With the latest breakthroughs in artificial intelligence a whole new concept of food may soon radically change what we eat. And at the same time, some experts believe it could reduce global warming. If we were to start from scratch, and we want to figure out the most efficient way to deliver nutrition to the 7.1 billion people on this planet, the answer wouldn't be animals. Science would tell you to do something different. No longer based on animal ingredients, this is a food entirely based on plants - although it looks and tastes like the classic food.

Share the post "How artificial intelligence is changing our world – Intelligent Barbie dolls and other innovative uses – Part 2" Artificial intelligence is a field that has wide ranging applications across various industries as we saw in the first part of this series. Here are five other interesting uses of AI which will show you that with the right idea, there's literally no limit on what is possible with the technology. Have you ever wondered how an artificial neural network sees the world? DeepDream is made up of artificial neural networks (ANNs). This means that a computer is running stacked layers of artificial neurons which are used to process images.

This paper investigates the problem of cross-lingual transfer parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g., English). Existing model transfer approaches typically don't include lexical features, which are not transferable across languages. In this paper, we bridge the lexical feature gap by using distributed feature representations and their composition. We provide two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space. Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer. Furthermore, our framework is flexible enough to incorporate additional useful features such as cross-lingual word clusters. Our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages. It also significantly outperforms state-of-the-art delexicalized models augmented with projected cluster features on identical data. Finally, we demonstrate that our models can be further boosted with minimal supervision (e.g., 100 annotated sentences) from target languages, which is of great significance for practical usage.

As a reminder, all participants' lines will be in the listen-only mode. There will be an opportunity for you to ask questions after the presentation concludes. I would now like to hand the conference over to Mr. Aravind Viswanathan. Thank you and over to you, sir. We will begin the call with business highlights and overview by Abid, the Chief Executive Officer and Member of the Board, followed by the financial overview by our CFO, Jatin Dalal. Afterwards, the operator will open the bridge for Q&A with our management team. Before Abid starts, let me draw your attention to the fact that during this call, we may make certain forward-looking statements within the meaning of Private Securities Litigation Reform Act 1995. These statements are based on management's current expectations and are associated with uncertainties and risks, which may cause the actual results to differ materially from those expected. The uncertainties and risk factors are being explained in our detailed filings with the SEC. Wipro does not undertake any obligation to update the forward-looking statements to reflect events and circumstances after the date of filing thereof. The conference call will be archived and the transcript will be available on our website. Ladies and gentlemen, let me now hand it over to Mr. Abid. Today is the first opportunity for me to interact with all of you since I've taken over as the Chief Executive Officer of Wipro, and it's a special moment for me. While I will speak about the performance of our full quarter and the full fiscal year, I thought I will take this opportunity to begin by speaking about our ambition, our strategy and how we are going to execute this strategy. Since I got announced within two days, I was able to define and announce my structure and I had already preselected my leadership team which I announced on 6th of January, effective February 1. Over the past 80 days after I have taken over as CEO, I've had the opportunity to go around the globe and meet about 70 of our top 100 clients. And both with my leadership team and with the customers, I've had the opportunity to validate the strategy that we have been working on and this gives me a high level of confidence on the relevance of our overall strategy. Our ambition is to double our revenues to 15 billion by fiscal 2020 with a 23% operating margin.

PETALING JAYA: Malaysian Integrated Medical Professional Association (Mimpa) president Dr Dhesi Baha Raja has won the Pistoia Alliance Life Science Award for developing a mobile app that predicts dengue outbreaks. Dr Dhesi developed an AIME (Artificial Intelligence in Medical Epidemiology), which is a disease-prediction mobile platform that employs technology and data to give people prior warning of when disease outbreaks might occur. Dr Dhesi, who led the team of six people, which developed the app, said that winning the award, organised by the Pistoia Alliance of King's College London, proves and validates the technology used as a tool for dengue prevention. He told The Star Online on Wednesday that he is looking to bring the app to Malaysia within the next three months and believes that the technology would be useful to combat Malaysia's dengue problem. Dr Dhesi said that he would be working with mobile digital service provider Webe and the Health Ministry soon.

