Xinzhi Zhang

Reflections on the talk by Prof Ikhlaq Sidhu on Artificial Intelligence

“Will AI help the film directors to make better movies? – Yes! But will AI replace the film directors and become directors? – No!” – Prof Ikhlaq Sidhu.

Thus responded by Prof Sidhu when I asked, “do you think AI will replace film directors?” Fortunately, our dialogue was far wider than the above. Prof Ikhlaq Sidhu, Professor & Chief Scientist, UC Berkeley, and the Founding Director, Center for Entrepreneurship & Technology, offered a University-wide “Distinguished Lecture” on “How to Achieve Data Analytics and Artificial Intelligence in X” and a two-full-day masterclass on Big Data Analytics in Hong Kong Baptist University.

In the 1.5-hour of talk, Prof Sidhu reviewed the traditional applications of AI (for example, classification and scoring/prediction). He then reviewed the future directions of AI. Thirdly, he pinpointed several limitations of AI vis-à-vis human (which I will offer some of my humble reflections on this blog), and how to understand the future development of this field (another key point I’d like to address further here). Lastly, some strategies options were offered to HKBU. During the four-day visit of Prof Sidhu to HKBU (thanks to the Knowledge Transfer Office, especially Alfred), we got a chance to have more in-depth discussion on this theme. My three quick and immature reflections are indicated below.

# Reflection 1: AI and human

When the whole world is charmed (and feeling threatened perhaps) about the AlphaGo (and still remembering the perplexed look of Ke Jie), Prof Sidhu raised a question: “does this mean AI can do everything better than humans?” When the expected answer from the mass (as humans) might be “no” (but I have a large circle of tech-geek friends who would say yes, loudly and clearly), the mechanisms worth further explication. Prof Sidhu holds a very prudent and cautious view. AI can handle tasks that having certain outcomes (winning the GO); in a limited, in discrete conditions; where the human world may not always have the luxury of having certain outcomes (I wish I could know the path to become a billionaire), in unlimited and continuous conditions. Hence, one tentative statement here is, there will be more and more “specific AI” applications, focusing on one domain, handling one very specific type of tasks (AI-assisted financial planning, AlphaGo, auto-drivers, among others); but there are not “comprehensive AI” (do we have Blade Runner 2049 or Ghost in the Shell). That repeats the dialogue in the opening when I asked: “since AI can self-learn, and AI has been quite successful in many fields, will it success as well in creative industries, such as painting, music, poetics, and film making?”

# Reflection 2: Data and theory

“The end of theory.” We all still remember the Chris Anderson’s 2008 essay on Wired. One primary argument is that, with the huge amount of data, we don’t need theory any more, because the patterns, regulations, and insights will emerge from the data. Neither Prof Sidhu and other audiences directly mention this piece, but Sidhu raised the cautious in seeking ground truth (if my interpretation is correct): if the truth of a certain parameter is 12; but all the top-ranked Google pages say it should be 11, then people will believe the parameter to be 11.

So how to deal with the relationship between data and “theory?” Prof Sidhu raised three critical reflections: a. data is more valuable than algorithm; b. Algorithm is more important than the system; and c. algorithms, data, and computing data is growing faster than computing.

Personally I have a strong resonance from point (a). Tycho, the 16^th century Danish astronomer, who reached the human’s limitation to document decades-long astronomical and planetary observations. With the huge amount of data from Tycho, could later scientists, namely Kepler and Newton, accomplish scientific leap-forwards and raise new insights and propose new theories. In the big data era, we badly need more empirical data and observations that enabled by the accurate and comprehensive documentations of human behaviors, just like what Tycho did in several centuries ago. “Exactly! We still need theories and scientific methods: from observation to building up hypothesis and to make further inferences.” Thus responded Sidhu when I mentioned Tycho. (Credit should also be given to my high school geography teacher, when he told us the story of scientific progress in the human history).

# Reflection 3: AI, job losses, lifestyle, and beyond 

The final reflection started from an audience’s question, which was also a commonly-discussed one: how to respond to the job losses because of AI? The first half of Prof Sidhu’s response to the question echoed some economists’ observation: while AI may lead to some job losses, but new job positions will emerge at the same time. Secondly, Prof Sidhu used the example of sewing industry in the history. When the sewing machine was invented, about 50 workers could be replaced by one machine (50:1). Further, the cost of the clothes was also dropped; and sequentially, people’s lifestyle was also changed (buying two clothes vs having a full closet of clothes – far more than one’s needs). Hence, my humble thought is that, more studies are needed to examine the long-term impacts of AI on social change. Topics may include lifestyle, fashion, aesthetics, culture values, social values, and ideologies.

