The releases come from Meta’s Fundamental AI Research (FAIR) team which has focused on advancing AI through open research and collaboration for over a decade. As AI rapidly innovates, Meta believes working with the global community is crucial.

“By publicly sharing this research, we hope to inspire iterations and ultimately help advance AI in a responsible way,” said Meta.

Chameleon: Multi-modal text and image processing

Among the releases are key components of Meta’s ‘Chameleon’ models under a research license. Chameleon is a family of multi-modal models that can understand and generate both text and images simultaneously—unlike most large language models which are typically unimodal.

“Just as humans can process the words and images simultaneously, Chameleon can process and deliver both image and text at the same time,” explained Meta. “Chameleon can take any combination of text and images as input and also output any combination of text and images.”

Potential use cases are virtually limitless from generating creative captions to prompting new scenes with text and images.

Multi-token prediction for faster language model training

Meta has also released pretrained models for code completion that use ‘multi-token prediction’ under a non-commercial research license. Traditional language model training is inefficient by predicting just the next word. Multi-token models can predict multiple future words simultaneously to train faster.

“While [the one-word] approach is simple and scalable, it’s also inefficient. It requires several orders of magnitude more text than what children need to learn the same degree of language fluency,” said Meta.

JASCO: Enhanced text-to-music model

On the creative side, Meta’s JASCO allows generating music clips from text while affording more control by accepting inputs like chords and beats.

“While existing text-to-music models like MusicGen rely mainly on text inputs for music generation, our new model, JASCO, is capable of accepting various inputs, such as chords or beat, to improve control over generated music outputs,” explained Meta.

AudioSeal: Detecting AI-generated speech

Meta claims AudioSeal is the first audio watermarking system designed to detect AI-generated speech. It can pinpoint the specific segments generated by AI within larger audio clips up to 485x faster than previous methods.

“AudioSeal is being released under a commercial license. It’s just one of several lines of responsible research we have shared to help prevent the misuse of generative AI tools,” said Meta.

Improving text-to-image diversity

Another important release aims to improve the diversity of text-to-image models which can often exhibit geographical and cultural biases.

Meta developed automatic indicators to evaluate potential geographical disparities and conducted a large 65,000+ annotation study to understand how people globally perceive geographic representation.

“This enables more diversity and better representation in AI-generated images,” said Meta. The relevant code and annotations have been released to help improve diversity across generative models.

By publicly sharing these groundbreaking models, Meta says it hopes to foster collaboration and drive innovation within the AI community.

(Photo by Dima Solomin)

See also: NVIDIA presents latest advancements in visual AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Meta unveils five AI models for multi-modal processing, music generation, and more appeared first on AI News.

Customers were supposed to be able to drive up, loudly state their orders into the drive-thru microphone, and then have an AI record all of that, instead of a human crew member. Theoretically, the AI should be able to comprehend more complicated requests, offer recommendations based on past orders and also reduce error, all while simultaneously continuing to provide fast service and without getting bogged down by staffing problems.

Yet McDonald’s is already giving up on the AI drive-thru feature just a few years after introducing it. Viral videos exposed the system as overwhelmed with more simple orders, taking requests from the wrong cars and recommending peculiar food couplings, like ice cream and bacon. Following these public failures, McDonald’s declared that it will no longer use IBM for the partnership after July 31, 2024.

This spectacular flameout raises the question: does AI really have a role to play in the drive-thru of the future, or is ‘Burgerbot’ just destined to be the next in a long line of extremely smart things that fundamentally, scornfully refuse to understand humans and, thus, fail?

The pros of AI drive-thrus 

But McDonald’s woes aside, the benefits that attracted the company and competitors to AI drive-thrus in the first place haven’t disappeared. Labour costs are going up while staff are getting harder to find, so the prospect of having an AI take orders could offer significant cost reductions in the future.

AI systems also promise something extremely compelling: absolute understanding of even the most twisted, crazy customer issues or requests you can think of. Armed with prior ordering data, the AI was able to provide custom recommendations aimed at boosting average spend.

AI ordering, furthermore, could lead to an even speedier, less-friction-filled drive-thru experience. An AI could immediately process an order rather than requiring a human crew member to repeat it back. Those efficiencies could be even more significant during peak hours when cars are sprawled around the building and the drive-thru is jammed.

