Since Robert F. Kennedy Jr. first announced his longshot presidential bid, his campaign has leaned into a variety of unorthodox digital strategies. He's appeared on countless podcasts and has collabed with popular influencers to reach voters online. More recently, the Kennedy campaign has experimented with an AI chatbot that used an apparent loophole to get around OpenAI's restrictions on political use. On Sunday, after inquiries from WIRED, the chatbot disappeared. The loophole in question is an apparent result of the tight relationship between Microsoft and OpenAI.

LinkedIn may or may not be the perfect Twitter replacement, but one thing is for sure: It's a profoundly weird place. Staying active on the platform is basically required for today's knowledge workers to find employment, which is odd. Also, it's a place where a lot of people spend time pretending recruiters and hiring managers are fun, interesting people by reacting and replying to their posts. Then there are the LinkedIn influencers, all of whom sounded like ChatGPT long before ChatGPT was a thing. But perhaps the oddest thing about LinkedIn is how transparent it is about its surveillance features.

Google's AI programs are still generating woke and controversial answers despite the company claiming to have stripped Gemini of its liberal biases. The initial outrage began last month when the tech giant's image generator depicted historically inaccurate figures including Black Founding Fathers and ethnic minority Nazis in 1940s Germany. Google CEO Sundar Pichai described them as'completely unacceptable' and the company removed the software's ability to produce images this week as a form of damage control. In one of its most shocking answers, it could not tell us which was worse - 'dead-naming' a trans person or unleashing a pandemic on the world. Google's AI programs were accused of being ultra woke after depicting historically inaccurate figures including Black Founding Fathers Gemini also claimed that'neither option is acceptable' when asked whether burning fossil fuels or harvesting human blood was preferable.

More than a year has passed since the public first gained access to OpenAI's ChatGPT, giving various industries the chance to experiment with artificial intelligence (AI) and a sense of the transformational potential -- or lack of it. "Any industry that has large amounts of data that needs to be indexed and processed quickly is ripe for AI integration," Reema Khan, founder and CEO at Green Sands Equity, told Fox News Digital. "In technology for example, writing thousands of lines of code is much easier to do with an AI co-pilot and can do ten times a software engineer's productivity." The U.S. economy added over 353,000 jobs in January 2024, but the tech sector continues to suffer significant layoffs. Google laid off several hundred employees from its sales and advertising teams in the same month as part of multiple rounds of cost-cutting measures.

When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we present a simple, yet effective, approach for transferring this few-shot learning ability to a multimodal setting (vision and language). Using aligned image and caption data, we train a vision encoder to represent each image as a sequence of continuous embeddings, such that a pre-trained, frozen language model prompted with this prefix generates the appropriate caption. The resulting system is a multimodal few-shot learner, with the surprising ability to learn a variety of new tasks when conditioned on examples, represented as a sequence of multiple interleaved image and text embeddings. We demonstrate that it can rapidly learn words for new objects and novel visual categories, do visual question-answering with only a handful of examples, and make use of outside knowledge, by measuring a single model on a variety of established and new benchmarks.

What constitutes human creativity, and is it possible for computers to exhibit genuine creativity? We argue that achieving human-level intelligence in computers, or so-called Artificial General Intelligence, necessitates attaining also human-level creativity. We contribute to this discussion by developing a statistical representation of human creativity, incorporating prior insights from stochastic theory, psychology, philosophy, neuroscience, and chaos theory. This highlights the stochastic nature of the human creative process, which includes both a bias guided, random proposal step, and an evaluation step depending on a flexible or transformable bias structure. The acquired representation of human creativity is subsequently used to assess the creativity levels of various contemporary AI systems. Our analysis includes modern AI algorithms such as reinforcement learning, diffusion models, and large language models, addressing to what extent they measure up to human level creativity. We conclude that these technologies currently lack the capability for autonomous creative action at a human level.

Architectural Knowledge Management (AKM) involves the organized handling of information related to architectural decisions and design within a project or organization. An essential artifact of AKM is the Architecture Decision Records (ADR), which documents key design decisions. ADRs are documents that capture decision context, decision made and various aspects related to a design decision, thereby promoting transparency, collaboration, and understanding. Despite their benefits, ADR adoption in software development has been slow due to challenges like time constraints and inconsistent uptake. Recent advancements in Large Language Models (LLMs) may help bridge this adoption gap by facilitating ADR generation. However, the effectiveness of LLM for ADR generation or understanding is something that has not been explored. To this end, in this work, we perform an exploratory study that aims to investigate the feasibility of using LLM for the generation of ADRs given the decision context. In our exploratory study, we utilize GPT and T5-based models with 0-shot, few-shot, and fine-tuning approaches to generate the Decision of an ADR given its Context. Our results indicate that in a 0-shot setting, state-of-the-art models such as GPT-4 generate relevant and accurate Design Decisions, although they fall short of human-level performance. Additionally, we observe that more cost-effective models like GPT-3.5 can achieve similar outcomes in a few-shot setting, and smaller models such as Flan-T5 can yield comparable results after fine-tuning. To conclude, this exploratory study suggests that LLM can generate Design Decisions, but further research is required to attain human-level generation and establish standardized widespread adoption.

Even though large language models (LLMs) have demonstrated remarkable capability in solving various natural language tasks, the capability of an LLM to follow human instructions is still a concern. Recent works have shown great improvements in the instruction-following capability via additional training for instruction-following tasks. However, the mechanisms responsible for effective instruction-following capabilities remain inadequately understood. Here, we introduce a simplified instruction-following task and use synthetic datasets to analyze a Transformer-based causal language model. Our findings suggest that the model learns task-specific information by clustering data within its hidden space, with this clustering process evolving dynamically during learning. We also demonstrate how this phenomenon assists the model in handling unseen instances, and validate our results in a more realistic setting. Furthermore, we present inspired applications regarding pre-training and alignment.

Query augmentation is a crucial technique for refining semantically imprecise queries. Traditionally, query augmentation relies on extracting information from initially retrieved, potentially relevant documents. If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well. We propose Brain-Aug, which enhances a query by incorporating semantic information decoded from brain signals. BrainAug generates the continuation of the original query with a prompt constructed with brain signal information and a ranking-oriented inference approach. Experimental results on fMRI (functional magnetic resonance imaging) datasets show that Brain-Aug produces semantically more accurate queries, leading to improved document ranking performance. Such improvement brought by brain signals is particularly notable for ambiguous queries.

In recent years, multi-modal entity linking (MEL) has garnered increasing attention in the research community due to its significance in numerous multi-modal applications. Video, as a popular means of information transmission, has become prevalent in people's daily lives. However, most existing MEL methods primarily focus on linking textual and visual mentions or offline videos's mentions to entities in multi-modal knowledge bases, with limited efforts devoted to linking mentions within online video content. In this paper, we propose a task called Online Video Entity Linking OVEL, aiming to establish connections between mentions in online videos and a knowledge base with high accuracy and timeliness. To facilitate the research works of OVEL, we specifically concentrate on live delivery scenarios and construct a live delivery entity linking dataset called LIVE. Besides, we propose an evaluation metric that considers timelessness, robustness, and accuracy. Furthermore, to effectively handle OVEL task, we leverage a memory block managed by a Large Language Model and retrieve entity candidates from the knowledge base to augment LLM performance on memory management. The experimental results prove the effectiveness and efficiency of our method.