Earlier this month on 7 Oct, the Hong Kong Baptist University held a university-level Information Day, introducing undergraduate programmes (see this news issued by the University). The Department of Journalism is a part of this exciting event. This year the Department is joining hands with the Department of Computer Science (COMP) to launch a new interdisciplinary concentration eneitled “Data and Media Communication” (DMC), starting from 2018/19. Here is a simple official webpage. Basically, the new concentration – being added to the existing counterparts in the department – Chinese Journalism, International Journalism, and Financial Journalism – aiming at providing both data analytics training and journalistic education to students.  It is an effort for the department to meet the challenge of the transitioning news industry, as well as the willingness to march into the frontier of the interplay of data science and humanities/social sciences in general.

The event sailed through, with the great help from the six hard-working student helpers from other programmes, especially when we do NOT have any student to cheer for us! Mr Pili Hu, the newly appointed lecturer in our department, formerly the data manager at Inituim, offered an array of excellent data-driven journalism works, so that we could demonstrate and show the visitors the power of the combination of journalistic sensitivity, design, infographic, and data analytics. We also owe big favors to Melody, Hung Gor, Chung Gor, and not to mention the strong support from Alice, our Dept. Head, as well as Dean Prof Huang.

Well I guess it is not appropriate – also not the intention of this blog – for me to further promote and explicate this new concentration. But may I – as a humble associate director of this infant programme – share several observations and reflections from the information day and Q&As with the visitors.

First, the visitors’ enthusiasm and interests in data-driven journalism were far beyond our expectation. A simple indicator is that all the pamphlets and souvenirs were out of supply. High school students, parents, year 2 AD students, and faculty members from other academic units, gathered around our booth, asking questions. They were not just passers-by. Secondly, it seems that most students are intimidated by the term of “data” – they regarded “data” as “high math skills.” Actually, for the application of data science in journalism, “math skills” is not the most important ability, instead, it is the “data sense” that one should be cultivated – where to find data, how to interpret data, how to make sense of data, and “how to lie with statistics.” All these are not about “science” and “math,” but the same keen attention to the “misfortune of human beings” and the willingness to contribute to the public goods. Thirdly, it seems that there is an overlapping of the interests of “data-driven journalism” and “financial journalism.” Common-sensically, both are focusing on “data.” But I would say these two areas are distinct. To me, it appears that financial journalism is more specialized and having demanding requirements on specific domain knowledge, whereas data-driven journalism is more like a “telescope” (or microscope, a tool, a lens) to uncover the myths of the social realities. Finally, my humble understanding on the interplay between journalism and data science shares with the quotation of Prof Jonathan Zhu at CityU: “Students from humanities are worried about [how to compute]; whereas those who with science backgrounds are worried about [what to compute].” That’s the long-lasting and enduring challenge facing both clusters of students.

“The best time to plant a tree was 20 years ago. The second best time is now,” thus spoke the proverb. As a student using “traditional” quantitative methods, I was fortunate to be enrolled as one of the 178 participants in the 2017 summer school on “Artificial Society and Computational Social Science,” held by the School of Sociology and Anthropology at Sun Yat-Sen University (SYSU) in Guangzhou from 10 – 20 July, 2017. The summer school was coordinated by Professor Yucheng Liang (faculty of SYSU), and lectured by Dr Hong Zhang (faculty of SYSU), Dr Yongren Shi (Postdoc at Yale, who has published several high-calibrated articles on top journals such as American Sociological Review), as well as (the well-known top scholar) Professor James Evans (from U Chicago). The summer school lasted for non-stop 11 days, with a total of 57 credits. Thank goodness, I finished this summer school in the grilling and hectic season in Guangzhou.

The summer school covered two broad research areas, namely, the agent-based modeling (ABM) approach, and the computational social science (CSS) approach. The ABM approach takes the process of computer simulation based on several formulated rules, and explicates the process and dynamics among individuals (termed as “agents”), and the emergence of collective social phenomenon. In the research, the characteristics and rules of agents’ action, the rule of social interaction among these agents, and the social contexts in which the agents interact, are all defined and set-up by the researchers. The approach has been applied to a wide range of disciplines, from natural science (the symbiosis of animals, the penetration of fire in the forest), to social science such as the distribution of wealth, the formation of race segregation, and, as a recent article published on Journal of Communication explicated, the reciprocal influence of selective media exposure, interpersonal political talk, and political polarization! The ABM method tries to explain the world in a highly abstract and succinct fashion, and it manages to investigate the evolution of collective social facts (i.e., race segregation, ideological polarization) from simple individual’s actions and interacting rules (i.e., “I will move to another place if 30% of my neighborhoods are not the same race as me” – as depicted by the Schelling model).

