AI chips in 2020: Guide to cost-efficient AI training & inference

In last decade, machine learning, especially deep neural networks have played a critical role in the emergence of commercial AI applications. Deep neural networks were successfully implemented in early 2010s thanks to the increased computational capacity of modern computing hardware. AI hardware is a new generation of hardware custom built for machine learning applications.

As the artificial intelligence and its applications emerge, the race to develop cheaper and faster chips is likely to accelerate among tech giants. Companies can either rent these hardware on the cloud from cloud service providers like Amazon AWS or buy their hardware. Own hardware can result in lower costs if utilization can be kept high. If not, companies are better off relying on the cloud vendors.  Read more


AI Security in 2020: Defend against AI-powered cyberattacks

Businesses can implement AI-powered security solutions into their systems to protect against online & offline security issues. Though AI is an effective solution to protect organizations from cyberattacks, it also enables attackers to launch complex, automated attacks.

Another aspect of AI security is the security of machine learning systems powering decision making of companies and autonomous systems. It has been proven that simple changes in inputs can cause these systems to fail, enabling attackers another attack surface. Therefore, companies need to consider security when implementing AI solutions. Read more


Autonomous Things in 2020: In-Depth Guide

Though self-driving vehicles take the front seat when we talk about autonomous things, autonomous robots and drones can also make a difference for businesses. These technologies can lead to partial or full automation of tasks involving humans today.

Transportation, retail, security and military are some of the industries with early examples of autonomous things. Autonomy will eventually revolutionize every industry.

What is Autonomous Things (AuT)?

Autonomous Things (AuT), or the Internet of Autonomous Things (IoAT), are devices that work on specific tasks autonomously without human interaction thanks to AI algorithms. These devices include robotics, vehicles,  drones, autonomous smart home devices and autonomous software.  Read more


Augmented Analytics in 2020: Democratization of Analytics

 AI capabilities such as machine learning, natural language processing and computer vision and to some degree other technologies like AR and VR are poised to augment analytics activities like preparing data and identifying insights. Augmented analytics will enable companies to run more efficient and effective analytics departments and internalize data-driven decision making and enable employees to become citizen data scientists.

What is augmented analytics?

Cognitive or AI-driven or augmented analytics all mean modern analytics: analytics that leverages the latest advances in AI algorithms such as deep learning. When you google these terms, you may find slightly different explanations  Read more


Cognitive Computing = AI. Let’s not create another fancy term

Cognitive computing is another term for AI and cognitive analytics is analytics that leverages the latest advances in AI.

Wikipedia agrees with that. Wikipedia states that:

At present, there is no widely agreed upon definition for cognitive computing in either academia or industry.

And then goes on to explain that cognitive computing includes AI. We couldn’t agree more.

Though there is some verbiage around how cognitive differs from AI, we have not seen any rational  description of that difference. Vendors and some industry analysts and pundits make ill-defined distinctions between the 2 technologies but as a computer scientist and AI industry analyst, I can’t tell the difference. So unless someone comes along with a clear definition of how cognitive computing is different from AI, we act as though they are the same. Read more