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Artificial intelligence is shaping healthcare in Canada and around the world. The role of AI in delivering care will evolve and, indeed, grow. As a series of tools, it offers opportunities for patients and practitioners; yet, as with any technology, it comes with risks. As with any tool, AI will only be as good as those who create and operate it. Given who we are - and our track record - that ought to give us pause.Understanding, interrogating, and mobilizing new technologies requires care, diligence, and diversity. When it comes to equitable, accessible healthcare, we require heaps and heaps of such considerations. So, in this episode we ask: What does artificial intelligence mean for the future of equitable, accessible healthcare? On this episode of Open to Debate, David Moscrop talks with Dr. Rim Khazal, an expert on AI policy who holds a PhD in Neuroscience from Carleton University.

A.I, what do we think about it? IDK but I'm sure we make zero sense. Let us know what you think @beeroverchatter 

Ben Goertzel is one of the most interesting minds in the artificial intelligence community. He is the founder of SingularityNET, designer of OpenCog AI framework, formerly a director of the Machine Intelligence Research Institute, Chief Scientist of Hanson Robotics, the company that created the Sophia Robot. He has been a central figure in the AGI community for many years, including in the Conference on Artificial General Intelligence. Support this podcast by supporting these sponsors: - Jordan Harbinger Show: https://jordanharbinger.com/lex/ - MasterClass: https://masterclass.com/lex This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this

Brian Thomas, Agency Data Scientist at NASA Headquarters, discusses artificial intelligence.

Darrell West is vice president of the Brookings Institution and director of Governance Studies at Brookings. John Allen is the president of the Brookings Institution and a retired U.S. Marine Corps four-star general. Together, they are the authors of the book, "Turning Point: Policymaking in the Era of Artificial Intelligence," a broad look at the impact that artificial intelligence systems are likely to have on everything from the military, to health care, to vehicles and transportation, and to international great power competition. They joined Benjamin Wittes to discuss the book and the question of how we should govern AI systems. What makes for ethical uses of AI? What makes it scary? What are the anxieties that people have about artificial intelligence and to what extent are the fears legitimate?Support this show http://supporter.acast.com/lawfare. See acast.com/privacy for privacy and opt-out information.

We mark the 20th anniversary of the Iraq War by revisiting some of the classic clips from our cowed and deferential news media circa 2003. PLUS: What is artificial intelligence and what does it mean for the future? Pulitzer-winning columnist Thomas Friedman doesn't know, but that didn't stop him from writing about it.

We mark the 20th anniversary of the Iraq War by revisiting some of the classic clips from our cowed and deferential news media circa 2003. PLUS: What is artificial intelligence and what does it mean for the future? Pulitzer-winning columnist Thomas Friedman doesn't know, but that didn't stop him from writing about it.

We mark the 20th anniversary of the Iraq War by revisiting some of the classic clips from our cowed and deferential news media circa 2003. PLUS: What is artificial intelligence and what does it mean for the future? Pulitzer-winning columnist Thomas Friedman doesn't know, but that didn't stop him from writing about it.

In scholarly publishing, artificial intelligence chatbots (AICs)—advanced conversational programs—are emerging as generative artificial intelligence (AI) tools that can support the peer review process by automating tasks, such as identifying methodological flaws, verifying references, and improving language clarity. These tools hold potential to enhance efficiency, reduce reviewer workload, and address inconsistencies in peer review quality. However, their integration also raises significant challenges, including concerns about reliability, ethical implications, and transparency in decision-making. Peer review, a critical component of scholarly publishing, is already under strain due to increasing demands on reviewers and longstanding issues such as variability in review quality and potential biases. AICs have been proposed as tools to assist and augment the peer review process, for example, by ensuring clarity, completeness, and logical stringency of content. However, little is known about how peer reviewers perceive their role, utility, and potential impact. This study will conduct an international cross-sectional survey to assess peer reviewers’ attitudes and perceptions towards AICs in the peer review process. The survey aims to explore reviewers’ familiarity with AI, perceived benefits and challenges, ethical concerns, and anticipated future roles for AICs in peer review. Findings will provide insights to guide the development of policies and best practices for the ethical and effective integration of AICs in peer review.

In this Fully-Connected episode, Chris and Daniel explore the geopolitics, economics, and power-brokering of artificial intelligence. What does control of AI mean for nations, corporations, and universities? What does control or access to AI mean for conflict and autonomy? The world is changing rapidly, and the rate of change is accelerating. Daniel and Chris look behind the curtain in the halls of power.

