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.

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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.

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

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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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Project P.I. leverages generative AI and computer vision technologies to detect issues such as damaged products or incorrect colours and sizes before they reach customers. The AI model not only identifies defects but also helps uncover the root causes, enabling Amazon to implement preventative measures upstream. This system has proven highly effective in the sites where it has been deployed, accurately identifying product issues among the vast number of items processed each month.

Before any item is dispatched, it passes through an imaging tunnel where Project P.I. evaluates its condition. If a defect is detected, the item is isolated and further investigated to determine if similar products are affected.

Amazon associates review the flagged items and decide whether to resell them at a discount via Amazon’s Second Chance site, donate them, or find alternative uses. This technology aims to act as an extra pair of eyes, enhancing manual inspections at several North American fulfilment centres, with plans for expansion throughout 2024.

Dharmesh Mehta, Amazon’s VP of Worldwide Selling Partner Services, said: “We want to get the experience right for customers every time they shop in our store.

“By leveraging AI and product imaging within our operations facilities, we are able to efficiently detect potentially damaged products and address more of those issues before they ever reach a customer, which is a win for the customer, our selling partners, and the environment.”

Project P.I. also plays a crucial role in Amazon’s sustainability initiatives. By preventing damaged or defective items from reaching customers, the system helps reduce unwanted returns, wasted packaging, and unnecessary carbon emissions from additional transportation.

Kara Hurst, Amazon’s VP of Worldwide Sustainability, commented: “AI is helping Amazon ensure that we’re not just delighting customers with high-quality items, but we’re extending that customer obsession to our sustainability work by preventing less-than-perfect items from leaving our facilities, and helping us avoid unnecessary carbon emissions due to transportation, packaging, and other steps in the returns process.”

In parallel, Amazon is utilising a generative AI system equipped with a Multi-Modal LLM (MLLM) to investigate the root causes of negative customer experiences.

When defects reported by customers slip through initial checks, this system reviews customer feedback and analyses images from fulfilment centres to understand what went wrong. For example, if a customer receives the wrong size of a product, the system examines the product labels in fulfilment centre images to pinpoint the error.

This technology is also beneficial for Amazon’s selling partners, especially the small and medium-sized businesses that make up over 60% of Amazon’s sales. By making defect data more accessible, Amazon helps these sellers rectify issues quickly and reduce future errors.

(Photo by Andrew Stickelman)

See also: X now permits AI-generated adult content

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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OpenAI claims its commitment to designing AI models with safety in mind has often thwarted the threat actors’ attempts to generate desired content. Additionally, the company says AI tools have enhanced the efficiency of OpenAI’s investigations.

Detailed threat reporting by distribution platforms and the open-source community has significantly contributed to combating IO. OpenAI is sharing these findings to promote information sharing and best practices among the broader community of stakeholders.

Disrupting covert IO

In the past three months, OpenAI disrupted several IO operations using its models for various tasks, such as generating short comments, creating fake social media profiles, conducting open-source research, debugging simple code, and translating texts.

Specific operations disrupted include:

• Bad Grammar: A previously unreported operation from Russia targeting Ukraine, Moldova, the Baltic States, and the US. This group used OpenAI’s models to debug code for running a Telegram bot and to create political comments in Russian and English, posted on Telegram.
• Doppelganger: Another Russian operation generating comments in multiple languages on platforms like X and 9GAG, translating and editing articles, generating headlines, and converting news articles into Facebook posts.
• Spamouflage: A Chinese network using OpenAI’s models for public social media activity research, generating texts in several languages, and debugging code for managing databases and websites.
• International Union of Virtual Media (IUVM): An Iranian operation generating and translating long-form articles, headlines, and website tags, published on a linked website.
• Zero Zeno: A commercial company in Israel, with operations generating articles and comments posted across multiple platforms, including Instagram, Facebook, X, and affiliated websites.

The content posted by these operations focused on various issues, including Russia’s invasion of Ukraine, the Gaza conflict, Indian elections, European and US politics, and criticisms of the Chinese government.

Despite these efforts, none of these operations showed a significant increase in audience engagement due to OpenAI’s models. Using Brookings’ Breakout Scale – which assesses the impact of covert IO – none of the five operations scored higher than a 2, indicating activity on multiple platforms but no breakout into authentic communities.

