different approach to know where it is. You may want to take a look at the video clip showing the car driving by itself.

The car is equipped with a laser and a series of video cameras scanning the road. The captured images are fed to a computer that recognise the exact location, avoiding the issues of GPS localisation in areas hiding the position of the GPS satellites.

Obviously the system can recognise any kind of obstacles, pedestrian included, and take appropriate action. An iPad on the dashboard provides the interface to the driver-passenger.

The car, or shall we call it a robot?, is not completely autonomous. If it feels the situation is getting too complex to be managed stops and asks the passenger to get back into the driver seat.

Conversely, when it feels that it could drive it is offering the driver to sit back, relax, and let it take control.

What is nice in the approach taken by the Oxford researchers is that the whole system can be installed on any modern car (since these new cars are basically equipped with fly-by-wire technologies) at a cost of just 5,000£ and they expect to be able to lower this price to 100£ in just a few years.

They are calling this “assisted driving”, so it is clearly a notch below the Google car but if you are waiting for a Google car at your car dealer, you might be in for a 15 years wait. On the contrary, this assisted driving may start to be offered by car manufacturers in just a few years time.

When we walk we just do it, without thinking about it. Actually, that is not true. We don’t realise the processing intensive activity that takes place in our brain to make our walking possible.

A four legged robot…

This has become apparent when scientists tried to create walking robots.

At the University of Wyoming computer scientists are trying to inject brain processing power in robots to make their walking smooth and flexible.

The first task this brain has to accomplish is to understand that it is in a body that has four legs (or six or eight…). It might seem trivial but it is the very first thing that needs to be done.

To do this the scientists are mimicking the evolutionary process of biological systems through a brain that can learn and evolve. This led to very strange, and ineffective, walking attempts but in a short time the brain learnt what was good and what was not. The result is a walking that not just is smooth and effective but that can change to adapt to different terrain condition.

You can get more information, and watch a little clip, through the link I inserted.

What is really interesting me is that we are now capable of creating programs that learns by doing and evolve. The programmer is no longer creating a program to perform a specific function (walking) rather is creating a program that can become aware of its environment and has a goal to achieve. To achieve the goal the program tries several strategies and learn through experience.

Just two weeks ago I posted some (gloomy) thoughts on the shrinking of jobs as a result of technology evolution, and in particular of the ones in robotic, cloud and big data areas. Now I just stumbled on an article reinforcing those thoughts.

The fact is, our drive towards better processes and products, i.e. more efficient, less expensive, smarter, is making some human activities redundant, in the sense that they can be done equally well, actually better, by a machine, and often by re-engineering the process and just getting rid of the activity. This can be the case when one transforms atoms into bits and move the bits in the Cloud for processing. Smart processing can replace the “brain” and there are no more atoms, so no “hands” are needed.  The transformation of atoms into bits, and when needed of bits into atoms can be done by sensors, robots and other machinery.

Take the example of the Hunt Library at the NC State University. The library contains two million books and their management would require many librarians just for retrieving and storing them. The solution chosen was to create a robotised library that is actually managed by a number of robots controlled by a computer. A customer input is request through a computer and this instruct the right robot to retrieve the book and deliver it to the customer. Same goes when the customer returns the book. The system reads the book identity and a robot will place it back at the right location.

Human beings (as well as several animal species) have a knack to get a feeling that something is going on by observing how other people are behaving. In particular our brains are very well in tracking the gaze of other people and whenever they detect a convergence of “gazing” they get arouse and focus their attention on that converging point.

This is how in a social gathering, at a party or in a square, at the office or at a metro station, we are continuously scanning other people’s gaze (and if someone is staring at us, making us the convergent point of gazing we rapidly get uneasy…).

Robots have a difficult time in understanding what is going on. For them any single input has basically the same importance and they have to work out what could really be important. This has been the issue that some researchers at the Carnegie Mellon Robotic Institute have tried to solve.

