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The Australian Academy of Science and the Australian Academy of Law are delivering their annual joint symposium for 2021. This year the topic is Artifical Intelligence: Future directions in technology and law.

Speakers will each give a 10-minute presentation, followed by a Q&A session. The event will be moderated by The Hon Dr Annabelle Bennett AC FAA FAAL SC.

Professor Lyria Bennett Moses FAAL: Director of Allens Hub for Technology, Law and Innovation at UNSW, Sydney in the Faculty of Law and Justice. Professor Bennett Moses has written about the limitations of AI and data-driven approaches to decision-making in government, law enforcement and the legal system, and why it is crucial for everyone to understand how smart machines are impacting on our society.

Professor Sventha Venkatesh FAA FTSE: Co-Director, Applied Artificial Intelligence Institute at Deakin University, Alfred Deakin Professor, ARC Laureate Fellow, Co-Director of Applied Artificial Intelligence Institute and a leading Australian computer scientist who has made fundamental and influential contributions to the field of activity and event recognition in multimedia data.

Professor Toby Walsh FAA: Scientia Professor of Artificial Intelligence at the University of NSW a leading researcher in Artificial Intelligence, a Laureate Fellow and Scientia Professor of Artificial Intelligence in the School of Computer Science and Engineering at UNSW Sydney, and leader of the Algorithmic Decision Theory group at CSIRO Data61.

Mr Edward Santow FAAL: served as Australias Human Rights Commissioner from 2016-2021. He recently started as Industry Professor - Responsible Technology at the University of Technology Sydney. He leads a major UTS initiative to build Australias strategic capability in AI and new technology. This will support Australian business and government to be leaders in responsible innovation by developing and using AI that is powerful, effective and fair.

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Artificial Intelligence: Future directions in technology and law - Australian Academy of Science

Chatbot Serves as Virtual College Adviser

Colleges have had success with chatbotsfor a few years, but high school students can now benefit from the first nationally accessible (and free) AI college adviser chatbot, Oli. The tool is the result of apartnership between Common App and Mainstay(formerly AdmitHub). Oli stands ready to help students around the clock with a wide range of tasks, such as selecting the right school, completing college and scholarship applications and understanding financial aid forms. It responds to questions via text and also sends users deadline reminders, updates and resources several times a week. When extra help is required, Oli connects students with a trained college adviser fromCollege Advising Corps.

RELATED:Counselors take an online approach to helping high school students with college decisions.

While education leaders and policymakers have been pushing tutoring as a solution to thecrisis of COVID-19 learning disruption, supporting every student in need with a human tutor isnt feasible nor affordable. Researcher Neil Heffernan, a computer science professor and director of the Learning Sciences and Technologies graduate program at Worcester Polytechnic Institute, is working on technology to support human tutors.

These AI-powered tutor chatbots would democratize private tutoring, something thats not available to many students. Heffernan believes that on-demand, AI-driven tutor chatbots are an important addition to the learning experience and will easily integrate across a schools system existing technology.

To me, AI is just a set of simple tools that we can use, in this case, to figure out some problems that teachers and kids are persistently having, says Heffernan. The real magic is giving human tutors and teachers a little bit of information on whats going on so they can be more efficient.

AI-enabled chatbots are likely coming soon to a school near you to help with class scheduling, tutoring, college applications, collecting feedback and a lot more.

Teachers shouldnt be scared, Heffernan says. Like many things in AI, the chatbots are going to slowly come in and, I hope, actually help kids when humans are not available.

KEEP READING:Schools can use artificial intelligence to keep students engaged in online learning.

Link:
Chatbots Allow Educators to Delegate Repetitive Tasks and Focus on Teaching - EdTech Magazine: Focus on K-12

Why dont you go join them? asked Ozioma. Showing up behind Amaka on the balcony, the landlady lit an English-brand cigarette, leaned against the railings, and peered down.

I used to be the dance queen of our village, Ozioma went on, her eyes hazy with nostalgia. Not trying to brag here, but not a single boy could take his eyes off me. My father hated when I danced, though. He threatened to hit me every time he caught me dancing.

Did you listen to him?

Ozioma laughed heartily. Why on earth would a child give up what they love because their parents said no? Eventually, I found a way that could allow me to at least finish the dance.

What was it? asked Amaka.

I would wear an Agbogho Mmuo every time I danced.