The negatives – and where McDonald has stumbled

Of course, the viral blunders that shut down McDonald’s AI drive-thru dreams tells a cautionary tale true to life when it comes to some of the real pitfalls with which this new tech grapples. Despite years of training, McDonald’s AI system still seemed to frequently fail at the very core task of taking accurate orders.

And such failures during the ordering process could result in customer dissatisfaction and expensive order inaccuracies – which in turn could wipe out any potential labour savings. And then there is the looming question of whether AI will ever be able to understand the constant stream of idiosyncratic phrasings, accents, and off-menu requests as well as humans.

The McDonald’s experience also hints at the “uncanny valley” challenge of AI-powered ordering. While often impressive, the current state of AI can still produce responses that seem almost human, but not quite – leading to an unsettling experience for some customers. A human voice actor may be required to dub over the AI.

The future of AI drive-thrus 

Despite pulling the plug on its initial system, the fast food giant still believes AI will be a big player in the drive-thrus of the future. The IBM project, which testing gave the company “confidence that a voice ordering solution for drive-thru will be part of our restaurants’ future,” is part of a broader effort by McDonald’s to develop “long-term, scalable solutions,” a company spokesperson said.

Chipotle, Wendy’s, Taco Bell, and Pizza Hut are among a number of other big chains that are also making big moves to integrate AI drive-thru ordering into their businesses. The stakes are high, with the entire industry standing to save billions in labour costs if this technology works.

The consensus among most experts, however, is that AI drive-thru ordering is just beginning and that major milestones must still be met before it goes truly universal. But with AI language models advancing so fast, it may not be long before the ordering-caution and other ordering issue plaguing McDonald’s will be a thing of the past.

There may also be a phased handoff period where passengers have the choice of interacting with an AI or a human flight attendant. It might build rapport and increase trust over time, which might be an advantage for AI drive-thrus.

But at the end of the day, it sounds like McDonalds was still a few months to years ahead of the curve and just rolled this out more aggressively than it should have right away. Or, as one Silicon Valley investor quipped yesterday: “You can’t be a pioneer without taking a few arrows in the back.” For the fast-food industry, streamlining drive-thrus with AI remains the next frontier – it’s just going to take more time.

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Acknowledging past shortcomings in machine learning utilisation, Snap’s CEO Evan Spiegel announced a new, assertive strategy to integrate AI and machine learning technologies into its services, marking a substantial departure from its long-term focus on revising its advertising approach.

In an interview with Bloomberg, Spiegel emphasised the need to improve their machine learning capabilities to reach cutting-edge standards. “We needed to improve there and bring together some of our most senior machine learning folks to just talk about what it would look like for us to get to state of the art and really invest,” he stated.

Soon afterward, Snap debuted its newest generative AI technology that allows phone cameras to create more lifelike lenses—the features on the app that let you turn into a dog or have giant bug eyes—when recording videos and taking photos. Snapchat hopes that this change will help it compete more effectively with other social media platforms.

Snap has been a pioneer in augmented reality (AR) technology, which layers digital effects onto real-world images or videos. Although Snap still operates in the shadow of larger rivals such as Meta, the company is making a significant bet on more sophisticated and, frankly, more fun AR lenses. They hope these will attract new users and advertisers to the Snapchat platform.

The company also unveiled that AR developers can now create AI-powered lenses, and Snapchatters will be able to extensively use these lenses in their content. Additionally, Snap announced a new iteration of its developer program: Lens Studio. This more advanced version of the software, introduced late last year, initially allowed creators to build their own AR experiences for Snapchat. Now, it extends to websites and other apps.

With the improved Lens Studio, Snap’s CTO Bobby Murphy said that the time required to create AR effects would be dramatically reduced from weeks to minutes or hours, and that it would also facilitate the development of more sophisticated work. “What’s fun for us is that these tools both stretch the creative space in which people can work, but they’re also easy to use, so newcomers can build something unique very quickly,” Murphy explained in an interview with Reuters.

The new Lens Studio includes a suite of generative AI tools, such as an AI assistant that can answer developers’ questions if they need help. Another tool allows artists to type a prompt and automatically generate a three-dimensional image that they can use for their AR lens, eliminating the need to develop a 3D model from scratch.

Early AR technologies only allowed users to perform simple tasks, such as placing a hat on someone’s head in a video. However, according to Murphy, Snap’s improvements will make it kind of hard to tell whether a digital hat is actually being worn, with the hat moving seamlessly with the person’s movements and the lighting on the hat matching the video perfectly.

