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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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.

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Recently, Arm unveiled new chip blueprints and software tools aimed at enhancing smartphones’ ability to handle AI tasks more efficiently. But they didn’t stop there – Arm also implemented changes to how they deliver these blueprints, potentially accelerating adoption.

Arm is evolving its solution offerings to maximise the benefits of leading process nodes. They announced the Arm Compute Subsystems (CSS) for Client, their latest cutting-edge compute solution tailored for AI applications in smartphones and PCs.

This CSS for Client promises a significant performance leap – we’re talking over 30% increased compute and graphics performance, along with an impressive 59% faster AI inference for AI, machine learning, and computer vision workloads.

While Arm’s technology powered the smartphone revolution, it’s also gaining traction in PCs and data centres, where energy efficiency is prized. Though smartphones remain Arm’s biggest market, supplying IP to rivals like Apple, Qualcomm, and MediaTek, the company is expanding its offerings.

They’ve launched new CPU designs optimised for AI workloads and new GPUs, as well as software tools to ease the development of chatbots and other AI apps on Arm chips.

But the real gamechanger is how these products are delivered. Historically, Arm provided specs or abstract designs that chipmakers had to translate into physical blueprints – an immense challenge arranging billions of transistors.

For this latest offering, Arm collaborated with Samsung and TSMC to provide physical chip blueprints ready for manufacturing, which was a huge time saver.

Samsung’s Jongwook Kye praised the partnership, stating their 3nm process combined with Arm’s CPU solutions meets soaring demand for generative AI in mobiles through “early and tight collaboration” in the areas of DTCO and PPA maximisation for an on-time silicon delivery that hit performance and efficiency demands.

TSMC’s head of the ecosystem and alliance management division, Dan Kochpatcharin echoed this, calling the AI-optimised CSS “a prime example” of Arm-TSMC collaboration helping designers push semiconductor innovation’s boundaries for unmatched AI performance and efficiency.

“Together with Arm and our Open Innovation Platform® (OIP) ecosystem partners, we empower our customers to accelerate their AI innovation using the most advanced process technologies and design solutions,” Kochpatcharin emphasised.

Arm isn’t trying to compete with customers, but rather enable faster time-to-market by providing optimised designs for neural processors delivering cutting-edge AI performance.

As Arm’s Chris Bergey said, “We’re combining a platform where these accelerators can be very tightly coupled” to customer NPUs.

Essentially, Arm provides more refined, “baked” designs customers can integrate with their own accelerators to rapidly develop powerful AI-driven chips and devices.

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 has some history in the AI space. He co-founded OpenAI in 2015 alongside the current CEO, Sam Altman, and others. However, he parted ways with the company in 2018 due to disagreements over its priorities and direction, specifically OpenAI’s move away from open-source AI models and towards proprietary, closed models that they sell access to. Musk became one of OpenAI’s most vocal critics after that.

Fast-forward to July of last year, and Musk formally re-entered the generative AI arena with the announcement of xAI. Since then, the company has been aggressively working on building advanced AI systems that it claims will be “truthful, competent, and maximally beneficial for all of humanity.”

One of xAI’s first offerings is Grok, an edgier version of OpenAI’s ChatGPT. It is currently available only to X Premium subscribers. According to benchmarks shared by xAI, Grok-1 outperformed models like Llama-2-70B and GPT-3.5 but still lagged behind Anthropic’s Claude3, OpenAI’s GPT-4, Google’s Gemini, and Meta’s open-source Llama 3.

This latest funding round saw investments from big names, including Andreessen Horowitz, Sequoia Capital, and even Saudi Arabian Prince Al Waleed bin Talal. Last year, filings showed xAI was looking to raise up to $1 billion in equity investments, and just a few months ago, reports surfaced that they had increased their target to a whopping $6 billion—a figure Musk initially denied.

Developing the hardware capable of powering AI at this sophisticated level is no small feat and certainly is costly. Just last week, a report revealed that xAI would need an astonishing 100,000 of Nvidia’s current H100 chips for a supercomputer to power an upgraded version of Grok, each costing between $30,000 to $40,000.

Musk has ambitious plans for these powerful resources. He reportedly informed investors that the goal is to launch this new data centre by fall 2025, marking a significant step forward in xAI’s technological capabilities.

Continuing this AI race for chips, talent, and technology will be expensive. Big tech firms have already invested billions into AI startups like Anthropic, on top of what companies like Google, Apple, Amazon, Microsoft, and Meta are investing in their own AI projects.

