I always thought of history more like a movie than a series of real events. I'd seen people all over the internet creating similar images – colorized Roman statues, colorized photos; and I wanted to give it a shot," Saladin wrote on her blog.

Seven years have passed since world leaders met in New York and agreed on 17 Sustainable Development Goals (SDGs) to solve major challenges such as poverty, hunger, inequality, climate change and health. The pandemic has undoubtedly diverted attention from some of these issues in the last couple of years. But even before COVID-19, the UN was warning that progress in meeting the SDGs was not advancing at the speed or scale needed. Greeting them in 2030 will be difficult. The pandemic has demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society.

InstaDeep is an EMEA leader in delivering decision-making AI products. Leveraging their extensive know-how in GPU-accelerated computing, deep learning, and reinforcement learning, they have built products, such as the novel DeepChain platform, to tackle the most complex challenges across a range of industries. InstaDeep has also developed collaborations with global leaders in the AI ecosystem, such as Google DeepMind, NVIDIA, and Intel. They are part of Intel's AI Builders program and are one of only 2 NVIDIA Elite Service Delivery Partners across EMEA. The InstaDeep team is made up of approximately 155 people working across its network of offices in London, Paris, Tunis, Lagos, Dubai, and Cape Town, and is growing fast.

Air pollution is a major public health problem worldwide although the lack of data is a global issue for most low and middle income countries. Ambient air pollution in the form of fine particulate matter (PM2.5) exceeds the World Health Organization guidelines in Rwanda with a daily average of around 42.6 microgram per meter cube. Monitoring and mitigation strategies require an expensive investment in equipment to collect pollution data. Low-cost sensor technology and machine learning methods have appeared as an alternative solution to get reliable information for decision making. This paper analyzes the trend of air pollution in Rwanda and proposes forecasting models suitable to data collected by a network of low-cost sensors deployed in Rwanda.

The US government has said it will fund data-gathering on the conflict in Ukraine. In addition to laying the groundwork for war-crime prosecutions, the move would share critical, real-time data with humanitarian organizations. The newly established Conflict Observatory will use open source investigation techniques (OSINT) and satellite imagery to monitor the conflict in Ukraine and collect evidence of possible war crimes. Outside organizations and international investigators would be able access the resulting database, a US State Department spokesperson confirmed in an email. Partners for the Conflict Observatory include Yale University's Humanitarian Research Lab, the Smithsonian Cultural Rescue Initiative, artificial intelligence company PlanetScape Ai, and Esri, a geographic information systems company, according to a State Department press release.

You could be forgiven for believing that we've already achieved the era of autonomous vehicles. Tesla, the electric car manufacturer run by Elon Musk, refers to a version of its Autopilot software as "Full Self Driving". The company released a (misleadingly edited) video of an autonomous vehicle navigating city streets, its drivers' hands on their lap – a style replicated by enthusiasts. Musk has repeatedly assured in speeches and interviews that autonomous vehicles were one to two years away – or, as he put it in 2015, a "solved problem" because "we know what to do and we'll be there in a few years." But the existing Autopilot technology has not yet realized those promises and, as a new New York Times documentary illustrates, the gap in expectation and reality has led to several deadly crashes.

This paper studies the cooperative learning of two generative flow models, in which the two models are iteratively updated based on the jointly synthesized examples. The first flow model is a normalizing flow that transforms an initial simple density into a target density by applying a sequence of invertible transformations. The second flow model is a Langevin flow that runs finite steps of gradient-based MCMC toward an energy-based model. We start from proposing a generative framework that trains an energy-based model with a normalizing flow as an amortized sampler to initialize the MCMC chains of the energy-based model. In each learning iteration, we generate synthesized examples by using a normalizing flow initialization followed by a short-run Langevin flow revision toward the current energy-based model. Then we treat the synthesized examples as fair samples from the energy-based model and update the model parameters with the maximum likelihood learning gradient, while the normalizing flow directly learns from the synthesized examples by maximizing the tractable likelihood. Under the short-run non-mixing MCMC scenario, the estimation of the energy-based model is shown to follow the perturbation of maximum likelihood, and the short-run Langevin flow and the normalizing flow form a two-flow generator that we call CoopFlow. We provide an understating of the CoopFlow algorithm by information geometry and show that it is a valid generator as it converges to a moment matching estimator. We demonstrate that the trained CoopFlow is capable of synthesizing realistic images, reconstructing images, and interpolating between images. Normalizing flows (Dinh et al., 2015; 2017; Kingma & Dhariwal, 2018) are a family of generative models that construct a complex distribution by transforming a simple probability density, such as Gaussian distribution, through a sequence of invertible and differentiable mappings. Due to the tractability of the exact log-likelihood and the efficiency of the inference and synthesis, normalizing flows have gained popularity in density estimation (Kingma & Dhariwal, 2018; Ho et al., 2019; Yang et al., 2019; Prenger et al., 2019; Kumar et al., 2020) and variational inference (Rezende & Mohamed, 2015; Kingma et al., 2016).

Royal Mail is building a fleet of 500 drones to carry mail to remote communities all over the UK, including the Isles of Scilly and the Hebrides. The postal service, which has already conducted successful trials over Scotland and Cornwall, will create more than 50 new postal drone routes over the next three years as part of a new partnership with London company Windracers. Drones, or UAVs (uncrewed aerial vehicles), can help reduce carbon emissions and improve the reliability of island mail services, Royal Mail claims. They offer an alternative to currently-used delivery methods that can be affected by bad weather – ferries, conventional aircraft and land-based deliveries. They can also take off from any flat surface (sand, grass or tarmac) providing it is long enough.

"For many supported languages, even the largest languages in Africa that we have supported - say like Yoruba, Igbo, the translation is not great. It will definitely get the idea across but often it will lose much of the subtlety of the language," Google Translate research scientist Isaac Caswell told the BBC.

As we continue to develop machine learning Operations (MLOps), we need to think of machine learning (ML) development and deployment flow as a Directed Acyclic Graph (DAG). DAG is a scary acronym, but so are LTSM, DNN, backward propagation, GAN, transformer, and many others. I think using "pipeline" is wrong. The problem with "pipeline" is that it is slang. I can assure you the human brain is not a "pipeline."