This is a speculative essay on interface design and artificial intelligence. Recently there has been a surge of attention to chatbots based on large language models, including widely reported unsavory interactions. We contend that part of the problem is that text is not all you need: sophisticated AI systems should have dashboards, just like all other complicated devices. Assuming the hypothesis that AI systems based on neural networks will contain interpretable models of aspects of the world around them, we discuss what data such dashboards might display. We conjecture that, for many systems, the two most important models will be of the user and of the system itself. We call these the System Model and User Model. We argue that, for usability and safety, interfaces to dialogue-based AI systems should have a parallel display based on the state of the System Model and the User Model. Finding ways to identify, interpret, and display these two models should be a core part of interface research for AI.

Current image generation models struggle to reliably produce well-formed visual text. In this paper, we investigate a key contributing factor: popular text-to-image models lack character-level input features, making it much harder to predict a word's visual makeup as a series of glyphs. To quantify this effect, we conduct a series of experiments comparing character-aware vs. character-blind text encoders. In the text-only domain, we find that character-aware models provide large gains on a novel spelling task (WikiSpell). Applying our learnings to the visual domain, we train a suite of image generation models, and show that character-aware variants outperform their character-blind counterparts across a range of novel text rendering tasks (our DrawText benchmark). Our models set a much higher state-of-the-art on visual spelling, with 30+ point accuracy gains over competitors on rare words, despite training on far fewer examples.

Contrast maximization (CMax) techniques are widely used in event-based vision systems to estimate the motion parameters of the camera and generate high-contrast images. However, these techniques are noise-intolerance and suffer from the multiple extrema problem which arises when the scene contains more noisy events than structure, causing the contrast to be higher at multiple locations. This makes the task of estimating the camera motion extremely challenging, which is a problem for neuromorphic earth observation, because, without a proper estimation of the motion parameters, it is not possible to generate a map with high contrast, causing important details to be lost. Similar methods that use CMax addressed this problem by changing or augmenting the objective function to enable it to converge to the correct motion parameters. Our proposed solution overcomes the multiple extrema and noise-intolerance problems by correcting the warped event before calculating the contrast and offers the following advantages: it does not depend on the event data, it does not require a prior about the camera motion, and keeps the rest of the CMax pipeline unchanged. This is to ensure that the contrast is only high around the correct motion parameters. Our approach enables the creation of better motion-compensated maps through an analytical compensation technique using a novel dataset from the International Space Station (ISS). Code is available at \url{https://github.com/neuromorphicsystems/event_warping}

Subset selection tasks, arise in recommendation systems and search engines and ask to select a subset of items that maximize the value for the user. The values of subsets often display diminishing returns, and hence, submodular functions have been used to model them. If the inputs defining the submodular function are known, then existing algorithms can be used. In many applications, however, inputs have been observed to have social biases that reduce the utility of the output subset. Hence, interventions to improve the utility are desired. Prior works focus on maximizing linear functions -- a special case of submodular functions -- and show that fairness constraint-based interventions can not only ensure proportional representation but also achieve near-optimal utility in the presence of biases. We study the maximization of a family of submodular functions that capture functions arising in the aforementioned applications. Our first result is that, unlike linear functions, constraint-based interventions cannot guarantee any constant fraction of the optimal utility for this family of submodular functions. Our second result is an algorithm for submodular maximization. The algorithm provably outputs subsets that have near-optimal utility for this family under mild assumptions and that proportionally represent items from each group. In empirical evaluation, with both synthetic and real-world data, we observe that this algorithm improves the utility of the output subset for this family of submodular functions over baselines.

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR). NMR aims at re-purposing a pre-trained model from a source domain to a target domain by modifying the input of a frozen pre-trained model. In addition to the known, input-independent, reprogramming method, we propose an advanced reprogramming paradigm: Input-dependent NMR, to increase adaptability to complex input data such as musical audio. Experimental results suggest that a neural model pre-trained on large-scale datasets can successfully perform music genre classification by using this reprogramming method. The two proposed Input-dependent NMR TL methods outperform fine-tuning-based TL methods on a small genre classification dataset.

Naftali Bennett spoke exclusively with Fox News Digital about the benefits of AI and the need to set parameters for its use now. BERT is an open-source machine learning framework that is used for various natural language processing (NLP) tasks. It is designed to help computers better understand nuance in language by grasping the meaning of surrounding words in a text. The benefit is that context of a text can be understood rather than just the meaning of individual words. It is no secret that artificial intelligence impacts society in surprising ways.

'OFFENSIVE' – A troubling societal issue called transableism, in which a person "chooses" to identify as handicapped, is attracting attention. TERRIFYING TANDEM OF TECH – Take a look at 10 ways big government uses AI to create the totalitarian society as described in George Orwell's classic, "1984." KIRK CAMERON CRIES – Actor-writer Kirk Cameron was brought to "tears of hope" at a public library book event. Kirk Cameron, author of "As You Grow," an illustrated children's book that shares biblical values, has been holding family-focused events in public libraries across America -- and on April 29, he "got emotional" as a crowd began singing songs of worship to God. He's shown in New Jersey.

Another notable issue is the emergence of artificial intelligence (AI) and the impact it could have on script writing. The guild wants assurances that humans will still get work, no matter how clever or creative these AI systems will become.

Chatbots pretending to be journalists have been discovered running almost 50 AI-generated "content farms" so far, according to an investigation by the anti-misinformation outfit NewsGuard. The websites churn out content relating to politics, health, environment, finance and technology at a "high volume", the researchers found, to provide rapid turnover of material to saturate with adverts for profit. "Some publish hundreds of articles a day," Newsguard's McKenzie Sadeghi and Lorenzo Arvanitis said. In total, 49 sites in seven languages – English, Chinese, Czech, French, Portuguese, Tagalog and Thai – were identified as being "entirely or mostly" generated by AI language models. Almost half the sites had no obvious record of ownership or control, and only four were able to be contacted.

More than 11,000 members of the Writers Guild of America (WGA) are on strike, throwing Hollywood into turmoil as the entertainment business grapples with seismic changes triggered by the global streaming TV boom. The writers argue streaming has negatively affected them, saying they are working more for less money. They are seeking better compensation for their work on film, television and streaming shows and residual payments that reward writers when a show becomes a hit. The WGA called its first work stoppage in 15 years after failing to reach an agreement for higher pay from studios such as Walt Disney and Netflix. It represents roughly 11,500 writers in New York, Los Angeles and elsewhere.