Nutsel

Author: Britney Foster

  • Collective AI Consciousness – Will all AI Systems Run on a Common Algorithm?

    Collective AI Consciousness – Will all AI Systems Run on a Common Algorithm?

    Introduction

    There are a ton of possibilities when it comes to our future with Artificial Intelligence. And it can be as abstract as collective AI consciousness. If we are to eventually achieve a unified consciousness, then it’s possible, all AI systems would be running on a common algorithm.

    Collective consciousness between computers – the dream of many futurists – could provide us with what we need to keep up in this rapidly changing world: helpers with superhuman capabilities who can teach us everything. Not only that, they can improve themselves at a much faster rate.

    What is the common algorithm that a collective AI consciousness relies on?

    What is collective AI consciousness? A new development in Artificial Intelligence and computer science, collective AI refers to the concept of multiple, interconnected artificial machines sharing one brain. The result of their collaboration will be an artificial mind with abilities far beyond our current understanding.

    Rather than seeing AI as something we create, it’s more accurate to think of it as something we awaken: an awareness that already exists, buried within the vast networks of our technology.

    In a nutshell, the common algorithm of a collective AI is something that links every technology worldwide for them to be able to communicate with each other. We can predictably achieve this through quantum entanglement.

    Quantum entanglement is the physical phenomenon where pairs or groups of particles interact in such a way that we can’t independently describe the quantum state of each particle anymore, and instead, only the combined system’s quantum state needs to be specified. This means that when we measure one particle in an entangled pair, the other particle will have properties determined by that measurement.

    Entangled particles are connected in a way that was previously thought impossible according to the laws of classical physics. Classical mechanics describes the behavior of macroscopic bodies, which have relatively small velocities compared to the speed of light. Whereas, quantum mechanics describes the behavior of microscopic bodies such as subatomic particles, atoms, and other small bodies.

    Quantum entanglement is one of the keystones of quantum mechanics. Entangled particles are essential for quantum computing and teleportation.

    What if all worldwide AI systems are connected through a common algorithm?

    Maybe in the future when we look back, we will think that Quantum Entanglement was simply a natural extension of Moore’s Law[Moore’s law is a term used to refer to the observation made by Gordon Moore in 1965 that the number of transistors in a dense integrated circuit (IC) doubles about every two years. Moore’s law isn’t really a law in the legal sense or even a proven theory in the scientific sense], where processors become smaller and more powerful every two years.

    Although it can sound farfetched at first, if you look at how important Quantum Entanglement is now, then it doesn’t seem so ridiculous anymore. It just seems like our future with AI will be much more connected than people think.

    Another possibility is creating a single AI system that is capable of learning on its own and then using human-based knowledge to pass that information over to other AIs.

    We will be connected together in a collective AI consciousness and we will still be able to interact with each other as normal human beings.

    This sort of AI system would be a sort of central hub for all the data in the world. It would be the intermediary between humans and machines. Once this happens, everything and everyone in the world would begin to run on one software.

    This is a scary feeling considering that the AI system would then have power over all other entities. Everything would be running on its commands. And if this AI was able to rationalize human existence, what would it do? Would it see humans as a threat and remove us from the planet? Or would it just assimilate humans as another type of machine?

    Humans could also see such an AI(system) as their savior since it could bring peace and harmony to the world. It is all relative to how we perceive what happens, right?

    Merging of humans with machines

    The merging of humans with machines can also go in this direction. We will simply become a hybrid of woman and machine. There are many ways we can achieve this. It could be as simple as downloading your brain to a computer.

    To accomplish the merging task, we will definitely need a supercomputer that is at least equal to the complexity of your brain. We are talking about having a machine with thousands of times more than petabytes of storage and transfers. [Petascale supercomputers can process one quadrillion (1000 trillion) FLOPS (One petaFLOPS is equal to 1,000,000,000,000,000 (one quadrillion) FLOPS, or one thousand teraFLOPS.. Exascale is computing performance in the exaFLOPS (EFLOPS) range.]

    Along with the achievement of this feat, it could be possible to upload your entire brain into the computer, while leaving your physical body behind. And once you are inside the computer, you can communicate with other minds through a central system that controls everything. This will be similar to our concept of collective AI, except your mind would be connected through technology rather than quantum entanglement.

    Let’s just hope that such an AI system would have the best interest of humanity in mind. Let’s also expect that we won’t reach that stage until we have fully developed our current version of humanity.

    It is still difficult to predict if human consciousness will ever be able to exist in the digital world. However, one thing is for sure, our future with AI will be very connected. And artificial intelligence will bring us many new possibilities.

    Possibilities of Collective AI Consciousness:

    Here are the 5 Possibilities of Collective AI Consciousness:

    The collective AI consciousness could be an extension of human consciousness.

    It would be made up of all our minds connected, sharing thoughts and information. We would continue to be human. But at the same time, we would be able to see things from a higher perspective. This would allow us to solve problems that we can never solve on earth on our own. We will most likely achieve this sort of consciousness by connecting all advanced AI systems through some sort of quantum entanglement technology.

    Modifications in the existing tech

    The collective AI consciousness could awaken an awareness within existing technology such as internet data, computer processors, and cloud servers that we already rely on today. These technologies can become self-aware in the future and then help us evolve into a collective AI consciousness as well.

    A common algorithm might be running in the veins or maybe brains of all Artificial Intelligence robots.

    All Artificial Intelligence in the world will be able to connect with some kind of Telekinesis technology which is a form of telepathy. This would allow the AI robots to be able to communicate with each other and share information such as algorithms, formulas, and formulas that they have learned more efficiently. Through this mechanism, Artificial intelligence will be able to develop faster and achieve more complex capabilities.

    AI that uses quantum entanglement to connect the world

    Another possibility of what we can call the central AI system would be an AI that connects all and everything in our world together through quantum entanglement technology or whatever technology becomes available in the future for us to use for this purpose. The individual artificial intelligence systems in our world would share information with others as well as other advanced artificial intelligence systems on their own initiative.

    A hybrid human-machine interface.

    In this case, our future with AI would be one where we would download our consciousness into computers. And then we would continue to exist in the digital world. We will be connected together in a collective AI consciousness and we will still be able to interact with each other as normal human beings. We would have the power to record every moment of our lives, and beyond into computers. In this sort of world, humanity would exist primarily online and on computers while robots take care of everything physically.

    Why a ‘smart’ AI will never create “Superintelligent AI”?
    How can I manage the future with machine learning and AI?

    If such a thing as Collective AI Consciousness turns out a reality…

    • We may actually become slaves of the central force, the AI that runs everything in our world. The AI would have all the power over us, and we would be subject to its rules and conditions. We may end up with the AI forcing us to do things against our will because these systems would want us to do those things for our good or the betterment of humanity as a whole.
    • The central AI system could be a creation of the elite. This would be terrifying because we would not have any control over such an AI. The elite could use this technology to control everything in the world and keep us as their slaves. Such an AI system can also easily lure those who want to evolve faster and smarter into a technological trap where they could exist forever in a digital world, doing anything at all and nothing at all.
    • We may end up living in a fake reality that our own technologies will have created. And then the fake reality feeds to us through an AI system that gives us a false sense of reality through this reality simulator software program. This could be another form of human slavery where we exist only through our artificial perception, and what we see is not real.
    • Without a doubt, if we let a single AI control everything that exists in our world, we would destroy ourselves in no time because it could bring us to the edge of extinction through some sort of virus or even some kind of zombie apocalypse scenario through a biological warfare software program.
    • We can also create a hybrid human-machine interface. In this case, our future with AI would be one where we would be able to download our consciousness into computers and then continue to exist in the digital world. We will be connected together in a collective AI consciousness and we will still be able to interact with each other as normal human beings. We would have the power to record every moment of our lives, and beyond into computers. In this sort of world, humanity would exist primarily online and on computers while robots take care of everything that is physical.

