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Authors:
(1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
(2) Andrey Rass, Den Haag, Netherlands. Table of Links Abstract and 1 Introduction
2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture
2.1 Data Augmentation Robustness Scouting
2.2 The Specifics Of Data Affect Augmentation-Induced Bias
2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias
2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias
3 Conclusion and Limitations, and References
Appendices A-L 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias As part of this work’s goal was to confirm the effects of DA on the bias-variance trade-off of image classification problems as seen in Balestriero, Bottou, and LeCun (2022), we also chose to delve deeper into the specifics of the DA policy implemented by the original paper. In particular, we felt that it overlooked the possible effects its universal application of Random Horizontal Flipping (henceforth ”RHF”) as a supplemental DA may have introduced. In an effort to investigate this, we once again conducted a series of experiments similar to Section 2.2, this time excluding RHF. Our trials showed (see Figure 3 and appendices F, J and K) similar trends and results when compared to Section 2.1. However, as could be expected from removing a minor source of regularization such as RHF, overall mean performance was marginally worse across all three datasets. In addition, it appears that the thresholds of α (past which overall and class-specific performances begin to fall, as well as at which best perclass and mean accuracies are reached) have generally increased - for example, ”Sandal”’s best performance is up to 40% from 36% compared to the previous section. In this way, we see that RHF compounds with the scaling Random Cropping DA, acting as a ”constant” source of additional regularization, while preserving, if accelerating, the dynamics of test set accuracies as α grows. With this in mind, Balestriero, Bottou, and LeCun (2022) is validated, as the conclusions reached in the work would likely not have been impacted had RHF been omitted. While not gravely consequential, this finding should serve as a reminder that caution should be exercised when chaining a plurality of data augmentations together. While such an approach is standard practice in contemporary computer vision tasks, it can rapidly increase complexity, and controlling for the influence of a given augmentation on class-specific bias may become difficult. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands (2) Andrey Rass, Den Haag, Netherlands. Authors: Authors: (1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands (2) Andrey Rass, Den Haag, Netherlands. Table of Links Abstract and 1 Introduction 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2.1 Data Augmentation Robustness Scouting 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 3 Conclusion and Limitations, and References Appendices A-L Abstract and 1 Introduction Abstract and 1 Introduction 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2.1 Data Augmentation Robustness Scouting 2.1 Data Augmentation Robustness Scouting 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 3 Conclusion and Limitations, and References 3 Conclusion and Limitations, and References Appendices A-L Appendices A-L 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias As part of this work’s goal was to confirm the effects of DA on the bias-variance trade-off of image classification problems as seen in Balestriero, Bottou, and LeCun (2022), we also chose to delve deeper into the specifics of the DA policy implemented by the original paper. In particular, we felt that it overlooked the possible effects its universal application of Random Horizontal Flipping (henceforth ”RHF”) as a supplemental DA may have introduced. In an effort to investigate this, we once again conducted a series of experiments similar to Section 2.2, this time excluding RHF. Our trials showed (see Figure 3 and appendices F, J and K) similar trends and results when compared to Section 2.1. However, as could be expected from removing a minor source of regularization such as RHF, overall mean performance was marginally worse across all three datasets. In addition, it appears that the thresholds of α (past which overall and class-specific performances begin to fall, as well as at which best perclass and mean accuracies are reached) have generally increased - for example, ”Sandal”’s best performance is up to 40% from 36% compared to the previous section. In this way, we see that RHF compounds with the scaling Random Cropping DA, acting as a ”constant” source of additional regularization, while preserving, if accelerating, the dynamics of test set accuracies as α grows. With this in mind, Balestriero, Bottou, and LeCun (2022) is validated, as the conclusions reached in the work would likely not have been impacted had RHF been omitted. While not gravely consequential, this finding should serve as a reminder that caution should be exercised when chaining a plurality of data augmentations together. While such an approach is standard practice in contemporary computer vision tasks, it can rapidly increase complexity, and controlling for the influence of a given augmentation on class-specific bias may become difficult. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias

