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Machine learning systems have gotten extremely good at generating stock imagery from just a few bits of text. AI tools like Open AIs DALL-E have quickly become a favorite among artists, allowing them to generate extremely specific and surreal images by typing things like cats playing chess in space or shrimp sitting on a park bench contemplating life.

Artist David OReilly took this even further, using the generative systems to create an entire animated childrens cartoon called Bartak. The result is a storybook-esque nightmare world that feels like the machine learning equivalent of being lobotomized. Characters faces melt into digital oblivion while a chipper AI-generated voice narrates the story in an extremely unsettling non-language that sounds like a Disney Channel host speaking in tongues.

OReilly, a 3D artist who is well-known for creating these kinds of disturbing animations, describes the short as a sneak peak of a series that uses the awesome power of AI to create the perfect kids entertainment. In an Instagram post, OReilly claims that a full season order of 75,000 episodes is now being generatedwhich may or may not be true, given his track record of unsettling one-off provocations. (OReilly could not be reached for comment)

DALL-E and other natural language processing systems are known for their ability to generate uncannily accurate results. Previous systems like GPT-3, which is frequently used by chatbots, have been used to create AI dungeon text adventures and even occult rituals that feel disturbing realisticso much so that its often difficult to distinguish the systems output from a real human.

Researchers have found that these systems are also prone to generating results that reproduce racist and sexist stereotypes. In an analysis of DALL-E, Open AIs researchers found that typing things like CEO would exclusively generate images of white men, while typing nurse would produce images of Southeast Asian women.

As a weird art project, OReillys use of the tool seems relatively benign, however. And his fans seem to be in on the joke.

Its really inspiring to see how well Bartak has helped my kids understand the world around them, and taught me how to be a better parent! writes one Instagram commenter of the extremely cursed cartoon. My kids are so much smarter as a result. You want to see the excitement in their eyes, especially at the hands of a show like this.

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AI Created This Extremely Cursed Children's Cartoon - VICE

Advanced technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have become a cornerstone of successful modern clinical trials, integrated into many of the technologies enabling the transformation of clinical development.

The health and life sciences industrys dramatic leap forward into the digital age in recent years has been a game-changer with innovations and scientific breakthroughs that are improving patient outcomes and population health. Consequently, embracing digital transformation is no longer an option but an industry standard. Lets explore what that truly means for clinical development.

An accelerated path to better results

Over the years, technology has equipped clinical leaders to successfully reduce costs while accelerating stages of research and development. These technologies have aided in the structurization of complex data environmentsa need created by the exponential growth in data sources containing valuable information for clinical research.

Today, the volume, variety and velocity of structured and unstructured data generated by clinical trials are outpacing traditional data management processes. The reality is that there is simply too much data coming from too many sources to be manageable by human teams alone. As a response to this, AI/ML technologies have proven in recent years to hold the remarkable potential to automate data standardization while ensuring quality control, in turn easing the burden on researchers with minimal manual intervention.

Once the collection and streamlining of data is compiled within a single automated ecosystem, clinical trial leaders begin to benefit from faster and smarter insights driven by the application of machine analysis. These include the creation of predictive and prescriptive insights that can aid researchers and sites to uncover best practices for future processes. Altogether, these capabilities can improve research outcomes, patients experience and safety.

A look into compliance and privacy

When we think about the use of patient data, privacy and compliance adherence must be a consideration. The bar is set high for any technology being implemented into clinical trial execution.

Efforts must adhere to Good Clinical Practice (GcP) and validation requirements that ensure an outcome is valid by it being predictable and repeatable. Additionally, there must be transparency and explainability around how any AI algorithm makes decisions to prove correctness and avoidance of any potential bias. This is becoming more essential than ever from a compliance perspective as regulators look at algorithms as part of what they base their approvals on.

Keeping the h(uman) in healthcare

The goal of implementing AI/ML in clinical research is not to replace humans with digital tools but to increase their productivity through high-efficiency human augmentation and the automation of mundane tasks. Before the application of advanced technologies to clinical trials, there was an unmet need for an agile methodology where researchers and organizers could solely focus on critical requirements and the delivery of results.

The intelligent application of technology allows for human interaction with AI models to bring better outcomes to research, and even in its most advanced stage, data science technology never replaces the human data scientist. It does, however, provide a mutually beneficial circumstance wherein the augmentation of workflows allows data scientists to ease data burden while AI models flourish through human feedback. This continuous learning by an AI model is known as Continuous Integration/Continuous Delivery (CI/CD).

