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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.

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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.

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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.

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Scientists use machine learning to predict narcissistic traits based on neural and psychological features - PsyPost