“As 2023 draws to a close with breathless predictions about the future of artificial intelligence, some may wonder whether judges are about to become obsolete. I am sure we are not—but equally confident that technological changes will continue to transform our work,” stated Roberts.

Roberts stressed the intrinsic value of human judgement, asserting that machines could not fully replace the nuanced decisions made by individuals.

In his report, Roberts pointed out the importance of subtle factors such as a trembling hand, a momentary hesitation, or a fleeting break in eye contact—aspects that machines might struggle to discern accurately. The Chief Justice underlined the public’s inherent trust in human judgement over AI when it comes to evaluating such nuances.

However, Roberts expressed legitimate concerns about the potential drawbacks of AI in the legal domain. He warned against the possibility of AI-generated fabricated answers or “hallucinations,” citing instances where lawyers used AI-powered applications to submit briefs that referenced imaginary cases.

Additionally, Roberts highlighted the risks associated with AI influencing privacy and the potential for bias in decisions in discretionary matters like flight risk and recidivism.

Despite these apprehensions, Roberts acknowledged the positive aspects of incorporating AI in the legal system. He recognised AI’s potential to democratise access to legal advice and tools, particularly benefiting those who cannot afford legal representation.

As the legal world adapts to AI, Chief Justice Roberts’ reflections underscore the importance of striking a balance between harnessing its substantial benefits while managing the potentially devastating risks.

(Image Credit: DOD photo by Navy Petty Officer 1st Class Carlos M. Vazquez II under CC BY 2.0 DEED license)

See also: AI & Big Data Expo: Ethical AI integration and future trends

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 Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post US Chief Justice: AI won’t replace judges but will ‘transform our work’ appeared first on AI News.

During the discussion, Grande provided insightful perspectives on the current challenges, how Edge Impulse is helping address these struggles, and the tremendous promise of on-device AI.

Key hurdles with edge AI adoption

Grande highlighted three primary pain points companies face when attempting to productise edge machine learning models, including difficulties determining optimal data collection strategies, scarce AI expertise, and cross-disciplinary communication barriers between hardware, firmware, and data science teams.

“A lot of the companies building edge devices are not very familiar with machine learning,” says Grande. “Bringing those two worlds together is the third challenge, really, around having teams communicate with each other and being able to share knowledge and work towards the same goals.”

Strategies for lean and efficient models

When asked how to optimise for edge environments, Grande emphasised first minimising required sensor data.

“We are seeing a lot of companies struggle with the dataset. What data is enough, what data should they collect, what data from which sensors should they collect the data from. And that’s a big struggle,” explains Grande.

Selecting efficient neural network architectures helps, as does compression techniques like quantisation to reduce precision without substantially impacting accuracy. Always balance sensor and hardware constraints against functionality, connectivity needs, and software requirements.

Edge Impulse aims to enable engineers to validate and verify models themselves pre-deployment using common ML evaluation metrics, ensuring reliability while accelerating time-to-value. The end-to-end development platform seamlessly integrates with all major cloud and ML platforms.

Transformative potential of on-device intelligence

Grande highlighted innovative products already leveraging edge intelligence to provide personalised health insights without reliance on the cloud, such as sleep tracking with Oura Ring.

“It’s sold over a billion pieces, and it’s something that everybody can experience and everybody can get a sense of really the power of edge AI,” explains Grande.

Other exciting opportunities exist around preventative industrial maintenance via anomaly detection on production lines.

Ultimately, Grande sees massive potential for on-device AI to greatly enhance utility and usability in daily life. Rather than just raw data, edge devices can interpret sensor inputs to provide actionable suggestions and responsive experiences not previously possible—heralding more useful technology and improved quality of life.

Unlocking the potential of AI on edge devices hinges on overcoming current obstacles inhibiting adoption. Grande and other leading experts provided deep insights at this year’s AI & Big Data Expo on how to break down the barriers and unleash the full possibilities of edge AI.

“I’d love to see a world where the devices that we were dealing with were actually more useful to us,” concludes Grande.

