Artificial Intelligence (AI) job recruitment is growing at an accelerated rate. This was the substance of what I shared with host Cisco Cotto during my appearance on CBS Radio affiliate WBBM News Radio 780 on Thursday, September 12, 2019. Please click here to listen to my segment in its entirety. Below are the questions Cisco asked me as well as my responses in italics. I hope you enjoy this.

This specification describes how a system implemented as computer programs on one or more computers in one or more locations can generate a sequence of audio data that includes a respective audio sample at each of multiple time steps . For example, the sequence of audio data can represent speech in a particular natural language or a piece of music.

Maria Bartiromo has been covering business news for 30 years, and she's got her eye on the next big wave: artificial intelligence. The Fox Business Network anchor, who recently re-signed with the network for a multiyear deal, is releasing an hour-long investigative documentary about artificial intelligence. The segment, which has been in the works for a year now, includes interviews with chief executive officers of major companies including IBM IBM, -0.76% and Ford. Fox News parent company Fox Corp FOXA, 0.72% was previously owned by MarketWatch parent News Corp NWS, -0.21%. Artificial intelligence isn't just making demands to Siri on Apple's iPhones, AAPL, -1.46% or telling your Google GOOG, -0.71% email inbox to identify spam.

On first sight, coming with a definition for "AI arts" does not sound hard. AI (an abbreviation for the term Artificial Intelligence) refers to computers being able to perform many human-like cognitive tasks, such as playing games of chess and Go, recognizing content in images, translating between languages, selecting best candidates in a job search based on their CVs, and so on. This is how AI has been traditionally understood, and we can extend this concept to the arts. Following this logic, "AI arts" would refer to humans programing computers to create with a significant degree of autonomy new artifacts or experiences that professional members of the art world recognize as belonging to "contemporary art." Or, we can teach computers skills of artists from some earlier historical period and expect that professional art historians recognize new artifacts the computer creates as possible art from this period.

On first sight, coming with a definition for "AI arts" does not sound hard. AI (an abbreviation for the term Artificial Intelligence) refers to computers being able to perform many human-like cognitive tasks, such as playing games of chess and Go, recognizing content in images, translating between languages, selecting best candidates in a job search based on their CVs, and so on. This is how AI has been traditionally understood, and we can extend this concept to the arts. Following this logic, "AI arts" would refer to humans programing computers to create with a significant degree of autonomy new artifacts or experiences that professional members of the art world recognize as belonging to "contemporary art." Or, we can teach computers skills of artists from some earlier historical period and expect that professional art historians recognize new artifacts the computer creates as possible art from this period.

Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at https://github.com/TF2-Engine/TF2. Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.

Could Apple be about to release the HomePod 2, a smaller version of its Siri smart speaker? That's a question we've been asking ourselves for a while now now – and while Apple's iPhone X launch event on September 12, 2018 didn't reveal what the next HomePod will look like, the HomePod 2 could finally be on the horizon. Nearly a year has passed since then, and the iPhone 11 launch saw no mention of the new Apple HomePod Mini – so, everything is pointing to a 2020 release date. The speculation is that the next version of the HomePod, the Apple HomePod 2, may be a more compact version of the original, with the name Apple HomePod Mini being rumored. According to a Bloomberg report in July 2018, Apple may have been looking to release the HomePod 2 sometime in early 2019, which would make sense based on the release date of the original HomePod – of course, it never actually materialized.

In this work we aim to understand and estimate the importance that a neural network assigns to various aspects of the data while learning and making predictions. Here we focus on the recognizing textual entailment (RTE) task and its application to fact verification. In this context, the contributions of this work are as follows. We investigate the attention weights a state of the art RTE method assigns to input tokens in the RTE component of fact verification systems, and confirm that most of the weight is assigned to POS tags of nouns (e.g., NN, NNP etc.) or their phrases. To verify that these lexicalized models transfer poorly, we implement a domain transfer experiment where a RTE component is trained on the FEVER data, and tested on the Fake News Challenge (FNC) dataset. As expected, even though this method achieves high accuracy when evaluated in the same domain, the performance in the target domain is poor, marginally above chance.To mitigate this dependence on lexicalized information, we experiment with several strategies for masking out names by replacing them with their semantic category, coupled with a unique identifier to mark that the same or new entities are referenced between claim and evidence. The results show that, while the performance on the FEVER dataset remains at par with that of the model trained on lexicalized data, it improves significantly when tested in the FNC dataset. Thus our experiments demonstrate that our strategy is successful in mitigating the dependency on lexical information.

Can artificial intelligence become an organic life form? Can it behave, or will it behave in the same way that biological life behaves? I don't think we can classify A.I. as a species, it's a technology. The only way we can make A.I conscious is to incorporate biology into it. We have to be able to integrate bacteria, microorganisms into the artificial intelligence.

Machine learning involves two processes that require compute – training and inference.