This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the early days of artificial intelligence, computer scientists have been dreaming of creating machines that can see and understand the world as we do. The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data. In recent years, computer vision has taken great leaps thanks to advances in deep learning and artificial neural networks. Deep learning is a branch of AI that is especially good at processing unstructured data such as images and videos.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the early days of artificial intelligence, computer scientists have been dreaming of creating machines that can see and understand the world as we do. The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data. In recent years, computer vision has taken great leaps thanks to advances in deep learning and artificial neural networks. Deep learning is a branch of AI that is especially good at processing unstructured data such as images and videos.

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.

Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time series data. However, their performance degrades substantially when applied to real-world data, especially long-term data with complex behaviors (e.g., long-term trend across years, mid-term seasonality across months, and short-term local variation across days). To address the modeling of such complex data with different behaviors at different frequencies (time spans), we propose a novel progressive learning paradigm of NODEs for long-term time series forecasting. Specifically, following the principle of curriculum learning, we gradually increase the complexity of data and network capacity as training progresses. Our experiments with both synthetic data and real traffic data (PeMS Bay Area traffic data) show that our training methodology consistently improves the performance of vanilla NODEs by over 64%.

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities. However, for dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem where there is a need to represent the changing nodes, attributes, and edges over time. The evolution of search engine responses to user queries in the last few years is partly because of the role of KGs such as Google KG. KGs are significantly contributing to various AI applications from link prediction, entity relations prediction, node classification to recommendation and question answering systems. This article is an attempt to summarize the journey of KG for AI.

We have seen some of the most promising use cases of the Artificial intelligence system in the healthcare field recently. In one such example, Google's AI lab DeepMind employed the futuristic tech to accurately detect the disease in Breast Cancer patients. Bacteria has increasingly become resistant to antibiotic drugs over the years as infections have become more dangerous. This has been amply manifested in the recent COVID-19 outbreak. Efforts seem to be gathering pace to find a vaccination or cure for the disease which has rocked the World.

Scientists estimate that up to 90pc of the carbon emissions from industrial use of fossil fuels could be captured in this way, although the practice requires a continued supply of suitable locations to store the captured carbon. The researchers from MIT caution that their algorithm is only as effective as the data which it has been fed. They plan to give it even more data in the future to train it to better analyse seismic waves. Laurent Demanet, a professor of applied mathematics and one of the authors of the paper, told MIT News: "Using this neural network will help us find the missing frequencies to ultimately improve the subsurface image and find the composition of the Earth." MIT's research was funded by petroleum refining business Total SA and the US Air Force.

After returning from summer vacation in September 1928, bacteriologist Alexander Fleming found a colony of bacteria he'd left in his London lab had sprouted a fungus. Curiously, wherever the bacteria contacted the fungus, their cell walls broke down and they died. Fleming guessed the fungus was secreting something lethal to the bacteria--and the rest is history. Fleming's discovery of penicillin and its later isolation, synthesis, and scaling in the 1940s released a flood of antibiotic discoveries in the next few decades. Bacteria and fungi had been waging an ancient war against each other, and the weapons they'd evolved over eons turned out to be humanity's best defense against bacterial infection and disease.

The European Commission's new digital plan proposes the creation of so-called data spaces, where industrial data can be shared across sectors. While this would be an important step towards a single market for good quality, interoperable data, it remains to be seen whether the EU member states will be able to reach a consensus on the plan and unlock the required funding. The Commission sees the limited access to data held by both public and private actors as one of the main obstacles to the development of AI technologies in Europe. As such, the idea of data spaces is an important step towards strengthening the EU's position in AI. The more data that is shared, especially among businesses, the more sophisticated algorithms can become.

Banjo relies on info scraped from social media, satellite imaging data and the real-time info from law enforcement. Banjo claims its "Live Time Intelligence" AI can identify crimes -- everything from kidnappings to shootings and "opioid events" -- as they happen. Banjo presents many of the same concerns that similar companies have encountered. One of the strongest arguments against surveillance practices by Clearview AI has been that the company's data storage and security protocols were untested and unregulated. As Clearview AI proved earlier this month, that can lead to massive data leaks.