Is your weekend a bit bare-bones? Here's something that could entertain you for a minute or two. The chatbot Grok-2 is now free for everyone to fool around with on X. We knew this was coming and, well, now it's here. There are some limitations for those who don't want to plunk down 8 (or more) each month for X Premium. The free tier only allows for ten messages in each two-hour period.

Robot-assisted feeding has the potential to improve the quality of life for individuals with mobility limitations who are unable to feed themselves independently. However, there exists a large gap between the homogeneous, curated plates existing feeding systems can handle, and truly in-the-wild meals. Feeding realistic plates is immensely challenging due to the sheer range of food items that a robot may encounter, each requiring specialized manipulation strategies which must be sequenced over a long horizon to feed an entire meal. An assistive feeding system should not only be able to sequence different strategies efficiently in order to feed an entire meal, but also be mindful of user preferences given the personalized nature of the task. We address this with FLAIR, a system for long-horizon feeding which leverages the commonsense and few-shot reasoning capabilities of foundation models, along with a library of parameterized skills, to plan and execute user-preferred and efficient bite sequences. In real-world evaluations across 6 realistic plates, we find that FLAIR can effectively tap into a varied library of skills for efficient food pickup, while adhering to the diverse preferences of 42 participants without mobility limitations as evaluated in a user study. We demonstrate the seamless integration of FLAIR with existing bite transfer methods [19, 28], and deploy it across 2 institutions and 3 robots, illustrating its adaptability. Finally, we illustrate the real-world efficacy of our system by successfully feeding a care recipient with severe mobility limitations. Supplementary materials and videos can be found at: https://emprise.cs.cornell.edu/flair .

"China will use AI to disrupt elections in the US, South Korea and India, Microsoft warns" one read. "China Is Using AI to Sow Disinformation and Stoke Discord Across Asia and the US," another claimed. The headlines were based on a report published earlier this month by Microsoft's Threat Analysis Center which outlined how a Chinese disinformation campaign was now utilizing artificial technology to inflame divisions and disrupt elections in the US and around the world. The campaign, which has already targeted Taiwan's elections, uses AI-generated audio and memes designed to grab user attention and boost engagement. But what these headlines and Microsoft itself failed to adequately convey is that the Chinese government-linked disinformation campaign, known as Spamouflage Dragon or Dragonbridge, has so far been virtually ineffective.

With its simple mix of ingredients and high nutritional value, it's no surprise pasta is one of the most popular foods in the world. Despite dating back thousands of years, the age-old question still remains – which pasta shape is the best for holding sauce? To mark World Pasta Day, MailOnline turned to online AI tool ChatGPT for the answer, and it came up with some controversial results. Top of the list was cascatelli, a relatively new pasta from America with a curved shape and distinctive ruffles, deliberately designed to carry sauce. Also in the top six were spaghetti, penne and the'bow tie' pasta farfalle – but an expert claims a lot depends on the type of sauce too.

To used for actions and comestibles; Section 3 presents a address this shortcoming, we propose a high-level representation representation of recipes as bipartite graphs; Section 4 considers of recipes as labelled bipartite graphs where the acceptability of recipes; Section 5 presents definitions first subset of nodes denotes the comestibles involved in the for comparing recipes; Section 6 presents definitions for recipe (ingredients, intermediate food items, final products, composition of recipes from subrecipes; Section 7 presents i.e. dishes, and by-products) and the second subset of nodes substitution based on changing the type of nodes; Section 8 denotes actions on those comestibles. The edges reflect the presents substitution based on changing the structure of the (possibly partial) sequence of steps taken in the recipe going graph; Section 9 discusses related literature; and Section 10 from the ingredients to final products.

Existing novice-friendly machine learning (ML) modeling tools center around a solo user experience, where a single user collects only their own data to build a model. However, solo modeling experiences limit valuable opportunities for encountering alternative ideas and approaches that can arise when learners work together; consequently, it often precludes encountering critical issues in ML around data representation and diversity that can surface when different perspectives are manifested in a group-constructed data set. To address this issue, we created Co-ML -- a tablet-based app for learners to collaboratively build ML image classifiers through an end-to-end, iterative model-building process. In this paper, we illustrate the feasibility and potential richness of collaborative modeling by presenting an in-depth case study of a family (two children 11 and 14-years-old working with their parents) using Co-ML in a facilitated introductory ML activity at home. We share the Co-ML system design and contribute a discussion of how using Co-ML in a collaborative activity enabled beginners to collectively engage with dataset design considerations underrepresented in prior work such as data diversity, class imbalance, and data quality. We discuss how a distributed collaborative process, in which individuals can take on different model-building responsibilities, provides a rich context for children and adults to learn ML dataset design.

We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of an abstract nature. Our method allows users to quickly and easily sketch a shape, and then maps the sketch into the latent space of a part-aware neural implicit shape architecture. SENS analyzes the sketch and encodes its parts into ViT patch encoding, then feeds them into a transformer decoder that converts them to shape embeddings, suitable for editing 3D neural implicit shapes. SENS not only provides intuitive sketch-based generation and editing, but also excels in capturing the intent of the user's sketch to generate a variety of novel and expressive 3D shapes, even from abstract sketches. We demonstrate the effectiveness of our model compared to the state-of-the-art using objective metric evaluation criteria and a decisive user study, both indicating strong performance on sketches with a medium level of abstraction. Furthermore, we showcase its intuitive sketch-based shape editing capabilities.

In the next five years, it is likely that AI will begin to reduce employment for college-educated workers. As the technology continues to advance, it will be able to perform tasks that were previously thought to require a high level of education and skill. This could lead to a displacement of workers in certain industries, as companies look to cut costs by automating processes. While it is difficult to predict the exact extent of this trend, it is clear that AI will have a significant impact on the job market for college-educated workers. It will be important for individuals to stay up to date on the latest developments in AI and to consider how their skills and expertise can be leveraged in a world where machines are increasingly able to perform many tasks.

To get started begin by ploughing the land to prepare it for sowing the seeds. Wait for few years and watch them grow, keep watering and adding fertilizers to get a good quality produce. After the corn, wheat and apples are ripe harvest them and don't forget to pick the bark of cinnamon trees. Get the corn and process it to make sugar meanwhile also mill and grind the wheat to make flour. Boil the saline water at medium heat until the whole water is evaporated and only salt remains.

I cannot put exact words on my frustration. I have been a Data Scientist for 3 years and I have been struggling for most of these 3 years to explain to my family what the purpose and day to day activities of my jobs are. First of all, my family only speaks French and Data Scientist does not translate fully to French. Officially, the French government proposed a translation: "expert en métadonnées". Spoiler Alert, it means nothing to anyone.