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.

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

The post The rise and fall of AI at the McDonald’s drive-thru appeared first on AI News.

Let’s delve into how AI is revolutionising online slot gaming in the UK and what this means for both players and developers.

AI integration in slot machine development

The development of slot machines has come a long way from their mechanical beginnings to the sophisticated digital versions we see today. AI is at the forefront of this evolution, significantly enhancing the creation and functionality of these games.

Enhanced game design

AI enables developers to create more dynamic and visually appealing games. By analysing vast amounts of player data, AI can generate themes, graphics, and narratives that are tailored to the preferences of different player demographics. This means that games can be more immersive and engaging, drawing players into captivating storylines and visually stunning environments that are continually evolving based on player interactions.

Adaptive gameplay

One of the most exciting applications of AI in slot machine development is the ability to adjust gameplay in real-time. AI algorithms monitor player behaviour and adapt the difficulty and features of the game accordingly. For example, if a player is struggling, the game might become slightly easier to keep them engaged, or if a player is doing exceptionally well, the game might present more challenges to maintain excitement. This adaptive gameplay ensures that players remain interested and challenged, providing a more satisfying gaming experience.

Personalised player experience

Personalisation is a key aspect of modern online gaming, and AI is instrumental in delivering a customised experience for each player.

Machine learning for player insights

AI uses machine learning to analyse player behaviour and preferences, allowing for highly personalised game suggestions and promotional offers. By understanding what types of games a player enjoys and how they like to play, AI can recommend new games or bonuses that are most likely to appeal to them. This level of personalisation enhances the player’s experience and increases their engagement with the platform.

Customised in-game experience

Beyond recommendations, AI also customises the gameplay itself. For instance, AI can adjust game mechanics, such as bonus frequencies and difficulty levels, based on individual player data. This means that each gaming session is unique and tailored to the player’s specific preferences, making the gaming experience more enjoyable and engaging.

Enhancing security and fairness

Security and fairness are critical components of online gaming, and AI plays a significant role in ensuring both.

Fraud detection and prevention

AI is highly effective in identifying and preventing fraudulent activities. By continuously monitoring player behavior and transaction patterns, AI can detect anomalies that may indicate fraudulent actions. When such activities are detected, AI can intervene in real-time to prevent losses and protect players and operators alike. This capability is crucial for maintaining the integrity and trustworthiness of online gaming platforms.

Ensuring fair play

Ensuring fair play is paramount in online gaming, and AI enhances this by maintaining the integrity of random number generators (RNG). AI algorithms ensure that the outcomes of slot games are truly random and free from manipulation. This transparency is essential for player trust, as it guarantees that every player has an equal chance of winning based on luck, without any external interference.

AI’s role in responsible gambling

AI is also a powerful tool in promoting responsible gambling practices, helping to mitigate the risks associated with gambling addiction.

Predictive analytics for problem gambling

AI can analyse playing patterns to identify behaviours that may indicate problem gambling. By using predictive analytics, AI can spot early signs of addiction, such as excessive spending or extended gaming sessions. This allows gaming platforms to intervene proactively, offering support and resources to players who may be at risk.

Intervention strategies

Once potential problem gambling behavior is identified, AI can implement intervention strategies. This might include sending notifications to players about their gaming habits, providing self-assessment tools, or limiting the amount of time and money a player can spend on the platform. These strategies are designed to help players maintain a healthy relationship with gambling and prevent the escalation of problematic behaviours.

Future prospects

Looking ahead, the potential of AI in the online slot gaming industry is immense. As AI technology continues to advance, we can expect even more sophisticated and personalised gaming experiences. AI-driven innovation will likely lead to entirely new types of games that we cannot yet imagine, offering unparalleled levels of engagement and excitement.

AI is revolutionising the online slot gaming industry in the UK, offering enhanced game design, personalised player experiences, improved security, and responsible gambling measures. As the technology continues to evolve, the future of online slot gaming looks incredibly promising, with AI playing a pivotal role in shaping this exciting landscape. For players and developers alike, the integration of AI presents endless possibilities for innovation and improvement in the world of online gaming.

The post The impact of AI on online slot gaming in the UK appeared first on AI News.