What? Amakas eyes widened. The Agbogho Mmuo was the sacred mask of northern Igbo, representing maiden spirits as well as the mother of all living creation.

See, my father had your exact expression when he saw me with the mask. He had no choice but to bow down, to show his respect to the mask and the goddess it embodies. Of course, after I was done with the dance, with the mask stripped off, I would get my share of scolding, said Ozioma, beaming with pride, as if the memory had temporarily brought her back to the days when she was a young girl.

Upon hearing Oziomas story, Amaka felt an idea, blurry and shapeless, darting across his mind like a fish. He scrunched up his face, thinking. The mask

Yes, child. The mask is where my power came from.

Strip off the mask? Strip off the mask, murmured Amaka.

All of a sudden, he leapt to his feet and kissed Ozioma on the cheek. Thank you, oh thank you, my dance queen! He dashed back to his room, leaving behind the hustle and bustle of the parade and a very confused Ozioma.

Maybe spinning a lie and putting it in FAKAs mouth wont make his followers abandon their idol, Amaka told Chi via video chat that afternoon, excited with his new discovery. But stripping off its mask and revealing the hidden puppet master might.

No one knows who the puppet master is, though, Chi replied.

Exactly! Amaka beamed. Cant you see? It means that the puppet master can be anyone.

So, youre suggesting that

I can strip off FAKAs mask and make him any person you want him to be.

Chi fell silent in the video chat.

Youre a fucking genius, Chi finally muttered.

Ndewo, Amaka said, preparing to sign off.

Wait, Chi looked up. It means that you need to create a face that exists in reality.

Yes.

A face that can fool all the anti-fake detectors, added Chi, musing. Think about the color distortion, the noise pattern, the compression rate variation, the blink frequency, the biosignal is it doable?

I need time, said Amaka. And unlimited cloud AI computing power.

Ill get back to you. Chi logged off.

Amaka gazed at his own reflection in the dimming monitor screen. The adrenaline rush that had initially washed over him had faded. He saw on his face not excitement, but exhaustion and an unsettled feeling, as if he had betrayed a guardian spirit watching from above.

In theory anyone could fake a perfect image or video, at least well enough to fool the existing anti-fake detectors. The problem was the costcomputing power.

Fakes and their detectors were engaged in an eternal battle, like Eros and Thanatos. Amaka had his work cut out for him, but he was determined to succeed in achieving his singular goal: the creation of a real, human face.

Read the original:
Artificial Intelligence and the Gods Behind the Masks - WIRED

An international research from several universities including Maastricht University (UM) has proposed a standardized registry for artificial intelligence (AI) work in biomedicine. Aim is to improve the reproducibility of results and create trust in the use of AI algorithms in biomedical research and, in the future, in everyday clinical practice. The scientists presented their proposal in the scientific journal Nature Methods.

In the last decades, new technologies have made it possible to develop a wide variety of systems that can generate huge amounts of biomedical data. For example in cancer research. At the same time, completely new possibilities have developed for examining and evaluating this data using artificial intelligence methods. AI algorithms in intensive care units, e.g., can predict circulatory failure at an early stage. That is based on large amounts of data from several monitoring systems by processing a lot of complex information from different sources at the same time.

Read the complete press release here.

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This great potential of AI systems leads to an unmanageable number of biomedical AI applications. Unfortunately, the corresponding reports and publications do not always adhere to best practices or provide only incomplete information about the algorithms used or the origin of the data. This makes assessment and comprehensive comparisons of AI models difficult. The decisions of AIs are not always comprehensible to humans and results are seldomly fully reproducible. This situation is untenable, especially in clinical research, where trust in AI models and transparent research reports are crucial to increase the acceptance of AI algorithms and to develop improved AI methods for basic biomedical research.

To address this problem, an international research team including the UM has proposed the AIMe registry forartificialintelligence in biomedical research, a community-driven registry that enables users of new biomedical AI to create easily accessible, searchable and citable reports that can be studied and reviewed by the scientific community.

The freely accessible registry is available athttps://aime-registry.organd consists of a user-friendly web service that guides users through the AIMe standard and enables them to generate complete and standardised reports on the AI models used. A unique AIMe identifier is automatically created, which ensures that the report remains persistent and can be specified in publications. Hence, authors do not have to cope with the time-consuming description of all facets of the AI used in articles for scientific journals and simply refer to the report in the AIMe registry.