The computational social science (CSS) can be roughly defined, by Professor Evans, as the “method to use computers to generate data, discover patterns or generate and test explanations that you could not have without them” (in-class lecture notes, taking by the author). It is based on massive amount of data generated from social media, digital traces, as well as the digitalization of existing “non-digital” text materials. The “Declaration” of this field was raised by David Lazer (2009), which has been widely quoted and featured. In the second session of the summer school, Dr Yongren Shi introduced several cutting-edged works. For example, is science political polarized? (Intuitively one may say “no!” – because one always believes that “science” is (ought to be) objective, non-ideologically tilted, and being free from the partisanship’s influence). However, massive Amazon book purchase records revealed a complex and disturbing scenario (see the work “Millions of online book co-purchases reveal partisan differences in the consumption of science” by Shi and his associates). He also mentioned another recent work on the strategic development of organization.

As a “late comer” of this field, I was deeply fascinated and charmed by the insightful (and sometimes “crazy”) research ideas and the rigorous operationalization and implementation of this field. The interdisciplinary nature also generates a lot of rich research ideas, and advancing our understanding on the nature of human behaviors. I was also moved by all the teaching faculties (including the five voluntary student teaching assistants), who voluntarily organized this workshop and generously shared their latest work, all for FREE! (Yes, the summer school was zero charged). I was also surprised to find the hard-working and eagerness of all the participants attending this summer school. Some of them were already ranked as Associate Professor or above. In sum, there remains much to be further reflected and digested from this fruitful summer.

# Figure 1. Professor James Evan is lecturing. The photo was took by the author.

# Figure 2. A snapshot of the main campus of SYSU.

# Figure 3. My humble “Letter of Certificate”

On June 7^th, a number of entertainment public social media accounts in mainland China (see the news report by the South China Morning Post) were shut down by the authority. According to the Cyberspace Administration of China, the censorship authority for all new media platforms, these entertainment accounts cast a threat to public order, as they are likely to promote vulgar contents such as violence and pornography, disclosing too much celebrities’ privacy and unhealthy personal lives, or containing monstrous and exaggeratedly bizarre pictures or videos. Another review by the New York Times pinpointed the tricky aspect of this issue: when entertainment news and sports news were previously regarded as “safe and free” areas of news reporting, the cracking down of these public accounts indicated an extended mode of content regulation. Another perplexed puzzle is that, defined as a crucial component of “cultural industry,” the strategic development of entertainment and new media have been written into several waves of Five-Year Plans.

My quick reflection on this “news occurrence” is twofold. First, based on a humble guess, the motivation for the authority to initiate such censorship – like some of those content regulations imposing on entertainment contents but unlike those on politically sensitive or “national security-related” contents – is that they believe ordinary audiences might imitate or legitimize those lifestyles defined to be “unhealthy” and “negative,” such as, but not limited to, extramarital affairs, law violation, showing off, binge drinking and drug abuse, plus (possibly) excessively materialistic lifestyles. A strong media effect is presumed. The third-person effect can also spell the logic out: only a small group of smart people can distinguish the right from wrong, whilst the general netizens are less enlightened.

Secondly, the real paradox is that, in a context where the media system is relatively not free (of course, it depends on the benchmark of “free” – a handy reference is that how much “independence,” or “dependence” the media agencies can enjoy), media has the function of “empowerment:” everything appears on the media is legitimate and endorsed by the authorities. This is a myth that should be corrected, and one crucial way, though may not be the best one, is to grant a more open and diverse media landscape and return the right for decision-making back to the audiences themselves. In the vein of entertainment (or just like sports), perhaps the neo-liberal interpretation of market functions perfectly. One can always vote for or against his or her (entertainment) idols (setting aside running the risk of objectifying those celebrities for a while). The Media System Dependency theory argues that people rely on media the most when the situation they are living in is uncertain and unstable. Based on the theory, perhaps a rapid development of entertainment media, though somewhat not that decent for a while, is the result of limited competence in reporting issues on government, politics, and the public. This in turn promotes a rapid growth of online content providers. One may ask, what will happen if the entertainment media is constrained as well? In balancing the benefits and demands among media, politics, stakeholders, and audience, there is still a long way to proceed.

(This picture is in the public domain and it is obtained from: https://pixabay.com/en/news-information-reading-1483109/)