Any explanation that faithfully explains this type of model needs to be in agreement with this invariance property.

Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection methods tend to rely too heavily on the dialogue contexts or over-emphasize the new information in the selected knowledge, resulting in the selection of repetitious or incongruous knowledge and further generating repetitive or incoherent responses, as the generation of the response depends on the chosen knowledge. To address these shortcomings, we introduce a Coherent and Engaging Topic Transition (CET2) framework to model topic transitions for selecting knowledge that is coherent to the context of the conversations while providing adequate knowledge diversity for topic development. Our CET2 framework considers multiple factors for knowledge selection, including valid transition logic from dialogue contexts to the following topics and systematic comparisons between available knowledge candidates. Extensive experiments on two public benchmarks demonstrate the superiority and the better generalization ability of CET2 on knowledge selection. This is due to our well-designed transition features and comparative knowledge selection strategy, which are more transferable to conversations about unseen topics. Analysis of fine-grained knowledge selection accuracy also shows that CET2 can better balance topic entailment (contextual coherence) and development (knowledge diversity) in dialogue than existing approaches.

Interpretability methods are valuable only if their explanations faithfully describe the explained model. In this work, we consider neural networks whose predictions are invariant under a specific symmetry group. This includes popular architectures, ranging from convolutional to graph neural networks. Any explanation that faithfully explains this type of model needs to be in agreement with this invariance property. We formalize this intuition through the notion of explanation invariance and equivariance by leveraging the formalism from geometric deep learning. Through this rigorous formalism, we derive (1) two metrics to measure the robustness of any interpretability method with respect to the model symmetry group; (2) theoretical robustness guarantees for some popular interpretability methods and (3) a systematic approach to increase the invariance of any interpretability method with respect to a symmetry group. By empirically measuring our metrics for explanations of models associated with various modalities and symmetry groups, we derive a set of 5 guidelines to allow users and developers of interpretability methods to produce robust explanations.

From Apple to Coca-Cola, many of the world's biggest brands have logos that are instantly recognised by people around the world. But what if these logos had been designed by famous artists? Tech company Gnatta has used artificial intelligence (AI) to reimagine a number of logos in the styles of artists such as Banksy, Picasso and Monet. The system pieces together a completely new logo based on the artist's previous creations - can you guess what famous logos they are? Using AI communication tech company Gnatta has reimagined a number of company's logos from Nike to Toblerone in the styles of artists such as Banksy, Picasso and Monet - can you guess what brands these are based on?

The Lipstick Index, as it's known, was a phrase coined by Leonard Lauder, chairman of the board at Estee Lauder, in the early noughties when it became clear in times of economic crisis, sales of color cosmetics - in particular lipstick - soar as an affordable way to treat yourself. Last year, sales of cosmetics plummeted. With bustling make-up counters in department stores closed for much of 2020, sales of designer brand cosmetics were down by more than 40% according to market research firm NPD, which equates to a loss of £500 million (around $689 million and AU$902 million). Meanwhile, sales of more affordable cosmetics in supermarkets fell by 22% in the UK, according to the Top Products survey from retail trade magazine, The Grocer, adding a further £183 million loss (around $256 million / AU$218 million). A combination of the rise of working from home and mandatory masks meant thousands of us, including this writer, ditched our make-up bags altogether.

AI, AR, VR, or any form of virtual reality, isn't a new concept within the beauty industry, but it certainly is a remarkable one. Virtual reality has always stirred a web of speculation and curiosity amongst tech-savvy enthusiasts, and whether the enriching digital-based experience was put to the test by mass brands like Nike and IKEA, or luxury fashion houses such as Gucci and Louis Vuitton, augmented reality is the once-niche concept that is turning traditional ways of shopping and experiencing products into a revolutionary trend. It isn't surprising that the pandemic has helped accelerate the digital innovation in the beauty space. With retail shops temporarily closing their doors around the world, consumers have been driven online to fuel their beauty needs. To help make the online shopping experience easier, retailers are using AI technology -- and it's working.

The next time you spot a lip color that you like, you can quickly use AI to find a corresponding shade in Chanel's range of lipsticks. The company is announcing today its new Lipscanner app that lets you use your phone's camera to identify a hue -- whether it's on someone's lips or just the color of your favorite purse. Then, it'll suggest a match from Chanel's "lip universe," which includes more than 400 products encompassing different finishes and shades. This isn't the first time a beauty company has made an app to identify lipstick shades -- YSL's Perso system will even print out the exact color you scan, making it more sophisticated. Lipscanner will also allow you to virtually "try on" the lipstick to see if you like it.

The creation of a perfume is often treated as a bespoke art. The French pride themselves on centuries in the olfactory business, and professional scent masters -- often referred to as "noses" -- spend decades learning the craft, apprenticing under masters. Giant cosmetic companies such as Coty and Estée Lauder write huge checks to storied fragrance agencies, which will employ meticulous perfume chemists, scrupulous in the art of aromachology. A common theme here is that the skill of developing a fragrance is extremely valuable -- and extremely human. Scent is, after all, the sense that science says has the strongest ability to evoke memories, or trigger emotions and moods.

If you're wondering what the future of fashion looks like, it looks like an early '90s robot fever dream built for Web 2.0. Chanel took to the catwalk at Paris Fashion Week on Tuesday to show off its tech-inspired, Spring-Summer 2017 collection (yes, they're already that much cooler than you). Chanel creative director Karl Lagerfeld described it as an exercise in "intimate technology." Here's what we know for sure: Lagerfeld has been watching a lot of Daft Punk film clips, and next month's fashion magazines are going to be filled with "pixelated tweed." The ready-to-wear collection was shown off in Chanel's customary Paris runway venue, the Grand Palais.