Microsoft, in particular, has struck a multi-billion-dollar partnership with OpenAI, whose CEO, Sam Altman, is reportedly pursuing trillions more to revamp the global chip industry.

Tesla will continue to hire AI engineers for self-driving cars, even though Elon Musk has previously stated a preference for not bringing most of those AI and robotics capabilities in-house, to allow greater focus on external ventures. He talks about breaking free from the traditional constraints of auto manufacturing as part of a broader agenda.

At the same time, Tesla is gearing up for its annual meeting on June 13, where shareholders will start voting on whether to reinstate Musk’s record-breaking $56 billion pay package, a significant development given Musk’s substantial influence across various tech sectors.

Over the years, Musk has made substantial contributions to AI research, and now his venture, xAI, is at the forefront of AI developments. It will be fascinating to watch how these efforts unfold and see which of xAI’s contributions will secure a place in the annals of AI history during this rapid evolution.

See also: Elon Musk’s xAI open-sources Grok

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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The two-day event will feature a virtual leaders’ session, co-chaired by British Prime Minister Rishi Sunak and South Korean President Yoon Suk Yeol, and a subsequent in-person meeting among Digital Ministers. UK Technology Secretary Michelle Donelan, and Korean Minister of Science and ICT Lee Jong-Ho will co-host the latter.

This summit builds upon the historic Bletchley Park discussions held at the historic location in the UK last year, emphasising AI safety, inclusion, and innovation. It aims to ensure that AI advancements benefit humanity while minimising potential risks and enhancing global governance on tech innovation.

“The summit we held at Bletchley Park was a generational moment,” stated Donelan. “If we continue to bring international governments and a broad range of voices together, I have every confidence that we can continue to develop a global approach which will allow us to realise the transformative potential of this generation-defining technology safely and responsibly.”

Echoing this sentiment, Minister Lee Jong-Ho highlighted the importance of the upcoming Seoul Summit in furthering global cooperation on AI safety and innovation.

“AI is advancing at an unprecedented pace that exceeds our expectations, and it is crucial to establish global norms and governance to harness such technological innovations to enhance the welfare of humanity,” explained Lee. “We hope that the AI Seoul Summit will serve as an opportunity to strengthen global cooperation on not only AI safety but also AI innovation and inclusion, and promote sustainable AI development.”

Innovation remains a focal point for the UK, evidenced by initiatives like the Manchester Prize and the formation of the AI Safety Institute: the first state-backed organisation dedicated to AI safety. This proactive approach mirrors the UK’s commitment to international collaboration on AI governance, underscored by a recent agreement with the US on AI safety measures.

Accompanying the Seoul Summit will be the release of the International Scientific Report on Advanced AI Safety. This report, independently led by Turing Prize winner Yoshua Bengio, represents a collective effort to consolidate the best scientific research on AI safety. It underscores the summit’s role not only as a forum for discussion but as a catalyst for actionable insight into AI’s safe development.

The agenda of the AI Seoul Summit reflects the urgency of addressing the challenges and opportunities presented by AI. From discussing model safety evaluations, to fostering sustainable AI development. As the world embraces AI innovation, the AI Seoul Summit embodies a concerted effort to shape a future where technology serves humanity safely and delivers prosperity and inclusivity for all.

See also: US and Japan announce sweeping AI and tech collaboration

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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The ML Olympiad – organised by groups including ML GDE, TFUG, and other ML communities – aims to provide developers with hands-on opportunities to learn and practice machine learning skills by tackling real-world challenges.

Over the previous two rounds, an impressive 605 teams participated across 32 competitions, generating 105 discussions and 170 notebooks.

This year’s lineup includes challenges spanning areas like healthcare, sustainability, natural language processing (NLP), computer vision, and more. Competitions are hosted by expert groups and developers from around the world.
Here are this year’s challenges:

• Smoking Detection in Patients

Hosted by Rishiraj Acharya (AI/ML GDE) in collaboration with TFUG Kolkata, this competition tasks participants with predicting smoking status using bio-signal ML models.

• TurtleVision Challenge

Organised by Anas Lahdhiri under MLAct, this challenge calls for the development of a classification model to differentiate between jellyfish and plastic pollution in ocean imagery.

• Detect Hallucinations in LLMs

Luca Massaron (AI/ML GDE) presents a unique challenge of identifying hallucinations in answers provided by a Mistral 7B instruct model.

• ZeroWasteEats

Anushka Raj, alongside TFUG Hajipur, seeks ML solutions to mitigate food wastage, a critical concern in today’s world.

