Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. HAT recognizes that AI will function as a teammate instead of simply a tool in collaboration with humans. Effective human-AI teams need to be capable of taking advantage of the unique abilities of both humans and AI while overcoming the known challenges and limitations of each member, augmenting human capabilities, and raising joint performance beyond that of either entity. The National AI Research and Strategic Plan 2023 update has recognized that research programs focusing primarily on the independent performance of AI systems generally fail to consider the functionality that AI must provide within the context of dynamic, adaptive, and collaborative teams and calls for further research on human-AI teaming and collaboration. However, there has been debate about whether AI can work as a teammate with humans. The primary concern is that adopting the "teaming" paradigm contradicts the human-centered AI (HCAI) approach, resulting in humans losing control of AI systems. This article further analyzes the HAT paradigm and the debates. Specifically, we elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) and apply it to represent HAT under the HCAI umbrella. We believe that HAIJCS may help adopt HAI while enabling HCAI. The implications and future work for HAIJCS are also discussed. Insights: AI has led to the emergence of a new form of human-machine relationship: human-AI teaming (HAT), a paradigmatic shift in human-AI systems; We must follow a human-centered AI (HCAI) approach when applying HAT as a new design paradigm; We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT for developing effective human-AI teaming

From the social sciences to machine learning, it has been well documented that metrics to be optimized are not always aligned with social welfare. In healthcare, Dranove et al. (2003) showed that publishing surgery mortality metrics actually harmed the welfare of sicker patients by increasing provider selection behavior. We analyze the incentive misalignments that arise from such average treated outcome metrics, and show that the incentives driving treatment decisions would align with maximizing total patient welfare if the metrics (i) accounted for counterfactual untreated outcomes and (ii) considered total welfare instead of averaging over treated patients. Operationalizing this, we show how counterfactual metrics can be modified to behave reasonably in patient-facing ranking systems. Extending to realistic settings when providers observe more about patients than the regulatory agencies do, we bound the decay in performance by the degree of information asymmetry between principal and agent. In doing so, our model connects principal-agent information asymmetry with unobserved heterogeneity in causal inference.

The notion of margin loss has been central to the development and analysis of algorithms for binary classification. To date, however, there remains no consensus as to the analogue of the margin loss for multiclass classification. In this work, we show that a broad range of multiclass loss functions, including many popular ones, can be expressed in the relative margin form, a generalization of the margin form of binary losses. The relative margin form is broadly useful for understanding and analyzing multiclass losses as shown by our prior work (Wang and Scott, 2020, 2021). To further demonstrate the utility of this way of expressing multiclass losses, we use it to extend the seminal result of Bartlett et al. (2006) on classification-calibration of binary margin losses to multiclass. We then analyze the class of Fenchel-Young losses, and expand the set of these losses that are known to be classification-calibrated.

We prove closed-form equations for the exact high-dimensional asymptotics of a family of first order gradient-based methods, learning an estimator (e.g. M-estimator, shallow neural network, ...) from observations on Gaussian data with empirical risk minimization. This includes widely used algorithms such as stochastic gradient descent (SGD) or Nesterov acceleration. The obtained equations match those resulting from the discretization of dynamical mean-field theory (DMFT) equations from statistical physics when applied to gradient flow. Our proof method allows us to give an explicit description of how memory kernels build up in the effective dynamics, and to include non-separable update functions, allowing datasets with non-identity covariance matrices. Finally, we provide numerical implementations of the equations for SGD with generic extensive batch-size and with constant learning rates.

The soap opera-like ousting and swift return of OpenAI CEO Sam Altman produced plenty of fodder for ironic quips online but it also exposed some serious fault lines. One such critique I enjoyed was: "How are we supposed to solve the AI alignment problem if aligning just a few board members presents an insurmountable challenge?" As the company behind ChatGPT, OpenAI may be one of the more recognizable names, but artificial intelligence is more than one company. It's a technology of immense consequence, yet it remains almost entirely unregulated. The E.U. has a chance to meaningfully tackle that challenge--but not if it bends the knee to Big Tech's ongoing onslaught. Inspirational Members of the European Parliament have so far been standing firm in the face of incredible pressure, in an effort to save this landmark legislation.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The potential threat posed by the rapid development of artificial intelligence (AI) means safeguards need to be built in to systems from the start rather than tacked on later, a top U.S. official said on Monday. "We've normalized a world where technology products come off the line full of vulnerabilities and then consumers are expected to patch those vulnerabilities. We can't live in that world with AI," said Jen Easterly, director of the U.S. Cybersecurity and Infrastructure Security Agency.

FOX News correspondent Benjamin Hall previews his'Special Report' interview with Ukraine President Volodymyr Zelenskyy, who says now is not the time for a peace deal with Russia. The wife of the Ukrainian military intelligence chief who once vowed to "keep killing Russians anywhere on the face of this world until the complete victory of Ukraine" has been poisoned, reports say. Marianna Budanova, the spouse of Kyrylo Budanov, is suffering from poisoning by heavy metals, Reuters is reporting, citing Ukrainian media. The alleged incident happened weeks after the Russian government said it will pursue terrorism charges against Budanov and three other military officials in connection to drone strikes on Russian territory and regions of Ukraine currently being held by Russian invading forces. It is unclear who is behind the reported poisoning or when it happened.

How do we gain access to artificial intelligence's huge potential benefits – such as devising new life-saving drugs or finding new ways to teach children – without opening a box of horrors? If we're not careful, AI could be a Frankenstein monster. It might eliminate nearly all jobs. It could lead to autonomous warfare. Even such a mundane goal as making as many paper clips as possible, critics of AI argue, could push an all-powerful AI to end all life on Earth in pursuit of more clips.

This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training dataset. We show an adversary can extract gigabytes of training data from open-source language models like Pythia or GPT-Neo, semi-open models like LLaMA or Falcon, and closed models like ChatGPT. Existing techniques from the literature suffice to attack unaligned models; in order to attack the aligned ChatGPT, we develop a new divergence attack that causes the model to diverge from its chatbot-style generations and emit training data at a rate 150x higher than when behaving properly. Our methods show practical attacks can recover far more data than previously thought, and reveal that current alignment techniques do not eliminate memorization.

We introduce a diffusion-based generative model to describe the distribution of galaxies in our Universe directly as a collection of points in 3-D space (coordinates) optionally with associated attributes (e.g., velocities and masses), without resorting to binning or voxelization. The custom diffusion model can be used both for emulation, reproducing essential summary statistics of the galaxy distribution, as well as inference, by computing the conditional likelihood of a galaxy field. We demonstrate a first application to massive dark matter haloes in the Quijote simulation suite. This approach can be extended to enable a comprehensive analysis of cosmological data, circumventing limitations inherent to summary statistic -- as well as neural simulation-based inference methods.