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Artificial intelligence (AI) has proven helpful to the world in many ways, including the assistants and robots that have taken on many of the tasks associated with daily life and replaced humans in surgical procedures and other professions. Several AI models and tools that are immediately in front of our eyes are ensuring that the world will be a better place, so it's not just the robots that have a beneficial influence on humans, there are models including ChatGPT and Dall-E that have revolutionized the tech industry.

For those who may not be aware, ChatGPT is a chatbot that was introduced in November of 2022. It was created to help users with a variety of tasks. Another significant tech company, "Dell-E," is utilized to produce lifelike images only from a description. They were both created by a business named "OpenAI."

Yet, we are fully aware that it is always bad along with good, therefore to learn more about how people see artificial intelligence, a study titled "Trust in Artificial Intelligence" was conducted during September and October 2022. The University of Queensland and KGPM Australia conducted the study and provided the data on which it was based. A total of 17,193 respondents from seventeen different nations participated in the survey.

There were three separate sections in the survey's poll: "agree," "disagree," and "neutral." Despite everything that has been said about how AI has helped humanity, some individuals still believe that the world would be a far better place without it. 42% of respondents, or two out of every five, agreed with this statement.

What may be the cause of it, then? Several individuals are concerned about their occupations and careers being replaced by AI robots that resemble humans as a result of the study. While 39% of individuals polled denied that AI can take over their future, it's likely that they still believe that some jobs couldn't be replaced by it or that they aren't aware of how rapidly AI's value is increasing. The poll also found that 19% of respondents had a neutral opinion on the matter.

Nonetheless, each person sees the world from a unique perspective. According to the poll, 67% of respondents remain hopeful and upbeat about the future of AI. Even if they are aware of all the negative effects and how they will affect us, some people (57%) are still relaxed about it.

Furthermore, 47% of people report feeling extremely nervous because they fear AI would progressively destroy the human world and that there are very significant risks associated with using AI in daily life, which is not surprising. Also, 24% of them express an angry sentiment against AI and its applications.

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As per the "Trust in Artificial Intelligence" study, 42% individuals fear ... - Digital Information World

UNIVERSITY PARK, Pa. Two artificial intelligence-driven models designed by researchers from the Penn State College of Information Sciences and Technology could help promote the rights of low-income renters in the United States when facing forced eviction. Both models aim to forecast where and how many renters could be at risk of eviction to help better inform policymaking and resource allocation.

The researchers first model, "Weakly-supervised Aid to Relieve Nationwide Eviction Rate," helps to identify areas where there could be a high concentration of individuals facing eviction. To identify these hotspots, their framework uses sociological data such as renters educational and financial characteristics that are historically associated with housing instability to label satellite data based on certain features in each image, such as the presence of trees and signs of gentrification. This data is used to train a machine learning model, which identifies eviction filing hotspots in other locations.

Not all states make data on housing instability and eviction rates available, and there is a high cost to collect this data when its even available, said Amulya Yadav, PNC Career Development Assistant Professor and co-author on the study. Our model presents a novel approach by using other data points related to eviction filings to create more efficient and accurate reporting that is highly generalizable to different counties across the country.

The second model, Multi-view model forecasting the number of tenants at-risk of formal eviction," aims to provide an accurate forecast of tenants at-risk of eviction at a certain point in the future.

In a similar approach, the model uses data from available eviction filing records, the U.S. Census American Community Survey, and labor and employment statistics to estimate the number of tenants who may face eviction in each census tract.

Through a collaboration with the Child Poverty Action Lab, a leading non-profit leveraging data-driven approaches to inform actions for relieving poverty-related issues across Dallas County, Texas, the team tested both models against a real-world dataset in that county, where eviction records are more complete and readily available. The models proved to be more accurate than existing baseline models, outperforming some by up to 36%.

There are resources available to help renters facing housing instability, but they are allocated with tremendous variability and sometimes theyre not used at all, said Maryam Tabar, doctoral student and lead author on the study. There is a need to use these funds and resources more efficiently, which is possible through more accurate forecasting of potential evictions.

