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Entertainment and Media Businesses Put Your All Into Generative AI

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Entertainment and Media Businesses Put Your All Into Generative AI

The media industry is inherently creative, and recognizing this is essential to appreciating the industry’s eagerness and willingness to attempt new things. In keeping with the industry mentality that actively seeks out innovation rather than avoids it, its purpose to provide compelling content also motivates it to investigate methods of augmenting creative talents while concurrently reducing aspects of production procedures.

Accordingly, generative artificial intelligence (AI) is a game-changer that will boost the creative activities of the sector by creating new opportunities for inspiration, teamwork, and content production. Media professionals may use generative AI to improve their creative output, automate processes, and create ground-breaking experiences that enthrall and please viewers in fresh and immersive ways.

The “Generative AI Tracker®” illuminates the ethical, legal, and social ramifications of generative AI implementation while revealing the cutting-edge potential and new applications of generative AI in the media sector.

Media Industry’s AI Future is Being Cast by Innovators

A wave of innovation has been sparked by the commercialization of generative AI to meet the diverse and complex needs of the media business. The firms driving these advances are divided into three groups by PYMNTS Intelligence: players for AI audience engagement, players for AI creative development, and players for AI operational efficiency.

Businesses that fall under the category of AI creative enhancement players use generative AI to provide richer media experiences. Examples of these partnerships are Immerso AI with Yotta and Microsoft Xbox with Inworld AI. Businesses in the AI audience engagement and insight sector include Telly and Displace. By using generative AI to evaluate customer activity, these organizations are able to forecast patterns of consumer involvement, make informed business decisions, and target advertising material through increasingly seamless interactive consumer experiences. In the meantime, generative AI tools are provided by AI operational efficiency players, which are embodied by businesses like Reduct.Video. These tools increase the effectiveness of operational components of content generation and management in the media sector.

AI Envisions A Future for the Media Industry

Science fiction meets reality with generative AI’s inventive potential and applications in the media sector. They question accepted practices for the production, distribution, and consumption of content. These factors make the media sector an ideal place for this technology to be tested.

It should come as no surprise that it is already using generative AI to target marketing and content delivery. The technology has the ability to transform conventionally passive information consumption into customized, participatory user experiences, going beyond applications with an economic foundation. For instance, the gaming sector is leading the way in the application of generative AI to create more immersive gaming environments that improve the gameplay’s character and environment elements.

In the meanwhile, the news media sector is carefully investigating the potential of generative AI, if only to simplify certain intricate back-end aspects of the content creation process that do not compromise journalistic integrity. This is because they are constrained by strict ethical standards and the need to maintain public trust.

The Ethical and Social Conundrums Associated with Media Use of Generative AI

The media sector is already facing criticism for its part in spreading false and misleading information. Emerging generative AI technologies are becoming widely accessible, which increases the likelihood that these already-existing issues may worsen and affect the industry as a whole.

As a result, many of the most well-known generative AI technologies’ outputs are now being closely examined by creatives and content producers, who then raise concerns about plagiarism. The opaque decision-making procedures used by LLMs complicate problems and make it difficult to understand the reasoning behind any particular outcome. One effect is that there’s a greater chance of a legal battle between the tech and media sectors. This might have a significant impact on how generative AI applications develop in the future in both industries.

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Timescale Introduces Advanced AI Vector Database Extensions for PostgreSQL

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A PostgreSQL cloud database provider recently declared the availability of two brand-new, open-source extensions that greatly improve the scalability and usability of its data retrieval from vector databases for artificial intelligence applications.

Using PostgreSQL, an open-source relational database, for vector data retrieval is made possible by the new extensions, pgvectorscale and pgai. This is essential for developing AI applications and specialized contextual search.

AI programmers can add data to high-dimensional arrays using vector databases, connecting them based on their contextual relationships with each other. Vector databases store data using contextualized meanings, where the “nearest neighbor” can be used to connect them, in contrast to typical relational databases. For example, a cat and a dog have a closer meaning as family pets than does an apple. When an AI searches for semantic data, including keywords, documents, photos, and other media, this speeds up the information-finding process.

