"The biggest change in human history will take place in the next decade," warns Aidan Meller, a Briton who ran an art gallery for 20 years until he became a pioneer by launching the world's first creative robot, Ai-Da. Introduced in 2019 as "the first humanoid artist," Ai-Da not only creates poems, paintings and sculptures, but also draws inspiration from the highest cultural references. Her name is not random either; it is a tribute to Ada Lovelace, a British mathematician considered the first computer programmer, also known for being the only legitimate daughter of the poet Lord Byron. Ai-Da's next action will be at the Giardini of the Venice Biennale on April 23. It will be the first time in the 120-year history of the Biennale that a robot artist will exhibit their work alongside that created by humans.

The dynamics of the global commercial artificial intelligence market continues to change over time, thanks to the persistent advancements in technology. This research report offers a detailed and insightful assessment of the global commercial artificial intelligence market, taking primary trends and the future prospects of this market in consideration. Various segments of this market, based on a number of parameters, have also been evaluated to gain a clear overview of the dynamics in the worldwide commercial artificial intelligence market. The global commercial artificial intelligence market demonstrates a highly fragmented and competitive business landscape. The leading companies in this market, including NVIDIA, Intel, IBM, Google, Microsoft, AWS, General Vision, GE, Siemens, and Mitsubishi Electric, are all competing on the basis of R&D, innovations, and new product launches.

Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We achieve our goal by leveraging and combining a pretrained language-image model (CLIP), to steer the edit towards a user-provided text prompt, with a denoising diffusion probabilistic model (DDPM) to generate natural-looking results. To seamlessly fuse the edited region with the unchanged parts of the image, we spatially blend noised versions of the input image with the local text-guided diffusion latent at a progression of noise levels. In addition, we show that adding augmentations to the diffusion process mitigates adversarial results. We compare against several baselines and related methods, both qualitatively and quantitatively, and show that our method outperforms these solutions in terms of overall realism, ability to preserve the background and matching the text. Finally, we show several text-driven editing applications, including adding a new object to an image, removing/replacing/altering existing objects, background replacement, and image extrapolation. Code is available at: https://omriavrahami.com/blended-diffusion-page/

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-available satellite imagery, can be used to produce cost-effective and high spatial-resolution crop maps. However, accessing ground truth data for supervised learning is especially challenging in developing countries due to factors such as smallholding and fragmented geography, which often results in a lack of crop type maps or even reliable cropland maps. Our area of interest for this study lies in Himachal Pradesh, India, where we aim at producing an open-access binary cropland map at 10-meter resolution for the Kullu, Shimla, and Mandi districts. To this end, we developed an ML pipeline that relies on Sentinel-2 satellite images time series. We investigated two pixel-based supervised classifiers, support vector machines (SVM) and random forest (RF), which are used to classify per-pixel time series for binary cropland mapping. The ground truth data used for training, validation and testing was manually annotated from a combination of field survey reference points and visual interpretation of very high resolution (VHR) imagery. We trained and validated the models via spatial cross-validation to account for local spatial autocorrelation and selected the RF model due to overall robustness and lower computational cost. We tested the generalization capability of the chosen model at the pixel level by computing the accuracy, recall, precision, and F1-score on hold-out test sets of each district, achieving an average accuracy for the RF (our best model) of 87%. We used this model to generate a cropland map for three districts of Himachal Pradesh, spanning 14,600 km2, which improves the resolution and quality of existing public maps.

In speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring backends are commonly used, namely cosine scoring or PLDA. Both have advantages and disadvantages, depending on the context. Cosine scoring follows naturally from the spherical geometry, but for PLDA the blessing is mixed -- length normalization Gaussianizes the between-speaker distribution, but violates the assumption of a speaker-independent within-speaker distribution. We propose PSDA, an analogue to PLDA that uses Von Mises-Fisher distributions on the hypersphere for both within and between-class distributions. We show how the self-conjugacy of this distribution gives closed-form likelihood-ratio scores, making it a drop-in replacement for PLDA at scoring time. All kinds of trials can be scored, including single-enroll and multi-enroll verification, as well as more complex likelihood-ratios that could be used in clustering and diarization. Learning is done via an EM-algorithm with closed-form updates. We explain the model and present some first experiments.

