Фил Роули: « Упредительный маркетинг»
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Фил Роули, директор по глобальным инновациям PHD, медиа-мыслитель и всемирно известный digital-стратег, рассказывает как подготовиться к революции искусственного интеллекта в индустрии рекламы и коммуникаций.
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Фил Роули: « Упредительный маркетинг»
- 23. 2021- Bid speed the main source of competitive advantage for media agencies SENTIENCE 2021 MARKETING 2021 MEDIA 2022 MARKETING 2023 CONSUMER 2024 MARKETING 2028 CONSUMER 2029 TECHNOLOGY
- 28. SENTIENCE Exponential development of technology see's machines with human levels of intelligence 2029 - 2029 TECHNOLOGY 2029 MARKETING
Editor's Notes
- Good afternoon – my name is Phil Rowley, I am the Global Innovation Director at PHD’s Global Strategy Unit PHD is bit obsessed with Artificial Intelligence – and we’re working to make some logical next step predictions about it’s like effect on marketing. These have been written up into a book called Sentience. Would like to bring the book to life for you. To do that I want you to come on a journey with me. To the year 2029. Where AI has asserted a change on us greater than that of the internet. But before we get to that. I want us to take of a run up. Let’s just take a step back. To, arguably, where it all started.
- To 1936, Princeton University, where a young English mathematician named Alan Turing published a paper which became the foundation of computer science. In it Turing presented a theoretical machine that can solve any problem that can be described by simple instructions encoded on a paper tape. In essence he’d invented the concept of the computer.
- Fast forward to 1943 and the creation of artificial neural networks . The eccentric self-taught mathematician Walter Pitts of University of Chicago and neurophysiologist Warren McCulloch were the first ever to describe the concept of an artificial neural network and show that it can be used to calculate any computable function. They also suggested that such networks, because they can be tuned based on experience, could learn … and thereby ultimately, eventually, become intelligent.
- Seven years later and the ultimate test of computer intelligence was developed, by Alan Turing. The Turing Test asked: “can a computer fool a panel of human judges into believing that it’s human?” If so, then we might as well consider it to be a thinking machine. During a Turing Test, the judges would ask the machine a series of questions through a text-only medium. If the machine can fool the humans into thinking that it’s a human, then it’s passed the test.
- At about the same time, in 1951, Marvin Minsky and Dean Edmonds, students at Princeton University, become the first to actually build a neural network computer. This primitive neural network was only able to simulate about 40 neurons, nothing compared to the 100 billion neurons in the human brain, but it was a remarkable start.
- …and led the way to Artificial Intelligence as a specific field of study, in 1956, when computer scientist John McCarthy spearheaded a two-month workshop at Dartmouth College, along with Minsky and two other students. The workshop brought together ten researchers from across the U.S. to study a wide range of topics and show off their work … the most lasting thing to come out of the workshop is an agreement to adopt McCarthy’s new name for the field: artificial intelligence.
- And then in 1965 an important insight emerged. Not specifically AI related but very important underpinning for AI was the concept of Moore’s Law – by Gordon Moore, the co-founder of Intel. The rule observes that computers will progressively get twice as fast and cost half as much every 12–18 months. It describes exponential change. To get a sense of how exponential change works, consider this: if a person was to take 30 linear steps along a straight line (1, 2, 3, 4…) they’d end up 30 paces or, say, 30 meters away from their starting point. However, if they took 30 exponential steps (1, 2, 4, 8, 16…) they’d end up one billion meters away or have gone around the planet over 26 times.
- We then went into, what is termed, the AI winter – when people became disillusioned by the progress. This period lasted until the 1990s. However in this winter, an important foundation for AI was born – IOT (Internet of Things) Computer students at Carnegie-Mellon University wanting to know the inventory level of their local Coke machine. In 1982, they wired up the Coke vending machine with micro-switches to report on levels in the racks in the machine.