• Predicting Wellness

Hosted by Ankit Kumar Verma and TFUG Prayagraj, this competition involves predicting the percentage of body fat in men using multiple regression methods.

• Offbeats Edition

Ayush Morbar from Offbeats Byte Labs invites participants to build a regression model to predict the age of crabs.

• Nashik Weather

TFUG Nashik challenges participants to forecast the weather condition in Nashik, India, leveraging machine learning techniques.

• Predicting Earthquake Damage

Usha Rengaraju presents a task of predicting the level of damage to buildings caused by earthquakes, based on various factors.

• Forecasting Bangladesh’s Weather

TFUG Bangladesh (Dhaka) aims to predict rainfall, average temperature, and rainy days for a particular day in Bangladesh.

• CO2 Emissions Prediction Challenge

Md Shahriar Azad Evan and Shuvro Pal from TFUG North Bengal seek to predict CO2 emissions per capita for 2030 using global development indicators.

• AI & ML Malaysia

Kuan Hoong (AI/ML GDE) challenges participants to predict loan approval status, addressing a crucial aspect of financial inclusion.

• Sustainable Urban Living

Ashwin Raj and BeyondML task participants with predicting the habitability score of properties, promoting sustainable urban development.

• Toxic Language (PTBR) Detection

Hosted in Brazilian Portuguese, this challenge by Mikaeri Ohana, Pedro Gengo, and Vinicius F. Caridá (AI/ML GDE) involves classifying toxic tweets.

• Improving Disaster Response

Yara Armel Desire of TFUG Abidjan invites participants to predict humanitarian aid contributions in response to disasters worldwide.

• Urban Traffic Density

Kartikey Rawat from TFUG Durg calls for the development of predictive models to estimate traffic density in urban areas.

• Know Your Customer Opinion

TFUG Surabaya presents a challenge of classifying customer opinions into Likert scale categories.

• Forecasting India’s Weather

Mohammed Moinuddin and TFUG Hyderabad task participants with predicting temperatures for specific months in India.

• Classification Champ

Hosted by TFUG Bhopal, this competition involves developing classification models to predict tumour malignancy.

• AI-Powered Job Description Generator

Akaash Tripathi from TFUG Ghaziabad challenges participants to build a system that automatically generates job descriptions using Generative AI and chatbot interface.

• Machine Translation French-Wolof

GalsenAI presents a challenge of accurately translating French sentences into Wolof, offering a platform to enhance language translation capabilities.

• Water Mapping using Satellite Imagery

Taha Bouhsine of ML Nomads tasks participants with water mapping using satellite imagery for dam drought detection.

Google is supporting each community host this round through its Google for Developers program.

Participants are encouraged to search for “ML Olympiad” on Kaggle, follow #MLOlympiad on social media, and get involved in the competitions that most interest them.

With such a diverse array of real-world machine learning challenges, the ML Olympiad represents an excellent opportunity for developers to put their skills to the test and gain valuable experience.

(Image Credit: Google)

See also: Microsoft: China plans to disrupt elections with AI-generated disinformation

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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The post ML Olympiad returns with over 20 challenges appeared first on AI News.

Because LLMs have an ability to analyse large amounts of data quickly and generate coherent text, there is growing understanding of the potential to improve services across a range of sectors including healthcare, law, education and in financial services including banking, insurance and financial planning.

This report, which is the first to explore the adoption of LLMs across the finance ecosystem, shows that people working in this area have already begun to use LLMs to support a variety of internal processes, such as the review of regulations, and are assessing its potential for supporting external activity like the delivery of advisory and trading services.

Alongside a literature survey, researchers held a workshop of 43 professionals from major high street and investment banks, regulators, insurers, payment service providers, government and legal professions.

The majority of workshop participants (52%) are already using these models to enhance performance in information-orientated tasks, from the management of meeting notes to cyber security and compliance insight, while 29% use them to boost critical thinking skills, and another 16% employ them to break down complex tasks.

The sector is also already establishing systems to enhance productivity through rapid analysis of large amount of text to simplify decision making processes, risk profiling and to improve investment research and back-office operations.

When asked about the future of LLMs in the finance sector, participants felt that LLMs would be integrated into services like investment banking and venture capital strategy development within two years.

They also thought it likely that LLMs would be integrated to improve interactions between people and machines, for example dictation and embedded AI assistants could reduce the complexity of knowledge intensive tasks such as the review of regulations.

