With the UK heading to the polls, the role of AI in guiding voter decisions is under scrutiny. ChatGPT, a generative AI tool, has been tested on its ability to provide insights into the upcoming general election. Despite its powerful pattern-matching capabilities, experts emphasise its limitations and potential biases, given that AI tools rely on their training data and accessible online content.
ChatGPT suggested a strong chance of a Labour victory in the UK based on current polling when prompted about the likely outcomes of the election. However, AI’s predictions can be flawed, as demonstrated when a glitch led ChatGPT to declare Labour as the election winner prematurely incorrectly. This incident prompted OpenAI to refine ChatGPT’s responses, ensuring more cautious and accurate outputs.
ChatGPT can help voters navigate party manifestos, outlining the priorities of major parties like Labour and the Conservatives. By summarising key points from multiple sources, the AI aims to provide balanced insights. Nevertheless, the psychological impact of AI-generated single answers remains a concern, as it could influence voter behaviour and election outcomes.
Why does it matter?
The use of AI for election guidance has sparked debates about its appropriateness and reliability. While AI can offer valuable information, the importance of critical thinking and informed decision-making must be balanced. As the election date approaches, voters are reminded that their choices hold significant weight, and participation in the democratic process is crucial.
A new International Monetary Fund (IMF) report reveals that Nigeria and several other developing nations need more digital infrastructure to effectively deploy AI. The shortfall persists despite Nigeria’s recent launch of its first Multilingual Large Language Model (LLM) and unveiling of an AI strategy in April. The IMF’s ‘AI Preparedness Index,’ which evaluates 174 economies, highlights that many developing countries like Nigeria are trailing in AI readiness due to inadequate digital infrastructure.
According to the Index’s interactive map, most African nations, with exceptions like Namibia, Botswana, and South Africa, exhibit low preparedness for AI. Wealthier economies are generally better equipped for AI adoption, and the IMF warns that the disparity could exacerbate existing global inequalities. The report suggests that while AI has the potential to enhance productivity and expand opportunities in countries like Nigeria, it may also widen the gap between those who can leverage the technology and those who cannot.
To address Nigeria’s challenges, the IMF recommends that emerging markets and developing economies invest heavily in digital infrastructure and worker training. For advanced economies, the priority should be on expanding social safety nets and fostering AI innovation and integration. International coordination is also essential to establish regulations that protect against AI risks and abuses while building public trust in the technology.
Amazon’s AWS, the leading global cloud computing provider, is intensifying efforts to draw the public sector into the realm of AI amidst fierce competition with Microsoft and Google in the generative AI domain. The initiative aims to demonstrate AI’s potential to enhance public services across health, security, and non-profit sectors, leveraging technologies like ChatGPT to streamline operations and improve outcomes.
Over two years, AWS has allocated a substantial $50 million fund to support public sector entities in exploring AI applications, offering cloud computing credits, training, and technical expertise to kickstart innovative projects. Currently serving thousands of government agencies, academic institutions, and nonprofits worldwide, AWS seeks to transition AI concepts into practical solutions that can effectively address public sector challenges.
Dave Levy, AWS’s vice president overseeing global public sector operations, highlighted the importance of moving from conceptualisation to implementation in public sector AI projects, underscoring the need for robust support to navigate complexities and achieve meaningful impacts. The push comes amid heightened competition as Microsoft and Google Cloud aggressively pursue public sector AI adoption, aiming to leverage vast datasets and AI capabilities to revolutionise service delivery and operational efficiency.
Amazon’s AWS remains committed to addressing challenges such as data privacy, security, and ethical considerations surrounding AI adoption in the public sector, emphasising rigorous security protocols and readiness for large-scale deployment.
Why does it matter?
As generative AI continues to evolve, AWS’s strategic focus on public sector adoption underscores its belief in AI’s transformative potential, aiming to lead the charge in integrating advanced technologies into governmental and non-governmental organisations worldwide.
A recent report from Citigroup predicts a significant rise in banking profits, driven by the adoption of AI, with projections soaring to nearly $2 trillion by 2028 from the current $1.7 trillion. While AI has traditionally been used to optimise products and boost productivity, its role is expanding to include customer-facing tasks, similar to how ATMs revolutionised cash withdrawals.
Leading this transformation is DeepBrain AI, a startup based in California founded by Eric Seyoung Jang in 2016. Initially focusing on chatbots, the company now develops AI-powered avatars used by major South Korean banks like KB Kookmin Bank to manage frequently asked questions, thereby reducing the workload on human bankers.
Despite concerns that AI could replace human jobs, the Citigroup report and experts suggest that technology adoption historically leads to more job creation. For instance, the number of compliance officers in the United States has tripled since 2000. While AI may alter the nature of banking roles, it is unlikely to eliminate them. Roles are expected to evolve, necessitating new skills that complement AI technologies.
DeepBrain AI continues to innovate, expanding into sectors such as retail and broadcasting. In banking, their AI avatars are transitioning from in-branch kiosks to mobile banking apps, aiming to offer personalised, face-to-face conversational services.
Micron Technology surpassed revenue expectations in the third quarter due to robust demand for its memory chips, particularly in AI applications. However, its fourth-quarter revenue forecast, while meeting expectations, disappointed investors, leading to a 7.2% drop in after-hours trading.
The forecast failed to meet recent optimistic projections fuelled by the AI boom, which tempered market enthusiasm following a significant rise in Micron’s stock price earlier in the month. Micron’s Chief Business Officer emphasised their strategic advantage in AI chip supply, highlighting robust ongoing demand for their products in the years ahead.
Why does it matter?
Micron remains a prominent player in high-bandwidth memory chips essential for advanced AI systems, positioning it strongly in the semiconductor market. The company’s performance typically establishes benchmarks for the broader chip sector, influencing market sentiment toward competitors like Nvidia, whose stock declined following Micron’s earnings report.