Attacker trends

Investigations into these influence operations revealed several trends:

• Content generation: Threat actors used OpenAI’s services to generate large volumes of text with fewer language errors than human operators could achieve alone.
• Mixing old and new: AI was used alongside traditional formats, such as manually written texts or copied memes.
• Faking engagement: Some networks generated replies to their own posts to create the appearance of engagement, although none managed to attract authentic engagement.
• Productivity gains: Threat actors used AI to enhance productivity, summarising social media posts and debugging code.

Defensive trends

OpenAI’s investigations benefited from industry sharing and open-source research. Defensive measures include:

• Defensive design: OpenAI’s safety systems imposed friction on threat actors, often preventing them from generating the desired content.
• AI-enhanced investigation: AI-powered tools improved the efficiency of detection and analysis, reducing investigation times from weeks or months to days.
• Distribution matters: IO content, like traditional content, must be distributed effectively to reach an audience. Despite their efforts, none of the disrupted operations managed substantial engagement.
• Importance of industry sharing: Sharing threat indicators with industry peers increased the impact of OpenAI’s disruptions. The company benefited from years of open-source analysis by the wider research community.
• The human element: Despite using AI, threat actors were prone to human error, such as publishing refusal messages from OpenAI’s models on their social media and websites.

OpenAI says it remains dedicated to developing safe and responsible AI. This involves designing models with safety in mind and proactively intervening against malicious use.

While admitting that detecting and disrupting multi-platform abuses like covert influence operations is challenging, OpenAI claims it’s committed to mitigating the dangers.

(Photo by Chris Yang)

See also: EU launches office to implement AI Act and foster innovation

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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This allows OpenAI to incorporate conversations from Reddit into ChatGPT and other new products, echoing a previous agreement that the platform had with Google, reportedly valued at $60 million.

The partnership enables OpenAI to better sample the datasets on which their models are trained, allowing AI systems to become more precise and context-aware. For human communication and natural language processing, this means models like ChatGPT can stay continually updated with one of the vastest collections of public discourse available, enabling them to respond more effectively.

As part of this collaboration, Reddit will be able to develop and release new AI-powered tools for its users and moderators, utilising OpenAI’s advanced language models. This collaboration could result in more effective moderation tools and a set of features specifically designed to help users better understand thread information. Features might include summarising content or assisting users in forming responses to replies without having to write everything from scratch.

The primary goal of these features is to refine language interactions for all users. Moreover, as part of this partnership, OpenAI will serve as an advertising partner, enabling Reddit to offer ads that are more tailored and relevant, utilising OpenAI’s capacity to capture the subtleties of user behaviour.

The reaction of Reddit’s community to this partnership remains uncertain, but their historical engagement and vocal opposition to unfavourable executive decisions, such as those during the protests over API pricing, suggest they may respond vigilantly. The acceptance of this partnership will hinge critically on OpenAI’s ability to maintain user privacy and adhere to Reddit’s platform norms.

From OpenAI’s viewpoint, partnering with Reddit signifies a crucial strategic development. It positions the company to highlight its prominent AI technology in direct competition with giants such as Google and Microsoft, and crucially, within the integral realm of social media. For Reddit, this collaboration could provide a substantial advantage over less progressive platforms, potentially reshaping its image and attracting more users.

The partnership offers great promise but also raises critical ethical and methodological questions. Integrating real-time user-generated data to advance AI capabilities could lead to privacy violations and potentially restrict users’ freedom of expression. Additionally, this application of AI might clash with existing ethical norms.

Steve Huffman, the CEO of Reddit, supports the integration, stating it will promote more relevant content and improve community engagement, consistent with the vision of a connected internet. However, navigating the implications of this deal is complex, particularly in light of Reddit’s history with data scraping issues and recent copyright disputes.

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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This new category not only boosts Microsoft’s ventures but also positions it to compete directly with AI-led advancements from tech giants such as Alphabet and Apple. During an event at Microsoft’s campus in Redmond, Washington, CEO Satya Nadella unveiled the Copilot+ PCs. These computers, developed in collaboration with manufacturers like Acer and Asustek Computer, align with Microsoft’s stock prices reaching near all-time highs amid Wall Street’s expectations that AI will significantly boost earnings for major tech firms.