Snap also eventually plans to create AR lenses that cover everything from your head to your toes—not just your face. Building a new wardrobe for individuals is really hard to do right go right now, said Murphy. Through its generative AI capabilities, Snap will provide advanced AR experiences to distinguish Snapchat from its peers and attract new users, even though it might struggle to gain users relative to its scale compared with giants like Meta.

See also: NVIDIA presents latest advancements in visual AI

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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“Artificial intelligence, and generative AI in particular, represents a pivotal technological advancement,” said Jan Kautz, VP of learning and perception research at NVIDIA.

“At CVPR, NVIDIA Research is sharing how we’re pushing the boundaries of what’s possible — from powerful image generation models that could supercharge professional creators to autonomous driving software that could help enable next-generation self-driving cars.”

Among the over 50 NVIDIA research projects being presented, two papers have been selected as finalists for CVPR’s Best Paper Awards – one exploring the training dynamics of diffusion models and another on high-definition maps for self-driving cars.

Additionally, NVIDIA has won the CVPR Autonomous Grand Challenge’s End-to-End Driving at Scale track, outperforming over 450 entries globally. This milestone demonstrates NVIDIA’s pioneering work in using generative AI for comprehensive self-driving vehicle models, also earning an Innovation Award from CVPR.

One of the headlining research projects is JeDi, a new technique that allows creators to rapidly customise diffusion models – the leading approach for text-to-image generation – to depict specific objects or characters using just a few reference images, rather than the time-intensive process of fine-tuning on custom datasets.

Another breakthrough is FoundationPose, a new foundation model that can instantly understand and track the 3D pose of objects in videos without per-object training. It set a new performance record and could unlock new AR and robotics applications.

NVIDIA researchers also introduced NeRFDeformer, a method to edit the 3D scene captured by a Neural Radiance Field (NeRF) using a single 2D snapshot, rather than having to manually reanimate changes or recreate the NeRF entirely. This could streamline 3D scene editing for graphics, robotics, and digital twin applications.

On the visual language front, NVIDIA collaborated with MIT to develop VILA, a new family of vision language models that achieve state-of-the-art performance in understanding images, videos, and text. With enhanced reasoning capabilities, VILA can even comprehend internet memes by combining visual and linguistic understanding.

NVIDIA’s visual AI research spans numerous industries, including over a dozen papers exploring novel approaches for autonomous vehicle perception, mapping, and planning. Sanja Fidler, VP of NVIDIA’s AI Research team, is presenting on the potential of vision language models for self-driving cars.

The breadth of NVIDIA’s CVPR research exemplifies how generative AI could empower creators, accelerate automation in manufacturing and healthcare, while propelling autonomy and robotics forward.

(Photo by v2osk)

See also: NLEPs: Bridging the gap between LLMs and symbolic reasoning

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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While LLMs like ChatGPT have demonstrated impressive performance on various tasks, they often struggle with problems requiring numerical or symbolic reasoning.

NLEPs follow a four-step problem-solving template: calling necessary packages, importing natural language representations of required knowledge, implementing a solution-calculating function, and outputting results as natural language with optional data visualisation.

This approach offers several advantages, including improved accuracy, transparency, and efficiency. Users can investigate generated programs and fix errors directly, avoiding the need to rerun entire models for troubleshooting. Additionally, a single NLEP can be reused for multiple tasks by replacing certain variables.

The researchers found that NLEPs enabled GPT-4 to achieve over 90% accuracy on various symbolic reasoning tasks, outperforming task-specific prompting methods by 30%

Beyond accuracy improvements, NLEPs could enhance data privacy by running programs locally, eliminating the need to send sensitive user data to external companies for processing. The technique may also boost the performance of smaller language models without costly retraining.

However, NLEPs rely on a model’s program generation capability and may not work as well with smaller models trained on limited datasets. Future research will explore methods to make smaller LLMs generate more effective NLEPs and investigate the impact of prompt variations on reasoning robustness.

The research, supported in part by the Center for Perceptual and Interactive Intelligence of Hong Kong, will be presented at the Annual Conference of the North American Chapter of the Association for Computational Linguistics later this month.

(Photo by Alex Azabache)

See also: Apple is reportedly getting free ChatGPT access

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post NLEPs: Bridging the gap between LLMs and symbolic reasoning appeared first on AI News.