- 1990 and the AI winter was over. Ray Kurzweil, using the law of accelerating returns – an evolution of Moore’s Law - , made a series of accurate predictions In his 1990 book The Age of Intelligent Machines, Kurzweil predicts that a computer will beat a human chess champion by 1998. He was out by only year … it happened a year earlier than he predicted …
- Whilst in the AI winter a Carnegie Mellon University computer scientist named Geoff Hinton persevered with neural networks – pioneering an approach that use multiple layers of virtual neurons. By 2006, he had created what we now call the deep learning algorithms. Whereas Machine Learning requires an algorithm to be taught and supervised by a human, Deep learning allowed for AI to carry-out unsupervised learning and action – by just reading every pixel/sound wave and optimising action based on target outcome. This was a major major breakthrough. 2011 and another major AI event…
- Same year and another landmark moment. Apple bought this, largely, unknown company called Siri. The endorsement of Apple – was a defining moment for AI AI had officially gone mainstream.
- Audio: this video is mute – present over it (the robot in the video is called Baxter BTW) By 2013 the broader academic community started to engage in the implications of AI. A report by a group of Oxford University researchers published in 2013 concluded that 47 percent of all jobs were at risk of being replaced by AI within the next two decades. That, of course, didn’t stop the innovation…
- Meanwhile, Google announced that it had just completed the mapping of the exact location of every business, every household, and every street number in the entirety of France. Google used machine learning algorithms and its street-view database — hundreds of millions of images — and had somebody manually go through a few hundred images and circle the street numbers in them. Then Google’s engineers wrote a program that said, essentially, “You figure out what’s unique about those circled things, find them in the other 100 million images, and then read the numbers that you find.” It took Google one hour.
- By 2015, just about every major automobile company was working on some form of autonomous vehicle — including the driver-on-demand service Uber, who in 2015 revealed that they were building their AI research lab in Pittsburgh to “kickstart autonomous taxi fleet development.”
- Also in 2015, an important foundation for the future of AI happened with Samsung changing its T&Cs to kindly let you know that your overheard conversations may be captured and transmitted to a third party. However, any anxiety about data privacy was short-lived…as we then all started worrying something even more significant… the existential threat AI posed to humanity…
- …with Elon Musk, Steve Wozniak, Bill Gates and Stephen Hawking all warning of the dangers of AI. With Musk saying they would be more dangerous that ‘nukes’. And with Hawking famously saying that success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks. From 2015 onwards we really started to see the affects of AI, ripple up onto the shores of marketing…
- That’s the history. But now let talk marketing. Lets talk advertising. Let’s talk the future By 2018 marketers started to target based not just on what people have clicked-on but by what they actually know and their opinions. Using non PII-data gleaned from the overheard conversations – voice and text. Yahoo!’s 20 years of historic emails, 256 petabytes of data, was a treasure trove for deep-learning algorithms.
- Reading and overhearing conversations started to allow VPAs (Virtual Personal Assistants) to take a more active role in making recommendations to us. For marketers the focus turns towards affecting the VPA’s AI-derived ‘quality/relevance-score’ This quality score was based on over-heard and canvased opinion but also on the actual performance of products. Laundry detergents were assessed on how clean clothes are by IoT washing machines, petrol quality assessed by cars. This, incidentally, was one of the greatest unseen applications for IOT along with connected products alerting the user’s VPA when they are close to expiration or renewal — more on this later.
- At the same time we entered the Preemptive Marketing era. This is where individuals were algorithmically-identified as high-potentials based on patterns of behaviour that were too complex for data analysts to identify. This type of analysis had already been successfully deployed in other areas such as predicting where crimes are more likely to happen. This finally allowed data-strategies to become much more sophisticated —prior to 2019 agencies selected cookie-pools of people captured from click-based activities over the last few days or weeks, with no deep understanding if that audience has already fulfilled on that purchase. With Preemptive Marketing, it was not just the bid that was real-time — the data was finally real-time too. Winning the ability to be the suggested brand/product for these prospects as they popped into a high-propensity or an active-request state became the subject of increasing and intense focus, with the combination of bid price and quality-relevance score the crucial determinants.