Watch our full interview with Alessandro Grande below:

(Photo by Niranjan _ Photographs on Unsplash)

See also: AI & Big Data Expo: Demystifying AI and seeing past the hype

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 Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post AI & Big Data Expo: Unlocking the potential of AI on edge devices appeared first on AI News.

This collaboration will capitalise on Absci’s Integrated Drug Creation platform—seamlessly integrating with AstraZeneca’s expertise in oncology, aiming to expedite the discovery of a potentially game-changing cancer therapy.

Under the agreement, Absci will deploy its pioneering generative AI technology to craft a therapeutic candidate antibody tailored for a specific oncology target. The collaboration encompasses an upfront commitment, substantial R&D funding, milestone payments, and royalties on future product sales.

Sean McClain, Founder & CEO of Absci, said: “AstraZeneca is a leader in developing novel treatments in oncology, and we are excited to collaborate with them to design a therapeutic candidate antibody with the potential to improve the lives of cancer patients.”

Absci’s Integrated Drug Creation platform combines generative AI and scalable wet-lab technologies, generating proprietary data by scrutinising millions of protein-protein interactions. This data fuels Absci’s proprietary AI models, facilitating the design of antibodies that are later validated through wet-lab experiments.

This accelerated approach, completing the entire cycle within approximately six weeks, enhances the probability of successful development outcomes for biologic drug candidates.

Puja Sapra, PhD, SVP of Biologics Engineering & Oncology Targeted Delivery at AstraZeneca, commented: “This collaboration is an exciting opportunity to utilise Absci’s de novo AI antibody creation platform to design a potential new antibody therapy in oncology.”

The announcement follows Absci’s recent publication on the design and validation of de novo antibodies using their state-of-the-art ‘zero-shot’ generative AI model.

The collaboration between Absci and AstraZeneca should further help to demonstrate how AI can be used to revolutionise drug discovery.

(Photo by National Cancer Institute on Unsplash)

See also: AI & Big Data Expo: AI’s impact on decision-making in marketing

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 Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post Absci and AstraZeneca forge AI partnership to discover cancer treatments appeared first on AI News.

The focal point of Smith’s presentation was a strategic experiment conducted by his team to explore the potential of AI in reducing noise and bias inherent in decision-making. Smith began by addressing the common perception of decision-making and the often-overlooked influence of human biases and external factors.

“Let’s recognise that we’re not the best at making decisions, that there are some issues when we make decisions—just as there are some issues when AI makes some decisions,” said Smith.

Smith advocates for combining the strengths of both human and AI decision-makers.

The strategic experiment involved a comprehensive analysis of the human decision-making process, where the team pitted AI against a human team in running a Facebook travel campaign. Smith delved into the intricacies of the human brain’s dual components—the amygdala for intuitive thinking and the prefrontal cortex for reasoning.

Notably, Smith drew attention to the concept of “noise,” a term he differentiated from bias, describing it as the variance in decision-making that introduces inconsistencies. He supported this with examples from various professions, such as judges delivering differing sentences based on external factors.

The challenges within the marketing and advertising space were highlighted, particularly the difficulty of managing a vast number of variables—illustrated by a campaign with a staggering 83 million variations.

“There’s no way that a human can realistically go through 83 million [ad variation] combinations,” said Smith. “AI is better at picking out important signals in large data sets.”

Initially, the results of the strategic experiment showed humans outperforming the AI-optimised campaign, However, the AI campaign quickly pulled away:

While acknowledging AI’s flaws — including bias — Smith advocated for a collaborative approach, envisioning a balance between human intuition and AI assistance. He highlighted the importance of recognising human limitations and leveraging AI to reduce decision-making flaws.

The presentation concluded with key takeaways, encouraging the recognition of human decision-making limitations, leveraging AI to reduce flaws, and finding the right balance between human input and AI assistance.

See also: Ampere Computing: Unlocking a Path to the Sustainable Cloud

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 Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post AI & Big Data Expo: AI’s impact on decision-making in marketing appeared first on AI News.