Welcome to the brave new world of securo-feminism*. In the long tradition of systems of patriarchal violence representing themselves as the solution to patriarchal violence, the ongoing expansion of draconian "war on terror" measures is being advertised as an advance for women's rights. For instance, countries like the United Kingdom have extended anti-terrorism provisions to now not only strip citizenship from "terrorists", but also from (some of) those convicted of sexual abuse: a "double punishment" reserved exclusively for dual nationals and suspected dual nationals, predominantly Muslims and other racialised targets from former colonies in the Global South. Simultaneously, the British government itself is threatening the rights and safety of abuse survivors and others fleeing violence, with its proposed new bill to "secure the borders" by penalising asylum seekers for arriving by unauthorised routes (never mind that such penalties flagrantly violate international refugee law). In the United States, President Joe Biden's "feminist" credentials include the introduction of new justice mechanisms to address sexual assault within the military: packaged in the same piece of legislation escalating American "defence" spending to unprecedented heights, surpassing even the previous record set by his predecessor Donald Trump.

Artificial Intelligence has become so integrated into the technology stack that it goes largely unnoticed. When you take a picture with a newer iPhone, for instance, the phone collects a couple of frames and analyses it at the pixel level to identify what is in the scene and balance the exposure to produce a crisp, detailed final picture. All this happens in a fraction of the time it took you to read that sentence. Outside of turning you into a better photographer, AI is verifying your identity and enabling all the smart solutions that make your personal admin more convenient. There's an AI engine doing an instant credit assessment to approve your application in minutes.

The conversation is on hold. The Edge community has hit the road... or they're staying home. Preparing for the academic year to begin, wrapping up projects and starting new ones, celebrating with family and friends or contemplating in solitude. After a hiatus, Edge is pleased to revive Summer Postcards: Edgies reporting in from wherever they are and on whatever they're doing, as the dog days wind out and the season comes to a close. As the world slowly returns to a "new normal" with enduring COVID restrictions in the midst of renewed vaccine freedoms, this year's collection is a testament to change (temporary and lasting), a consideration of loss (will travel ever be like it was?), and a celebration of questions (that still need answering). The hammock may be away until next year, but the memories remain. I spent the summer writing and revising the final section of a longish novel I started in 2019. It seems now as though I've been from 1946 to 2021 on my hands and knees. Various lockdowns have been a liberation from obligations and the luggage carousel, and I've never known such sweet and total focus for months on end. We have the luxury of living in the country--no shortage of big skies and moody walks. All our few breaks were in the UK--Scotland, the Lake District, the West country. Even in our remote part of the Lakes, I had to keep on writing--as in photo. The best novel I read this summer was Sandro Veronesi's The Hummingbird. Best non-fiction was Peter Godfrey Smith's Metazoa: Animal Life and the Birth of the Mind. I gave time also to some wonderful novellas--perfect fictional form for you too-busy scientists. IAN MCEWAN is a novelist whose works have earned him worldwide critical acclaim. He is the recipient of the Man Booker Prize for Amsterdam (1998), the National Book Critics' Circle Fiction Award, and the Los Angeles Times Prize for Fiction for Atonement (2003). His most recent novel is Machines Like Me. In 2019, Časlav Brukner and myself were walking on a beach on Lamma Island, near Hong Kong, marvelling together at the astonishing strangeness of quantum phenomena. This summer, the conversation with Časlav has continued on another island, and quite an island: Lesbos, the northern Greek island near the Turkish coast. Lesbos is the place where lyrical poetry was born. Here lived Sappho and Alcaeus.

Dr Chris Kpodar, the Chairman and Chief Executive Officer of Solomon Investments Ghana Limited, says artificial intelligence (AI) represents the future and that should be the direction Ghana should be going. It is the pathway to achieving increased efficiency, lower human error rates and improved workflows among other highpoints. AI refers to the use of simulation of human intelligence in machines that are programmed to think like humans and mimic their actions and may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. Dr Kpodar, who is also a Chief Technical Advisor for the Centre for Greater Impact Africa (CGIA), said AI had become necessary in all spheres of life and gradually becoming the future. Ghana like all other developing countries must join the technological drive through strategic policies, he stated at a forum organized by the Ghana News Agency (GNA) in Tema.

The AfDB announced last year that it had approved a grant of just over $1m to support the creation of AI-backed systems to process customer complaints for the national banks of Ghana and Rwanda, and the Competition and Consumer Protection Commission of Zambia. A chatbot, or chatterbot, is a software application used to conduct an online chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. With literally millions of data points being created in a single day at major banks, humans are unable to comb through all the information fast enough. Automated AI systems can flag up potentially fraudulent activity and push this to skilled staff in the form of alerts, allowing personnel to focus on the most important tasks easily. For some banks, the idea of adopting AI solutions can seem like a complex undertaking, especially for those institutions that have legacy infrastructure where data is stored in disparate silos.