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Courtesy of Khoa Luu

One of the presentations at CVPR 2023

The annual conference on Computer Vision and Pattern Recognition, CVPR 2023, in Vancouver, Canada, attracted global attention tovarious uses of artificial intelligence, drawing over 1,000 attendees from more than 75 countries.

Khoa Luu, assistant professor in the Department of Electrical Engineering and Computer Science and area chair of the main conference, said, "This is one of the frontier AI conferences. This is why it attracts a lot of tech companies like Google, Amazon, Facebook, Microsoft, et cetera," he said. "These companies see emerging technologies and next generation AI products within this conference and attract a lot of research and scientists."

The conference received over 9,000 submissions, with 2,359 papers ultimately accepted for presentation. The U of A had a notable presence at CVPR 2023. Its doctoral students showcased their work at the main conference and in several workshops.

Naga VS Raviteja Chappa, a Ph.D. student, achieved third place at the best paper competition of the ninth International Workshop on Computer Vision in Sports. Xuan-Bac Nguyen, also a Ph.D. student, received the prestigious best reviewer award at the main conference. These students also served as panelists to review other submissions to the main conference.

Luu expressed his commitment to encouraging student participation in conferences.

"I do my best to secure grants and encourage graduate students to attend this conference," Luu said. "I do this so they can learn how to become professional researchers in the future. This conference gives them the opportunity to meet corporate attendees, communicate and interact with professionals in the industry. Our students often just stay in the computer lab creating code during their academic careers. They need to reach out and, you know, see and feel the beautiful insights and desires within the industry. It is more than just coding or being a robot machine. They need to see how things are applied and practiced. They need to learn how to present in a professional way and how to communicate with other people. I think this is a critical skill for a graduate student. They can learn this from the conference, and they must learn that they cannot just stay in the lab."

To facilitate student participation, various grants and sponsorships played a pivotal role. Luu expressed his gratitude to the sponsors, including the U of A, for supporting the students' research and enabling their enriching experiences at CVPR 2023.

Not only did Luu serve as the area chair for the main conference, he also was the co-organizer of the CVPR 2023 Precognition Workshop. He did this in collaboration with Aurora Innovation Inc., Google Research, Carnegie Mellon University, University of Houston and HKUST (Guangzhou).

Luu would like to give special thanks to all those who collaborated and sponsors:

Computer Vision and Pattern Recognition 2024 will be held June 17-21, 2024, at the Seattle Convention Center.

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Engineering Professor Helps Head Up Successful 'Frontier' Artificial ... - University of Arkansas Newswire

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Scientific discovery in the age of artificial intelligence - Nature.com

In a recent Blue Jays baseball game, I observed a concerning trend: multiple obvious balls were being called as strikes. This had a detrimental effect on the game, as the confused batters were swinging at pitches that were clearly outside the strike zone, resulting in additional strikes and ultimately leading to their quick outs.

This issue highlights the need for accurate determination of the crux or essence of the game. Fortunately, artificial intelligence (AI) has the potential to fulfill this role. Surprisingly, AI technology already exists but is currently unused in the context of baseball.

By harnessing the power of AI, the game could benefit from precise and unbiased assessments of whether a pitch is a ball or a strike. AI algorithms could analyze the trajectory and location of each pitch, taking into account the individual batters strike zone. This would eliminate the subjective human element and ensure consistency in the game.

Moreover, AI could enhance other aspects of baseball as well. It could be used to accurately determine if a runner is safe or out, reducing the uncertainty and controversy surrounding close calls. Additionally, AI could aid in the analysis of player performance, providing valuable insights for coaches and strategists.

Implementing AI technology in baseball would require a collaborative effort from baseball organizations, technology developers, and governing bodies. Embracing AI could revolutionize the sport, making it fairer and more objective.

In conclusion, the use of artificial intelligence in baseball has the potential to address the issue of incorrect calls and improve the overall fairness and accuracy of the game. With the existing technology just waiting to be utilized, it is time for baseball to tap into the power of AI and embrace its benefits.

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The Role of Artificial Intelligence in Baseball - Fagen wasanni

Artificial Intelligence (AI) is a rapidly evolving technology that has the potential to disrupt and enhance various industries. It is changing the way we work, learn, and operate businesses. Mobile applications are utilizing AI to intelligently search for solutions and provide expanded possibilities.

Simply put, AI combines computer science and robust datasets to enable problem-solving. It includes sub-fields such as machine learning and deep learning. AI has already been applied in various ways, from voice or language recognition in mobile apps to facial recognition software used by law enforcement agencies.

According to Nicole Alexander, the Head of Global Marketing at Meta and a Professor of Marketing and Technology at NYU, technology permeates every element of our society. It is AI technology that is becoming increasingly integrated into our everyday lives. For example, mobile apps can predict how individuals or their children may look in the future, and tasks previously performed by humans are now computerized with AI.

While advocating for the cautions and protections required in dealing with this revolutionizing era of AI, Alexander emphasizes the importance of governance, responsibility, and diverse training sets to prevent harm and maximize AI benefits. As an ecosystem develops around AI and rules are explored, businesses should tap into what AI can do for them. Small and large companies need to develop responsible and ethical AI systems, considering marginalized communities.

