Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of large language models (LLMs) to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts. The code is released at https://github.com/Qrange-group/SUR-adapter.

Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-level intelligence. Among their numerous skills, the translation abilities of LLMs have received considerable attention. Compared to typical machine translation that focuses solely on source-to-target mapping, LLM-based translation can potentially mimic the human translation process which might take preparatory steps to ensure high-quality translation. This work explores this possibility by proposing the MAPS framework, which stands for Multi-Aspect Prompting and Selection. Specifically, we enable LLMs first to analyze the given source sentence and induce three aspects of translation-related knowledge: keywords, topics, and relevant demonstrations to guide the final translation process. Moreover, we employ a selection mechanism based on quality estimation to filter out noisy and unhelpful knowledge. Both automatic (3 LLMs x 11 directions x 2 automatic metrics) and human evaluation (preference study and MQM) demonstrate the effectiveness of MAPS. Further analysis shows that by mimicking the human translation process, MAPS reduces various translation errors such as hallucination, ambiguity, mistranslation, awkward style, untranslated text, and omission. Source code is available at https://github.com/zwhe99/MAPS-mt.

We introduce the sparse modern Hopfield model as a sparse extension of the modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a memory-retrieval dynamics whose one-step approximation corresponds to the sparse attention mechanism. Theoretically, our key contribution is a principled derivation of a closed-form sparse Hopfield energy using the convex conjugate of the sparse entropic regularizer. Building upon this, we derive the sparse memory retrieval dynamics from the sparse energy function and show its one-step approximation is equivalent to the sparse-structured attention. Importantly, we provide a sparsity-dependent memory retrieval error bound which is provably tighter than its dense analog. The conditions for the benefits of sparsity to arise are therefore identified and discussed. In addition, we show that the sparse modern Hopfield model maintains the robust theoretical properties of its dense counterpart, including rapid fixed point convergence and exponential memory capacity. Empirically, we use both synthetic and real-world datasets to demonstrate that the sparse Hopfield model outperforms its dense counterpart in many situations.

Last week, it seemed that OpenAI--the secretive firm behind ChatGPT--had been broken open. The company's board had suddenly fired CEO Sam Altman, hundreds of employees revolted in protest, Altman was reinstated, and the media dissected the story from every possible angle. Yet the reporting belied the fact that our view into the most crucial part of the company is still so fundamentally limited: We don't really know how OpenAI develops its technology, nor do we understand exactly how Altman has directed work on future, more powerful generations. This was made acutely apparent last Wednesday, when Reuters and The Information reported that, prior to Altman's firing, several staff researchers had raised concerns about a supposedly dangerous breakthrough. At issue was an algorithm called Q* (pronounced "Q-star"), which has allegedly been shown to solve certain grade-school-level math problems that it hasn't seen before.

Earlier this month, OpenAI's board abruptly fired its popular CEO, Sam Altman. The ouster shocked the tech world and rankled Altman's loyal employees, the vast majority of whom threatened to quit unless their boss was reinstated. After a chaotic five-day exile, Altman got his old job back--with a reconfigured, all-male board overseeing him, led by ex-Salesforce CEO and former Twitter board chair Bret Taylor. Right now, only three people sit on this provisional OpenAI board. Immediately prior to the failed coup, there were six.

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.

To meet these rising expectations, many procurement teams are turning to advanced analytics, AI, and machine learning (ML) to transform the way they make smart business buying decisions and create value for the organization. AI and ML tools have long helped procurement teams automate mundane and manual procurement processes, allowing them to focus on more strategic initiatives. But recent advances in natural language processing (NLP), pattern recognition, cognitive analytics, and large language models (LLMs) are "opening up opportunities to make procurement more efficient and effective," says Julie Scully, director of software development at Amazon Business. The good news is procurement teams are already well-positioned to capitalize on these technological advances. Their access to rich data sources, ranging from contracts to invoices, enables AI/ML solutions that can illuminate the insights contained within this data.

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.

Kurt "The CyberGuy" Knutsson explains new Google Maps features, powered by AI. Artificial intelligence is influencing nearly all aspects of life in 2023. From education to the workplace to creative endeavors, AI is making its mark on our everyday lives. Google Bard product lead Jack Krawczyk sat down with Fox News Digital for an interview in New York City recently to discuss how generative AI frontrunner Google Bard has developed to accommodate people's lifestyles. As just one example, Krawczyk mentioned that parents can use Google Bard to snap a photo of their craft drawer -- then ask the AI tool what kind of art can be made using the available supplies.

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.