These PCs are engineered to manage a greater share of AI tasks locally, reducing reliance on cloud data centres. Priced from $1,000, the devices are slated to begin shipping on June 18. The lineup includes laptops from established manufacturers such as Dell Technologies, HP, and Samsung Electronics, complemented by two models from Microsoft itself.

Technical innovations and product range

The Surface Laptop, Microsoft’s inaugural Copilot+ device, features a Qualcomm Snapdragon X series system-on-chip. This chip includes a built-in AI accelerator capable of delivering up to 45 tera operations per second (TOPS) of computing power. Available in 13.5-inch and 15-inch models, these laptops offer up to 64 gigabytes of RAM and 1 terabyte of flash storage.

Joining the Surface Laptop is the new Copilot+ version of the Surface Pro, a versatile two-in-one PC. This 10.6-inch tablet converts into a laptop with a detachable keyboard, known as the Flex Keyboard, which also functions as a wireless connector and stylus charger for the Surface Pro.

Both the Surface Laptop and Surface Pro are equipped with the Snapdragon X processor, available with up to 32 gigabytes of RAM and one terabyte of storage. The high-end models feature an OLED display, enhancing visual quality significantly.

A distinctive feature of the Copilot+ series is “Recall,” which allows the device to log all user activities—from web browsing to voice chats—and store this data locally. This functionality creates a searchable history that users can access to revisit past activities, even months later.

Yusuf Mehdi, head of consumer marketing at Microsoft, announced at the press event that the company anticipates selling 50 million AI PCs in the coming year. He emphasised that the enhanced capabilities of AI assistants, now operating directly on these PCs, will make a compelling case for consumers considering upgrades.

Despite a 15% decline in global PC shipments last year, according to Gartner, Microsoft is optimistic about the potential market share for its Copilot+ PCs.

Analyst Ben Bajarin from Creative Strategies remarked: “People just need to be convinced that the device experience alone justifies this entirely new category of Copilot+ machines.”

Echoing Intel’s 2011 strategy with the “Ultrabook,” which aimed to compete against Apple’s MacBook Air, Microsoft’s introduction of the “Copilot+” category is poised to redefine the slim-form Windows laptop market.

Microsoft also revealed that GPT-4o, the latest advancement from OpenAI’s ChatGPT, will soon be integrated into Copilot. Additionally, the new series of Surface Pro tablets and Surface Laptops will incorporate Qualcomm chips based on Arm Holdings’ designs, utilising the innovative Prism technology to adapt software originally designed for Intel and AMD processors for use on Arm-based systems.

At a recent demonstration, Microsoft showcased how its new devices, running Adobe photo editing software, outperformed an Apple device, just weeks after Apple introduced a new AI-focused chip expected to enhance future laptops.

Challenges and opportunities in the evolving PC market

Facing strong competition in the traditional PC market, Microsoft continues to lead in integrating the latest AI trends into consumer products. This strategic initiative, fueled by its partnership with OpenAI, aims to secure a leadership position in the fiercely competitive AI industry, challenging rivals like Alphabet.

Just last week, both OpenAI and Alphabet’s Google introduced new AI technologies that support real-time, voice-responsive interactions, establishing new benchmarks for AI voice assistants. Meanwhile, Windows PC manufacturers are contending with increased pressure from Apple, whose custom Arm-based chips have set new standards in battery life and performance.

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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This is according to research from enterprise analytics AI firm Alteryx.

Since the release of ChatGPT by OpenAI in November 2022, there has been significant buzz about the transformative potential of generative AI, with many considering it one of the most revolutionary technologies of our time. 

With a significant 79% of organisations reporting that generative AI contributes positively to business, it is evident that a gap needs to be addressed to demonstrate AI’s value to consumers both in their personal and professional lives. According to the ‘Market Research: Attitudes and Adoption of Generative AI’ report, which surveyed 690 IT business leaders and 1,100 members of the general public in EMEA, key issues of trust, ethics and skills are prevalent, potentially impeding the successful deployment and broader acceptance of generative AI.

The impact of misinformation, inaccuracies, and AI hallucinations

These hallucinations – where AI generates incorrect or illogical outputs – are a significant concern. Trusting what generative AI produces is a substantial issue for both business leaders and consumers. Over a third of the public are anxious about AI’s potential to generate fake news (36%) and its misuse by hackers (42%), while half of the business leaders report grappling with misinformation produced by generative AI. Simultaneously, half of the business leaders have observed their organisations grappling with misinformation produced by generative AI.

