Android Taking Our Place In The Society

What initially was portrayed as science fiction in movies would soon turn into reality when robots learn to imitate and perform human actions and behaviour.

Social learning is fundamental in psychology and engineering which explains that new behaviour can be learnt by imitating and observing others. What if this fundamental theory could be applied in robot learning? How would it affect you if a robot were to imitate your very behaviour?

Let’s suppose you put on a shoe and start kicking a football. As a human, you understand ball as an object that is to be played with. A robot, using smallest configuration can also be instructed to replicate your behaviour. However, would a robot be able to distinguish the difference between a ball and a chair? What if robot kicks a chair along with the ball as it is learning to imitate your kicking? This error may be caused by the fact that the robot will identify both the chair and the ball as an object and treat them equally.

Furthermore, using an experimental approach, engineers have further tried to understand the extent a robot will imitate an action, and would the robot be able to identify the action relevancy? However, due to lack of emotional tendencies in a developing robot, its actions would be accepted as radical in our society. But this is just one of the several complications that limit the learning and innovation in robots as the risk of advancement in robots outweighs the benefit.

Through the means of constant repetitions, an animations device or a developing robot can adapt playback of the actions. However, this repetitive playbacks are non-interactive, as the robots do not evolve and change their actions based on the environment. Instead, the robots glue themselves to the repetition movements. Another method that has been successful, to some extent, has been teaching a robot how to navigate a maze using simple perception. Using a dog that is will adept at manuevering the environment, the robot is able to learn its way through the puzzle and store the solution in its hard drives for future purposes

I wonder if you enjoy sarcasm and irony? Well, who doesn’t!

As we talk with our friends and families, we like to throw in a sarcastic comments to brighten the conversation. But I hate to break it to you that another issue that sends shiver down the spine of the engineers and researchers is what if the robot is unable to distinguish sarcasm and irony from seriousness as it is learning to imitate. This would motivate the robot to perform actions based on the sarcastic comment that would otherwise be unnecessary or dangerous.

On the other hand, should the social learning in robots be successful, you would soon have a intelligent companion who would bring comfort to your life and be your butler for as little as 399$.  JUST GO EASY ON THE SARCASM!

Citation:

Breazeal, C., & Scassellati, B. (2002). Robots that imitate humans. Trends in Cognitive Sciences6(11), 481–487. https://doi.org/10.1016/s1364-6613(02)02016-8

Robots may replace humans. (2016, October 7). Deccan Herald. https://www.deccanherald.com/content/574634/robots-may-replace-humans-nursing.html

A Massive New Library of 3D Images Could Help Your Robot Butler Get Around Your House. (2017, April 26). Communications of the ACM. https://cacm.acm.org/news/216399-a-massive-new-library-of-3d-images-could-help-your-robot-butler-get-around-your-house/fulltext

Why Python Is So Popular Even Though It’s Super Slow

Even those unfamiliar with coding have probably heard of Python. Python is considered one of the most popular and in-demand programming languages. According to a recent Stack Overflow survey, Python has ranked foremost among its competitors like Java, C, and C++. But what makes Python the number one choice?

Python was developed to be simple to learn and use. Recently, Python has gained a reputation as a very user- and beginner-friendly language. It is simple to learn because it closely resembles commands in English, and as a result, it improves code readability. Python is also more error-tolerant, so you’ll be able to compile and run your program up until the problematic section without any problems. As a result, it has dethroned Java as the most widely used beginner language.

Since Python is a dynamically typed language, which means that it doesn’t know about the type of the variable until the code is run, it is incredibly flexible and enables us to use modular components created in other programming languages. For example, you could import a C++ program as a module into Python. Although this sounds promising, Python’s flexibility results in it being slower than other popular programming languages. Because it requires numerous references from the computer to validate the meaning of each statement.

How Python Manages to Retain Its Popularity Despite Being Slow

As opposed to other programming languages like C++ and Java, Python is significantly more efficient, which is the main factor in its popularity. It is much more concise and expressive, and it uses fewer lines of code and less time to perform the same tasks thanks to its emphasis on code readability in both design and philosophy. For instance, even though Python programs are slower than Java programs, they can be developed much more quickly because Python codes are approximately 3 times shorter than Java codes.

