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MADISON, Wis.University of Wisconsin-Madison researchers said they were proud to publish a groundbreaking paper on a new MRI machine-learning network.

They determined how brightly colored scans can help surgeons recognize, and accurately remove, an intracerebral hemorrhage (ICH), or bleeding in the brain.

Walter Block, a professor of medical physics and biomedical engineering, leads the research team that developed a special algorithm to support doctors who must act quickly and with precision to extract a brain bleed.

The trick is to visualize it and quantify it so that the surgeon has the information they need, Block said.

Tom Lilieholm a PhD candidate and lead author of the research created the specific algorithm for the new color-coded MRI machine-learning network.

We got pretty high accurate segmentations out of the machine here, 96% accurate clot, 81% accurate edema, he said, showing off one of the studys MRI slides.

Lilieholm said it can show a surgeon in less than a minute just how much of the hemorrhage they can safely remove.

Its really kind of useful to have that, and to have robust data to compare against, Lilieholm said. Thats where Matt kind of came in.

The Matt Lilieholm was referring to is NFL player Matt Henningsen.

Henningsen is from Menomonee Falls. Before becoming a Denver Bronco, he attended UW-Madison, where he excelled on the football field and in the classroom. He earned a bachelors and masters degree from the university.

My task would be to identify the location of the intracerebral hemorrhage and segment both the clot and the edema surrounding the clot, and then move on to every single layer of that image, Henningsen said.

Henningsen spent more than 100 hours gathering data for this new research on brain bleeds. He said he was excited and grateful for the opportunity to be part of this collaboration.

The UW-trained bioengineer and football player said he hopes this project can eventually support and improve something his football profession fears: traumatic brain injury.

You cant diagnose concussion with an MRI currently, he said. But I mean, maybe in the future, if youre able to, you can use machine-learning to potentially detect certain abnormalities that the human eye couldnt necessarily detect or things of that sort. Maybe we could get somewhere.

Read more:
UW scientists and NFL player create new MRI machine-learning ... - Spectrum News 1

Artificial Intelligence (AI) has emerged as one of the most transformative and rapidly growing fields in the technology industry. It is also one of the most discussed topics. While people are busy discussing how AI is a threat to existing jobs, it wouldn't be fair to not talk about the plethora of career opportunities it has opened for professionals across various domains.

From machine learning engineer to AI consultant, there are a variety of roles awaiting your exploration. In this article, we have listed the top 5 career options in AI, that you can consider if you wish to try your luck in this evolving field.

Artificial Intelligence (AI) refers to the development of computer systems, to perform tasks that typically require human intelligence. From understanding natural language, recognising patterns, learning from data, and making decisions, it aims to solve complex problems, make predictions, and improve processes.

Well, there is no doubt that the use of AI technology has expanded significantly in the past few years. While some people are worried about losing their jobs because of AI, its important to notice the positive side. As per a report from the Economic Times, AI is expected to create some 97 million new jobs by 2025.

But what is it, that you need to work in AI? To answer that here is a list of hard and soft skills that, if acquired can ensure a successful career in this field:

Strong foundation in programming languages such as Python, Java, or C++ to develop AI algorithms and models.

Good understanding of mathematical concepts, like linear algebra, calculus, and statistics to design and optimise AI algorithms.

Proficiency in machine learning techniques such as supervised and unsupervised learning, with a sound knowledge of various algorithms and frameworks like TensorFlow or PyTorch.

AI is a complex technology. Hence, it demands strong problem-solving skills to tackle the issues hand efficiently.

With the complexities of the AI concept, comes the need for effective communication with non-technical stakeholders.

AI is a constantly developing field. Hence, an innovative mindset can be useful in exploring AIs full potential.

A technology as complex as AI isnt harnessed by one person alone. So, get ready to become a team player, if you wish a career in this domain.

There is no doubt that the job prospects in AI are highly promising with the increasing demand for AI specialists in fields like machine learning, natural language processing, data science, and many more. Lets explore the average salary, qualifications, and skills requirement for these five job roles offered by AI:

A machine learning engineer is a person in IT who focuses on researching, building, and designing self-running artificial intelligence (AI) systems to automate predictive models.