The integration of human capacity and technology results in accelerated efficiency, improved compliance and superb patient personalization. Furthermore, regardless of how efficient algorithms become, the decision-making power will always belong to humans.

Envisioning a bold future

AI/ML strategies are redefining the clinical development cycle like never beforeand as the industry leaps into new frontiers, digital transformation is leading the way to incredible advancements that will revolutionize the space forever. Leaders today have the opportunity to apply advanced technologies to solve historically complicated problems in the field.

Already, weve seen better site selection, more effective risk-based quality management, improved patient monitoring and safety, enhanced patient recruitment and engagement, and improved overall study qualityand this is just the beginning.

Photo: Blue Planet Studio, Getty Images

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The role of AI and machine learning in revolutionizing clinical research - MedCity News

Creating a machine learning agent to ease interactions between participants in virtual and augmented reality environments such as the metaverse in order to help them achieve their goals and address conflict situations. This is the main objective ofGuestXR, the European project under the coordination of professor Mel Slater, from theFaculty of Phycologyand the Institute of Neurosciences of the University of Barcelona UB (UBNeuro).

As part of the GuestXR project (A Machine Learning Agent for Social Harmony in eXtended Reality), coordinated by the Eurecat technological centre, a machine learning system called guest will be designed, and it will work as an agent able to examine the individual and group behaviour of the participants by drawing on existing models from the neuroscience and social psychology standpoint. Moreover, Deep Reinforcement Learning will train the Guest to learn over time both in simulations and real meetings and it will be more efficient in social interaction, informs UB in a press release.

In these lines, the agent will take part in the conversation in different ways, performing a range of multisensory actions through music or changes in space, among others, notes the director of Eurecat Audiovisual Technologies, Adan Garriga.

Each meeting in virtual and augmented reality has a purpose, be it explicit (for instance, reaching an agreement on a topic) or implicit (such as enjoyment). The basic idea of the guest is to help the group of people to accomplish this purpose. This involves an exciting research with a strong interdisciplinary element, which opens new paths, and hopefully, contributes to the success of virtual and augmented reality, notes Professor Mel Slater.

In order to modulate this social interaction, the guest will carry out a series of multisensory actions, through, for instance, visual and auditory features to create specific states of mind and stimulate a relaxed environment when it identifies a conflict, highlights researcher Umut Saying, from Eurecat Audiovisual Technologies.

This project is designed to help offering solutions to the existing challenges in online environments of social interaction such as online harassment, for which there isnt a specific European legislation, says the coordinator of the Eurecat GuestXR project, Aurora Ses.

The intervention of the machine learning system will initially be tested in conflict resolution situations, interactions with participants with hearing difficulties and contexts that lead to polarized debates such as climate change.

Two open calls to include other innovative use cases to test the effectiveness of the system are expected. One case will be aimed at society in general including companies and associations, among others while the other will aimed at the field of arts.

The GuestXR technology will be carried out under the Ethics by design approach, which entails considering the potential ethical problems derived from the use of artificial intelligence (respect for human autonomy, prevention of harm, fairness, etc.).

The GuestXR consortium, coordinated by the UB and the Eurecat technology centre, is formed by eight organizations from six countries with multidisciplinary teams of the field of the expanded reality, machine learning, artificial intelligence, social psychology, neuroscience of emotions, multisensory integration, ethics of research and technology transfer. Among the collaborators are the University of Maastricht (the Netherlands), the University of Warsaw (Poland), the University of Reichman (Israel), the National Institute for Research in Digital Science and Technology (INRIA, France), and the companies Virtual Bodyworks and G.tec medical engineering GMBH.

Building the metaverse in Europe: open source collaboration platforms gain momentum

After Mark Zuckerbergs public announcement that Facebook is rebranding itself as Meta, the metaverse has once again become a buzzword.

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Machine learning tool improves conflict management in the metaverse - Innovation Origins

Research and innovation in machine learning in the mining industry operations and technologies sector has declined in the last quarter but remains higher than it was a year ago.

The most recent figures show that the number of related patent applications in the industry stood at 16 in the three months ending March up from 10 over the same period in 2021.

Figures for patent grants related to followed a different pattern to filings stagnating from one in the three months ending March 2021 to one in the same period in 2022.

