While solving mathematical systems is accomplished by analytical software, formulating a problem as a set of mathematical operations has been typically done manually by domain experts. Recent machine learning methods have shown promise in converting textual problem descriptions to corresponding mathematical formulations. This paper presents an approach that converts linear programming word problems into mathematical formulations. We leverage the named entities in the input and augment the input to highlight these entities. Our approach achieves the highest accuracy among all submissions to the NL4Opt Competition, securing first place in the generation track.

In the interest of interpreting neural NLI models and their reasoning strategies, we carry out a systematic probing study which investigates whether these models capture the crucial semantic features central to natural logic: monotonicity and concept inclusion. Correctly identifying valid inferences in downward-monotone contexts is a known stumbling block for NLI performance, subsuming linguistic phenomena such as negation scope and generalized quantifiers. To understand this difficulty, we emphasize monotonicity as a property of a context and examine the extent to which models capture monotonicity information in the contextual embeddings which are intermediate to their decision making process. Drawing on the recent advancement of the probing paradigm, we compare the presence of monotonicity features across various models. We find that monotonicity information is notably weak in the representations of popular NLI models which achieve high scores on benchmarks, and observe that previous improvements to these models based on fine-tuning strategies have introduced stronger monotonicity features together with their improved performance on challenge sets.

If you've never picked a raspberry, well, first of all that's too bad, because a fresh raspberry is a beautiful thing. But second, and more immediately relevant in this case, you would not know that there is a technique to it that, surprisingly, robots aren't super good at because they tend to be… crushy. But this robo-berry designed by Swiss researchers could usher in a new era of gentle, automated robo-pickers. The secret to picking a raspberry is to grip it just enough to get purchase and then pull it downwards off the little stem, apparently called the "receptacle," which seems backwards. Seems simple -- and it is, but only our hands are among the most sensitive and finely controlled constructions in the universe, the culmination of a hundred million years of evolution, outdone only by (I suspect) raccoons.

Google's do-it-yourself AIY kits released last year are already a great way to learn the ins and outs of designing a smart home speaker powered by Google Assistant, but they always came with a caveat: You needed to bring your own Raspberry Pi to the party. But with an update available today, Google is giving you everything you need right in the box. Once again available in two flavors, Voice and Vision, Google's new kits are a one-stop solution for building the next-generation of AI devices, and include a Raspberry Pi Zero WH, micro USB connection cable, and pre-provisioned SD card. Each kit also comes with the appropriate hardware you'll need to get your smart device up and running: Google's AIY Voice Kit includes everything you need to make a smart speaker. Google's AIY Vision Kit includes everything you need to make a smart camera.

As the field of artificial intelligence grows and enters the marketing mainstream it brings with it a host of new vocabulary and subtlety. At Adweek, a panel on AI declared it and machine learning to be the same thing for the purposes of their discussion. But while the terms might be interchangeable from an event perspective, they equate to very different things. As the fields of machine learning and AI become more entwined in our rhetoric it's important we have a clear understanding of the difference – the last thing the industry needs is another "native", where definitions vary wildly from one provider to another. An algorithm is a series of instructions written by a programmer for software to follow.

Octopus-like robots are plucking strawberries in Spain, in the US machines are vacuuming apples off the trees, and in the UK they are feeding and milking cows. Robots are taking over fields around the world, and last week food and rural affairs secretary Andrea Leadsom suggested they could help replace the thousands of EU workers who currently help put food on British tables. And it is not just Brexit that is forcing the agricultural industry to embrace the next phase of mechanisation. Farmers are already having to rethink their operations in the face of higher minimum pay – mainly a result of the national living wage for over-25s, which came into effect last year. Robotic milking machines, in which cows queue up to milk themselves, are now mainstream, while systems tat automatically feed or track the health of livestock are on the rise.