Google Is Paying Publishers to Test an Unreleased Gen AI Platform
Google Is Paying Publishers to Test an Unreleased Gen AI Platform In exchange for a five-figure sum, publishers must use the tool to publish 3 stories per day By Mark Stenberg Google launched a private program for a handful of independent publishers last month, providing the news organizations with beta access to an unreleased generative artificial intelligence platform in exchange for receiving analytics and feedback, according to documents seen by ADWEEK. As part of the agreement, the publishers are expected to use the suite of tools to produce a fixed volume of content for 12 months. In return, the news outlets receive a monthly stipend amounting to a five-figure sum annually, as well as the means to produce content relevant to their readership at no cost. “In partnership with news publishers, especially smaller publishers, we’re in the early stages of exploring ideas to potentially provide AI-enabled tools to help journalists with their work,” a Google representative said in a statement. “These tools are not intended to, and cannot, replace the essential role journalists have in reporting, creating and fact-checking their articles.” The AI-enabled results could throttle readership to an ‘apocalyptic’ degree. The beta tools let under-resourced publishers create aggregated content more efficiently by indexing recently published reports generated by other organizations, like government agencies and neighboring news outlets, and then summarizing and publishing them as a new article. Other gen AI experiments Google has released over the past two years include the codenamed Genesis, which can reportedly produce whole news articles and was privately demonstrated to several publishers last summer, according to The New York Times. Others, including Search Generative Experience and Gemini, are available for public use and threaten to upend many of the commercial foundations of digital publishing. The program is part of the Google News Initiative, which launched in 2018 to provide publishers with technology and training. Although many of its programs indisputably benefit the publishers involved, the broader reception of GNI has been mixed. Google has used GNI to drum up positive press and industry goodwill during moments of reputational duress, and many of the commercial problems it aims to solve for publishers were created by Google in the first place, said Digital Content Next CEO Jason Kint. “The larger point here is that Google is in legislative activity and antitrust enforcement globally for extracting revenue from the publishing world,” Kint said. “Instead of giving up some of that revenue, it’s attacking the cost side for its long-tail members with the least bargaining power.” Details of the program Google first shared a call for news organizations to apply to test the emerging technologies in an October edition of the Local Independent Online News newsletter. GNI began onboarding publishers in January, and the yearlong program kicked off in February. According to the conditions of the agreement, participating publishers must use the platform to produce and publish three articles per day, one newsletter per week and one marketing campaign per month. To produce articles, publishers first compile a list of external websites that regularly produce news and reports relevant to their readership. These sources of original material are not asked for their consent to have their content scraped or notified of their participation in the process—a potentially troubling precedent, said Kint. When any of these indexed websites produce a new article, it appears on the platform dashboard. The publisher can then apply the gen AI tool to summarize the article, altering the language and style of the report to read like a news story. The resulting copy is underlined in different colors to indicate its potential accuracy: yellow, with language taken almost verbatim from the source material, is the most accurate, followed by blue and then red, with text that is least based on the original report. A human editor then scans the copy for accuracy before publishing three such stories per day. The program does not require that these AI-assisted articles be labeled. The platform cannot gather facts or information that have not already been produced elsewhere, limiting its utility for premium publishers. Articles produced by the platform could also draw traffic away from the original sources, negatively affecting their businesses. The process resembles the ripping technique newly in use at Reach plc, except that in this case, the text is pulled from external sources. “I think this calls into question the mission of GNI,” Kint said. “It’s hard to argue that stealing people’s work supports the mission of the news. This is not adding any new information to the mix.” https://www.adweek.com/media/google-paying-publishers-unreleased-gen-ai/
Grazie Daniela, molto interessante. Soprattutto questo paragrafo:
The beta tools let under-resourced publishers create aggregated content more efficiently by indexing recently published reports generated by other organizations, *like government agencies and neighboring news outlets*, and then summarizing and publishing them as a new article.
