Training and deploying sophisticated AI models occur within vast, power-hungry data centres. A “typical AI-focused data centre consumes as much electricity as 100 000 households,” the IEA notes, with the largest facilities under construction projected to demand 20x times that amount.

Surging data centre investments

Global investment in data centres has nearly doubled since 2022, reaching half a trillion dollars in 2024, sparking concerns about escalating electricity needs.

While data centres accounted for approximately 1.5% of global electricity consumption in 2024 (around 415 terawatt-hours, TWh,) their local impact is far more significant. Consumption has grown annually by about 12% since 2017, vastly outpacing overall electricity demand growth.

The US leads this consumption (45%), followed by China (25%) and Europe (15%). Almost half of US data centre capacity is concentrated in just five regional clusters.

Looking ahead, the IEA projects global data centre electricity consumption to more than double by 2030 to reach approximately 945 TWh. To put that in context, that’s slightly more than Japan’s current total electricity consumption.

AI is pinpointed as the “most important driver of this growth”. The US is projected to see the largest increase, where data centres could account for nearly half of all electricity demand growth by 2030. By the decade’s end, US data centres are forecast to consume more electricity than the combined usage of its aluminium, steel, cement, chemical, and other energy-intensive manufacturing industries.

The IEA’s “Base Case” extends this trajectory, anticipating around 1,200 TWh of global data centre electricity consumption by 2035. However, significant uncertainties exist, with projections for 2035 ranging from 700 TWh (“Headwinds Case”) to 1,700 TWh (“Lift-Off Case”) depending on AI uptake, efficiency gains, and energy sector bottlenecks.

Fatih Birol, Executive Director of the IEA, said: “AI is one of the biggest stories in the energy world today – but until now, policymakers and markets lacked the tools to fully understand the wide-ranging impacts.

“In the United States, data centres are on course to account for almost half of the growth in electricity demand; in Japan, more than half; and in Malaysia, as much as one-fifth.”

Meeting the global AI energy demand

Powering this AI boom requires a diverse energy portfolio. The IEA suggests renewables and natural gas will take the lead, but emerging technologies like small modular nuclear reactors (SMRs) and advanced geothermal also have a role.

Renewables, supported by storage and grid infrastructure, are projected to meet half the growth in data centre demand globally up to 2035. Natural gas is also crucial, particularly in the US, expanding by 175 TWh to meet data centre needs by 2035 in the Base Case. Nuclear power contributes similarly, especially in China, Japan, and the US, with the first SMRs expected around 2030.

However, simply increasing generation isn’t sufficient. The IEA stresses the critical need for infrastructure upgrades, particularly grid investment. Existing grids are already strained, potentially delaying around 20% of planned data centre projects globally due to complex connection queues and long lead times for essential components like transformers.

The potential of AI to optimise energy systems

Beyond its energy demands, AI offers significant potential to revolutionise the energy sector itself.

The IEA details numerous applications:

• Energy supply: The oil and gas industry – an early adopter – uses AI to optimise exploration, production, maintenance, and safety, including reducing methane emissions. AI can also aid critical mineral exploration.
• Electricity sector: AI can improve forecasting for variable renewables, reducing curtailment. It enhances grid balancing, fault detection (reducing outage durations by 30-50%), and can unlock significant transmission capacity through smarter management—potentially 175 GW without building new lines.
• End uses: In industry, widespread AI adoption for process optimisation could yield energy savings equivalent to Mexico’s total energy consumption today. Transport applications like traffic management and route optimisation could save energy equivalent to 120 million cars, though rebound effects from autonomous vehicles need monitoring. Building optimisation potential is significant but hampered by slower digitalisation.
• Innovation: AI can dramatically accelerate the discovery and testing of new energy technologies, such as advanced battery chemistries, catalysts for synthetic fuels, and carbon capture materials. However, the energy sector currently underutilises AI for innovation compared to fields like biomedicine.

