California lawmakers intend to shelve legislation that would have required Google to pay news outlets for distributing their content, and in its place announced a new public-private partnership between the state and the tech giant that will fund programs to research artificial intelligence and bolster local journalism. The plan lays out a commitment of nearly 250 million over the next five years, with one-fourth of the money coming from state taxpayers and three-fourths of it coming from Google and possibly other private donors. The money will go toward two new initiatives administered by UC Berkeley's Graduate School of Journalism: a fund to distribute millions of dollars to California news outlets, and an "AI accelerator" to develop ways for journalists to use the powerful technology. "This agreement represents a major breakthrough in ensuring the survival of newsrooms and bolstering local journalism across California -- leveraging substantial tech industry resources without imposing new taxes on Californians," Gov. Gavin Newsom said in a statement. "The deal not only provides funding to support hundreds of new journalists, but helps rebuild a robust and dynamic California press corps for years to come, reinforcing the vital role of journalism in our democracy."

Google announced this week that it would begin the international rollout of its new artificial intelligence-powered search feature, called AI Overviews. When billions of people search a range of topics from news to recipes to general knowledge questions, what they see first will now be an AI-generated summary. Google touted AI Overviews at its annual I/O developer conference as a way of delivering customers quick answers and simplifying the online search experience, but it also has another effect on the way that people engage with the internet: keeping users, and advertisers, on Google.com. "Google will do the googling for you," said Liz Reid, head of Google Search. While Google was once mostly a portal to reach other parts of the internet, it has spent years consolidating content and services to make itself into the web's primary destination.

Google's dominance as the most visited website has been undisputed since it rose to prominence as the leading search engine in the early 2000s. However, that position could now be facing its biggest ever threat, with the arrival of new artificial intelligence (AI) chatbots such as ChatGPT, which can answer people's questions online. Google is countering by developing its own AI products. But its chatbot, Bard, didn't have the most auspicious start. This month, a Google advert showed that Bard had provided an inaccurate answer to a question about the James Webb space telescope.

Online advertising revenues account for an increasing share of publishers' revenue streams, especially for small and medium-sized publishers who depend on the advertisement networks of tech companies such as Google and Facebook. Thus publishers may benefit significantly from accurate online advertising revenue forecasts to better manage their website monetization strategies. However, publishers who only have access to their own revenue data lack a holistic view of the total ad market of publishers, which in turn limits their ability to generate insights into their own future online advertising revenues. To address this business issue, we leverage a proprietary database encompassing Google Adsense revenues from a large collection of publishers in diverse areas. We adopt the Temporal Fusion Transformer (TFT) model, a novel attention-based architecture to predict publishers' advertising revenues. We leverage multiple covariates, including not only the publisher's own characteristics but also other publishers' advertising revenues. Our prediction results outperform several benchmark deep-learning time-series forecast models over multiple time horizons. Moreover, we interpret the results by analyzing variable importance weights to identify significant features and self-attention weights to reveal persistent temporal patterns.

Social media sites play a major role in increasing false information it may be for a profitable business, generating advertising revenue for publishers who create and publish stories that go viral. The more clicks a story gets, the more money online publishers make through advertising revenue and for many publishers' social media is an ideal platform to share content and drive web traffic. Artificial Intelligence (AI) now enables the mass creation of what is known as "Deepfakes" -- creating synthetic videos that closely resemble real videos. Deepfakes are fake videos created using digital software, machine learning and face-swapping. They are computer-created artificial videos in which images are combined to create new footage that depicts events, statements and actions that never actually happened.

Options for targeting digital advertising in a way that doesn't rely on cookies are increasing, thanks to advances in predictive analytics and AI that will ultimately lessen the current dominance of Google, Facebook, and other large-scale content aggregators. Google announced earlier this month that it will no longer allow third-party cookies to collect data via its Chrome browser. Many companies have historically relied on those cookies to better target their digital advertising, as the cookies enable digital ad networks and social media sites to create a profile of an end user without knowing specifically who that individual is. While that approach doesn't necessarily breach anyone's privacy, it does give many users the feeling that some entity is tracking the sites they visit in a way that makes them uncomfortable. Providers of other browsers, such as Safari from Apple and the open source Firefox browser, have already abandoned third-party cookies.

For online advertising in e-commerce, the traditional problem is to assign the right ad to the right user on fixed ad slots. In this paper, we investigate the problem of advertising with adaptive exposure, in which the number of ad slots and their locations can dynamically change over time based on their relative scores with recommendation products. In order to maintain user retention and long-term revenue, there are two types of constraints that need to be met in exposure: query-level and day-level constraints. We model this problem as constrained markov decision process with per-state constraint (psCMDP) and propose a constrained two-level reinforcement learning to decouple the original advertising exposure optimization problem into two relatively independent sub-optimization problems. We also propose a constrained hindsight experience replay mechanism to accelerate the policy training process. Experimental results show that our method can improve the advertising revenue while satisfying different levels of constraints under the real-world datasets. Besides, the proposal of constrained hindsight experience replay mechanism can significantly improve the training speed and the stability of policy performance.

One of the few bright spots this past year was supposed to be the revival of journalism. And to be sure, it's been a great time for muckraking, with newsrooms bringing home scoop after scoop on the Trump administration. Subscriptions to everything from the New York Times to Mother Jones are up. And for the first time in decades, trust in news media is rising too: Today, 54 percent of the public have confidence in journalists to tell the truth, while only 36 percent trust the president. Here's where the story turns more complicated.

One of the few bright spots this past year was supposed to be the revival of journalism. And to be sure, it's been a great time for muckraking, with newsrooms bringing home scoop after scoop on the Trump administration. Subscriptions to everything from the New York Times to Mother Jones are up. And for the first time in decades, trust in news media is rising too: Today, 54 percent of the public have confidence in journalists to tell the truth, while only 36 percent trust the president. Here's where the story turns more complicated.

IBM announced that Oovvuu, an Australian technology start-up, has launched a Watson powered video-on-demand news platform to connect viewers to the most relevant video and news content, while generating vital new advertising revenues for global media and news organisations. As media organisations struggle to maintain advertising revenues, news and editorial teams are shrinking and the quality of journalism is being challenged. News organisations around the world are relinquishing advertising revenue to social platforms, with estimates that Facebook and Google represent 85 per cent of digital revenue and growing. Building new revenue streams are critical not only to the survival of media and news organisations but also their ability to maintain and deliver quality journalism. Oovvuu connects the world's best video content from 40 broadcast partners, such as ABC, BBC, and Bloomberg, with the most read-news articles using the power of IBM's Watson to match videos with breaking news.