But participants also acknowledged that the technology poses risks which will limit its usage. Financial institutions are subject to extensive regulatory standards and obligations which limits their ability to use AI systems that they cannot explain and do not generate output predictably, consistently or without risk of error.

Based on their findings, the authors recommend that financial services professionals, regulators and policy makers collaborate across the sector to share and develop knowledge about implementing and using LLMs, particularly related to safety concerns. They also suggest that the growing interest in open-source models should be explored and could be used and maintained effectively, but that mitigating security and privacy concerns would be a high priority.

Professor Carsten Maple, lead author and Turing Fellow at The Alan Turing Institute, said: “Banks and other financial institutions have always been quick to adopt new technologies to make their operations more efficient and the emergence of LLMs is no different. By bringing together experts across the finance ecosystem, we have managed to create a common understanding of the use cases, risks, value and timeline for implementation of these technologies at scale.”

Professor Lukasz Szpruch, programme director for Finance and Economics at The Alan Turing Institute, said: “It’s really positive that the financial sector is benefiting from the emergence of large language models and their implementation into this highly regulated sector has the potential to provide best practices for other sectors. This study demonstrates the benefit of research institutes and industry working together to assess the vast opportunities as well as the practical and ethical challenges of new technologies to ensure they are implemented safely.”

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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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Creator Fund also participated, along with, MMC Ventures, Moonfire Ventures and Rockmount Capital and leading angel investors. 

Stanhope AI was founded as a spinout from University College London, supported by UCL Business, by three of the most eminent names in neuroscience and AI research – CEO Professor Rosalyn Moran (former Deputy Director of King’s Institute for Artificial Intelligence), Director Karl Friston, Professor at the UCL Queen Square Institute of Neurology and Technical Advisor Dr Biswa Sengupta (MD of AI and Cloud products at JP Morgan Chase). 

By using key neuroscience principles and applying them to AI and mathematics, Stanhope AI is at the forefront of the new generation of AI technology known as ‘agentic’ AI.  The team has built algorithms that, like the human brain, are always trying to guess what will happen next; learning from any discrepancies between predicted and actual events to continuously update their “internal models of the world.” Instead of training vast LLMs to make decisions based on seen data, Stanhope agentic AI’s models are in charge of their own learning. They autonomously decode their environments and rebuild and refine their “world models” using real-time data, continuously fed to them via onboard sensors.  

The rise of agentic AI

This approach, and Stanhope AI’s technology, are based on the neuroscience principle of Active Inference – the idea that our brains, in order to minimise free energy, are constantly making predictions about incoming sensory data around us. As this data changes, our brains adapt and update our predictions in response to rebuild and refine our world view. 

This is very different to the traditional machine learning methods used to train today’s AI systems such as LLMs. Today’s models can only operate within the realms of the training they are given, and can only make best-guess decisions based on the information they have. They can’t learn on the go. They require extreme amounts of processing power and energy to train and run, as well as vast amounts of seen data.  

By contrast, Stanhope AI’s Active Inference models are truly autonomous. They can constantly rebuild and refine their predictions. Uncertainty is minimised by default, which removes the risk of hallucinations about what the AI thinks is true, and this moves Stanhope’s unique models towards reasoning and human-like decision-making. What’s more, by drastically reducing the size and energy required to run the models and the machines, Stanhope AI’s models can operate on small devices such as drones and similar.  

“The most all-encompassing idea since natural selection”

Stanhope AI’s approach is possible because of its founding team’s extensive research into the neuroscience principles of Active Inference, as well as free energy. Director Indeed Professor Friston, a world-renowned neuroscientist at UCL whose work has been cited twice as many times as Albert Einstein, is the inventor of the Free Energy Theory Principle. 

Friston’s principle theory centres on how our brains minimise surprise and uncertainty. It explains that all living things are driven to minimise free energy, and thus the energy needed to predict and perceive the world. Such is its impact, the Free Energy Theory Principle has been described as the “most all-encompassing idea since the theory of natural selection.” Active Inference sits within this theory to explain the process our brains use in order to minimise this energy. This idea infuses Stanhope AI’s work, led by Professor Moran, a specialist in Active Inference and its application through AI; and Dr Biswa Sengupta, whose doctoral research was in dynamical systems, optimisation and energy efficiency from the University of Cambridge. 

Real-world application

In the immediate term, the technology is being tested with delivery drones and autonomous machines used by partners including Germany’s Federal Agency for Disruptive Innovation and the Royal Navy. In the long term, the technology holds huge promise in the realms of manufacturing, industrial robotics and embodied AI. The investment will be used to further the company’s development of its agentic AI models and the practical application of its research.  