The figures are compiled by GlobalData, which tracks patent filings and grants from official offices around the world. Using textual analysis, as well as official patent classifications, these patents are grouped into key thematic areas and linked to key companies across various industries.

Machine learning is one of the key areas tracked by GlobalData. It has been identified as being a key disruptive force facing companies in the coming years, and is one of the areas that companies investing resources in now are expected to reap rewards from. The figures also provide an insight into the largest innovators in the sector.

Honeywell International was the top innovator in the mining industry operations and technologies sector in the latest quarter. The company, which has its headquarters in the US, filed nine related patents in the three months ending March. That was up from three over the same period in 2021.

It was followed by the US-based Caterpillar with six patent applications, and Japan-based Komatsu (1 applications).

Innovative Industrial Technologies

Process Instrumentation Systems and Bulk Solids Sensors for the Mining Sector

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Machine learning innovation among mining industry companies dropped off in the last quarter - Mining Technology

Cancer is arguably the greatest health challenge of our time. During the past 50 years, clinical advances have substantially reduced the mortality rate for people with cancer, but new breakthroughs often require years of trial and error in the lab.

An innovative partnership between The University of Texas at Austins Machine Learning Lab, Oden Institute for Computational Engineering and Sciences and Dell Medical School aims to speed up those discoveries, saving lives in the process. What would have previously taken years in the lab can potentially be accomplished in days with the appropriate computing simulations.

The research collaboration is possible because of a $10 million leadership gift from Dheeraj and Swapna Pandey.

The biggest promise of computational oncology is personalized medicine, Dheeraj Pandey said. The ability for us to answer questions that save precious lives. More importantly, the field is attempting to break silos between physics, biology, and computing researchers who are fighting indefatigably against cancer.

UT researchers will integrate two emerging disciplines computational oncology and machine learning to transform the future of cancer care. Machine learning applies algorithms to large data sets to build classifiers that can make accurate predictions, even in complex biological and chemical domains. Computational oncology uses physics-based and data-driven advanced mathematical and computational approaches to model tumors, calibrate patient-specific models, and simulate patient responses to potential treatment options.

Modeling and simulation occur across a spectrum of scales, from the cellular level to the organ level of the human body. The models can be theory-driven, knowledge-driven, or data-driven. Or, increasingly, a combination of all three. Substantial computational skills and capabilities, as well as medical knowledge, are required to capture the individuality of each cancer patients situation for accurate decision making at all levels.

UT Austin has a unique environment that enables the interdisciplinary research critical to tackling societal grand challenges such as personalized care for cancer patients, said Karen Willcox, director of the Oden Institute. We are thrilled to build a new partnership with the Machine Learning Lab, building on the Oden Institutes strength in computational oncology and our existing partnerships with Dell Med, MD Anderson Cancer Center and the Texas Advanced Computing Center. Computational medicine is a top priority for the Oden Institute, and the generosity of the Pandey family is a game changer in taking our efforts to a new level.

The Oden Institute and its Center for Computational Oncology sit at the forefront of developing mechanism-based modeling techniques that optimize treatment and outcomes for an individual patient. The Machine Learning Laboratory is the universitys headquarters for machine learning and artificial intelligence.

A new wave of machine learning is creating predictive models that are transforming science, said Adam Klivans, director of the Machine Learning Lab and NSF-funded Institute for Foundations of Machine Learning. Our technologies can anticipate new biological and chemical interactions to advance the automated discovery of new treatments.

Currently, cancer biologists and chemists rely on trial and error to determine what treatments will be most effective. Connecting university research with community providers is central to the mission of Dell Med. Through initiatives such as the Livestrong Cancer Institutes, Dell Med translates leading-edge research into high-quality clinical trials and patient-focused precision medicine.

Time is critical when treating cancer, said Gail Eckhardt, director of the Livestrong Cancer Institutes at Dell Med. The Pandeys gift brings us that much closer to the day when clinicians and researchers can integrate patient data and computational methods to individualize therapy, thereby improving the lives of patients with cancer.

Computational approaches are the key to accelerating progress against cancer, said David Jaffray, chief technology and digital officer at The University of Texas MD Anderson Cancer Center. This investment will further the collaborative, team science approach we have developed with the leadership at UT Austin. Together, we are building a critical mass of talent to use the power of data and computing to make real progress against this terrible disease.

Read the feature story to learn more about this partnership.

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UT Researchers Aim to Change the Cancer Equation - UT News - University of Texas