Should this legislation come into force, it could penetrate the defences that many in Silicon Valley have built against such detailed scrutiny of AI development and deployment processes.

Since the public release of OpenAI’s ChatGPT, backed by Microsoft 18 months ago, there has been significant growth in interest and investment in generative AI technologies. These applications, capable of writing text, creating images, and producing audio content at record speeds, have attracted considerable attention. However, the rise in AI activity accompanying these changes prompts an intriguing question: How do AI developers actually source the data needed to train their models? Is it through the use of unauthorised copyrighted material?

Implementing the AI Act

The EU’s AI Act, intended to be implemented gradually over the next two years, aims to address these issues. New laws take time to embed, and a gradual rollout allows regulators the necessary time to adapt to the new laws and for businesses to adjust to their new obligations. However, the implementation of some rules remains in doubt.

One of the more contentious sections of the Act stipulates that organisations deploying general-purpose AI models, such as ChatGPT, must provide “detailed summaries” of the content used to train them. The newly established AI Office has announced plans to release a template for organisations to follow in early 2025, following consultation with stakeholders.

AI companies have expressed strong resistance to revealing their training data, describing this information as trade secrets that would provide competitors with an unfair advantage if made public. The level of detail required in these transparency reports will have significant implications for both smaller AI startups and major tech companies like Google and Meta, which have positioned AI technology at the center of their future operations.

Over the past year, several top technology companies—Google, OpenAI, and Stability AI—have faced lawsuits from creators who claim their content was used without permission to train AI models. Under growing scrutiny, however, some tech companies have, in the past two years, pierced their own corporate veil and negotiated content-licensing deals with individual media outlets and websites. Some creators and lawmakers remain concerned that these measures are not sufficient.

European lawmakers’ divide

In Europe, differences among lawmakers are stark. Dragos Tudorache, who led the drafting of the AI Act in the European Parliament, argues that AI companies should be required to open-source their datasets. Tudorache emphasises the importance of transparency so that creators can determine whether their work has been used to train AI algorithms.

Conversely, under the leadership of President Emmanuel Macron, the French government has privately opposed introducing rules that could hinder the competitiveness of European AI startups. French Finance Minister Bruno Le Maire has emphasised the need for Europe to be a world leader in AI, not merely a consumer of American and Chinese products.

The AI Act acknowledges the need to balance the protection of trade secrets with the facilitation of rights for parties with legitimate interests, including copyright holders. However, striking this balance remains a significant challenge.

Different industries vary on this matter. Matthieu Riouf, CEO of the AI-powered image-editing firm Photoroom, compares the situation to culinary practices, claiming there’s a secret part of the recipe that the best chefs wouldn’t share. He represents just one instance on the laundry list of possible scenarios where this type of crime could be rampant. However, Thomas Wolf, co-founder of one of the world’s top AI startups, Hugging Face, argues that while there will always be an appetite for transparency, it doesn’t mean that the entire industry will adopt a transparency-first approach.

A series of recent controversies have driven home just how complicated this all is. OpenAI demonstrated the latest version of ChatGPT in a public session, where the company was roundly criticised for using a synthetic voice that sounded nearly identical to that of actress Scarlett Johansson. These examples point to the potential for AI technologies to violate personal and proprietary rights.

Throughout the development of these regulations, there has been heated debate about their potential effects on future innovation and competitiveness in the AI world. In particular, the French government has urged that innovation, not regulation, should be the starting point, given the dangers of regulating aspects that have not been fully comprehended.

The way the EU regulates AI transparency could have significant impacts on tech companies, digital creators, and the overall digital landscape. Policymakers thus face the challenge of fostering innovation in the dynamic AI industry while simultaneously guiding it towards safe, ethical decisions and preventing IP infringement.

In sum, if adopted, the EU AI Act would be a significant step toward greater transparency in AI development. However, the practical implementation of these regulations and their industry results could be far off. Moving forward, especially at the dawn of this new regulatory paradigm, the balance between innovation, ethical AI development, and the protection of intellectual property will remain a central and contested issue for stakeholders of all stripes to grapple with.

See also: Apple is reportedly getting free ChatGPT access

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

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Many games are being totally revolutionised by AI stepping onto the scene, so let’s get into the nitty-gritty of which games are changing, what’s happening, and how AI is leaving its footprint on this world of online casino games!