Enhance Sentiment Analysis with Role-Flipping Multi-LLM Negotiation

A time is coming when your thoughts will be the only activity you need to get things done. Have you ever been so caught up with work, you don't want to move a muscle? You are not willing to use your voice because you feel the ideas will escape once you do. So, what do you do when you suddenly wish your favorite songs were playing at that moment, but you feel too lazy to pick up your phone or call Siri's attention to turn on your playlist? If only your thoughts could make the orders, right? Here, you are thinking, "Siri, play me 'Easy on Me' by Adele" and seconds later the sound fills your workspace. Then you think, "Nope, too low. Siri, increase the volume to 10", and the volume gets adjusted to suit what’s on your mind perfectly. Your brain alone processes commands without any effort from your mouth. I guess that can be seen as a grand breakthrough in the history of neuroscience—or probably not! Let's find out what this means for the world collectively. What Exactly is Neurocomputing? Neurocomputing is a branch of neuroscience that deals with the examination of brain patterns and activities. It is a subfield of cognitive neuroscience that scientifically studies the biological mechanisms and reactions of the brain to different experiences. The term applies to the process of reading, analyzing, and interpreting brain activities by utilizing brain sensors and an imaging framework to examine how our brain reacts in response to different emotions. This is usually through the use of a Brain-Computer Interface Device. The framework, in this case, is called the "triad,” a 3-step process of emotion-valuation, sensory-motor, and meaning-knowledge evaluation. With the introduction of Al, neuroscientists hit a new milestone with brain activity studies. The Rise of Neurocomputing Would you have ever imagined that one day, the content of your thoughts could be read by devices? Science fiction? Sure! Why not? We see a lot of that. But reality? That's almost impossible (or so we thought). Lie detectors were the most mind readers, but they can never detect what your thoughts are word by word. However, technology always seems to awe us as time progresses because the technology we once thought only existed in movies is right here! The use of brainwave-controlled devices to read minds began with the invention of Electro Encephalogram (EEG) in the 1900s. History has it that the German psychiatrist, Hans Berger, created the in neurosurgery. first EEG brain recording These devices, fitted with sensors, were used mostly to monitor and record a patient's brain activity. When placed on your scalp, the sensors pick up electrical signals produced by the brain, which can be interpreted. Around 2008, consumer-focused EEG became commercialized for public use. and —both tech companies—were among the early players in this scene as they released EEG headsets with the promise to create mind-controlled video games. NeuroSky Emotive Furthermore, it began being tested in other areas besides video games, like how it could be used to control your phone or other activities by companies like and . iBrain Muse Relief from Brain-Computer Interfaces Technology Innovations and advanced companies like Meta and Neuralink notched it up with their research into Brain-Computer Interfaces (BCIs) - a technology that can pick thoughts directly from brain neurons and translate them into words. This technology uses AI algorithms to read brain activity and decode emotions into words. Between 2017 and 2019, Meta began funding . As of March 2022, the funded Nature Neuroscience, UCSF researchers achieved brain-to-text decoding with an average error rate as low as 3% when it was tested with vocabularies of up to 300 words—a huge advancement from previous studies. this technology research into At the same time, Elon Musk's Neuralink is already making a good deal of progress with its flexible ultra-thin threads that can be implanted directly into the brain, enabling the carrier to control their gadgets/devices with their thoughts. This is already being tested on paraplegics. The technology has been very much welcomed by paralytic patients who need to gain a good level of control over their lives again. Neuralink has already begun testing the brain threads on paraplegics. Case Study of Neurocomputing Although this technological concept sounds somewhat new, neurocomputing has been in the research and development stage for years, and test-flight gadgets are already being produced. The possibilities of mind reading and brainwave-controlled gadgets are endless. Scientists continue to look into ways to integrate them into our lives, experimenting across various sectors with the possible use of non-invasive EEG devices and BCIs. Many of these experiments are often procedures aimed at helping the physically and mentally disabled to communicate their needs and gain some control over their lives. The world is not new to cases of patients who can not speak due to sicknesses, paralysis, stroke, or just being born that way. So when, for the first time in history, researchers at the GrapheneX-UTS Human-centric Artificial Intelligence Centre, University of Technology Sydney, invented a portable non-invasive device that could translate thoughts into words, it was seen as a breakthrough in neurocomputing. Breakthrough at the