Neural Information Processing Systems http://nips.cc/

In next-generation communications and networks, machine learning (ML) models are expected to deliver not only accurate predictions but also well-calibrated confidence scores that reflect the true likelihood of correct decisions. This paper studies the calibration performance of an ML-based outage predictor within a single-user, multi-resource allocation framework. We first establish key theoretical properties of this system's outage probability (OP) under perfect calibration. Importantly, we show that as the number of resources grows, the OP of a perfectly calibrated predictor approaches the expected output conditioned on it being below the classification threshold. In contrast, when only one resource is available, the system's OP equals the model's overall expected output. We then derive the OP conditions for a perfectly calibrated predictor. These findings guide the choice of the classification threshold to achieve a desired OP, helping system designers meet specific reliability requirements. We also demonstrate that post-processing calibration cannot improve the system's minimum achievable OP, as it does not introduce new information about future channel states. Additionally, we show that well-calibrated models are part of a broader class of predictors that necessarily improve OP. In particular, we establish a monotonicity condition that the accuracy-confidence function must satisfy for such improvement to occur. To demonstrate these theoretical properties, we conduct a rigorous simulation-based analysis using post-processing calibration techniques: Platt scaling and isotonic regression. As part of this framework, the predictor is trained using an outage loss function specifically designed for this system. Furthermore, this analysis is performed on Rayleigh fading channels with temporal correlation captured by Clarke's 2D model, which accounts for receiver mobility.

The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: 'Examine policies and guidelines', 'Think about social impacts', 'Harness understanding of the technology', 'Indicate use', 'Critically engage with outputs', 'Access secure versions', and'Look at user agreements'. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI's benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies.

Set in undetermined future society where robotics are a major part of everyday life, from performing household chores to construction and more, Runaway follows Officer Jack Ramsay (Tom Selleck), head of the so-called'Runaway Squad'. His hi-tech unit deals with out of control robots, intervening where humans may be endangered by the machines' malfunctions. When Ramsay discovers a plot by criminal genius Luther (Gene Simmons) to sell advanced microchips and cutting edge weaponry to the highest bidder, he finds himself taking on not only Luther, but also the dangerous killer's deadly robotic creations. Written and directed by the late Michael Crichton, Runaway continues the theme of the dangers of technology affecting the lives of humans that had been previously visited in his previous works Westworld and Looker. Reviewing Runaway now, thirty years after it's original release, is a real eye opener to Crichton's foresight.

This paper demonstrates our vision for knowledge graphs that assist machines to reason about the cause of signals observed by sensors. We show how the approach allows for constructing smarter surveillance systems that reason about the most likely cause (e.g., an attacker breaking a window) of a signal rather than acting directly on the received signal without consideration for how it was produced.

Reid Simmons has stopped trying to guess what students will come up with next. As head of Carnegie Mellon University's undergraduate program in artificial intelligence, Simmons watches what some of the most creative minds are doing with AI, and they never cease to amaze him. And now as the director of the newly opened JPMorgan Chase & Co. AI Maker Space, Simmons will have a front row seat for collaborative and transformative developments. "We want students from all over the university -- from engineering, business and fine arts -- to come and use their creativity to make interesting things happen," Simmons said. "Giving students the freedom to let their imaginations run wild is really what this space is all about."

Reid Simmons has stopped trying to guess what students will come up with next. As head of Carnegie Mellon University's undergraduate program in artificial intelligence, Simmons watches what some of the most creative minds are doing with AI, and they never cease to amaze him. And now as the director of the newly opened JPMorgan Chase & Co. AI Maker Space, Simmons will have a front row seat for collaborative and transformative developments. "We want students from all over the university -- from engineering, business and fine arts -- to come and use their creativity to make interesting things happen," Simmons said. "Giving students the freedom to let their imaginations run wild is really what this space is all about."

When Southern Cross Austereo (SCA) become an early-stage investor in Melbourne-based Sonnant, Which-50.com Surprisingly, Sonnant CEO Tony Simmons responded by offering a demonstration to explain how it all worked. Sonnant styles itself as a "transformational artificial intelligence (AI) and machine learning (ML) company that provides content discovery for the spoken word". Simmons explained that the key to Sonnant's success was their initial decision to train the AI to understand the Australian accent in phase one. Anyone who's tried to make a booking at a restaurant while in America will know what I mean." Australian English is most associated with monophthongs (single vowels), where there are approximately 20 distinct sounds compared to American English, with only 16 sounds. Also difficult for the AI are Australian diphthongs, the timing between two vowel sounds and the tendency for a falling second sound. An accurate transcript of an analogue recording is necessary to map the ...

As students across the US return to school, many are adapting to virtual learning amid the ongoing pandemic. But critics claim Edgenuity, an online learning program adopted by tens of thousands of schools, is flawed. Edgenuity grades assignments and quizzes using artificial intelligence and one middle-schooler was able to outsmart the system. By adding a jumble of relevant keywords to their answers, such as'word salad,' the student figured out they could trick Edgenuity's scoring algorithm and earn perfect scores on short-answer tests - his grade went from a 50 to 100. Lazare used the word salad technique for this question about Constantinople.

The level of a person's raw intelligence, as measured by aptitude tests such as IQ scores, is generally pretty stable for most people during adulthood. While it's true that there are things you can do to fine tune your natural capabilities, such as doing brain exercises, puzzle solving, and getting optimal sleep – the amount of raw brainpower you have is difficult to increase in any meaningful or permanent way. For those of us who constantly strive to be high-performers in our fields, this seems like bad news. If we can't increase our processing power, then how can we solve life's bigger problems as we move up the ladder? The good news is that while raw cognitive abilities matter, it's how you use and harness those abilities that really makes the difference.