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Disclaimer: This article is sponsored content and should not be considered as financial or investment advice. Always do your own research before making any financial decisions. The opinions expressed in this article are those of the author and do not necessarily reflect the views of CryptoGlobe.

Recently, ATPBot announced that it now supports all Binance and Kraken users to implement AI automatic trading through API, providing users with more opportunities to trade cryptocurrencies. Say goodbye to subjective judgment and decision-making based on experience, and let each of your transactions be carried out on the basis of a high probability of winning. ATPBot exists to make investing easier, more efficient, and more trustworthy.

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Facts have proved that artificial intelligence has the ability to process and analyze massive data, which has advantages in various fields. ATPBot is a typical example of artificial intelligence making significant contributions in the field of quantitative trading. Similar to ChatGPTs ability to understand and process natural language, ATPBot provides investors with a scientific, standardized and effective investment method in the world of AI quantitative trading.

ATPBot determines the timing and price of buying and selling by backtesting a large amount of data and algorithms, reducing emotional interference and human errors, while improving investment efficiency and stability, making it the ChatGPT of artificial intelligence quantitative trading.

ATPBot is a platform that focuses on AI quantitative trading strategy development and asset value-added management services. It uses the advantages of artificial intelligence technology to develop and implement quantitative trading strategies for users.

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See more here:

ATPBot now supports Binance and Kraken exchanges, Allowing ... - CryptoGlobe

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

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.

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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.

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Modernizing fraud prevention with machine learning - Help Net Security

Japans leading developer of new AI technologies Fujitsu accelerates its commitment to open source innovation with new projects hosted in LF AI & Data Fujitsu Limited, The Linux Foundation

Tokyo, San Francisco, September 15, 2023

Fujitsu Limited and the Linux Foundation today marked the official launch of Fujitsus automated machine learning and AI fairness technologies as open source software (OSS) ahead of Open Source Summit Europe 2023, running in Bilbao, Spain, from September 19-21, 2023. The two projects will offer users access to software that automatically generates code for new machine learning models, as well as a technology that addresses latent biases in training data.

The Linux Foundation approved the incubation of two new projects, "SapientML" and "Intersectional Fairness" on August 24 to encourage developers worldwide to further experiment and innovate with AI and machine learning technologies, with plans to host future activities like hackathons to engage and build a community to promote open source AI.

With these projects, Fujitsu and the Linux Foundation aim to further democratize AI to realize a world in which developers everywhere can easily and securely use the latest technologies on open platforms to create new applications and find innovative solutions to challenges facing business and society.

Offering AI technologies as OSS to developers worldwide opens up new opportunities for innovation across various industries by lowering the barrier of entry. We are excited to work together with Linux Foundation to contribute to the further advancement and spread of AI by launching Fujitsus AI technologies for the Linux Foundations open source projects SapientML and Intersectional Fairness.

We anticipate that offering Fujitsu's automated machine learning and AI fairness technologies as OSS will greatly contribute to the advancement and diffusion of AI. The Linux Foundation welcomes these two projects and looks forward to building the future of AI together.

Fujitsu holds the largest number of AI-related inventions in Japan, with 970 patents from 2014 to October 2022, and in April 2023, launched Fujitsu Kozuchi (code name) - Fujitsu AI Platform. Kozuchi enables users to rapidly and securely test advanced AI technologies and has offered a wide range of customers and partners access to some of Fujitsus most advanced AI technologies.

AI represents one of the most rapidly developing technologies of our time, contributing to the solution of various societal and industrial issues. However, this ongoing development requires advanced expertise in the development and operation of AI technologies, and concerns about the fairness of AI technology keep increasing. To enable further spread and enhancement of AI, a platform to openly share AI technologies with engineers worldwide represents an important prerequisite.

As a non-profit technology consortium, the Linux Foundation approves approximately a dozen selected technologies as OSS projects annually for the development of open technologies. Fujitsu is providing automated machine learning and AI fairness technologies as OSS via Linux Foundation, enabling developers around the world to access and widely use Fujitsu's technology at the source code level to accelerate technological advancement and the development of new applications, while also addressing concerns around fairness and transparency that remain a critical priority in the field of AI ethics.

Fujitsu has been further providing its technologies for automated machine learning and AI fairness as Fujitsu AutoML and Fujitsu AI Ethics for Fairness, together with various AI technologies and GUIs via the Fujitsu Kozuchi (code name) - Fujitsu AI Platform. Moving forward, Fujitsu will offer technology updates for each project on its AI platform.

Fujitsus purpose is to make the world more sustainable by building trust in society through innovation. As the digital transformation partner of choice for customers in over 100 countries, our 124,000 employees work to resolve some of the greatest challenges facing humanity. Our range of services and solutions draw on five key technologies: Computing, Networks, AI, Data & Security, and Converging Technologies, which we bring together to deliver sustainability transformation. Fujitsu Limited (TSE:6702) reported consolidated revenues of 3.7 trillion yen (US$28 billion) for the fiscal year ended March 31, 2023 and remains the top digital services company in Japan by market share. Find out more: http://www.fujitsu.com.

The Linux Foundation is the worlds leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects are critical to the worlds infrastructure including Linux, Kubernetes, Node.js, ONAP, PyTorch, RISC-V, SPDX, OpenChain, and more. The Linux Foundation focuses on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org. The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see its trademark usage page: http://www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

Fujitsu Limited Public and Investor Relations Division Inquiries

All company or product names mentioned herein are trademarks or registered trademarks of their respective owners. Information provided in this press release is accurate at time of publication and is subject to change without advance notice.

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Fujitsu and the Linux Foundation launch Fujitsu's automated ... - Fujitsu