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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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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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.

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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The company argued that advanced AI tools like ChatGPT require such broad training that adhering to copyright law would be utterly unworkable.

In written testimony, OpenAI stated that between expansive copyright laws and the ubiquity of protected online content, “virtually every sort of human expression” would be off-limits for training data. From news articles to forum comments to digital images, little online content can be utilised freely and legally.

According to OpenAI, attempts to create capable AI while avoiding copyright infringement would fail: “Limiting training data to public domain books and drawings created more than a century ago … would not provide AI systems that meet the needs of today’s citizens.”

While defending its practices as compliant, OpenAI conceded that partnerships and compensation schemes with publishers may be warranted to “support and empower creators.” But the company gave no indication that it intends to dramatically restrict its harvesting of online data, including paywalled journalism and literature.

This stance has opened OpenAI up to multiple lawsuits, including from media outlets like The New York Times alleging copyright breaches.

Nonetheless, OpenAI appears unwilling to fundamentally alter its data collection and training processes—given the “impossible” constraints self-imposed copyright limits would bring. The company instead hopes to rely on broad interpretations of fair use allowances to legally leverage vast swathes of copyrighted data.

As advanced AI continues to demonstrate uncanny abilities emulating human expression, legal experts expect vigorous courtroom battles around infringement by systems intrinsically designed to absorb enormous volumes of protected text, media, and other creative output. 

For now, OpenAI is betting against copyright maximalists in favour of near-boundless copying to drive ongoing AI development.

(Photo by Levart_Photographer on Unsplash)

See also: OpenAI’s GPT Store to launch next week after delays

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 Digital Transformation Week and Cyber Security & Cloud Expo.

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The post OpenAI: Copyrighted data ‘impossible’ to avoid for AI training appeared first on AI News.

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular.

At Canon, Zheng’s team focuses on ethically integrating AI into business by first mapping current and potential AI applications across areas like marketing and e-commerce. They then analyse and assess risks to ensure compliance with regulations.

Canon is actively mapping out AI applications and assessing risks, as Grace explained, “to align with regulations such as the EU legislations.”

As founder of Kosh Duo, Zheng also provides coaching to help businesses scale up through the use of AI marketing and data-driven approaches. She coaches professionals on achieving greater recognition and rewards by leveraging AI tools as well.

A key challenge she encounters is misunderstandings around what AI truly means – many conflate it solely with chatbots like ChatGPT rather than appreciating the full breadth of machine learning, neural networks, natural language processing, and more that enable today’s AI.

“There’s a lot of misconceptions, definitely. One of the biggest fears, as I touched on, is the very generic understanding that GPT equals AI,” says Zheng. “[Kosh Duo] provides coaching services to businesses to scale to the next level using AI marketing and data-driven approaches.”

When asked about trends to watch, Zheng emphasised the need for continual learning given how rapidly the field evolves. She expects that 2024 will be an “awakening year” where businesses truly grasp AI’s potential and individuals appreciate the need to evaluate their current skillsets.

The interview highlighted the transformative but often misunderstood power of AI in business and the importance of developing specialised skills to properly harness it. Zheng stressed that with the right ethical foundations and coaching, AI and machine learning can become positive forces to drive growth rather than something to fear.

Watch our full interview with Grace Zheng below:

(Photo by Benjamin Davies on Unsplash)

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 Cyber Security & Cloud Expo and Digital Transformation Week.

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The post AI & Big Data Expo: Ethical AI integration and future trends appeared first on AI News.

During the discussion, Grande provided insightful perspectives on the current challenges, how Edge Impulse is helping address these struggles, and the tremendous promise of on-device AI.

Key hurdles with edge AI adoption

Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.

“A lot of the companies building edge devices are not very familiar with machine learning,” says Grande. “Bringing those two worlds together is the third challenge, really, around having teams communicate with each other and being able to share knowledge and work towards the same goals.”

Strategies for lean and efficient models

When asked how to optimise for edge environments, Grande emphasised first minimising required sensor data.

“We are seeing a lot of companies struggle with the dataset. What data is enough, what data should they collect, what data from which sensors should they collect the data from. And that’s a big struggle,” explains Grande.

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. Always balance sensor and hardware constraints against functionality, connectivity needs, and software requirements.

Edge Impulse aims to enable engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics, ensuring reliability while accelerating time-to-value. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms.

Transformative potential of on-device intelligence

Grande highlighted innovative products already leveraging edge intelligence to provide personalised health insights without reliance on the cloud, such as sleep tracking with Oura Ring.

“It’s sold over a billion pieces, and it’s something that everybody can experience and everybody can get a sense of really the power of edge AI,” explains Grande.

