When you think about AI, you don't typically think about agriculture. But imagine how much easier farmers' lives would be if they could use computer vision to track livestock or detect pests in their fields. Just one problem: How can an enterprise leverage AI if they don't already have a team of data scientists? This is a pressing question not only in agriculture but also in a wide range of industrial businesses, such as manufacturing and logistics. After all, data scientists are in short supply! In this podcast, we explore how companies can deploy computer vision with their existing staff--no expensive hiring or extensive training required. We explain how to democratize AI so non-experts can use it, the possibilities that come from making AI more accessible, and unexpected ways AI transforms a range of industries. Our guests this episode are Elizabeth Spears, Co-Founder and Chief Product Officer for Plainsight, a machine learning lifecycle management provider for AIoT platforms, and Bridget Martin, Director of Industrial AI & Analytics of the Internet of Things Group at Intel . In her current role, Elizabeth works on innovating Plainsight's end-to-end, no-code computer vision platform. She spends most of her time focusing on products offered by Plainsight, particularly thinking of what new products to build, what order to build them in, and why they are needed. Bridget focuses on building up the knowledge and understanding that occur during the process of adopting AI, especially in an industrial space.

Amid global economic turmoil, using AI to improve online experiences and extract the most value from every investment is more important than ever. The advertising industry is a pioneer of AI and machine learning; harnessing the technologies to deliver personalised experiences that ensure the right content is put in front of the right people at the right time. AI News caught up with Tim van Kasteren, Head of Engineering at Adevinta, to learn more about how one of Europe's online classifieds leaders is using AI. AI News: From the top, how is AI improving online classifieds? Tim van Kasteren: Online classifieds are a form of two-sided marketplaces where a buyer and a seller come together to close a deal.

Through decades of support, Microsoft was an instrumental force helping China become the AI powerhouse it is today. Now, as the very thought of a U.S. company partnering in tech projects in China draws scrutiny from lawmakers, national security hawks, and human rights advocates, Microsoft could be forced to grapple with tough decisions surrounding the thriving AI ecosystem it fostered there. Microsoft established its research lab in Beijing in 1998, when it was a pioneer paving the way for AI research and business collaborations between the U.S. and China. It was three years before China joined the World Trade Organization, a time when President Bill Clinton actively pushed for closer trade ties with the country, and when AI was mostly the stuff of sci-fi pipe dreams. Since then, Microsoft Research Asia, or MSRA, has been known as one of the most influential hubs of AI research in the world, advancing speech recognition, natural language and image processing, and other deep-learning work, spreading its discoveries far and wide. Elements of research conducted at MSR China have been used to build Microsoft's advertising, chatbots, Bing search, Windows, Xbox, Azure Cloud, and other products used everywhere.

Q: How do you define AI in the spectrum of technology? John Edwards, vice president, Healthcare Solutions Consulting, SoftServe: We talk about artificial intelligence (AI). It's still a pipe dream that a robot would be thinking and feeling and being able to replace what a human brain does. But what AI was originally intended to do was to automate some set of activities that a person would typically do that you can create rules using computers to be able to replace that and do it more efficiently. And so, it is evolved to be a set of techniques that have their roots in statistics.

The face of a Connecticut farmer thought to be a vampire when he died of tuberculosis in the 19th century has been seen for the first time since his corpse was mutilated and tossed into a grave. The disease turns people's skin a pale yellow, their eyes become red and swollen and they sometimes have bloodstains around their mouth from coughing, which was believed to be signs of the undead about 200 years ago. The man's skeleton, buried in a casket with'JB55' engraved on it, was used to performed a DNA analysis that was fed to a machine learning system to predict what he may have looked like before being riddled with the disease. The results showed he had fair skin, brown or hazel eyes, brown or black hair and some freckles. The man, a farmer who lived in Connecticut, died of tuberculosis in the 19th century, which led people to believe he was a vampire.

Ukraine has developed drones that are capable of finding targets autonomously, a Ukrainian military leader has claimed, raising the prospect that the ongoing Russia-Ukraine war could see the first confirmed use of'killer robots' in armed conflict. Ukrainian Lieutenant Colonel Yaroslav Honchar gave details in an interview with Ukrainian news agency UNIAN on 13th October. Honchar is co-founder of Aerorozvidka ("Aerial Intelligence"), a team of around a thousand volunteer drone enthusiasts and technologists set up in 2014 to develop and use drones and other technology. Honchar says their drones already fly scout missions autonomously and mentions the possibility of automated strikes, but did not say such strikes had been carried out. Aerorozvidka declined to comment on the issue when asked by New Scientist.

There's an age-old adage in biology: structure determines function. In order to understand the function of the myriad proteins that perform vital jobs in a healthy body--or malfunction in a diseased one--scientists have to first determine these proteins' molecular structure. But this is no easy feat: protein molecules consist of long, twisty chains of up to thousands of amino acids, chemical compounds that can interact with one another in many ways to take on an enormous number of possible three-dimensional shapes. Figuring out a single protein's structure, or solving the "protein-folding problem, can take years of finicky experiments. But earlier this year an artificial intelligence program called AlphaFold, developed by the Google-owned company DeepMind, predicted the 3-D structures of almost every known protein--about 200 million in all. DeepMind CEO Demis Hassabis and senior staff research scientist John Jumper were jointly awarded this year's $3-million Breakthrough Prize in Life ...

Eric Schmidt has prodded the Pentagon for years to hurry along its software-buying process. Today the AI tech investor and former Google CEO is more determined than ever to urge government decision-makers to pick up the pace, but not just when it comes to buying more software for the Defense Department. Schmidt wants the government to implement his sweeping blueprint to fight what he considers an existential threat to democracy posed by China's AI plans, an effort that could also bolster his own commercial AI interests. He says the U.S.'s national security and economic leadership are dependent upon spending billions to procure smarter software, bolster AI research, and build the country's computer science talent pool. And he says he knows better than the Pentagon itself how to remove the bureaucratic blockades preventing more agile use of AI by the government. But at the same time, Schmidt's venture capital firm Innovation Endeavors has invested in companies that have received multimillion-dollar contracts from federal agencies. Some of those investments and contracts -- reported here for the first time -- were granted between 2016 and 2021 while Schmidt chaired two influential government initiatives, the Pentagon's Defense Innovation Board and the National Security Commission on Artificial Intelligence.

In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifies a latent variable to pick the relationship to exhibit in the next sentence and the story continuer generates the next sentence while expressing the selected relationship in a coherent way. Our automatic and human evaluations demonstrate that ReLiSt is able to generate stories with relationships that are more faithful to desired relationships while maintaining the content quality. The relationship assignments to sentences during inference bring interpretability to ReLiSt.

Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different. In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC. Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference. Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically. Experimental results on three KG datasets show that MoSE outperforms state-of-the-art MKGC methods. Codes are available at https://github.com/OreOZhao/MoSE4MKGC.