Leveraging the power of NVIDIA’s AI foundry service on Microsoft Azure, Amdocs aims to meet the escalating demand for data processing and analysis in the telecoms sector.

The telecoms industry processes hundreds of petabytes of data daily. With the anticipation of global data transactions surpassing 180 zettabytes by 2025, telcos are turning to generative AI to enhance efficiency and productivity.

NVIDIA’s AI foundry service – comprising the NVIDIA AI Foundation Models, NeMo framework, and DGX Cloud AI supercomputing – provides an end-to-end solution for creating and optimising custom generative AI models.

Amdocs will utilise the AI foundry service to develop enterprise-grade LLMs tailored for the telco and media industries, facilitating the deployment of generative AI use cases across various business domains.

This collaboration builds on the existing Amdocs-Microsoft partnership, ensuring the adoption of applications in secure, trusted environments, both on-premises and in the cloud.

Enterprises are increasingly focusing on developing custom models to perform industry-specific tasks. Amdocs serves over 350 of the world’s leading telecom and media companies across 90 countries. This partnership with NVIDIA opens avenues for exploring generative AI use cases, with initial applications focusing on customer care and network operations.

In customer care, the collaboration aims to accelerate the resolution of inquiries by leveraging information from across company data. In network operations, the companies are exploring solutions to address configuration, coverage, or performance issues in real-time.

This move by Amdocs positions the company at the forefront of ushering in a new era for the telecoms industry by harnessing the capabilities of custom generative AI models.

(Photo by Danist Soh on Unsplash)

See also: Wolfram Research: Injecting reliability into generative AI

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 Digital Transformation Week.

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

The post Amdocs, NVIDIA and Microsoft Azure build custom LLMs for telcos appeared first on AI News.

Yet something continues to be amiss. Or rather, something amiss continues to be added in.

One of the biggest issues with LLMs are their ability to hallucinate. In other words, it makes things up. Figures vary, but one frequently-cited rate is at 15%-20%. One Google system notched up 27%. This would not be so bad if it did not come across so assertively while doing so. Jon McLoone, Director of Technical Communication and Strategy at Wolfram Research, likens it to the ‘loudmouth know-it-all you meet in the pub.’ “He’ll say anything that will make him seem clever,” McLoone tells AI News. “It doesn’t have to be right.”

The truth is, however, that such hallucinations are an inevitability when dealing with LLMs. As McLoone explains, it is all a question of purpose. “I think one of the things people forget, in this idea of the ‘thinking machine’, is that all of these tools are designed with a purpose in mind, and the machinery executes on that purpose,” says McLoone. “And the purpose was not to know the facts.

“The purpose that drove its creation was to be fluid; to say the kinds of things that you would expect a human to say; to be plausible,” McLoone adds. “Saying the right answer, saying the truth, is a very plausible thing, but it’s not a requirement of plausibility.

“So you get these fun things where you can say ‘explain why zebras like to eat cacti’ – and it’s doing its plausibility job,” says McLoone. “It says the kinds of things that might sound right, but of course it’s all nonsense, because it’s just being asked to sound plausible.”

What is needed, therefore, is a kind of intermediary which is able to inject a little objectivity into proceedings – and this is where Wolfram comes in. In March, the company released a ChatGPT plugin, which aims to ‘make ChatGPT smarter by giving it access to powerful computation, accurate math[s], curated knowledge, real-time data and visualisation’. Alongside being a general extension to ChatGPT, the Wolfram plugin can also synthesise code.

“It teaches the LLM to recognise the kinds of things that Wolfram|Alpha might know – our knowledge engine,” McLoone explains. “Our approach on that is completely different. We don’t scrape the web. We have human curators who give the data meaning and structure, and we lay computation on that to synthesise new knowledge, so you can ask questions of data. We’ve got a few thousand data sets built into that.”

Wolfram has always been on the side of computational technology, with McLoone, who describes himself as a ‘lifelong computation person’, having been with the company for almost 32 of its 36-year history. When it comes to AI, Wolfram therefore sits on the symbolic side of the fence, which suits logical reasoning use cases, rather than statistical AI, which suits pattern recognition and object classification.

