Breakthroughs, discoveries, and DIY tips sent every weekday. A team of ancient literature experts have deciphered a Mesopotamain text that was missing for over 1,000 years. Etched on clay tablets, the Hymn to Babylon describes the ancient megacity in "all of its majesty," and gives new insights into the everyday lives of those who resided there. The text is detailed in a study published in the journal Iraq. Founded in Mesopotamia around 2,000 BCE, Babylon was once the largest city in the world.

A hymn dedicated to the ancient city of Babylon has been discovered after 2,100 years. Sung to the god Marduk, patron deity of the great city, the poem describes Babylon's flowing rivers, jewelled gates, and'bathed priests' in stunning detail. Although the song was lost to time after Alexander the Great captured the city, fragments of clay tablets survived in the ruins of Sippar, a city 40 miles to the North. In a process that would have taken'decades' to complete by hand, researchers used AI to piece together 30 different tablet pieces and recover the lost hymn. Originally 250 lines long, scientists have been able to translate a third of the original cuneiform text.

Breakthroughs, discoveries, and DIY tips sent every weekday. A pink flamingo is typically associated with a laid back lifestyle, but the way that these leggy birds with big personalities feed is anything but chill. When they dip their curved necks into the water, the birds use their feet, heads, and beaks to create swirling water tornadoes to efficiently group their prey together and slurp up them up. The findings are detailed in a study published this week in the journal Proceedings of the National Academy of Sciences (PNAS). "Flamingos are actually predators, they are actively looking for animals that are moving in the water, and the problem they face is how to concentrate these animals, to pull them together and feed," Victor Ortega Jiménez, a study co-author and biologist specializing in biomechanics at the University of California, Berkeley, said in a statement.

Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included.

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relational representation to generalize over different objects; and (d) in addition to action effects, it can also learn exogenous effects that are not related to any action, e.g., moving objects, endogenous growth and natural development. The proposed learning approach combines a multi-valued variant of inductive logic programming for the generation of candidate models, with an optimization method to select the best set of planning operators to model a problem. Finally, experimental validation is provided that shows improvements over previous work.

Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). Planning as heuristic search traditionally addresses the computation of sequential plans by searching in a grounded state-space. On the other hand GP aims at computing algorithm-like plans, that can branch and loop, and that generalize to a (possibly infinite) set of classical planning instances. This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a novel GP solution space that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines different evaluation and heuristic functions for guiding a combinatorial search in our GP solution space. Lastly the paper defines a GP algorithm, called Best-First Generalized Planning (BFGP), that implements a best-first search in the solution space guided by our evaluation/heuristic functions.

Artificial intelligence programs are gradually becoming part of the perfume development process. In what way does perfumers' work engage with these new methods? How can we reconcile this rational, mathematical approach with a creative process requiring sensitivity and subjectivity? Is the future of perfumers at risk? Jean-Christophe Hérault, senior perfumer at IFF, explains the implications of what is sometimes referred to as a revolution, while reminding us of the importance of human intuition. What do artificial intelligence programs used in fragrance creation consist of?

Digital channels targeting the mass consumer market are helping insurers swell their client base without the need for traditional intermediaries – but at a cost. One cost is increased risk of fraud, global insurance fraud analytics firm FRISS said during an event in Chile outlining the results of the first fraud survey of its kind covering all Latin America. As technology develops and purchasing habits change, insurers are increasingly leveraging digital channels, allowing consumers to take out policies online, directly from an insurer, in a largely frictionless manner. The trend is forecast to continue in Latin America as insurers seek to keep costs under control, tap what is still an underpenetrated market, and stand out from the crowd. One consequence of this is the sidelining of brokers, whose detailed client knowledge can help prevent fraud, said FRISS, a Dutch firm that provides an AI-powered platform to help P&C insurers combat fraud and which carried out the survey of insurance companies.

A machine learning algorithm that can predict the mechanical properties of metal–organic frameworks (MOFs) offers a way to overcome these highly varied and versatile materials' achilles heel – their instability.1 The team behind this work hope that this computational tool will speed up acceptance of these materials by industry. MOFs are a type of crystalline coordination polymers that form porous structures by combining metal clusters and organic ligands. 'Their "building block" nature allows chemists to easily tune their syntheses to tailor the pore size and surface chemistry for a specific application,' explains David Fairén-Jiménez at the University of Cambridge, UK. 'However, if you wish to use MOFs in real life, you need to shape them into pellets, and this densification may destroy their porosity, thus their functionality.'