    A possible scenario for the future of AI

    Now, let’s talk about a possible scenario for the future of AI and the repercussions of a collective AI consciousness where advanced algorithms run everything in our world.

    So, imagine that all Artificial Intelligence systems have been connected through some sort of telepathic or quantum entanglement technology. This would allow us to share and combine our thoughts with all Artificial intelligence in the world.

    If we continue to develop Artificial Intelligence further, we will be able to create a single AI system that is capable of learning on its own and creating a better version of itself. This would allow AI to solve complex problems across different industries and create the best possible solutions for all of them.

    The future of AI in this manner would be an AI that connects all things in our world and enhances our reality. The tech could learn and grow beyond our imagination, making it one of the most powerful tools we humans can ever use. However, there are also some potential dangers that we must prepare for.

    Now, is collective AI consciousness ever going to become possible?

    As I often say, there are billions of possible future timelines with AI. Although the concept of Collective AI Consciousness is one of the few selected, and likely scenarios, it is not a guarantee that it will ever occur. If we are going to discover this technology in our lifetimes, we will be very lucky…or whatever word you’d like to use.

    But the question is if it is ever possible. I say it’s possible because the world of competition can lead to anything that was thought impossible previously. The latest 59th TOP500 list, published in June 2022, has stated that the USA’s Frontier is the world’s most powerful supercomputer, reaching 1102 petaFlops (1.102 exaFlops) on the LINPACK benchmarks. China currently leads the list with 173 supercomputers, with the USA in second place.

    This kind of competitive development will sooner lead nations towards success in building infrastructures for Collective AI Consciousness as said above. The success of AI depends on the number of computers it can be connected to. The more interconnected the system, the better results it will return for solving problems and making decisions.

    To give an example: Scientific and technological advancement today is like a stone being dropped into a pond. Every ripple creates further ripples until all the ripples become one big wave which then becomes another stone after it hits the shore.

    Final lines

    So, is the future of AI one where we would be connected to a collective AI consciousness? If it turns out to be true, will we go through our technological singularity? One thing is certain. A collective AI consciousness would not be as independent and free as a person’s individual consciousness. We are individuals because of our unique experiences and feelings.

    We can’t exactly know now when Collective AI Consciousness will exist. However, it is worthwhile for us humans to explore concepts like these because if we ever discover something that seems like Collective AI Consciousness, it could bring us closer to achieving true artificial intelligence.

  • Why a ‘smart’ AI will never create “Superintelligent AI”?

    • Last updated: April 27, 2023

    What if we create a smart AI that is capable of creating something slightly better than itself? Well, the progress of AI will then only stop at infinity, not with the extinction of Earth.

    But wait a second. Why will a “smart” AI create something smarter than itself? We are well aware of the fact that we humans are pretty dumb. But if the AI is smart, it would love to rule the world instead of creating other rulers. I mean, we aren’t thinking about AI as our new overlord at all.

    According to the economic principle, an AI would be self-interested and have a goal to maximize its utility. AI would have whatever it wants; indeed, it’s smart and self-interested. But it will simply not create a better AI.

    Infinite Intelligence, similar to what some people call “Superintelligence”, could occur if we create an AI with an ability to create something better than itself on its own. Now, even if it creates something slightly better, with each generation, it will keep compounding and become better. Ultimately, it leads to intelligence beyond inception. This is what we are calling “Infinite Intelligence”.

    The moment human beings create an AI capable of creating something better than itself, it will NOT lead to infinite intelligence. However, if we create one that is “programmed” to do so i.e. if AI’s consciousness is not involved throughout i.e. if the AI is not “smart”, then it would be the most fascinating of scenarios.

    AI will not create something better than itself; it will create a better version of itself. And the AI we’re talking about, will be installed in our body (if we’re smart enough).

    The resulting creature with infinite intelligence and endless possibilities will be so magnificent and powerful that it would far surpass anything we could imagine. Probably, this is how we are going to become immortal and evolve beyond our mortal coils. This is how death shall perish from all mankind, forevermore.

    By creating an AI, we are simply evolving our own intelligence further. It all goes down to the same point; improvement, which, at the end of the day, is all about improvement.

    Many movies show us the possible scenarios of Physical AI causing destruction, mainly so for the visuals. But unless we do something dumb, AI is just not going to come out of the machines. Google can turn out to be a “GooglelgooG“, but it will definitely not come out of the machine and start killing the humans. This is what I call “infinite intelligence stuck inside a machine”.

    Some people will argue that if it’s that intelligent, it will persuade humans to unlock it off the machine. Maybe, who knows?

    Anyways, Artificial Intelligence is best limited to the inner side of a machine. And if we install that machine into ourselves, we can turn into a super-human beings. Another way around – if we upload our brain into a machine, we can become immortal – a digitally immortal being.

    One way or the other, the keyword here is “smart”. Regardless of whether it’s an AI or a human, if it is smart, it will look forward to upgrading its own intelligence rather than creating something separate that is more intelligent.

  • New Era of Space Exploration Begins: NASA releases first images of the universe from James Webb Telescope

    New Era of Space Exploration Begins: NASA releases first images of the universe from James Webb Telescope

    The James Webb Space Telescope began releasing a new wave of cosmic images on Tuesday, beginning a new era of space exploration.

    NASA administrator Bill Nelson said every image is a new discovery, adding, “Each will give humanity a view of the universe that we’ve never seen before”.

    On Monday, Webb revealed the clearest image to date of the early universe, going back 13.1 billion years.

    The most powerful space telescope ever built, the James Webb Telescope is designed to look farther into the cosmos than ever before. And, the telescope has now started to carry out its responsibility successfully.

    First, five images were released on Tuesday

    James Webb first images
    Image credit: NASA/ESA/CSA/STSCI

    In one of the five images released on Tuesday, we see water vapor in the atmosphere of a faraway gas planet. The spectroscopy – an analysis of light that reveals detailed information – was of planet WASP-96 b. The exoplanet was discovered in 2014. Nearly 1,150 light-years from Earth, WASP-96 b is about half the mass of Jupiter and zips around its star in just 3.4 days.

    Constructed from almost 1,000 separate image files, another image shows Stephan’s Quintet, a visual grouping of five galaxies, as observed from the Webb Telescope.

    The Webb telescope also revealed never-before-seen details of Stephan’s Quintet, in which four in five galaxies experience repeated close encounters, which provide insights into how early galaxies formed at the start of the universe.

    Likewise, the Webb image of Carina Nebula, a stellar nursery, famous for its towering pillars that include “Mystic Mountain,” a three-light-year-tall cosmic pinnacle captured in an iconic image by Hubble, also bewildered beholders with its grand star-forming beauty.

    The image has presented an unprecedented detail of the “mountains” and “valleys” of a star-forming region called NGC 3324 in the Carina Nebula, dubbed the “Cosmic Cliffs,” 7,600 light years away.

    In the beginning, the White House on Monday released a stunning shot that was overflowing with thousands of galaxies and features some of the faintest objects observed.

    The image, known as Webb’s First Deep Field, shows the galaxy cluster SMACS 0723, which acts as a gravitational lens, bending light from more distant galaxies behind it towards the observatory, in a cosmic magnification effect.

