Intro
Jordan [00:00:00]:
Will we even realize what work will look like in a few years? I'm not sure. But that's one of the things that we're going to be talking about today in everyday AI. Your daily podcast livestream and newsletter going over the latest AI news. So today we're going to talk about a lot of things. Really excited for the show. We're going to talk about IBM hasn't given up on Watson, leadership qualities are changing with AI. And is an AI chat bot going to be coming to a drive through near you? So, as you'll see, if you're joining us live, we have Ayman Rashid. He is the technology leader of digital transformation at Eisner Amper Amen. Thank you so much for joining us.
Aimann [00:00:46]:
Good to be here.
IBM's Watson X: From Jeopardy to Enterprise and Beyond
Jordan [00:00:47]:
Jordan all right, so let's start on IBM and Watson. Well, maybe people don't know. So IBM was really early to the party with their original AI supercomputer called Watson. They kind of sold that piece. So they're bringing back Watson X, which is just their second attempt to go all in on AI and helping to provide that to clients. I think the thing that's really interesting here is they're partnering with Hugging Face. So for the everyday person, if you've never heard of Hugging Face, they are essentially an open source I don't know if you'd say alternative, but they are an open source alternative to OpenAI and chat GPT, and they have a whole suite of programs. But Aven, I know you have some experience actually working with IBM Watson. So what are your thoughts on this kind of reinvigoration of Watson X?
Aimann [00:01:42]:
Yeah, so I don't know a whole lot about Watson X just yet because they recently announced, but I don't know if you remember, maybe the audience remembers almost ten years ago, Watson debuted on Jeopardy. Do you remember that episode? And back then, it was very like pie in the sky, out of reach. This is like bleeding edge technology. It's never going to materialize anytime soon. But actually, believe it or not, Watson and Watson's suite of solutions has been around on the enterprise level for a long time. And this is something that I didn't realize. But when you go on, for example, I think it was American Airlines and you talk to their chat agent, it is an AI. And it's embedded so seamlessly that you can't even tell. Right.
So I think a lot of these enterprises have been using IBM's products on the enterprise level because they offer, like, a hybrid, which means you can host it on the cloud or on premise servers, and they have what's called a cloud pack, and it's kind of like an all integrated foundational service with Watson assistant on top of it. You can train the model. You can use it to consume a database of information, and then people can chat with the assistant to get different information about your website or get help with whatever. So IBM has been around for a while. I think Watson X is meant to be more consumer facing, so maybe it's meant to be a little bit more user friendly, a little bit more within reach. It's less enterprise grade. So hopefully that means for smaller businesses, it's a little bit easier to although IBM enterprise solutions aren't necessarily expensive.
Jordan [00:03:28]:
Right.
Aimann [00:03:28]:
But it's just a different market entirely. Yeah, I need to definitely read up more on it. It's exciting, and they've been doing great work and not getting a whole lot of credit for it. And I think everyone except OpenAI has been kind of left behind from a marketing perspective, but not necessarily from a capability perspective. And that's why we're super early in the Hype cycle, for sure.
The Debate on the Widespread Use of AI
Jordan [00:03:54]:
It should be interesting to see. So, yeah, second kind of big story for today on everyday AI is a new study that was written about in Forbes, but the study is from Citrix, just talking about leadership qualities in general are changing with the future of AI. So I found this interesting, the results of this survey, they said in ten years, 86% of people feel that AI will be widespread, kind of in their day to day work. I was actually thrown off by that number. But even what are your first takes on just hearing that number in ten years? 86%. Feel that way.
Aimann [00:04:32]:
That's pretty high. 86%. I mean, that's 14% away from 100%. I guess the question is, what does widespread mean? Right? I mean, everyone knows that AI is already out there. The question is, how widespread is it going to be? That remains to be seen. I think there is on both sides of toast, I would say maybe the 14% that said no don't really understand AI all that well.
Jordan [00:05:00]:
Right?
Aimann [00:05:01]:
Or maybe they think it's going to have limited uses, but people, the younger generation is using it a lot right now, and over time, that's just going to get close to 100%. But I think it's partially ignorance.
AI Ordering Systems: The Future of Fast Food?
Jordan [00:05:15]:
Yeah. And maybe one thing that will bring it to 100%. Kind of our third news story of the day. So Wendy's is working with Google to essentially integrate current language models into their offerings in their drive through. So I know these have been around for a while in different phases of testing, but I think this might be the first time that at least I've seen publicly a company like Google working with a fast food chain, just training it on their terminology. The example in the story today is saying, hey, JBC is Junior break and cheeseburger, right? What are your thoughts on maybe an improved version of this AI coming to drive through?
