We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to "spin" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a "meta-backdoor" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call "pseudo-words," and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models.

Conversational designers have training and expertise in crafting engaging conversations. Generally conversations are crafted around products and services. Hence a big part of the process is to improve the conversations by focusing & improving the design. For conversational interfaces, a big cause of missed intents from user utterances are new product and new services. The problem here is customers want to chat to your chatbot based on advertising and marketing, but the intents have not been updated.

Consider an alternative history for the war in Ukraine. Intrepid Ukrainian Army units mount an effort to pick off Russian supply convoys. But rather than rely on sporadic air cover, the Russian convoys travel under a blanket of cheap drones. The armed drones carry relatively simple artificial intelligence (AI) that can identify human forms and target them with missiles. The tactic claims many innocent civilians, as the drones kill nearly anyone close enough to the convoys to threaten them with anti-tank weapons.

On a sunny morning last December, Iyus Ruswandi, a 35-year-old furniture maker in the village of Gunungguruh, Indonesia, was woken up early by his mother. A technology company was holding some kind of "social assistance giveaway" at the local Islamic elementary school, she said, and she urged him to go. When he got there, representatives of Worldcoin were collecting emails and phone numbers, or aiming a futuristic metal orb at villagers' faces to scan their irises and other biometric data. Two months before Worldcoin appeared in Ruswandi's village, the San Francisco–based company called Tools for Humanity emerged from stealth mode. The company's website described Worldcoin as an Ethereum-based "new, collectively owned global currency that will be distributed fairly to as many people as possible."

Highly accurate different horizon-based wind speed forecasting facilitates a better modern power system. This paper proposed a novel astute hybrid wind speed forecasting model and applied it to different horizons. The proposed hybrid forecasting model decomposes the original wind speed data into IMFs (Intrinsic Mode Function) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). We fed the obtained subseries from ICEEMDAN to the transformer network. Each transformer network computes the forecast subseries and then passes to the fusion phase. Get the primary wind speed forecasting from the fusion of individual transformer network forecast subseries. Estimate the residual error values and predict errors using a multilayer perceptron neural network. The forecast error is added to the primary forecast wind speed to leverage the high accuracy of wind speed forecasting. Comparative analysis with real-time Kethanur, India wind farm dataset results reveals the proposed ICEEMDAN-TNF-MLPN-RECS hybrid model's superior performance with MAE=1.7096*10^-07, MAPE=2.8416*10^-06, MRE=2.8416*10^-08, MSE=5.0206*10^-14, and RMSE=2.2407*10^-07 for case study 1 and MAE=6.1565*10^-07, MAPE=9.5005*10^-06, MRE=9.5005*10^-08, MSE=8.9289*10^-13, and RMSE=9.4493*10^-07 for case study 2 enriched wind speed forecasting than state-of-the-art methods and reduces the burden on the power system engineer.

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a theoretical approach. Using empirical data from Brazil, we compare how well different machine learning models transfer knowledge between two different disease pairs: (i) dengue and Zika, and (ii) influenza and COVID-19. In the theoretical analysis, we generate data using different transmission and recovery rates with an SIR compartmental model, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers during pandemic response.

AI-powered camera traps give Gabon wildlife rangers a new tool in the fight against poaching and biodiversity loss. With around 24 million hectares of forest, Gabon is a biodiversity hotspot, including hosting one of the largest populations of the critically endangered African forest elephant (Loxodonta cyclotis). Now, researchers from the University of Stirling, UK, are using a new kind of camera trap to help monitor and protect this and other species. Traditional monitoring systems use cameras deployed in the field, but the results are often collected and analysed months later. "This tells you where the animals were," says Robin Whytock, who led the team behind the new camera traps.

They call it Jenny; a robotic solution with Artificial Intelligence, AI capabilities. Jenny has come to alter the status quo, disrupting the traditional method of multi-level marketing. With its AI capabilities, Jenny eliminates the degrading methods of public sharing of printed literature and the beggarly system of selling supplements and other health-based products in the market, at bus stops and on the streets. Built by Strategic Business Techspace for Wealth Solution Dynasty, WSD, Jenny has given a new impetus to multi-level marketing and handed WSD the bragging right as the first tech-based MLM outfit. The chatbox AI solution is innovative and multitasks as a marketer and customer relations interface between WSD and its public. It is sitting on the company's social media handles- WhatsApp, Facebook, Instagram, Twitter and even on its website.

Necessity is the mother of invention, and Rwanda's government seems to understand this more than most with the launch of the Centre of the Fourth Industrial Revolution (C4IR). "With the advent of the Fourth Industrial Revolution and the rapid innovations witnessed during the Covid-19 pandemic, there is an increased urgency to develop digital and technological capacities to build more resilient systems for a healthier society and more sustainable economy," said Rwandan Minister of Information Communication Technology and Innovation Paula Ingabire. Ingabire made the comment in a media statement posted on the World Economic Forum's (WEF) website. Rwanda has launched its C4IR, saying it will "work with stakeholders around the world to design and pilot new approaches to technology governance that foster innovation in an inclusive and responsible manner". Some of the projects that the C4IR is already working on are the country's artificial intelligence (AI) policy and laws on the protection of personal data and privacy.

AI-powered camera traps are being used for more than just documenting and monitoring animals -- they have also been a crucial tool in protecting the local wildlife from poachers, such is the case in Gabon in Central Africa. Congo, and Congo Basin, in particular, offer incredible biodiversity with roughly 400 species of mammals and 1,000 species of birds that reside in the largest area of forest preserve -- 80% of Gabon is covered in forests -- out of all African nations, reports Appsilon. Out of these diverse species are endangered wildlife -- elephants, bonobos, lowland gorillas, and chimpanzees, which are at the forefront of the country's so-called "Green Gabon" movement. It seeks to develop sustainable logging while preserving wildlife, with the help of various tracking systems using satellite imagery as well as camera traps on the ground. To help maintain Gabon's biodiversity, researchers from the University of Stirling in the United Kingdom have begun using a new kind of camera trap.