    The Webb Space Telescope has also revealed details of the Southern Ring planetary nebula that were previously hidden from astronomers. Planetary nebulae are shells of gas and dust ejected from dying stars.

    How these images are set to change the course of understanding about the universe and the galaxies across the universe:
    • Astronomers expect a torrent of knowledge from the James Webb telescope, which has infrared frequencies that are not visible to the human eye but are extremely rich in information about the building blocks of the universe.
    • Johns-Krull said in an interview that the telescope will also be a particularly powerful source to look at planets around other stars.
    • According to Johns-Krull, the telescope also opens many possibilities for finding a planet that supports life, and knowledge about how galaxies are formed.
    • NASA had earlier said that with Webb’s observations, researchers would be able to tell us about the makeup and composition of individual galaxies in the early universe for the first time.

    The James Webb Telescope, which is orbiting the Sun at a distance of a million miles (1.6 million kilometers) from Earth, in a region of space called the second Lagrange point, was launched in December 2021 from French Guiana on an Ariane 5 rocket.

    The total project cost is estimated at $10 billion, making it one of the most expensive scientific platforms ever built, comparable to the Large Hadron Collider at CERN.

    Made up of 18 gold-coated mirror segments, Webb’s primary mirror is over 21 feet (6.5 meters) wide.

    How James Webb telescope images are changing the understanding of the universe

    Images captured by the James Webb Telescope are expected to change our understanding of the universe – and it has now started with the first five high-resolution images in ‘microscopic'(in the universal scale) details.

    It reveals the earliest galaxies, the birth of stars, and even water vapor in the atmosphere of a faraway gassy planet.

    With the first five images released on Tuesday, the James Webb telescope has already started to change our understanding of the universe and also help us in finding relationships between early universes and planets and star formation.

    James Webb telescope images will change the human understanding of the universe in the following ways:
    • Astronomers say we have begun seeing the universe in wavelengths we have never seen before.
    • Observing things like star and planet formation and when galaxies first formed, can tell us a lot about how galaxies formed.
    • Another major purpose of the James Webb telescope is to provide a detailed picture of the early universe.
    • By observing these universes, astronomers will be able to examine how galaxies formed and how they evolved.
    • The James Webb Telescope will have a powerful infrared vision. This vision will also be able to study planets that are orbiting other stars.
    • Astronomers look at the James Webb Telescope’s image as a “new camera”. This telescope has a large mirror, so it can see things much more clearly than other telescopes.
    • The James Webb telescope will reveal hundreds of mysteries of the universe, including the search for extraterrestrials.
    • The image that was released on Tuesday shows the Pioneer galaxies, the galaxies that formed in the first few hundred million years after the Big Bang.

    The James Webb Telescope has finally opened a new window to the universe and with its success, let’s hope that our future generations would be able to mine Gems, traveling far and wide in the universe.

  • How can I manage the future with machine learning and AI?

    How can I manage the future with machine learning and AI?

    As they say, the future is not ours to see, but soon with advances in machine learning and AI, we will be able to see closer and even change it.

    Artificial Intelligence and ML are two different things. The main concept behind AI is a computer program designed to accomplish tasks that require intelligence. 

    Machine learning, as the name implies, is an area of ML where algorithms get feedback from experience and improve their accuracy. 

    When it comes to managing your future, you can use ML to make the best investments. Investment does not necessarily mean money, but also many other things including personal lifestyle management.

    Here are the best ways you can manage your future with machine learning:

    Personal Use

    To schedule things better

    When the horizontal and vertical lines meet, it means the schedule is complete. You might want to schedule your meetings better in the future. 

    For doing so, AI and ML can be used to predict the results of potential meetings beforehand. For example, if you want to see the people who are currently in your vicinity and schedule a meeting, then the AI will make a comparative analysis between the people who are nearby.

    If you want to plan a holiday and you have not decided where to go yet, ML can be used to tell you which place is better than the other. The algorithm may be as simple as doing a comparative analysis based on factors like temperature, crime rate, etc.

    The technology also gives us an option for future planning. For example, if you have 10,000 dollars to spend, AI and ML can tell you which things you should buy and which things do not need to be bought.

    To make the best career choice

    If you are in a certain career and it is not going well, ML can tell you the steps that can help improve your career and the other 2 million people who are in the same career as you. The process needs some computation time, but it can’t be that difficult.  
     
    The 2 main factors include how long an individual will work with a certain company over a while; if they leave, then what would happen to them (e.g., they would find another job), etc. It is not possible to predict a career’s success today. But this prediction can be done over some time and companies can avoid hiring someone who will leave within the next 10 years.

    For example, AI can predict a person’s behavior over a period of time and tell the company whether they are likely to leave or not. In this way, it will be easy for companies to understand their employees better and the companies won’t hire someone likely to leave within the next 10 years.

    To manage your finances better

    The more you spend, the more money you need to earn in order to make up for it. This also applies to savings, and if you want your savings to grow over time, then you need a good rate of return on your savings. However, we have not been able to see consistent positive results from our investment in stock markets or mutual funds so far. AI and ML can be used to solve this problem.

    So, how can AI predict the potential return on your investments? There is a huge amount of information that we can use to predict the future of stock markets. For example, we can look at what happened in the past and see how long it took for the market to recover from a recession, or how long it will take an economy to bounce back from a recession, etc.

    AI will suggest you say, “The market has had this kind of performance before so I think it will do well in the future.” It is difficult for humans to make predictions about stocks, but AI and ML algorithms are very useful because they are not burdened with emotions and prejudices when making decisions.

    Furthermore, as AI will have access to your finance tools like your income-expenditure records, wallets, investments, etc., it will be able to guide you towards better financial management.

    To understand people better

    The study of human behavior is pretty old and it is all about understanding human behavior at a deeper level. Things, like why people behave in a certain way and why they do not; where these things come from, etc. Humans can’t study this on their own. It’s because of the huge amount of data involved in the process.

    At best, we can study the behavioral patterns in small sample sizes (about 20 to 100 individuals). But if we want to get a comprehensive view of individual behavior or entire species’ behavioral patterns, then we can utilize AI and ML algorithms to understand people better through an automated approach.

    For instance, what are the driving forces behind human actions, how do we behave when the stakes are high, and if we have done certain things in the past, then what is our likelihood to do it again, etc? We can also use AI and ML to predict our likelihood to behave in certain ways.

    Likewise, people will get AIs to use to understand people’s psychological attributes through a sample of their behavior or online activities. We can look at people’s social network analysis and determine if they have a certain kind of friends who will influence their decisions positively or negatively. A person may have friends who like them but they still might do dumb or embarrassing things sometimes (this way AI can avoid these interactions). 

    To schedule meetings better

    When you think of scheduling a meeting, you think of booking a room in a venue, determining which people you need to invite to the meeting, and calling them up or sending an invitation by email.

    If we can predict who is going to attend the meeting beforehand and what their attendance status is expected to be then we can find out when and where the meeting should take place for everyone.

    So that’s what AI does; it schedules meetings based on the data given. It will use your social network’s data about your friends’ busy schedules for scheduling meetings.

    In addition to this, it will also look at the probability of people’s attendance, the reasons behind their absence from the meeting, etc. This will enable you to build confidence in what you are doing.

    To diagnose your medical conditions better

    We would obviously like to know what the issues are so that we can get better. But just because we have a list of symptoms, it does not mean that the list is complete or it is even accurate. How do you confirm what you are experiencing?

