ML

Although the exact numbers of the market potential of IoT varies between various estimates, McKinsey sets it to be between $2.7(€2.19) to $6.2(€5.04) trillion by 2025. It is estimated that the number of connected devices is set to increase approximately five fold, from 27 billion in 2017 to 125 billion by 2030. However, this potential can be leveraged only

The Internet of Things (IoT) is hotter than ever and in 2018 there will be more collaboration between market leaders, even as the community of 300+ platforms begins inevitable consolidation. According to Matt Smith, CTO at Software AG, artificial intelligence (AI) and machine learning (ML) will begin to infiltrate IoT platforms and devices and new

SAP SE reports that nearly four out of 10 consumers who own in-home assistants are considering using them for holiday shopping this year — to purchase gifts for others (16%), research deals and savings for gifts (15%), and for their own purchases (7%). These findings derive from a survey commissioned by SAP of 1,000 U.S.

Dataiku, an enterprise solution for collaborative advanced analytics and machine learning, announced that it has achieved Amazon Web Services (AWS) Machine Learning (ML) Competency status. This designation recognises Dataiku for enabling machine learning for everyone,

Qualcomm Technologies, Inc., a subsidiary of Qualcomm Incorporated, and SenseTime Group Limited announced plans to collaborate on artificial intelligence (AI) and machine learning (ML) for future mobile and IoT products.

Cisco, has announced its intent to acquire MindMeld Inc., a privately held artificial intelligence (AI) company based in San Francisco. MindMeld has pioneered the development of a unique AI platform that enables customers to build intelligent and human-like conversational interfaces for any application or device.

What is normal? It’s important to remember there is no silver bullet in security, and there is no evidence at all that tools such as ML and AI can solve the problem. ML is good at finding similarities between things (such as spam emails), but it is not so good at locating anomalies.

A recurring claim at security conferences is that “security is a big data / machine learning (ML) / artificial intelligence (AI) problem”. This is unfortunately wildly optimistic, and wrong in general. While certain security problems can be addressed by ML/AI algorithms, in general the problem of detecting a malicious actor amidst the vast trove of