There’s a lot being said about AI these days that’s science fiction. One person who knows the facts is David Schmaier, President and Chief Product Officer of Salesforce. Here, he talks in detail about the many unseen ways AI is being used now, how it will profoundly stimulate innovation and benefit humanity, the rise of robots, and more.
Chapter 1: What is artificial intelligence and how is it used today?
Artificial intelligence, whether people realize it or not, is all around us. It's being used in our daily lives in unexpected ways with unexpected results. Where is it being used now and what's next? Hi, everyone. I'm Lynn Thoman, and this is Three Takeaways. On Three Takeaways, I talk with some of the world's best thinkers, business leaders, writers, politicians, newsmakers and scientists.
Each episode ends with three key takeaways to help us understand the world and maybe even ourselves a little better. Today, I'm excited to be with David Schmeier. David is the President and Chief Product Officer of Salesforce. Salesforce is an American cloud-based software company, which is the world's largest enterprise software firm.
They are one of the leaders in technology, and their clients include 90% of Fortune 500 companies. In 2020, Salesforce replaced ExxonMobil in the Dow Jones Industrial Average. Salesforce has invested a billion dollars in generative AI startups, which David also oversees. Welcome, David, and thanks so much for joining Three Takeaways today.
Ben, it's great to see you again, and thanks so much for having me. It is my pleasure. Let's start by talking about how AI works. You believe there are four or five stages of AI. Can you tell us what they are? And then I'm going to ask you about each one in turn.
Chapter 2: What are the different stages of AI?
I'd be happy to, Lynn. AI has been around since the dawn of computers. So it was first talked about in the 40s and the 50s. And Alan Turing came up with something called the Turing test, where if someone could carry on a conversation
back then by teletype and you couldn't tell whether it was computer or not that was deemed artificial intelligence as we call it today and the idea was the neurons that we have in our brain could also work there could be computer oriented neurons that could sort of think just like we do as human beings.
And so based on that idea, there was predictive AI, the first type of AI that really became popular. And the second is the big boom that we're in now that really changes everything, in my opinion, which is called generative AI. And the third is agents. And then robots are going to be the next big wave, and that's coming now, too.
And this is all on the road to what many of the AI startups call AGI, which is artificial general intelligence, where the AI can do everything that a human can do, but it can do it faster.
Let's start with predictive AI, your stage one of AI. How does it work?
Predictive AI is a mathematical model that looks at the past data and uses that data to literally predict what the future will be. So I buy something on an amazon.com website and it predicts and recommends what are the next things I should buy, product A, B, and C. So that's all based on the mathematical model of predictions from the past to the future.
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Chapter 3: How does predictive AI work?
You believe that stage one of AI is predictive AI and stage two is generative AI. How is generative AI different than predictive AI?
ChatGPT is based on this new transformer architecture. That's the T in GPT. And that's based on a paper that was authored by a division of Google called the Google Brain Division, where a number of AI scientists figured out that you could take AI and train it on a set of words. That had never been done before or had been contemplated.
And so what they did is they originally trained the original version of ChatGPT, and I think the numbers are 500 million words. And then they went to billions of words. And then they went from there with like each model, each one of these is trained on more and more words and more and more content. So it's literally in the billions now.
And in order to crunch all of those words to train it, it literally costs billions of dollars now. and takes billions of dollars of NVIDIA GPUs, basically NVIDIA chips, on a supercomputer that can now predict the next word or the next sentence or the next paragraph. So yes, it uses AI, but it's trained on an entirely different data source, not data, but content.
That algorithm to predict words can also be used on other kinds of content, like images, like videos, like movies. And so now there's this concept of multimodal AI models where we started out using it to train it on words. Now you can train the AI on images, on videos, on sounds. All of the five senses, if you will, can interact with the AI.
And you have AI that can cross these different modalities so that maybe I talk to the AI and it gives me the answer in text and then it generates a movie out of it. Or I can ask it, tell me what is a horse?
write a poem about a horse and now generate a movie of a horse running through a meadow for me and i can do all the above fundamentally it's generating new answers yeah it's generating new content versus new predictions about the data
So that's the fundamental difference.
Yeah. Predictive AI literally is predicting the data in the future. Generative AI is generating the content that you asked it to generate.
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Chapter 4: What distinguishes generative AI from predictive AI?
Can you give some more examples of generative AI and what it can do? Sure.
It can create the essay. It can create the document. It can create the doctoral thesis. It can create the movie. It can create the poem. It can create anything. And so now the AI can not only read the data, but it can understand the semantic meaning of what's being said, whether you're texting with it or talking with it or interacting with it across any of these modalities.
So that's the big unlock because now it's working like the human brain where it literally understands and then it can take action just like people do.
David, can you explain what an agent is, how you think about agents?
