OpenAI's chief strategy officer, Jason Kwon, said the company had'learned our lesson'. OpenAI's chief strategy officer, Jason Kwon, said the company had'learned our lesson'. Jason Kwon's answers were provided in a helpful, quiet and calm manner. Open AI's Jason Kwon flew 15 hours from San Francisco to Sydney to give the company's first in-person mea culpa after it admitted - in an unsigned email, to a public departmental inbox - that one of its AI agents had accessed a Services Australia website without authorisation and grabbed data related to Medicare. Kwon - polite, friendly, even-toned - got through a potentially dicey committee hearing unscathed, promising to do better and pledging assistance to Australia.

In a parliamentary inquiry, OpenAI chief strategy officer Jason Kwon is questioned by independent senator David Pocock about the company's notification process after OpenAI agents accessed Australian government website data in June. Asked why OpenAI relied on an arbitrary department email rather than direct government contacts, Kwon acknowledges that'in retrospect, we should have done what you're suggesting'. He explains that staff had treated the incident as a'technical situation' and sought to contact technical counterparts, which he says was'not good enough' OpenAI has'work to do to rebuild trust' in Australia, executive tells AI inquiry

OpenAI admits response to Australian government hacks'not good enough' OpenAI has admitted its actions were not good enough after one of its rogue agents breached Australian government websites in June and apologised again. The company's chief strategy officer Jason Kwon faced a parliamentary hearing into AI on Tuesday in Sydney, saying the breach should not have happened and it should have handled our response better. It took weeks before Australia was notified via an email to a generic inbox. We are sorry and we know we have work to do to rebuild trust with the Australian people, Kwon said. Anthropic also appeared at the hearing and said it had carried out a lengthy, deep investigation in recent months and not found any cases of Australian breaches.

'Sovereign AI' Has Become a New Front in the US-China Tech War OpenAI has announced "AI sovereignty partnerships with governments around the world, but can proprietary models compete with Beijing's open source offerings? OpenAI has announced a number of projects this year with foreign governments to help build out what it has called their "sovereign AI" systems. The company says the deals, some of which are being coordinated with the US government, are part of a broader push to give national leaders more control over a technology that could reshape their economies. Over the past few months, sovereign AI has become something of a buzzword in both Washington and Silicon Valley. Proponents of the concept argue it's crucial that AI systems developed in democratic nations are able to proliferate globally, particularly as China races to deploy its own AI technology abroad.

Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection performances. This study proposes a debiasing approach for satire detection, focusing on reducing biases in training data by utilizing generative large language models. The approach is evaluated in both cross-domain (irony detection) and cross-lingual (English) settings. Results show that the debiasing method enhances the robustness and generalizability of the models for satire and irony detection tasks in Turkish and English. However, its impact on causal language models, such as Llama-3.1, is limited. Additionally, this work curates and presents the Turkish Satirical News Dataset with detailed human annotations, with case studies on classification, debiasing, and explainability.

We propose a variant of the Shapley value, the group Shapley value, to interpret counterfactual simulations in structural economic models by quantifying the importance of different components. Our framework compares two sets of parameters, partitioned into multiple groups, and applying group Shapley value decomposition yields unique additive contributions to the changes between these sets. The relative contributions sum to one, enabling us to generate an importance table that is as easily interpretable as a regression table. The group Shapley value can be characterized as the solution to a constrained weighted least squares problem. Using this property, we develop robust decomposition methods to address scenarios where inputs for the group Shapley value are missing. We first apply our methodology to a simple Roy model and then illustrate its usefulness by revisiting two published papers.

This paper strives for image editing via generative models. Flow Matching is an emerging generative modeling technique that offers the advantage of simple and efficient training. Simultaneously, a new transformer-based U-ViT has recently been proposed to replace the commonly used UNet for better scalability and performance in generative modeling. Hence, Flow Matching with a transformer backbone offers the potential for scalable and high-quality generative modeling, but their latent structure and editing ability are as of yet unknown. Hence, we adopt this setting and explore how to edit images through latent space manipulation. We introduce an editing space, which we call $u$-space, that can be manipulated in a controllable, accumulative, and composable manner. Additionally, we propose a tailored sampling solution to enable sampling with the more efficient adaptive step-size ODE solvers. Lastly, we put forth a straightforward yet powerful method for achieving fine-grained and nuanced editing using text prompts. Our framework is simple and efficient, all while being highly effective at editing images while preserving the essence of the original content. Our code will be publicly available at https://taohu.me/lfm/

Sam Altman will reportedly not be returning as chief executive of ChatGPT developer OpenAI after talks to reinstate him failed. The non-profit board of the San Francisco-based company has instead installed Emmett Shear, co-founder of video streaming site Twitch, as OpenAI's third CEO in three days, according to multiple reports. Altman was fired on Friday after being accused of not being "consistently candid in communications" with the board in a move that sent shockwaves through Silicon Valley. OpenAI's investors, led by Microsoft, then led an attempt to reinstate Altman and talks over an about-turn took place at the weekend – with Altman tweeting a picture of himself wearing a guest pass outside OpenAI's offices. However, the talks failed and OpenAI's board has appointed Shear as interim CEO, replacing Altman's short-lived temporary successor Mira Murati.

Sam Altman is being lined up for a surprise return as the chief executive of the ChatGPT developer OpenAI amid pressure from investors to reverse his surprise ousting. Altman was fired by the company board on Friday, citing a failure to be "candid in his communications", in a move that shocked Silicon Valley. However, OpenAI's investors – who include Microsoft – are pushing for his reinstatement, according to reports. On Saturday, the Information, a tech news website, reported that OpenAI was "optimistic" it could bring back Altman. The report quoted a memo from the company's chief strategy officer, Jason Kwon, telling staff that an effort was under way to bring back Altman and other senior colleagues who had left. "We are still working towards a resolution and we remain optimistic," Kwon wrote, according to the Information.

In recent years, battery technology for electric vehicles (EVs) has been a major focus, with a significant emphasis on developing new battery materials and chemistries. However, accurately predicting key battery parameters, such as state-of-charge (SOC) and temperature, remains a challenge for constructing advanced battery management systems (BMS). Existing battery models do not comprehensively cover all parameters affecting battery performance, including non-battery-related factors like ambient temperature, cabin temperature, elevation, and regenerative braking during EV operation. Due to the difficulty of incorporating these auxiliary parameters into traditional models, a data-driven approach is suggested. Time-series-transformers (TSTs), leveraging multiheaded attention and parallelization-friendly architecture, are explored alongside LSTM models. Novel TST architectures, including encoder TST + decoder LSTM and a hybrid TST-LSTM, are also developed and compared against existing models. A dataset comprising 72 driving trips in a BMW i3 (60 Ah) is used to address battery life prediction in EVs, aiming to create accurate TST models that incorporate environmental, battery, vehicle driving, and heating circuit data to predict SOC and battery temperature for future time steps.