Several of the signers have already publicly agreed to some similar actions to those in the White House's pledge. Before OpenAI rolled it out its GPT-4 system widely, it brought in a team of running outside professions to exercises, a process known as "redteaming." Google has already said in a blog post it is developing a watermarking, which companies and policymakers have touted as a way to address concerns that AI could supercharge misinformation.

Today, the White House was proud to announce it has received "voluntary commitments" from tech companies like Microsoft, Meta, and OpenAI to support forthcoming regulation on artificial intelligence. At first blush, it's a reassuring gesture from tech companies who hold "human extinction" in the palm of their hands, but Americans should take their lip service with a grain of salt. More than a decade after Mark Zuckerberg coined the mantra "move fast and break things," the public is finally realizing the serious negative effect that social media platforms have had on youth mental health, and the brokenness in our democracy and public health that Big Tech has left in its wake. Now, Big Tech wants to "launch and iterate" a new lab experiment on society writ large, this time with artificial intelligence. Leaders in the field agree that "smart regulation" is needed to avoid serious harm to humanity, but AI is already woven into the fabric of the mainstream's daily lives.

The White House has struck a deal with major AI developers--including Amazon, Google, Meta, Microsoft, and OpenAI--that commits them to take action to prevent harmful AI models from being released into the world. Under the agreement, which the White House calls a "voluntary commitment," the companies pledge to carry out internal tests and permit external testing of new AI models before they are publicly released. The test will look for problems including biased or discriminatory output, cybersecurity flaws, and risks of broader societal harm. Startups Anthropic and Inflection, both developers of notable rivals to OpenAI's ChatGPT, also participated in the agreement. "Companies have a duty to ensure that their products are safe before introducing them to the public by testing the safety and capability of their AI systems," White House special adviser for AI Ben Buchanan told reporters in a briefing yesterday.

Over the past few weeks I have been using GPT-4 to help me cook. Need a substitute for an ingredient you forgot to buy? GPT can suggest an alternative. Time to clear out the cupboards? Simply type: "Please create a recipe using two eggs, a jar of borlotti beans, a potato, a leek, and the scrapings on the bottom of a jar of pickle." I'm always polite, and so is GPT. It thinks for a moment – then whips up the instructions for an unusual but edible hash and even wishes me bon appétit.

GOP Rep. Nancy Mace spoke exclusively with Fox News Digital about her thoughts on the rapidly advancing AI sector, as Congress races to get ahead of the burgeoning technology. AI is not going to kill us. Nor is AI going to save us. Instead, AI has the potential to help us change. Very few are considering the opportunities this new technology offers to clean up government.

The advent of Chat-GPT has led to a surge of interest in Embodied AI. However, many existing Embodied AI models heavily rely on massive interactions with training environments, which may not be practical in real-world situations. To this end, the Maniskill2 has introduced a full-physics simulation benchmark for manipulating various 3D objects. This benchmark enables agents to be trained using diverse datasets of demonstrations and evaluates their ability to generalize to unseen scenarios in testing environments. In this paper, we propose a novel two-stage fine-tuning strategy that aims to further enhance the generalization capability of our model based on the Maniskill2 benchmark. Through extensive experiments, we demonstrate the effectiveness of our approach by achieving the 1st prize in all three tracks of the ManiSkill2 Challenge. Our findings highlight the potential of our method to improve the generalization abilities of Embodied AI models and pave the way for their ractical applications in real-world scenarios. All codes and models of our solution is available at https://github.com/xtli12/GXU-LIPE.git