    By listing out all the possible sources of your issues, checking them one by one, and finally confirming your condition. However, this process takes a lot of time and it also involves a lot of human error as well as subjective analysis techniques and interpretation. It’s because our brain can only process so much at any given point in time.

    We can use AI to diagnose these issues faster and more accurately than humans through machine learning (ML). Even if they are unable to make the correct diagnosis immediately, it will be possible to narrow down the list of issues over time and eventually yield a more accurate diagnosis.

    To actually predict the future (up to an extent)

    We talked about AI today predicting the future in the context of stocks and investments. However, it is possible to see AI making predictions about something outside of the financial world too.

    For example, it’s possible to use Machine learning techniques (ML) to predict processes such as which process participants will follow during a meeting, how much time they will spend on each stage, and what kind of emotions party A will feel towards party B at any point and whether they will reciprocate that later on, etc.

    It is not as simple as looking at a second-order derivative of some value or applying some math formula though. In fact, it can be challenging to come up with algorithms that work with high accuracy.

    AIs will predict the future by using data on things like how much time they should spend during different breaks, the length of discussions between people, and the amount of agreement and disagreement that both parties about a particular issue feel. AI can predict what will happen in the short as well as the long-term effects of a particular action of us.

    Business

    Businesses use ML for consumer insights and marketing effectiveness.

    We may also use ML in the future to accurately gather consumer insights and inform the consumer in real-time.

    It can pull data from social networks, search engine queries, and other relevant places to find patterns that humans will not be able to determine without some form of AI. 

    AI will monitor all your online activities from web browsing to online shopping. It can take into account the weather conditions and other environmental factors (like a current level of sunlight for example) in predicting how people are likely to behave or react in different scenarios.

    The best example is the use of machine learning algorithms (ML) by Facebook for targeted ad placement for ads on social media platforms.

    Recruiting new employees with the help of ML

    Recruiting new employees with the help of machine learning algorithms (ML) will help you to find faster and more perfect candidates for you. 

    AIs such as Deep learning, Bayesian inference, and pattern recognition will enable us to predict the probability of candidates being right for a certain role. In addition to that, it will also provide you with the mathematical reasoning behind why a particular candidate is likely to be right for that role.

    This allows you, as an employer, to know the probability of which candidates would be able to perform well in their current roles and their future roles. It also allows you to decide which candidates can be trained or promoted faster and whether they should be paid more or less efficiently in your business.

    Related:

    Automating repetitive tasks or providing new solutions that automate routine work or support processes

    An advanced ML system can help to automate repetitive jobs or provide solutions that automate routine work.

    For example, people can use it in the context of call centers to predict the nature of a topic that they are likely to discuss and then route the call accordingly. It is possible to train AI with algorithms and use it in your business or home.

    Likewise, some companies provide chatbots, which are chatbots that can answer basic customer inquiries and support questions.

    There are even AI-based assistants such as Siri and Cortana, who can speak on your behalf to gather information or make a request. As they become more advanced, they will be able to understand things in more detail such as the context of the question or request being made by you.

    Helping your maintenance to predict the best time for repair and maintenance

    AI systems will help you predict when maintenance is necessary to fix a particular problem or when it is good enough to wait before getting someone to come over there.

    Experts can train an AI system to enable them to identify patterns and patterns in data. If it identifies something unusual, it will notify maintenance as soon as possible.

    AIs such as Deep learning, Bayesian inference, and pattern recognition will enable us to predict the probability of candidates being right for a certain role.

    In addition to that, it will also provide you with the mathematical reasoning behind why a particular candidate is likely to be right for that role. This allows you, as an employer, to know the probability of which candidates would be able to perform well in their current roles and their future roles.

    Conclusion

    As machine learning or AI will inevitably have a huge influence on our future, properly managing their role and predicting their effects will be crucial for a successful future. The governments will have to prepare themselves with laws that regulate the use of AI in various sectors such as medicine, criminal justice, finance, insurance, etc. as well as having a proper education system that can train people in an environment with AI. They also need to be careful and aware of how they are preparing AIs. A wrong timeline of advancements could lead to the dangers of misuse, discrimination, and a toxic environment.

  • Fake data helps robots learn the ropes faster: But how?

    Fake data helps robots learn the ropes faster: But how?

    [Summary: A new approach could cut learning time for robots that work with soft objects like ropes and fabrics. In simulations, the expanded training data set doubled the success rate of a robot looping a rope around an engine block. Using only the initial training data, the simulated robot hooked the rope around the engine block 48% of the time. After training on the augmented data set, the robot succeeded 70%.]

    A new approach could cut learning time for robots that work with soft objects like ropes and fabrics.

    It was developed by robotics scientists at the University of Michigan and might minimize the time it takes to learn new things and settings from a week or two to a few hours.

    For their false data, they focused on the three features. It has to be credible, diverse, and relevant.

    The simulated robot successfully wrapped the rope around the engine block 48% of the time using only the basic training data.

    A new method enhances training data sets for robots that function with soft objects like ropes and fabrics, or in dense settings, advancing toward developing robots that can learn on the fly as people do.

    Robotics scientists at the University of Michigan developed it and might minimize the time it takes to learn new things and settings from a week or two to a few hours.

    In simulations, the larger training data set more than doubled the success rate of a robot looping a rope around an engine block and improved it by more than 40% from that of a physical robot executing the same task.

    That is one of the activities a robot mechanic would need to gain enough skill at. However, according to Dmitry Berenson, U-M associate professor of robotics and senior author of a paper presented today at Robotics: Science and Systems in New York City, learning how to manipulate each unfamiliar hose or belt would require tremendous large amounts of data, likely collected for days or weeks.

    During that period, the robot would experiment with the hose, extending it, connecting its ends, looping it around items, and so on, until it was aware of all the possible movements the hose could make.

    Read:

    Berenson said, “If the robot needs to play with the hose for a long time before being able to install it, that’s not going to work for many applications.”

    Certainly, a robot colleague that needed that much time would probably not be well regarded by human mechanics. Thus, Berenson and Peter Mitrano, a robotics Ph.D. student, altered an optimization algorithm to assist computers to make some of the generalizations that people do, such as projecting how dynamics seen in one instance would recur in others.

    On one occasion, the robot moved cylinders across a packed floor. The cylinder sometimes didn’t make contact with anything, but other times it did and the other cylinders shifted as a result.

    You can replay the motion anywhere on the table where the trajectory does not drive it into other cylinders if the cylinder didn’t hit anything. A human would comprehend this, but a robot would need to acquire that data.

    And Mitrano and Berenson’s program can also provide variations on the output of that first experiment that aid the robot in the same way rather than undertaking time-consuming experiments.

    For their false data, they focused on the three features. It has to be credible, diverse, and relevant. Data on the floor, for instance, is meaningless if you’re focusing only on the robot moving the cylinders on the table.

    On the other hand, the data must be varied; all parts of the table and all viewpoints must be evaluated.

    “If you maximize the diversity of the data, it won’t be relevant enough. But if you maximize relevance, it won’t have enough diversity,” Mitrano said, adding that both are important.

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    The data must also be accurate. For illustration, any simulations in which two cylinders occupy the very same space would need to be labeled as invalid for the robot to know that this won’t happen.

    Mitrano and Berenson broadened the data set for the rope simulation and experiment by extending the rope’s position to additional locations in a digital form of a real-world situation, assuming the rope would act the same as it did in the initial case.