This time last year Steve Wozniak was sounding a cautionary note about the future of Artificial Intelligence (AI), warning that computers would one day take over from humans and joking that we might even end up as their pets. In a recent interview with Australia's ABC TV's Lateline the engineering genius appeared more sanguine about the future of self-aware, super-intelligent Artificial Intelligence and much more concerned with the real world killer robots that are all but with us: Lethal Autonomous Weapon Systems (LAWS). The Apple co-founder maintains that human-level Artificial Intelligence won't happen for "a very long time": It might take 200 years before they are really fully able to operate all of their needs in the world, until then they're going to need human beings … I'm not really worried at all. It's very scary to make autonomous weapons that are just following some programmed set of instructions … even when you're driving a car there is no one set of rules … if a lane is closed off you have to do something against the rules … I don't think it's a good idea at all. I don't think we can really stop it.

Which industries will come to the fore in the next decade, and beyond, and become hubs of innovation? According to former State Department official Alec Ross, they won't be the industries that have dominated technology thus far. Instead, artificial intelligence (AI), genomics and robotics will lead the way. On Tuesday, the Italian Embassy in Washington, D.C., held an event to discuss Ross' recently published book, The Industries of the Future. He expounded on the book's themes and highlighted what it will take for individuals, companies and countries to harness the changes that he sees coming to the global economy.

In this paper we show that the problem of deciding the consistency of a knowledge base in the Description Logic ALCM is ExpTime-complete. The M stands for meta-modelling as defined by Motz, Rohrer and Severi. To show our main result, we define an ExpTime Tableau algorithm as an extension of an algorithm for ALC by Nguyen and Szalas.

Probabilistic models with weighted formulas, known as Markov models or log-linear models, are used in many domains. Recent models of weighted orderings between elements that have been proposed as flexible tools to express preferences under uncertainty, are also potentially useful in applications like planning, temporal reasoning, and user modeling. Their computational properties are very different from those of conventional Markov models; because of the transitivity of the “less than” relation, standard methods that exploit structure of the models, such as variable elimination, are not directly applicable, as there are no conditional independencies between the orderings within connected components. The best known algorithms for general inference inthese models are exponential in the number of statements. Here, we present the first algorithms that exploit the available structure. We begin with the special case of models in the form of chains; we present an exact O(n^3) algorithm, where n is the total number of elements. Next, we generalize this technique to models in which the set of statements are comprised of arbitrary sets of atomic weighted preference formulas (while the query and evidence are conjunctions of atomic preference formulas), and the resulting exact algorithm runs in time O(m * n^2 * n^c), where m is the number of preference formulas, n is the number of elements, and c is the maximum number of elements in a linear cut (which depends both on the structure of the model and the order in which the elements are processed)—therefore, this algorithm is tractable for cases in which c can be bounded to a low value. Finally, we report on the results of an empirical evaluation of both algorithms, showing how they scale with reasonably-sized models.

Bisimulation provides structural conditions to characterize indistinguishability between nodes on graph-like structures from an external observer. It is a fundamental notion used in many areas. However, many applications use graphs where nodes have data, and where observers can test for equality or inequality of data values (e.g., asking the attribute "name" of a node to be different from that of all its neighbors). The present work constitutes a first investigation of "data aware"' bisimulations on data graphs. We study the problem of computing such bisimulations, based on the observational indistinguishability for XPath — a language that extends modal logic with tests for data equality. We show that in general the problem is pspace-complete, but identify several restrictions that yield better complexity bounds (coNP, ptime) by controlling suitable parameters of the problem; namely, the amount of em non-locality allowed, and the class of models considered (graph, DAG, tree). In particular, this analysis yields a hierarchy of tractable fragments.