According to PwC’s report on XAI, AI has a $15.7 trillion of opportunity by 2030. However, as AI tools become more advanced, more computations are done in a “black box” that humans can hardly comprehend. This lack of explainability is unable to satisfy the need for transparency, trust, and a good understanding of expected business outcomes. Explainability is key to enterprise adoption of AI because people don’t easily trust a machine’s recommendations that they don’t thoroughly understand.
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 become more widespread, 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’ Sagemaker service 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.
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.
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.
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