On the other hand, there have been instances where the most expensive resource and primary concern have been computer run time. However, speed has diminished in importance since computers, servers, and other technologies have become more reasonable than ever. Today, project development time is more crucial than the execution speed of the programming languages, due to increased employee costs. Shorter development time not only saves money but also increases your competitiveness. It enables faster prototypes and innovations, helping companies to beat the competition. In short, the time you can save in development will likely be more cost-effective than the performance and execution speed you get in the programming. This is the factor that makes python a turning point for companies. Considering the pros and cons of these facts, which option would you choose as a CEO?

Python is used to fuel myriad data processing tasks in enterprises all over the world and is the second most popular tool for analytics and data science. Thousands of machine learning projects use Python libraries like OpenCV for computer vision and TensorFlow for neural networks. JPMorgan, for instance, forecasts the financial markets using Python. The NSA further employs Python for security-related intelligence analysis and cryptography.

In conclusion, even though Python is slower than other languages, it is widely used because of its efficiency, ability to optimize employees in businesses to reduce the need for labor, promises of competitiveness through quick innovation, and usage in a variety of fields thanks to its extensive library.

Sources:

Saabith, AL Sayeth, M. M. M. Fareez, and T. Vinothraj. “Python current trend applications-an overview.” International Journal of Advance Engineering and Research Development 6, no. 10 (2019).

Srinath, K. R. “Python–the fastest growing programming language.” International Research Journal of Engineering and Technology 4, no. 12 (2017): 354-357.

Author:

Yusuf Ünlü

 

How to text a plant

Unfathomable as it may seem, a smartphone is all you will need to text your plant. All you need to do is to open a chatbot, type in your message, and hit send. Your plant will then reply to your question just like one of your friends would. Researchers have been experimenting with the idea of texting such mute living things for years and have recently concocted a concoction that does just that.

Based on IoT (Internet of Things) and Fuzzy Logic, an automatic smart farming system with a chatbot interface was created. Through an eight-week trial, researchers were able to successfully converse with orchids to find out what it needs during their growth and consequently tweak their living conditions based on their responses.

Why is this significant, you may ask?

Well, the reality is that plants are unable to thrive in all conditions – at least at the current moment – which is not a good outlook considering that we consume a wide variety of plants. Therefore, this research and breakthroughs are pivotal for farmers to boost growth rates and yields. You might then find it rather ironic that orchids grown normally have a higher average growth rate as compared to those in the automated smart farming. However, automated smart farming yielded a more consistent growth rate and a higher end yield – which are more favourable for farmers.

However, the core question of how to text a plant remains unanswered up till now.

Just like a language translation service that helps interpret one language into another language that one can comprehend, the automatic smart farming system translates the answers from the plants in response to the questions from humans. For example, if you ask a plant in this smart farming system “How are you?”, the answer of the (plant) chatbot can be “I am cold” or “I am feeling hot” depending on the temperature, the humidity and the soil moisture where the plant is. To achieve this human-plant chatting, the system uses Natural Language Understanding (NLU), Natural Language Processing (NLP), and pattern matching to analyse the user’s questions and return the response to the user. Figure 1 depicts this workflow.

Figure 1: Chatbot’s workflow

The replies from the chatbot reflect the plant’s needs based on existing environmental information. The smart farming systems contain sensors that measure the temperature, humidity, soil moisture, and sunlight from the server. These measurements are mapped to human-liked responses using Fuzzy logic, such as “I am cold” in the above example. Figure 2 shows the working process of chatbot in the Orchid demo farm where the automatic smart farming system was deployed.

Figure 2: The working process of chatbot in Orchid demo farm

The performance evaluation of the Orchid demo farm yields satisfactory results – with the average accuracy of conversation between chatbot and orchids being 0.71 in harmonic mean, 0.75 in precision, and 0.6 in recall. Chatting with orchids would help farmers to understand the plant condition and timely respond to their needs for better growth in the long-term while reducing fertilizer and preventing giving excessive water to the plants.

Therefore, imagining a day when we can text back and forth with our plants isn’t very unrealistic – it’s just a matter of time.

Wiangsamut, Samruan & Chomphuwiset, Phatthanaphong & Khummanee, Suchart. (2019). Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot. Advances in Science, Technology and Engineering Systems Journal. 4. 10.25046/aj040522.

