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Predictive AI is Still the Mainstay, Even Though Generative AI is Becoming More Popular

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Predictive AI is Still the Mainstay, Even Though Generative AI is Becoming More Popular

The implementation of completely new procedures and infrastructure is not necessary for generative AI.

Enterprise CEOs and boards of directors have made generative artificial intelligence (genAI) a top focus since the introduction of ChatGPT in November 2022. For example, according to a PwC survey, 84% of CIOs anticipate using genAI in 2024 to support a new business model. Without a question, genAI is a truly revolutionary technological advancement. However, it’s also critical to keep in mind that this is only one type of AI and that not all use cases will benefit from its utilization.

The definition of artificial intelligence evolves with time. A program that played tic tac toe would have been considered a form of artificial intelligence fifty years ago; not so much these days. However, the history of AI can be broadly divided into three groups.

Conventional Analytics: For the past forty years, businesses have employed analytical business intelligence (BI). However, as technology has evolved and grown more complex, the term “BI” has been replaced with “analytics.” In general, analytics uses historical data to uncover insights about past events.

Predictive artificial intelligence (AI) is a forward-thinking technology that uses historical data analysis to identify patterns that may be applied to the present to produce precise future projections.

Generative AI: GenAI examines text, photos, audio, and video content to create new content based on user requirements.

“We work with a lot of chief data and artificial intelligence officers (CAIOs),” said Thomas Robinson, COO at Domino, “and, at most, they see generative AI accounting for 15% of use cases and models. Predictive AI is still the workhorse in model-driven businesses, and future models are likely to combine predictive and generative AI.”

Predictive and generative AI are actually already being used in tandem in certain use cases. For example, reports on preliminary diagnoses can be generated by studying radiological pictures, or reports on stocks that are expected to rise in the near future can be produced by mining stock data. This means that companies will require a shared platform for creating fully functional AI, according to CIOs and CTOs.

Every kind of AI has its own stack and is not treated as such in complete AI development and deployment. It’s true that genAI might need a little more power from some GPUs, and networking might need to be strengthened for better performance in some parts of the system, but constructing a new stack from the ground up isn’t necessary unless a company is operating a genuinely massive genAI deployment on the order of Microsoft or Meta.

Additionally, testing and governance procedures don’t have to be entirely redesigned. Predictive AI-powered mortgage risk models, for instance, need to undergo extensive testing, validation, and ongoing oversight, much as genAI’s large language models (LLMs). Once more, there are distinctions, like the well-known issue with “hallucinations” with genAI. However, risk management procedures for genAI will typically resemble those for predictive AI.

One in five Fortune 100 firms rely on Domino’s Enterprise AI platform to handle AI tools, data, training, and deployment. Teams working on MLOps and AI can use this platform to manage all aspects of AI, including generative and predictive AI, from a single control center. Organizations may enable full AI development, deployment, and management by consolidating MLOps under a single platform.

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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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