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Artificial Intelligence

Why Symbolic AI Is Extremely Critical For Business Operations?

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Why Symbolic AI Is Extremely Critical For Business Operations?

A symbolic AI can explain business insights and assist it in achieving all of its goals.

Even as many businesses experiment with AI using rudimentary machine learning (ML) and deep learning (DL) models, a new sort of AI called symbolic AI is emerging from the lab, with the potential to transform both AI’s function and its relationship with its human overseers. There are two groups in AI history: symbolic AI and non-symbolic AI, each of which takes a distinct approach to build an intelligent system. The symbolic method tried to create an intelligent system with explainable actions based on rules and knowledge, whereas the non-symbolic method aimed to create a computational system modeled after the human brain. The ultimate objective of computer science is to create an AI system capable of thinking, logic, and learning. Most AI systems today, on the other hand, only have one of the two abilities: learning or reasoning. While symbolic approaches excel in thinking, explaining, and managing large data structures, they struggle to establish their symbols in the perceptual world.

To address problems, Symbolic AI employs a top-down approach (example: chess computer). “You’ll find what you’re searching for if you look hard enough.” Search is the symbolic AI technique. The computer’s step-by-step testing of potential solutions and confirmation of the results is referred to as “search” in this scenario. A chess computer that “imagines” millions of different future moves and combinations and then “decides” which moves have the highest probability of winning based on the results is a good illustration of this. The analogy to the human mind is obvious: everyone who has spent a significant amount of time playing a board or strategy game has at least once “played through” motions in their thoughts before reaching a choice. Neural networks can help traditional AI algorithms since they add a “human” gut feeling to them, reducing the number of movements that need to be computed. By integrating these technologies, AlphaGo was able to defeat a human in a game as complex as Go. If the computer had computed all possible movements at each step, this would not have been possible.

The difficulty of modifying ideas once they were stored in a rule’s engine was one of the key stumbling blocks of symbolic AI or GOFAI. Expert systems are monotonic, which means that the more rules you add, the more information is encoded in the system, but new rules cannot destroy previous knowledge. Monotonic is a term that refers to only one direction. Machine learning algorithms may be retrained on fresh data, they are better at recording provisional information that may be retracted later if required; for example, when data is non-stationary, they will modify their parameters depending on that new data.

The second issue with symbolic thinking is that the computer does not understand what the symbols signify, implying that they are not necessarily related to any other non-symbolic representations of the world. This contrasts with neural networks, which may connect symbols to vectorized representations of data, which are just translations of raw sensory input.

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The obvious question then becomes, “Who are the symbols for?” Are they of any use to machines? Why should robots employ symbols if they allow human sapiens to communicate and manage information based on underlying physiological constraints? Why can’t machines communicate using vectors or some noisy language shared by dolphins and fax machines? Let us make a prediction: when machines do learn to communicate with one another intelligibly, it will be in a language that humans will be unable to comprehend. Perhaps the bandwidth of words is insufficient for high-bandwidth devices. Perhaps they require additional dimensions to express themselves clearly. Language is only a keyhole in a door that has been bypassed by machines. The natural language might, at best, be an API that AI provides to humans so that they can ride on its coattails; at worst, it could be a diversion from what actual machine intelligence entails. However, we have conflated it with the pinnacle of success since natural language is how we demonstrate our intelligence.

Benefits:

Knowledge Graph creation: We create a Knowledge Graph for our clients as a starting point for constructing any chatbot or voice assistant. The Knowledge Graph is the data structure of the future, according to us. It will serve as the foundation for all future AI-based applications.

Process implementation: Digitization and preparation of organizational data are unavoidable for businesses. As a result, the creation of a Knowledge Graph is unavoidable sooner or later. Onlim establishes the organizational procedures and workflows that will be necessary for the future for frequent knowledge documentation and update.

Decades of experience are brought to bear: The Onlim team has a combined knowledge of graph construction expertise of many decades. Customers may benefit and learn from this large knowledge base as they move toward their goal of implementing a chatbot/voice assistant.