Timescale’s AI product lead, Avthar Sewrathan, told SiliconANGLE in an interview that while most of this data is kept in very popular, high-performance vector databases, not all of the data used by services is kept in vector databases. Thus, in the same context, there are occasionally several data sources.

“AI is being incorporated into every organization in the world, in some form or another, whether through the development of new apps that capitalize on the power of large language models or through the redesign of current ones,” stated Sewrathan. Therefore, CTOs and technical teams must decide whether to employ a distinct vector database or a database they are already familiar with while figuring out how to use AI. Encouraging Postgres to be a better database for AI is the driving force behind these enhancements.

Building on the open-source foundation of the original expansion, pgvectorscale, enables developers to create more scalable artificial intelligence (AI) applications with improved search performance at a reduced cost.

According to Sewrathan, it incorporates two innovations: Statistical Binary Quantization, which is an enhancement of standard binary quantization that helps reduce memory use, and DiskANN, which can offload half of its search indexes to disk with very little impact on performance. DiskANN is capable of saving a significant amount of money.

In comparison to the widely used Pinecone vector database, PostgreSQL was able to attain 28x lower latency for 95% and 16x greater query throughput for approximate nearest neighbor queries at 99% recall, according to Timescale’s benchmarks of pgvectorscale. Since pgvectorscale is written in Rust instead of C, PostgreSQL developers will have more options when developing for vector support.

The next addition, pgai, is intended to facilitate the development of retrieval-augmented generation, or RAG, solutions for search and retrieval in applications using artificial intelligence. In order to lessen the frequency of hallucinations—which occur when an AI boldly makes erroneous statements—RAG blends the advantages of vector databases with the skills of LLMs by giving them access to current, reliable information in real-time.

Building precise and dependable AI systems requires an understanding of this technique. OpenAI conversation completions from models like GPT-4o are built directly within PostgreSQL with the first release of pgai, which facilitates the creation of OpenAI embeddings rapidly.

The most recent flagship model from OpenAI, the GPT-4o, offers strong multimodal capabilities like video comprehension and real-time speech communication.

According to Sewrathan, PostgreSQL’s vector functionality builds a strong “ease of use” bridge for developers. This is significant because many firms currently use PostgreSQL or other relational databases.

Because it streamlines your data architecture, adding vector storage and other features via an extension is much easier, according to Sewrathan. “One database is all you have.” It has the ability to store several data kinds simultaneously. That has been extremely beneficial because without it, there would be a great deal of complexity, data synchronization, and data deduplication.

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Apple is Updating Siri and Giving it new Generative AI Capabilities

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The release of iOS 18, macOS updates, and other significant announcements marked the beginning of Apple’s yearly Worldwide Developers Conference (WWDC) 2024 yesterday. The launch of the eagerly awaited new iteration of Apple’s voice assistant, Siri, was the most notable of these. By means of a brand-new system dubbed “Apple Intelligence,” the revised Siri is equipped with stronger generative AI capabilities.

With these enhanced artificial intelligence capabilities, Apple has enabled Siri to perform better, becoming more contextually aware, natural, and deeply ingrained in the Apple environment. The incorporation of ChatGPT into this change promises more intelligent responses and new AI-powered functionality. The updated Siri, according to Apple, is “more natural, more contextually relevant, and more personal,” and it may speed and streamline routine activities. Let’s examine each of the recently added features of Apple’s sophisticated voice assistant in depth.

New style

Activating a bright light that encircles the screen edges is just one of the many features of the redesigned Siri. Increased user engagement is the goal of this graphic makeover. Apple has added onscreen awareness to Siri, which goes beyond aesthetics and allows the virtual assistant to take actions based on what’s on the screen. Customers may now ask Siri to locate and act upon book recommendations received via Messages or Mail, or to add a new address straight from a text message to a contact card.

