MEMBER
Kathleen Walch
MEMBER TYPE
Think Tank
FOCUS
Research, Machine Learning, and Artificial Intelligence
WORK
Principal Analyst at Cognilytica, Forbes & TechTarget Contributor, AI Today Host, and ATARC AI Group Chair
CONTACT
CogWorld think tank members include some of today’s greatest minds in AI and future proofing.
Code 7893
SUMMARY
Kathleen is a serial entrepreneur, savvy marketer, AI and Machine Learning expert, and tech industry connector. She is a principal analyst, managing partner, and founder of Cognilytica, an AI research and advisory firm, and co-host of the popular AI Today podcast.
ARTICLES
In heavily regulated industries such as healthcare, digital innovation can be slow to progress. However, once organizations push towards digital transformation and innovation, the benefits that can be achieved such as revenue growth, patient volume, and cost of care can provide tremendous value. Healthcare organizations are looking for an approach to cost-effective and technically efficient build-out to help on their digital transformation journeys. With investments shifting from core EMRs to infrastructure solutions that enable flexibility and adaptability, healthcare organizations are looking to digital innovation to solve these key issues. In an upcoming Enterprise Data &AI presentation on May 5, 2022, Vignesh Shetty, SVP & GM Edison AI And Platform, GE Healthcare Digital will discuss GE Healthcare’s digital health platform and how it’s helping companies in the healthcare sector on their AI and data journey.
Just a few decades ago shopping was done by physically going into retail stores or shopping through mail order magazines. Then, the shopping experience was revolutionized with the internet, and always connected mobile phones. Artificial Intelligence is now making significant changes in the way people buy and sell online, from creating more personalized experiences to targeted marketing, crafting tailored messages to be delivered at the right time and through the right channel or AI enabled chatbots to interact with customers at any time of the day.
The pace of adoption for AI and machine learning continues unabated with global, widespread, adoption and usage. It’s not just companies that are taking note of the tremendous value AI can provide them. Countries and governments around the world are also seeking competitive advantages by harnessing the power of AI. Governments that can take advantage of the tremendous transformation presented by AI and cognitive technologies can position themselves for global competitiveness in the future. As a result, countries around the world are adopting AI strategies to provide roadmaps, funding, education, and strategies needed to differentiate themselves and become leaders in different areas related to AI and cognitive technology.
Agriculture and farming is one of the oldest and most important professions in the world. Humanity has come a long way over the millennia in how we farm and grow crops with the introduction of various technologies. As the world population continues to grow and land becomes more scarce, people have needed to get creative and become more efficient about how we farm, using less land to produce more crops and increasing the productivity and yield of those farmed acres.
For many years banks have been at the forefront of using technology to help with both front-of-house and back-of-house operations. It’s no surprise then that banks are adopting AI to help in a variety of ways.
The UK has played an important role in the history and development of AI. Alan Turing, a British mathematician, is considered to be the father of theoretical computer science and has deep roots in AI as well. In addition to crafting the foundations for modern computing, Turing envisioned the Turing test, which aims to determine a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
Artificial intelligence has been around for many decades. However, In the past decade, interest and investment in AI have skyrocketed. In the past few years, companies and organizations in just about every industry and across the world are adopting a wide range of cognitive technology solutions spanning the seven patterns of AI with applications in finance, retail, automotive, healthcare, government applications and many more industries showing adoption.
Companies of all sizes are implementing AI, ML, and cognitive technology projects for a wide range of reasons in a disparate array of industries and customer sectors. Some AI efforts are focused on the development of intelligent devices and vehicles, which incorporate three simultaneous development streams of software, hardware, and constantly evolving machine learning models. Other efforts are internally-focused enterprise predictive analytics, fraud management, or other process-oriented activities that aim to provide an additional layer of insight or automation on top of existing data and tooling. Yet other initiatives are focused on conversational interfaces that are distributed across an array of devices and systems. And others have AI & ML project development goals for public or private sector applications that differ in more significant ways than these.
Many enterprises, vendors, and startups often confuse the role of data scientist and data engineers. While the overlap of these roles is substantial they’re not particularly interchangeable.
AI brings mixed emotions and opinions when referenced in the context of jobs. AI will eliminate the need for many different kinds of jobs in many different categories. But at the same time, AI will create new jobs in many categories. Is AI an overall job killer or job creator?
As AI continues to become a focus for an increasing number of enterprises, these organizations are realizing how important it is to have the right people and skills in place. In particular, there has recently been a significant increase in demand for data scientists in organizations as AI, various applications of machine learning (ML), non-ML predictive analytics, and other so-called “big data” approaches continue to gain traction in the enterprise. In fact, the significant demand for data scientists has led to the talent crunch that we’re seeing across many enterprises and organizations. However, given that 80% of an AI project has to do with data preparation and data engineering activities, perhaps organizations should really be searching for data engineers even more than data scientists?
AI systems are constantly evolving. Machine learning models learn from data and experience, and once they are released into the real world, they need to continually be monitored, tested, and retrained on an ongoing basis. It also needs to be created with ethical and responsible frameworks in place.