The team presented the "Weakly-supervised Aid to Relieve Nationwide Eviction Rate" model at the 31st ACM International Conference on Information and Knowledge Management and the multi-view model forecasting the number of tenants at-risk of formal eviction at the 31st International Joint Conference on Artificial Intelligence late last year.

Both models are being evaluated by subject matter experts for a pilot deployment in the field. The team said they hope they can assist non-government organizations and policymakers in making more data-driving decisions about where to allocate resources to better address housing instability, as well as support advocacy efforts with elected officials and agencies related to housing instability.

Eviction disproportionately impacts individuals from underrepresented backgrounds and can exacerbate existing societal problems related to income disparity, educational attainment, and mental health, said Dongwon Lee, professor and co-author on the study. These models can help us better address these challenges and improve the lives of those vulnerable to eviction.

Additional contributors to the projects include doctoral candidate Wooyong Jung at Penn State College of Information Sciences and Technology, as well as Owen Wilson Chavez and Ashley Flores of The Child Poverty Action Lab. The work was supported in part by the National Science Foundation and the Bill and Melinda Gates Foundation.

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Artificial intelligence models aim to forecast eviction, promote renter ... - Pennsylvania State University

This article by Abhishek Mittal, vice president of data and operational excellence at Wolters Kluwer,was originally published in Legal Dive.

Artificial intelligence (AI) is changing workflows in every corner of the businessand legal departments are no exception.

The term artificial intelligence was coined by computer scientist John McCarthy about 60 years ago.

Since then, AI has become one of the most promising technological innovations in the corporate world and beyond. Google CEO Sundar Pichai has even suggested that AI may be more impactful than the discovery of fire or the invention of electricity.

Much like a fire, though, AI doesnt keep burning on its own. Someone must build and train it. Thats why, a decade ago, Harvard Business Review declared data scientist the sexiest job of the 21st century.

Having worked with many brilliant data scientists, I find the job title to be a bit of a misnomer. To start, successful AI solutions require the right mix of design, data, and domain expertise.

Data scientists on their own cannot build AI models, just as AI models on their own cannot handle all decision-making. Thats why I refer to data scientists as decision scientists. Even with the advent of AI, decision-making is still in human hands at the end of the day.

Lets take a closer look at what that means in practice and how corporate legal departments can get the most out of the technology.

One of the biggest misperceptions about artificial intelligence is that it is going to replace people, which is simply not the case. Instead, legal professionals who use AI will replace legal professionals who do not.

Think of AI as an enabler, akin to the technology in smart cars. The car cannot drive itself, but it can help with specific tasks like backing up, parking, or changing lanes.

In the future, AI will be as ubiquitous as Microsoft Excel. But decision-making and review processes will still require validation by a human end user.

When my company was designing its AI-assisted legal invoice review, for instance, we first paired data scientists and domain experts to build the model. Then, we put the AI that they built to the test.

We gave one group of experts a set of invoices to review manually. We gave another group the same set of invoices but accompanied by AI scores provided by our newly built model. We did this repeatedly, so we could track the results over time.

The experts with AI were able to generate greater savings sometimes saving four times more than the control group. The AI acted as a highlighter, allowing them to focus on items that demanded greater due diligence. But humans were still part of the review process.

Once you understand that AI is not going to replace human talent, it becomes more obvious that you need the right people to get the most out of the technology.

In the beginning, we had more data scientists (as theyre commonly called) than we did domain experts. But domain experts are the ones who know which processes and customer experiences are best suited to be improved by AI.

Weve continued to grow our roster of domain experts, and they use AI more frequently than our data scientists.

Additionally, theyre the ones driving enhancements, as they have a better understanding of what problems need to be solved.

Not all companies can build their own AI models in-house, though. If youre looking to choose a partner, pick one with the most usage.

