This week, 5 reasons why many businesses are still in AI adolescence.
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SEE: Algorithms as a Service: The Future of Computing
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Three Steps To Start Crafting The Life You Deserve
Wendy Fong, founder of Chief Gigs and talent expert, helps clients achieve optimum performance and thrive in a dynamic and diverse world.
I keep turning down lucrative job offers.
The conversation with my sister usually goes something like this:
Me: “I was talking to a potential client about a project and they recruited me for a full-time job instead.”
Her: “Don’t they know you don’t want to work?”
“Don’t want to work” isn’t really the best way to put it. Prior to starting my own business, I worked extremely hard. I lovetackling challenging problems and building teams, and I have remained close friends with coworkers throughout my career.
This is not a post about my wonderful track record or how great I think I am, but it’s actually about understanding what you value in life. I started my company at the end of 2019 after I was fired from my last job. In the beginning, it was an uphill battle—I was insecure about whether I would be successful, and I was constantly looking for another full-time job. Basically, I had one foot in and one foot out.
5 Ways To Use Tech To Find Time To Find New Clients
The number one pain for three out of five people who are in the business of selling their expertise is finding new clients.
Technology moves fast, there’s no doubt about that. Those who are in the business of selling their expertise—agency owners, business coaches, speakers, authors, and strategic consultants—need to find the time to find new clients.
Luckily, there are resources like bestselling author, speaker, and marketing strategist Minal Sampat to help us keep up. (We met when I helped her edit her bestselling book Why Your Marketing Is Killing Your Business.) Here’s what she has to say about growing your business using today’s tech:
Why startup fundamentals are key to AI strategy
Using the “technology startup mentality” to successfully integrate data science tools in commercial contexts
In his book Zero to One, Peter Thiel explores the core fundamentals that successful technology startups have in common. The takeaway: startups should embrace seven pivotal traits — otherwise, queue red flags. Here’s why great AI innovation projects also exhibit the fundamentals of successful technology startups, from the perspective of a Bay Area data scientist working in digital strategy consulting.
This post will explore the similarities (and some of the differences) between technology startups and digital transformation, with a focus on applied AI. The intended audience includes data practitioners (DS, DE, etc.), technologists, and business executives — as both technical and non-technical folks closely touch AI integration.
Strategic courage in an age of volatility
Today’s challenging environment requires business leaders to hone their edge in three critical areas: insights, commitment, and execution.
The late Brazilian car-racing champion Ayrton Senna once said, “You cannot overtake 15 cars in sunny weather, but you can when it’s raining.” Well, there’s been no shortage of downpours in recent years. We’re living in a world where new shocks—the war in Ukraine, the return of inflation—have been layered onto earlier shocks—a deadly global pandemic, supply chain disruptions—that in turn were layered onto, and dramatically accelerated, long-standing trends such as digitization and sustainability.
In almost all our recent conversations, CEOs, board members, and other business leaders share with us a common sentiment: this combination of shocks has created perhaps the most challenging environment management teams have ever faced—and one that likely won’t change anytime soon. We have entered an age of volatility.
How can we train a computer to feel?
When a neural network uses so much data it becomes “universal”
Computers are great at many things. Your laptop can perform thousands of computations in fractions of a second, render entire cityscapes to a hairline’s resolution, and store massive libraries of information on a hard-drive that is barely the size of your palm.
But, despite these strengths, there are also many seemingly mundane things that computers can’t do. Computers can’t feel. Neither can they intuit the world as easily as we can. Consider the task of sentiment analysis, for instance, where we aim to use automated processes to swiftly identify whether opinions in a body of text are positive or negative.
Ask any person to read a product review paragraph and summarize what the writer is feeling; for them it’s a trivial task that can be completed with high accuracy. “The reviewer liked the product,” our human reader can swiftly conclude. It’s not so simple for a computer, who has no “natural propensity” for anything, let alone language and emotional perception. All actions of the computer must be taught to it. So how do we teach it to “feel”, or, at least, to recognize patterns that indicate feelings? How can we translate this task into something a computer can learn to do?
Let’s explore some of the considerations and principles behind designing a natural language processing (NLP) model.
Featured Article
5 reasons why many businesses are still in AI adolescence
Here’s what businesses can learn from the small group of organizations that already use artificial (AI) to their competitive advantage.
If the world’s largest companies were people, most would be in their teenage years when it comes to using Artificial Intelligence (AI).
According to new research from Accenture on AI maturity, 63% of 1,200 companies were identified as “Experimenters,” or companies that are stuck in the experimentation phase of their AI lives. They have yet to leverage the technology’s full potential to innovate and transform their business, and they risk leaving money on the table.
This is money that the most AI-mature organizations are already pocketing. While the “AI adults” (dubbed Achievers in the research) are only a small group — representing 12% of companies — they are reaping big rewards: By outperforming their peers on AI, they are increasing their revenue growth by 50% on average. How? Because they master key capabilities in the right combination by having command of the technology itself — including data, AI and cloud — as well as their organizational strategy, responsible use of AI, C-suite sponsorship, talent and culture.