Personalisation in the slots

First up: personalisation. AI really shines when it comes to personalising the slots, because an AI can analyse each player’s individual behaviour and start tailoring what the game shows to match. Imagine you’re playing at your favourite slot, and you get a bunch of free spins come up – but none of them are quite what you wanted and they’re just not doing it for you today. We all know that feeling of disappointment… and honestly, it gets directed at the company, because why don’t they know you better than that? Isn’t marketing meant to be good these days?

Well, AI is changing all that and cutting the frustration that comes with it! It is capable of tracking what bonuses you use and what games you play (and even when and how you play them), and that means that suddenly, casinos can offer much more tailored options when you’re playing on the slots. Free spins for your favourite game ever, just as you sit down to relax on a Friday night? Yes, that’s much more likely now! 

Personalised bonus games? These are also creeping onto the scene, along with game features that are specifically honed to tick your “like” box and give you the best possible gaming experience. And it’s only because of AI that this is becoming possible – sure, casinos tried to offer this kind of personalisation in the past, but it was simply too much for humans to manage.

Of course, you must have been playing the slots for a while for this to work; the AI is dependent on having historical data of how you play and when you play to bring up these offers. The longer you’ve been playing and the more consistent your patterns are, the better the AI will be able to come up with offers that are perfect for you. As this system improves, maybe we’ll see slots that are completely unique for each individual – it could happen! And as AI becomes increasingly accepted and legislation is put into place for it, we’re going to see more and more innovation in this space.

Learning about bluffing in poker

How do you teach a computer to bluff? We’re not going to pretend it’s easy; it’s proven a major challenge for those building AIs, getting a computer to mimic a human’s ability to deceive other players. However, we’re pretty much there, and AIs can now be incorporated into the online world of poker – one of the most popular casino games on the planet.

So, first off, they have created an AI that’s good at poker; there’s been major progress in advancing how the absolute best AI can play, and it’s doing well. However, that’s not actually enough for casinos: they don’t want an AI that can beat human players every time, because who would ever play against that? They need an AI that can understand nuance, make mistakes occasionally, and lose – but in convincing ways that are still satisfying to play against. Now that’s a real challenge!

But if they’re successful, there’s going to be big rewards: some people would much rather play against a computer than other humans, provided the computer makes a satisfying opponent. This is likely to be an ongoing process as the AIs master how to play in each context, but it already looks promising to us! Of course, there are wary about teaching computers how to lie effectively… after all, sci-fi books and films have shown us exactly why that could be a bad idea. For the casino industry, though, it’s looking tantalising. 

Conclusion

AI isn’t “big” in most casino games yet, because it hasn’t had time to infiltrate them… but we’re likely to see it edging in from the fringes and changing more and more things about how we play and enjoy games online as the years come. It’s overly exciting to imagine how it might revolutionise classic games like poker, blackjack, roulette, the slots, and more. However, we’re just going to have to “wait and see” here, because AI is only just unfolding its metaphorical wings and starting to flap.

The post AI in casino games: A whole new world waiting to be dealt appeared first on AI News.

According to a Bloomberg report by Mark Gurman, “Apple isn’t paying OpenAI as part of the partnership.”

Instead, the Cupertino-based company is leveraging its massive user base and device ecosystem as currency.

“Apple believes pushing OpenAI’s brand and technology to hundreds of millions of its devices is of equal or greater value than monetary payments,” Gurman’s sources explained.

Gurman notes that OpenAI could find a silver lining by encouraging Apple users to subscribe to ChatGPT Plus, priced at $20 per month. If subscribers sign up through Apple devices, the iPhone maker will likely even claim a commission.

Apple’s AI strategy extends beyond OpenAI. The company is reportedly in talks to offer Google’s Gemini chatbot as an additional option later this year, signalling its intent to provide users with diverse AI experiences without necessarily having to make such major investments itself.

The long-term vision for Apple involves capturing a slice of the revenue generated from monetising chatbot results on its operating systems. This move anticipates a shift in user behaviour, with more people relying on AI assistants rather than traditional search engines like Google.

While Apple’s AI plans are ambitious, challenges remain. The report highlights that the company has yet to secure a deal with a local Chinese provider for chatbot features, though discussions with local firms like Baidu and Alibaba are underway. Initially, Apple Intelligence will be limited to US English, with expanded language support planned for the following year.