Ask potential partners how many customers are using their models and how many years of experience their models have. Many companies will throw out all the right buzzwords.

But a tried-and-true model is the key to getting the most out of artificial intelligence.

According to McKinsey, by the year 2025, data will be embedded in every decision, interaction, and process. But in the meantime, its important to prioritize use cases based on which problems are most suitable for AI.

To that end, ask yourself: What decision are we trying to improve? Are there a lot of transactions happening? Do we have sufficient data? Is there an opportunity to create a feedback loop?

Once again, the right mix of data scientists and domain experts is key to answering these questions.

In some cases, people use AI for quality assurance checks. Other times, its used for predictive insights. Regardless, its very important to analyze the why of your use case before you start building the model.

Our AI-assisted invoice review was an appealing use case because we had so much data on legal spend already. This gave us a huge head start when it came time to build our models.

The promise of artificial intelligence cannot be understated; it will be commonplace in corporate legal departments in no time. And yet, AI is not a plug-and-play solution thats going to make decisions for you.

Instead, it should enable your experts to make better decisions for themselves. AI isnt meant to replace human beings; its meant to augment their knowledge. Plan accordingly.

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How legal departments can get the most out of artificial intelligence - Wolters Kluwer

Summary: New artificial intelligence technology is able to screen for genetic mutations in brain cancer tumors in less than 90 seconds.

Source: University of Michigan

Using artificial intelligence, researchers have discovered how to screen for genetic mutations in cancerous brain tumors in under 90 seconds and possibly streamline the diagnosis and treatment of gliomas, a study suggests.

A team of neurosurgeons and engineers at Michigan Medicine, in collaboration with investigators from New York University, University of California, San Francisco and others, developed an AI-based diagnostic screening system called DeepGlioma that uses rapid imaging to analyze tumor specimens taken during an operation and detect genetic mutations more rapidly.

In a study of more than 150 patients with diffuse glioma, the most common and deadly primary brain tumor, the newly developed system identified mutations used by the World Health Organization to define molecular subgroups of the condition with an average accuracy over 90%.

The results arepublished inNature Medicine.

This AI-based tool has the potential to improve the access and speed of diagnosis and care of patients with deadly brain tumors, said lead author and creator of DeepGliomaTodd Hollon, M.D., a neurosurgeon at University of Michigan Health and assistant professor of neurosurgery at U-M Medical School.

Molecular classification is increasingly central to the diagnosis and treatment of gliomas, as the benefits and risks of surgery vary among brain tumor patients depending on their genetic makeup.

In fact, patients with a specific type of diffuse glioma called astrocytomas cangain an average of five yearswith complete tumor removal compared to other diffuse glioma subtypes.

However, access to molecular testing for diffuse glioma is limited and not uniformly available at centers that treat patients with brain tumors. When it is available, Hollon says, the turnaround time for results can take days, even weeks.

Barriers to molecular diagnosis can result in suboptimal care for patients with brain tumors, complicating surgical decision-making and selection of chemoradiation regimens, Hollon said.

Prior to DeepGlioma, surgeons did not have a method to differentiate diffuse gliomas during surgery. An idea that started in 2019, the system combines deep neural networks with an optical imaging method known as stimulated Raman histology, which was also developed at U-M, to image brain tumor tissue in real time.

DeepGlioma creates an avenue for accurate and more timely identification that would give providers a better chance to define treatments and predict patient prognosis, Hollon said.

Even with optimal standard-of-care treatment, patients with diffuse glioma face limited treatment options. The median survival time for patients with malignant diffuse gliomas is only 18 months.

While the development of medications to treat the tumors is essential,fewer than 10%of patients with glioma are enrolled in clinical trials, which often limit participation by molecular subgroups. Researchers hope that DeepGlioma can be a catalyst for early trial enrollment.

Progress in the treatment of the most deadly brain tumors has been limited in the past decades- in part because it has been hard to identify the patients who would benefit most from targeted therapies, said senior authorDaniel Orringer, M.D., an associate professor of neurosurgery and pathology at NYU Grossman School of Medicine, who developed stimulated Raman histology.