Moreover, the reliability of information provided by generative AI has been questioned. Feedback from the general public indicates that half of the data received from AI was inaccurate, and 38% perceived it as outdated. On the business front, concerns include generative AI infringing on copyright or intellectual property rights (40%), and producing unexpected or unintended outputs (36%).

A critical trust issue for businesses (62%) and the public (74%) revolves around AI hallucinations. For businesses, the challenge involves applying generative AI to appropriate use cases, supported by the right technology and safety measures, to mitigate these concerns. Close to half of the consumers (45%) are advocating for regulatory measures on AI usage.

Ethical concerns and risks persist in the use of generative AI

In addition to these challenges, there are strong and similar sentiments on ethical concerns and the risks associated with generative AI among both business leaders and consumers. More than half of the general public (53%) oppose the use of generative AI in making ethical decisions. Meanwhile, 41% of business respondents are concerned about its application in critical decision-making areas. There are distinctions in the specific areas where its use is discouraged; consumers notably oppose its use in politics (46%), and businesses are cautious about its deployment in healthcare (40%).

These concerns find some validation in the research findings, which highlight worrying gaps in organisational practices. Only a third of leaders confirmed that their businesses ensure the data used to train generative AI is diverse and unbiased. Furthermore, only 36% have set ethical guidelines, and 52% have established data privacy and security policies for generative AI applications.

This lack of emphasis on data integrity and ethical considerations puts firms at risk. 63% of business leaders cite ethics as their major concern with generative AI, closely followed by data-related issues (62%). This scenario emphasises the importance of better governance to create confidence and mitigate risks related to how employees use generative AI in the workplace. 

The rise of generative AI skills and the need for enhanced data literacy

As generative AI evolves, establishing relevant skill sets and enhancing data literacy will be key to realising its full potential. Consumers are increasingly using generative AI technologies in various scenarios, including information retrieval, email communication, and skill acquisition. Business leaders claim they use generative AI for data analysis, cybersecurity, and customer support, and despite the success of pilot projects, challenges remain. Despite the reported success of experimental projects, several challenges remain, including security problems, data privacy issues, and output quality and reliability.

Trevor Schulze, Alteryx’s CIO, emphasised the necessity for both enterprises and the general public to fully understand the value of AI and address common concerns as they navigate the early stages of generative AI adoption.

He noted that addressing trust issues, ethical concerns, skills shortages, fears of privacy invasion, and algorithmic bias are critical tasks. Schulze underlined the necessity for enterprises to expedite their data journey, adopt robust governance, and allow non-technical individuals to access and analyse data safely and reliably, addressing privacy and bias concerns in order to genuinely profit from this ‘game-changing’ technology.

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.

The post Ethical, trust and skill barriers hold back generative AI progress in EMEA appeared first on AI News.

This collaboration combines Tech Mahindra’s range of AI offerings, TechM amplifAI0->∞, and IBM’s watsonx AI and data platform with AI Assistants.

Customers can now combine IBM watsonx’s capabilities with Tech Mahindra’s AI consulting and engineering skills to access a variety of new generative AI services, frameworks, and solution architectures. This enables the development of AI apps in which organisations can use their trusted data to automate processes. It also provides a basis for businesses to create trustworthy AI models, promotes explainability to help manage risk and bias, and enables scalable AI adoption across hybrid cloud and on-premises environments.

According to Kunal Purohit, Tech Mahindra’s chief digital services officer, organisations focus on responsible AI practices, and incorporating generative AI technologies to revitalise enterprises. 

“Our work with IBM can help advance digital transformation for organisations, adoption of GenAI, modernisation, and ultimately foster business growth for our global customers,” Purohit added.

To further enhance business capabilities in AI, Tech Mahindra has established a virtual watsonx Centre of Excellence (CoE), which is already operational. This CoE functions as a co-innovation centre, with a dedicated team tasked with maximising synergies between the two companies and producing unique offerings and solutions based on their combined capabilities.

The collaborative offerings and solutions developed through this partnership could help enterprises achieve their goals of constructing machine learning models using open-source frameworks while also enabling them to scale and accelerate the impact of generative AI. These AI-driven solutions have the potential to aid organisations enhance efficiency and productivity responsibly. 