Other exciting opportunities exist around preventative industrial maintenance via anomaly detection on production lines.

Ultimately, Grande sees massive potential for on-device AI to greatly enhance utility and usability in daily life. Rather than just raw data, edge devices can interpret sensor inputs to provide actionable suggestions and responsive experiences not previously possible—heralding more useful technology and improved quality of life.

Unlocking the potential of AI on edge devices hinges on overcoming current obstacles inhibiting adoption. Grande and other leading experts provided deep insights at this year’s AI & Big Data Expo on how to break down the barriers and unleash the full possibilities of edge AI.

“I’d love to see a world where the devices that we were dealing with were actually more useful to us,” concludes Grande.

Watch our full interview with Alessandro Grande below:

(Photo by Niranjan _ Photographs on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

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 Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post AI & Big Data Expo: Unlocking the potential of AI on edge devices appeared first on AI News.

Representatives from Adobe, Cohere, IBM, Nvidia, Palantir, Salesforce, Scale AI, and Stability attended the White House for the announcement. These eight companies have pledged to play a pivotal role in promoting the development of safe, secure, and trustworthy AI.

The Biden-Harris Administration is actively working on an Executive Order and pursuing bipartisan legislation to ensure the US leads the way in responsible AI development that unlocks its potential while managing its risks.

The commitments made by these companies revolve around three fundamental principles: safety, security, and trust. They have committed to:

1. Ensure products are safe before introduction:

The companies commit to rigorous internal and external security testing of their AI systems before releasing them to the public. This includes assessments by independent experts, helping guard against significant AI risks such as biosecurity, cybersecurity, and broader societal effects.

They will also actively share information on AI risk management with governments, civil society, academia, and across the industry. This collaborative approach will include sharing best practices for safety, information on attempts to circumvent safeguards, and technical cooperation.

2. Build systems with security as a top priority:

The companies have pledged to invest in cybersecurity and insider threat safeguards to protect proprietary and unreleased model weights. Recognising the critical importance of these model weights in AI systems, they commit to releasing them only when intended and when security risks are adequately addressed.

Additionally, the companies will facilitate third-party discovery and reporting of vulnerabilities in their AI systems. This proactive approach ensures that issues can be identified and resolved promptly even after an AI system is deployed.

3. Earn the public’s trust:

To enhance transparency and accountability, the companies will develop robust technical mechanisms – such as watermarking systems – to indicate when content is AI-generated. This step aims to foster creativity and productivity while reducing the risks of fraud and deception.

They will also publicly report on their AI systems’ capabilities, limitations, and areas of appropriate and inappropriate use, covering both security and societal risks, including fairness and bias. Furthermore, these companies are committed to prioritising research on the societal risks posed by AI systems, including addressing harmful bias and discrimination.

These leading AI companies will also develop and deploy advanced AI systems to address significant societal challenges, from cancer prevention to climate change mitigation, contributing to the prosperity, equality, and security of all.

The Biden-Harris Administration’s engagement with these commitments extends beyond the US, with consultations involving numerous international partners and allies. These commitments complement global initiatives, including the UK’s Summit on AI Safety, Japan’s leadership of the G-7 Hiroshima Process, and India’s leadership as Chair of the Global Partnership on AI.

The announcement marks a significant milestone in the journey towards responsible AI development, with industry leaders and the government coming together to ensure that AI technology benefits society while mitigating its inherent risks.

(Photo by Tabrez Syed on Unsplash)

See also: UK’s AI ecosystem to hit £2.4T by 2027, third in global race

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 Digital Transformation Week.

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The post White House secures safety commitments from eight more AI companies appeared first on AI News.

The v3.1 iteration of the benchmark suite has seen record participation, boasting over 13,500 performance results and delivering up to a 40 percent improvement in performance. 

What sets this achievement apart is the diverse pool of 26 different submitters and over 2,000 power results, demonstrating the broad spectrum of industry players investing in AI innovation.

Among the list of submitters are tech giants like Google, Intel, and NVIDIA, as well as newcomers Connect Tech, Nutanix, Oracle, and TTA, who are participating in the MLPerf Inference benchmark for the first time.

David Kanter, Executive Director of MLCommons, highlighted the significance of this achievement:

“Submitting to MLPerf is not trivial. It’s a significant accomplishment, as this is not a simple point-and-click benchmark. It requires real engineering work and is a testament to our submitters’ commitment to AI, to their customers, and to ML.”

MLPerf Inference is a critical benchmark suite that measures the speed at which AI systems can execute models in various deployment scenarios. These scenarios span from the latest generative AI chatbots to the safety-enhancing features in vehicles, such as automatic lane-keeping and speech-to-text interfaces.