Sequoia Capital is leading a $16 million investment in Dust, a French startup founded by former OpenAI researcher Stanislas Polu. Dust specialises in crafting bespoke AI bots for enterprises, leveraging advanced language models like OpenAI’s GPT and Google’s Gemini.
Its solutions cater to various business needs, such as customer support, sales analytics, and software code management. Instead of creating AI models from the ground up, Dust integrates its software with existing AI platforms such as Slack.
Why does it matter?
The funding round was geared towards facilitating Dust’s expansion into the US market and supporting its global ambitions. Despite facing competition from established entities and fellow startups, Dust maintains agility by using its flexibility to thrive in the competitive landscape of AI automation tools.
Small businesses in the United States increasingly turn to AI to improve their operations, although many encounter significant challenges during implementation. According to research conducted by Morning Consult on behalf of Visa, 52% of small businesses have already integrated AI, and an additional 50% plan to do so within the next two years.
Despite these advancements, 90% of businesses face obstacles in adopting AI, citing difficulties such as understanding how to use AI tools (47%), navigating various options (36%), and concerns regarding compatibility and security (31% and 26%, respectively). The study also identifies marketing as the most promising area for AI adoption among businesses not currently using AI, indicating widespread optimism about AI’s potential to increase efficiency and generate revenue.
Denise Press, Visa’s head of small business for North America, emphasised the dual challenge of acknowledging AI’s importance while grappling with the practicalities of implementation. She recommends that businesses begin with low-risk AI applications, such as automating tasks like drafting press releases, to become familiar with the technology’s advantages.
Why does it matter?
There is a collective encouragement for small businesses to cautiously and optimistically embrace AI, utilising partnerships and expertise from vendors to navigate these transformative technologies effectively. However, the challenges highlighted—such as learning curves, security concerns, and navigating options—underscore the need for tailored support and guidance.
European defence technology startup Helsing is currently in negotiations to secure nearly $500 million from investors in Silicon Valley, including Accel and Lightspeed Venture Partners, amounting to $4.5 billion. This valuation marks a significant increase, tripling the company’s value in less than a year, possibly driven by heightened global conflicts which in turn are prompting a surge in private investments within the military supply sector.
Specialising in AI-based software for defence, Helsing was established in 2021 and works with AI to analyse extensive data from sensors and weapons systems, providing real-time battlefield intelligence to assist military decision-making processes. The company’s software is also contributing to the advancement of AI capabilities for drones in Ukraine.
Sources familiar with the negotiations revealed that Accel and Lightspeed will be new investors in Helsing, potentially joined by General Catalyst, a previous investor in the company. If finalised, this deal would position Helsing as one of Europe’s most valued artificial intelligence startups in terms of worth, at par with Paris-based Mistral, an AI startup that recently secured €600 million at a valuation nearing €6 billion. The reluctance of venture investors to engage with defence tech firms has notably shifted, particularly in the US and Europe, driven by escalating tensions between major powers and the ongoing conflict in Ukraine, leading to increased defence expenditure by nations.
NATO’s recent allocation of its €1 billion ‘innovation fund’ towards European tech firms points towards a notable shift, with Europe rapidly closing the investment gap in defence and dual-use technologies as compared to the US. The evolving landscape of modern warfare, as is the case in the Ukrainian conflict, emphasises the transition towards software-defined technologies over traditional hardware, enabling military forces to enhance strategic capabilities.
Why does it matter?
Helsing has forged partnerships with established defence contractors in Europe, such as Germany’s Rheinmetall and Sweden’s Saab, to integrate AI into existing platforms like fighter jets. Collaborating with Airbus, the startup is also developing AI technologies for application in both manned and unmanned systems.
YouTube is negotiating with major record labels to license their songs for AI tools that clone popular artists’ music. The negotiations aim to secure the content needed to legally train AI song generators and launch new tools this year. Google-owned YouTube has offered upfront payments to major labels like Sony, Warner, and Universal to encourage artists to participate, but many remain opposed, fearing it could devalue their work.
Previously, YouTube tested an AI tool called ‘Dream Track,’ which allowed users to create music clips mimicking well-known artists. However, only a few artists participated, including Charli XCX and John Legend. YouTube now hopes to sign up dozens more artists to expand its AI song generator tool, though it won’t carry the Dream Track brand.
Why does it matter?
These negotiations come as AI companies like OpenAI are making licensing agreements with media groups. The proposed music deals would involve one-off payments to labels rather than royalty-based arrangements. YouTube’s AI tools could become part of its Shorts platform, competing with TikTok and other similar platforms. As these discussions continue, major labels are also suing AI startups for allegedly using copyrighted recordings without permission, seeking significant damages.
Bearing AI has unveiled an enhanced Deployment Planner tailored for tramp shipping companies following the success of its liner shipping version. Unlike liners, tramp operators manage one-off contracts between ports, posing challenges for emissions optimisation and profitability.
The latest tool leverages advanced AI to analyse extensive historical and real-time data, offering actionable insights into critical operational issues. It allows chartering, operations, and environmental teams to experiment with vessel deployments, optimising environmental impact and profitability.
Built on robust machine learning models, the Deployment Planner predicts end-of-year performance for every vessel, even those without scheduled contracts. The aforementioned capability provides deep insights into emissions management, enabling tramp operators to achieve superior performance amidst dynamic scheduling.
Kristofer Maanum, Senior Product Leader at Bearing AI, highlighted the tool’s significance for tramp operators, facilitating informed decisions that balance efficiency and sustainability across varied contract scenarios. The Deployment Planner emerges as a crucial asset for managing fleet efficiency, mitigating compliance risks, and optimising operational costs in tramp shipping.