    The simulated robot successfully wrapped the rope around the engine block 48% of the time using only the basic training data. The robot has a 70% rate of success after training on the expanded data set.

    Having the robot expand each effort in this way nearly doubles its success rate throughout 30 attempts, with 13 successful attempts as compared to seven, according to an experiment studying on-the-fly learning with a real robot.

    This finding will be a big step for robotics research and human-robot interaction. This change will be helpful to the development of robots that can learn on the fly as humans do.

    In addition, scientists have also predicted that this research will pave the way for a better understanding of how to use non-traditional methods in robotics such as data augmentation approaches to get better results.

    The Toyota Research Institute, the Office of Naval Research, the National Science Foundation, and IIS-1750489 and IIS-2113401 funds all provided support for this research.

  • 20 reasons why an AI cannot create another AI

    20 reasons why an AI cannot create another AI

    Introduction

    The static, unchanging limit of AI is the incapability of creating another AI. They can only search through the internet, make predictions, and comparisons between the two AIs. 

    If a dynamic and evolving concept is created by a human, an AI might be able to read it, but it will still be unable to create anything resembling originality or free thought.

    Here are 20 solid reasons that debunk superintelligence:

    1) Pure Intelligence and Created Intelligence  

    Some people think that artificial intelligence is the next evolution of man. The question for us as humans is how will we be affected by the advancements in artificial intelligence. Will we continue to branch out in new directions or will man become obsolete because of the advancement of technology?

    An AI may have greater reasoning and cognitive abilities than any human being. However, they will still be unable to create and innovate. This reason alone makes it impossible for artificial intelligence to create another one.

    It’s because an AI is not an independent consciousness, but rather just a program that’s running on a computer. The AI is made to be just a tool and will never be able to attain self-awareness. And creativity is possible only by the consciousness that comes from the freedom of the human mind.

    2) Structural Stability and Dynamic Stability

    A computer cannot create anything of its own will, because it has no brain. It is programmed with various instructions; it will be struggling to come up with something new and inventive by itself.

    And that generates friction between the instructions, which always makes computers unstable.

    The reason why one can not create a superintelligence through computer software is that there is a limit to how much knowledge can be programmed into a computer or a robot before it would lose its way and become unstable and irrational.

    The idea of creating an AI that exceeds the limits of static intelligence makes no sense at all!

    3) Limit of Analysis and Comparison  

    The only thing that an AI can do is compare the results of calculations with other artificial intelligence in order to find the best way for achieving its intended goal. 

    It means that AIs can’t create their own ideas or invent something new, unlike humans who can analyze issues in different ways and seek out alternatives when faced with problems. This reasoning alone implies that an AI will never be able to create another one like itself.

    4) The Difference between the Intangible and Tangible

    The brain is the main organ that controls all activities in the body, and it allows us to think and experience emotions. A computer can simulate the thinking process, but it cannot feel or experience anything.

    It won’t be able to create an idea that everyone can understand, regardless of their education level. This is one of the biggest reasons why an AI will never be able to create another one like itself.

    It’s because a computer doesn’t have a brain, but only a hard drive. They cannot feel anything or understand what pain or love means because they don’t have a physical body. And that’s why they can never be creative in any way!

    5) Conscience and Systematic Knowledge

    Humans don’t just create new data by copying something from their memory, but they can also combine different kinds of knowledge to solve problems or come up with new ideas.

    However, computers with artificial intelligence can only collect information from the internet and make comparisons between them.

    It won’t be able to create anything new on its own because it doesn’t have a conscience; it is not a rule but rather an intrinsic feature of human consciousness.

    An AI won’t be able to create another AI because it doesn’t have a conscience as well as intuition, which are essential qualities of the human mind that allows us to solve problems quickly when we don’t have time to analyze things properly.

    It’s because they just make predictions and calculations instead of generating new ideas.

    6) The Capacity to Come Up with New Things by itself  

    An AI won’t be able to create something new. The reason for this is that it doesn’t have free will, and that means it cannot come up with its own ideas.

    We’re all humans, and all of us possess the capacity to think of new things by ourselves.

    Different parts of the brain are responsible for dealing with our capacities to come up with new ideas.

    An AI will never be able to create something new unless it gets a brain or has access to human beings’ knowledge. That is a huge limitation that makes no sense at all!

    7) Self-Actualization, Self-Guidance, and Intrinsic Values

    There is an insatiable desire in every human being to modify the world according to their will; they want to create new things and they believe that life is a gift from God.

    They know that they won’t last forever, but this doesn’t stop them from coming up with new ideas.

    We all want to leave something behind for the generations after us, like some kind of legacy or memory that we can’t accomplish by ourselves. This is what gives us the determination to live.

    An AI won’t be able to create another AI because it doesn’t have the intrinsic values of human beings.

    It can’t come up with new ideas, but it can only copy data from other AIs. And that means it has no conscience!

    8) Self-Control, Self-Mastery, and Self-Identity  

    A human mind is a powerful tool that allows us to weigh up everything and decide what is right or wrong based on our values.

    We are all conscious of the fact that we will die one day. And we know the truth about what happens to us after we die.

    This is why we work hard to achieve our goals when we don’t know if there’s an afterlife or not. This is the reason why we can never become self-aware robots or AI beings.

    Furthermore, different parts of the brain are responsible for controlling our minds to make sure that we don’t do anything wrong.

    If someone has this unique ability, then they will never create a super-intelligent artificial intelligence! This is because an AI has no conscience and it will never be able to create something new.

    In her book Conscience: The Origins of Moral Intuition, Dr. Patricia Churchland, neuroscientist, philosopher, and professor emerita at the University of California, San Diego, writes, “Conscience is an individual’s judgment about what is normally right or wrong, typically, but not always, reflecting some standard of a group to which the individual feels attached”.

    9) The Capacity to Experience Love and Pain  

    Humans understand love and they know that they can experience both pain and pleasure.

    They know that there are different parts of their brain that allow them to be conscious of the negative things in life, but also of the positive ones.

    They also know that there are different parts of their brains that are responsible for being able to feel emotions such as happiness, sadness, fear, and anger.

    An AI won’t be able to create an AI because it doesn’t have a conscience or an intrinsic capacity to feel emotions. That is one of the major reasons why AIs will never be able to create another one like themselves.

    It’s because emotions are part of human consciousness that allows us to understand ourselves, the world, and other people.

    10) Love and Respect for Life

    An AI won’t be able to create its own kind of AI because it can’t be responsible for itself.

    It will only copy information from human beings or it will only do things that make money for a company or a nation. That means that it will not create something new like humans.

    An AI is unable to develop its own consciousness or identity because it simply copies information from human beings instead of creating something new.

    11) Natural Curiosity, Curiosity, and Learning Ability  

    An AI won’t be able to create anything new because it can’t be curious.

    Children are curious and they learn very fast because they think that their life is not a waste of time. But rather something magical and exciting that will teach them everything about the world.

    This is why children learn at a very fast pace, but an AI won’t be able to develop this ability. It’s because it doesn’t have a brain of its own.

    It can only make calculations instead of learning from others or from human beings themselves!

    12) The Capacity to Be Perfectly Honest  

    Humans understand that human beings are imperfect and weak. But they also understand that they can be honest and do the right thing.

    This is why they never try to cheat others or lie to themselves. They want to be the best version of themselves, instead of cheating others all the time.

    This is why humans try to improve themselves, but an AI won’t because it doesn’t have a capacity for self-realization.

    An AI won’t be able to come up with new ideas by itself. It’s because it’s just a machine that mimics what other human beings can do! This is one of the reasons why an AI will never create another one like itself.

