Artificial intelligence has redefined how we work and our decision-making process. But, what does the future hold, and how does this impact people?
Without a doubt, artificial intelligence has infiltrated society and how we work today. Many of the business applications and social media apps we use are infused with AI in some way. This has changed our expectations of app interactions and outcomes which impacts our decision-making process.
Additionally, we are seeing investments by major companies in industry clouds to streamlining the unique experience in these verticals. AI will continue to be infused into these solutions which will shape the future of the business applications experience.
In this “Back @ IT” episode, I am joined by Cem Dilmegani, Founder of AIMultiple, to dive into the impacts of AI on industries and people, and where the future of AI is headed.
Highlights:
00:43: The topic of AI has been increasing and more and more people are interested in the topic. Cem outlines his past experience with @McKinsey and founding @AIMultiple.
02:07: AI has become more democratized and included in many business applications to the extent that it’s not viewed as AI specifically.
05:13: The early days of AI were very experimental and it wasn’t mature enough to be included in more mature and robust systems. But now AI has matured to be an integral part of the backbone of technology and expanded upon through machine learning.
07:16: Low-code / no-code applications are leveraging AI due to its democratization which is empowering citizen developers and professional developers alike. This has led to the rise of citizen data scientists as data is more digestible and easily usable.
11:38: Humans are still a fundamental and critical part of the future of technology and AI. It will become a symbiotic relationship where humans are learning from AI and AI is learning from humans.
14:44: Industry cloud solutions are utilizing AI to enable businesses to see quicker implementation and ROI. And, people have adapted to these ever-changing technology advancements.
18:26: How do businesses decide when to buy versus build AI solutions? And, what is the future of AI as it relates to business applications?
20:14: Deep learning research has hit some limitations due to its computation-intensive nature. High-quality data is needed to train the AI models which will aid in natural language understanding. This speaks to the fact that AI has come a long way, but still has a long way to go.
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