Author: Yee Hui Min

Could solar energy replace fossil fuels and save the planet?

Fossil fuels are actively destroying our planet. In fact, according to the European Environment Agency (2021), about two thirds of greenhouse gas emissions worldwide can be sourced back to the burning of fossil fuels. As we continuously seek to replace these non-renewable energy sources by green alternatives, solar energy comes in. Solar energy is a renewable energy source, which is simply defined as “any energy generated by the sun” (National Geographic Society, n.d.). One of the most popular ways that it is harnessed is through solar panels. The image below (Fig. 1) shows the CO2 emissions of solar and wind energy (renewable energy sources) compared to coal and natural gas (non-renewable energy sources). From this, we can see how significantly solar energy can reduce CO2 emissions and protect our climate.

Fig. 1: A comparison of the CO2 emissions from different energy sources (1)

You may be wondering: if solar energy is so great, why haven’t more fossil fuel power stations been replaced by solar farms? Well, solar panels aren’t exactly the most efficient energy producers. Solar panels tend to have an efficiency of around 15-20%, while coal can have an efficiency of up to 40%, and natural gas of up to 60% (Parkman, 2022). This makes it challenging to replace non-renewable energy sources by solar panels, as much more space and resources are needed to produce the same amount of energy. Now, is there any way we can solve this problem? Could we make solar energy more efficient?

In 2014, four researchers from different scientific institutes in Islamabad did an experiment in which they explored how mirroring and cooling systems affect the efficiency of solar panels. Mirroring is a process in which on top of the sun reaching a solar panel directly, a mirror is placed so that light reflects from this mirror to the solar panel. This allows more sunlight to reach the solar panel at once. (See Fig. 2.) Cooling systems simply help to keep the solar panel cool.

Fig. 2: Mirroring system on a solar panel (2)

To understand why mirroring and cooling systems could make solar panels more efficient, let’s take a look at how a solar panel works. Solar panels are made of smaller components, called solar cells. These cells are made of two layers of semiconducting materials that are contaminated with different impurities. This produces an unequal distribution of free electrons between the layers; the n-type (negative-type) layer contains free electrons, while the p-type (positive-type) layer contains “holes”, which are basically empty spaces for electrons. As photons from the sun reach the solar cell, the loosely bound electrons in the n-type layer enter an excited state and escape their respective atoms. With help of an external circuit, the electrons are then able to flow toward the p-type layer, where atoms are hungry for electrons. The flow of electrons in the external circuit produces electricity. (Arshad et al, 2014). See Fig. 3.

Fig. 3: A simplified depiction of how a solar panel works (3)

Now, to explain how mirroring can increase efficiency, we highlight the idea that photons from the sun reaching the solar panel release electrons from their respective atoms. As more photons reach the solar panel, more electrons will be free-bounded, and therefore more electric current can flow. Therefore, as photons are reflected from a mirror to the solar panel, more photons reach the solar panel and so more energy is produced. 

This however introduces us to a new issue: resistance. Resistance blocks the flow of a current, lowering the amount of energy production. As photons reach the solar panel, this energy is absorbed by electrons, which then speed up and collide with one another, increasing the temperature of the panel. An increase in temperature causes an increase in resistance, hindering the flow of the current. In fact, the efficiency of a solar panel is estimated to decrease by 0.5% for every degree that its temperature rises. (Arshad et al, 2014). This is where cooling systems come to play: cooling systems can help to keep the solar panel at a good operating temperature, which minimizes the effects of resistance on energy production.

As we have now developed a brief understanding of solar panels, as well as mirroring and cooling systems, let’s revisit the experiment. Researchers conducted an experiment in which they measured power outputs of solar cells without any mirroring or cooling systems, with only mirroring and no cooling systems, and with both mirroring and cooling systems. After collecting this data, they created graphs comparing the irradiance, power and efficiency seen over time of these different conditions. By analyzing these graphs, the researchers were able to recognize that the best results are seen when both mirroring and cooling systems are installed to a solar panel.