The two systems appear directly opposed, but with more commonality than you may think. “Where I see it, [approaches to AI] all share something in common, which is all about using the machinery of computation to automate knowledge,” says McLoone. “What’s changed over that time is the concept of at what level you’re automating knowledge.

“The good old fashioned AI world of computation is humans coming up with the rules of behaviour, and then the machine is automating the execution of those rules,” adds McLoone. “So in the same way that the stick extends the caveman’s reach, the computer extends the brain’s ability to do these things, but we’re still solving the problem beforehand.

“With generative AI, it’s no longer saying ‘let’s focus on a problem and discover the rules of the problem.’ We’re now starting to say, ‘let’s just discover the rules for the world’, and then you’ve got a model that you can try and apply to different problems rather than specific ones.

“So as the automation has gone higher up the intellectual spectrum, the things have become more general, but in the end, it’s all just executing rules,” says McLoone.

What’s more, as the differing approaches to AI share a common goal, so do the companies on either side. As OpenAI was building out its plugin architecture, Wolfram was asked to be one of the first providers. “As the LLM revolution started, we started doing a bunch of analysis on what they were really capable of,” explains McLoone. “And then, as we came to this understanding of what the strengths or weaknesses were, it was about that point that OpenAI were starting to work on their plugin architecture.

“They approached us early on, because they had a little bit longer to think about this than us, since they’d seen it coming for two years,” McLoone adds. “They understood exactly this issue themselves already.”

McLoone will be demonstrating the plugin with examples at the upcoming AI & Big Data Expo Global event in London on November 30-December 1, where he is speaking. Yet he is keen to stress that there are more varied use cases out there which can benefit from the combination of ChatGPT’s mastery of unstructured language and Wolfram’s mastery of computational mathematics.

One such example is performing data science on unstructured GP medical records. This ranges from correcting peculiar transcriptions on the LLM side – replacing ‘peacemaker’ with ‘pacemaker’ as one example – to using old-fashioned computation and looking for correlations within the data. “We’re focused on chat, because it’s the most amazing thing at the moment that we can talk to a computer. But the LLM is not just about chat,” says McLoone. “They’re really great with unstructured data.”

How does McLoone see LLMs developing in the coming years? There will be various incremental improvements, and training best practices will see better results, not to mention potentially greater speed with hardware acceleration. “Where the big money goes, the architectures follow,” McLoone notes. A sea-change on the scale of the last 12 months, however, can likely be ruled out. Partly because of crippling compute costs, but also because we may have peaked in terms of training sets. If copyright rulings go against LLM providers, then training sets will shrink going forward.

The reliability problem for LLMs, however, will be forefront in McLoone’s presentation. “Things that are computational are where it’s absolutely at its weakest, it can’t really follow rules beyond really basic things,” he explains. “For anything where you’re synthesising new knowledge, or computing with data-oriented things as opposed to story-oriented things, computation really is the way still to do that.”

Yet while responses may vary – one has to account for ChatGPT’s degree of randomness after all – the combination seems to be working, so long as you give the LLM strong instructions. “I don’t know if I’ve ever seen [an LLM] actually override a fact I’ve given it,” says McLoone. “When you’re putting it in charge of the plugin, it often thinks ‘I don’t think I’ll bother calling Wolfram for this, I know the answer’, and it will make something up.

“So if it’s in charge you have to give really strong prompt engineering,” he adds. “Say ‘always use the tool if it’s anything to do with this, don’t try and go it alone’. But when it’s the other way around – when computation generates the knowledge and injects it into the LLM – I’ve never seen it ignore the facts.

“It’s just like the loudmouth guy at the pub – if you whisper the facts in his ear, he’ll happily take credit for them.”

Wolfram will be at AI & Big Data Expo Global. 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 Cyber Security & Cloud Expo and Digital Transformation Week.

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

The post Wolfram Research: Injecting reliability into generative AI appeared first on AI News.

