On Thursday, Intel debuted "Clover Falls," an AI companion chip that the company will ship as a platform-level enhancement for commercial PCs, and presumably later for consumer PCs as well. Intel described Clover Falls, aka the Intel Visual Sensing Controller, as a "secure companion chip that helps make PCs more smart and secure through the power of Intel artificial intelligence." It will be mounted on the laptop's motherboard, "bringing new low-power capabilities to the PC and helping it sense and adapt to its surroundings," according to a blog post published by the company. So what exactly will Clover Falls do? One example that Intel gave claimed that the Clover Falls module could help the system automatically adjust display brightness after detecting user presence.

These include H20.ai - an open source machine learning One; Active.ai - a conversational AI platform used by over five large Ms. Patwardhan shared her perspective from her past experiences in Facial recognition is the face of future for video banking. Not in the database yet was the verdict! Ms. Patwardhan highlighted that biometrics and digital identity can "One rolls its eyes cutely while greeting customers and the other "Has anyone watched Black Mirror?" she asked. "AI will continue to grow and have many positive

Banking group Standard Chartered, Liverpool FC's shirt sponsor, is aiming to use robots or artificial intelligence to offer bespoke wealth advice or handle customer enquiries. The Asia-facing bank has hired thousands of extra compliance officers in a bid to stick to regulations. In 2013 the bank was fined 650m by US authorities for breaching sanctions on trading with Iran. Last year it announced its regulatory costs had risen 44% to 447m due to an increase in investment in its financial crime risk compliance capability. Now, chief executive Bill Winters wants to tap into technology to avoid further regulatory problems.

This study presents an evaluation of WordNet-based semantic similarity and relatedness measures in tasks focused on concept similarity. Assuming similarity as distinct from relatedness, the goal is to fill a gap within the current body of work in the evaluation of similarity and relatedness measures. Past studies have either focused entirely on relatedness or only evaluated judgments over words rather than concepts. In this study, first, concept similarity measures are evaluated over human judgments by using existing sets of word similarity pairs that we annotated with word senses. Next, an application-oriented study is presented by integrating similarity and relatedness measures into an algorithm which relies on concept similarity. Interestingly, the results find metrics categorized as measuring relatedness to be strongest in correlation with human judgments of concept similarity, though the difference in correlation is small. On the other hand, an information content metric, categorized as measuring similarity, is notably strongest according to the application-oriented evaluation.