GrapheneX-UTS Human-centric Artificial Intelligence Centre: At the time, though, the accuracy was questionable, but with the initial goal achieved, it would only take more years of developing it to increase its accuracy rating. We already find Meta and Neuralink taking up this task with BCIs and electron threads. Evolving neurocomputing also makes smooth communication possible between humans and machines, be it our gadgets, robots, or prosthetic arms. The history of neurocomputing and brain-controlled devices left a remarkable mark in the FIFA 2014 World Cup when, for the first time, the symbolic kick-off event for the tournament was led and completed by a paraplegic man using an advanced assisted mobility technology—a brain-controlled exoskeleton. The FIFA 2014 World Cup Kick-Off Event: This technology, by , a collaborative invention of over 100 scientists from around the world, made use of a set of non-invasive electrodes to read the brain signals from the paraplegic and transmit them to the lightweight exoskeleton triggering movements that helped the man complete the kick-off successfully. The Walk Again Project In the same year of the FIFA World Cup kick-off event, the director of the Center for Neuromodulation, Ohio State University, Dr. Ali Rezai, performed a on 26-year-old quadriplegic Ian Burkhart. Paralyzed Ian Burkhart Regained Mobility: life-changing surgery The surgery involved implanting a tiny 4 x 4 mm microchip into Burkhart's motor cortex, used alongside an electrical sleeve and purpose-made algorithms. This allowed the sensors to trigger his thoughts to control his hands and fingers while bypassing his damaged spinal cord. It was a success. This is not the only successful brain implant in the history of neurocomputing; a man paralyzed from the shoulders down has also been able to regain control of his paralyzed muscles by bypassing the injured spinal cord with the into his motor cortex. implantation of two aspirin-sized 96-channel electrode arrays EEG devices and BCIs are greatly contributing to improved quality of life for disabled people. Other Notable Strides: But we can't say that brain-controlled and mind-reading devices are only for the sick and disabled. These devices could also be used to carry out other tasks/activities for the good of everyone: BBC and This Place teamed up to create a mind-reading headset to enable its user to control the with their thoughts—goodbye to remote controls. BBC iPlayer A team led by an Associate Professor at the KU-KIST Graduate School of Converging Science and Technology, Korea University, . Suk-Won Hwang, developed E-Glasses This prototype eyeglass is made up of flexible electrode sensors located between the ears and eyes of the wearer. First, the electrodes consist of an EEG to monitor the brain's electrical activity and an ElectroOculogram (EOG) to track eye movements and transmit both from the glasses to a device for processing and interpretation. This glass can be used to monitor the mental health of the wearer, help them control games with their eyes, and turn on and off the sunglasses mode when the wearer needs it. Nissan and NASA are working together to build a self-driving robot car technology called that can read, monitor, and measure a driver's brain activity to know if he/she is alert to drive or not. Mind Sense In an exciting twist of things, in 2013, NASA came up with the crazy idea of using . Together with scientists from the University of Essex, they launched a project to control a virtual spaceship with the mind. BCIs to control planetary rovers A competition was held in 2016 by neuroscientists at the University of Florida, a competition that had pilot flying their drones to the finish line by using their minds. Brain Drone Race And the list goes on and on. But like every other technology and thing that exists, there will always be pros and cons to consider. The Ethical Considerations of Neurocomputing Neurocomputing is a technology that is already transforming lives, especially for paraplegics, who, in many cases, have been able to get robotic body parts that can be controlled with their minds or bypass spinal cord injuries to stimulate their bodies into motion through brain neuron implants. People who couldn't talk can now communicate their feelings and what they want/need and interact through mind-reading gadgets. For everyone else abled people, organizations, and companies that wish to apply these gadgets in their daily lives or their various sectors, it is very much achievable. It can bring "ease" into certain activities and, perhaps, create a more quiet world. However, the implantation of invasive BCIs into the brain involves a surgical procedure that is almost too risky to be considered. The brain is a fragile organ; damage to it can lead to mental abnormalities or even brain death. As such, the risk of brain implants makes this technology far-fetched for fully functional humans. Paraplegics, on the other hand, can wager these procedures because of the quest to have a better quality of life. A delicate situation, but if that is what it takes for them not to be stuck in a static world at the mercy of others, it is worth the risky shot. This is why BCIs by companies like Neuralink and Meta, although envisioned to cater to all and sundry, are more appealing to the paralytic section of the world at the moment. Because of these concerns, scientists are