The result of the collaboration is the Siemens Industrial Copilot, a powerful AI assistant designed to enhance collaboration between humans and machines in the manufacturing sector. The tool enables rapid generation, optimisation, and debugging of complex automation code, significantly reducing simulation times from weeks to minutes.

At the core of this collaboration is the integration of Siemens Industrial Copilot with Microsoft Teams, connecting design engineers, frontline workers, and various teams across business functions. This integration simplifies virtual collaboration, empowering professionals with new AI-powered tools and simplifying tasks that previously required extensive time and effort.

Empowering industries with Generative AI

Satya Nadella, Chairman and CEO of Microsoft, expressed the immense potential of this collaboration, stating: “With this next generation of AI, we have a unique opportunity to accelerate innovation across the entire industrial sector.”

Siemens CEO Roland Busch echoed this sentiment; emphasising the revolutionary impact on design, development, manufacturing, and operations.

The companies envision AI copilots becoming integral in industries such as manufacturing, infrastructure, transportation, and healthcare.

Schaeffler AG – a leading automotive supplier – is already embracing generative AI, enabling engineers to generate reliable code for industrial automation systems. Siemens Industrial Copilot will work to reduce downtimes.

Facilitating virtual collaboration

To facilitate virtual collaboration, Siemens and Microsoft are launching Teamcenter for Microsoft Teams; an application that utilises generative AI to connect functions across the product design and manufacturing lifecycle.

This integration will allow millions of workers who previously lacked access to Product Lifecycle Management (PLM) tools to contribute seamlessly to the design and manufacturing processes.

The collaboration between Microsoft and Siemens looks set to be an excellent case study of how AI empowers industries and professionals, revolutionising traditional workflows and fostering global innovation.

(Photo by Sezer Arslan on Unsplash)

See also: Bob Briski, DEPT®:  A dive into the future of AI-powered experiences

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 IoT Tech Expo and Digital Transformation Week.

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

The post Microsoft and Siemens revolutionise industry with AI-powered Copilot appeared first on AI News.

At the core of DEPT®’s approach is the strategic utilisation of large language models. Briski articulated the delicate balance between the ‘pioneering’ and ’boutique’ ethos encapsulated in their tagline, “pioneering work on a global scale with a boutique culture.”

While ‘pioneering’ and ’boutique’ evokes innovation and personalised attention, ‘global scale’ signifies the broad outreach. DEPT® harnesses large language models to disseminate highly targeted, personalised messages to expansive audiences. These models, Briski pointed out, enable DEPT® to comprehend individuals at a massive scale and foster meaningful and individualised interactions.

“The way that we have been using a lot of these large language models is really to deliver really small and targeted messages to a large audience,” says Briski.

However, the integration of AI into various domains – such as retail, sports, education, and healthcare – poses both opportunities and challenges. DEPT® navigates this complexity by leveraging generative AI and large language models trained on diverse datasets, including vast repositories like Wikipedia and the Library of Congress.

Briski emphasised the importance of marrying pre-trained data with DEPT®’s domain expertise to ensure precise contextual responses. This approach guarantees that clients receive accurate and relevant information tailored to their specific sectors.

“The pre-training of these models allows them to really expound upon a bunch of different domains,” explains Briski. “We can be pretty sure that the answer is correct and that we want to either send it back to the client or the consumer or some other system that is sitting in front of the consumer.”

One of DEPT®’s standout achievements lies in its foray into the web3 space and the metaverse. Briski shared the company’s collaboration with Roblox, a platform synonymous with interactive user experiences. DEPT®’s collaboration with Roblox revolves around empowering users to create and enjoy user-generated content at an unprecedented scale. 

DEPT®’s internal project, Prepare to Pioneer, epitomises its commitment to innovation by nurturing embryonic ideas within its ‘Greenhouse’. DEPT® hones concepts to withstand the rigours of the external world, ensuring only the most robust ideas reach their clients.

“We have this internal project called The Greenhouse where we take these seeds of ideas and try to grow them into something that’s tough enough to handle the external world,” says Briski. “The ones that do survive are much more ready to use with our clients.”