To do that they tried to understand how people are tracking attention in a crowd by placing a camera on each person to track their gaze and computing any convergence of gazing.

This, as shown in the figure below, produces some maps of the social space highlighting those areas where there is a convergence of gazing.

Interestingly, it turns out that people gazes intersect not just when there is something they are looking at but also whenever there is someone they are listening to. This provides the information about who if attracting the ears of people, not just their eyes.

The era of social robots is not too far away. Expect to see them mingle in our social circles starting in the next decade and become commonplace in the following ones. At that point we will start to wonder which is which, and that might be embarrassing…

According to the 6 ages of evolution classified by Ray Kurzweil we are going to see in the 6th age the coming  together of bio and artificial objects, designed by us, creating a seamless continuum where it will be useless to try to distinguish one form the other.

Schematics for a symbiotic sensor, part bio cells and part electronics

Many researchers are working at this frontier of science by creating interfaces to support interaction between bio and artefacts. A number of bio sensors have already been developed (like algae that can fluoresce when detecting a specific substance in their ambient) and more are being created in the labs as new ways of mixing bio and artefacts are found.

Researchers at the Newcastle University have published a paper (accessible for free till the end of 2012 here, a general presentation of the research can be found here) showing how to create a symbiotic sensor, partly consisting of modified cells and partly of electronics.

The goal is to be able to detect light, as Nature does -in many different ways indeed- a first step to create a bio-synthetic eye.

What the researchers did is to modify a gene in an ovary cell of an hamster so that when a light beam illuminate the cell the gene is activated and produces Nitron Oxyde (NO). The NO can be easily detected by an electrode generating an electrical signal that is used to indicate the presence of light,

The interest in having a bio symbiotic sensor is that the cells used to sense the presence of light multiply and and can successfully overcome unfavourable ambient through adaptation, something that can be much more difficult for an artefact.

This is but a very small step in the direction of a bio-synthetic robot but clearly shows the work that is going on. Kurzweil feels that we will see this kind of bio robots in the third decade of this century (e.g a robot equipped with bio senses, like bio eyes, bio skin and also with bio muscles) and that in the fourth fifth decades we will see robots with a brain that will be difficult to distinguish from ours in terms of behaviour.

Both scaring and exciting…

I guess anyone of us had the Golden Arches experience: getting in line to order a Big Mac, and seeing just beyond the counter a number of boys and girls cooking hamburgers, adding BLT, wrapping them and placing them on the shelf to be picked up.

Now Momentum Machines has created a robot that can prepare up to 400 hamburger per hour, seasoning, packaging and wrapping them ready to eat.

More than that. The robots starts to work on your order as soon as you place it. Hamburgers are not prepared in advance, but right when the order is placed, and the same goes for the slicing of the tomatoes and all the other trimmings.

On the left hand side you can see the schematics of the robotic processing of your order. Since everything is made at the time of order you can have an hamburger that is 1/3 beef and 2/3 bison or whatever you whim is. You can select to have tomatoes but no lettuce and so on.

According to Momentum Machines there are many advantages with their product: flexibility, freshness and therefore quality, speed and proper consistent hygiene.

They also state the huge savings in personnel cost, and this can of course be true but at the same time provides a further indication that a lot of jobs are on the way of being replaced by machines.

We have had this kind of concerns for at least two centuries and that has been proved false. As new machine can take up the work of workers they also open the door to employment in new professions. However, in the last decade, and more so since 2007, studies are pointing out that for the first time since the Industrial Age, the work destroyed by machine is not offset by new working opportunities being created.

This is bad news, or at least is something that will require a rethinking of the rules of the game upon which our Society is based today. Add to this the saturation of markets in several areas (like cell phones in developed countries but soon in developing countries as well) and you see that we are moving from an expansion phase to a stable phase. Since our financial engines are running on an expansion model this creates big problems (see what is happening in the automotive sectors where we now have lost the expansion “drive” and cars are only bought to substitute old ones).

I think we need to get ready for dramatic paradigm changes in the next decade…