#172: Making AI implementation a Reality, with Felipe Flores host of Data Futurology and Kathleen Walch and Ron Schmelzer, Hosts of AI today podcast
AI is taking centre stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing.
Open up any newspaper or social media feed these days and it’s easy to spot the tidal wave of hype that artificial intelligence (AI) has unleashed. Within this hype lies a lot of opportunities, but without the right knowledge and tools, it can be hard to identify the real breakthroughs.
For the first time in Data Futurology podcast, we’ll be having a two-in-one episode.
In the first part of this episode, Felipe Flores is interviewed by AI today podcast hosts Kathleen Walch and Ron Schmelzer.
Cognilytica's AI Today podcast focuses on relevant information about what's going on today in the world of artificial intelligence.
It is quite true that AI is no more confined to innovation labs only. It is now being praised for its amazing potential to transform businesses.
In the latter part of this episode, Felipe interviews Kathleen and Ron to talk about certain challenges and finding out the real potential of AI.
Enjoy the show!
Thanks to our sponsor Talent Insights Group!
What we discussed:
13:39 Maybe you're coming from traditional BI, traditional data analytics, how do you see these concepts marrying as bi gone away? Where are we with all that?
17:44 What are you seeing as some of the biggest trends emerging in data and data science today?
22:11 What are you hearing as some of the common data themes, and maybe even some surprising insights that some of your guests have brought up in any of your conversations?
27:10 What do you believe the future of AI is in general, and its application to organizations and beyond?
48:39 What have you learned about this data that can help me further my understanding, or look for things that I didn't know that was in there?
01:24:42 What is actually the problem that we're trying to solve? What is the data that should go into it? What should we be optimizing for?
Quotes:
We want to bring content to the community that helps people get value out of AI, make more dreams a reality and to highlight different use cases, different challenges, bring different perspectives of how people have been overcoming these challenges.
A lot of people especially, you know, they're finding that a lot of the putting AI in practice is pretty mundane. It's not nearly like making autonomous robots and self-driving vehicles. A lot of it really looks like predictive analytics and pattern and anomaly recognition, all the things that we like to talk about in our various seven patterns.
What we're seeing in the market, there are more and more AI capabilities that are kind of like sneaking into the world of BI and being delivered through the eye.
AI is helping, BI get more embedded in more places make it more accessible. And I think that there's a lot more to give in in that space when you focus on the human to computer interaction. There's a lot more that we can do there.
We do need to find ways that companies do need to respond and need to be agile, and we're gonna have to come out of this, hopefully, this will motivate people to pay much more attention to their data and ways to basically gain insights from it.
The ones that I seek out are people that are different to me, different in terms of their background, their journey. And their industry experience, or anything that's different, I'm really, really attracted to in terms of learning from that.
I love the human side, and how to better incentivize better, motivate better drive adoption is a key piece.
And I love seeing the strategy side. And that strategy is kind of like, you know, in one line it’s saying, What do you want to be? Where are you today? And then what's the road to get there?
And if you're not solving a business problem, then don't do it. You're gonna waste a lot of time, and resources, and money. And you're going to, you know, go down this rabbit hole to build this AI application that really nobody's using and it's not solving a problem. So don't do it.
As we continue, to mature in our adoption of the use and use of AI, I think that everyone's going to become an AI practitioner, in the same way, that everyone has become a computer practitioner.
What we found is that a lot of these, these companies are running projects ad hoc, and don't have a best practices methodology in place that can be incredibly frightening. And it also is incredibly wasteful, because they're wasting resources and time and money.
We always say don't do AI just to do AI. So make sure that you're actually solving a real business problem.
We talked about which algorithms to pick, well, not every algorithm is the right algorithm for that problem. So, understand what it is you're trying to do. And then you can pick different algorithms based on that.
People fear, automation, people fear AI, they think it's going to take their jobs, they feel threatened by that. You need to work with people and say, we're not trying to replace you, we're trying to have you do your job better.
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