The findings of the experiment show that mirroring and cooling systems significantly increase the power outputs of a solar cell. Mirroring alone already provides a clear improvement in the efficiency of a solar panel: a 32% increase is seen in the power outputs. With the help of cooling systems, this efficiency is increased by another 20%. Hence, if both mirroring and cooling systems are used on solar panels, their efficiency can be increased by a whopping 52%! 

One thing that the researchers realized however, was that during early and late hours of the day, the cooling system actually decreased the efficiency of the solar panel, as it prevented enough energy from reaching the electrons to release them from their respective atoms in order to create a flow of current. Therefore, a way that solar panels could be made even more efficient is by deactivating cooling systems during these hours to make sure the energy production is not hindered. 

The results of this experiment gives us hope for a green future. By using various mechanisms, such as mirroring and cooling systems, we can increase the efficiency of solar energy by large amounts, making it a realistic alternative to non-renewable energy sources. So, to answer the very question of this blog post: yes, solar energy can replace fossil fuels and effectively save our planet. We just need to continue its research so we can make it a reliable energy source for our future. Let’s go solar!

Fig. 4: Let’s go solar! (4)

 

The study:

R. Arshad, S. Tariq, M. U. Niaz and M. Jamil, “Improvement in solar panel efficiency using solar concentration by simple mirrors and by cooling,” 2014 International Conference on Robotics and Emerging Allied Technologies in Engineering (iCREATE), 2014, pp. 292-295, doi: 10.1109/iCREATE.2014.6828382.

Bibliography:

Energy and climate change. (n.d.). European Environment Agency. 

https://www.eea.europa.eu/signals/signals-2017/articles/energy-and-climate-change

Solar Energy | National Geographic Society. (n.d.). 

https://education.nationalgeographic.org/resource/solar-energy/

Parkman, K. (2022, May 12). Solar energy vs. fossil fuels. ConsumerAffairs. 

https://www.consumeraffairs.com/solar-energy/solar-vs-fossil-fuels.html

Image sources: 

  1. Herr, C. (2022, July 3). Are Solar Panels Better than Coal? Climate Solution Center. https://climatesolutioncenter.com/are-solar-panels-better-than-coal/
  2. How to boost any solar panel output by 75%. (n.d.). https://geo-dome.co.uk/article.asp?uname=solar_mirror
  3. How Solar Panels Work? (n.d.). NT Energy Solutions. http://www.nt-energysolutions.com/en/Article/Detail/101877
  4. SolarResource.org. (2019, October 10). Best Solar Energy Memes From Around The Web. Solar Resource. https://solarresource.org/best-solar-energy-memes-around-webz/

 

Mold as pets

10 000 years ago people started to domesticate animals and plants. It is very easy to come up with examples: dogs and cats as pets, sheep, cows, pigs, turkeys, wheat, and peas as food sources, horses, donkeys, and camels as working or draft animals, and cotton as a source of fiber. However, there exists another group of domesticated organisms that probably won’t come to your mind when you hear the word “domestication”, even though you see it everywhere. Do you ever go out to a bar or a nice restaurant, or maybe visit a sushi place? In every one of these places, you meet some domesticated microbial species. The yeast gives us beer and bread, certain bacteria help to make wine, and Koji mold is used in soy sauce production.
Let us look at one specific mold that evolved to be our pet. It is P. roqueforti, it is a mold fungus that is used for blue-veined cheese production, so let’s call it “cheese mold”. The scientists got strains of “cheese mold” from different types of blue-veined cheese and from silage, lumber, and spoiled foods. They divided strains into 4 genetic-related groups: 2 not connected to cheese and 2 “cheese-type”. Two cheese ones were named “Roquefort” and “non-Roquefort”. I find their choice of name to be quite creative. Their names are quite transparent about what they stand for: the type of mold that has been used to make original Roquefort shared by several regional farms, and the type used in cheese industrial production for other blue-veined cheese sorts, like Gorgonzola.
The purpose of the study was to describe the history of P. roqueforti domestication and humans’ influence on “cheese mold”. For that, the researchers need a sample of wild P. roqueforti, which hasn’t been discovered yet. However, they could say that two independent acts of domestication took place, one for “non-Roquefort” and one for “Roquefort”. Two “cheese-type” groups were also compared, and it turned out that “Roquefort” has fewer traits that are important for “cheese mold”. I was quite surprised by this. For example, compared to industrial molds used, “Roquefort” slowly grows on cheese, doesn’t tolerate high salt amounts (salt is used as cheese preservative), and it lacks traits necessary for aroma production, which is an essential blue-veined cheese characteristic. The researchers suggested two explanations for that. The first says that “roquefort” type of “cheese mold” was actually adapting to live not on cheese, but on bread. Sometimes there are happy accidents! It is based on the fact that the samples of mold used for further production were preserved and grown on bread. The second hypothesis explains the slow pace of growth. The farmers chose slow-growing mold over fast-growing intentionally. Why? This particular sort of cheese is made from ewe’s milk, which is available only from February to July, and there was no good way to store milk or cheese for half of the year. So slow-growing mold allowed the cheese to get ready to eat in a longer time after having it in milk form than a faster-growing mold would have. If you’re anything like me eating cheese year-round is really important, so its awesome that farmers figured this out.
Compared to researching animals and plants, it is more difficult to investigate the history of microbes. However, the thing that will help is the discovery of the wild strain of P. roqueforti. This will provide scientists with the material to compare domesticated strains with, so they will be able to tell us more about their histories.
Dumas, Emilie, et al. “Independent Domestication Events in the Blue‐Cheese Fungus Penicillium Roqueforti.” Molecular Ecology, 3 Feb. 2020, 10.1111/mec.15359.

