After getting through the setup pleasantries, that's all you're left with when you start a new draft on the Freewrite Alpha. No spell check, no AI-powered notes on your grammar, and most certainly no other browser tabs to distract you from the ultimate goal of getting words down on the page. Instead, Freewrite has taken its already distraction-free writing experience and shrunk the price tag some by cutting the Alpha's screen down to almost nothing. I might not be a novelist, but between news posts and reviews, I write somewhere in the region of 20,000 words a week. So, I thought, what better way to test a writing machine than to use it exclusively for a full week, to see how it holds up to the rigors of the online journalist's grind?

The robot dogs had a dance-off. As the world celebrated #InternationalDanceDay, a unique duo took the stage, or rather, the screen, to showcase a different kind of choreography. Spot, the quadruped robot developed by Boston Dynamics, found a new friend in Sparkles, a dazzlingly dressed counterpart designed to explore the fusion of robotics, art and entertainment. At first glance, the video in question seems like a whimsical animation straight out of a children's show. Yet, this is no fiction.

In the contemporary film industry, accurately predicting a movie's earnings is paramount for maximizing profitability. This project aims to develop a machine learning model for predicting movie earnings based on input features like the movie name, the MPAA rating of the movie, the genre of the movie, the year of release of the movie, the IMDb Rating, the votes by the watchers, the director, the writer and the leading cast, the country of production of the movie, the budget of the movie, the production company and the runtime of the movie. Through a structured methodology involving data collection, preprocessing, analysis, model selection, evaluation, and improvement, a robust predictive model is constructed. Linear Regression, Decision Trees, Random Forest Regression, Bagging, XGBoosting and Gradient Boosting have been trained and tested. Model improvement strategies include hyperparameter tuning and cross-validation. The resulting model offers promising accuracy and generalization, facilitating informed decision-making in the film industry to maximize profits.

Assessing response quality to instructions in language models is vital but challenging due to the complexity of human language across different contexts. This complexity often results in ambiguous or inconsistent interpretations, making accurate assessment difficult. To address this issue, we propose a novel Uncertainty-aware Reward Model (URM) that introduces a robust uncertainty estimation for the quality of paired responses based on Bayesian approximation. Trained with preference datasets, our uncertainty-enabled proxy not only scores rewards for responses but also evaluates their inherent uncertainty. Empirical results demonstrate significant benefits of incorporating the proposed proxy into language model training. Our method boosts the instruction following capability of language models by refining data curation for training and improving policy optimization objectives, thereby surpassing existing methods by a large margin on benchmarks such as Vicuna and MT-bench. These findings highlight that our proposed approach substantially advances language model training and paves a new way of harnessing uncertainty within language models.

Large Language Model (LLM) -in-the-loop applications have been shown to effectively interpret the human user's commands, make plans, and operate external tools/systems accordingly. Still, the operation scope of the LLM agent is limited to passively following the user, requiring the user to frame his/her needs with regard to the underlying tools/systems. We note that the potential of an LLM-Agent User Interface (LAUI) is much greater. A user mostly ignorant to the underlying tools/systems should be able to work with a LAUI to discover an emergent workflow. Contrary to the conventional way of designing an explorable GUI to teach the user a predefined set of ways to use the system, in the ideal LAUI, the LLM agent is initialized to be proficient with the system, proactively studies the user and his/her needs, and proposes new interaction schemes to the user. To illustrate LAUI, we present Flute X GPT, a concrete example using an LLM agent, a prompt manager, and a flute-tutoring multi-modal software-hardware system to facilitate the complex, real-time user experience of learning to play the flute.

NEXT-GEN BATTLE: Modern warfare is changing rapidly, and harnessing artificial intelligence is key to staying ahead of America's adversaries. Modern warfare is rapidly changing -- and artificial intelligence may only speed up that process. FUNNY BOT: A team of university researchers in the Netherlands says they've developed an artificial intelligence (AI) platform that can recognize sarcasm, according to a new report. AI (artificial intelligence) letters are placed on a computer motherboard in this illustration taken on June 23, 2023. 'OUTCOMPETE CHINA': A bipartisan group of U.S. senators on Wednesday joined in a call to boost American funding of artificial intelligence research.