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.

The post Snap introduces advanced AI for next-level augmented reality appeared first on AI News.

Artificial intelligence (AI), once thought to be the exclusive domain of big cities and tech hubs, is making its way to the furthest corners of rural Ireland, empowering locals with cutting-edge tools to boost their businesses and preserve their traditional crafts.

It is all part of Microsoft’s ambitious ‘Skill Up Ireland’ initiative, which aims to provide every person in Ireland with the opportunity to learn AI skills. The program has partnered with the Irish government and various organisations to deliver AI training and resources to communities across the country, leaving no one behind in the era of rapid technological advancement.

One recent beneficiary of this program is Andrew Murray, the general manager of the 22-room Doonmore Hotel on Inishbofin. A native of the island, Murray comes from a family that has lived on Inishbofin for generations, with his parents founding the hotel in 1969. Despite the remote location, Murray is eager to embrace AI as a tool to streamline his operations and save time.

“What I’m interested in the most is the power of AI to save time for people like me,” Murray said. “Because time is the most precious thing we have, and it’s finite. There are only 24 hours in a day.”

Through an AI introduction class, Murray discovered the possibilities of tools such as Microsoft Copilot, an AI-powered assistant for everything from scheduling to data analysis to creating content. He intends to use these tools to oversee things like scheduling staff and inventory management as well as invoicing and pricing – tasks that he has normally spent hours, if not days, doing completely manually.

But Murray is not alone in his enthusiasm for AI on Inishbofin. Catherine O’Connor, a weaver who draws inspiration from the island’s natural colors and textures, has also embraced the technology. Initially wary of the AI training, O’Connor quickly became “absorbed by it” once she realised its potential to help her market her handmade scarves, table runners, and wall hangings.

“Every piece has a story behind it,” O’Connor explained. “You can get a scarf at the five-and-dime store, but a handmade scarf takes hours and hours to make. It’s a totally different level. So you have to find the right words to use.”

Now, with the help of Copilot, O’Connor can write engaging descriptions of her creations for marketing her craft on a proper e-commerce platform and help people understand her work more accurately and visualise the creation.

Another Copilot user, Inishbofin-based florist Patricia Concannon, plans to also use Copilot to make her website and Instagram captions more engaging which should prove useful in helping her reach new customers and attracting a wider audience for her floral displays.

The AI training on Inishbofin is just one element of Microsoft’s wider ‘Skill Up Ireland’ programme aimed at upskill and reskill over in Ireland, which includes Dream Space, an immersive learning experience to introduce STEM and AI skills to every one of the country’s one million students and their teachers.

Kevin Marshall, head of Learning & Skills for Microsoft Ireland, said the rapid growth in the prevalence of AI in the last few years has necessitated upskilling and reskilling programmes. He continued: “At the same time, with the explosion of generative AI in the last 18 months, there’s a real need to educate people on what this is, to show them that it’s not black magic.

The challenge, however, lies in the ever-evolving nature of AI technology. “The teaching is non-invasive, it’s collaborative,” Marshall explained. “The programs teach the basic foundations and core principles of AI. Here’s what it can do. Here are the risks and the ethical issues. Here are the opportunities. And here’s where you go play with it.”

Programmes like ‘Skill Up Ireland’ are an opportunity for rural communities like Inishbofin not to be left behind through the digital divide as AI significantly impacts industries and the way that we live and work. Audrey Murray, a felt artist and teaching assistant on the island, summed it up: “AI has to be another step, I suppose, bringing us closer to the world and bringing the world here.”

And with Microsoft’s promise of creating AI skills for all in Ireland, the remote extremities of the Emerald Isle are on the brink of being catapulted into the future, when the very latest technologies are melded with ancient skills and lifeways. Meanwhile, for the inhabitants of Inishbofin, the opportunities are yet to reveal themselves.

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 AI comes to Ireland’s remote Islands through Microsoft’s ‘Skill Up’ program appeared first on AI News.

“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.

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

The post NVIDIA presents latest advancements in visual AI appeared first on AI News.

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.

The post EU AI legislation sparks controversy over data transparency appeared first on AI News.

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.

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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

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