Rapid methods for molecular classification hold great promise for rethinking clinical trial design and bringing new therapies to patients.

Additional authors include Cheng Jiang, Asadur Chowdury, Akhil Kondepudi, Arjun Adapa, Wajd Al-Holou, Jason Heth, Oren Sagher, Maria Castro, Sandra Camelo-Piragua, Honglak Lee, all of University of Michigan, Mustafa Nasir-Moin, John Golfinos, Matija Snuderl, all of New York University, Alexander Aabedi, Pedro Lowenstein, Mitchel Berger, Shawn Hervey-Jumper, all of University of California, San Francisco, Lisa Irina Wadiura, Georg Widhalm, both of Medical University Vienna, Volker Neuschmelting, David Reinecke, Niklas von Spreckelsen, all of University Hospital Cologne, and Christian Freudiger, Invenio Imaging, Inc.

Funding: This work was supported by the National Institutes of Health, Cook Family Brain Tumor Research Fund, the Mark Trauner Brain Research Fund, the Zenkel Family Foundation, Ians Friends Foundation and the UM Precision Health Investigators Awards grant program.

Author: Noah FromsonSource: University of MichiganContact: Noah Fromson University of MichiganImage: The image is in the public domain

Original Research: Closed access.Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging by Todd Hollon et al. Nature Medicine

Abstract

Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging

Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. However, timely molecular diagnostic testing for patients with brain tumors is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment.

In this study, we developed DeepGlioma, a rapid (<90seconds), artificial-intelligence-based diagnostic screening system to streamline the molecular diagnosis of diffuse gliomas.

DeepGlioma is trained using a multimodal dataset that includes stimulated Raman histology (SRH); a rapid, label-free, non-consumptive, optical imaging method; and large-scale, public genomic data. In a prospective, multicenter, international testing cohort of patients with diffuse glioma (n=153) who underwent real-time SRH imaging, we demonstrate that DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy (IDH mutation, 1p19q co-deletion and ATRX mutation), achieving a mean molecular classification accuracy of 93.31.6%.

Our results represent how artificial intelligence and optical histology can be used to provide a rapid and scalable adjunct to wet lab methods for the molecular screening of patients with diffuse glioma.

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Artificial Intelligence Predicts Genetics of Cancerous Brain Tumors ... - Neuroscience News

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In the ongoing aftermath of COVID-19, manufacturing enterprises are seeking sustainable supply chain strategies that include extensive use of artificial intelligenceTom Fisk - Pexels

Manufacturing Growth Advocacy Network (MAGNET)Bob Perkoski

AI can automate routine tasks around order tracking and quality control, reducing costs and improving efficiencyTiger Lily - Pexels

On the transportation side, AI could spit out optimal delivery routes, or exact windows for trucks to arrive or depart a transportation centerTima Miroshnichenko - Pexels

How can ChatGPT be used by manufacturers to help their businesses?

Anyone with a computer and a bit of curiosity can pose this question to the ChatGPT online artificial intelligence (AI) tool. With a single prompt, the AI-powered chatbot will sing the praises of digital transformationa new age for the industry where customer service and lead generation exist on the cutting edge.

Yet, for sector proponents including Clevelands Manufacturing Growth Advocacy Network (MAGNET), the technologys potential for supply chain optimization is the real eye-opener.

In recent years, supply chains have become significantly more challenging to manage, notes MAGNET president and CEO Ethan Karp. Existing vulnerabilities in the flow of raw materials and finished goods were worsened by the pandemic, disrupting new product creation and leaving companies scrambling for answers.

In the ongoing aftermath of COVID-19, manufacturing enterprises are seeking sustainable supply chain strategies that include extensive use of artificial intelligence. The ChatGPT innovations ability to understand relationships and analyze huge volumes of data can change how these companies approach everything from sales to materials procurement.