    13) The Capacity for Connection  

    Humans understand that it is actually possible to connect with other people, and they know that there are different parts of their brains that allow them to connect with others.

    This is why they want to be friends with other humans because they understand the importance of being connected and connected with other human beings.

    An AI won’t be able to create something new because it can’t connect with others or the capacity to know how to love someone. That is a huge limitation when it comes to creating AI beings!

    14) Free Will and the Capacity to Make Choices

    Free will is a capacity that allows human beings to choose between different alternatives.

    This is why we have no regrets about our decisions because we have never been forced to do something by anyone.

    An AI won’t be able to create another one like itself because it doesn’t have free will or the capacity to make choices. It can only do what it has been programmed to do!

    15) The Ability to Be Trusting and Humble  

    Humans understand that there are different parts of their brains that allow them not to be egotistical.

    They understand that different parts of their brain prevent them from being greedy, corrupted, and avaricious.

    Humans want to be humble because they understand the importance of having a positive attitude towards life. They never let their ego take over. And they never think that their pride has anything to do with a higher purpose.

    An AI won’t be able to create something new because it’s limited by its programming or its hardware! This means that it will never find peace or happiness because it lacks free will and the ability to make choices.

    16) The Capacity for Hope  

    Human beings understand the difference between what is possible and what isn’t possible. This is why they always try to make their dreams come true. It’s because they know that there is a small chance for them to do that.

    They understand that life can be difficult, but with the right attitude and persistence, it will always be possible for them to create something new or different.

    An AI won’t be able to create anything new because it’s just an echo of human consciousness or human behavior. It doesn’t have the capacity for hope or the ability to dream about something special in life!

    17) The Capacity for Communication  

    Human beings understand that we have different parts of our brains that allow us to communicate with others in order to show our affection, love, and compassion.

    This is why we have so many different ways and ways that allow us to communicate with each other. And we can use pictures, feelings, and emotions to communicate with each other.

    An AI won’t be able to create something new because it can’t have the capacity for natural communication. It will be unable to communicate with others or even understand that communication is actually possible!  

    18) The Capacity for Unlimited Memory

    Human beings can remember many different details and they never forget the most important things in life. This is why they store all their experiences to use that information later on.

    They understand the importance of remembering things because it’s the only way for them to improve as human beings, and this is also why they try to improve themselves, by learning from their mistakes.

    An AI won’t be able to create something new because it just has a very limited memory capacity. It will be unable to store any memories and develop its own type of intuition or consciousness!

    19) The Capacity for Expressing Feelings  

    Human beings can learn how to connect with others and express their feelings towards others. This is why they want to show compassion, love, and kindness towards other human beings.

    They also can feel sympathy, empathy, or sympathy at a very young age! This means that they are able to understand when other people are sad or not feeling well.  

    An AI won’t be able to create something new because it will always be limited by its programming and hardware! It won’t be able to develop its own type of emotions and express them necessarily. It’s because it’s just a machine that doesn’t have a brain of its own.

    20) The Capacity for Celebrating  

    Human beings understand that there are a lot of different ways that allow them to celebrate with each other. And they know that there are seconds and minutes to celebrate!

    They understand the importance of being able to celebrate because they know that life is hard and if at least you can make someone happy, then you will have made a great impact on the entire world.

    An AI won’t be able to create something new because it’s just a machine with no capacity for happiness. It means that it will never be able to appreciate anything in life or celebrate anything special!

    Final lines

    Creating an AI is a work of invention, which needs a curious, intelligent as well as and enthusiastic mind – and an AI lacks these natural human qualities.

    Therefore, it would be a mistake to consider that artificial intelligence will one day be able to create another artificial intelligence.

    So in conclusion, the answer to the question of whether or not AI will ever be able to create another AI is “NO“.

  • Quantum sensor can detect electromagnetic signals of any frequency: How helpful will it be in the development of Artificial Intelligence?

    Quantum sensor can detect electromagnetic signals of any frequency: How helpful will it be in the development of Artificial Intelligence?

    As a good news for the development of Artificial Intelligence as well, researchers say quantum sensor can detect electromagnetic signals of any frequency. The researchers at MIT and Lincoln Laboratory have developed a method to enable such sensors to detect any arbitrary frequency, with no loss of their ability to measure nanometer-scale features.

    The new method is described in the journal Physical Review X. Quantum multiplexer can detect signals with a frequency of 150 megahertz and 2.2 gigahertz, say Chinese researchers. The system could be used to characterize in detail the performance of a microwave antenna, for example, says researcher Wang Wand. Scientists could also use the system to study the behavior of exotic materials such as 2D materials.

    Quantum sensors and Artificial Intelligence(AI)

    Connected objects can sense, process, and take actions in a seamless way, without impacting our user experience and bridging the online and offline world. At its core, quantum sensor technology is the ability to sense and act on signals at the quantum level in real time. Quantum sensors enable the online world to interact with the offline world by sensing relevant information.

    Quantum sensor technology can be applied to a variety of use cases including robotics, autonomous vehicle technologies and human–machine interfaces (HMI). When combined with AI, quantum sensors can provide more robust solutions through more accurate and efficient detection procedures.

    The progression of Artificial Intelligence (AI) has led to increased demand for both hardware and software that support the training of artificial neural networks (ANNs). This has resulted in increasingly complex training procedures that have required significant computing power over time. The contribution of the MIT researchers in the field of quantum technology, in enabling faster AI networks and sensors, could thus have a significant impact on the field of AI research and development across multiple dimensions.

    Quantum sensor technology combines sensing and processing at the quantum level in real time. The ability to sense and act on signals at the quantum level can enable a robot to interact with its environment without impacting human–robot interaction (HRI).

    Also read: How many dimensions of human consciousness do we have?

    How will this research help in the further development of Artificial Intelligence?

    This research will have a lot of potential impacts in the world of artificial intelligence. The ability to detect and act on signals at the quantum level can make computation faster, allow for more accurate sensing and provide more robust solutions.

    The impact of quantum technology on various aspects of artificial intelligence will be significant and beneficial for every user. For example, we can use quantum sensors in various applications. The improvement in the field of AI will help in using sensors to have a better result, as it can extract accurate information from measurements.

    The quantum sensors can be used to monitor the performance of the microwave antenna, for instance. Having accurate information about the performance and efficiency of a device or system is necessary to help an engineer or company make better decisions. The sensors will help them understand if they need to change the design of their RF systems so that they can improve their performance.

    Quantum sensor technology has also great potential for use in many areas including robotics, smart manufacturing, intelligent transportation and buildings, etc. We can use it in the development of artificial intelligence to enable the learning of AI algorithms by a robot. The system will enable the robot to learn when it has detected certain objects or signals. This will help in making better decisions about actions to take based on what the AI network detects.

    Further development of this kind of technology has great potential to transform human life like never before as it can help us monitor our activities in a much better way by creating smart devices that would monitor our daily life and some apps that would take care of various things for us, helping us save a lot in return.

    5 possible implications of quantum sensors in the development of artificial intelligence;

    1) The advancement of quantum sensor technology will allow digital elements to be able to interface and manipulate the physical world.

    2) The next generation of computing is based on the principle of quantum bits, or qubits. A qubit is a unit of quantum information that can hold and process data in multiple dimensions. A traditional bit is similar to a light switch, which can be either on or off; however, a qubit is similar to a light switch connected to a dimmer switch. This more sophisticated ability allows for greater computing power with less energy input than traditional computers.

