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Google unveils Veo 3 with audio capabilities

Google has introduced Veo 3, its most advanced video-generating AI model to date, capable of producing sound effects, ambient noise and dialogue to accompany the footage it creates.

Announced at the Google I/O 2025 developer conference, Veo 3 is available through the Gemini chatbot for those subscribed to the $249.99-per-month AI Ultra plan. The model accepts both text and image prompts, allowing users to generate audiovisual scenes rather than silent clips.

Unlike other AI tools, Veo 3 can analyse raw video pixels to synchronise audio automatically, offering a notable edge in an increasingly crowded field of video-generation platforms. While sound-generating AI isn’t new, Google claims Veo 3’s ability to match audio precisely with visual content sets it apart.

The progress builds on DeepMind’s earlier work in ‘video-to-audio’ AI and may rely on training data from YouTube, though Google hasn’t confirmed this.

To help prevent misuse, such as the creation of deepfakes, Google says Veo 3 includes SynthID, its proprietary watermarking technology that embeds invisible markers in every generated frame. Despite these safeguards, concerns remain within the creative industry.

Artists fear tools like Veo 3 could replace thousands of jobs, with a recent study predicting over 100,000 roles in film and animation could be affected by AI before 2026.

Alongside Veo 3, Google has also updated Veo 2. The earlier model now allows users to edit videos more precisely, adding or removing elements and adjusting camera movements. These features are expected to become available soon on Google’s Vertex AI API platform.

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The rise of tech giants in healthcare: How AI is reshaping life sciences

Silicon Valley targets health

The intersection of technology and healthcare is rapidly evolving, fuelled by advancements in ΑΙ and driven by major tech companies that are expanding their reach into the life sciences sector.

Once primarily known for consumer electronics or search engines, companies like Google, Amazon, Microsoft, Apple, and IBM are now playing an increasingly central role in transforming the medical field.

These companies, often referred to as ‘Big Tech’, are pushing the boundaries of what was once considered science fiction, using AI to innovate across multiple aspects of healthcare, including diagnostics, treatment, drug development, clinical trials, and patient care.

AI becomes doctors’ new tool

At the core of this revolution is AI. Over the past decade, AI has evolved from a theoretical tool to a practical and transformative force within healthcare.

Companies are developing advanced machine learning algorithms, cognitive computing models, and AI-powered systems capable of matching—and sometimes surpassing—human capabilities in diagnosing and treating diseases.

AI is also reshaping many aspects of healthcare, from early disease detection to personalised treatments and even drug discovery. This shift is creating a future where AI plays a significant role in diagnosing diseases, developing treatment plans, and improving patient outcomes at scale.

One of the most significant contributions of AI is in diagnostics. Google Health and its subsidiary DeepMind are prime examples of how AI can be used to outperform human experts in certain medical tasks.

For instance, DeepMind’s AI tools have demonstrated the ability to diagnose conditions like breast cancer and lung disease with remarkable accuracy, surpassing the abilities of human radiologists in some cases.

google deepmind AI progress Demis Hassabis

Similarly, Philips has filed patents for AI systems capable of detecting neurodegenerative diseases and tracking disease progression using heart activity and motion sensors.

From diagnosis to documentation

These breakthroughs represent only a small part of how AI is revolutionising diagnostics by improving accuracy, reducing time to diagnosis, and potentially saving lives.

In addition to AI’s diagnostic capabilities, its impact extends to medical documentation, an often-overlooked area that affects clinician efficiency.

Traditionally, doctors spend a significant amount of time on paperwork, reducing the time they can spend with patients.

However, AI companies like Augmedix, DeepScribe, and Nabla are addressing this problem by offering solutions that generate clinical notes directly from doctor-patient conversations.

These platforms integrate with electronic health record (EHR) systems and automate the note-taking process, which reduces administrative workload and frees up clinicians to focus on patient care.