Quantum sensors as an answer for all scientific problems?

Dark matter, quantum mechanics, neutrinos, all these names probably already make you feel quite confused, even if you have heard about them before. What’s more, you can think now “what do I need to know about them? Why should I be interested in this topic if I can go out and build a snowman or scroll social media for the next three hours?”. Fortunately, we have an answer for that question. Let’s start with some simple explanation: what exactly are quantum sensors?

Quantum sensors are detectors, which work not really different than microscopes, telescopes or.. our eyes! However, their characteristic is that they can see and measure things much smaller than any human could ever do. How do they work? You probably already guessed by their name – yes, there are using so-called “quantum mechanics” and more precisely – its features.

Try to imagine it: as you use your senses, like sight or touch to recognize and understand the world surrounding you, the same do quantum sensors – but instead of ears or eyes, they use atoms and their quantum properties. They can recognize even really small changes in their environment, such as magnetic field or, what we should focus on, energy.

But how is it really related to this mysterious “neutrinos”?  And how people can use them in daily life? Okay, maybe we should start with Dark Vader of our Universe – dark matter. People, even scientists, don’t really know what it is. To describe it, we need to know its properties – we have to use our sensors. By measuring the differences in energy, scientist can mathematically express its real structure and try to predict how does this “matter” behaves – and if it is a matter at all.

And this knowledge might be very important for the further exploration of our galaxy. Can you imagine? Knowing properties of this one thing, could significantly change our view of the Universe and provide many information about stars millions kilometers away! Isn’t it fascinating? And from this point you can already imagine how many “impossible” aims humanity could achieve, things most of us have seen only watching science fiction movies.

Now we should focus a little on details. Let’s assume that our dark matter is a real matter. So, shouldn’t it be made of some small particles? Such as normal matter like you or the chair you are sitting on is built of atoms? Good question, and quantum sensors say definitely yes! The experts already found such particles using, of course, quantum sensors to detect so small changes in energy, that any other machine couldn’t even see!

Okay, if this mysterious cosmic spaghetti is already better known to you, now let’s move on to the another tiny objects – neutrinos. As some of you may remember from chemistry classes there were already some similar name. Yes, you remember correctly – we think about neutrons. But what, apart from the name, is similar about them? So, both are particles, both without charge. However, neutrino is much smaller and lighter, doesn’t really interact with anything and basically, is almost unnoticeable. You can imagine: neutron is like an orange lying on the table. It can fall down, you can hit it, it is quite heavy and has something inside. Neutrino? In this scale it would be rather a speck of dust, something you usually don’t care about at all. Unless you are allergic.

