This Week's Top 5 Stories in AI
How Google is Using AI to Connect Humans and Animals
Google leverages its advanced large language models (LLMs), such as Gemini, to power enterprise-ready AI solutions across industries.
By integrating LLMs into platforms, Google empowers global businesses to build custom chatbots, automate information retrieval and deploy intelligent agents that enhance customer engagement and operational efficiency.
Now, the company has announced that it’s using LLMs for a very different and unique purpose: Dolphin translation.
Google has developed a specialised large language model (LLM) designed to analyse dolphin communication patterns.
The model, DolphinGemma, processes the clicks, whistles and burst pulses that form dolphin vocalisations.
The project is a collaboration between Google, researchers at Georgia Institute of Technology (Georgia Tech) and the Wild Dolphin Project (WDP), which conducts field research on Atlantic spotted dolphins.
DolphinGemma functions as an audio-in, audio-out model that processes sequences of dolphin sounds to identify patterns and structure, similar to how language models (LMs) for human communication predict subsequent words in sentences.
AI Data Centre Power Demand: The Sustainability Challenge
The energy requirements of AI systems have emerged as a global market constraint, with datacentres worldwide now consuming 460 terawatt-hours of electricity annually.
Dr. Vanessa Just, Founder and Chief Executive Officer of JUS.TECH GmbH, a sustainability-focused technology consultancy, says: “Today's data centres already consume lots of power: Globally 460 terawatt-hours (TWh) of electricity are needed annually. That's equivalent to the entire country of Germany.”
This escalation in power consumption, driven by the computational requirements of AI model training and inference, presents systemic challenges to global electricity grids and sustainability targets, according to research from Arm.
The study covers 650 business leaders across multiple industries.
Accenture’s Path to AI Success with Cognitive Digital Brains
Enterprises across the world are facing difficult decisions on how to implement AI systems that can act with increasing autonomy.
Accenture’s research has found that corporate leaders have started to confront the practical challenges of this AI implementation, particularly when deploying AI cognitive systems that can operate with minimal human oversight.
These autonomous AI capabilities – referred to as “cognitive digital brains” in technical circles – are embedding institutional knowledge, workflows and value chains directly into systems that can understand and act with autonomy at a higher level than before.
Yet as AI becomes more independent, humans' trust around it is wavering – so Accenture has found four emerging trends of AI in its Technology Vision 2025 report to advise businesses on how to stay ahead in the AI race whilst building trust.
“As AI increasingly acts autonomously on behalf of people, trust is emerging as the foundation of the “digital brain” that enterprises can now create,” says Julie Sweet in a LinkedIn post, Chair and Chief Executive Officer at Accenture.
The Rise of the Chief AI Officer: Explained
Britain's largest companies are competing for AI talent at board level as AI becomes essential for business operations.
As a result, nearly half of Financial Times Stock Exchange (FTSE) 100 companies have now established dedicated AI leadership positions, according to new research.
The findings come from the All In: The Corporate AI Leadership Race report published by pltfrm, an AI-focused executive recruitment firm.
The study documents how UK blue-chip companies are restructuring their leadership teams to incorporate specialised AI expertise.
- 48% have dedicated CAIO or equivalent
- 42% were appointed in the last year alone and 65% since January 2023
- Three dominant career backgrounds for CAIOs: Data science (50%), consulting (21%) and engineering and tech (17%)
- Two emerging AI leadership archetypes: The innovation-driven “Savant” vs. the governance-focused “Shepherd”
Furthermore, only 4% of current AI leadership positions are held by executives with primarily academic backgrounds – suggesting companies currently prioritise commercial and implementation expertise over theoretical knowledge.
The Power of TSMC’s A14 Chip Process for Future AI
As AI workloads continue to drive unprecedented demand for advanced semiconductors, the global chip manufacturing industry faces mounting pressure to deliver more powerful and energy-efficient solutions.
In this high-stakes environment, Taiwan Semiconductor Manufacturing Company (TSMC) maintains its position as the critical linchpin in the global technology supply chain, producing chips for companies including Apple, AMD, Nvidia and Qualcomm.
The semiconductor manufacturing market has become increasingly complex since 2022, with geopolitical tensions and supply chain vulnerabilities prompting major economies to invest billions in domestic chip production capabilities.
Now, TSMC has unveiled its next-generation A14 process technology at its North America Technology Symposium in Santa Clara, California.
This is a step forward from the company's N2 process, scheduled to enter production later this year – as the A14 technology is designed to accelerate AI capabilities through improved computing performance and power efficiency – addressing growing concerns about the energy consumption of large AI systems.
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