Collaboration is key to navigating challenges

Despite the potential, significant barriers hinder AI’s full integration into the energy sector. These include data access and quality issues, inadequate digital infrastructure and skills (AI talent concentration is lower in energy sectors,) regulatory hurdles, and security concerns.

Cybersecurity is a double-edged sword: while AI enhances defence capabilities, it also equips attackers with sophisticated tools. Cyberattacks on utilities have tripled in the last four years.

Supply chain security is another critical concern, particularly regarding critical minerals like gallium (used in advanced chips,) where supply is highly concentrated.

The IEA concludes that deeper dialogue and collaboration between the technology sector, the energy industry, and policymakers are paramount. Addressing grid integration challenges requires smarter data centre siting, exploring operational flexibility, and streamlining permitting.

While AI presents opportunities for substantial emissions reductions through optimisation, exceeding the emissions generated by data centres, these gains are not guaranteed and could be offset by rebound effects.

“AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments, and companies – how we use it,” said Dr Birol.

“The IEA will continue to provide the data, analysis, and forums for dialogue to help policymakers and other stakeholders navigate the path ahead as the energy sector shapes the future of AI, and AI shapes the future of energy.”

(Photo by Javier Miranda)

See also: UK forms AI Energy Council to align growth and sustainability goals

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 inaugural meeting of the council will see members agree on its core objectives, with a central focus on how the government’s mission to become a clean energy superpower can support its commitment to advancing AI and compute infrastructure.

Unveiled earlier this year as part of the government’s response to the AI Opportunities Action Plan, the council will serve as a crucial platform for bringing together expert insights on the significant energy demands associated with the AI sector.

Concerns surrounding the substantial energy requirements of AI data centres are a global challenge. The UK is proactively addressing this issue through initiatives like the establishment of new AI Growth Zones.

These zones are dedicated hubs for AI development that are strategically located in areas with access to at least 500MW of power—an amount equivalent to powering approximately two million homes. This approach is designed to attract private investment from companies looking to establish operations in Britain, ultimately generating local jobs and boosting the economy.

Peter Kyle, Secretary of State for Science, Innovation, and Technology, said: “The work of the AI Energy Council will ensure we aren’t just powering our AI needs to deliver new waves of opportunity in all parts of the country, but can do so in a way which is responsible and sustainable.

“This requires a broad range of expertise from industry and regulators as we fire up the UK’s economic engine to make it fit for the age of AI—meaning we can deliver the growth which is the beating heart of our Plan for Change.”

The Council is also expected to delve into the role of clean energy sources, including renewables and nuclear, in powering the AI revolution.

A key aspect of its work will involve advising on how to improve energy efficiency and sustainability within AI and data centre infrastructure, with specific considerations for resource usage such as water. Furthermore, the council will take proactive steps to ensure the secure adoption of AI across the UK’s critical energy network itself.

Ed Miliband, Secretary of State for Energy Security and Net Zero, commented: “We are making the UK a clean energy superpower, building the homegrown energy this country needs to protect consumers and businesses, and drive economic growth, as part of our Plan for Change.

“AI can play an important role in building a new era of clean electricity for our country and as we unlock AI’s potential, this Council will help secure a sustainable scale up to benefit businesses and communities across the UK.”

In a parallel effort to facilitate the growth of the AI sector, the UK government has been working closely with energy regulator Ofgem and the National Energy System Operator (NESO) to implement fundamental reforms to the UK’s connections process.

Subject to final sign-offs from Ofgem, these reforms could potentially unlock more than 400GW of capacity from the connection queue. This acceleration of projects is deemed vital for economic growth, particularly for the delivery of new large-scale AI data centres that require significant power infrastructure.

The newly-formed AI Energy Council comprises representatives from 14 key organisations across the energy and technology sectors, including regulators and leading companies. These members will contribute their expert insights to support the council’s work and ensure a collaborative approach to addressing the energy challenges and opportunities presented by AI.

Among the prominent organisations joining the council are EDF, Scottish Power, National Grid, technology giants Google, Microsoft, Amazon Web Services (AWS), and chip designer ARM, as well as infrastructure investment firm Brookfield.