The spotlight of MLPerf Inference v3.1 shines on the introduction of two new benchmarks:

• An LLM utilising the GPT-J reference model to summarise CNN news articles garnered submissions from 15 different participants, showcasing the rapid adoption of generative AI.

• An updated recommender benchmark – refined to align more closely with industry practices – employs the DLRM-DCNv2 reference model and larger datasets, attracting nine submissions. These new benchmarks are designed to push the boundaries of AI and ensure that industry-standard benchmarks remain aligned with the latest trends in AI adoption, serving as a valuable guide for customers, vendors, and researchers alike.

Mitchelle Rasquinha, co-chair of the MLPerf Inference Working Group, commented: “The submissions for MLPerf Inference v3.1 are indicative of a wide range of accelerators being developed to serve ML workloads.

“The current benchmark suite has broad coverage among ML domains, and the most recent addition of GPT-J is a welcome contribution to the generative AI space. The results should be very helpful to users when selecting the best accelerators for their respective domains.”

MLPerf Inference benchmarks primarily focus on datacenter and edge systems. The v3.1 submissions showcase various processors and accelerators across use cases in computer vision, recommender systems, and language processing.

The benchmark suite encompasses both open and closed submissions in the performance, power, and networking categories. Closed submissions employ the same reference model to ensure a level playing field across systems, while participants in the open division are permitted to submit a variety of models.

As AI continues to permeate various aspects of our lives, MLPerf’s benchmarks serve as vital tools for evaluating and shaping the future of AI technology.

Find the detailed results of MLPerf Inference v3.1 here.

(Photo by Mauro Sbicego on Unsplash)

See also: GitLab: Developers view AI as ‘essential’ despite concerns

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 Digital Transformation Week.

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

The post MLPerf Inference v3.1 introduces new LLM and recommendation benchmarks appeared first on AI News.

The Global AI Ecosystem platform is developed with support from AI Industry Analytics (AiiA) and Deep Knowledge Group. Designed as a universally accessible space for community interaction, collaboration, content sharing, and knowledge exchange, it has become a vital hub for AI enthusiasts and professionals.

AiiA, in its Global AI Economy Size Assessment report, conducted groundbreaking research showcasing the rapid expansion of the UK’s AI industry.

With over 8,900 companies operating in the sector, the UK AI economy’s valuation of £1.36 trillion underscores its substantial contribution to the national GDP. Approximately 4,100 investment funds are dedicated to AI, with 600 of them based in the UK.

A robust workforce of 500,000 UK-based AI specialists is driving innovation, solidifying the nation’s position in the global AI landscape. This skilled workforce not only bolsters GDP growth but also acts as a safety net against unemployment.

The UK government’s active prioritisation of its national AI agenda is a significant factor in this remarkable growth. Last month, UK Deputy PM Oliver Dowden called AI the most ‘extensive’ industrial revolution yet.

With 280 ongoing projects harnessing AI technology, the UK’s commitment to AI is clear. AI is a major pillar of the country’s national industrial strategy, making the UK one of the most proactive nations in shaping its AI future.

Dmitry Kaminskiy, Founder of AI Industry Analytics (AiiA) and General Partner of Deep Knowledge Group, said:

“Despite an economic downturn and other challenges, the UK stands as an undoubtable, dynamic, and proactive leader in the global AI arena, having surpassed £1.3 trillion in 2023 and projected to reach £2.4 trillion by 2027.

There is no question that AI is poised to be the major driver for economic growth, fuelling the further development of the entire UK DeepTech industry, and creating a cumulative, systemic, positive impact on the full scope of the nation’s integral infrastructure.”

Key cities like London, Cambridge, Manchester, and Edinburgh have emerged as leading AI hubs, fostering collaboration and providing access to essential resources. With nearly 5,000 AI companies in London alone, it competes with entire countries on the global AI stage and solidifies its European leadership status.

AiiA’s estimation of the UK AI economy size used AI algorithms to map the global AI industry, profiling 50,000 companies, 20,000 investors, 2,000 AI leaders, and 2,500 R&D hubs. Building upon previous reports, it conducted the most comprehensive assessment of the Global AI Economy to date, projecting a global AI economy exceeding £27.2 trillion ($34 trillion) by 2027.

The UK’s position as a hub for science, R&D, DeepTech, and AI governance places it in good stead for leveraging AI as a core engine of technological progress and driving economic growth.

(Image Credit: Global AI Ecosystem)

See also: UK government outlines AI Safety Summit plans

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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