In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration and before establishing connections. Early fake profile identification is crucial to maintaining the platform's integrity since it prevents imposters from acquiring the private and sensitive information of legitimate users and from gaining an opportunity to increase their credibility for future phishing and scamming activities. This work uses textual information provided in LinkedIn profiles and introduces the Section and Subsection Tag Embedding (SSTE) method to enhance the discriminative characteristics of these data for distinguishing between legitimate profiles and those created by imposters manually or by using an LLM. Additionally, the dearth of a large publicly available LinkedIn dataset motivated us to collect 3600 LinkedIn profiles for our research. We will release our dataset publicly for research purposes. This is, to the best of our knowledge, the first large publicly available LinkedIn dataset for fake LinkedIn account detection. Within our paradigm, we assess static and contextualized word embeddings, including GloVe, Flair, BERT, and RoBERTa. We show that the suggested method can distinguish between legitimate and fake profiles with an accuracy of about 95% across all word embeddings. In addition, we show that SSTE has a promising accuracy for identifying LLM-generated profiles, despite the fact that no LLM-generated profiles were employed during the training phase, and can achieve an accuracy of approximately 90% when only 20 LLM-generated profiles are added to the training set. It is a significant finding since the proliferation of several LLMs in the near future makes it extremely challenging to design a single system that can identify profiles created with various LLMs.

Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities. However, previous work does little to explore what environment state information is provided to LLM actors via language. Exhaustively describing high-dimensional states can impair performance and raise inference costs for LLM actors. Previous LLM actors avoid the issue by relying on hand-engineered, task-specific protocols to determine which features to communicate about a state and which to leave out. In this work, we propose Brief Language INputs for DEcision-making Responses (BLINDER), a method for automatically selecting concise state descriptions by learning a value function for task-conditioned state descriptions. We evaluate BLINDER on the challenging video game NetHack and a robotic manipulation task. Our method improves task success rate, reduces input size and compute costs, and generalizes between LLM actors.

We aim to determine the extent and content of guidance for authors regarding the use of generative-AI (GAI), Generative Pretrained models (GPTs) and Large Language Models (LLMs) powered tools among the top 100 academic publishers and journals in science. The websites of these publishers and journals were screened from between 19th and 20th May 2023. Among the largest 100 publishers, 17% provided guidance on the use of GAI, of which 12 (70.6%) were among the top 25 publishers. Among the top 100 journals, 70% have provided guidance on GAI. Of those with guidance, 94.1% of publishers and 95.7% of journals prohibited the inclusion of GAI as an author. Four journals (5.7%) explicitly prohibit the use of GAI in the generation of a manuscript, while 3 (17.6%) publishers and 15 (21.4%) journals indicated their guidance exclusively applies to the writing process. When disclosing the use of GAI, 42.8% of publishers and 44.3% of journals included specific disclosure criteria. There was variability in guidance of where to disclose the use of GAI, including in the methods, acknowledgments, cover letter, or a new section. There was also variability in how to access GAI guidance and the linking of journal and publisher instructions to authors. There is a lack of guidance by some top publishers and journals on the use of GAI by authors. Among those publishers and journals that provide guidance, there is substantial heterogeneity in the allowable uses of GAI and in how it should be disclosed, with this heterogeneity persisting among affiliated publishers and journals in some instances. The lack of standardization burdens authors and threatens to limit the effectiveness of these regulations. There is a need for standardized guidelines in order to protect the integrity of scientific output as GAI continues to grow in popularity.

Large language models have proven themselves highly flexible, able to solve a wide range of generative tasks, such as abstractive summarization and open-ended question answering. In this paper we extend the capabilities of LLMs by directly attaching a small audio encoder allowing it to perform speech recognition. By directly prepending a sequence of audial embeddings to the text token embeddings, the LLM can be converted to an automatic speech recognition (ASR) system, and be used in the exact same manner as its textual counterpart. Experiments on Multilingual LibriSpeech (MLS) show that incorporating a conformer encoder into the open sourced LLaMA-7B allows it to outperform monolingual baselines by 18% and perform multilingual speech recognition despite LLaMA being trained overwhelmingly on English text. Furthermore, we perform ablation studies to investigate whether the LLM can be completely frozen during training to maintain its original capabilities, scaling up the audio encoder, and increasing the audio encoder striding to generate fewer embeddings. The results from these studies show that multilingual ASR is possible even when the LLM is frozen or when strides of almost 1 second are used in the audio encoder opening up the possibility for LLMs to operate on long-form audio.