AI is not only a playground for tech entrepreneurs but also a growing conversation in government. It has positive implications for healthcare systems, urban planning, and the needs of communities. Alexander prepares graduate students to understand the positive effects of AI and urges executives to embrace their role and responsibility in decision-making. By understanding the underlying effects of AI, leaders can develop AI systems that align with their organizations values and communicate effectively with new employees.

In conclusion, AI is transforming various aspects of our lives and businesses. As it continues to evolve, it is crucial to prioritize responsible and ethical AI development, while considering marginalized communities and the impact on society as a whole.

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The Role of Artificial Intelligence in Everyday Life and Business - Fagen wasanni

Contributor: Sergio Vasquez Bronfman, Associate Professor of Digital Transformation - ESCP Business School. | Published: 7 August 2023

Sergio Vasquez Bronfman, Associate Professor of Digital Transformation - ESCP Business School. 3 August 2023

Since the early years of artificial intelligence (AI), several examples have shown the risks of an inappropriate use of it.

First, there is ELIZA, the first conversational robot developed by professor Joseph Weizenbaum at the MIT in the late 60s. This artificial intelligence program simulated a session with a psychiatrist. Weizenbaum introduced this program to some psychiatrists and psychoanalysts in order to show that a machine could not really imitate a human being. He was surprised when he saw many of them delighted to see ELIZA working as if it were a real psychiatrist, and even promote its use to develop psychiatry and psychoanalysis on a large scale and at low cost. Weizenbaum reacted by calling on psychiatrists and psychoanalysts: How can you imagine for a moment to delegate something as intimate as a session with one of you to a machine?

A second example is the Soviet false nuclear alarm of September 1983, when their computerized missile warning system reported four nuclear missile launches from the USA. As the number of missiles detected was very small, the Soviet officer on duty at the time disobeyed procedure and told his superiors that he thought it was a false alarm (normally, a nuclear attack would involve dozens or even hundreds of nuclear missiles). Fortunately, his advice was followed, preventing a Soviet retaliation that could have been the start of a nuclear war between the Communist countries and the free world. It was later established that the false alarm had been created by a misinterpretation of the data by the Soviet artificial intelligence software.

Finally, we can refer to the case of Eric Loomis, a repeat offender with a criminal record, sentenced to 6 years in prison by the Supreme Court of the State of Wisconsin (in the USA), in 2017. This conviction was based at least in part on the recommendation of an AI-based software program called Compas, which is marketed and sold to the courts. The program is one incarnation of a new trend in artificial intelligence: one that aims to help judges make better decisions. Loomis later claimed that his right to a fair trial had been violated because neither he nor his lawyers had been able to examine or challenge the algorithm behind the recommendation.

These examples (and many others) gave rise to important political and ethical debates since at least the 1970s, about which tasks we should delegate to AI and which we should not, even if it is technologically possible. Already important at that time, these issues have come back even more strongly with the new wave of AI, based on neural networks and deep learning, which has led to amazing results, the latest example being ChatGPT and other products of generative AI. There are essentially two main approaches: on the one hand, there is the ethics that should be injected into AI programs, and on the other, the ethics of the use of artificial intelligence, i.e. the tasks that can be delegated to it.

As for the first alternative, several examples show that AI systems can lead to biased results because the data on which they work are biased. It would then be enough to correct these biases for AI to work properly. But the problem is much more complex than that, because data does not capture everything about most real problems. Data is a proxy of reality, which usually is much more complex. In particular, data cannot capture the current and future context. The limitations of teaching an algorithm to understand right and wrong should warn against overconfidence in our ability to train them to behave ethically. We can go even further and say that machines, because they are machines, will never behave ethically because they cannot imagine what a good life would be and what it would take to live it. They will never be able to behave morally per se because they cannot distinguish between good and evil.

In a seminal book, Computer Power and Human Reason, Joseph Weizenbaum poses an essential question: are there ideas that will never be understood by a machine because they are related to goals that are inappropriate for machines? This question is essential because it goes to the core of the existence (or not) of a fundamental difference between human beings and machines. Weizenbaum argues that the comprehension of humans and machines is of a different nature. Human comprehension is based on the fact of having a brain, but also a body and a nervous system, and of being social animals, something a machine will never be (even if social robotics is undergoing significant development nowadays, something that Weizenbaum imagined nearly 50 years ago). The basis on which humans make decisions is totally different from that of AI. The key point is not whether computers will be able to make decisions on justice, or high-level political and military decisions, because they probably will be able to. The point is that computers should not be entrusted to perform these tasks because they would necessarily be made on a basis that no human being could accept, i.e. only on a calculation basis. Therefore, these issues cannot be addressed by questions that start with can we? The limits we must place on the use of computers can only be stated in terms of should we?

The fundamental ethical issue of AI thus seems to us to be the transfer of responsibility from the human being to the machine (I didnt kill her, it was the autonomous car!, I didnt press the nuclear button, it was the artificial intelligence!). Even if in the European Union the GDPR (General Data Protection Regulation) prevents decisions about humans from being made by a computer, we know how things are in justice administrations and HR departments: people are always overwhelmed, and they will not take the time to discuss the advice given by AI (Nothing personal, Bob; we just asked the AI and it said that you should be fired. But we made the decision!). The facts described and analysed here show that since we currently dont know how to make computers wise, we should not delegate them tasks that require wisdom. Rather than trying to teach algorithms to behave ethically, the real issue is: Who is responsible here?

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Which tasks shouldn't we delegate to artificial Intelligence? | theHRD - The HR Director Magazine