The Apple-OpenAI deal represents a novel approach to collaboration in the AI space, where brand exposure and technological integration are valued as much as, if not more than, direct financial compensation.

See also: Musk ends OpenAI lawsuit while slamming Apple’s ChatGPT plans

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Musk had initially sued OpenAI in March 2024, alleging breach of contracts, unfair business practices, and failure in fiduciary duty. He claimed that his contributions to the company were made “in exchange for and in reliance on promises that those assets were irrevocably dedicated to building AI for public benefit, with only safety as a countervailing concern.”

The lawsuit sought remedies for “breach of contract, promissory estoppel, breach of fiduciary duty, unfair business practices, and accounting,” as well as specific performance, restitution, and damages.

However, Musk’s filings to withdraw the case provided no explanation for abandoning the lawsuit. OpenAI had previously called Musk’s claims “incoherent” and that his inability to produce a contract made his breach claims difficult to prove, stating that documents provided by Musk “contradict his allegations as to the alleged terms of the agreement.”

The withdrawal of the lawsuit comes at a time when Musk is strongly opposing Apple’s plans to integrate ChatGPT into its operating systems.

During Apple’s keynote event announcing Apple Intelligence for iOS 18, iPadOS 18, and macOS Sequoia, Musk threatened to ban Apple devices from his companies, calling the integration “an unacceptable security violation.”

Despite assurances from Apple and OpenAI that user data would only be shared with explicit consent and that interactions would be secure, Musk questioned Apple’s ability to ensure data security, stating, “Apple has no clue what’s actually going on once they hand your data over to OpenAI. They’re selling you down the river.”

Since bringing the lawsuit against OpenAI, Musk has also created his own AI company, xAI, and secured over $6 billion in funding for his plans to advance the Grok chatbot on his social network, X.

While Musk’s reasoning for dropping the OpenAI lawsuit remains unclear, his actions suggest a potential shift in focus towards advancing his own AI endeavours while continuing to vocalise his criticism of OpenAI through social media rather than the courts.

See also: DuckDuckGo releases portal giving private access to AI models

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

The post Musk ends OpenAI lawsuit while slamming Apple’s ChatGPT plans appeared first on AI News.

At its core, the ChatGPT Prompt Generator is designed to simplify the process of crafting tailored prompts for ChatGPT. By leveraging the tool’s intuitive interface, users can effortlessly create prompts that align with their specific needs, whether they’re seeking assistance with customer support, content creation, or creative writing endeavors.

ChatGPT prompt generator tool features and benefits

The beauty of this tool lies in its user-friendly approach. With just a few clicks, users can select the desired action, such as ‘Create’, ‘Explain’, ‘Analyse’ or ‘Write’, and then specify the focus area. This level of customization ensures that the generated prompts are contextually relevant and tailored to the user’s requirements.

But the true power of the ChatGPT Prompt Generator extends beyond mere convenience. By automating the prompt creation process, the tool saves users valuable time and effort, enabling them to engage with ChatGPT in a more efficient and productive manner. Gone are the days of generic or irrelevant responses – every conversation is now tailored to the user’s specific needs.

One of the standout features of this tool is its ability to understand natural language and adapt to various contexts. Powered by cutting-edge AI technology, the ChatGPT Prompt Generator ensures that the generated prompts are thoughtful, contextually appropriate, and designed to elicit meaningful responses from ChatGPT.

Whether you’re a business professional seeking to streamline customer interactions, a content creator looking to generate engaging material, or a writer exploring new creative avenues, the ChatGPT Prompt Generator is your ultimate companion. By harnessing the power of AI, this tool empowers you to unlock the limitless potential of ChatGPT and elevate your conversations to new heights.

For those seeking to explore the vast realm of AI tools further, the AI Tools Directory at AI Parabellum is a treasure trove of resources. This comprehensive directory curates a wide range of AI-powered tools, spanning various domains and applications, ensuring that users can find the perfect solution for their specific needs.

Final words

In the rapidly evolving world of AI, the WebUtiliy’s ChatGPT Prompt Generator stand as beacon of innovation, empowering users to harness the power of cutting-edge technology and unlock new realms of possibility. Embrace the future of AI-driven conversations.

The post ChatGPT Prompt Generator: Unleashing the power of AI conversations appeared first on AI News.