Take Audio Analytic, who have announced an experimental baby monitor, running AI-powered sound recognition on an Arm Cortex-M0+ -based processor. This processor, used in devices such as bank smartcards, has an ultra-low energy footprint. The monitor uses real-time on-device analysis of a baby crying, incorporated with a simple LED warning light that flicks from green to red when the device recognizes the baby’s cry. This alert system illustrates how to apply AI where it is most useful, taking into account constraints of networking, power consumption, and processing.

Closer to home, we see utility companies using smart meters for electricity and gas. Such meters transmit data on usage either to an individual walking by the building, or directly back to company headquarters. They have built-in processing and storage capabilities, allowing them to gather and process relevant information and await a passing sensor that they can send the data packets to, rather than requiring someone to approach each device, read it, and manually enter information into their logs.

AI-based devices such as these are more about serious function than science fiction, demonstrating the pervasiveness of AI today. They provide functional, and sometimes critical, capabilities for their users as well as producing huge efficiencies and savings for the companies that employ them. The fact that different use cases require unique architectural choices means that decision-makers need to understand the capabilities, patterns, and tradeoffs of AI-capable processors running in the cloud, edge, and endpoint.

So, how to make the right choices? In this paper, we review processor and platform options, then consider several use cases both to illustrate the potential of AI today and show how to devise, define, and deploy the right AI for the job.

The post Artificial Intelligence Where You Need It appeared first on Gigaom.

Given the rapid pace of technology, security has often lagged behind the implementation of mobile and IoT in the enterprise. While most CISOs have a mobile security plan that includes mobile device management (MDM), most MDMs do not adequately protect systems and data from intrusions via mobile device applications from unsecured WiFi networks. Furthermore, MDM lacks the capability to extend to IoT devices that run independently of mobile devices. This has resulted in a new category of applications focused on mobile threat detection (MTD). MTD extends the standard capabilities to IoT devices, with the goal of catching more threats, reducing false-positives,  and minimizing the time required to detect and remediate.

This market landscape report focuses on the players in the industry who are offering this next generation of security protection to the enterprise. This report will provide C-level executives and security professionals with an overview of threat detection and management solutions available in the mobile and IoT markets.

Key findings:

• MTD products have advanced significantly in the last few years, and are now well integrated with most standard MDM products on the market. These products have been narrowed to focus on a two-horse race between iOS and Android-based devices.
• Differentiations between MTD products focus on two main areas: 1) deep-learning capabilities; 2) extended functionality. Choices for security professionals will be determined by specific needs beyond the basic protections offered as well as by looking at vendor reputation for areas such as zero-day threats and ransomware attacks.
• Baseline enterprise requirements, including preventing man-in-the-middle attacks, jailbreaking, and phishing attacks, have become table stakes for MTD providers.
• IoT continues to be an issue for MTD products that traditionally have only focused on mobile device and network protection. IoT based devices that connect via mobile OS’ can take advantage of MTD to assist in protecting against threats, but this protection does not extend to independent devices connecting to networks directly.
• The proliferation of IoT devices is becoming a growing concern in the threat detection area, and several companies are exploring how to best handle the security implications. This will become a key differentiator for many MTD product offerings over the next two to three years.
• Many security executives and professionals are still behind in dealing with the threats associated with the use of autonomous IoT devices in their enterprise.

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