Aimann [00:05:54]:
Damon, I could have sworn I heard that McDonald's or another fast food chain has an AI ordering system. Yeah, I think it was McDonald's. And obviously they only piloted it in like, one or two locations, but it was pretty bad. But that could be for several reasons, but I think that it's not all that far fetched to believe that it's going to become the norm for ordering systems and to free up the humans so that they can cook instead of taking down orders. I think it's going to happen. And when it does happen, it's going to be ubiquitous and it's going to work well enough where no one's going to even notice.
Google's Caution with AI
Jordan [00:06:38]:
Yeah, it'll be sad. That piece on my resume working the drive through at Culverts will no longer get me much clout. All right, let's transition a little bit. So Google has their big I O conference tomorrow, I believe, and they're supposed to be announcing just a lot of updates to their current AI offerings and their suite of products.
Personally, I feel Microsoft has really been pushing the envelope. But even in your role in digital transformation, what do you think that I know we don't know exactly what Google will announce, but how do you feel with just the big announcements coming from companies like Google and Microsoft and AMD and Nvidia? It seems like all the biggest companies are saying publicly on their earnings call everywhere. They're going all in, it seems, on AI. So what does that mean both for everyday people like you and me, but also even working in digital transformation? What does this mean when all these companies go all in?
Aimann [00:07:44]:
Yeah. So word on the street is that Google has a lot more than it's showing us and they're just taking precautions. And I also heard that leadership put their foot down was like, we can't be so risk averse. I think there's a lot of trepidation because of potential lawsuits based off the information AI gives you. It's a very gray area because AI is a loose cannon. Right. I mean, you train it on data set and it says whatever it thinks is right. That kind of represents the good and the bad of human nature. Right. We're all loose cannons, we're all creative minds, and we all have a lot we can say that can either be wrong or offensive or whatever. Now, package that in a commercial setting and let it loose and you have no idea what's going to happen.
So I think Google is being very cautious, but that could work against them if they take too long. So I think it'll be interesting. I think it's obviously good for the consumer that more competition comes out because I'm sure their model is different than Chat GPT or GPT Four, rather the GPT four model in terms of the types of responses you get and maybe what it's good at versus what it's bad at. But I hope they really come out of the gate swinging and not hold back, because that would be best for everybody.
Middle-sized Businesses Prioritizing AI for Efficiency
Jordan [00:09:10]:
Yeah. And you bring up an interesting point because I do believe that you can make the argument. Obviously the technology is there, but there's obviously things holding them back. Even yesterday on the show that we talked about Apple CEOs specifically even saying that, taking specific, dedicated and slow approach, do you think that even in your role at Eisner Amper, do you think that larger enterprise companies are taking a similar approach? Are they also taking like, hey, we know the technology is there, but let's wait?
Aimann [00:09:47]:
I wouldn't say they're saying let's wait. I think they're exploring conceptually and not implementing just yet. I would say middle. So we work with middle sized and enterprise size businesses, and I would say the middle ones are preoccupied with improving their operational efficiency and trying to automate certain things that are slowing them down and getting on the right systems. So especially if they're pivoting towards enterprise level in terms of their annual revenue, enterprise companies have a lot more at stake because there's already even IBM itself is replacing a decent amount of its workforce with AI.
So if you're not doing it on an enterprise level, you're going to quickly become obsolete. So they do have to pay attention. The question is to what extent can they actually implement AI and either increase the productivity on a per worker basis or reduce the amount of labor they need or the amount of resources they need? And nobody likes to talk about that, but that is disinflationary. It would help with the economy overall if more companies did it. But again, it's new and these tools are still kind of coming out and being refined, and it's going to be the trailblazers that really set the stage and show a good example for everybody else.
Unleashing Innovative AI Solutions: The Key Challenges
Jordan [00:11:07]:
Yeah. Amen, you mentioned something that we talked about, I think, last week on the show. So yeah, IBM saying, I think, 7800 jobs, that they were not going to fill them and instead were going to be essentially filled by AI. And then you also talked about with these mid to enterprise size clients, like, what is the extent that they either are or should be implementing AI? So not asking you to look into the future, but if you had to look into the crystal ball today, what could it look like? I mean, can medium sized and enterprise clients like the IBMS of the world and those smaller, obviously, let's say Fortune 500 and companies doing 20 million, 50 million, 100 million a year, so you're smaller to medium sized clients, can they take moves like IBM and say, hey, this 20% of our work, this could go to AI.
Aimann [00:12:02]:
They can. Now, the challenge here is that they have to get really creative, dig deep, and understand what specific use cases or what business case can they make for implementing AI, because the problem, I guess, with AI is that it's not a cookie cutter solution. You can't just take AI and toss it on your desk and say, hey, we've implemented AI. It's very much unique to what they need. And they have to do some engineering and some creative thinking and design thinking. We conduct enterprise design thinking workshops where we take the client through a journey that helps them understand what they need to implement from a modernization perspective or a digital transformation perspective. That's very important so that they can understand what the ROI is from doing this uplift with AI.