Read next: More focus on the social impact of AI

Read more here:
AIMe A standard for artificial intelligence in biomedicine - Innovation Origins

A new artificial-intelligence technology for heart imaging can potentially improve care for patients, allowing doctors to examine their hearts for scar tissue while eliminating the need for contrast injections required for traditional cardiovascular magnetic resonance imaging.

A team of researchers who developed the technology, including doctors at UVA Health,reports the success of the approach in a new article in the scientific journal Circulation. The team compared its AI approach, known as virtual native enhancement, with contrast-enhanced cardiovascular magnetic resonance scans now used to monitor hypertrophic cardiomyopathy, the most common genetic heart condition. The researchers found that virtual native enhancement produced higher-quality images and better captured evidence of scar in the heart, all without the need for injecting the standard contrast agent required for cardiovascular magnetic resonance scans.

This is a potentially important advance, especially if it can be expanded to other patient groups, said researcher Dr.Christopher Kramer, the chief of the Division of Cardiovascular Medicine at UVA Health, Virginias only designated Center of Excellence by theHypertrophic Cardiomyopathy Association. Being able to identify scar in the heart, an important contributor to progression to heart failure and sudden cardiac death, without contrast, would be highly significant. Cardiovascular magnetic resonance scans would be done without contrast, saving cost and any risk, albeit low, from the contrast agent.

Hypertrophic cardiomyopathy is the most common inheritable heart disease, and the most common cause of sudden cardiac death in young athletes. It causes the heart muscle to thicken and stiffen, reducing its ability to pump blood and requiring close monitoring by doctors.

The new virtual native enhancement technology will allow doctors to image the heart more often and more quickly, the researchers say. It also may help doctors detect subtle changes in the heart earlier, though more testing is needed to confirm that.

The technology also would benefit patients who are allergic to the contrast agent injected for cardiovascular magnetic resonance scans, as well as patients with severely failing kidneys, a group that avoids the use of the agent.

The new approach works by using artificial intelligence to enhance T1-maps of the heart tissue created by magnetic resonance imaging. These maps are combined with enhanced MRI cines, which are like movies of moving tissue in this case, the beating heart. Overlaying the two types of images creates the artificial virtual native enhancement image.

Based on these inputs, the technology can produce something virtually identical to the traditional contrast-enhanced cardiovascular magnetic resonance heart scans doctors are accustomed to reading only better, the researchers conclude. Avoiding the use of contrast and improving image quality in [cardiovascular magnetic resonance] would only help both patients and physicians down the line, Kramer said.

While the new research examined virtual native enhancements potential in patients with hypertrophic cardiomyopathy, the technologys creators envision it being used for many other heart conditions as well.

While currently validated in the [hypertrophic cardiomyopathy] population, there is a clear pathway to extend the technology to a wider range of myocardial pathologies, they write. [Virtual native enhancement] has enormous potential to significantly improve clinical practice, reduce scan time and costs, and expand the reach of [cardiovascular magnetic resonance] in the near future.

The research team consisted of Qiang Zhang, Matthew K. Burrage, Elena Lukaschuk, Mayooran Shanmuganathan, Iulia A. Popescu, Chrysovalantou Nikolaidou, Rebecca Mills, Konrad Werys, Evan Hann, Ahmet Barutcu, Suleyman D. Polat, HCMR investigators, Michael Salerno, Michael Jerosch-Herold, Raymond Y. Kwong, Hugh C. Watkins, Christopher M. Kramer, Stefan Neubauer, Vanessa M. Ferreira and Stefan K. Piechnik.

Kramer has no financial interests in the research, but some of his collaborators are seeking a patent related to the imaging approach. A full list of disclosures is included in the paper.

The research was made possible by work funded by the British Heart Foundation, grant PG/15/71/31731; the National Institutes of Healths National Heart, Lung and Blood Institute, grant U01HL117006-01A1; the John Fell Oxford University Press Research Fund; and the Oxford BHF Centre of Research Excellence, grant RE/18/3/34214. The research was also supported by British Heart Foundation Clinical Research Training Fellowship FS/19/65/34692, National Institute for Health Research (NIHR) Oxford Biomedical Research Centre at The Oxford University Hospitals NHS Foundation Trust, and the National Institutes of Health.

To keep up with the latest medical research news from UVA, subscribe to theMaking of Medicineblog.

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New Artificial Intelligence Technology Poised to Transform Heart Imaging - University of Virginia