Maximum convenience: Online takes care of the nitty-gritty details in the background, enabling businesses to concentrate on data preparation and addition. The Online Conversational AI platform allows you to easily edit or alter any information at any time.

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A comprehensive approach: Online provides a complete experience by assisting clients through all stages of the process. From storing information in the form of a Knowledge Graph to providing chatbots or voice assistants with the capacity to absorb facts, respond appropriately, and allow users to complete desired transactions such as purchases, the possibilities are endless.

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Artificial Intelligence

IoT Connect: How To Manage Connected Assets Efficiently

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IoT Connect: How To Manage Connected Assets Efficiently

In the telecom industry, proper connectivity is the most important foundation. Especially today, in times of online globalization, the amount of different devices exchanging information with each other is tremendous. And that provides a lot of problems to deal with. IoT Connect was design to manage this process. Care to learn more?

The basics

Right on the start, we should explain the term itself. The Internet of Things (IoT) is a concept by which clearly identifiable objects can directly or indirectly collect, process or exchange data via a smart electrical installation or a computer network. The concept is used in the processing industry, city management, healthcare, home appliances… almost everywhere, really. Including telecommunication business.

Device identity management

A cellphone. Personal computers. ATMs at your bank. Every electronic device is using online communication these days. While doing so, they receive a unique digital identity. IoT solutions are used to control and manage these identities along with the devices themselves.

An average smart home has some IoT devices, too. Heating system, for example, with remote access that can be achieved by device communication. It needs to be properly managed, don’t you think? And what about more critical devices like security locks? IoT telecommunications apply to them as well.

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How to do it right

By implementing iot connect software. Be advised that many hardware companies and different system integrators alongside other solution providers are already accustomed to this IoT platform. It is a part of their daily routine because they understand how important management of connected assets really is.

It allows anyone to automate their activities with self-service and contact libraries in order to boost up response and overall efficiency levels that follow it. Especially, when their devices don’t seem to operate in a way they are supposed to.

What are the benefits for the telecom industry?

Comprehensive IoT projects provide stability for users, yes. But they also provide a way to sell more IoT telecom services composed of a particular device, connectivity capabilities and IoT applications with different everyday-use features. Clearly, this is the way a company specialized in telecommunications can connect itself with customers on a deeper level. That can render opportunities for new enterprises, enabling tools for better quality of services and profits to be gained from them. Telecom IoT solutions are therefore something worth looking into, don’t you agree?

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Is Developing Human Brain Cells-Based AI Models Ethical?

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Is Developing Human Brain Cells-Based AI Models Ethical?

Exploring the ethical choices of brain cell-based AI models in the recent future

In December 2021, Melbourne-based Cortical Labs grew groups of neurons (brain cells) that were incorporated into a computer chip. The resulting hybrid chip works because both brains and neurons share a common language: electricity. In silicon computers, electrical signals travel along metal wires that link different components together. In brains, neurons communicate with each other using electric signals across synapses (junctions between nerve cells). In Cortical Labs’ Dishbrain system, neurons are grown on silicon chips. These neurons act as the wires in the system, connecting different components. The major advantage of this approach is that the neurons can change their shape, grow, replicate, or die in response to the demands of the system. Dishbrain could learn to play the arcade game Pong faster than conventional AI systems. The developers of Dishbrain said: “Nothing like this has ever existed before, it is an entirely new mode of being. A fusion of silicon and neuron.”

Cortical Labs believes its hybrid chips could be the key to the kinds of complex reasoning that today’s computers and AI cannot produce. Another start-up making computers from lab-grown neurons, Koniku, believes its technology will revolutionize several industries including agriculture, healthcare, military technology, and airport security. Other types of organic computers are also in the early stages of development. While silicon computers transformed society, they are still outmatched by the brains of most animals. For example, a cat’s brain contains 1,000 times more data storage than an average iPad and can use this information a million times faster. The human brain, with its trillion neural connections, is capable of making 15 quintillion operations per second.