An enhanced capacity for linguistic comprehension

Apple’s Siri now features richer language-understanding capabilities, allowing it to process and respond to user commands more naturally. This improvement ensures Siri can maintain context across multiple interactions, even if users stumble over their words. Additionally, users can now type to Siri and switch seamlessly between text and voice inputs, offering more flexible ways to interact with the assistant.

Siri’s compatibility with outside applications

Because of the new App Intents API, one of the most notable aspects of the new Siri is its ability to perform actions in a variety of apps—both those developed by Apple and those by outside developers. This means that programmers can give Siri specific commands to execute within their apps. For example, users may ask Siri to “send the photos from the barbecue on Saturday to Malia” using a message app, or “make this photo pop” in a photo editing software. Interactions between various apps and services can now be done more easily thanks to this added capabilities.

Apple and openAI collaborate to power Siri

Notably, Apple and OpenAI have teamed to enhance Siri’s generative AI capabilities by integrating ChatGPT technology. With this integration, Siri can respond with greater sophistication and manage jobs that are more complicated. Users of Apple’s Mac and iPhone operating systems will be able to access ChatGPT through updates, which will improve features like text and content production. Apple’s plan to integrate cutting-edge AI technologies and maintain its competitiveness in the IT industry includes this relationship.

Apple uses sophisticated Siri to protect user privacy

Users can be reassured by Apple that Siri and the new AI capabilities in its devices will respect its strict privacy policies. While the company will rely on the cloud without storing user data there for more power-intensive operations, certain AI functions will process data directly on the device. This strategy aligns with Apple’s goal of striking a balance between improved usefulness and consumer privacy.

The new Siri will only be available on a few chosen Apple devices

The newest iPads, Macs, and iPhones will be the only devices that can utilize this sophisticated Siri experience. Most of Siri’s new features, which are powered by Apple Intelligence, will only be available on the iPhone 15 Pro, iPhone 15 Pro Max, iPads, and Macs with M1 CPUs or later.

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EU Introduces an AI-Driven “Digital Twin” of the Planet

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Today, the European Commission unveiled the initial iteration of Destination Earth (DestinE), an AI-driven simulator designed to increase the precision of climate projections.

Two models—one for extreme weather events and another for adapting to climate change—are included in the initial edition of DestinE. With the use of these models, the Earth’s climate will be closely observed, predicted, and simulated.

According to EU antitrust chief Margrethe Vestager, “DestinE means that we can observe environmental challenges which can help us predict future scenarios – like we have never done before.”

The LUMI supercomputer located in Finland is one of the high-performance computers (EuroHPC) that power DestinE. To accelerate data processing, the developers have integrated this with AI.

Vestager stated, “This first phase shows how much we can achieve when Europe puts together its massive supercomputing power and its scientific excellence.”

The main model will, however, probably change over time, and by the end of this decade, a digital duplicate of the Earth should be finished.

Digital Twin of the Earth

Want to test how a heatwave will impact food security? Or if a storm will flood a certain city? Or the best places to position your wind farm? All of that could be possible using the digital twin of the Earth.

The digital twin uses a sizable data lake to fuel its simulations and forecasts. Satellites like those used in the EU’s Copernicus program are the source of this data. It will also originate from vast amounts of public data as well as IoT devices situated on the ground.

Future iterations of the digital twin of Earth will incorporate data from forests, cities, and oceans, pretty much anyplace on Earth that scientists can analyze data.

In 2022, the EU launched DestinE for the first time. The digital twin will be constructed with funding exceeding €300 million.

With today’s launch, the first phase comes to a conclusion and the second phase begins, with a combined funding commitment of over €150 million for both.

As the final Digital Europe program 2025–2027 is presently being prepared, its approval will determine the funding for the third stage.

Organizations working on this kind of technology are not limited to the EU. The Earth-2 digital replica was introduced by Nvidia in March. As stated by the powerhouse in chip manufacturing, the model is currently being used by the Taiwanese government to more accurately forecast when typhoons will hit land.

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