This collaborative framework, uniting the energy and technology sectors, aims to ensure seamless coordination in speeding up the connection of energy projects to the national grid. This is particularly crucial given the increasing number of technology companies announcing plans to build data centres across the UK.

Alison Kay, VP for UK and Ireland at AWS, said: “At Amazon, we’re working to meet the future energy needs of our customers, while remaining committed to powering our operations in a more sustainable way, and progressing toward our Climate Pledge commitment to become net-zero carbon by 2040.

“As the world’s largest corporate purchaser of renewable energy for the fifth year in a row, we share the government’s goal to ensure the UK has sufficient access to carbon-free energy to support its AI ambitions and to help drive economic growth.”

Jonathan Brearley, CEO of Ofgem, added: “AI will play an increasingly important role in transforming our energy system to be cleaner, more efficient, and more cost-effective for consumers, but only if used in a fair, secure, sustainable, and safe way.

“Working alongside other members of this Council, Ofgem will ensure AI implementation puts consumer interests first – from customer service to infrastructure planning and operation – so that everyone feels the benefits of this technological innovation in energy.”

This initiative aligns with the government’s Clean Power Action Plan, which focuses on connecting more homegrown clean power to the grid by building essential infrastructure and prioritising projects needed for 2030. The aim is to clear the grid connection queue, enabling crucial infrastructure projects – from housing to gigafactories and data centres – to gain access to the grid, thereby unlocking billions in investment and fostering economic growth.

Furthermore, the government is streamlining planning approvals to significantly reduce the time it takes for infrastructure projects to get off the ground. This accelerated process will ensure that AI innovators can readily access cutting-edge infrastructure and the necessary power to drive forward the next wave of AI advancements.

(Photo by Vlad Hilitanu)

See also: Tony Blair Institute AI copyright report sparks backlash

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 AI-powered vehicle represents a significant leap forward in marine technology and underwater robotics. Capable of conducting complex underwater inspections without human intervention, it promises to dramatically enhance efficiency and slash costs associated with underwater surveys and inspections.

Traditionally, offshore wind site inspections have been manual, labour-intensive processes. Beam’s autonomous solution offers a radical departure from this approach, enabling data to be streamed directly back to shore. This shift allows offshore workers to concentrate on more intricate tasks while reducing inspection timelines by up to 50%, resulting in substantial operational cost savings.

Brian Allen, CEO of Beam, said: “We are very proud to have succeeded in deploying the world’s first autonomous underwater vehicle driven by AI. Automation can revolutionise how we carry out inspection and maintenance of offshore wind farms, helping to reduce both costs and timelines.”

Beyond improved efficiency, Beam’s technology elevates the quality of inspection data and facilitates the creation of 3D reconstructions of assets alongside visual data. This deployment marks a crucial step in Beam’s roadmap for autonomous technology, with plans to extend this AI-driven solution across its fleet of DP2 vessels, ROVs, and autonomous underwater vehicles (AUVs) throughout 2025 and 2026.

“Looking ahead to the future, the potential of this technology is huge for the industry, and success in these initial projects is vital for us to progress and realise this vision. This wouldn’t be possible without forward-thinking customers like SSE Renewables who are willing to go on the journey with us,” explained Allen.

The Seagreen wind farm, operational since October 2023, is the world’s deepest fixed-bottom offshore wind farm. Beam’s project at Seagreen has provided crucial insights into the potential of autonomous technology for large offshore wind superstructures. The data collected by the AI-driven vehicle will support ongoing operational reliability at the site, offering valuable information on areas such as marine growth and potential erosion at the foundations.

Matthew Henderson, Technical Asset Manager – Substructure and Asset Lifecycle at SSE Renewables, commented: “At SSE, we have a mantra that ‘if it’s not safe, we don’t do it.’ Beam’s technology demonstrates that autonomous inspections can reduce the personnel we need to send offshore for planned inspections, while speeding up planned works and collecting rich data-sets to inform asset integrity planning.