This can only be matched today by massive supercomputers using vast amounts of energy. The human brain only uses about 20 watts of energy or about the same as it takes to power a lightbulb. It would take 34 coal-powered plants generating 500 megawatts per hour to store the same amount of data contained in one human brain in modern data storage centers. Companies do not need brain tissue samples from donors, but can simply grow the neurons they need in the lab from ordinary skin cells using stem cell technologies. Scientists can engineer cells from blood samples or skin biopsies into a type of stem cell that can then become any cell type in the human body.

People will no doubt be much more willing to donate skin cells for research than their brain tissue. One of the barriers to brain donation is that the brain is seen as linked to your identity. But in a world where we can grow mini-brains from virtually any cell type, does it make sense to draw this type of distinction? If neural computers become common, we will grapple with other tissue donation issues. In Cortical Lab’s research with Dishbrain, they found human neurons were faster at learning than neurons from mice. Might there also be differences in performance depending on whose neurons are used? Might Apple and Google be able to make lightning-fast computers using neurons from our best and brightest today? Would someone be able to secure tissues from deceased geniuses like Albert Einstein to make specialized limited-edition neural computers?

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Such questions are highly speculative but touch on broader themes of exploitation and compensation. Consider the scandal regarding Henrietta Lacks, an African-American woman whose cells were used extensively in medical and commercial research without her knowledge and consent.

Henrietta’s cells are still used in applications that generate huge amounts of revenue for pharmaceutical companies (including recently to develop COVID vaccines. The Lacks family still has not received any compensation. If a donor’s neurons end up being used in products like the imaginary Nyooro, should they be entitled to some of the profit made from those products?

As recently discussed in a study there are no evidence neurons on a dish have any qualitative or conscious experience so cannot be distressed and without pain receptors, cannot feel pain. Neurons have evolved to process information of all kinds – being left completely unstimulated, as currently done all over the world in labs, is not a natural state for a neuron. All this work does is allow neurons to behave as nature intended at their most basic level. Humans have used animals to do physical labor for thousands of years, despite often leading to negative experiences for the animals. Would using organic computers for cognitive labor be any more ethically problematic than using an ox to pull a cart? We are in the early stages of neural computing and have time to think through these issues. We must do so before products like the “Nyooro” move from science fiction to the shops.

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How Can An Algorithm Tell The Emotions Of A Pig?

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How Can An Algorithm Tell The Emotions Of A Pig?

Is it possible to comprehend what a pig is feeling when it screeches or grunts? A group of scientists from Switzerland, Denmark, Germany, France, Norway, and the Czech Republic have discovered a means to decipher pig language. Researchers were able to capture the feelings of the pigs using artificial intelligence in the study released in Scientific Reports by collecting their songs in various settings.

The AI pig translator, which identifies a pig’s emotion through oinks, snuffles, grunts, and squeals, helps in better treatment of these animals on farms.

Using 7,414 noises from 411 pigs, the researchers created an algorithm to determine if the pigs were experiencing good, negative, or mixed emotions.

The recordings were made in a variety of scenarios encountered by commercial pigs, both favorable and bad, from birth to death.

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Positive occurrences include piglets sucking from their mothers and being reunited with their families after being separated.

Separation, piglet battles, castration, and killing were among the emotionally draining scenarios.

“We show in this research that animal noises can provide important information about their emotions.” “We also show that an algorithm can be used to analyze and understand pig emotions, which is a big step toward bettering animal welfare in farming,” says Elodie Briefer in a report published by The Guardian.

The researchers discovered more high-frequency calls, like screeches or grunts, in negative situations after examining over 7000 audio recordings to determine whether there was a pattern in the noises while experiencing specific emotions.

Simultaneously, low-frequency noises such as barks and grunts were heard in instances where the pigs were experiencing positive or negative emotions. The authors found the circumstances between the extremes to be particularly intriguing. The researchers discovered a new pattern after a more thorough analysis of the sound data that indicated what the pigs felt in specific scenarios in greater detail.

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