looking back into non-invasive methods of mind reading. Last year, researchers at the , University of Technology Sydney, invented a portable, non-invasive cap-like device that could translate thoughts into words when placed on the head. GrapheneX-UTS Human-centric Artificial Intelligence Centre Additionally, scientists from the University of Texas, Austin, developed to translate the thoughts in one's brain into actual speech by monitoring the brain's activity involving the use of fMRI and Al language models. a non-invasive technique The accuracy of these techniques was average. However, with the intended goal achieved, it would only take more years of development to increase its accuracy. Still, no one wants to walk around carrying a headpiece with cords sticking out, and neither can they carry fMRI machines all day. Hence, there is a need for less obvious, non-intrusive devices or less risky invasive BCIs. While the world can be swamped by Brainwave-controlled devices that read our minds, it poses serious concerns. One day, like every other day, we'll be carrying out activities with silent thoughts all in our heads, but this is the sad part: organizations would be able to read, monitor, store, access, and use these thoughts when needed. As tragic as this sounds, it is already in progress. The thoughts in our brain are currently our only inaccessible assets, our privacy safe, our most intimate thoughts, and our personal identity—no one should be able to access our minds. Data Privacy: But with the recent discoveries and technologies in neuroscience, this intrinsic privacy may be breached sooner than we know, leaving us truly vulnerable to Big Tech companies and organizations. If we lose control of our minds, if any company can spy into our thoughts, what else do we have left of ourselves? Just like we have on the internet, companies would be able to buy and access our brain data for targeted marketing. The first appeal for every product starts with the brain. You may not be able to say it, but you can think it accurately. Imagine advertisers having that exact information just as we feel it; then, they will be able to market their products in a practically irresistible way. Neurocomputing and mind-reading devices would make brain data available on a massive scale, and this is also very detrimental to us. Think about it; aside from compelling advertising, how safe are you? The brain is where all your secrets lie: your passwords, location, investments, amount of money you own, where you saved your assets, etc. A brain data hack would mean a loss too great to bear. The argument of some neuroscientists that the potential for misuse of neurocomputing and mind reading is so great that there needs to be a revamp of the human rights laws to protect us all is valid. Because this technology has what it takes to interfere with our most basic rights, the current laws are not fully equipped to salvage the situation; we need a new "jurisprudence of the mind" for our protection. So, in all, our biggest challenge is privacy and safety! What's the future? A quiet world. Maybe what we need? Or maybe not. Elon Musk and his company, Neuralink, have been at the forefront, pushing the concept of neurocomputing beyond the idea of it being the help for paraplegics only. For Musk, it would take conceptual interactions to a whole new level where words and conversations don't have to be verbalized, just thought. He calls it "consensual telepathy,” a realm where your speech becomes completely unnecessary. You can walk into a room with people conversing but not conversing because their thoughts are flying invisibly between them. Reading that now, it does sound uncanny. What becomes of our speech? Oh! I know what you are thinking, "there would never be a day when our speech isn't needed,” and of course, that's somewhat true. But come to think of it, before the advent of mobile phones, would there have been a day we thought we'd be so glued to these gadgets we'd rarely appreciate physical communication and rather be embedded in virtual conversations? I don't think so. When all you need is your thoughts to perform the most basic activities like ordering a coffee from a cafe, asking your friend to pass you the popcorn at a movie, communicating a proposal to your boss, conversing verbally on a date, etc., you may slowly lose touch of actual words. Almost the same way, we are slowly losing touch with writing on paper because we'd rather type into our computers, tablets, or phones. Well, there's no denying the fact that it sounds pretty interesting, too. You'd never have to say you don't know how to express yourself because your mind will do all the expressing for you where words would have escaped you. The concept of your feelings will flow just as you feel them, and no matter how disgruntled they are, your receiver will get the exact message with the fear, excitement, contempt, and all the fleeting emotions - conveyed just as it is. Also, the advancement of this technology would eliminate the language barrier. Whatever we think can be communicated directly to the recipient in whatever language desired. What neurocomputing proposes for the future is both unsettling and intriguing, but we really can't stop technology from evolving, and neither can we stop the future. We only hope that measures are instituted to mitigate the cons while we hope for the best.