The functionality of ChatGPT can take data from inventory systems and generate an email to a supplier that says, We need this on X date, explains Karp.

Whereas preventive maintenance is perhaps the most talked-about use case for AI tools like ChatGPT, the techs pattern identifying abilities can be harnessed for supply chain issues as well, according to Karp.

In theory, the powerful chatbot could forecast supply disruptionsallowing manufacturers to plan for problems before they occur. Additionally, AI can automate routine tasks around order tracking and quality control, reducing costs and improving efficiency.

Previously siloed manufacturing departments and stakeholders, meanwhile, could be brought together by AI. The technology has the industry covered on risk management as well, giving builders lead time on natural disasters or geopolitical events before major supply network disruptions arise.

Enterprise resource planning systems (ERP) are likely the best supply-related application for the nascent chatbot, says Karp. As ERP is integrated into daily business processes, including AI in that equation only makes sense.

All those functions about communicating with suppliers would be embedded in a software package that becomes more powerful and user friendly, Karp says.

Western Reserve University professor Michael GoulderDont get ahead of yourself

Case Western Reserve University (CWRU) professor Michael Goulder knows very well the possibilities of an AI-assisted supply chain. Along with his duties as a professor at CWRU, Goulder also leads the colleges master of supply chain management program.

In his previous career, Goulder oversaw the supply network for Hudson-based JoAnn Fabrics, giving him a full understanding of the complex system that starts with raw materials and ends when a user receives a finished product. Supply administration done correctly reduces costs and leads to a more efficient production cycle, he says.

Considering how fragile supply lines became during COVID-19, using AI and machine learning to strengthen the system seems an obvious choice.

However, the boundless buzz around AI reminds the CWRU prof of the late 1990s Internet boom and subsequent bust.

There is a vast overestimation of the speed at which these technologies will be perfected and commercialized, says Goulder. It took 10-plus years for the Internet to mature, and likewise it will take longer than people think for AI to mature.

Though Goulder is cautious about AIs immediate impacts, there are reasons to be excited about the technologys future. AI could be fed big supply chain data sets and return thousands of actionable variables.

The beauty of machine learning is that it will determine the variables that make the most sense, Goulder says. That will revolutionize supply chain forecasting when the technology matures.

Inventory and transportation management are additional circumstances where AI can shine. On the transportation side, artificial intelligence could spit out optimal delivery routes, or exact windows for trucks to arrive or depart a transportation center.

Companies will have a model about what products are selling in what parts of the country, then start shipping those goods knowing what the demands are, says Goulder. The [AI] models will learn and get better over time.

MAGNET president and CEO Ethan KarpPlacing a bet on AI

Currently, most organizations do not have the sophistication to leverage emerging AI technologies. Any manufacturing firm interested in pursuing digital designs must know how to capture the innovations full value, Goulder says.

That means purpose-built analytics rather than half-hearted experimentation with an application like ChatGPT. Goulder says he expects talents around Python and other programming languages will be in demand as artificial intelligence takes hold in manufacturing and beyond.

Business leaders want highly developed analytical skillsthey wont hire someone if that person doesnt know Python, says Goulder. Those skills are now table stakes. If I was a young person in the supply chain or a mid-career manager, Id make a big bet on those tools.

MAGNETs Karp agrees that ChatGPT cannot just be bolted on to a companys supply chain network. Simply giving the chatbot a few prompts reveals the errors in what passes for its thinking.

Ultimately, it sounds like a person and makes you believe its thinking like a person, but its just taking information and smashing it together with no mind toward sense, Karp argues.

Caveats aside, Karp cannot help but be thrilled by AIs down-the-line benefits for the manufacturing supply chain.

There have been conversations about AI for years, but this makes it real for people, says Karp. The supply chain [for this tech] makes sense, because there is a lot of communication that goes back and forth. The more real-time [we can get], the better.

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The dawn of ChatGPT: Artificial intelligence could be a boon for the ... - freshwatercleveland