So this “invisible” neutrinos, are so small that measuring them in any way seems impossible. But not for quantum sensors. Most importantly, scientists using beta decays (which you might hear about sometimes in TV, when speaking about atomic power stations) acquire neutrinos and thanks to quantum sensors, approximate their mass. What’s more, they can also detect so-called “cosmic relic neutrinos” which emerge from our space and which can provide another substantial properties of our Universe. These small, “old” particles are like little dinosaurs’ bones, buried underground, just waiting for some bold archaeologists to discover them. Here, quantum sensors are our shovels.

One example of the detector; you probably won’t understand it, but, at least, you can enjoy the colours.

As you can already see, quantum sensors may have more applications than more people can imagine. They not only sound cool – they also do a really cool stuff. And learning about Universe structure or super-small particles with additional history of space are only scratching the surface. By many different methods and multiply methods of using quantum sensors, scientists can measure other things too and make another worldwide discoveries.

Electricity changed our lives. Computers revolutionized our world. And what quantum sensors can do?

References:

Golwala, Sunil R. and Figueroa-Feliciano, Enectali et al. Novel Quantum Sensors for Light Dark Matter and Neutrino Detection. Annual Review of Nuclear and Particle Science 2022 72:1, 419-446. https://doi.org/10.1146/annurev-nucl-102020-112133

Author: Aleksandra Franc

Ditch Your Studies, Play Games Instead!

Let’s be real, who likes studying? At least, not me! I would rather play some video games instead, 100%! Many students like myself haven’t found their most efficient traditional studying method, and that is absolutely devastating. But fear not, the new breakthrough is here! Rather than studying the old-fashioned way, soon you might be able to learn through games! Although still developing and incomplete, I guarantee this is a promising research. Buckle up, ladies and gentlemen!

This study was conducted in Helsinki, with the 33 pre-schooler participants being in areas of “discrimination” (poor environmental factors, low parental education levels, unemployed parents-families, single parent families, and immigrant families). These kids were grouped into three, the first one being the intervention group (playing a simple math game “Lola’s World”), the second one being the control group (playing the alphabetical game “Lola’s ABC”) and the third one being the passive control group who didn’t get to play any games (sucks for them!).

Lola's Learning World – Math edition - Lola Panda

(Disclaimer: I am not sponsored nor endorsed, and this is not an advetisement! This is just a visual aid to show what the game looks like.)

The three groups first took “Early Numeracy Test” (ENT) to examine their mathematical knowledge, which yielded pretty underwhelming results. Then, each and every one of their performances were taken and named the pre-study ENT scores. After, those groups of kids played the predetermined games mentioned above for 15 minutes over three weeks. And ta-da! The joyous playtime is over, it was time for them to take another ENT.

After some time, the results came out! There’s good news and bad news. Which one do you wanna hear first? I’m assuming you chose bad news. So, the results weren’t as good as expected. They didn’t see a significant difference between the post-study ENT results between those three groups. What I mean is that none of those groups stood out in terms of ENT scores after the game-playing phase. However, here comes the good news. They did see a significant improvement within the intervention group, a.k.a. the ones that played the math game! They saw how the kids playing “Lola’s World” boosted their ENT scores after the study!

The aim of this study was just to check if game-based mathematical learning was doing low-performing children any good, and behold, the study showed exactly that! More could be improved in future studies; however this study represents a valuable attempt to prove that game-based learning can support studies. It is also worth mentioning that many similar studies have been conducted and all yielded identical results. It was also heavily hinted that low performing children should be assisted more on the instructional side, not just with interactive games like “Lola’s World”. This research also opens up more opportunities to conduct similar researches, and can serve as a guideline for one!

Absolutely, this could be the answer to all of us wondering what the most efficient way of studying could be. Although still undergoing development here and there, at least for sure now we know how games can support studies through this precious study! Be happy guys, in the near future you might not need those textbooks, just games!

 

Aunio, P., Mononen, R. The effects of educational computer game on low-performing children’s early numeracy skills – an intervention study in a preschool setting. European Journal of Special Needs Education Vol. 33 No. 5, pp. 677-691 (2018). https://doi.org/10.1080/08856257.2017.1412640

 

Cheers,

Indira Sutjipto

Why do fish keep getting smaller?

