Univariate and multivariate feature selection methods can be used for biomarker discovery in analysis of toxicant exposure. Among the univariate methods, differential expression analysis (DEA) is often applied for its simplicity and interpretability. A characteristic of methods for DEA is that they treat genes individually, disregarding the correlation that exists between them. On the other hand, some multivariate feature selection methods are proposed for biomarker discovery. Provided with various biomarker discovery methods, how to choose the most suitable method for a specific dataset becomes a problem. In this paper, we present a framework for comparison of potential biomarker discovery methods: three methods that stem from different theories are compared by how stable they are and how well they can improve the classification accuracy. The three methods we have considered are: Significance Analysis of Microarrays (SAM) which identifies the differentially expressed genes; minimum Redundancy Maximum Relevance (mRMR) based on information theory; and Characteristic Direction (GeoDE) inspired by a graphical perspective. Tested on the gene expression data from two experiments exposing the cod fish to two different toxicants (MeHg and PCB 153), different methods stand out in different cases, so a decision upon the most suitable method should be made based on the dataset under study and the research interest.

We describe two theorem proving tasks -- premise selection and internal guidance -- for which machine learning has been recently used with some success. We argue that the existing methods however do not correspond to the way how humans approach these tasks. In particular, the existing methods so far lack the notion of a state that is updated each time a choice in the reasoning process is made. To address that, we propose an analogy with tasks such as machine translation, where stateful architectures such as recurrent neural networks have been recently very successful. Then we develop and publish a series of sequence-to-sequence data sets that correspond to the theorem proving tasks using several encodings, and provide the first experimental evaluation of the performance of recurrent neural networks on such tasks.

This week, Nvidia reported earnings and revenues that were down compared to a year ago. But they did signal a return to growth after a couple of week quarters as the company worked off inventory pile-ups related to the collapse of cryptocurrency mining. People aren't buying graphics cards to mine for cryptocurrency anymore, but they are beefing up their gaming PCs to play high-end games, and developers are now embracing Nvidia's Turing architecture in its RTX graphics cards, said Jensen Huang, CEO of Nvidia, in an analyst call this week. But the artificial intelligence chip market had a pause with a slowdown in hyperscale deployments in data centers. We caught up with Huang for a few minutes on Thursday to talk about the state of the gaming market.

Yes, it is indeed one of the most burning paradoxes of modern times. Do you really have to fear the fact that artificial intelligence (AI) will snatch away your only way to livelihood? Well, both history and facts says a different story. The way I see it, here is humanity's best shot to dedicate all mundane and repetitive tasks to bots while we can engage ourselves into something more creative that would further the cause of the business. If you are a pizza delivery boy in London, you don't have to worry for a second about your job loss when you see, the self-driving robots doing the delivery.

I just returned from attending several spring digital marketing conferences – Adobe Summit and Martech West. In both the art of the possible was on full display and got me thinking about whether fully-automated AI-driven digital marketing could ever be a thing, and what realistic automation goals look like. Digital marketing continues to be on the leading edge of AI advances and high-tech innovations. Surveys repeatedly indicate AI professionals aim their efforts at infusing intelligence into digital marketing. And every day we're inundated with news of more advances in marketing automation such as: From all this, some might conclude the end of human-powered marketing is close at hand. But others, unconvinced by these tenuous signs, might simply respond, "Poppycock!"

NEW DELHi: The NITI Aayog has drawn up a plan for creating an institutional framework for artificial intelligence (AI) in the country.

Very little is known about the New York Police Department's use of facial recognition technology. What kind of facial recognition is in place? Which databases are being used? How many people have been scanned, and why? How long is their data retained?

Sharp jawline, doe-brown eyes and fluttery eye-lashes; Sophia, the world's first AI-powered humanoid is quite a stunner with impressive features. Besides looks, she boasts of an admirable sense of humour. Designed by Hong Kong-based company Hanson Robotics, Sophia has many human values like wisdom, compassion and kindness. She is also capable of expressing her emotions, holding eye contact, recognizing faces and understanding human speech. Well, Sophia is not the only humanoid robot the world is gushing over.

To integrate volatile renewable sources into the energy supply, capacities of the power grid have to be increased. The need for new lines can be reduced by better utilization of existing lines as a function of weather conditions. To this end, researchers of Karlsruhe Institute of Technology (KIT) work on self-learning sensor networks to model the cooling effect of weather based on real data. In favorable conditions, the line's power transmission can be enhanced in this way. To transport power from producers to consumers, to prevent temporary shutdown of plants that generate power from regenerative sources, in particular at high wind intensities, and to ensure high supply security in general, considerable extension of the existing grid infrastructure is required.