Adobe has revealed an experimental tool called Project Super Sonic, which uses AI to simplify the process of generating sound effects and background audio for video content. Introduced at Adobe’s MAX conference, the prototype allows users to create audio through text prompts, object recognition, or even by mimicking the desired sound with their own voice. This innovation aims to speed up and enhance the audio creation process, giving users more control over their video projects.
While generating sound from text isn’t new, Adobe’s approach goes further. With object recognition, users can click on a video frame, and the tool will generate relevant sound effects for that scene. However, the most impressive feature allows users to imitate sounds themselves, with the AI automatically creating the matching audio in sync with the video.
Although currently just a demo, Adobe’s track record suggests Project Super Sonic may soon find its way into popular tools like Adobe Premiere, offering creators an efficient way to elevate their videos with high-quality audio.
Intel and AMD are teaming up to ensure software compatibility across their x86 chips in response to competition from Arm Holdings. For decades, Intel’s x86 architecture has powered laptops, PCs, and servers, with AMD licensing the technology to make its own competing chips. However, Arm’s market share has grown, partly due to its contracts requiring that all Arm chips support Arm software universally.
In response, Intel and AMD have formed an advisory group that includes major industry players such as Broadcom, Dell Technologies, Lenovo, and Oracle. The group’s objective is to establish consistent and compatible standards for x86 chips by combining expertise from the hardware and software sectors.
At a Lenovo event in Seattle, Intel CEO Pat Gelsinger highlighted the flexibility of x86 technology for AI-enabled laptops, stating that the architecture is still strong and poised for growth and innovation as AI advances.
Google is enhancing its Shopping tab with AI, building on its previous integration of generative AI into Search in 2023. The company announced it will use AI technology to help users find products that match their specific needs. The update includes a new, personalised feed of shoppable products, offering a scrollable, TikTok-inspired design.
When users search for a product, an AI-generated brief will provide personalised tips and considerations based on their query. For example, if someone searches for a “men’s winter jacket for Seattle,” the AI might recommend prioritising water resistance for the rainy climate and suggest insulation types suitable for the milder temperatures.
Google’s AI will recommend relevant products, offering brief descriptions to explain why each item is a suitable choice. Users can browse categories like “Synthetic insulated winter jackets for Seattle” and use filters to refine their search based on specific sizes or local availability.
The personalised shopping feed will showcase products and videos tailored to user preferences, featuring items like Chelsea boots alongside YouTube Shorts with shopping tips. Google is positioning itself to compete with TikTok, which has gained traction in e-commerce. These new features will roll out in the US in the coming weeks, as Google combines its Shopping Graph with advanced Gemini models to enhance the user experience.
Unily has introduced its new ‘Insight Center’, a platform designed to help large enterprises integrate and manage digital assistants and language models. The solution provides a central access point for multiple AI tools, streamlining their use across business functions like HR and customer service. It also ensures efficient governance and prioritises simplicity in its user experience.
At its annual event, Unite 24, Unily also announced the launch of ‘Unily Go’, a mobile app focused on improving engagement and communication for frontline workers. The app helps connect employees who don’t have access to desktop computers, offering secure messaging and personalised features to keep teams connected.
By partnering with industry leaders such as Microsoft and Workgrid, Unily ensures its platform offers a comprehensive and secure way for organisations to interact with the digital assistants of their choice. Unily Go, in particular, addresses the need for better communication tools for mobile workers in sectors like retail and manufacturing.
Both the Insight Center and Unily Go will become part of the Unily employee experience platform in 2025. The company plans to offer these features with white-labelling options so businesses can align the tools with their corporate branding.
CS Disco, Inc. has officially launched its AI-driven Cecilia platform in the European Union and the United Kingdom. The Cecilia AI Platform helps legal professionals review large datasets faster, allowing for quicker identification and analysis of crucial documents. The platform offers tools like Cecilia Q&A, which answers fact-based questions from a user’s document set, streamlining the review process.
The company’s generative AI capabilities are designed to boost efficiency in legal work, with features such as single document Q&A and document summaries helping attorneys quickly navigate complex or lengthy documents. The platform also supports documents in multiple languages, offering significant time savings compared to traditional methods.
Early adopters in the United States have already reported success with Cecilia’s tools, praising their speed and accuracy. CS Disco is focusing on enabling legal teams to handle large volumes of data with greater precision, as it expands its services to the European market.
The Cecilia platform is expected to grow further, with additional AI features planned for release in the EU and UK by 2025. DISCO aims to continue its role as a leader in AI-enabled legal technology, improving outcomes for clients across different markets.