There's a lot more creativity required because AI is not quite there in terms of general intelligence. I mean, it's not even close to general intelligence, in my opinion, but it can fool you into believing that. So you have to definitely create use cases that are well suited for the current models of AI or invest a lot of money into a totally separate, totally different supervised or unsupervised learning using neural networks that it's going to be very resource intensive. So if you want something out of the box, you have to know exactly what you're doing. If you want something that's unique, you need to know what you're doing and you need a lot of money. So those are the two bottlenecks, I would say.
The Future of AI Training: Using APIs
Jordan [00:13:41]:
Which of those two options? So kind of the cookie cutter out of the box versus the companies. You kind of started to mention some machine learning and supervised unsupervised. Which of those two routes do you think is going to be more widely traveled in the near future? People just saying, like, hey, we're going to go the cookie cutter approach with this AI or hey, we're going to invest and we're going to build something out specifically for us?
Aimann [00:14:07]:
Neither. I think that the most common approach will be to just use large language models and use an existing API to prompt it. I would say that's a class called generative AI, which is just helping AI fill in the blanks, as opposed to teaching AI something brand new and then asking it to literally take over a certain part of your company. With Auto, GPT and some of the AI agents, we have kind of different models where we can continuously prompt. Again, that's large language models. When you get into the supervised and unsupervised learning and building your own deep learning system, you have to have very specific game plan. It's a lot harder and it's a lot more out of reach for now until the hardware catches up and becomes extremely cheap. So I think that AI training as a service will eventually become a thing, and eventually it'll be such that you use an API to feed a data. For example, Amazon, you can use their streaming platform, I think it's called kinetic, to basically firehose data into an AI training model. That's simple. But right now, it does still require a level of expertise that not a lot of companies have. But if they don't get on that, especially on the enterprise level, they will be left behind. So it's do or die. This is kind of the inflection point for the industries.
The Future of White-Collar Jobs with AI
Jordan [00:15:41]:
Yeah, I do think that is kind of where the people at least who understand AI and who are following it, they are starting to say, hey, this is a do or die time for big companies. So I said I wasn't going to make you look into the crystal ball, but now I'm just going to put you on the spot here. So as we are low on time, give me your quick hot take on AI. Are we all going to be out of work? Are we all going to be just working on prompt engineering? The average everyday person who just has a desk job, what is their role going to look like in five years?
Aimann [00:16:19]:
It's funny you say that. Okay, so I'll give you the pessimistic and the optimistic view. Okay.
Jordan [00:16:27]:
Love it.
Aimann [00:16:27]:
Pessimistic view is most white collar jobs where you're shuffling data around is going to be more it's going to be more a scenario where a company will say like, oh, do we need a human for this AI to help manage it or not? Otherwise the AI can just kind of function on its own, and you have kind of an orchestrator at the top, like a sea level. Or maybe you still need some middle management, but not nearly as much management as you need now. And then that AI will take on the role of checking in with individual resources. Imagine you get a video, call Jordan right now, and it's from, I don't know, Brad Pitt, right? And it's a fake Brad Pitt, but it's so good. It's a deep fake. His voice is real, and he's talking to you about what you did. He's basically your manager, and he's also your therapist, by the way, because he can do whatever, and he's just making sure you're delivering on your KPIs, which are easy to set.
I mean, that to me, seems like the future, unfortunately, and it lacks the human touch, but it skyrockets productivity. And at the end of the day, when it comes to capitalism, productivity is king. And that does result in better and higher standard of living for everybody. Now, that also should mean hopefully everyone has more time on their hands and products and services are less expensive, which means we can spend more time in communities and with our families and all of that stuff.
Pessimistic view is that we all just get wiped out and just doing everything for us. But there's no UBI, meaning universal basic income. There's no safety nets, and we all either have to become jugglers or some other entertainment service that can't be replaced by AI until the robots start dancing better than us, and then we're really in trouble. But I joke around with my friends that I need to go learn how to be a mechanic real quick, just in case, because they can't take over. Although with electric cars, even the mechanic is being kind of transformed. There's a lot I could say about the future, but I think it's going to be interesting and I think that the people who are most willing to experiment and really lead and take risks are going to be the ones on top. But we'll see. All right, well, it's not a bad idea to get a farm.
Outro: Win ChatGPT Premium
Jordan [00:18:59]:
Yeah. Thank you for both the pessimistic and the optimistic. I mean, hopefully, we all just have our own personal Brad Pitt KPI therapist, helping us through our daily lives. But amen again, thank you for joining us on the show. Thank you. If you are watching live or if you're listening to this on the podcast, as a reminder, please go to your everydayai.com, sign up for the newsletter. In there, you'll learn about we're giving away two year long premium subscriptions to check GPT, so that way you can build your own personal Brad Pitt assistant. So thank you again for tuning in and we hope to see you back tomorrow and every day. Thank you.
Aimann [00:19:43]:
Thanks, Jordan.