Does charitable giving still matter but need to change? Philanthropy, the use of private assets for public good, has been much criticised in recent years. Do elite philanthropists wield too much power? Is big-money philanthropy unaccountable and therefore anti-democratic? And what about so-called "tainted donations" and "dark money" funding pseudo-philanthropic political projects? The COVID-19 pandemic has amplified many of these criticisms, leading some to conclude that philanthropy needs to be fundamentally reshaped if it is to play a positive role in our future. Rhodri Davies, drawing on his deep knowledge of the past and present landscape of philanthropy, explains why it's important to ask what philanthropy is for because it has for centuries played a major role in shaping our world. Considering the alternatives, including charity, justice, taxation, the state, democracy and the market, he examines the pressing questions that philanthropy must tackle if it is to be equal to the challenges of the 21st century.

The goal of image interpretation is to convert raw image data into me- ingful information. Images are often interpreted manually. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form. Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi?cant area of research activity. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. These can p- ducereasonableresultsonsimpleimages,butthepresenceofnoise,occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Furthermore,- belling an object is often only the ?rst step of the interpretation process. In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. A convenient way of achieving this is to use a ?exible model to encode information such as the expected size, shape, appearance, and position of objects in an image. The use of ?exible models was popularized by the active contour model, or 'snake' [98]. A snake deforms so as to match image evidence (e.g., edges) whilst ensuring that it satis?es structural constraints. However, a snake lacks speci?city as it has little knowledge of the domain, limiting its value in image interpretation.