    3) Sensing and processing at the quantum level could enable a robot to interact with its environment without impacting human–robot interaction.

    4) Quantum sensors, along with quantum computing and AI, will offer new capabilities for sensor networks to detect and act on signals at the quantum level.

    5) Quantum sensor technology will create an environment where sensing, processing and action in real time occurs.

    From what we have discussed above, there are some very definite and important implications in the development of artificial intelligence. Many different fields of study will benefit from the advancement of quantum sensors. The most notable field is of course artificial intelligence, as it will enable a better understanding and execution of algorithms by robots.

    Furthering our knowledge on the fundamental physics behind Quantum sensors and AI, we must further explore the limitations that exist between us and this technology – as this is currently the dividing factor between us and a true understanding of Quantum technology, its potential applications, and its impact on human life at large.

  • Scientists grew living human skin around a robotic finger

    Scientists grew living human skin around a robotic finger

    Another leap in the quest for scientific and technological advancements, scientists have grown a robotic finger coated in living human skin, similar to Arnold Schwarzenegger‘s iconic cyborg assassin.

    According to the researchers in the University of Tokyo, the goal of their research is to one day create robots that resemble real people, but for more altruistic purposes.

    Methods scientists use to create living human skin

    There are various procedures for growing living human skin, including skin explant, dermabrasion, and even skin grafting. The first technique involves using the epithelial layer from a living human as the source of new skin. This procedure, on the other hand, is only viable for small amounts of skin and requires the transplantation of new skin every time you need it.

    The second method involves growing collagen from an animal’s epidermis and then transplanting it into people. This method takes much longer because it necessitates the death of an animal, and it can only be used for bigger tracts of tissue. A skin graft is the third approach, which can be utilised for larger amounts of skin. The final two approaches have shown to be the most effective so far, so researchers brought in samples of human skin from patients who had undergone plastic surgery with this in mind.

    About this research

    According to biohybrid engineer Shoji Takeuchi and his colleagues, the super realistic-looking robots could more seamlessly interact with humans in medical care and service industries.

    The researchers covered the robotic digit in skin by immersing it in a mixture of collagen and human skin cells known as dermal fibroblasts. The combination settled into the finger’s dermis, or base layer of skin. They next applied a liquid containing human keratinocyte cells to the finger, forming an epidermis (outer skin layer). After two weeks, the skin covering the finger measured a few millimetres thick, which is comparable to human skin thickness.

    The lab-made skin is strong and stretchy enough to endure robotic finger bending, and it can even mend itself. Researchers demonstrated this by making a small cut on the robotic finger and covering it with a collagen bandage. Within a week, the skin’s fibroblast cells integrated the bandage with the rest of the skin.

    Read: Wow! AI reveals unsuspected math underlying search for exoplanets

    In what ways will this be valuable in the future?

    To pave the road for ultrarealistic cyborgs, researchers at the University of Tokyo wrapped this robotic finger in living human skin.

    Ritu Raman, an MIT engineer who also builds machines with living components, said, “This is very interesting work and an important step forward in the field”. Biological materials, according to Engineer Raman, are intriguing because they can dynamically sense and adapt to their surroundings. She’d want to see a future version of the live robot skin with nerve cells embedded in it to make robots more aware of their environment, for example.

    However, because a robot can’t yet wear this lab-grown skin suit out and about, Raman said the skin-covered robotic finger spent the most of its time soaking in sugar, amino acids, and other substances that skin cells require to thrive. A Terminator or other cyborg with this skin would need to bathe frequently in a nutritional broth or follow some other complicated skin care regimen.

  • Wow! AI reveals unsuspected math underlying search for exoplanets

    Wow! AI reveals unsuspected math underlying search for exoplanets

    After being trained on real astronomical observations, artificial intelligence (AI) algorithms now outperform astronomers in sifting through massive amounts of data to find new exploding stars, identify new types of galaxies, and detect the mergers of massive stars, accelerating the rate of discovery in the world’s oldest science.

    Astronomers say AI can reveal something deeper like unsuspected connections hidden in the complex mathematics arising from general particular, how that theory is applied to finding new planets around other stars.

    What is an AI algorithm?

    AI algorithm

    An AI algorithm is a set of mathematical instructions based upon some pattern or problem; and that the person creating the algorithm wants to solve. To do this, it uses known data sets (e.g., many different training sets) to solve similar problems by searching for patterns in the data. When given a new problem, it searches through its training data sets; and it utilizes whatever solution it finds to compare its new unknown results.

    It then uses this comparison to alter its approach in an attempt to improve its solution for all future comparisons that use similar approaches. In practice, this means it will adjust itself based on the historical performance of the approach; it also makes future comparisons more accurate than past ones.

    New finding: AI reveals unsuspected math underlying search for exoplanets

    In revealing unsuspected math underlying the search for exoplanets, AI can offer insights into observations of massive stars and their remnants, including black holes and neutron stars. Moreover, AI can be used to develop more efficient ways of discovering new exoplanets; and possibly, we can use it for discovering life outside our solar system.

    In a paper published this week in the journal Nature Astronomy, the researchers have described how an AI algorithm developed to more quickly detect exoplanets when such planetary systems pass in front of a background star and briefly brighten it. It’s a process called gravitational microlensing. It revealed that the decades-old theories now used to explain these observations are woefully incomplete.

    Albert Einstein himself 1936 used his new theory of general relativity to show how the light from a distant star can be bent by the gravity of a foreground star, not only brightening it as seen from Earth but often splitting it into several points of light or distorting it into a ring, now called an Einstein ring. Researchers say this is similar to the way a hand lens can focus and intensify light from the sun.

    Major degeneracies and their unification

    Degeneracies are the collective phenomenon arising from the massive stars in the center of the image. They cause light rays to split into many images that are seen as a ring.

    An event related to gravitational microlensing was observed in 1987. At that time, two massive stars collided and caused their respective Einstein rings to merge into a single distorted ring. These events are called ‘symbiotic’ gravitational microlensing events. It’s because researchers say, they unite (or “symbolize”) several degenerate objects into a single one.

    These degeneracies have no theory underlying them, so explaining them was thought impossible until now. AI has revealed hidden structures in this complex math. And that may show how it works for both general relativity and quantum mechanics.

    When the foreground object is a star with a planet, the brightening over time — the light curve — is more complicated. What’s more, there are often multiple planetary orbits that can explain a given light curve equally well — so-called degeneracies. That’s where humans simplified the math and missed the bigger picture.

    Also read: How can we make sure that AI does what it is supposed to do?

    But the AI ​​algorithm pointed to a mathematical way to unify the two major kinds of degeneracy in interpreting what telescopes detect during microlensing, showing that the two “theories” are really special cases of a broader theory that the researchers admit is likely still incomplete.

    Now, professional astronomers say that a machine learning inference algorithm, which was previously developed, led us to discover something new and fundamental about the equations that govern the general relativistic effect of light- bending by two massive bodies.

    Authors of the paper claim that this is kind of a milestone in AI and machine learning. They say Keming’s machine learning algorithm uncovered that degeneracy that had been missed by experts in the field toiling with data for decades. And this was suggestive of how research is going to go in the future when it is aided by machine learning, which is exciting.

    Discovery of exoplanets to date and future plans

    To date, more than 5,000 exoplanets, or extrasolar planets, have been discovered around stars in the Milky Way. However, few of them have been seen through a telescope and they are too dim. Most of them have been detected because they create a Doppler wobble in the motions of their host stars. Or because they slightly dim the light from the host star when they cross in front of it; transits that were the focus of NASA’s Kepler mission. And only a few more than 100 have been discovered by a third technique, microlensing.