Antoine Guerin

Fish for centuries have been getting smaller due to overfishing, so, if you love your favorite seafood dish, maybe it’s time to have it just a bit less often. 80% of fish populations across the world are being overfished or fished at maximum capacity right now. Fish make up at least 15% of the protein intake of almost 2.9 billion people. That’s more than a third of the population of the entire world! Many fishermen rely on fish for their livelihoods and as food for their own families. These fishermen select the biggest fish to market, leaving many of the smaller fish behind. This is partially because of regulations that have set minimum size limits but not all have maximum size limits. Over time, this means that only the fish with genes that make them smaller are reproducing. Eventually, these fish can no longer carry eggs and so, they have issues reproducing because of how minuscule they have become. The baby fish will be so small when born many of them have no chance at life, but they will instead die young. Every generation of fish that get smaller will mean that fishermen and markets will be receiving fewer fish and smaller ones at that. This also drives up the price of fish because them becoming less common, and that’s a tragedy for fish eaters around the globe.

How can we avoid this fishtastrophy? By reducing the amount of fish eaten, allowing natural populations of fish to reproduce naturally, and placing a maximum size restriction on fish for fishermen, fish populations can continue to thrive while only being marginally disrupted by the fishing industry. Humans have been fishing for thousands of years without majorly disrupting the size of fish and their populations, but because of increasing demand, it is growing more and more difficult to sustainably fish natural species. Cutting back on the fishing taking place in overexploited populations could help them recover from potentially decades of damage. They did this by taking the biggest fish of every generation similar to how large fish have been harvested in lakes and oceans for centuries. Fish generations have an average length of 3-7 years, so that’s anywhere from 18-42 years to almost eliminate an entire population of fish. However, it takes nearly 12 generations of fish to reach an average size again which is twice as long as it took to almost wipe them out! For reference, that’s over 8 decades, so while fish can regain their size in time, it takes a lifetime of work, effort, and resources for them to do so. Cutting back on the fishing taking place in overexploited populations could help them recover from many decades of damage and potential annihilation. Fish across the world can continue to thrive with just a small helping hand.

Fish are an incredibly important natural resource for many communities globally. It is therefore of utmost importance that these fish species and populations we have relied on for thousands of years remain stable. With just some changes in regulation and proper enforcement, you will be able to enjoy fish for many decades to come.

Tibbetts, John. “NATURAL RESOURCES: Reversing Human Impacts on Fish Evolution.” Environmental Health Perspectives, vol. 117, no. 5, May 2009, 10.1289/ehp.117-a197.

This AI is better at detecting cancer than your doctor

You probably clicked on this blog because you’re either very excited or terrified at the idea of encountering a robot at your next visit to the doctor’s office. Let me first reassure – or disappoint you – we’re not talking about robots here. Instead, this blog post is about artificial intelligence (AI)-assisted cancer detection. While this may at first sound less cool than robots, it could actually save many lives by detecting cancer more accurately and earlier than doctors currently can.

The most common cancer today and the second deadliest one among women is breast cancer. A recent study by a team around Christian Leibig found that when AI and a radiologist – doctors who analyse breast cancer screening images – work together, they can identify breast cancer more accurately than radiologists by themselves.

So how does this system work? Firstly, let’s talk about AI. The kind of AI used here is based on a technology called computer vision, where AI learns to recognize patterns from a huge number of breast screening images. When it is shown a new image, it can thus identify if the patterns for cancer (or certain types thereof) are present. Moreover, the AI algorithm studied by Leibig and his team also tells you how confident it is in its findings. When working together with a radiologist, the images which the AI assistant is not sure about will then be looked at by the doctor. Only those which the AI assistant is very certain about are automatically forwarded to the next step in the hospital’s procedure. For example, the AI’s findings may be passed on to the ‘consensus conference’, where several doctors decide whether to give a cancer diagnosis.

Leibig and his team compared how well AI-assisted radiologists did when detecting cancer on over a million breast cancer screening images, to how well radiologists and AI each did on their own. The scientists found that the AI-assisted radiologists scored the highest, both at detecting when cancer was present, as well as when it wasn’t. This means that the AI-assisted radiologist is more likely to be correct in telling you that you have cancer, and also that it is more likely to be right when it tells you that you don’t, compared to radiologists working alone.

