We took a break from analysing the present and asked an artificial intelligence to generate the vehicles these people have (plausibly) never designed. The results are not those that could have been achieved six months ago, nor those that could be achieved six months from now, and they tell us more about the software's research capacity and the different (often disturbingly asymmetrical) availability of information on Charlotte Perriand or Frank Lloyd Wright. But beware: they also tell us what and how much of these names are part of the cultural heritage floating around the web at this very moment, ready to generate astonishing epiphanies or legendary misunderstandings, depending on what we want to ask of them.

Transformers Interpret is a model explainability tool designed to work exclusively with the transformers package. In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files. Positive attribution numbers indicate a word contributes positively towards the predicted class, while negative numbers indicate a word contributes negatively towards the predicted class.

This article was published as a part of the Data Science Blogathon. Hey, are you working on a data science project, solving a problem statement related to data science, or experimenting with a statistical test to make further decisions and handling the most repeatedly cited statistical term, 'correlation'? Willing to correctly interpret these statistical terms (covariance, parametric and non-parametric correlation)? Which method will outperform or underperform in your problem statement in a specific scenario (i.e., sensitive to outliers, type of distribution, etc.)? Working on sample data and willing to know the probable error of the coefficient?

Algorithms determine who we interact with in our newsfeeds. They teach robots to learn, and to imagine. In many ways, algorithms have made software developers the rulers of the modern world. But what are they exactly? Where did they come from?

Stability AI, developer of the Stable Diffusion text-to-image generative AI model, announced Amazon's AWS as its preferred cloud platform.

GE has created a smart mixer that could help take more of the sting out of baking. For one thing, the Profile Smart Mixer with Auto Sense has a built-in scale that will enable you to measure ingredients directly in the mixing bowl -- just be sure to add each ingredient slowly so you don't plop in too much. It uses motor torque feedback to monitor the texture and viscosity of a mix. According to Crate and Barrel, which snagged the smart mixer as an exclusive product, the appliance will shut off when it detects that your concoction is ready for the next step. That should help you avoid having overmixed dough that results in overly chewy cookies.

Volkswagen's electric car lineup is now mature enough that it's introducing second-generation models -- and it appears the company is taking some criticism to heart. VW has teased a redesign of the ID.3 that addresses complaints about the first version while upgrading the technology. The compact EV now sports a "matured" design with a supposedly sharper-looking exterior and higher-quality interior materials. Importantly, it's also more functional -- there's a larger 12-inch infotainment display, two cupholders in the center console and a removable luggage compartment floor. The tech may be the centerpiece.

In the past, Deft said he felt frustrated when younger esports players or players he felt had inconsistent performances ended up winning Worlds. For example, Faker, who is the same age as Deft, has won Worlds three times, while Deft repeatedly lost in quarterfinals in previous years. But Deft said that if he felt frustrated about how his 2022 teammates were younger than him and had more easily achieved success, it would be unkind of him to feel this way since they were all on the same team.

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.

Can you trust what AI is telling you? It's a new AI model from OpenAI that's designed to chat in a conversational manner. Stuck for ideas on what to talk to a machine about, I decided to interview ChatGPT about the ethics of AI. Would it have the level of self-awareness to be honest about its own dangers? Would it even be willing to answer questions on how it behaves? And while ChatGPT started off by being commendably upfront about the ethics of what it does, it eventually descended into telling outright lies.