“As we move further offshore, and into deeper waters, the ability to collect high-quality inspection data in a low-risk manner is imperative to us delivering our Net Zero Acceleration Programme.”

As Beam prepares to roll out its AI-driven inspection technology across its fleet in 2025 and 2026, this deployment aligns with the company’s mission to revolutionise offshore wind operations by making them more efficient and cost-effective—further supporting the global energy transition.

The success of this AI-powered underwater vehicle at Seagreen wind farm not only demonstrates the potential of autonomous technology in offshore wind inspections but also sets a new standard for safety, efficiency, and data quality in the industry. Such innovations will play a crucial role in ensuring the sustainability and cost-effectiveness of offshore wind energy.

See also: Hugging Face is launching an open robotics project

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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Introducing lightweight, industry-specific AI models for individual licence, SAS hopes to equip organisations with readily deployable AI technology to productionise real-world use cases with unparalleled efficiency.

Chandana Gopal, research director, Future of Intelligence, IDC, said: “SAS is evolving its portfolio to meet wider user needs and capture market share with innovative new offerings,

“An area that is ripe for SAS is productising models built on SAS’ core assets, talent and IP from its wealth of experience working with customers to solve industry problems.”

In today’s market, the consumption of models is primarily focused on large language models (LLMs) for generative AI. In reality, LLMs are a very small part of the modelling needs of real-world production deployments of AI and decision making for businesses. With the new offering, SAS is moving beyond LLMs and delivering industry-proven deterministic AI models for industries that span use cases such as fraud detection, supply chain optimization, entity management, document conversation and health care payment integrity and more.

Unlike traditional AI implementations that can be cumbersome and time-consuming, SAS’ industry-specific models are engineered for quick integration, enabling organisations to operationalise trustworthy AI technology and accelerate the realisation of tangible benefits and trusted results.

Expanding market footprint

Organisations are facing pressure to compete effectively and are looking to AI to gain an edge. At the same time, staffing data science teams has never been more challenging due to AI skills shortages. Consequently, businesses are demanding agility in using AI to solve problems and require flexible AI solutions to quickly drive business outcomes. SAS’ easy-to-use, yet powerful models tuned for the enterprise enable organisations to benefit from a half-century of SAS’ leadership across industries.

Delivering industry models as packaged offerings is one outcome of SAS’ commitment of $1 billion to AIpowered industry solutions. As outlined in the May 2023 announcement, the investment in AI builds on SAS’ decades-long focus on providing packaged solutions to address industry challenges in banking, government, health care and more.

Udo Sglavo, VP for AI and Analytics, SAS, said: “Models are the perfect complement to our existing solutions and SAS Viya platform offerings and cater to diverse business needs across various audiences, ensuring that innovation reaches every corner of our ecosystem. 

“By tailoring our approach to understanding specific industry needs, our frameworks empower businesses to flourish in their distinctive Environments.”

Bringing AI to the masses

SAS is democratising AI by offering out-of-the-box, lightweight AI models – making AI accessible regardless of skill set – starting with an AI assistant for warehouse space optimisation. Leveraging technology like large language models, these assistants cater to nontechnical users, translating interactions into optimised workflows seamlessly and aiding in faster planning decisions.

Sgvalo said: “SAS Models provide organisations with flexible, timely and accessible AI that aligns with industry challenges.

“Whether you’re embarking on your AI journey or seeking to accelerate the expansion of AI across your enterprise, SAS offers unparalleled depth and breadth in addressing your business’s unique needs.”

The first SAS Models are expected to be generally available later this year.

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.

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

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The extended partnership between Wipro and IBM combines the former’s extensive industry expertise with IBM’s leading AI innovations. The collaboration seeks to develop joint solutions that facilitate the implementation of robust, reliable, and enterprise-ready AI solutions.

The Wipro Enterprise AI-Ready Platform harnesses various components of the IBM watsonx suite, including watsonx.ai, watsonx.data, and watsonx.governance, alongside AI assistants. It offers clients a comprehensive suite of tools, large language models (LLMs), streamlined processes, and robust governance mechanisms, laying a solid foundation for the development of future industry-specific analytic solutions.

