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Will AI Make Buying A New Home Easier Than Ever?

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Will AI Make Buying A New Home Easier Than Ever?

Artificial intelligence and machine learning are slowly making their way into various aspects of our lives. For the average person, however, the use of AI goes on behind the scenes. Aside from virtual assistants like Siri and Alexa, most people would find it hard to pinpoint where it actually affects them.

But this is changing, as the usage of machine learning is becoming prevalent in every industry. We all understand that companies have profiles on us made from endless data points we didn’t even have to manually input. This has made it easier for companies to market to us, creating a great environment for small businesses, and it will make life easier for individuals as well.

One of the ways it will soon make life easier is in the process of becoming a new homeowner. Here’s how.

Homeowners’ insurance and AI

In the insurance industry, Lemonade has been making waves ever since it launched. One of the most recent and significant advancements has been in the use of AI for homeowners insurance. While still maintaining the human touch for closing complex claims when necessary, their AI assistant Maya helps people through the process of insuring their homes.

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The AI behind Lemonade’s insurance makes it possible for the company to more accurately assess risk, set fair premiums for each individual policyholder, and quickly process claims.

Insurance has always been one of the most stats-heavy industries. With AI to process far more data far more quickly, insurance companies can evolve to better serve every customer.

Identifying neighborhoods and homes

Machine learning has already impacted the way we buy homes. After all, any good property website will highlight the best fit for every online visitor. However, there is still a long way that AI can go to further improve the process.

In the near future, virtual assistants will not just find the houses best suited to buyers, but take them through the home in a virtual setting as well. Along the way, the AI bot will point out the traits of the home that the individual will be most interested in.

But it won’t begin and end with the home. The entire neighborhood will be assessed, with AI showing the potential new homeowner the exact ways in which the neighborhood will accommodate them and their family. Instead of looking for homes in neighborhoods that you think you’ll like, you will be led to neighborhoods that are almost definitely right for you.

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Pricing a home

Housing prices are not always based on actual value. Variance in supply and demand can create markets where homes are over-or undervalued. Determining exactly how much you should pay for a home is practically impossible. However, AI can use live information to collate the specifications and features of a particular home and compare it to other homes just like it.

This will give buyers leverage for negotiations with the seller or vice versa. Even if the seller won’t budge, the buyer can take a look at one of the equivalent properties selling at a lower price. In some cases, the buyer will return to the seller with an understanding of why they might pay more for this particular home, but they will benefit from not having to grapple with buyer’s remorse.

Processing the transfer

Processing the transfer is the admin no one wants to deal with. The good news is that it is already becoming more streamlined, with the ability to get the paperwork done immediately. AI will take it further by connecting all the parties involved, following up when someone has not signed a contract and getting the transfer done more quickly than ever before. AI is already making buying a home easier. As technology progresses and more companies adopt AI, it will give a whole new outlook to potential buyers of homes.

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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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