Del resto, cosa c'è di più sicuro, per giornali in ristrettezze economiche (cioè quasi tutti), che riciclare propaganda governativa a basso costo? Con buona pace del ruolo politico del giornalismo. -- _______________ *Maurizio Borghi* Università di Torino https://www.dg.unito.it/persone/maurizio.borghi Co-Director Nexa Center for Internet & Society <https://nexa.polito.it/>
Buongiorno, Maurizio. Vediamo se si realizzerà la previsione di Daron Acemoglu, Get Ready for the Great AI Disappointment Rose-tinted predictions for artificial intelligence’s grand achievements will be swept aside by underwhelming performance and dangerous results. In the decades to come, 2023 may be remembered as the year of generative AI hype, where ChatGPT became arguably the fastest-spreading new technology in human history and expectations of AI-powered riches became commonplace. The year 2024 will be the time for recalibrating expectations. Of course, generative AI is an impressive technology, and it provides tremendous opportunities for improving productivity in a number of tasks. But because the hype has gone so far ahead of reality, the setbacks of the technology in 2024 will be more memorable. More and more evidence will emerge that generative AI and large language models provide false information and are prone to hallucination—where an AI simply makes stuff up, and gets it wrong. Hopes of a quick fix to the hallucination problem via supervised learning, where these models are taught to stay away from questionable sources or statements, will prove optimistic at best. Because the architecture of these models is based on predicting the next word or words in a sequence, it will prove exceedingly difficult to have the predictions be anchored to known truths. Anticipation that there will be exponential improvements in productivity across the economy, or the much-vaunted first steps towards “artificial general intelligence”, or AGI, will fare no better. The tune on productivity improvements will shift to blaming failures on faulty implementation of generative AI by businesses. We may start moving towards the (much more meaningful) conclusion that one needs to know which human tasks can be augmented by these models, and what types of additional training workers need to make this a reality. Some people will start recognizing that it was always a pipe dream to reach anything resembling complex human cognition on the basis of predicting words. Others will say that intelligence is just around the corner. Many more, I fear, will continue to talk of the “existential risks” of AI, missing what is going wrong, as well as the much more mundane (and consequential) risks that its uncontrolled rollout is posing for jobs, inequality, and democracy. We will witness these costs more clearly in 2024. Generative AI will have been adopted by many companies, but it will prove to be just “so-so automation” of the type that displaces workers but fails to deliver huge productivity improvements. The biggest use of ChatGPT and other large language models will be in social media and online search. Platforms will continue to monetize the information they collect via individualized digital ads, while competition for user attention will intensify. The amount of manipulation and misinformation online will grow. Generative AI will then increase the amount of time people spend using screens (and the inevitable mental health problems associated with it). There will be more AI startups, and the open source model will gain some traction, but this will not be enough to halt the emergence of a duopoly in the industry, with Google and Microsoft/OpenAI dominating the field with their gargantuan models. Many more companies will be compelled to rely on these foundation models to develop their own apps. And because these models will continue to disappoint due to false information and hallucinations, many of these apps will also disappoint. Calls for antitrust and regulation will intensify. Antitrust action will go nowhere, because neither the courts nor policymakers will have the courage to attempt to break up the largest tech companies. There will be more stirrings in the regulation space. Nevertheless, meaningful regulation will not arrive in 2024, for the simple reason that the US government has fallen so far behind the technology that it needs some time to catch up—a shortcoming that will become more apparent in 2024, intensifying discussions around new laws and regulations, and even becoming more bipartisan. https://www.wired.com/story/get-ready-for-the-great-ai-disappointment/ Un caro saluto, Daniela ________________________________________ Da: Maurizio Borghi <maurizio.borghi@unito.it> Inviato: mercoledì 28 febbraio 2024 09:57 A: Daniela Tafani; Nexa Oggetto: Re: [nexa] Google Is Paying Publishers to Test an Unreleased Gen AI Platform Grazie Daniela, molto interessante. Soprattutto questo paragrafo:
The beta tools let under-resourced publishers create aggregated content more efficiently by indexing recently published reports generated by other organizations, like government agencies and neighboring news outlets, and then summarizing and publishing them as a new article.
Del resto, cosa c'è di più sicuro, per giornali in ristrettezze economiche (cioè quasi tutti), che riciclare propaganda governativa a basso costo? Con buona pace del ruolo politico del giornalismo. -- _______________ Maurizio Borghi Università di Torino https://www.dg.unito.it/persone/maurizio.borghi Co-Director Nexa Center for Internet & Society<https://nexa.polito.it/>
On 28/02/24 10:54, Daniela Tafani wrote:
More and more evidence will emerge that generative AI and large language models provide false information and are prone to hallucination—where an AI simply makes stuff up, and gets it wrong. Hopes of a quick fix to the hallucination problem via supervised learning, where these models are taught to stay away from questionable sources or statements, will prove optimistic at best. Because the architecture of these models is based on predicting the next word or words in a sequence, it will prove exceedingly difficult to have the predictions be anchored to known truths.
