Part of the power of AI and deep learning is that AI training can indiscriminately learn things we don’t explicitly instruct it to learn. Unfortunately, it can pick up on trends that we would rather it not – such as the inherent gender bias in our use of language. Companies
New tools are enabling organizations to invite and leverage non-data scientists – say, domain data experts, team members very familiar with the business processes, or heads of various business units – to propel their AI efforts. There are advantages to empowering these internal “citizen data scientists,” but also risks. Organizations
We noted last year in Blockchain: Changing The Nature Of Doing Business And Re-imagine How To Manage Tangible And Digital Assets, that exciting and creative enterprise use cases built on blockchain-powered systems are driving real productivity and value at scale. As organizations begin to understand blockchain’s utility and promise, they’re
Computers were once seen as more or less infallible machines that simply processed discrete inputs into discrete outputs, whose calculations were never wrong. If a problem ever arose in a calculation or business process, it was definitionally caused by human error, not the computer. But as machines encroach on ever-more
Since the first computer was built, businesses and consumers have enjoyed a progression toward simpler and more intimate interactions with technology. Professors wielding punch cards gradually gave way to business people brandishing PCs and, more recently, mobile and wearable devices. In a sense, the connection to the digital world has
Too many leaders succumb to fear of missing out (FOMO) when new tech trends emerge and demand that something – anything – using the new tech be implemented immediately. This leads to wasted investment, missed opportunity and disillusionment about the new landscape. Emerging technologies are critical and demand attention and
ChatGPT, from OpenAI, shows the power of AI to take on tasks traditionally associated with “knowledge work.” But the future won’t just involve tasks shifting from humans to machines. When technology enables more people to complete a task, with help from a machine, the result is typically entirely new systems
I’m sure you are familiar with the old saying that a rising tide lifts all boats. There is the other side of that coin, perhaps not as well known, namely that a receding tide sinks all ships. Bottom-line, sometimes the tide determines whether you are going up or going down.
Understanding how to evaluate and manage algorithmic performance could be the difference between success and failure. This article outlines a six-step approach for defining what to measure and monitor. Central to this approach is to work out where the waste is by measuring failure states. These are critical to monitoring
You’re frustrated. Two functional leaders are pulling you into a nasty turf war when you need them to collaborate. You’re writing a frustrated reply, when a friend stops you. They recommend more appropriate wording, and that you ask the functional leaders to schedule a meeting to discuss conflicting priorities and
The COVID-19 pandemic has increased the focus on the use of artificial intelligence (AI) across the life sciences organization, from R&D to manufacturing, supply chain, and commercial functions. During the pandemic, company leadership and management realized that they could run many aspects of their business remotely and with digital solutions.
Facial recognition technology has entered the mass market, with our faces now able to unlock our phones and computers. While the ability to empower machines with the very human ability to identify a person with a quick look at their face is exciting, it’s not without significant ethical concerns. Suppose