Average salary: 7L - 14L per year

Qualification: Bachelor or higher degree in computer science, machine learning, or a related field.

Skills required:

AI research scientists work on AI research with a focus on developing new algorithms and technologies. They aim to push the boundaries of AI by collaborating with academic institutions, research organisations, and leading tech companies.

Average salary: 29L - 42L per year

Qualifications: Bachelors degree in computer science, engineering, or similar fields.

Skills required:

NLP engineers specialise in developing AI systems that can understand, interpret, and generate human language. They work on applications like chatbots, virtual assistants, and language translation tools.

Average salary: 6L - 14L per year

Qualification:Bachelors degree in computer science, data science, engineering, or a related field.

Skills required:

Robotics engineers help design and develop intelligent robotic systems that can interact with the environment. They are also responsible for conducting research and developing new applications for existing robots.

Average salary: 4L - 9L per year

Qualification: Bachelor's degree in computer science or a related field.

Skills:

AI Consultants are professionals who provide expertise and guidance in implementing AI solutions. They assess business needs in order to recommend AI strategies and oversee AI project implementations.

Average salary: 9L - 19L per year

Qualification:Bachelor's or master's degree in computer science, data science, business, or related field.

Skills:

The field of AI offers diverse career opportunities, each with its unique focus and requirements. Hence, in order to succeed, it is crucial to develop the necessary skills and stay updated with the latest advancements. Remember that with the right preparation and commitment, you can aim for a fulfilling and rewarding career in AI.

Read more:
AI careers demystified: The ultimate guide to AI jobs - YourStory

In a new study published in the journal Social Neuroscience, researchers employed machine learning techniques to predict individual differences in narcissistic personality traits using distinct structural brain features. The study represents the first-ever attempt to harness machine learning for deciphering the neural underpinnings of narcissism.

Narcissistic traits encompass characteristics such as grandiosity, a constant need for admiration, a lack of empathy, entitlement, manipulative behavior, envy, arrogance, fragile self-esteem, and difficulties in maintaining healthy relationships. These traits reflect a self-centered and often arrogant perspective, where individuals may believe they are superior to others and expect special treatment.

When narcissistic traits are severe and persistent, they may lead to a diagnosis of narcissistic personality disorder, a complex clinical construct often comorbid with other psychological disorders such as borderline personality, substance abuse, antisocial tendencies, and anxiety. However, diagnosing narcissistic personality disorder can be challenging, as it relies on self-reported and observed behaviors, thoughts, and feelings. This is because there are no clear biological markers for the disorder, making it difficult to objectively assess the disorder.

The researchers sought to develop predictive models that could estimate an individuals narcissistic traits based on their brain structure and personality features. This has practical implications for psychology and clinical assessments. Predictive models could potentially help identify individuals at risk of developing narcissistic traits or assist in the assessment and treatment of personality disorders.

In our Lab, the Clinical and Affective Neuroscience Lab, we are particularly interested in understanding the neural fingerprint of personality. Especially personality disorders. We all have a personality that ranges from normal to abnormal traits and we believe it is of fundamental importance understanding it, explained study author Alessandro Grecucci, a professor of affective neuroscience and neurotechnology at the University of Trento.

The researchers conducted a study using data from the MPI-Leipzig Mind Brain-Body dataset, which included structural MRI and questionnaire data from 135 healthy participants. Eligibility criteria included good health, no medication, and no history of substance abuse or neurological diseases. The participants demographic and behavioral data were recorded.

Using a machine learning technique called Kernel Ridge Regression, the researchers found that specific brain regions were linked to narcissistic traits, including the orbitofrontal cortex, Rolandic operculum, angular gyrus, rectus, and Heschls gyrus. These regions are associated with emotion processing, social cognitive processing, and auditory perception.

The findings provide evidence that even such an intimate thing such as personality, the inner core of who we are, can be scientifically studied and predicted from our brain, Grecucci told PsyPost. In our lab, we are trying to develop neuro-predictive models of personality and other affective relevant dimensions. One day, these studies may help clinicians to characterize eventual difficulties before they turn into a full disorder.

