This week, Why is Decision Intelligence important? Know how to understand your data better.
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.
This Week’s Top Reads:
- Stepping up: What COOs will need to succeed in 2023 and beyond
- Using sound & machine learning to model the world
- How to manage risk as AI spreads throughout your organisation
- The Keys to Launching an InsurTech Startup in a Challenging Environment
- In machine learning, synthetic data can offer real performance improvements
We hope you find this information valuable and would appreciate your help sharing it with others who may be interested.
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Stepping up: What COOs will need to succeed in 2023 and beyond
COOs are increasingly important to corporations, but what skills will they need to perform at their best? Business leaders weigh in.
How many of us can name a famous COO? The role of the COO was low profile to begin with, and in the early 2000s, a trend toward flatter organizations and more hands-on CEOs took hold. In 2000, 48 percent of Fortune 500 and S&P 500 companies had a COO1 ; by 2018, that number had dropped to an all-time low of 32 percent.
But COOs are making a comeback. As of 2022, 40 percent of leading companies had a COO, with the financial and energy sectors leading the way at 48 percent. Furthermore, the role itself has changed—it’s bigger, bolder, and more transformative than ever.
Using sound & machine learning to model the world
This machine-learning system can simulate how a listener would hear a sound from any point in a room.
Imagine the booming chords from a pipe organ echoing through the cavernous sanctuary of a massive, stone cathedral.
The sound a cathedral-goer will hear is affected by many factors, including the location of the organ, where the listener is standing, whether any columns, pews, or other obstacles stand between them, what the walls are made of, the locations of windows or doorways, etc. Hearing a sound can help someone envision their environment.
Researchers at MIT and the MIT-IBM Watson AI Lab are exploring the use of spatial acoustic information to help machines better envision their environments, too. They developed a machine-learning model that can capture how any sound in a room will propagate through the space, enabling the model to simulate what a listener would hear at different locations.
How to manage risk as AI spreads throughout your organisation
Start small but dream big
As AI spreads throughout the enterprise, organizations are having a difficult time balancing the benefits against the risks. AI is already baked into a range of tools, from IT infrastructure management to DevOps software to CRM suites, but most of those tools were adopted without an AI risk-mitigation strategy in place.
Of course, it’s important to remember that the list of potential AI benefits is every bit as long as the risks, which is why so many organizations skimp on risk assessments in the first place.
Many organizations have already made serious breakthroughs that wouldn’t have been possible without AI. For instance, AI is being deployed throughout the health-care industry for everything from robot-assisted surgery to reduced drug dosage errors to streamlined administrative workflows. GE Aviation relies on AI to build digital models that better predict when parts will fail, and of course, there are numerous ways AI is being used to save money, such as having conversational AI take drive-thru restaurant orders.
The Keys to Launching an InsurTech Startup in a Challenging Environment
No. 1, build an amazing team internally with people that are smarter than you.
The insurance industry has faced an onslaught of challenges over the past couple of years, from the global COVID-19 pandemic to economic and social inflation, a talent shortage, a changing climate, growing ransomware attacks, and the Russia-Ukraine war.
These topics were all discussed at the Insuretech Connect conference in September at Mandalay Bay Hotel and Casino in Las Vegas. During one session on how insurers can navigate turbulent times in the industry, Everest Re CEO Juan Andrade said “there’s never been a more important time in the industry to be able to deal with various kinds of risks.”
In machine learning, synthetic data can offer real performance improvements
Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.
Teaching a machine to recognize human actions has many potential applications, such as automatically detecting workers who fall at a construction site or enabling a smart home robot to interpret a user’s gestures.
To do this, researchers train machine-learning models using vast datasets of video clips that show humans performing actions. However, not only is it expensive and laborious to gather and label millions or billions of videos, but the clips often contain sensitive information, like people’s faces or license plate numbers. Using these videos might also violate copyright or data protection laws.
And this assumes the video data are publicly available in the first place — many datasets are owned by companies and aren’t free to use.
Do you ever feel inundated with data when making a decision? So much so that you don’t know where to start. And even when you do start, it seems like you can never get a holistic view of what’s happening in your business.
You’re not alone. The average CEO is involved in 140 tasks a week, requiring many decisions. Fifty percent of their decisions were made in nine minutes or less. But there is hope. Decision intelligence is the new trending tool that can help you make sense of all your data, get a clear view of your business, and help support better decision-making.
Decision intelligence (DI) is an analytics field focusing on automated decision-making. It uses artificial intelligence (AI) techniques such as machine learning, natural language processing (NLP), and predictive modelling to automate data analysis and inform decisions. DI is very effective in fraud detection, customer churn prediction, and price optimisation.
This article will discuss what DI is, how it works, and some real-world applications. We will also provide tips on how to get started with Decision Intelligence in your organisation.