This week, three trusted CEO Digital Strategies to face the new world.
Welcome to The Digital Eye, your weekly roundup of the latest technology news.
Our team of experts has scoured the internet for the most exciting and informative articles so that you can stay up-to-date on Digital, Data, Blockchain, AI & Analytics, and Digital Transformation.
- Everything You’ve Ever Wanted to Know About Machine Learning
- Want To Drive The Future Of Your Business? Consider A Role In People Analytics
- If data is the new oil, synthetic data is an ethical and renewable alternative
- Deep Dive: How AI content generators work
- How Diveplane uses explainable AI to bolster AI adoption
SEE: Algorithms as a Service: The Future of Computing
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Everything You’ve Ever Wanted to Know About Machine Learning
Putting the fun in fundamentals! A collection of short videos to amuse beginners and experts alike
Looking for a fun introduction to AI with a sense of humor? Look no further than Making Friends with Machine Learning (MFML), a lovable free YouTube course designed with everyone in mind. Yes, everyone. If you’re reading this, the course is for you!
Short form videos: Most of the videos below are 1–5 minutes long, which means you get to upgrade your knowledge in bite-sized, well, bites. Tasty bites! Dive right in at the beginning or scroll down to find the topic you’d like to learn more about.
Long form videos: For those who prefer to learn in 1–2 hour feasts, the course is also available as 4 longer installments here.
Want To Drive The Future Of Your Business? Consider A Role In People Analytics
What makes People Analytics such an important emerging career?
Historically, we tend to put people in professional boxes based on their natural skill sets. If a recent graduate loves numbers and analytics, they’re told to pursue a role in finance or join a business analytics team.
If a candidate loves understanding what makes people tick, they may find themselves drawn toward HR and People Strategy. But in today’s people-forward and data-driven workforce, these old rules are antiquated—and these two seemingly opposite skill sets can be incredibly powerful when brought together.
I recently spoke with Patty Smith, People Analytics Partner at Cruise, who is proof that there are a lot of choices on the People side of business for individuals who are drawn to data sets as much as they are to solving human-centric challenges.
If data is the new oil, synthetic data is an ethical and renewable alternative.
The challenge for AI developers is where to source this data.
In 2006, the British mathematician Clive Humfry coined the now-familiar phrase, ‘Data is the new oil’. However, 16 years from his pronouncement, the realities and consequences of this “new oil” have caused many to turn to a renewable alternative to power AI: synthetic data.
Move over software; it’s now artificial intelligence “eating the world” — and its seemingly insatiable appetite for training data means developers urgently need a new source of accurate, privacy-compliant data to fuel their models. Training a simple visual recognition AI requires upwards of 100,000 perfectly-annotated, privacy-compliant images. The challenge for AI developers then is where to source this data — and in high-enough volume.
Deep Dive: How AI content generators work
Developing an AI tool that can completely replace a person while matching human authors will take time.
Artificial intelligence (AI) has been steadily influencing business processes, automating repetitive and mundane tasks even for complex industries like construction and medicine.
While AI applications often work beneath the surface, AI-based content generators are front and center as businesses try to keep up with the increased demand for original content. However, creating content takes time, and producing high-quality material regularly can be difficult. For that reason, AI continues to find its way into creative business processes like content marketing to alleviate such problems.
AI can effectively personalize content marketing to the audience it is aimed at, according to David Schubmehl, research vice president for conversational AI and intelligent knowledge discovery at IDC.
How Diveplane uses explainable AI to bolster AI adoption
Partnerships are the way forward, Diveplane says.
“Black box” artificial intelligence (AI) systems are designed to automate decision-making, mapping a user’s features into a class predicting individual behavioral traits such as credit risk, health status, and so on, without revealing why. This is problematic, not only because of the lack of transparency, but also because of potential biases inherited by algorithms from human prejudices or any hidden elements in the training data that may result in unfair or incorrect decisions.
As AI continues to proliferate, there is an increasing need for technology companies to demonstrate the ability to trace back through the decision-making process, a functionality called explainable AI. This would essentially help them understand why a certain prediction or decision was made, what the important factors were in making that prediction or decision, and how confident the model is in that prediction or decision.
Featured Article
Three Trusted CEO Digital Strategies To Face The New World
Today’s times require visionary, inspired CEOs, not timid retrenchment. Are you and your organization ready?
Article by @VentureBeat
Artificial Intelligence (AI) everywhere has the potential to transform every business and improve the life of every person on the planet. In fact, every day we hear about AI breaking new ground, from detecting cancer and playing Minecraft, to creating “sentient” chatbots and generating compelling art. The goal of AI is simple: To accelerate “data to insights.” We have seen tremendous progress in the basic AI ingredients — the exponential growth of “data, compute and algorithms.”
Data, as measured by the total number of bytes, is in zettabytes. Compute, as measured by hardware execution capacity of operations per second, is in petaflops to exaflops, and algorithms, as measured by the number of parameters in a neural network, have exceeded a trillion.