Artificial intelligence (AI) is predicted to be the next disruptive wave in media and society more generally.
MIT professor Max Tegmark’s latest book, Life 3.0, gives a nice hypothesis that the first indication artificial general intelligence (AGI) has happened will occur in media as it offers the best influence/financial returns equation to help the AGI scale.
But in a Chatham House discussion by some of the industry’s leading venture investors* at the Global Corporate Venturing Symposium in London last month the view by some was that AGI, artificial intelligence that is equally or more powerful than human intelligence, was about 30 years away.
And while there are plenty of reasons to doubt predictions in AI, as Rodney Brooks, Panasonic professor of robotics at MIT and founder of Rethink Robotics, wrote in his post, the seven deadly sins of AI predictions, Amara’s Law means: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”
If AGI does occur in about 30 years’ time then everyone involved the roundtable is statistically likely to be still alive and potentially working.
But the short-term effects from AI-related developments are already having impact through work done in areas such as natural language generation (NLG), machine or deep learning and generative adversarial networks (GANs) and these developments were the focus for much of the discussion in the roundtable.
Before this discussion started in earnest with a case study on Urbs Media’s NLG joint venture with Press Association as the final part of digital disruption from content creation and dissemination to include production of scalable stories off data, there was an observation on the evolution of market capitalisations of media companies against disruptive tech peers.
The main media groups’ market caps have grown over the past two decades but at a rate outpaced by some players from the broader tech industry, such as electronics manufacturer Apple, diversified internet conglomerate Alphabet, social media company Facebook, software developer Microsoft, and Chinese groups, such as Tencent, Alibaba and Baidu, that were either not actively involved in the media space or did not/barely existed 20 years ago.
With these tech disruptors seen as the most focused on AI, particularly through corporate venturing units, such as Google’s Gradient Ventures, will this further exaggerate the gap in value and potentially a tipping point for media companies?
While participants noted the shift to digital marketing and the increasing importance of data analytics, much of the discussion revolved around the general paradigm shift driven by artificial intelligence (AI).
Some of the participants expressed that, in terms of content production, what fundamentally has changed in the past decades is having better equipment and streams of data while the journalistic job remains essentially the same. While this has spurred the rise of data-based journalism and allowed for a certain degree of automation, at present no machine can write a story or an article better than a human.
Some noted that one of the hottest issues is rather content distribution over the online channels, which are dominated by companies like Facebook and Alphabet. Among other things, participants appeared to agree that there are opportunities on the monetisation side in the distribution and in terms of reinventing subscriptions models.
In terms of quality of AI-generated media output it was also expressed that it is largely contingent on the quality of data available, so companies like Google and Facebook with large amounts of data hold a clear competitive advantage.
Other applications of AI that were mentioned during the discussion were in image recognition or image analysis (used by e-retail and e-commerce platforms). It was noted that may AI companies are sector-agnostic, so media companies should try to use them to help analyse the large quantities of data. This way, media companies can streamline internal processes, which would allow them to reduce the need for data analysts and scientists.
One of the participants drew the distinction between AI and machine learning (ML). Participants pointed to examples of ML used to identify fake news – to enable users to verify the veracity of a story.
ML technology is also used to identify what people are wearing in video content (such as movies and TV shows – read Ben Evans’s post on video’s importance here) and matching fashion retailers that may offer similar clothing. It was also noted that ML and AI have wide possible applications in call centres, human resources and recruiting technology, real time multi-language transcription, translations and localization, brand safety, customer services. One of the participants cited an example of a recruiting chatbot, with which job seekers were willing to spend more than 30 minutes on average. There was also an example of the ability to hire a bot to design a logo for a company.
During the discussion, it became clear that not all media markets are alike and the applicability of AI and ML may greatly vary across geographies. In Asia, media tend to be highly regulated, which necessitates editors to make sure no important stakeholders are offended or that content is not adult-entertainment in nature.
The end of the discussion revolved around interesting existing strategies for media investors. Participants in room spoke of the new app market and ecosystem around Tencent´s WeChat or Meituan Dianping’s so-called super apps as great examples of how media can be converged with other commercial activities. However, it was noted that one of the biggest challenges in integrating media, entertainment and commerce, such as purchase attire worn by stars on a TV shows, lies in the difference in margins (double digits in media and up to 10% in e-retail). They also highlighted the importance of creating a relationship with end users. One of participants noted that consolidation is happening in the sector and in order to be a winner, one must first strive for scale. Participants generally agreed on the importance of partnering and collaboration, in recognition that with new business models through the blockchain and opportunities for decentralisation (arguments against here) the next generation of winners might not the be same as the current tech giants.
It was generally agreed that while media and telecom is the number one industry to be disrupted by AI, at the moment the overall focus is placed on complementarity – simplifying and organising the job of the humans – rather than replacement of human labour. However, the most astounding example given was from one company, which has managed to automate the equivalent of 40,000 jobs. The company has, nonetheless, managed to provide the people involved with other jobs, as it has expanded tremendously, although it has obviously hired fewer than it could have without such automation.
Participants also touched on the issue of ethical concerns in media investing in a world encroached by AI. One of them pointed out that they often have to ask themselves whether to invest in gaming companies, as games that children and teenagers play may be very addictive. On a much broader scale, the more pressing ethical apprehensions touch on whether it is really ethical to invest in automation, AI or ML towards people who may be out of their job at some point in the future.
Or as Henry Kissinger, sectary of state for former US presidents Richard Nixon and Gerald Ford, said in the June issue of Atlantic magazine: “Philosophically, intellectually – in every way – human society is unprepared for the rise of artificial intelligence.”
Kissinger gave three areas of special concern: unintended results from AI, changing human thought processes and values and making inexplicable conclusions. Management consultant Venkatesh Rao in his Breaking Smart blog similarly worries that AI will mean “our world will start to seem increasingly non-anthropocentric”.
This will matter less if people are changed – transhumanism – of course. Interestingly, at about the same time 60 years ago that the first wave of AI optimism was coming out psychologist Carl Jung published the Undiscovered Self and pointed out that knowledge is not the same understanding and “ultimately everything depends on the quality of the individual”.
My thanks to all the individual contributors to the discussion and Kaloyan Andonov for the notes and write-up of the roundtable itself.
*Sector focus: Media Industry Roundtable
Moderated by: James Mawson, Editor in Chief, Global Corporate Venturing
Tony Askew, Founder Partner, REV Venture Partners
Azeem Azhar, Chief, Exponential View
Jörn Caumanns, CFO, Bertelsmann Investments
Boon Ping Chua, CEO, SPH Ventures
Camilla Dolan**, Principal, Burda Principal Investments
Alex Dunsdon, Saatchinvest/Bakery
Solomon Elliott, Founder, The Student View
Ling Ge**, chief Europe representative, Tencent
Megumi Ikeda, GM of Europe and MD, Hearst Ventures
Ilonka Jankovich, managing partner, Randstad Innovation Fund
Mike Martin, Sky Ventures
Mike Redding, head, Accenture Ventures
Alan Renwick, Director, RADAR AI Limited
Vinay Solanki, Head of Commercial Growth Fund, Channel 4
Eze Vidra, Founder, Reimagine Ventures, ex-GV
Kaloyan Andonov, GCV Analytics – notes
** Registered