WEBVTT - How to Compete and Win with Artificial Intelligence

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<v Speaker 1>Well, was that a venture partners They invest in what

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<v Speaker 1>is called AI first companies. We're talking about artificial intelligence.

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<v Speaker 1>Those are companies and entrepreneurs that are applying machine learning

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<v Speaker 1>to the real world across the industries and functionality. So

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<v Speaker 1>let's get into it with Ash Fontana, managing director of Zetta.

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<v Speaker 1>Before Zetta, he launched syndicates at angel List, his new

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<v Speaker 1>book The AI First Company, How to Compete and Win

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<v Speaker 1>with Artificial Intelligence, and Ash getting up early joining us

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<v Speaker 1>on the phone from Sydney, Australia, where it's I think

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<v Speaker 1>six thirty in the morning. Nice to have you here,

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<v Speaker 1>Welcome to Bloomberg. Thank you so much for having on

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<v Speaker 1>your side. Well, it's a pleasure. How are you and

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<v Speaker 1>tell us about this past year, um, covid this book.

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<v Speaker 1>What's it been like? Mm hmm, Well, I'm well it

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<v Speaker 1>was it was sort of a funny year, and that

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<v Speaker 1>it was a good year to write a book, um

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<v Speaker 1>to the inside and whatnot, um and finishing all those

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<v Speaker 1>all those loose ends that they're just lying around in

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<v Speaker 1>your house when you see you doing nothing else. So UM,

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<v Speaker 1>it was a good year in that regard. It was

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<v Speaker 1>a very funny year to be in the field of

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<v Speaker 1>artificial intelligence, because uh, you know, we've all heard this

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<v Speaker 1>from a few different people, but the acceleration was incredible,

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<v Speaker 1>and the imperative to automate as the sort of imperative

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<v Speaker 1>to get a better understanding of what's going on in

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<v Speaker 1>our world when we couldn't be out in the world

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<v Speaker 1>UM really increased, and so that the right into adoption

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<v Speaker 1>of these technologies really want through the root, meaning what like,

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<v Speaker 1>give us some examples of things that because I do

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<v Speaker 1>think ash it's fair to say that for a lot

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<v Speaker 1>of people, and I'm not making a judgment call, but

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<v Speaker 1>when we think of AI, it's often guided by Hollywood

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<v Speaker 1>interpretation of it, or we think about you know, controlling

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<v Speaker 1>our minds UM. But it's already in our world and

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<v Speaker 1>it's making decisions about things. M Yeah, indeed it is.

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<v Speaker 1>And I think there's two aspects of this UM to

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<v Speaker 1>bring it down to sort of really examples, you know.

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<v Speaker 1>One is what do you do when there aren't people

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<v Speaker 1>around the aren't have people in close contact with each

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<v Speaker 1>other in a place like a warehouse, Well, you have

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<v Speaker 1>to use the machine. So firstly, understand what people would

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<v Speaker 1>be doing that warehouse or the usual process let's observe

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<v Speaker 1>it with some cameras and try to break it down

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<v Speaker 1>with some sort of system that analyzes the feed from

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<v Speaker 1>those cameras. Um. And then too, how do you move

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<v Speaker 1>things around when there are people there to do that?

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<v Speaker 1>People can't be there to do that, it's not safe

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<v Speaker 1>for them to do that, and to use robots, and

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<v Speaker 1>robots are funny. That's sort of like a system that

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<v Speaker 1>has lots of different bits of artificial intelligence in it.

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<v Speaker 1>So you know, that's one sort of pretty tangible example.

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<v Speaker 1>But you know what else happened last year was just

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<v Speaker 1>seven these systems were used. Whether they were energy systems

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<v Speaker 1>like electricity good, whether they were economic systems like what

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<v Speaker 1>happened in markets, they just started, um doing things you

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<v Speaker 1>haven't seen them do before. And you know what artificial

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<v Speaker 1>intelligence really is, I know people think of it a

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<v Speaker 1>lot on the time. Is something that you know, is

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<v Speaker 1>like a weird sort of mind. Um, It's really just

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<v Speaker 1>like a bunch of physical models strung together. And so

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<v Speaker 1>when all these systems started going awry, and I really

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<v Speaker 1>helped us understand what's going on by doing statistical analysis

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<v Speaker 1>on the flow of power through degrees when no one's

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<v Speaker 1>striving to work and everyone's staying at home. Why what

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<v Speaker 1>was happening there? How do we actually manage better how

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<v Speaker 1>we move energy around the grid in that situation that

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<v Speaker 1>we've mada sine before while and AI can sort of

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<v Speaker 1>very quickly make a new model that helps you generate

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<v Speaker 1>a prediction in that new world rather than you have

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<v Speaker 1>to rely on your old models that were used to

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<v Speaker 1>generate predictions under normal uses usage patterns of electricity. UM.