Ci sono però casi, quali la valutazione dell'affidabilità di chi chiede un prestito, in cui l'imprecisione - eufemisticamente detto - dei SALAMI è ben nota e non è considerata un problema. Come scrive Brett Scott in Cloud Money (2022), capitolo 9. And there are countless possible mis-categorisations. To use a light-hearted example, I discovered that Twitter’s Robo-Sherlocks have me categorised – among other things – as a possible ‘working-class mom’. In Twitter’s case this may mean I’m shown adverts for cut-price family holidays, but in the case of a bank it could mean getting blacklisted from credit, or even its opposite – getting enticed into over-indebtedness. Regardless, ***if bluntly auto-categorising can help a bank make money by allowing it to churn through many more customers at low cost, the bank will try to do just that.*** Much in the way YouTube does not care if it gets video suggestions wrong for 20 per cent of people, so too will banks write off those customers who find themselves punished by the imprecision of their systems. ***The motivation in automating ‘intelligence’ is not to seek the truth and nuance of every case: it is to optimise profit at scale,*** and, unless it hurts the bottom line, financial institutions will not discard a system if they discover it only has the capabilities of a lazy holiday intern. Questo vale in particolare per il celebrato microcredito: Poorer people are unprofitable if an institution has to spend too much time serving or thinking about them, so the solution is to automate the process of doing both. Rebus sic stantibus, monopolisti e oligopolisti possono tenersi i SALAMI traendone profitto, anche se è possibile che chi deve misurarsi con la concorrenza ne tragga invece un danno economico. Ma "competition" - come scriveva Peter Thiel - "is for losers". Possiamo estendere queste considerazioni anche all'AI generativa e ai LLM? A presto, MCP
Il 28/02/2024 14:43, Maria Chiara Pievatolo ha scritto:
Ci sono però casi, quali la valutazione dell'affidabilità di chi chiede un prestito, in cui l'imprecisione - eufemisticamente detto - dei SALAMI è ben nota e non è considerata un problema. Come scrive Brett Scott in Cloud Money (2022), capitolo 9.
Rebus sic stantibus, monopolisti e oligopolisti possono tenersi i SALAMI traendone profitto, anche se è possibile che chi deve misurarsi con la concorrenza ne tragga invece un danno economico. Ma "competition" - come scriveva Peter Thiel - "is for losers".
Sì, è così. Gli effetti sono quelli della "so-so automation", per usare un'espresseione di Acemoglu. Nel caso del reclutamento automatizzato, ad esempio, le aziende evitano di investire risorse nella procedura di reclutamento, ma evitano così anche di trovare i candidati migliori: https://www.bbc.com/worklife/article/20240214-ai-recruiting-hiring-software-...
Possiamo estendere queste considerazioni anche all'AI generativa e ai LLM?
A qualcuno servono, certo. Provo a cominciare l'elenco. Servono 1. ai venture capitalists che vi hanno investito, ai quali non serve che una tecnologia sia utile o che funzioni; serve soltanto che le persone credano che funzioni, per un tempo sufficientemente lungo da rendere possibile un ritorno sugli investimenti; 2. alle aziende edtech, per impadronirsi di una parte dei magri stipendi dei docenti e di tutti i dati e i metadati degli studenti; 3. ai governi interessati a presentare un taglio dei servizi come un'innovazione tecnologica; 4. alle piattaforme, per trattenere gli utenti nei loro ecosistemi chiusi e disporre così della loro attenzione e dei loro dati e metadati; 5. in tutti gli ambiti in cui abbia luogo uno scambio di testi in cui il mittente non ha voglia di scrivere e il destinatario non ha intenzione di leggere (anche nell'ambito della ricerca scientifica e della revisione paritaria); 6. nei rapporti tra capitale e lavoro, a rafforzare il primo a danno del secondo: la forza contrattuale dei lavoratori è schiacciata dalla prospettiva, pur infondata, di una loro generale sostituibilità con macchine e robot; 7. a truffe, manipolazioni e propaganda su vasta scala. ... A presto, Daniela