Jo Debecker, Managing Partner & Global Head of Wipro FullStride Cloud, said: “This expanded partnership with IBM combines our deep contextual cloud, AI, and industry expertise with IBM’s leading AI innovation capabilities.”

A key aspect of this collaboration is the establishment of the IBM TechHub@Wipro, a centralised tech hub aimed at supporting joint client pursuits. This initiative will bring together subject matter experts, engineers, assets, and processes to drive and support AI initiatives.

Kate Woolley, General Manager of IBM Ecosystem, commented: “We’re pleased to reach this new milestone in our 20-year partnership to support clients through the combination of Wipro’s and IBM’s joint expertise and technology, including watsonx.”

The Wipro Enterprise AI-Ready Platform offers infrastructure and core software for AI and generative AI workloads, enhancing automation, dynamic resource management, and operational efficiency in the enterprise. Moreover, it caters to specialised industry use cases, such as banking, retail, health, energy, and manufacturing, offering tailored solutions for customer support, marketing, feedback analysis, and more.

Nagendra Bandaru, Managing Partner and President of Wipro Enterprise Futuring, highlighted the flexibility of the platform, stating: “Wipro’s Enterprise AI-Ready Platform will allow clients to easily integrate and standardise multiple data sources augmenting AI- and GenAI-enabled transformation across business functions.”

In addition to facilitating AI governance through the AI lifecycle, the platform prioritises responsible AI practices, ensuring transparency, data protection, and compliance with relevant laws and regulations.

As part of this collaboration, Wipro associates will undergo training in IBM hybrid cloud, AI, and data analytics technologies, further enhancing their capabilities in developing joint solutions.

(Photo by Carson Masterson on Unsplash)

See also: Reddit is reportedly selling data for AI training

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.

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

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“The UK is one of the world’s most advanced AI economies, and AI technology is already having a transformative impact on our economy and society,” said UK Science Minister George Freeman.

“But there is tremendous potential to do more.”

The AI for Decarbonisation programme is part of the wider £1 billion Net Zero Innovation Portfolio that aims to accelerate the commercialisation of low-carbon technologies, systems and business models.

AI for Decarbonisation will consist of two initial stages:

• Stage one will provide up to £500,000 of funds to create a virtual centre of excellence on AI innovation and decarbonisation through March 2025.
• Stage two is where the remaining £1 million will be used to fund innovative AI-powered decarbonisation projects.

The AI for Decarbonisation programme offers an exciting opportunity to leverage and develop the UK’s outstanding expertise in the field,” adds Freeman.

“Putting this rapidly-evolving technology into action will enable us to save energy costs for businesses and households, create high-value, skilled jobs, and kickstart millions of pounds of private investment while supporting our net-zero targets.”

The UK is seeking to increase its energy independence following Russia’s invasion of Ukraine and the weaponisation of supplies.

New energy infrastructure to increase supply will take a long time to build, so reducing demand in the meantime is vital. Improving the insulation of homes and businesses while using technologies like AI to optimise processes are two leading solutions.

Specifically encouraged to bid for funding are AI projects that enable a faster transition to renewable energy, decarbonise industry by improving energy productivity and fuel switching, and decrease emissions in the agricultural sector. 

The programme opens for applications on 22 November 2022 and closes on 19 January 2022.

(Photo by Martin Sepion 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.

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A report from Greenpeace earlier this month exposed Google as being one of the top three developers of AI tools for the fossil fuel industry. Greenpeace found AI technologies boost production levels by as much as five percent.

In an interview with CUBE’s John Furrier, the leader of Google’s CTO office, Will Grannis, said that Google will “no longer develop artificial intelligence (AI) software and tools for oil and gas drilling operations.”

The pledge from Google Cloud is welcome, but it must be taken in a wider context.

In 2019, Google Cloud’s revenue from oil and gas was approximately $65 million. A hefty sum, but less than one percent of all Google Cloud revenues. Furthermore, Google Cloud’s revenue from oil and gas decreased by about 11 percent despite overall revenue growing by 53 percent.