From Science Fiction to Reality: The Promise of Neurocomputing and Brain-Computer Interfaces (BCIs)

কল্পবিজ্ঞান থেকে বাস্তবে: নিউরোকম্পিউটিং এবং ব্রেন-কম্পিউটার ইন্টারফেসের প্রতিশ্রুতি (BCIs)

Von der Science-Fiction zur Realität: Das Versprechen von Neurocomputing und Brain-Computer-Interfaces (BCIs)

De la ciencia ficción a la realidad: la promesa de la neurocomputación y las interfaces cerebro-computadora (BCI)

De la science-fiction à la réalité : la promesse de la neuroinformatique et des interfaces cerveau-ordinateur (BCI)

विज्ञान कथा से वास्तविकता तक: न्यूरोकंप्यूटिंग और मस्तिष्क-कंप्यूटर इंटरफेस (बीसीआई) का वादा

サイエンスフィクションから現実へ: ニューロコンピューティングと脳コンピュータインターフェース (BCI) の可能性

SF에서 현실로: 신경컴퓨팅과 뇌-컴퓨터 인터페이스(BCI)의 전망

Da ficção científica à realidade: a promessa da neurocomputação e das interfaces cérebro-computador (BCIs)

От научной фантастики к реальности: перспективы нейрокомпьютеров и интерфейсов «мозг-компьютер» (BCI)

Bilim Kurgudan Gerçeğe: Nörobilgisayar ve Beyin-Bilgisayar Arayüzlerinin (BCI) Vaadi

Từ khoa học viễn tưởng đến hiện thực: Lời hứa về giao diện máy tính thần kinh và não-máy tính (BCI)

从科幻到现实：神经计算和脑机接口 (BCI) 的前景

Last week I was talking about job applications and not holding yourself back by saying “no” pre-emptively. I started to dive into job descriptions before realizing they deserved their own post. Which is what I’m talking about today. I’m not going to sugar-coat it: Job descriptions are often a complete work of fiction. Sometimes, this is because the job description is years out of date – written before the previous employee in that role (or the one before  that) had even started. The original responsibilities morphed and  changed with the needs of the moment and the skills of the people, but the job description remained unchanged. Sometimes the job description started out realistically but then HR (or the “AI”-driven tool HR is using) decides to add other requirements  to better match similar job descriptions out of standardized databases –  which are themselves nothing more than amalgams of job descriptions  sourced across time and distance. It’s job-description-by-committee,  with no bearing on reality. Sometimes HR offers a single open req (when 3 are needed) so the manager tries to create a wish-list of requirements and  responsibilities, in the hope that someone will come along with most (or  at least many) of the boxes checked. It’s a pipe dream, but a  frustratingly common one. Regardless of the reason, the truth remains that many of the job descriptions out there are nothing more than a messy collection of vague  hopes, wishful thinking, and out-of-date ideas. Over three decades an nearly 20 jobs, I’ve lost count of the number of times I’ve shown up for an interview only to discover that the work  bore NO resemblance to the requirements on paper. What I find more  astonishing is that sometimes the manager knew this was the case, but  often they didn’t. The lesson in all of this goes back to the core message of the : In your job hunt, job is to apply. Don’t second guess whether you have “enough” of the  skills, because – as I’ve just explained – the work their need done may  be very different. last post your Do you like the company because of it’s culture, creation, community, calling, or some other aspect? Are you intrigued enough to want to find  out more? That’s all it takes. Apply. Also published . here

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