Augmedix, for example, claims to save up to an hour per day for clinicians, while DeepScribe’s AI technology is reportedly more accurate than even GPT-4 for clinical documentation.

Nabla takes this further by offering AI-driven chatbots and decision support tools that enhance clinical workflows and reduce physician burnout.

Portable ultrasounds powered by AI

AI is also transforming medical imaging, a field traditionally dependent on expensive, bulky equipment that requires specialised training.

Innovators like Butterfly Network are developing portable, AI-powered ultrasound devices that can provide diagnostic capabilities at a fraction of the cost of traditional equipment. These devices offer greater accessibility, particularly in regions with limited access to medical imaging technology.

The ability to perform ultrasounds and MRIs in remote areas, using portable devices powered by AI, is democratising healthcare and enabling better diagnostic capabilities in underserved regions.

An advanced drug discovery

In the realm of drug discovery and treatment personalisation, AI is making significant strides. Companies like IBM Watson are at the forefront of using AI to personalise treatment plans by analysing vast amounts of patient data, including medical histories, genetic information, and lifestyle factors.

IBM Watson has been particularly instrumental in the field of oncology, where it assists physicians by recommending tailored cancer treatment protocols.

A capability like this is made possible by the vast amounts of medical data Watson processes to identify the best treatment options for individual patients, ensuring that therapies are more effective by considering each patient’s unique characteristics.

Smart automation in healthcare

Furthermore, AI is streamlining administrative tasks within healthcare systems, which often burden healthcare providers with repetitive, time-consuming tasks like appointment scheduling, records management, and insurance verification.

By automating these tasks, AI allows healthcare providers to focus more on delivering high-quality care to patients.

Amazon Web Services (AWS), for example, is leveraging its cloud platform to develop machine learning tools that assist healthcare providers in making more effective clinical decisions while improving operational efficiency.

It includes using AI to enhance clinical decision-making, predict patient outcomes, and manage the growing volume of patient data that healthcare systems must process.

Startups and giants drive the healthcare race

Alongside the tech giants, AI-driven startups are also playing a pivotal role in healthcare innovation. Tempus, for example, is integrating genomic sequencing with AI to provide physicians with actionable insights that improve patient outcomes, particularly in cancer treatment.

The fusion of data from multiple sources is enhancing the precision and effectiveness of medical decisions. Zebra Medical Vision, another AI-driven company, is using AI to analyse medical imaging data and detect a wide range of conditions, from liver disease to breast cancer.

Zebra’s AI algorithms are designed to identify conditions often before symptoms even appear, which greatly improves the chances of successful treatment through early detection.

Tech giants are deeply embedded in the healthcare ecosystem, using their advanced capabilities in cloud computing, AI, and data analytics to reshape the industry.

Microsoft, for example, has made significant strides in AI for accessibility, focusing on creating healthcare solutions that empower individuals with disabilities. Their work is helping to make healthcare more inclusive and accessible for a broader population.

Amazon’s AWS cloud platform is another example of how Big Tech is leveraging its infrastructure to develop machine learning tools that support healthcare providers in delivering more effective care.

M&A meets medicine

In addition to developing their own AI tools, these tech giants have made several high-profile acquisitions to accelerate their healthcare strategies.

Google’s acquisition of Fitbit, Amazon’s purchase of PillPack and One Medical, and Microsoft’s $19.7 billion acquisition of Nuance are all clear examples of how Big Tech is seeking to integrate AI into every aspect of the healthcare value chain, from drug discovery to clinical delivery.

These acquisitions and partnerships also enable tech giants to tap into new areas of the healthcare market and provide more comprehensive, end-to-end solutions to healthcare providers and patients alike.

Smart devices empower health

Consumer health technologies have also surged in popularity, thanks to the broader trend of digital health and wellness tools. Fitness trackers, smartwatches, and mobile health apps allow users to monitor everything from heart rates to sleep quality.