So an AI agent has a specific role. Maybe it's a customer service agent, or maybe it's a sales agent, or maybe it's a marketing agent that launches marketing campaigns for your company, or maybe it's an e-commerce agent that helps customers buy the right products on your e-commerce website. This gets to the higher level, sort of next level capabilities of the AI.
So we talked about how the generative AI can create words or sentence or documents. We talked about how it's multimodal. So now it can create images or videos, or it can do any combination thereof. Now, the next level of AI intelligence is what they call reasoning intelligence.
And many of the companies, OpenAI, and we built our own reasoning engine, which we call Atlas, where it can not only create the next word or the next sentence, but it can literally understand the semantic meaning of what's going on. So you and I are having a conversation right now, and billions of neurons in our brain are processing this data.
And then I'm understanding what you're saying, and you're understanding what I'm saying. Well, now the AI can start to do that too, which is really quite remarkable. So based on this generative technology, you can build reasoning engines that allow the AI to do very specific things for you.
And that's where, you know, really there's the aha moment in AI, where AI can do more than autocomplete sentences. It can, in fact, do things that humans can do. Can you give some examples? I'd be happy to. We at Salesforce think that AI agents are truly the next big part of this generative AI revolution. So we talked about predictive. We talked about generative.
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Chapter 5: What are the capabilities of generative AI?
Sure. We are working with one of the largest healthcare companies in the world. Healthcare is a big business for us. And there's a, what I would call fragmented customer experience with most healthcare companies today. You know, you get your labs, you go in for your physical, and then you get your diagnostic information. And then you go to a generalist who has to refer you to a specialist.
And it really is kind of maddening how complicated it is. And, you know, I'm not a doctor. And as far as I know, Lynn, I don't think you are. But you feel like you need to go to medical school because you're now the advocate for your own patient journey. And so what if you imagine this future where you had a medical concierge, your own AI physician,
that was working on your behalf every single day that was helping you throughout this entire process and reading the imagery looking at diagnostic information understanding your profile versus mine versus someone else's and it could really walk you through that whole experience that would include text or emailing it and be reading the email conversations back and forth. It would include voice.
So you might be talking to this AI agent. It might be not only looking at images, but reading images to understand what's going on. It might be accessing medical databases to look at other other people with similar types of lab results that you have to diagnose problems. And so our view is that AI works in concert with humans. So that might not all happen today entirely with AI.
And if there's a question that the AI can't answer, it transfers the call back and forth. So we think it's going to be AI and humans working together hand in hand. But this medical concierge example is a perfect example of how multimodal could be live in action in all of our lives coming soon.
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Chapter 6: What is the role of AI agents in customer service?
What are the implications of agents for labor? I've heard estimates that companies and organizations will need 20% or even 30% fewer employees. What do you think?
Well, there's no question that AI changes everything. And the world will never be the same with generative AI and with agents and with robots and ultimately AGI in the future. So we're going into this AI future full speed. Now, that doesn't mean that there won't be any jobs in the future. Every prior technology revolution like the Internet and e-commerce and social
pundits have theorized that all the jobs are going away. And in fact, what you would find if you examine those other technology trends is employment, in fact, increased. It didn't decrease due to those trends. And so we still go into the bank branch, even though we have digital banking and we still call people, even though we can also slack them or email them using the Internet.
So it's surprising that the world doesn't change quite as fast
as sometimes people imagine and this is an opportunity not a problem and it's going to reduce a lot of the monotonous work so it's really going to stimulate human creativity i believe it's really going to stimulate innovation and it's going to allow us to do the best work of our careers but there's no question it's going to change what people are going to do in the future what they did in the past there are certainly occupations
in the 1900s that are not thriving today. And there's new disciplines like computer programming or becoming a data scientist that didn't exist 100 years ago.
And the next stage after AI agents, you believe, will be robots, AI in a physical body, if you will.
Can you talk about that? It makes perfect sense, Lynn, if you think about it. So now I have an AI agent that I can talk to, that I can text with, that I can read my emails and understand them, that really understands who I am and what I want. And now I can put that AI agent into a physical device. And so that will happen in business. like the Waymo example in the autonomous car.
That will happen in the household where I think there'll be personal robots, literally right out of iRobot from Isaac Asimov and that AI future. We're going to put these AI agents and this semantic reasoning capability with guardrails
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Chapter 7: How does AI create personalized customer experiences?
But I believe that the overall effect will be very, very positive, that it will reduce the repetitive and monotonous work that will allow us to have incredible advances as we talked about in education and health care and make the world a better place.
Thank you, David. This has been wonderful. My pleasure. If you're enjoying the podcast, and I really hope you are, please review us on Apple Podcasts or Spotify or wherever you get your podcasts. It really helps get the word out. If you're interested, you can also sign up for the Three Takeaways newsletter at threetakeaways.com, where you can also listen to previous episodes.
You can also follow us on LinkedIn, X, Instagram and Facebook. I'm Lynn Toman, and this is Three Takeaways. Thanks for listening.