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<v Speaker 1>So it's really good at doing things like that, and

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<v Speaker 1>the advantage to it is that it's constantly able to

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<v Speaker 1>take in and make those decisions using real time data.

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<v Speaker 1>So as the data can change, as we saw a

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<v Speaker 1>year ago or a little bit more than a year ago,

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<v Speaker 1>the data points changed dramatically. Nobody would have predicted it initially,

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<v Speaker 1>right until, of course we started to see how significant

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<v Speaker 1>and how severe the pandemic was. But that's the advantage

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<v Speaker 1>of AI. Yeah, that's exactly right. UM. You know a

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<v Speaker 1>lot of these systems are trained in an environment that

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<v Speaker 1>represents a real world environment. But every time they get

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<v Speaker 1>a new observation, every time they take a new photo

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<v Speaker 1>of what's on a shelf and supermarket or what's how

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<v Speaker 1>things are moving around the warehouse. Will they make an

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<v Speaker 1>observation of how power is flowing through the grid, you know,

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<v Speaker 1>it's flowing to this part of the grid and not

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<v Speaker 1>that part of the grid. It learns and it updates

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<v Speaker 1>the view of the world. Now we do that too

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<v Speaker 1>as humans. Of course, we're always updating out view of

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<v Speaker 1>the world. But you know, we're actually very hesitant to

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<v Speaker 1>update out of view of the world because, um, we

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<v Speaker 1>like being able to make decisions quickly based on instinct,

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<v Speaker 1>and that requires having very solid models of the world.

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<v Speaker 1>Whereas AIS don't sort of has that bias. They don't

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<v Speaker 1>have that recency bias, so to speak, and they don't

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<v Speaker 1>have that need to be super intuitive about things. So

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<v Speaker 1>they're updating the view of the world very very quickly,

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<v Speaker 1>and that can be really helpful. And in the situation

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<v Speaker 1>so as what is an AI first company because you

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<v Speaker 1>invest in them, Yeah, and and our first company is

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<v Speaker 1>a company that genuinely puts AI artificial intelligence to start

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<v Speaker 1>every conversation, in the conversations about who they're going to

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<v Speaker 1>hire next, you go to hire people that you know

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<v Speaker 1>can build these models, build these these machine learning models

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<v Speaker 1>that eventually start looking like AIS. You know what Are

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<v Speaker 1>you going to put your money until you're gonna put

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<v Speaker 1>your money into acquiring valuable data? What policy concerns do

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<v Speaker 1>you have? Are you really going to play a role

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<v Speaker 1>in the data own data privacy? Are first companies really

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<v Speaker 1>put these conversations first so that they can genuinely build

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<v Speaker 1>this stuff rather than try to sprinkle it on top later.

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<v Speaker 1>So are these the company? So help me out here?

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<v Speaker 1>Are these the companies who are creating the AI infrastructure,

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<v Speaker 1>the AI algorithms? Because it sounds like we are moving

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<v Speaker 1>increasingly to a world where you know, every company, just

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<v Speaker 1>like retail became kind of a digital e commerce company,

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<v Speaker 1>had to adopt some kind of digital strategy. We saw

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<v Speaker 1>that a lot in the pandemic or companies in general.

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<v Speaker 1>Is it Are we moving towards a world where every

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<v Speaker 1>company is going to have some type of AI strategy

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<v Speaker 1>or AI first strategy? Yeah? I really think we are.

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<v Speaker 1>You know, the way you put it there, I think

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<v Speaker 1>it's how I would put it um and that is

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<v Speaker 1>all software will eventually become intelligent. Every bit of technology

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<v Speaker 1>that we use will have a bit of a conicutve

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<v Speaker 1>element to it. And in court we've seen so far

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<v Speaker 1>as a couple of really nice companies get started, like

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<v Speaker 1>UI Path and Palanteer and clubd Error and a couple

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<v Speaker 1>of these companies that have gone public. Now you know

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<v Speaker 1>they're really making the tools that other people build their eyes. Um,

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<v Speaker 1>they're not really a access companies themselves. They're not building

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<v Speaker 1>the AI s. But you know, now that they're really

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<v Speaker 1>well established and their products are mature, people can use

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<v Speaker 1>them in retail, in manufacturing, in healthcare to build their

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<v Speaker 1>own aies in those fields, combining their own experience of

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<v Speaker 1>how things work. You know, how diseases progress, how consumers

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<v Speaker 1>behave with the power of these models to get a

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<v Speaker 1>predictive system underway. But what to give me an idea

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<v Speaker 1>because you do have some investments and I've been looking

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<v Speaker 1>at your website earlier today, tell me what is an

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<v Speaker 1>AI or what are some of the AI first companies

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<v Speaker 1>that you have invested in, and just give us an

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<v Speaker 1>idea of what they're doing, just so that our our

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<v Speaker 1>audience and the ssters that are out there, because this

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<v Speaker 1>does sound like something that will become more popular potentially

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<v Speaker 1>going forward. Yeah, for sure. I think A really good

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<v Speaker 1>example is a company called Tractable, and what they do

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<v Speaker 1>is they basically help people get back on their feet

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<v Speaker 1>after being a disaster, you know, whether that's a weather

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<v Speaker 1>disaster or having a car crash. And the way they

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<v Speaker 1>do that they analyze images. So you have a little

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<v Speaker 1>fender vendor on the way to work, you take a

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<v Speaker 1>photo and they can analyze that photo a naked decision

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<v Speaker 1>almost on the spot about whether your car is going

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<v Speaker 1>to be repaired or whether it's a write off. And

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<v Speaker 1>that just gets the money in your account more quickly.