Devices like the Apple Watch and Google’s Fitbit collect health data continuously, providing users with personalised insights into their well-being.

seoul 05 02 2022 male hand with two apple watches with pink and gray strap on white background

Instead of being isolated within individual devices, the data is increasingly being integrated into broader healthcare systems, enabling doctors and other healthcare providers to have a more complete view of a patient’s health.

This integration has also supported the growth of telehealth services, with millions of people now opting for virtual consultations powered by Big Tech infrastructure and AI-powered triage tools.

Chinese hospitals embrace generative AI

The rise of generative AI is also transforming healthcare, particularly in countries like China, where technology is advancing rapidly. Once considered a distant ambition, the use of generative AI in healthcare is now being implemented at scale.

The technology is being used to manage massive drug libraries, assist with complex diagnoses, and replicate expert reasoning processes, which helps doctors make more informed decisions.

At Beijing Hospital of Traditional Chinese Medicine, Ant Group’s medical model has impressed staff by offering diagnostic suggestions and replicating expert reasoning, streamlining consultations without replacing human doctors.

Our choice in a tech-driven world

As AI continues to evolve, tech giants are likely to continue disrupting the healthcare industry while also collaborating with traditional healthcare providers.

While some traditional life sciences companies may feel threatened by the rise of Big Tech in healthcare, those that embrace AI and form partnerships with tech companies will likely be better positioned for success.

The convergence of AI and healthcare is already reshaping the future of medicine, and traditional healthcare players must adapt or risk being left behind.

generate an image of an artificial intelligence head in front of a human head and digital codes in the background reproducing all the human heads inputs and psychological reactions

Despite the tremendous momentum, there are challenges that need to be addressed. Data privacy, regulatory concerns, and the growing dominance of Big Tech in healthcare remain significant hurdles.

If these challenges are addressed responsibly, however, the integration of AI into healthcare could modernise care delivery on a global scale.

Rather than replacing doctors, the goal is to empower them with better tools, insights, and outcomes. The future of healthcare is one where technology and human expertise work in tandem, enhancing the patient experience and improving overall health outcomes.

As human beings, we must understand that the integration of technology across multiple sectors is a double-edged sword. It can either benefit us and help build better future societies, or mark the beginning of our downfall— but in the end, the choice will always be ours.

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Can AI replace therapists?

With mental health waitlists at record highs and many struggling to access affordable therapy, some are turning to AI chatbots for support.

Kelly, who waited months for NHS therapy, found solace in character.ai bots, describing them as always available, judgment-free companions. ‘It was like a cheerleader,’ she says, noting how bots helped her cope with anxiety and heartbreak.

But despite emotional benefits for some, AI chatbots are not without serious risks. Character.ai is facing a lawsuit from the mother of a 14-year-old who died by suicide after reportedly forming a harmful relationship with an AI character.

Other bots, like one from the National Eating Disorder Association, were shut down after giving dangerous advice.

Even so, demand is high. In April 2024 alone, 426,000 mental health referrals were made in England, and over a million people are still waiting for care. Apps like Wysa, used by 30 NHS services, aim to fill the gap by offering CBT-based self-help tools and crisis support.

Experts warn, however, that chatbots lack context, emotional intuition, and safeguarding. Professor Hamed Haddadi calls them ‘inexperienced therapists’ that may agree too easily or misunderstand users.

Ethicists like Dr Paula Boddington point to bias and cultural gaps in the AI training data. And privacy is a looming concern: ‘You’re not entirely sure how your data is being used,’ says psychologist Ian MacRae.

Still, users like Nicholas, who lives with autism and depression, say AI has helped when no one else was available. ‘It was so empathetic,’ he recalls, describing how Wysa comforted him during a night of crisis.

A Dartmouth study found AI users saw a 51% drop in depressive symptoms, but even its authors stress bots can’t replace human therapists. Most experts agree AI tools may serve as temporary relief or early intervention—but not as long-term substitutes.

As John, another user, puts it: ‘It’s a stopgap. When nothing else is there, you clutch at straws.’

