If you rode the metro in the Brazilian city of Sao Paulo in 2018, you might have come across a new kind of advertising. Glowing interactive doors featured content targeted at individuals, according to assumptions made by artificial intelligence based on their appearance. Fitted with facial recognition cameras, the screens made instantaneous decisions about passengers' gender, age and emotional state, then served them ads accordingly. Digital rights groups said the technology violated the rights of trans and non-binary people because it assigned gender to individuals based on the physical shape of their face, potentially making incorrect judgments as to their identity. It also maintained a strictly male-female model of gender, ignoring the existence of non-binary people.

It is important for sociable recommendation dialog systems to perform as both on-task content and social content to engage users and gain their favor. In addition to understand the user preferences and provide a satisfying recommendation, such systems must be able to generate coherent and natural social conversations to the user. Traditional dialog state tracking cannot be applied to such systems because it does not track the attributes in the social content. To address this challenge, we propose DEUX, a novel attribute-guided framework to create better user experiences while accomplishing a movie recommendation task. DEUX has a module that keeps track of the movie attributes (e.g., favorite genres, actors,etc.) in both user utterances and system responses. This allows the system to introduce new movie attributes in its social content. Then, DEUX has multiple values for the same attribute type which suits the recommendation task since a user may like multiple genres, for instance. Experiments suggest that DEUX outperforms all the baselines on being more consistent, fitting the user preferences better, and providing a more engaging chat experience. Our approach can be used for any similar problems of sociable task-oriented dialog system.

LAI (Leaf Area Index) is of great importance for crop yield estimation in agronomy. It is directly related to plant growth status, net assimilation rate, plant photosynthesis, and carbon dioxide in the environment. How to measure LAI accurately and efficiently is the key to the crop yield estimation problem. Manual measurement consumes a lot of human resources and material resources. Remote sensing technology is not suitable for near-Earth LAI measurement. Besides, methods based on traditional digital image processing are greatly affected by environmental noise and image exposure. Nowadays, deep learning is widely used in many fields. The improved FCN (Fully Convolutional Network) is proposed in our study for LAI measure task. Eighty-two cucumber images collected from our greenhouse are labeled to fine-tuning the pre-trained model. The result shows that the improved FCN model performs well on our dataset. Our method's mean IoU can reach 0.908, which is 11% better than conventional methods and 4.7% better than the basic FCN model.

Large pretrained language models like GPT-3 have acquired a surprising ability to perform zero-shot classification (ZSC). For example, to classify review sentiments, we can "prompt" the language model with the review and the question "Is the review positive?" as the context, and ask it to predict whether the next word is "Yes" or "No". However, these models are not specialized for answering these prompts. To address this weakness, we propose meta-tuning, which trains the model to specialize in answering prompts but still generalize to unseen tasks. To create the training data, we aggregated 43 existing datasets, annotated 441 label descriptions in total, and unified them into the above question answering (QA) format. After meta-tuning, our model outperforms a same-sized QA model for most labels on unseen tasks, and we forecast that the performance would improve for even larger models. Therefore, measuring ZSC performance on non-specialized language models might underestimate their true capability, and community-wide efforts on aggregating datasets and unifying their formats can help build models that understand prompts better.

Researchers at UniSA have developed a cost-effective new technique to monitor soil moisture using a standard digital camera and machine learning technology. The United Nations predicts that by 2050 many areas of the planet may not have enough fresh water to meet the demands of agriculture if we continue our current patterns of use. One solution to this global dilemma is the development of more efficient irrigation, central to which is precision monitoring of soil moisture, allowing sensors to guide'smart' irrigation systems to ensure water is applied at the optimum time and rate. Current methods for sensing soil moisture are problematic – buried sensors are susceptible to salts in the substrate and require specialised hardware for connections, while thermal imaging cameras are expensive and can be compromised by climatic conditions such as sunlight intensity, fog, and clouds. Researchers from The University of South Australia and Baghdad's Middle Technical University have developed a cost-effective alternative that may make precision soil monitoring simple and affordable in almost any circumstance.

The digital bank founded by Prof. Amnon Shashua, among the founders of the self-driving auto-tech company Mobileye, officially began operations on Sunday, promising to shake up the Israeli banking sector and inject badly needed competition. First Digital Bank, Israel's first new banking institution in 43 years, aims to use artificial intelligence and other technology to create a personal ambiance without the actual human contact that comes with neighborhood branches. "Netflix killed off Blockbuster, Spotify disrupted the music industry and Tesla has left Ford and Mitsubishi in the dust. Banking is one of the few industries that hasn't undergone a revolution. Big, long-standing names control the market with too little competition and offer exactly the same products," said First Digital Bank's CEO, Gal Bar-Dea.

The pandemic forced much of normal life to move online. Digitization, the conversion of materials to digital format, gave way to digitALization, the transformation of whole processes to online formats. This evolution had been occurring for some time, and Covid-19 accelerated it. It also proved that many processes that were previously only on-ground could be delivered effectively and conveniently online. We saw the effect of digitalization and disruption with music (Spotify), streaming video (Netflix) and shopping (Amazon) prior to the pandemic.

It's barely been a month since DJI unveiled a new drone, and the company already has another to show. Note the absence of the Mavic branding, indicating we might be seeing more of a shift in how the company pitches each level of quadcopter it offers. It's not clear if the "S" branding here is an Apple-style upgrade to the previous model, but the Air 2S certainly looks like someone gave the previous model a light facelift. The design seems near-identical bar a few smoother lines and an extra pair of front obstacle avoidance cameras. But what's important here is the camera it seems.

DJI has a new drone, the Air 2S, and it's one of the best drones I've ever flown. The Air 2S is externally nearly identical to last year's Mavic Air 2. It even uses the same batteries, which makes upgrading a little cheaper. There are some very welcome changes in this update. The Air 2S adds an object detection camera to the top of the drone, which improves the collision avoidance system. It really helps when you're flying toward something at high speed, since the drone pitches forward, rendering the front sensor slightly less effective.

It all started with an odd pile of shells: a pile that, upon closer inspection, fell apart like a flower losing its petals, introducing a burned-out nature documentarian named Craig Foster--and, in time, the world--to the octopus hiding cleverly inside. Known simply as "her," she would become the star of My Octopus Teacher, the Oscar-nominated Netflix documentary and surprise pandemic hit that told the story of Foster's unlikely relationship with that eight-armed mollusk. Released in September 2020, it arrived at the perfect moment. Audiences exhausted by lockdowns and unrelenting 2020-ness were primed for escape into the undersea fantasia of South Africa's kelp forests, where Foster met her. Best-selling books like The Soul of an Octopus and Other Minds: The Octopus, the Sea, and the Deep Origins of Consciousness had whetted public curiosity about these uncannily intelligent creatures with whom humans last shared a common ancestor 600 million years ago. Yet while most writing about octopuses emphasizes their ostensibly alien, unknowable nature,1 and serious, science-minded nature documentaries elevate concern about biodiversity over sentiment for a single animal, My Octopus Teacher defied convention. It embraced Foster's feelings for the octopus, which over the course of a year evolved from curiosity to care--even to love. And though her own feelings were left for viewers to interpret, the film's indelible impression was of nature populated by species who are not only beautiful and exquisitely evolved and ecologically important, but highly sentient, too. Nautilus talked to Foster about his octopus teacher and how getting to know her changed the way he thinks about nature. I write a lot about nature and biology and ecology, but in the last few years I've focused on the minds of animals and how we think about them.