While Google Cloud’s revenue from the oil and gas industry was declining, the public’s intolerance towards big polluters is increasing. The reputational damage caused to Google of continuing its relationship with polluters would likely have been more costly over the long-term.

This isn’t the first time Google has cut-off an AI-related relationship with a controversial industry to preserve its reputation.

Back in 2018, Google was forced into ending a contract with the Pentagon called Project Maven to build AI technologies for drones. Over 4,000 Google employees signed a petition demanding their management cease the project and never again “build warfare technology.”

Following the Project Maven backlash, Google CEO Sundar Pichai promised in a blog post the company will not develop technologies or weapons that cause harm, or anything which can be used for surveillance violating “internationally accepted norms” or “widely accepted principles of international law and human rights”.

Back in January, Pichai called for sensible AI regulation that does not limit the potential societal benefits.

PAX, a Dutch NGO, ranked Google among the safest companies developing AI while slamming rivals such as Amazon and Microsoft for being among the “highest risk” tech firms in the world.

(Photo by Zbynek Burival on Unsplash)

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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When the crisis ends, it will in some ways be a statistician’s dream. The amount of data which will be able to be extrapolated will be huge, for both scientists and businesses working in this field.

One such business is Airband Technologies. The UK- based company urges businesses, consumers and governments to ‘take the cleaner path’ through a smart, wearable air sensor. The sensor aims to help monitor air pollution and use intelligent routing with real-time data.

AI News caught up with Anton Fedotov (left), co-founder of Airband Technologies, to discuss his company’s vision for cleaner air, as well as the artificial intelligence (AI) technologies being employed to make it happen.

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AI News: Hi Anton. Tell me about your career to date and role and responsibilities at Airband?

Anton Fedotov: I began Airband with a group of co-founders a little over a year ago. We began with an idea, and have spent the year working hard on developing it into something real and physical. Throughout that time, I have been leading the process as CEO of the company – I am involved both in the development of our product, as well as the development of our business, including reaching out and finding prospective investors and clients.

AI: How did the concept for Airband come about?

AF: Our lead engineer was doing research on a project about air pollution, which led him to realise the sheer lack of data which is out there – there is almost nothing aside from a few stationary monitoring sites spread out across larger city. Even data which can be purchased comes from modelling the spread starting from a relatively small dataset. Seeing the development of IoT, crowdsourced, and wearable technologies was our inspiration for a wearable air sensor, which can contribute to a massive granular dataset and help solve the pollution problem.

AI: Tell us about Airband’s wearable air pollution monitor and how AI technologies are being used for it?

AF: Our wearable pollution monitor is a revolutionary device which measures the quality of the air around the user, rather than a fixed space. It provides personalised pollution readings for the area and for the day to people, and it helps employers keep track of and manage their employees’ exposure for high-risk jobs. Airband makes use of AI to maximise sensor potential and extrapolate data, as well as maintaining calibration across the network. Finally, our predictive AQI technology uses AI to predict air quality in gaps of the network, making it the most detailed map of air pollution ever.

AI: What are some of the partnerships Airband is putting together with other companies in this space and how important are partnerships in achieving your goals?

AF: Airband is always on the lookout for partnerships, which will be fundamental to achieving our goals. We are looking at partnerships with service and hardware providers in the IoT sphere to help us develop and deploy both our product and future products across industries. We also are looking for partners in the data science sphere in order to maximise the potential of the data we collect, and help complement our dataset with existing monitoring solutions.

AI: What other initiatives are interesting to you in the green tech space and why?

AF: There are many amazing initiatives in the green tech space which are going on at the moment – some companies are aiming to directly reduce pollution by creating air “Scrubbers” which could serve to clean up the air around busy streets. Other companies are working on making plastics recyclable or even entirely bio-degradable. Perhaps one of the most interesting angles is the companies bringing easier choices to environmentally minded consumers, by providing either information or alternatives when buying reusable products or travelling.