Furthermore, the researchers constructed a predictive model to determine an individuals narcissistic traits based on specific subscales from the NEO Personality Inventory-Revised, Short Dark Triad questionnaire, and the Personality Styles and States Inventory.

Individuals with higher levels of openness, characterized by a willingness to explore new experiences and ideas, were more likely to exhibit narcissistic traits. Lower levels of agreeableness, which involve being less cooperative, sympathetic, and considerate of others, were associated with narcissistic traits. Higher levels of conscientiousness, indicating self-discipline, organization, and goal-oriented behavior, were linked to narcissistic traits.

Additionally, the study found that abnormal personality traits, including Borderline, Antisocial, Addicted, Negativistic, and Insecure traits, were related to narcissistic traits. Machiavellianism, characterized by manipulative and deceitful behavior, also predicted narcissistic traits. This suggests that individuals with narcissistic traits may exhibit a combination of personality traits, some of which are outside the normal range.

In this and other studies, we are observing an emerging coherent pattern in different personality disorders, Grecucci said. Regions belonging to the same cortical-subcortical networks are at a forefront. This may lead to the development of a common personality network behind specific personality traits.

The study provides new insights into the neural underpinnings of narcissism. But as with all research, it includes some limitations. Firstly, the analysis focused solely on gray matter features, neglecting potential insights that could be gained from exploring white matter features or functional brain activity. Future research may benefit from a more comprehensive examination of various brain aspects. Secondly, while the study included a relatively larger sample size compared to previous research, it acknowledges the potential for even larger sample sizes to enhance brain-wide association analyses.

The researchers also believe that clinical personality models offer more robust and predictive insights into personality traits than non-clinical models.

Personality is a complex thing, and no one knows which is the best model of personality we should use to study this topic at a brain level, Grecucci explained. Contrary to the vast majority of studies that are using normal personality models (such as the Big Five), we are trying to make a claim that personalities can be better captured using clinical models such as the DSM-5 personality disorder axis. The clinical personalities offer such a strong characterization of what different personalities are that in my opinion they can be more predictive than other non-clinical models. In the end, personality disorders are just exaggerated personality traits that we all have.

The study, Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach, Khanitin Jornkokgoud, Teresa Baggio, Md Faysal, Richard Bakiaj, Peera Wongupparaj, Remo Job, and Alessandro Grecucci.

See the original post:
Scientists use machine learning to predict narcissistic traits based on neural and psychological features - PsyPost

Summary: Researchers have utilized advanced machine learning techniques to unveil the neural structure linked to narcissism, overcoming previous study limitations.

Employing Kernel Ridge Regression and Support Vector Regression, they predicted narcissistic personality traits based on brain organization and other personality aspects. A specific brain circuit, including regions like the lateral and middle frontal gyri, played a predictive role.

Furthermore, a combination of both conventional and abnormal personality traits could forecast narcissism.

Key Facts:

Source: Neuroscience News

Narcissisma term that often garners interest in both academic circles and daily conversations.

Often associated with pathological conditions, the neurological underpinnings of narcissism have remained a mystery. But recent advances in machine learning are shining a new light on this old enigma.

Past attempts to map the neural routes of narcissism have often fallen prey to inconsistent findings. Many of these inconsistencies were attributed to limitations such as low participant numbers or reliance on traditional univariate methods. These approaches were limiting the depth of insight possible into the intriguing world of narcissistic traits.

Determined to break past these barriers, a recent study employed cutting-edge machine learning techniques: Kernel Ridge Regression and Support Vector Regression.

These tools have the capability to discern and predict patterns in vast datasets, making them apt for an investigation into the intricate neural web of narcissism.

The aim was straightforward but ambitious: build a predictive model for narcissistic traits, relying on both neural structures and an array of personality features.

The results were both surprising and enlightening.

A specific brain circuit emerged as a powerful predictor of narcissistic personality traits. This circuit incorporates regions such as the lateral and middle frontal gyri, angular gyrus, Rolandic operculum, and Heschls gyrus.

The statistical significance (p<0.003) of this finding underscores its potential implications for both neuroscience and psychology.