- And, for the first time, VPAs start to feel, at least a little bit, sentient They start to reduce the gap between the external world and the user. The gap could be seen in the effort required in having to search for information, having to remember a birthday, having to click into various sites to organize any activity. Much of the information was there but it required us to cross a gap to get to it. We had to mentally flex ourselves out of shape in order to fit the disorganized, disconnected, decentralized world of information – for example missing a flight and having to change hotels, cancel the hire car, contact work colleagues, re-arrange diaries, call family members. We had to get our minds into the shapes of the different websites in order to operate them. By 2020 your VPA took care of the whole thing for you. It started to spend its entire time and focus managing your life — making everything easier, removing boundaries. And it started getting more things right than wrong. The VPA had access to the world and edited it for you. And with this, our sense of what the internet was dramatically contracted from an unfathomably large expanse of information into a simple human voice.
- And this changed our real relationships. Every one of us had a personal assistant in our ear - on call to manage the complexities of life. They alerted us that someone has called and summarize what they said and what they want — and asked you what you want to do. We started to speak naturally back to them and they understood and went back and communicated on our behalf. Often communicating with your friends or colleagues’ VPA … the two AIs making all the necessary arrangements themselves. This time-shifted and VPA-mediated form of communication became a new form of interaction and, over this decade, replaced a large amount of live conversations. Think of it like a Digital Concierge.
- …these AI prompts were driven by a biddable advertising model where the message was created algorithmically in a completely native and natural way. The messages were delivered by the VPA only when the quality-relevance score and the bid-price ratio trips a certain threshold. VPAs only pushed through messaging that it deemed relevant based on its knowledge about the individual it supports, meaning that only the most naturally relevant messaging made it through. Live communication – text and voice chat - was also augmented with information and content that supported and added to the conversation … some of which was again ad-funded native messaging.
- With global agencies and tech companies developing sophisticated bid-based software they reached a parity. At this point, as with the stock-market a well over decade earlier, speed become a new competitive advantage. Brands that could reach, or win in a bid opportunity within a fraction of a second ahead of their competitors would, over time, grow ahead of their competitors.
- By 2022, AI started to write the ads. Within standard ads, headlines, combined with offers and images, emerged in the relevant language and cultural context. We started to assess the creative work after it has already appeared. Algorithmic-driven creative and knowledge-driven targeting finally opened up the potential to move beyond segmentation and focus on individual level messaging — so each person had a communication targeted specifically at them, based on their unique situation (what they knew, what they liked, category purchase propensity, the brand purchase propensity, where they are in the purchase process etc.).
- Because our VPAs were tasked with carrying out many of our day-to-day tasks, including searching for options, our VPAs increasingly started to make decisions for us. Starting in 2023, for many purchase decisions, where there was little discernible difference between brands, the decision was simply made by an algorithm – and you were informed if you wanted to know. Fuelled by predictive algorithms – with data from IOT - VPAs would start to inform their user that, for example, the kettle is due a replacement and ask if they would like to see some options or if they want the VPA to just make the call. Depending on the category, thousands of these moments were now happening every day – with the bulk being low-interest frequently purchased items - and they became a major trigger for bid-based marketing fulfilment systems. So people were popping into an active purchase stage not when they decided to buy a product but when their products expire or run out.
- 2024 and marketers have all but adapted to this new world. Marketers primarily moved to new era of marketing to the machine – not the pre-frontal cortext.
- Of course a sizeable amount of marketing investment continued to be about affecting a non-rational response for decisions that were still made without the direct control of the VPA. Either by advertising reviews of products/services – a way of lifting perceived quality scores or, for the higher-interest purchase decisions categories such as autonomous cars, where the brand, and what it represents, were still an important factor in a human decision. For these categories the broadcast-outreach activity was single-mindedly about generating fame and talk-ability. In fact by 2028 it was hard to discern the difference between brands and entertainment platforms — such movies, entertainment spaces, branded-products, interactive-experiences. Brand advertising had gone the same way as the retail sector and become 100 percent recreational. Retail was now almost exclusively a social and entertainment experience.
- And so, in this world of strong-AI, we don't just use computers, we have relationships with them. They our the voice in our heads – and we will love having them around. That makes the world of 2015 look primitive and remarkable disconnected. Life was so much harder to navigate. This AI revolution has fundamentally changed marketing – it is now almost unrecognisable. And, more importantly, the AI revolution has radically reorganised the world in which we all live.
- Thank you.