New Delhi/Kolkata, India – A pair of black Dandy Pik Pik loafers covered in sharp, uneven spikes and shiny studs was part of the evidence before Judge Pratibha M Singh in an intellectual-property lawsuit brought by French luxury shoe brand Christian Louboutin against an Indian shoe manufacturer in a Delhi high court last year. Louboutin's lawyers had already regaled the court with anecdotes about the iconic status of their shoes. The signature stilettos, with their luxuriant red soles, had starred in movies like The Devil Wears Prada and Sex and The City, and were registered as a trademark in India and other countries, they said. Riding on the brand's reputation, the lawyers were now trying to make the point that spiked shoes, too, were unique to Christian Louboutin, and the defendant, Shutiq – The Shoe Boutique, was manufacturing and selling their designs in India illegally. Incriminating evidence presented to Judge Singh included testimony from ChatGPT, saying that Christian Louboutin is known for spiked men's shoes. Then there were photographs of Shutiq's 26 spiked and bedazzled shoes next to Louboutin originals, including Dandy Pik Pik.

Embodied conversational agents (ECAs) are paradigms of conversational user interfaces in the form of embodied characters. While ECAs offer various manipulable features, this paper focuses on a study conducted to explore two distinct levels of presentation realism. The two agent versions are photorealistic and animated. The study aims to provide insights and design suggestions for speech-enabled ECAs within serious game environments. A within-subjects, two-by-two factorial design was employed for this research with a cohort of 36 participants balanced for gender. The results showed that both the photorealistic and the animated versions were perceived as highly usable, with overall mean scores of 5.76 and 5.71, respectively. However, 69.4 per cent of the participants stated they preferred the photorealistic version, 25 per cent stated they preferred the animated version and 5.6 per cent had no stated preference. The photorealistic agents were perceived as more realistic and human-like, while the animated characters made the task feel more like a game. Even though the agents' realism had no significant effect on usability, it positively influenced participants' perceptions of the agent. This research aims to lay the groundwork for future studies on ECA realism's impact in serious games across diverse contexts.

"Robot's Delight – A Lyrical Exposition on Learning by Imitation from Human-Human Interaction" is a video submission that won Best Video at the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI 2017). The team also provides an in-depth explanation of the techniques and robotics in the video. Although social robots are growing in popularity and technical feasibility, it is still unclear how we can effectively program social behaviors. There are many difficulties in programming social robots -- we need to design hundreds or thousands of dialogue rules, anticipate situations the robot will face, handle common recognition errors, and program the robot to respond to many variations of human speech and behavior. Perhaps most challenging is that we often do not understand the reasoning behind our own behavior and so it is hard to program such implicit knowledge into robots.

Steve is the Head of Data Science and AI at Australian Computer Society, a proactive social media contributor and LinkedIn influencer. By employing the right AI technology for your business, you can accelerate growth. But business leaders should not forcefully include AI in their operations; instead, they should find specific workflows in which AI can provide maximum value. For instance, if you are in a restaurant business, you might want to use AI to generate weekly analytics by processing electronic bills. Many executives have a trust issue with up-and-coming technologies like AI regarding how they will fit into their business ecosystem.

An AI-focused neural network software engineer walks into a data shop says hello to the shopkeeper. "I'll have two data preparation functions, one testing and debugging toolset, a couple of application log tracking systems and a bag of potatoes," asks the engineer. Okay it's not a great joke, there's no punchline and the potatoes part is definitely just a ruse, but the way we might build the Artificial Intelligence (AI) functions of tomorrow has a kind of composabe, package-able feel. If it's not quite off-the-shelf AI, then its composable AI that brings together some of the core functions that smart systems use regularly. It's still down to our neural network engineer to know the recipe and peel the spuds, but we can start to shop for many of the individual components needed now.

In this post, you will learn about the concept of R-Squared in relation to assess the performance of multilinear regression machine learning model with the help of some real-world examples explained in simple manner. Once we have built a multilinear regression model, the next thing is to find out the model performance. The model performance can be found out by calculating the value of the Residual Standard Error (RSE) or the value of R-Squared. Residual Standard Error can be defined as the difference between the mean value of the prediction made by the model and the population mean value. In this article, we will learn the technique of evaluating the model performance using the value of R-Squared. Let's take an example of a real-world scenario where we went out for shopping some sarees, for upcoming festival, with our Grandma.

Until one and a half years ago, Devidas Lonkar from Chakan town of Pune district had to depend on local fertiliser and pesticide sellers to resolve diseases and fungal issues in his crops. Hailing from an agrarian background, the 26-year-old farmer grows sugarcane, cabbage, cauliflower as well as beetroot and groundnuts across a 7-acre plot. "I would describe the symptoms of fungus or disease to the shopkeeper, to which he would then suggest various pesticides and add-ons. It took me a while before realising that these shopkeepers only suggested chemicals with short-lived efficiency that would inevitably bring farmers back to them within a couple of months," he says. "This app ended up saving me a lot of money as well as time. Sitting at home, I can now diagnose plant diseases and have already saved about Rs 1-1.5 lakh in a year that I would otherwise spend on fertilisers," he mentions.

Imagine you've just walked into the corner store to find something quick for dinner. The shopkeeper asks "How may I help you?" and you explain you are looking for an easy but somewhat healthy dinner. The shopkeeper points out that she just started carrying both Lean Cuisines and Amy's Kitchen meals in the freezer section. She assessed your needs in the moment and served up exactly what you were looking for. After buying Lean Cuisines three nights in a row, you stop in again.

In-game shops are more than handy outlets to transform random metal scraps and tired old gear into new and useful items. Shops offer a reprieve from the action of whichever digital world you've entered, allowing you to take a moment, breathe and consider the situation from afar. Do you want to play as a gun-toting tank or a sneaky spy? Is your bow powerful enough for the battles ahead? Do you have enough health potions?

"Robot's Delight – A Lyrical Exposition on Learning by Imitation from Human-Human Interaction" is a video submission that won Best Video at the 2017 ACM/IEEE International Conference on Human-Robot Interaction (HRI 2017). The team also provides an in-depth explanation of the techniques and robotics in the video. Although social robots are growing in popularity and technical feasibility, it is still unclear how we can effectively program social behaviors. There are many difficulties in programming social robots -- we need to design hundreds or thousands of dialogue rules, anticipate situations the robot will face, handle common recognition errors, and program the robot to respond to many variations of human speech and behavior. Perhaps most challenging is that we often do not understand the reasoning behind our own behavior and so it is hard to program such implicit knowledge into robots.