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Understanding how proteins fold is key to unlocking how cells function and how their structures are built. When proteins fold incorrectly, it can cause cells to malfunction, leading to diseases like Alzheimer's and Parkinson's. Delving into the complexities of protein folding not only enriches our fundamental understanding of biological processes but also paves the way for innovative therapies, and novel drug discoveries.

The 2024 Boden Research Conference aims to bring together a multidisciplinary community of experts to deepen our understanding of protein folding dynamics and propel therapeutic innovation. The conference will assess current knowledge, identify crucial gaps, and guide future research in protein folding and misfolding. The conference will also explore how machine learning can revolutionise the search for new treatments and better diagnosis of diseases.

Join us to discover how the smallest proteins can solve some of our biggest health challenges. Together, we can advance scientific understanding and develop new solutions to combat debilitating diseases.

Date: 25th -26th September, 2024

Time: 8:00am -17:00 pm. A conference dinner will be organised on day one (25th September, 2024) at 18:00 pm

Deadline: Deadline for conference registration is 23:59 13th September, 2024 (AEST) , and deadline for abstract submission is 23:59 15th July, 2024 (AEST).

Cost: Free

Venue:Holmes Building (A09), Science Rd, The University of Sydney, Camperdown NSW 2050, Australia

Program: TBA

Contact: Professor Ken-Tye Yong, The University of Sydney: ken.yong@sydney.edu.au; Dr Morning Liu, The University of Sydney: xiaochen.liu@sydney.edu.au

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Australian Academy of Science Boden Research Conference 2024: Protein Folding: Mechanisms, Health, and Machine ... - Australian Academy of Science

In the seemingly never-ending war to protect their customers and institutions from fraud, an increasing number of financial institutions (FIs) are deploying machine learning (ML) and artificial intelligence (AI) tools to fight back.

And according to PYMNTS Intelligences Leveraging AI and ML to Thwart Scammers, a report created in collaboration withHawk, those efforts appear to be working.

The report, which is based on surveys with 200 FIs with more than $1 billion in assets under management, revealed that thoseFIs that now use ML or AI to mitigate fraud are seeing steep declines in common forms of fraud.

For instance, tech support impersonation and IRS imposter scams are two of the mostfrequently reported scams, yet FIs using ML or AI anti-fraud solutions were 17% less likely to report experiencing these leading scams than FIs relying solely on more traditional fraud prevention tools. Likewise, they were 18% less likely to report IRS imposter scams as a top concern.

They also reported lower rates of lottery, romance, utility, rentalandSocial Security scams.In fact,as the figure illustrates, FIs leveragingthe MLand AI technology reported lower incidents of nearly every common form of fraud.

The data also finds there is some room for improvement in both ML- and AI-based solutions.The tools were less successful in identifyingcharitable-donationscams. They also missed some fake debt-collection scams. This could be because these two scams are less common (and there would thus be less data for the solutions to work from).

Despite these small hurdles, FIs areapparentlyimpressed. Fifty-two percent of the FIs we surveyed plan to implement or increase their use of ML or AI fraud prevention models.In fact, FIs using AI or ML are 17% more likely to have plans in place to implement additional ML or AI solutions than their counterparts that do not use ML or AI fraud prevention solutions. In other words, many of the FIs now using the advancedtechnologies are ready to expand their ML and AItool chest.

Our study also found that by adopting ML or AI fraud-prevention models, FIs are not only stopping more bad actors from inflicting damage but also increasing the confidence their customers have that their accounts are protected. So, in turn, customer satisfaction rates are likely to increase as fraud levels decline.

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52% of FIs Plan to Lean on ML and AI to Combat Fraud - PYMNTS.com

Summary

Machine learning is sometimes considered a subset of overarching AI. But in the context of digital security, it may be better understood as a driving force, the fuel powering the engine.

The terms AI and machine learning are often used interchangeably by professionals outside the technology, managed IT and cybersecurity trades. But, truth be told, they are separate and distinct tools that can be coupled to power digital defense systems and frustrate hackers.

Artificial iIntelligence has emerged as an almost ubiquitous part of modern life. We experience its presence in everyday household robots and the familiar Alexa voice that always seems to be listening. Practical uses of AI mimic and take human behavior one step further. In cybersecurity, it can deliver 24/7 monitoring, eliminating the need for a weary flesh-and-blood guardian to stand a post.