AI: What can we expect from Airband (on the basis of business as usual) for the next 12 months and beyond?

AF: In the next 12 months, Airband hopes to deliver to initial business clients for businesses interested in protecting their employees. The development of our product and data network will be ongoing, and we should be able to start generating meaningful data, showcasing the utility of our product. Over the next 2-3 years, we hope to also expand into the consumer sector, and make the wearable air quality sensor available for all, at an affordable price.

Photo by Thomas Millot on Unsplash

Interested in hearing industry leaders like Airband discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London, and Amsterdam.

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Dr. Chris Fall, Director of DOE’s Office of Science, said: “Major scientific facilities at our DOE national laboratories are generating vast and growing amounts of data for researchers every day. Artificial intelligence and machine learning hold out new promise for managing this wealth of data as well as improving facility operations and aiding in experimental design.”

Proposals are likely to cover a wide variety of different challenges, including extracting information from complex data sets, managing facility operations in real-time, and optimising experiments through the creation of virtual laboratory environments, among other topics. The funding opportunity focuses on 18 DOE Office of Science user facilities, comprising of particle accelerators, accelerator test facilities, x-ray light sources, neutron scattering sources, and nanoscale science research centres, overseen by three major programme offices: basic energy sciences, high energy physics, and nuclear physics.

According to the latest Ericsson Mobility Report, data volumes in mobile networks are increasing at an exceptional rate and mobile data traffic is expected to grow fourfold by 2025, reaching up to 160 exabytes per month. This seems interesting and in fact offers all sorts of opportunities for communications service providers; however, there is a potential disadvantage of this rapidly increasing data traffic due to the impact on energy consumption and carbon footprint of mobile networks. But AI has the ability to solve this problem, Ericsson notes, as when deployed, communications service providers will be able to realise energy efficiencies on the radio network proactively. The technology does not just address site-related energy savings, but also operational efficiencies.

Photo by Bonnie Kittle on Unsplash

Interested in hearing industry leaders discuss subjects like this? Attend the co-located 5G Expo, IoT Tech Expo, Blockchain Expo, AI & Big Data Expo, and Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valle

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A report from KPMG last month explored how artificial intelligence would look to influence business. Among millennial respondents, AI came out on top as the biggest technological priority, ahead of IoT and 5G. Yet among those polled who said they were business leaders, it was less of a priority compared with robotic process automation (RPA). The report noted, however, that investing in RPA can be seen as a smoother route to future AI investment.

IT giant Infosys offers many opportunities for companies looking to invest in artificial intelligence to improve workplace productivity as well as enhance the customer experience. The company notes that customers are at different stages of their journeys depending on sector and use case; and that therefore a more nuanced approach is required.

Ahead of the AI & Big Data Expo event in Amsterdam later this month, AI News caught up with Dr. N. R. Srinivasa Raghavan, Chief Data Scientist, Data and Analytics at Infosys (left), to discuss the initiatives the company is putting together, as well as the industries set to benefit most long-term.

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AI News: Tell us about the initiatives Infosys is putting together in the realm of artificial intelligence – and what have customers been saying?

NR: Infosys is developing several industry-oriented AI solutions, frameworks, and workbenches that can help solve business problems. Specifically, AI is being positioned for enhancing the productivity of day to day operations of our clients through automation, enable decision making at tactical and operational levels, and for better customer experience.

Our customers are at different stages of embracing AI. Depending on their needs, Infosys is able to craft solutions driven by a global team of experts in data sciences and AI, and help deploy these solutions at enterprise scale.

AI: Do you agree that many companies/vendors are using ‘AI-washing’ (much in the same way as cloud-washing years ago) and overplaying their artificial intelligence capabilities? If so – what does this mean for users and the industry at large?

NR: We believe it is not as easy to be deceptive in AI as in cloud. AI is more of an outcome heavy tech than cloud, which is more of an infrastructure play. Therefore, one can verify if indeed pattern matching and predictive science of AI is behind any software. Also, the expertise of resources working on AI projects is discernible in the quality of the output.