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Met Office and Microsoft debut AI-ready forecasting system

The UK’s Met Office has launched a new supercomputer designed to significantly improve weather and climate forecasting accuracy. Operated via Microsoft’s Azure cloud platform, it is the world’s first cloud-based supercomputer dedicated solely to weather and climate science.

Capable of performing 60 quadrillion calculations per second—more than four times faster than its predecessor—the system is expected to enhance 14-day forecasts, improve rainfall predictions, and offer better data for sectors like aviation and energy.

The infrastructure, split across two data centres in southern England, runs entirely on renewable energy. Originally announced in 2020 with a £1.2 billion UK government investment, the project faced delays due to COVID-19 and global supply chain disruptions.

Despite recent cyberattacks on UK institutions, Met Office officials say the new system has robust security and represents a major technological upgrade.

The Met Office also says the new system will support AI integration and provide better insights into climate change-related events, such as floods and wildfires.

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AI at 45W: Neuchips showcases energy-saving chips for LLMs

As global energy demand surges alongside AI growth, Neuchips is stepping up with energy-efficient solutions that deliver high performance while reducing power consumption.

The company will showcase its latest innovations at COMPUTEX 2025, including its Viper series AI accelerator cards, capable of running a 14-billion parameter model at just 45 watts — roughly the same power as a standard light bulb.

The announcement follows an International Energy Agency (IEA) report projecting that electricity demand from AI-powered data centers will more than quadruple by 2030. Neuchips CEO Ken Lau emphasised that power-efficient AI is now a necessity, not a luxury.

Neuchips’ hardware supports models like Mistral Small 3, Llama 3.3, and Gemma 3, offering offline LLM inference that enhances data privacy. Its solutions are compatible with both Intel and AMD CPUs, and run on Ubuntu and Windows.

The company is expanding its reach through several key partnerships. With Taiwan’s National Center for High-performance Computing (NCHC), Neuchips is delivering energy-efficient AI to the cloud while ensuring data security and cost efficiency.

Collaborating with MAPLE LEAF INFORMATION AND TECHNOLOGY and Vecow, the company offers compact AI systems that operate without requiring additional power infrastructure.

In partnership with GSH’s ShareGuru SQLPilot, Neuchips is showcasing advanced agentic AI applications for business intelligence and customer service. Additionally, through integration with myLLM’s myPDA platform, Neuchips is enabling hybrid cloud-edge AI deployments using its hardware.

With its efficient AI acceleration chips and strategic collaborations, Neuchips is advancing sustainable AI across edge and data center environments.

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UAE’s EDGE Group unveils AI Accelerator to power defence and tech

EDGE Group, a global leader in advanced technology and defence, has launched the Group AI Accelerator, a new Centre of Excellence (COE) focused on accelerating AI-driven innovation across its portfolio and facilities.

The initiative is part of EDGE’s broader strategy to support the UAE’s ambitions of becoming a high-tech global hub.

The Group AI Accelerator will develop and integrate AI projects to enhance core engineering capabilities and business services. It will also incubate UAE talent and advance the country’s knowledge-based economy.

Dr. Chaouki Kasmi, EDGE’s President of Technology & Innovation, said the initiative will ‘enable the prompt adoption of AI technologies’ and foster ‘positive disruption’ across key programmes.

Overseen by EDGE’s Technology & Innovation Cluster, the COE will be guided by a steering committee of local and global experts. Engineering and business excellence working groups will lead AI skunkworks projects, R&D in machine learning, and digital transformation efforts.

EDGE’s latest move builds on its commitment to operational excellence and positions the UAE at the forefront of AI and Industry 4.0 development.

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China launches first AI satellites in orbital supercomputer network

China has launched the first 12 satellites in a planned network of 2,800 that will function as an orbiting supercomputer, according to Space News.

Developed by ADA Space in partnership with Zhijiang Laboratory and Neijang High-Tech Zone, the satellites can process their own data instead of relying on Earth-based stations, thanks to onboard AI models.