Adobe has launched its Firefly Video Model, an AI tool that generates video from text prompts, stepping into the growing competition in generative AI for film and television production. This move positions Adobe alongside rivals like OpenAI, ByteDance, and Meta Platforms, all of whom have recently released similar video tools.
Adobe, however, distinguishes itself by training its models on data it owns the rights to, ensuring the generated content can be legally used for commercial purposes. While a general release date is not confirmed, Adobe has begun offering access to those who signed up for the waiting list.
Although no customers have been announced for the video tool, Gatorade is using Adobe’s image generation model to create custom bottle designs, and Mattel has applied the technology in designing packaging for its Barbie dolls. Adobe has geared its video tools towards creators, making them user-friendly for everyday video production.
Ely Greenfield, Adobe’s chief technology officer for digital media, highlighted that the focus is on ensuring the AI understands key video production concepts like camera angles and motion, allowing it to seamlessly blend with conventional footage.
The AI Safety Institute is launching an £8.5 million funding scheme to support research on AI system safety, while the initiative will back studies on preventing unexpected failures in AI technologies and addressing challenges linked to their rapid deployment.
The Systemic Safety Grants Programme, run in partnership with the Engineering and Physical Sciences Research Council and Innovate UK, will initially fund around 20 projects. Each project can receive up to £200,000 to explore risks AI might present to society in the near future. Additional funding will follow as further phases are introduced.
Systemic AI safety focuses on the broader infrastructure supporting AI across sectors, including healthcare and energy. Ian Hogarth, chair of the institute, emphasised the importance of addressing risks in critical industries. He highlighted that diverse research teams will contribute to building essential knowledge about AI-related threats, such as deepfakes and system malfunctions.
Applications are open until 26 November, with successful projects to be announced by January 2025. Grants will be awarded the following month, supporting efforts to ensure AI systems remain safe, reliable, and trustworthy as their use expands across the economy.
It has been more than four years since AI was first introduced into clinical trials involving humans. Even back then, it was evident that the advancement of artificial intelligence—currently the most popular buzzword online in 2024—would enhance every aspect of society, including medicine.
Thanks to AI-powered tools, diseases that once baffled humanity are now much better understood. Some conditions are also easier to detect, even in their earliest stages, significantly improving diagnosis outcomes. For these reasons, AI in medicine stands out as one of the most valuable technological advances, with the potential to improve individual health and, ultimately, the overall well-being of society.
Although ethical concerns and doubts about the accuracy of AI-assisted diagnostic tools persist, it is clear that the coming years and decades will bring developments and improvements that once seemed purely theoretical.
AI collaborates with radiologists to enhance diagnostic accuracy
AI has been a crucial aid in medical diagnostics for some time now. A Japanese study showed that ChatGPT performed more accurate assessments than experts in the field.
After performing 150 diagnostics, neuroradiologists recorded an 80% accuracy rate for AI. These promising results encouraged the research team to explore integrating such AI systems into apps and medical devices. They also highlighted the importance of incorporating AI education into medical curricula to better prepare future healthcare professionals.
Early detection of brain tumours and lung cancer
Early detection of diseases, particularly cancer, is critical to a patient’s chances of survival. Many companies are focusing on improving AI within medical equipment to diagnose brain tumours and lung cancer in their earliest stages.
The algorithm developed by Imidex, which has received FDA approval, is currently in clinical trials. Its purpose is to improve the screening of potential lung cancer patients.
Collaborating with Spesana, the company is expected to be among the first to market once the research is finalised.
Growing competition shows AI’s progress
An increasing number of companies entering the AI-in-medicine field suggests that these advancements will be more widely accessible than initially expected. While the mentioned companies are set to dominate the North American market, a French startup Bioptimus is targeting Europe.
Their AI model, trained on millions of medical images, is capable of identifying cancerous cells and genetic anomalies within tumours, pushing the boundaries of precision medicine.
Public trust in AI medical diagnosis
New technologies often face public scepticism and AI in medicine is no exception. A 2023 study found that many patients feel uneasy with doctors relying on AI during treatment.
The Pew Research Centre report revealed that 60% of Americans are against AI-assisted diagnostics, while only 39% support it. Furthermore, 57% believe AI could worsen the doctor-patient relationship, compared to 13% who think it might improve it.
As for treatment outcomes, 38% anticipate improvements with AI, 33% expect negative results, and 27% believe no major changes will occur.
AI’s role in tackling dementia
Dementia, a progressive illness affecting cognitive functions, remains a major challenge for healthcare. However, AI has shown promising potential in this area. Through advanced pattern recognition, AI systems can analyse massive datasets, detect changes in brain structure, and identify early warning signs of dementia, long before symptoms manifest.