Nonetheless, there is possibly AI-washing in some very narrow areas like automation. It calls for better validations and governance to be put in place to weed these out.

AI: Which industries do you think are going to benefit most long-term from these kinds of technologies? How important is it that several emerging technologies – artificial intelligence, edge computing, blockchain – can all talk to each other and make each other better to provide greater business outcomes?

NR: Banking, financial services and insurance (BFSI), retail, consumer product goods and logistics (RCL), and services, utilities, resources and energy (SURE), in that order.

While AI and edge computing need to work in synchronization for cases like real time predictions, integration with blockchain will be essential in the data layers of AI. Especially where the reliability, traceability and ‘sovereignty’ of data that is feeding AI is concerned.

AI: What is the most exciting use case you have seen with artificial intelligence to date? (can be business or consumer)

NR: AI for consumer experience, AI in risk/fraud detection, AI for predictive maintenance, AI for security and AI for business functions like HR, Finance etc. These will typically be cases where there is rich and reliable data available for the AI models to work upon, as well as areas where there is executive ownership and sponsorship.

AI: What advice would you give to companies looking to embark upon or modify their digital transformation initiatives?

NR: AI is largely seen as the harbinger for an automated/human-augmented workplace. It is therefore essential to have AI experts to be part of the blue printing for the next-gen digital enterprises. AI cannot be telescoped into digital transformation programs in large enterprises. It is no more a commodity tech, but a strategic one.

AI: What are you looking forward to most at the AI & Big Data Expo and what will you be looking to tell attendees while there?

NR: We are looking forward to hearing from other participants on their success and learnings in implementing AI within Big Data context. We are eager to share our own experience in pushing the frontiers for AI and its adoption within enterprise settings.

Want to learn more about topics like this from thought leaders in the space? Find out more about the Edge Computing Expo, a brand new, innovative event and conference exploring the edge computing ecosystem.

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US-based AI semiconductor manufacturer Syntiant raised $25 million (£19.5m) in its latest series B funding round that was led by Microsoft Corp.’s venture fund: M12. Strategic investors like Amazon Alexa Fund, Applied Ventures, Intel Capital, Motorola Solutions Venture Capital and Robert Bosch Venture Capital were also part of this fundraiser. At this event, Syntiant also announced that it is expanding its board to five members by appointing Samir Kumar, managing director of M12, and Bret Johnsen, CFO of Space Exploration Technologies (SpaceX). He will serve as an independent director.

Syntiant is developing semiconductor solutions designed from the ground up for deep learning inference. Syntiant’s neural decision processors (NDPs) use an analogue neural network offers orders of magnitude lower power by extreme memory efficiency along with massively parallel computation with modest precision. This is done without the constraints of legacy processor architectures. The company enables always-on deep learning inference in battery-powered devices that are ideal for applications like hearing aids and IoT, smart speakers and mobile phones, etc.

Canadian language translation services provider Knowtions Research also raised $5 million (£3.9m) in series A funding round led by Information Venture Partners, with participation from Alibaba Entrepreneurs Fund. Knowtions intends to help health insurers in a time when the rapidly increasing healthcare costs are threatening to make insurance policies less affordable for people. The company wants to reverse this trend and is working towards transformation of such health insurers with the power of AI.

Knowtions has developed an AI platform, called Lydia, which helps health insurers unlock and use predictive insights in unusable health data. It learns how people seek medical care globally to make predictions on fraudulent behaviour and health risks. Insurers can use these predictions to create AI-assisted workflows that automate claims processing and personalise customer experience.

Grid4C, an Israeli developer of AI and machine learning solutions for the energy industry, has also raised a $5 million led by ICV — a venture capital firm focused on industrial technology, backed by French energy giant ENGIE and other leading utilities in Europe and in Asia. iAngels and AxessVentures were also among the list of investors.

The company is working with the leading utility companies across the world, delivering billions of predictions for millions of smart meters every day. The company’s analytics solutions leverage the ability of AI and data science to offer utilities with granular predictions and actionable insights for their operations and customer-facing applications.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

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