Professor Rosalyn Moran, CEO and co-founder of Stanhope AI, said: “Our mission at Stanhope AI is to bridge the gap between neuroscience and artificial intelligence, creating a new generation of AI systems that can think, adapt, and decide like humans. We believe this technology will transform the capabilities of AI and robotics and make them more impactful in real-world scenarios. We trust the math and we’re delighted to have the backing of investors like UCL Technology Fund who deeply understand the science behind this technology and their support will be significant on our journey to revolutionise AI technology.”

David Grimm, partner UCL Technology Fund, said: “AI startups may be some of the hottest investments right now but few have the calibre and deep scientific and technical know-how as the Stanhope AI team. This is emblematic of their unique approach, combining neuroscience insights with advanced AI, which presents a groundbreaking opportunity to advance the field and address some of the most challenging problems in AI today. We can’t wait to see what this team achieves.” 

Marina Santilli, sasociate director UCL Business, added “The promise offered by Stanhope AI’s approach to Artificial Intelligence is hugely exciting, providing hope for powerful whilst energy-light models. UCLB is delighted to have been able to support the formation of a company built on the decades of fundamental research at UCL led by Professor Friston, developing the Free Energy Principle.” 

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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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“This is a historic day,” said Italian lawmaker Brando Benifei, co-lead on the AI Act. “We have the first regulation in the world which puts a clear path for safe and human-centric development of AI.”

The AI Act will categorise AI systems into four tiers based on their potential risk to society. High-risk applications like self-driving cars will face strict requirements before being allowed on the EU market. Lower risk systems will have fewer obligations.

“The main point now will be implementation and compliance by businesses and institutions,” Benifei stated. “We are also working on further AI legislation for workplace conditions.”

His counterpart, Dragoş Tudorache of Romania, said the EU aims to promote these pioneering rules globally. “We have to be open to work with others on how to build governance with like-minded parties.”

The general AI rules take effect in May 2025, while obligations for high-risk systems kick in after three years. National oversight agencies will monitor compliance.

Differing viewpoints on impact

Reaction was mixed on whether the Act properly balances innovation with protecting rights.

Curtis Wilson, a data scientist at Synopsys, believes it will build public trust: “The strict rules and punishing fines will deter careless developers, and help customers be more confident in using AI systems…Ensuring all AI developers adhere to these standards is to everyone’s benefit.”

However, Mher Hakobyan from Amnesty International criticised the legislation as favouring industry over human rights: “It is disappointing that the EU chose to prioritise interests of industry and law enforcement over protecting people…It lacks proper transparency and accountability provisions, which will likely exacerbate abuses.”

Companies now face the challenge of overhauling practices to comply.

Marcus Evans, a data privacy lawyer, advised: “Businesses need to create and maintain robust AI governance to make the best use of the technology and ensure compliance with the new regime…They need to start preparing now to not fall foul of the rules.”

After years of negotiations, the AI Act signals the EU intends to lead globally on this transformative technology. But dissenting voices show challenges remain in finding the right balance.

(Photo by Tabrez Syed on Unsplash)

See also: OpenAI calls Elon Musk’s lawsuit claims ‘incoherent’

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The post EU approves controversial AI Act to mixed reactions appeared first on AI News.

The ambitious expansion was announced by former Tesla staff scientist Remi Cadene in a post on X:

In keeping with Hugging Face’s ethos of open source, Cadene stated the robot project would be “open-source, not as in Open AI” in reference to OpenAI’s legal battle with Cadene’s former boss, Elon Musk.

Cadene – who will be leading the robotics initiative – revealed that Hugging Face is hiring robotics engineers in Paris, France.

A job listing for an “Embodied Robotics Engineer” sheds light on the project’s goals, which include “designing, building, and maintaining open-source and low cost robotic systems that integrate AI technologies, specifically in deep learning and embodied AI.”

The role involves collaborating with ML engineers, researchers, and product teams to develop innovative robotics solutions that “push the boundaries of what’s possible in robotics and AI.” Key responsibilities range from building low-cost robots using off-the-shelf components and 3D-printed parts to integrating deep learning and embodied AI technologies into robotic systems.

Until now, Hugging Face has primarily focused on software offerings like its machine learning codebase and open-source chatbot. The robotics project marks a significant departure into the hardware realm as the startup aims to bring AI into the physical world through open and affordable robotic platforms.

(Photo by Possessed Photography on Unsplash)

See also: Google engineer stole AI tech for Chinese firms

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 BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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