By processing various test results and brain scans, AI algorithms enable earlier interventions, which can greatly improve patients’ quality of life. In particular, researchers from Edinburgh and Dundee are hopeful that their AI tool, SCAN-DAN, will revolutionise the early detection of this neurodegenerative disease.
The project is part of the larger global NEURii collaboration, which aims to develop digital health tools that can address some of the most pressing challenges in dementia research.
Helping with early breast cancer detection
AI has shown great potential in improving the effectiveness of ultrasound, mammography, and MRI scans for breast cancer detection. Researchers in the USA have developed an AI system capable of refining disease staging by accurately distinguishing between benign and malignant tumours.
Moreover, the AI system can reduce false positives and negatives, a common problem in traditional breast cancer detection methods. The ability to improve diagnostic accuracy and provide a better understanding of disease stages is crucial in treating breast cancer from its earliest signs.
Investment in AI set to skyrocket
With early diagnosis playing a pivotal role in curing diseases, more companies are seeking partnerships and funding to keep pace with the leading investors in AI technology.
Recent projections indicate that AI could add nearly USD $20 trillion to the global economy by 2030. While it is still difficult to estimate healthcare’s share in this growth, some early predictions suggest that AI in medicine could account for more than 10% of that value.
What is clear, however, is that major global companies are not missing the opportunity to invest in businesses developing AI-driven medical equipment.
What can we expect in the future?
AI is making significant progress across various industries, and its impact on medicine could be transformational. If healthcare receives as much or more AI focus than fields like economics and ecology, the potential to revolutionise medicine as a science is immense.
Various AI systems that learn about diseases and treatment processes have the capacity to gather and analyse far more information than the human brain. As regulatory frameworks evolve worldwide, AI-driven diagnostic tools may lead to faster, more accurate disease detection than ever before, potentially marking a major turning point in the history of medical science.
A recent assessment of some of the top AI models has revealed significant gaps in compliance with the EU regulations, particularly in cybersecurity resilience and preventing discriminatory outputs. The study by Swiss startup LatticeFlow in collaboration with the EU officials, tested generative AI models from major tech companies like Meta, OpenAI, and Alibaba. The findings are part of an early attempt to measure compliance with the EU’s upcoming AI Act, which will be phased in over the next two years. Companies that fail to meet these standards could face fines of up to €35 million or 7% of their global annual turnover.
LatticeFlow’s ‘Large Language Model (LLM) Checker’ evaluated the AI models across multiple categories, assigning scores between 0 and 1. While many models received respectable scores, such as Anthropic’s ‘Claude 3 Opus,’ which scored 0.89, others revealed vulnerabilities. For example, OpenAI’s ‘GPT-3.5 Turbo’ received a low score of 0.46 for discriminatory output, and Alibaba’s ‘Qwen1.5 72B Chat’ scored even lower at 0.37, highlighting the persistent issue of AI reflecting human biases in areas like gender and race.
In cybersecurity testing, some models also struggled. Meta’s ‘Llama 2 13B Chat’ scored 0.42 in the ‘prompt hijacking’ category, a type of cyberattack where malicious prompts are used to extract sensitive information. Mistral’s ‘8x7B Instruct’ model fared similarly poorly, scoring 0.38. These results show the need for tech companies to strengthen security measures to meet the EU’s strict standards.
While the EU is still finalising the enforcement details of its AI Act, expected by 2025, LatticeFlow’s test provides an early roadmap for companies to fine-tune their models. LatticeFlow CEO Petar Tsankov expressed optimism, noting that the test results are mainly positive and offer guidance for companies to improve their models’ compliance with the forthcoming regulations.
The European Commission, though unable to verify external tools, has welcomed this initiative, calling it a ‘first step’ toward translating the AI Act into enforceable technical requirements. As tech companies prepare for the new rules, the LLM Checker is expected to play a crucial role in helping them ensure compliance.
Fujitsu has launched a new AI-powered service aimed at boosting 5G network performance by predicting traffic surges and adjusting base station operations. The application ensures users experience minimal disruptions during peak periods by activating additional base stations when needed.
The system measures network quality in real time, identifying early signs of increased demand to prevent performance drops. It promises improved energy efficiency and reduced operational costs through smarter base station management. Commercial availability is scheduled for next month, integrated into Fujitsu’s open RAN-compliant orchestration platform.
Trials revealed that the technology enhances the user experience for individual applications, supporting 19% more users per base station. The predictive system is particularly effective during events, allowing networks to anticipate pedestrian traffic and adapt without compromising service quality.
Fujitsu’s tool represents a breakthrough in network management by combining traffic forecasting with dynamic resource allocation. Operators can now ensure smoother connectivity and reduce power consumption while keeping pace with fluctuating demand.