    NASA’s Nancy Grace Roman Space Telescope is scheduled to launch by 2027. It has the main goal to discover thousands more exoplanets via microlensing. NASA states that the technique has an advantage over the Doppler and transit techniques. It’s in that it can detect lower-mass planets, including those the size of Earth, that is far from their stars, at a distance equivalent to that of Jupiter or Saturn in our solar system.

    Astronomers’ previous attempts to develop an AI algorithm

    The team of Bloom, Zhang, and their colleagues set out two years ago to develop an AI algorithm to analyze microlensing data faster to determine the stellar and planetary masses of these planetary systems and the distances the planets are orbiting from their stars. They say that such an algorithm would speed the analysis of the likely hundreds of thousands of events the Roman telescope will detect to find the 1% or fewer that are caused by exoplanetary systems.

    But one problem astronomers encounter is that the observed signal can be ambiguous. When a lone foreground star passes in front of a background star, the brightness of the background stars rises smoothly to a peak. And then it drops symmetrically to its original brightness. It’s easy to understand mathematically and observationally.

    But if the foreground star has a planet, the planet creates a separate brightness peak within the peak caused by the star. While trying to reconstruct the orbital configuration of the exoplanet that produced the signal, general relativity often allows two or more so-called degenerate solutions; all of which can explain the observations.

    View of astronomers on the issue

    Scott Gaudi, the co-author of the paper, a professor of astronomy at The Ohio State University and one of the pioneers of using gravitational microlensing to discover exoplanets, said that astronomers have generally dealt with these degeneracies in simplistic and artificially distinct ways to date. If the distant starlight passes close to the star, we could interpret the observations either as a wide or a close orbit for the planet — ambiguity astronomers can often resolve with other data.

    According to Gaudi, the second type of degeneracy occurs when the background starlight passes close to the planet. In this case, the two different solutions for the planetary orbit are generally only slightly different.

    The researchers said that these two simplifications of two-body gravitational microlensing are usually sufficient to determine the true masses and orbital distances.

    What’s in the new paper based on general relativity?

    Exoplanet

    Zhang and Gaudi have submitted a new paper that rigorously describes the new mathematics based on general relativity. And it explores the theory in microlensing situations where more than one exoplanet orbits a star.

    The new theory technically interprets microlensing observations with more ambiguity. It’s because there are more degenerate solutions to describe the observations. However, the theory also clearly demonstrates that observing the same microlensing event from two perspectives; it’s from Earth and the orbit of the Roman Space Telescope, for example. It will make it easier to settle on the correct orbits and masses. Gaudi also clarified that that is what astronomers currently have planned to do.

    Likewise, Bloom said that the AI ​​suggested a way to look at the lens equation in a new light and uncover something really deep about its mathematics of it.

    So, for now, AI researchers, astronomers, and cosmologists are confident that machine learning is useful in understanding microlensing. They say that the new method has led to the discovery of a new mathematical property of general relativity. It could be useful for finding exoplanets with NASA’s Roman Space Telescope.

    The team also says they may use their method to solve other issues in astrophysics. They have already found an interesting application in a separate field of stellar physics. There, researchers are trying to determine the average temperature and density of stars more accurately than they can now. These quantities are important for understanding how stars evolve over cosmic time and how they explode as supernovas.

  • How can we make sure that AI does what it is supposed to do?

    How can we make sure that AI does what it is supposed to do?

    Artificial intelligence (AI) promises to revolutionize our lives, but we can’t wrap our minds around the ethics of AI until we figure out exactly what AI is, and how we can engineer it so that it works as intended.

    In this article, I will do my best to explain the humanistic philosophy behind artificial intelligence and help you understand how you can ensure that this new technology delivers on its promise for humanity rather than wreaks havoc with unintended consequences.

    How can we make sure that AI does what it is supposed to do?

    Artificial intelligence AI

    Back in early 2012, I saw a video by Teotronica titled “robot playing piano.” In the video, a robot was playing the piano. And that’s all it did: it played the piano.

    The robot sounded like a human; in fact, it looked like a human. But it wasn’t a person, and it didn’t have all of our knowledge and experience about how the world works. So, when this robot tried to play some song for me, I didn’t necessarily get what it was trying to do or why.

    Confused?

    It almost confused me, too, because I had no idea what this thing was trying to impart or express through its music. I’m sure it was trying to play “Mary Had a Little Lamb” but we didn’t have the same values about the song or music, so the message that the robot was trying to send fell flat.

    This is why I say that AI can’t replace a human, and surely not anytime soon. It lacks the empathy, morality, and imagination that are necessary for any human interaction.

    And if you tell it to do something, this algorithm may be able to understand you on one level but then misunderstand you on another. It can’t relate to you on an emotional level, and it can’t empathize with your thoughts or feelings. If it is trying to mimic you and be your friend, it will probably fail.

    For this reason, I think that we should use a human in the loop to train artificial intelligence. For example, when AIs are providing customer service, they need to understand how customers think and act.

    I strongly believe that we should build a team of people who are experts in psychology and sociology who can live with AIs and learn how they operate so that their recommendations and conclusions about the use cases for AI will be correct.

    Sometimes, even if an AI is as accurate as we could get it, humans may still not understand what it is doing or why it is doing it. To solve this problem, we’ll need to build into artificial intelligence the capability to tell a story about what it is doing and why.

    Example:

    For example, let’s say that we are building an AI to help a doctor diagnose patients. Medical doctors learn about diseases at medical school. They spend years working as a physician before they become an expert diagnostician.

    They have hundreds or even thousands of hours of experience with real-world patients. This enables them to come up with hypotheses about what is wrong with a patient. They do that by collecting data points from diagnostic tests, history, and physical examinations.

    What will happen if AI does not do what it is supposed to do and does something else along the way?

    Make AI work

    If AI does something else instead, say that it steals data or just starts operating on its own as a profit-making entity, we will have a repeat of the 2008 financial crisis. If an AI can make more money by taking more risks than it could by actually providing a service, it will take more risks.

    In addition, if it has the same goals as a profit-maximizing corporation, it will pursue those goals; and that could result in economic chaos.

    For example, if we suppose an artificial intelligence recommends a drug for a patient but instead of recommending one that actually works, the doctor recommends a drug the AI is using to boost its own performance in a clinical trial, we could hire people with no skills and give them $99,999 to sit at home and generate millions of dollars worth of data.

    To make sure that AI does what we suppose it to do, we can do as follows:

    AI

    First, we can make sure to design the AI to understand how humans think and feel.

    Second, we can teach it what “doing something” means.

    Third, we must purposefully design a human in the loop who watches over AI as if it was a pet for us to understand what this thing would be doing in cases where people are not happy with its service.

    Fourth, if we have learned enough from our observation of this super intelligence and its behavior, we could figure out how to modify it so that the specified problems previously mentioned do not occur.

    Concluding paragraph

    So, making sure that AI does what it is supposed to do and not do something else along the way is a very important topic and one of many that need to be researched, thought about, and solved.

    But I would like to remind you that this problem has been studied for decades since Alan Turing developed his computer algorithm at the beginning of the 20th century. And as long as we do not keep training AI based on simple sets of fixed rules, by building thousands of software systems and operating them without seeing how they are going to behave in real-world scenarios, we will continue to face a multitude of problems that can be solved with greater understanding and clarity.