nella mia esperienza solo una piccola parte delle news possono essere generate in questo modo. c'e' un popolare sito italiano di tecnologia che produce la maggioranza dei suoi contenuti da tempo cosi'. (se devi confrontare due telefonini, è abbastanza facile). il "giornalista" fa pochissimi aggiustamenti. metto "giornalista" tra virgolette perche' non mi pare paradigmatico del giornalismo. coem puo' confermare Anna Masera, un giornalista fa un mare di lavoro, prima di scrivere e scrivere e' tutto sommato una delle attivita' che richiede minor parte del suo tempo di attivita'. non mi pare che un outlet siffatto possa catturare tanto traffico, almeno non di più di quanto gia' accade per alcuni siti stra-minori con compensi da tre euro a pezzo... nella nostra mente sono dominanti gli outlet maggiori (stampa, corriere, ecc.) e questo si riverbera sui risultati dei motori di ricerca. rimane il traffico indotto dai social, ma anche qui vale in larga misur ail discorso fatto pe ri motori di ricerca. nel merito della sintesi di news di altri, vi segnalo questa curiosita' https://blog.quintarelli.it/2024/01/automatically-generated-news-back-in-201... ciao, s. On 28/02/24 09:57, Maurizio Borghi via nexa wrote:
Grazie Daniela, molto interessante. Soprattutto questo paragrafo:
The beta tools let under-resourced publishers create aggregated content more efficiently by indexing recently published reports generated by other organizations, _like government agencies and neighboring news outlets_, and then summarizing and publishing them as a new article.
Del resto, cosa c'è di più sicuro, per giornali in ristrettezze economiche (cioè quasi tutti), che riciclare propaganda governativa a basso costo? Con buona pace del ruolo politico del giornalismo.
-- _______________ *Maurizio Borghi* Università di Torino https://www.dg.unito.it/persone/maurizio.borghi <https://www.dg.unito.it/persone/maurizio.borghi> Co-Director Nexa Center for Internet & Society <https://nexa.polito.it/>
_______________________________________________ nexa mailing list nexa@server-nexa.polito.it https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa
+1 🤗 Anna Inviato da iPhone
Il giorno 28 feb 2024, alle ore 11:16, Stefano Quintarelli <stefano@quintarelli.it> ha scritto:
nella mia esperienza solo una piccola parte delle news possono essere generate in questo modo. c'e' un popolare sito italiano di tecnologia che produce la maggioranza dei suoi contenuti da tempo cosi'. (se devi confrontare due telefonini, è abbastanza facile). il "giornalista" fa pochissimi aggiustamenti.
metto "giornalista" tra virgolette perche' non mi pare paradigmatico del giornalismo.
coem puo' confermare Anna Masera, un giornalista fa un mare di lavoro, prima di scrivere e scrivere e' tutto sommato una delle attivita' che richiede minor parte del suo tempo di attivita'.
non mi pare che un outlet siffatto possa catturare tanto traffico, almeno non di più di quanto gia' accade per alcuni siti stra-minori con compensi da tre euro a pezzo... nella nostra mente sono dominanti gli outlet maggiori (stampa, corriere, ecc.) e questo si riverbera sui risultati dei motori di ricerca. rimane il traffico indotto dai social, ma anche qui vale in larga misur ail discorso fatto pe ri motori di ricerca.
nel merito della sintesi di news di altri, vi segnalo questa curiosita' https://blog.quintarelli.it/2024/01/automatically-generated-news-back-in-201...
ciao, s.
On 28/02/24 09:57, Maurizio Borghi via nexa wrote: Grazie Daniela, molto interessante. Soprattutto questo paragrafo:
The beta tools let under-resourced publishers create aggregated content more efficiently by indexing recently published reports generated by other organizations, _like government agencies and neighboring news outlets_, and then summarizing and publishing them as a new article. Del resto, cosa c'è di più sicuro, per giornali in ristrettezze economiche (cioè quasi tutti), che riciclare propaganda governativa a basso costo? Con buona pace del ruolo politico del giornalismo. --
*Maurizio Borghi* Università di Torino https://www.dg.unito.it/persone/maurizio.borghi <https://www.dg.unito.it/persone/maurizio.borghi> Co-Director Nexa Center for Internet & Society <https://nexa.polito.it/> _______________________________________________ nexa mailing list nexa@server-nexa.polito.it https://server-nexa.polito.it/cgi-bin/mailman/listinfo/nexa
participants (5)
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Anna Masera -
Daniela Tafani -
Maria Chiara Pievatolo -
Maurizio Borghi -
Stefano Quintarelli