Hence, when AI and doctors work together in identifying cancer, this can lead to a more accurate cancer diagnosis. Also, in this collaboration, humans always retain the final say, making this a rather safe way of using AI. What’s more, being able to focus on fewer, difficult images enables the radiologist to play to their strengths and do less – but nevertheless valuable – work. A reduced workload not only improves their quality of life, but also enables them to make more accurate diagnoses, since they are not exhausted from having looked at mountains of similar screening images all day. All this is ultimately for the best of patient and may save many lives: If an AI-assisted radiologist can find a patient’s cancer that would have otherwise gone undetected, or if the cancer can be found earlier than otherwise, the patient has better chances of healing thanks to AI.

So hopefully, after reading this, those excited about meeting a robot at the doctor’s office will be slightly less disappointed, knowing that AI could soon help their doctor when analysing cancer screening images. And those worried about AI technology entering their lives will hopefully also feel better, knowing that this form of AI technology is controlled and assisted by humans, and that it could actually save your life one day.

 

Source:

Leibig, C. et al. (2022) ‘Combining the strengths of radiologists and AI for breast cancer screening: a retrospective analysis’, The Lancet Digital Health, 4(7), doi: 10.1016/S2589-7500(22)00070-X

Author:

Lene Bierstedt

𝛑 appears in most non-expectable math identities.

In 1689, famous swiss professor Jakob Bernoulli paid attention to a quite specific mathematical problem, which is sum of infinite reverse squares, today it is known as Basel problem. Although formulation itself sounds a bit fancy for non-mathematician, it is just summation of series where 1 is divided by number squared:

Not too complicated if you managed to pass school math, but in the 17th century without calculators and computers, and limited knowledge about series it could take a while to find out the solution, especially considering that numbers go on to infinity…

 

46 years later, in 1735 swiss mathematician Leonard Euler was able to find out that infinite sum of reverse squares equals 𝛑2/6. Wow! Why would 𝛑 appear somewhere else than in circle area? Although Euler himself used very complicated math, proving this identity can be understood even by 9th graders, and yes, it involves circles. 

 

So why would 𝛑 appear in such non-related to geometry problem? Let’s find out!

 

Inverse squares appear in physics, in fact, if you imagine a light source at a distance one unit from you, the brightness received by your eye would be 1, but if it is at a distance two units away, you would see only one fourth of the original brightness. Hence, at distance three you see only one ninth of original brightness, this comes from properties of light and other physical quantities such as heat, sound, radio waves and etc. We can already see what are next steps… In the theoretical approach Basel problem can be represented within physical objects. 

 

But how can we manipulate our light sources to understand the math behind? 

It would be quite easy, in the picture above imagine yourself standing at point C, and the light source would be on point X. Due to properties of Pythagorean theorem (its inverse to be precise), the brightness received by your eyes from light source at point X would be the same if there were two same light sources at points A and B. Therefore we can “exchange” one light source to two other similar ones which lie on the apex of the right triangle. 

To refresh school memory, I remind you that if one of triangle’s side is circle’s diameter, and the third apex is on the side of circumference, than it would always be right triangle with perfect 90 degree angle. 

 

Can you already see where it is going? =) 

If you imagine yourself standing on point P and your light source is on point Q, where PQ is the diameter of a circle, it would be the same physical picture in terms of brightness received by your eye if there was a circle twice as big and there were two light sources on sides of a bigger circle. And we can keep adding light sources on the sides of a circle which is twice bigger until we end up standing on the edge of a circle of infinite circumference with the same brightness received by our eyes! 

 

Now, back to school geometry, consider that length of a circumference is 2𝛑r, and all over the circle on same evenly spaced distance there are our light sources which appear with same brightness to our eyes, but each would be in the inverse distance squared. Simple algebraic manipulations lead to the final cut: adding inverse squares up to infinity comes up to be… 𝛑2/6! Boom!

 

Knowing the properties of physical quantities, light in our case, and simple Euclidean geometry can help us to prove the Basel problem without too complicated formulas. 

 

In fact, there are tonnes of other modern methods to prove Basel problem, for example some other way would look something like this:

Too bad those guys did not pay attention in their geometry lessons in 8th grade!

Based on: No. 524 (July 2008), pp. 313-316 Johan Wästlund, “Summing inverse squares by euclidean geometry” http://www.math.chalmers.se/~wastlund/Cosmic.pdf

Filipp Volodin