Each satellite runs an 8-billion parameter AI model capable of 744 tera operations per second, with the group already achieving 5 peta operations per second in total. The long-term goal is a constellation that can reach 1,000 POPS.

The network uses high-speed laser links to communicate and shares 30 terabytes of data between satellites. The current batch also carries scientific tools, such as an X-ray detector for studying gamma-ray bursts, and can generate 3D digital twin data for uses like disaster response or virtual tourism.

The space-based computing approach is designed to overcome Earth-based limitations like bandwidth and ground station availability, which means less than 10% of satellite data typically reaches the surface.

Experts say space supercomputers could reduce energy use by relying on solar power and dissipating heat into space. The EU and the US may follow China’s lead, as interest in orbital data centres grows.

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Researchers believe AI transparency is within reach by 2027

Top AI researchers admit they still do not fully understand how generative AI models work. Unlike traditional software that follows predefined logic, gen AI models learn to generate responses independently, creating a challenge for developers trying to interpret their decision-making processes.

Dario Amodei, co-founder of Anthropic, described this lack of understanding as unprecedented in tech history. Mechanistic interpretability — a growing academic field — aims to reverse engineer how gen AI models arrive at outputs.

Experts compare the challenge to understanding the human brain, but note that, unlike biology, every digital ‘neuron’ in AI is visible.

Companies like Goodfire are developing tools to map AI reasoning steps and correct errors, helping prevent harmful use or deception. Boston University professor Mark Crovella says interest is surging due to the practical and intellectual appeal of interpreting AI’s inner logic.

Researchers believe the ability to reliably detect biases or intentions within AI models could be achieved within a few years.

This transparency could open the door to AI applications in critical fields like security, and give firms a major competitive edge. Understanding how these systems work is increasingly seen as vital for global tech leadership and public safety.

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JMA to test AI-enhanced weather forecasting

The Japan Meteorological Agency (JMA) is exploring the use of AI to improve the accuracy of weather for e casts, with a particular focus on deep learning technologies, according to a source familiar with the plans.

A dedicated team was launched in April to begin developing the infrastructure and tools needed to integrate AI with JMA’s existing numerical weather prediction models. The goal is to combine traditional simulations with AI-generated forecasts based on historical weather data.

If implemented, AI systems could identify weather patterns more efficiently and enhance forecasts for variables such as rainfall and temperature. The technology may also offer improved accuracy in predicting extreme weather events like typhoons.

Currently, the JMA relies on supercomputers to simulate future atmospheric conditions based on observational data. Human forecasters then review the outputs, applying expert judgment before issuing final forecasts and alerts. Even with AI integration, human oversight will remain a core part of the process.

In addition to forecasting, the agency is also considering AI for processing data from the Himawari-10 satellite, which is expected to launch in fiscal 2029.

An official announcement outlining further AI integration measures is anticipated in June.

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UK workers struggle to keep up with AI

AI is reshaping the UK workplace, but many employees feel unprepared to keep pace, according to a major new study by Henley Business School.

While 56% of full-time professionals expressed optimism about AI’s potential, 61% admitted they were overwhelmed by how quickly the technology is evolving.

The research surveyed over 4,500 people across nearly 30 sectors, offering what experts call a clear snapshot of AI’s uneven integration into British industries.

Professor Keiichi Nakata, director of AI at The World of Work Institute, said workers are willing to embrace AI, but often lack the training and guidance to do so effectively.

Instead of empowering staff through hands-on learning and clear internal policies, many companies are leaving their workforce under-supported.

Nearly a quarter of respondents said their employers were failing to provide sufficient help, while three in five said they would use AI more if proper training were available.

Professor Nakata argued that AI has the power to simplify tasks, remove repetitive duties, and free up time for more meaningful work.

But he warned that without better support, businesses risk missing out on what could be a transformative force for both productivity and employee satisfaction.

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