Google Identifies Smart Software Trends

January 18, 2022

Straight away the marketing document “Google Research: Themes from 2021 and Beyond” is more than 8,000 words. Anyone familiar with Google’s outputs may have observed that Google prefers short, mostly ambiguous phraseology. Here’s an example from Google support:

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When a Google document is long, it must be important. Furthermore, when that Google document is allegedly authored by Dr. Jeff Dean, a long time Googler, you know it is important. Another clue is the list of contributors which includes 32 contributors helpfully alphabetized by the individual’s first name. Hey, those traditional bibliographic conventions are not useful. Chicago Manual of Style? Balderdash it seems.

Okay, long. Lots of authors. What are the trends? Based on my humanoid processes, it appears that the major points are:

TREND 1: Machine learning is cranking out “more capable, general purpose machine learning models.” The idea, it seems, that the days of hand-crafting a collection of numerical recipes, assembling and testing training data, training the model, fixing issues in the model, and then applying the model are either history or going to be history soon. Why’s this important? Cheaper, faster, and allegedly better machine learning deployment. What happens if the model is off a bit or drifts, no worries. Machine learning methods which make use of a handful of human overseers will fix up the issues quickly, maybe in real time.,

TREND 2: There is more efficiency improvements in the works. The idea is the more efficiency is better, faster, and logical. One can look at the achievements of smart software in autonomous automobiles to see the evidence of these efficiencies. Sure, there are minor issues because smart software is sometimes outputting a zero when a one is needed. What’s a highway fatality in the total number of safe miles driven? Efficiency also means it is smarter to obtain machine learning, ready to roll models and data sets from large efficient, high technology outfits. One source could be Google. No kidding? Google?

TREND 3: “Machine learning is becoming more personally and communally beneficial.” Yep, machine learning helps the community. Now is the “community” the individual who works on deep dives into Google’s approach to machine learning or a method that sails in a different direction. Is the community the advertisers who rely on Google to match in an intelligent and efficient manner the sales’ messages to users human and system communities? Is the communally beneficial group the users of Google’s ad supported services? The main point is that Google and machine learning are doing good and will do better going forward. This is a theme Google management expresses each time it has an opportunity to address a concern in a hearing about the company’s activities in a hearing in Washington, DC.

TREND 4: Machine learning is going to have “growing impact” on science, health, and sustainability. This is a very big trend. It implicitly asserts that smart software will improve “science.” In the midst of the Covid issue, humans appear to have stumbled. The trend is that humans won’t make such mistakes going forward; for example, Theranos-type exaggeration, CDC contradictory information, or Google and the allegations of collusion with Facebook. Smart software will make these examples shrink in number. That sounds good, very good.

TREND 5: A notable trend is that there will be a “deeper and broader understanding of machine learning.” Okay, who is going to understand? Google-certified machine learning professionals, advertising intermediaries, search engine optimization experts, consumers of free Google Web search, Google itself, or some other cohort? Will the use of off the shelf, pre packaged machine learning data sets and models make it more difficult to figure out what is behind the walls of a black box? Anyway, this trend sounds a suitable do good, technology will improve the world that appears to promise a bright, sunny day even though a weathered fisherperson says, “A storm is a-coming.”

The write up includes art, charts, graphs, and pictures. These are indeed Googley. Some are animated. Links to YouTube videos enliven the essay.

The content is interesting, but I noted several omissions:

  1. No reference to making making decisions which do not allegedly contravene one or more regulations or just look like really dicey decisions. Example: “Executives Personally Signed Off on Facebook-Google Ad Collusion Plot, States Claim
  2. No reference to the use of machine learning to avoid what appear to be ill-conceived and possibly dumb personnel decisions within the Google smart software group. Example: “Google Fired a Leading AI Scientist but Now She’s Founded Her Own Firm
  3. No reference to anti trust issues. Example: “India Hits Google with Antitrust Investigation over Alleged Abuse in News Aggregation.”

Marketing information is often disconnected from the reality in which a company operates. Nevertheless, it is clear that the number of words, the effort invested in whizzy diagrams, and the over-wrought rhetoric are different from Google’s business-as-usual-approach.

What’s up or what’s covered up? Perhaps I will learn in 2022 and beyond?

Stephen E Arnold, January 18, 2022


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