Peter Norvig is a research director at Google and the co-author with Stuart Russell of the book Artificial Intelligence: A Modern Approach that educated and inspired a whole generation of researchers including myself to get into the field. This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on iTunes or support it on Patreon. Here's the outline

Artificial intelligence improvisation (AI improv) is an innovative form of improvised theatre that involves at least one human actor performing lines provided by an artificial intelligence (AI) chatbot (Lösel et al., 2020). While methods of incorporating chatbots into improvised theatre have been tested and refined for several years, this study aims to investigate whether watching AI improv can change people's impressions of chatbots and AI in general. To assess this, among other scales, we will use the recently developed ATTARI-12 (Stein et al., 2024): A unidimensional measure of general attitudes towards AI. One group of participants (group A) will be presented with a video of an AI improv scene. The effect of this intervention on participants’ impressions of AI (measured pre- and post-intervention) will be compared to the effect of watching a video of a generic improv scene (group B) and the effect of reading an AI-related article (group C). Based on results from a pilot survey, we expect our main intervention to generally have a positive impact on participants' attitudes towards AI, such as increasing their intention to use chatbots if possible.

On today's show, CEO of PORTpass Zak Hussein and Ritesh Kotak, a cybersecurity tech analyst join the show to talk about privacy and security concerns surrounding the private vaccine verification app Portpass. We discuss why there's a need for concrete protections from artificial intelligence threatening human rights with Sarit Mizrahi, a Ph.D. law candidate at the University of Ottawa. Plus, what can the world learn from the Canada-China prisoner swap? We hear from David Webster, an associate professor of history at Bishop's University.Learn more about your ad choices. Visit megaphone.fm/adchoices

AI-based recommendation and classification systems, especially those relying on data-intensive machine learning methods, are becoming increasingly important in various fields of application. These include various applications in the medical field – from diagnostic support to therapy recommendations. Especially in critical areas such as medicine, human control and supervision are among the key requirements for trustworthy AI systems. In order for human experts to be able to assess and evaluate AI-generated recommendations and classifications, it must be possible to trace the information on which an AI system arrives at a particular output. Hybrid human-AI teams offer the possibility for better decisions to be made in partnership than by humans or AI systems alone. For this to succeed, AI systems must be designed in such a way that people have a certain amount of trust in them on the one hand, but also critically reflect on the output of these systems on the other. This might also be affected by human's knowledge on AI-models and classification systems. The goal of this experiment is to assess the effect of explanations and the possibility of correction on trust and performance in the context of multi-class classification systems - while considering the covariate of AI-expertise.

AI-based recommendation and classification systems, especially those relying on data-intensive machine learning methods, are becoming increasingly important in various fields of application. These include various applications in the medical field – from diagnostic support to therapy recommendations. Especially in critical areas such as medicine, human control and supervision are among the key requirements for trustworthy AI systems. In order for human experts to be able to assess and evaluate AI-generated recommendations and classifications, it must be possible to trace the information on which an AI system arrives at a particular output. Hybrid human-AI teams offer the possibility for better decisions to be made in partnership than by humans or AI systems alone. For this to succeed, AI systems must be designed in such a way that people have a certain amount of trust in them on the one hand, but also critically reflect on the output of these systems on the other. This might also be affected by human's knowledge on AI-models and classification systems. The goal of this experiment is to assess the effect of explanations and the possibility of correction on trust and performance in the context of multi-class classification systems - while considering the covariate of AI-expertise.

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This study aims to systematically review the literature on the application of AI in RDM in university libraries.

Human flourishing, the optimal state of well-being, is essential for all. Over the past 20 years, substantial research has been devoted to understanding human flourishing. As the field of artificial intelligence rapidly advances, researchers argue that the existence of sentient AIs is only a matter of time and deliberate intent. Just as flourishing is important for humans, we propose that it should also be considered important for AIs. We are the first to explore the concept of AI flourishing. In this pioneering study, we aim to explore what AI flourishing might entail through a two-part design: In Part 1, we will prompt various state-of-the-art Large Language Models (LLMs) to describe what flourishing means to them as LLMs. Using their qualitative responses, we will conduct a thematic analysis to identify key themes and develop a preliminary AI flourishing framework. In Part 2, we will present the LLMs with the themes identified in Part 1 and ask them to rank the importance of each theme. We will compare this framework with existing human flourishing models to examine how LLMs conceptualize flourishing relative to humans. We will also compare the importance of each flourishing aspect with human priorities in flourishing. Drawing on the findings from both parts, we will discuss how human and AI flourishing overlap (likely reflecting the influence of human data on LLM training) and how they diverge, offering a novel theoretical contribution to the understanding of flourishing beyond the human context.

Pain management in non-communicative end-stage patients represents one of the greatest ethical and clinical challenges within palliative care, due to the multidimensional complexity of pain and the impossibility of resorting to verbal self-reports. Traditional tools, such as behavioral scales, have provided partial solutions, but still face limitations related to subjectivity and the workload of clinical staff. In this context, artificial intelligence (AI) emerges as a promising tool to improve pain assessment in this vulnerable population. Its ability to analyze multimodal patterns in real time-including physiological, behavioral, and neurological signals-offers opportunities for more objective and accurate assessment of suffering. This project aims to map the available scientific evidence on the use of AI technologies in the assessment of pain in non-communicative adult terminally ill patients, in order to identify advances, limitations, ethical implications and opportunities for implementation in palliative clinical settings.

A backup of the "Taxonomy of Artificial Intelligence" wiki (database name: aitaxonomywiki ) on Miraheze , self-generated by Miraheze itself every month to honor our backups commitment.