Kate Woolley, GM of IBM Ecosystem, emphasised the collaboration’s potential, adding that generative AI may serve as a catalyst for innovation, unlocking new market opportunities when built on a foundation of explainability, transparency, and trust. 

Woolley said: “Our work with Tech Mahindra is expected to expand the reach of watsonx, allowing even more customers to build trustworthy AI as we seek to combine our technology and expertise to support enterprise use cases such as code modernisation, digital labour, and customer service.”

This collaboration aligns with Tech Mahindra’s continuous endeavour to transform enterprises with advanced AI-led offerings and solutions, including their recent additions like Vision amplifAIer, Ops amplifAIer, Email amplifAIer, Enterprise Knowledge Search offering, Evangelize Pair Programming, and Generative AI Studio.

It is worth mentioning that the two companies have previously collaborated. Earlier this year, Tech Mahindra announced the opening of a Synergy Lounge in conjunction with IBM on the company’s Singapore campus. This Lounge seeks to accelerate digital adoption for APAC organisations. It aids in operationalising and leveraging next-generation technologies such as AI, intelligent automation, hybrid cloud, 5G, edge computing, and cybersecurity.

Beyond Tech Mahindra, IBM watsonx has been used in other collaborations to speed up the deployment of generative AI. Also happened early this year, the GSMA and IBM announced a new partnership to support the use and capabilities of generative AI in the telecom industry by launching GSMA Advance’s AI Training program and the GSMA Foundry Generative AI program.

In addition, there is a digital version of the program that covers both the commercial strategy and technology fundamentals of generative AI. This initiative uses IBM watsonx to provide hands-on training for architects and developers seeking in-depth practical generative AI knowledge.

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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In Abu Dhabi, the Technology Innovation Institute (TII) unveiled the Falcon 2 series. As reported by Reuters, this series includes Falcon 2 11B, a text-based model, and Falcon 2 11B VLM, a vision-to-language model capable of generating text descriptions from images. TII is run by Abu Dhabi’s Advanced Technology Research Council.

As a major oil exporter and a key player in the Middle East, the UAE is investing heavily in AI. This strategy has caught the eye of U.S. officials, leading to tensions over whether to use American or Chinese technology. In a move coordinated with Washington, Emirati AI firm G42 withdrew from Chinese investments and replaced Chinese hardware, securing a US$1.5 billion investment from Microsoft.

Faisal Al Bannai, Secretary General of the Advanced Technology Research Council and an adviser on strategic research and advanced technology, proudly states that the UAE is proving itself as a major player in AI. The release of the Falcon 2 series is part of a broader race among nations and companies to develop proprietary large language models. While some opt to keep their AI code private, the UAE, like Meta’s Llama, is making its groundbreaking work accessible to all.

Al Bannai is also excited about the upcoming Falcon 3 generation and expresses confidence in the UAE’s ability to compete globally: “We’re very proud that we can still punch way above our weight, really compete with the best players globally.”

Reflecting on his earlier statements this year, Al Bannai emphasised that the UAE’s decisive advantage lies in its ability to make swift strategic decisions.

It’s worth noting that Abu Dhabi’s ruling family controls some of the world’s largest sovereign wealth funds, worth about US$1.5 trillion. These funds, formerly used to diversify the UAE’s oil wealth, are now critical for accelerating growth in AI and other cutting-edge technologies. In fact, the UAE is emerging as a key player in producing advanced computer chips essential for training powerful AI systems. According to Wall Street Journal, OpenAI CEO Sam Altman met with investors, including Sheik Tahnoun bin Zayed Al Nahyan, who runs Abu Dhabi’s major sovereign wealth fund, to discuss a potential US$7 trillion investment to develop an AI chipmaker to compete with Nvidia.

Furthermore, the UAE’s commitment to generative AI is evident in its recent launch of a ‘Generative AI’ guide. This guide aims to unlock AI’s potential in various fields, including education, healthcare, and media. It provides a detailed overview of generative AI, addressing digital technologies’ challenges and opportunities while emphasising data privacy. The guide is designed to assist government agencies and the community leverage AI technologies by demonstrating 100 practical AI use cases for entrepreneurs, students, job seekers, and tech enthusiasts.

This proactive stance showcases the UAE’s commitment to participating in and leading the global AI race, positioning it as a nation to watch in the rapidly evolving tech scene.

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