Many people have had suspicions that their partner was cheating, but have questioned whether those feelings had any weight or were just their minds playing tricks on them. A'love rat' investigator, who only works for women, has shared her'loyalty check' that she claims will uncover breadcrumbs that leads to catching an unfaithful man. The check includes certain apps on their phone, files on their computer and how they use Google search. 'If the guy has a history of being secretive, that answer is almost always'Yes,'' she told DailyMail.com. 'Based on his personality, his profile, and things like that, I will approach them in the way that I think will work the best.'

This week, artificial intelligence caught up with the future – or at least Hollywood's idea of it from a decade ago. "It feels like AI from the movies," wrote the OpenAI chief executive, Sam Altman, of his latest system, an impressive virtual assistant. To underline his point he posted a single word on X – "her" – referring to the 2013 film starring Joaquin Phoenix as a man who falls in love with a futuristic version of Siri or Alexa, voiced by Scarlett Johansson. For some experts, that new AI, GPT-4o, will be an unsettling reminder of their concerns about the technology's rapid advances, with a key OpenAI safety researcher leaving this week following a disagreement over the company's direction. For others the GPT-4o release will be confirmation that innovation continues in a field promising benefits for all. Next week's global AI summit in Seoul, attended by ministers, experts and tech executives, will hear both perspectives, as underlined by a safety report released before the meeting that referred to potential positives as well as numerous risks.

The news presenter has a deeply uncanny air as he delivers a partisan and pejorative message in Mandarin: Taiwan's outgoing president, Tsai Ing-wen, is as effective as limp spinach, her period in office beset by economic under performance, social problems and protests. "Water spinach looks at water spinach. Turns out that water spinach isn't just a name," says the presenter, in an extended metaphor about Tsai being "Hollow Tsai" – a pun related to the Mandarin word for water spinach. This is not a conventional broadcast journalist, even if the lack of impartiality is no longer a shock. The anchor is generated by an artificial intelligence programme, and the segment is trying, albeit clumsily, to influence the Taiwanese presidential election. The source and creator of the video are unknown, but the clip is designed to make voters doubt politicians who want Taiwan to remain at arm's length from China, which claims that the self-governing island is part of its territory.

Heterogeneous graph learning aims to capture complex relationships and diverse relational semantics among entities in a heterogeneous graph to obtain meaningful representations for nodes and edges. Recent advancements in heterogeneous graph neural networks (HGNNs) have achieved state-of-the-art performance by considering relation heterogeneity and using specialized message functions and aggregation rules. However, existing frameworks for heterogeneous graph learning have limitations in generalizing across diverse heterogeneous graph datasets. Most of these frameworks follow the "pre-train" and "fine-tune" paradigm on the same dataset, which restricts their capacity to adapt to new and unseen data. This raises the question: "Can we generalize heterogeneous graph models to be well-adapted to diverse downstream learning tasks with distribution shifts in both node token sets and relation type heterogeneity?'' To tackle those challenges, we propose HiGPT, a general large graph model with Heterogeneous graph instruction-tuning paradigm. Our framework enables learning from arbitrary heterogeneous graphs without the need for any fine-tuning process from downstream datasets. To handle distribution shifts in heterogeneity, we introduce an in-context heterogeneous graph tokenizer that captures semantic relationships in different heterogeneous graphs, facilitating model adaptation. We incorporate a large corpus of heterogeneity-aware graph instructions into our HiGPT, enabling the model to effectively comprehend complex relation heterogeneity and distinguish between various types of graph tokens. Furthermore, we introduce the Mixture-of-Thought (MoT) instruction augmentation paradigm to mitigate data scarcity by generating diverse and informative instructions. Through comprehensive evaluations, our proposed framework demonstrates exceptional performance in terms of generalization performance.