Arthur Cole
VentureBeat
Fredericksburg, VA, United States
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Past articles by Arthur:
Finding an easier way to AI adoption
AI poses such a massive implantation challenge that getting it wrong is more likely than not. It will touch every digital aspect of the enterprise eventually, which means it is rife with pitfalls, namely, in the integration, training and execution phases of the rollout and, for many it will lead to a wholesale reworking of processes and even the business model itself. → Read More
Does AI sentience matter to the enterprise?
Does the enterprise need to be concerned with AI sentience? Not yet, experts say. Even if such an algorithm were to arise, would it be all that useful in a practical sense? → Read More
How AI is quietly revolutionizing the back office
The challenge for enterprises is to allow AI to place traditionally manual processes under fully automated control. → Read More
How AI helps improve the workforce
There is every reason to believe AI will help solve many of the challenges facing the modern workforce as it marches into a new economy. → Read More
The intelligent way to detect fraud
AI is best when tasked with finding hidden patterns within large datasets. So, it’s no wonder that AI applications are increasingly used to detect fraud. → Read More
Embedded AI: Rise of the intelligent device
For the most part, embedded AI will work behind the scenes, quietly working out system hiccups that would otherwise go unnoticed. It might lend some support to more flashy applications like natural language processing and intelligent analytics, but for the most part, it will focus on the finite responsibilities of its host device. → Read More
Turning AI failure into AI success stories
AI implementations share a number of key characteristics. Rather than simply unleashing the technology first on one goal and then another in a linear fashion, which is the habit for most traditional technology initiatives, a more effective approach is to direct it at three critical capabilities: business transformation, enhanced decision-making and systems and process modernization. → Read More
AI and low/no code: What they can and can’t do together
Low-code and no-code are intended to make it simpler to create new applications and services, so much so that even nonprogrammers can create the tools they need to complete their own tasks. If this technology can be combined with AI to help guide development efforts, there's no telling how productive the enterprise workforce can become in a few short years. → Read More
AI and low/no code: What they can and can’t do together
Low-code and no-code are intended to make it simpler to create new applications and services, so much so that even nonprogrammers can create the tools they need to complete their own tasks. If this technology can be combined with AI to help guide development efforts, there's no telling how productive the enterprise workforce can become in a few short years. → Read More
AI and low/no code: What they can and can’t do together
Low-code and no-code are intended to make it simpler to create new applications and services, so much so that even nonprogrammers can create the tools they need to complete their own tasks. If this technology can be combined with AI to help guide development efforts, there's no telling how productive the enterprise workforce can become in a few short years. → Read More
Finding AI’s low-hanging fruit
In some circles, the idea of going smaller with AI is catching on. Instead of a complete forklift upgrade across the entire business process, it’s better to do the easy stuff first. → Read More
The quest for explainable AI
Explainable AI seeks to infuse algorithms with transparency so that even lay users can ensure their AI is operating within acceptable bounds. → Read More
Advice for deploying AI in production environments
Deploying AI into real-world environments is probably the most crucial stage of its evolution because this is where it will finally prove itself to be a boon or a bane to the business model. → Read More
How to protect AI from cyberattacks – start with the data
AI is a digital technology, which means it can be compromised particularly when confronted by an intelligent attack. → Read More
The success of AI lies in the infrastructure
AI is already changing the way infrastructure is being designed all the way out to the edge. → Read More
MLops: The Key to Pushing AI into the Mainstream
MLops is still an emerging field, so it may be tempting to write it off as just another techy buzzword, but its track-record proves that when designed the right way and targeted at the proper goal: to maximize model performance and improve ROI, it pays off. → Read More
How AI is making real contributions (right now) to business models
The year 2022 is shaping up to be the year that AI finally starts to produce solid returns on the investments of the past few years. → Read More
Why AI democratization will bring more power to the enterprise
Using techniques like natural language processing (NLP) and neural networking, AI will very likely bring an end to the graphical user and even command line interfaces → Read More
How AI will change the data center and the IT workforce
As with most everything AI touches, the data center will become leaner, less costly to operate and achieve higher performance metrics. → Read More
How far should AI’s decision-making authority go?
In the near future, AI will be tasked with broad decision-making capabilities to streamline data flows, improve manufacturing processes, direct traffic and perform a wide range of other functions., This begs the question, where is the line between what AI should decide and what is best left for humans? → Read More