But the revelations didnt stop at neural structures. The research unearthed a compelling blend of normal (e.g., openness, agreeableness, conscientiousness) and abnormal (e.g., borderline, antisocial, insecure, addicted, negativistic, Machiavellianism) personality traits that could forecast narcissism.

This multi-dimensional approach, combining neural with psychological markers, has opened up a more holistic understanding of narcissistic traits.

This study stands as the first of its kind to deploy a supervised machine learning approach in the pursuit of decoding narcissism. It hints at a future where personality traits could be derived, not just from observable behaviors, but from a mix of neural and psychological features.

While these findings are a monumental step, they also pave the way for further inquiry. How might these insights transform therapeutic interventions? Could they enhance diagnostic precision? The confluence of neuroscience and machine learning promises not just answers, but a richer understanding of the human psyche.

This multi-faceted exploration of narcissism exemplifies how modern tools can rejuvenate classical investigations. As we continue to harness the combined power of neuroscience and machine learning, the horizons of personality research are bound to expand exponentially.

Author: Neuroscience News Communications Source: Neuroscience News Contact: Neuroscience News Communications Neuroscience News Image: The image is credited to Neuroscience News

Original Research: Closed access. Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach by Alessandro Grecucci et al. Social Neuroscience

Abstract

Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach

Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood.

Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods.

The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features.

In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from other relevant normal and abnormal personality features.

Results showed that a circuit including the lateral and middle frontal gyri, the angular gyrus, Rolandic operculum, and Heschls gyrus successfully predicted narcissistic personality traits (p<0.003).

Moreover, narcissistic traits were predicted by normal (openness, agreeableness, conscientiousness) and abnormal (borderline, antisocial, insecure, addicted, negativistic, machiavellianism) personality traits.

This study is the first to predict narcissistic personality traits via a supervised machine learning approach. As such, these results may expand the possibility of deriving personality traits from neural and psychological features.

See the original post:
Machine Learning Uncovers Neural Pathways of Narcissistic Traits - Neuroscience News

The number of digital transactions has skyrocketed. As consumers continue to spend and interact online, they have growing expectations for security and identity verification. As fraudsters become savvier and more opportunistic, theres an increased need for businesses to protect customers from fraud while still providing a seamless online experience.

At the same time, businesses have the ability to access more insights and data than ever before, but may not be leveraging the most effective technology solutions to accurately identify and authenticate consumers online.

Uncertain economic conditions and what feels like a barrage of new scams has made consumers and businesses more concerned about online fraud.

Experians 2023 U.S. Identity and Fraud Report found that over half of consumers feel like they are more of a fraud target than they were just one year ago. In addition, half of businesses report a high level of concern about fraud risk.

The report found that people worry most about identity theft (64%), stolen credit card information (61%) and online privacy (60%). On the other hand, businesses are concerned about authorized push payments fraud (40%) and transactional payment fraud (34%). Additionally, nearly 70% of businesses said that fraud losses have increased in recent years and most businesses reported that they plan to increase their fraud management budgets by at least 8% to as much as 19%.

Despite their plans to increase their fraud prevention budgets, data shows that businesses may not be completely aligned with consumer expectations.

For example, 85% of people report physical biometrics, such as facial recognition and fingerprints, as the authentication method that makes them feel most secure. However, that identity authentication method is currently used by just a third of businesses to detect and protect against fraud, showing there is still a disconnect between consumer preferences and what businesses are offering.

Finally, consumers not only stress the importance of better security, but they expect their online experiences to be frictionless. This is evident in the data while 51% considered abandoning a new account opening because of a negative experience, 37% said a bad experience caused them to take their business elsewhere. Its crucial for businesses to implement fraud solutions that are capable of properly verifying real customers while identifying and treating fraud and providing a positive experience.

Businesses understand the need to incorporate machine learning into their anti-fraud strategies.

The main benefits of incorporating machine learning into fraud management is that it can:

A multilayered approach to fraud that leverages data, machine learning and advanced analytics is crucial for businesses trying to stay ahead of fraud trends. Machine learning modernizes identification and fraud prevention, allowing businesses to fight new and old forms of fraud as they occur while providing their customers with a seamless, positive experience.

Read the rest here:
Modernizing fraud prevention with machine learning - Help Net Security