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When the topic of artificial intelligence and cars gets discussed – it’s a topic of frequent discussion online these days – it’s often in the single context of self-driving cars.
While Google is a lead in many discussions about self-driving experimentation (where a car is largely driven and controlled by a wide range of technologies), electric sports car maker Tesla gets a lot of press due to its stylish sports cars that capture imaginations with their high speeds, market-leading battery innovation, automated driving capability, and competitive prices.
Tesla’s also in the spotlight for owners of its cars getting killed in crashes where fingers point to the Autopilot technology in its cars being less than perfect for use on public roads that, combined with a driver not paying attention, is surely a recipe for lethal consequences.
Along with the ongoing discussions is lack of clarity over what people understand ‘self-driving’ to mean. In fact, there is no single definition of that phrase; rather, there is clear nuance in meaning as these definitions from the Automotive 2025: Industry without borders report (PDF) from the IBM Institute of Business Value (IBV) illustrate:
What is “self-driving”?
Automated: Driver must be present
- Partially – Driver monitors automatic functions, cannot perform non-driving tasks.
- Highly – System recognises its limitations and calls driver to take control, if needed. Driver can perform some non-driving tasks.
- Fully – System handles all situations autonomously without monitoring by driver. Driver allowed to perform non-driving tasks.
Autonomous: No driver required
- Limited – Designated areas where vehicles, infrastructure and the environment are controlled.
- Fully – Integrated with other vehicles in normal driving conditions.
Much of what we see today falls under the ‘automated’ definition. Indeed, according to the IBV report, it’s likely that we won’t see anything like full autonomous driving in the mainstream for a long time, certainly not much before 2025.
‘Automated’ is a different matter where we can expect to see considerable growth and more experimentation in the coming decade that will lead to partial mainstream use in a significant way.
Many elements are in play today, one of which – to circle back to the other conversation component that goes with ‘cars – is artificial intelligence, a notoriously tricky phrase to define with clarity that anyone can actually understand, but the definition on AlanTuring.net isn’t bad:
Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans.
In the broad automotive context, AI often conjures up images of robots driving cars and lends itself very well to an emotive, scifi-ish, picture of self-driving cars. If you saw the original Total Recall movie from 1990, you’ll remember Johnny Cab (see pic at top of page).
Yet AI is present in cars today in more fundamental, practical and visible ways that are largely to do with automating many driver-related tasks – making a phone call, navigating to a destination, or avoiding a collision, for instance.
The AI ingests data from sensors and other vehicle systems and makes decisions, the outcomes of which range from asking or alerting the driver, to taking actual action to avoid or minimise danger.
This is machine learning, a subset of AI, in action.
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Artificial intelligence and machine learning are two subjects in which I’ve long had interest, especially as they relate to organizational change, people’s behaviours and communication, and the future of work. This keen interest is a prime reason why I joined IBM earlier this year, and has intensified as I’ve got to know a great deal about cognitive computing and, of course, IBM Watson.
They are both areas where I have some clear views on what this means for organizational communication (including public relations), eg, automation in the workplace. So I was thrilled to be invited by Chip Griffin to join him in conversation in his latest episode of the “Chats with Chip” podcast to discuss such topics.
In “Chats with Chip,” we talk for a little over 30 minutes about the roles artificial intelligence and machine learning will have in the public relations industry. We talk about everything, from the automated creation of news stories by computers to the role that big data plays in communications, to the crucial role organizations must play in softening the social cost of such technological change, and a great deal more.
Take a listen:
If you prefer to read, here’s a transcript of our conversation.
*** UNVERIFIED TRANSCRIPT ***
Please review the audio before quoting to confirm accuracy of this unverified transcript.
Chip Griffin: Hi, this is Chip Griffin, and welcome to another episode of “Chats with Chip.” I am very pleased to have as my guest today, Neville Hobson. Neville, of course for our long time listeners is the co-founder of the FIR-podcast network along with Shel Holtz, and now he’s left the routine podcasting world behind and simply appears as a guest, but he’s also working for IBM, so welcome Neville and why don’t you tell us a little about what you are doing with IBM.
Neville Hobson: Yes, I will Chip. Thanks very much, indeed a pleasure to be chatting with you on this podcast. I joined IBM in January 2016 that’s about 6 months ago from as we’re talking today. A bit of a pivot actually. It’s not much to do with organizational communication in the sense of what I was doing before. It’s a lot to do with business transformation and a lot of corporate words like that that clients of IBM go through, so I tend to have conversations with people looking at the social elements of all of that in terms of sentiment analysis; in terms of how that enables people to make better decisions I suppose, and that’s very close to a topic that I am very keenly interested in, one of the reasons I went to IBM, which is this whole huge area of artificial intelligence machine learning and so forth and so on, epitomized in IBM Watson, and so that’s basically where I am at. A real career pivot I would add, so it’s a big change.
CG: Well it sounds very interesting, and I think it gives us a lot of fodder for things to talk about on this show because artificial intelligence machine learning, obviously Watson is at the pinnacle of that I think when people think of, you know, smart machines, but, you know, as we look at the communications industry, you know, whether you are on the PR side or the media side or marketing side there’s a lot of change that’s going to be happening I think in the coming years and really already has a little bit because of the rise of the machines as it were, and what it can do for you, and you know, one of the things I was struck by was a blog post you wrote earlier this year, and it had a prediction from [Gartner 00:02:07] where it says that, “20 percent of business content will be authored by machines, within the next 2 years,” and you know, we’ve certainly seen some stories.
There’s a company that produces for the Associated Press, automated financial report stories and now just recently came out said that they were going to automatically generate stories about Minor League Baseball games based on statistics that they were given in box scores and those sorts of things. First of all do you agree with that prediction? Do you think really that much of business content is going to come from machines in that sort of period of time?
NH: I’m not sure about most. I would say that the trend is quite clear and if we look at what machines, for want of a better word, are doing in this area, the AP is a very good example with the automated, some people are calling robo-journalism, where computer algorithms basically create the content and if you look at what exactly are they creating? It tends to be content that doesn’t require reasoning that doesn’t require, for want of a better word, deep cognition in terms of looking at things from different angles and presenting scenarios, they are reports largely and so you see things like the AP on sports reporting that other you mentioned recently about, I think it was baseball wasn’t it Chip?