This research project aims to explore how Artificial Intelligence (AI) and Machine Learning (ML) models are currently being applied to social media platforms for the prevention of substance use. The rapid expansion of social media has created a vast digital environment where substance-related content, peer influence, and behavioral patterns can be monitored and analyzed. AI/ML technologies have the potential to detect substance use trends, identify at-risk individuals, and optimize intervention strategies in real time. The primary objectives of this study are to (1) examine existing AI/ML applications in substance use prevention through social media analysis, (2) highlight challenges and opportunities associated with integrating AI/ML in digital public health initiatives. This research will synthesize evidence from recent studies, government reports, and AI-driven intervention programs to provide a comprehensive assessment of current advancements in the field. The expected outcomes include an in-depth understanding of how AI/ML models enhance prevention efforts, insights into ethical and privacy considerations, and recommendations for future AI-driven public health strategies. This study will contribute to the growing body of knowledge in digital health interventions and inform policymakers, healthcare professionals, and technology developers on best practice for leveraging AI/ML to curb substance use through social media platforms.

0:00:00 - Opening 0:04:56 - Working out EVERY DAY? 0:27:31 - What's better for a 7 y/o boy? Boxing, or Jiu Jitsu? And Why? 0:37:12 - How to prepare for Podcasts. Focus for Max Benefit. 0:46:30 - Inauthentic Extreme Ownership and Excessive Self-Blaming. 0:54:04 - Why Literature is good degree to pursue, and the importance of Language. 1:07:00 - Future Robots Soldiers in the Future. The Threat Artificial Intelligence. 1:12:56 - Effectiveness of Jocko's Favorite Submission Holds (Jiu Jitsu). 1:15:04 - Dealing with Push-Back from your team early in the plan. 1:22:28 - Opinions on leadership training West Point and other service Academies. 1:27:39 - Advantages and disadvantages of body types in Special Operations Forces. 1:30:26 - Dealing with emotions and being "hangry" when fasting. 1:44:16 - Difficulty learning Jiu Jitsu VS learning Wrestling. 2:05:42 - Loyalty in marriage to another Service Member. Loyalty to service vs. loyalty at home. 2:15:43 - Specific places and times to reflect on fallen comrades. 2:22:17 - Support, Cool Onnit , Amazon , JockoStore stuff, with Jocko White Tea and Psychological Warfare (on iTunes) . Extreme Ownership (book) , (Jocko's Kids' Book) Way of the Warrior Kid , and The Muster002 2:44:45 - Closing Gratitude. Support this podcast at - https://redcircle.com/jocko-podcast/exclusive-content

Given the heterogeneity of recommendations and the accelerating integration of AI into clinical care, a scoping review is needed to collate, categorize, and quantify the competencies that have been proposed for medical students. Unlike narrative reviews, a scoping review provides a transparent and reproducible methodology to ensure comprehensive coverage of the literature, minimize bias, and enable meaningful comparison of findings across sources. By applying constant-comparison coding and prevalence analysis, this review will identify where the literature converges on core AI competencies, where new skills are emerging, and where outlier proposals exist.

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Originally uploaded on June 12, 2023.

Originally uploaded on June 12, 2023.

Originally uploaded on June 12, 2023.

Interference on the Global Positioning System (GPS) infrastructure poses a threat to the nation's security and economy as systems become more dependent on the technology. The pervasiveness of GPS interference methods such as jamming and spoofing present multiple opportunities for adversaries to infiltrate and inject false data on systems as diverse as military, banking, shipping, ecommerce, transportation and other critical economic sectors. The study of GPS spoofing detection methods requires innovative and novel schemes to meet the challenge posed. With the increasing processing power of computer systems, artificial intelligence methods have become a prime candidate for application to the detection and reporting of these cyber threats. This thesis studied the application of machine learning and data analytics to identify false data injection attempts on military GPS. The study combined live and simulated GPS message traffic data to train and test machine learning algorithms to identify the threats. Applying both unsupervised and supervised learning methods to the dataset helped advance the study of the GPS spoofing problem and proved to be effective tools to monitor GPS traffic while serving as another layer of security to the GPS infrastructure.

24cm.xiv,479

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.

The military has recognized the need for automated decision aids to support battle management as warfighters become overwhelmed by shorter decision cycles, greater amounts of data, and more technology systems to manage. To date, much emphasis has focused on data acquisition, data fusion, and data analytics for gaining situational awareness in the battle space. However, a new frontier and opportunity exists for using this data to develop decision options and predict the consequences of military courses of action. This project studied the application of artificial intelligence (AI) to improve battle management decisions in the time-sensitive air and missile defense (AMD) environment. Specifically, this project studied current and future AI applications to the AMD kill chain with a model-based systems engineering (MBSE) approach. The team modeled the AMD kill chain by allocating time to the various kill chain functions and decisions, based on the time afforded by the incoming AMD threat. The team used simulations to analyze and demonstrate the use of automation in the kill chain functions to expedite decisions and improve AMD battle management.