Machine learning is sometimes considered a subset of overarching AI. But in the context of digital security, it may be better understood as a driving force, the fuel powering the engine. Using programmable algorithms, it recognizes sometimes subtle patterns. This proves useful when deployed to follow the way employees and other legitimate network users navigate systems. Although even discussions regarding AI and machine learning feel redundant, to some degree, they are a powerful one-two punch in terms of automating security decisions.

Integrating AI calls for a comprehensive understanding of mathematics, logical reasoning, cognitive sciencesand a working knowledge of business networks. The professionals who implement AI for security purposes must also possess high-level expertise and protection planning skills. Used as a problem-solving tool, AI can provide real-time alerts and take pre-programmed actions. But it cannot effectively stem the tide of bad actors without support. Enter machine learning.

In this context, machine learning emphasizes software solutions driven by data analysis. Unlike human information processing limitations, machine learning can handle massive swaths of data. What machine learning learns, for lack of a better word, translates into actionable security intel for the overarching AI umbrella.

Some people think about machine learning as a subcategory of AI, which it is. Others comprehend it in a functional way,i.e., two sides to the same coin. But for cybersecurity experts determined to deter, detectand repel threat actors, machine learning is the gasoline that powers AI engines.

Its now essential to leverage machine learning capabilities to develop a so-called intelligent computer that can defend itself, to some degree. Although the relationship between AI and machine learning is diverse and complex, an expert can integrate them into a cybersecurity posture with relative ease. Its simply a matter of repetition and the following steps.

When properly orchestrated and refined to detect user patterns and subtle anomalies, the AI-machine learning relationship helps cybersecurity professionals keep valuable and sensitive digital assets away from prying eyes and greedy digital hands.

First and foremost, its crucial to put AI and machine learning benefits in context. Studies consistently conclude that more than 80% of all cybersecurity failures are caused by human error. Using automated technologies removes many mistake-prone employees and other network users from the equation. Along with minimizing risk, these are benefits of onboarding these automated next-generation technologies.

Improved cybersecurity efficiency. According to the 2023 Global Security Operations Center Study, cybersecurity professionals spend one-third of their workday chasing downfalse positives. This waste of time negatively impacts their ability to respond to legitimate threats, leaving a business at higher than necessary risk. The strategic application of AI and machine learning can be deployed to recognize harmless anomalies and alert a CISO or vCISO only when authentic threats are present.

Increased threat hunting capabilities.Without proactive, automated security measures like MDR (managed detection and response), organizations are too often following an outdated break-and-fix model. Hackers breach systems or deposit malware, and then the IT department spends the remainder of their day, or week, trying to purge the threat and repair the damage. Cybersecurity experts have widely adopted the philosophy that the best defense is a good offense. A thoughtful AI-machine learning strategy can engage in threat hunting without ever needing a coffee break.

Cure business network vulnerabilities.Vulnerability management approaches generally employ technologies that provide proactive automation. They close cybersecurity gaps and cure inherent vulnerabilities by identifying these weaknesses and alerting human decision-makers. Unlike scheduling a routine annual risk assessment, these cutting-edge technologies deliver ongoing analytics and constant vigilance.

Resolve cybersecurity skills gap.Its something of an open secret that there are not enough trained, certified cybersecurity experts to fill corporate positions. Thats one of the reasons why industry leaders tend to outsource managed IT and cybersecurity to third-party firms. Outsourcing helps to onboard the high-level knowledge and skills required to protect valuable digital assets and sensitive information. Without enough cybersecurity experts to safeguard businesses, automation allows the resources available to companies to drill down and identify true threats. Without these advanced technologies being used to bolster network security, its likely the number of debilitating cyberattacks would grow exponentially.

The type of predictive analytics and swift decision-making capabilities this two-prong approach delivers has seemingly endless industry applications. Banking and financial sector organizations can not only use AI and machine learning to repel hackers but also ferret out fraud. Healthcare organizations have a unique opportunity to exceed Health Insurance Portability and Accountability Act (HIPAA) requirements due to the advanced personal identity record protections it affords. Companies conducting business in the global marketplace can also get a leg-up in meeting the EUs General Data Protection Regulation (GDPR) designed to further informational privacy.

Perhaps the greatest benefit organizations garner from AI and machine learning security automation is the ability to detect, respondand expel threat actors and malicious applications. Managed IT cybersecurity experts can help companies close the skills gap by integrating these and other advanced security strategies.

John Funk is a Creative Consultant at SevenAtoms. A lifelong writer and storyteller, he has a passion for tech and cybersecurity. When hes not found enjoying craft beer or playing Dungeons & Dragons, John can be often found spending time with his cats

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