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<v Speaker 1>It gets you back on the road more quickly, and

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<v Speaker 1>it for the insurance company makes the cost of process

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<v Speaker 1>in that claim a lot lower. And you know how

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<v Speaker 1>is that an AI first company, They had to for

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<v Speaker 1>two years before they got any customers, really go and

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<v Speaker 1>collect so much datas, so many different images, different ways

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<v Speaker 1>in which cars go through crashes, and how different panels

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<v Speaker 1>deform and whatnot, so that they could train their models

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<v Speaker 1>to the point where they can develop a really accurate

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<v Speaker 1>readoubt of the damage really quickly, just purely using computer

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<v Speaker 1>vision and not using getting a loss adjuster or having

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<v Speaker 1>someone come out and have a look at the car.

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<v Speaker 1>How much is that being used by the insurance industry,

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<v Speaker 1>the auto industry at this point. Yeah, it's being used

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<v Speaker 1>today by major insurers all around the world to make

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<v Speaker 1>decisions every day. Um, and then you know, you can

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<v Speaker 1>see how it could soon be used for other other purposes.

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<v Speaker 1>You know, if you're in a hail storm and your

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<v Speaker 1>riot gets damaged, well, they can take a photo from

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<v Speaker 1>space or from a drone and then analyze that photo

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<v Speaker 1>and quickly give you an assessment of the damage and

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<v Speaker 1>processes you're playing. You can see how it could be

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<v Speaker 1>used in all sorts of the types of situations. But today,

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<v Speaker 1>dozens and dozens of the world's largest insurance company these

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<v Speaker 1>working with them to process claims every single day, and

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<v Speaker 1>it's in the hands of people in their apps that

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<v Speaker 1>they've got on their phone and their their insurance company apps.

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<v Speaker 1>One thing I do want to ask you, because when

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<v Speaker 1>we're taking images pictures up above, I mean, I'm thinking

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<v Speaker 1>that there are some of our listeners or watchers on

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<v Speaker 1>YouTube that are just saying, well, wait a minute, Okay,

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<v Speaker 1>So now I start to get a little thinky about

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<v Speaker 1>AI and then the privacy concerns. What are the boundaries

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<v Speaker 1>that need to be set with these AI first companies. Yeah,

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<v Speaker 1>that's a great question, and you know, I'm all for

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<v Speaker 1>thinking about the role of government here as being a

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<v Speaker 1>really important one because in a sense, as as individuals

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<v Speaker 1>our data, my data, your data in itself is not

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<v Speaker 1>necessarily worth much, but as a collective data is worth

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<v Speaker 1>a lot to these companies. And you know, we can

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<v Speaker 1>work with government to make sure these companies respect our

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<v Speaker 1>privacy where they need to and properly compensate equal for

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<v Speaker 1>their data, um when they when they use it. I

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<v Speaker 1>think the EU is a real leading light here. Um.

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<v Speaker 1>You know, recently they released some legislation where they just

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<v Speaker 1>outright bands and applications I around facial recognition and whatnot,

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<v Speaker 1>and I think there are categorically some really scary uses

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<v Speaker 1>of it. And you know, we do have the power

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<v Speaker 1>through the legislature the branded stuff, um, but you know,

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<v Speaker 1>besides a couple of sort of very clear cases, it's

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<v Speaker 1>a more marginal consideration. And you know, I think having

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<v Speaker 1>better standards for auditing, being more upfront about what data

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<v Speaker 1>we're using, you know, with the cookie stuff that's hit

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<v Speaker 1>the news today, I think that's a really good approach.

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<v Speaker 1>But you know, looking to the EU and what they're doing,

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<v Speaker 1>I think an indication of what government do. Well look

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<v Speaker 1>forward to having you back and maybe at a time

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<v Speaker 1>where you don't have to get up so early, because

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<v Speaker 1>I do think this is an interesting area, uh and

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<v Speaker 1>certainly one that continues to development and really be a

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<v Speaker 1>part of our world. More broadly, Ash, thank you so much.

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<v Speaker 1>Ash Fontana, managing director of Zetta the his book The

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<v Speaker 1>AI First Company, How to Compete and Win with Artificial Intelligence,

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<v Speaker 1>joining us on the phone from Sydney, Australia,