While the allure of AI-driven solutions is undeniable, Briski underscored the need for caution. He warns that AI is not inherently transparent and trustworthy and emphasises the imperative of constructing robust foundations for quality assurance.

DEPT® employs automated testing to ensure responses align with expectations. Briski also stressed the importance of setting stringent parameters to guide AI conversations, ensuring alignment with the company’s ethos and desired consumer interactions.

“It’s important to really keep focused on the exact conversation you want to have with your consumer or your customer and put really strict guardrails around how you would like the model to answer those questions,” explains Briski.

In December, DEPT® is sponsoring AI & Big Data Expo Global and will be in attendance to share its unique insights. Briski is a speaker at the event and will be providing a deep dive into business intelligence (BI), illuminating strategies to enhance responsiveness through large language models.

“I’ll be diving into how we can transform BI to be much more responsive to the business, especially with the help of large language models,” says Briski.

As DEPT® continues to redefine digital paradigms, we look forward to observing how the company’s innovations deliver a new era in AI-powered experiences.

DEPT® is a key sponsor of this year’s AI & Big Data Expo Global on 30 Nov – 1 Dec 2023. Swing by DEPT®’s booth to hear more about AI and LLMs from the company’s experts and watch Briski’s day one presentation.

The post Bob Briski, DEPT®:  A dive into the future of AI-powered experiences appeared first on AI News.

This momentous agreement – signed in a ceremony at SK Seorin Building, Seoul – marks the culmination of discussions initiated by the Global Telco AI Alliance, a consortium launched in July 2023 by SK Telecom, Deutsche Telekom, E&, and Singtel.

This innovative partnership aims to create a telco-specific LLM that empowers global telcos to effortlessly and rapidly construct generative AI models. With a focus on multilingual capabilities (including German, English, and Korean), this LLM is designed to enhance customer services—particularly in areas like AI-powered contact centres.

Claudia Nemat, Member of the Board of Management for Technology and Innovation at Deutsche Telekom, said:

“AI shows impressive potential to significantly enhance human problem-solving capabilities.

To maximise its use, especially in customer service, we need to adapt existing large language models and train them with our unique data. This will elevate our generative AI tools.”

The collaboration also involves key AI industry players, such as Anthropic (Claude 2) and Meta (Llama2), enabling the co-development of a sophisticated LLM.

Anticipated to debut in the first quarter of 2024, the new telco-focused LLM will offer a deeper understanding of telecommunication service-related areas and customer intentions that surpass the capabilities of general LLMs.

One of the primary objectives of this collaboration is to assist telcos worldwide in developing flexible generative AI services, including AI agents. By streamlining the process of building AI-driven solutions like contact centres, telcos can save time and costs and open new avenues for business growth and innovation.

Ryu Young-sang, CEO of SK Telecom, commented:

“Through our partnership with Deutsche Telekom, we have secured a strong opportunity and momentum to gain global AI leadership and drive new growth.

By combining the strengths and capabilities of the two companies in AI technology, platform, and infrastructure, we expect to empower enterprises in many different industries to deliver new and higher value to their customers.”

This collaboration signifies a proactive response to the escalating demand for AI solutions within the telco industry, promising a paradigm shift in the traditional telecommunications landscape. The announcement follows SK Telecom’s $100 million investment in Anthropic in August.

See also: UMG files landmark lawsuit against AI developer Anthropic

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 Digital Transformation Week.

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

The post Deutsche Telekom and SK Telecom partner on telco-focused LLM appeared first on AI News.

According to a study conducted by BSI, 54 percent of UK respondents expressed excitement about AI’s potential to revolutionise medical diagnoses and 43 percent welcomed AI’s role in reducing food waste. However, there is a prevailing lack of trust.

This scepticism could hinder the integration of AI technologies in the NHS, which is currently grappling with challenges like the COVID-19 backlog and an ageing population. Almost half of Britons (49%) support the use of AI to alleviate pressure on the healthcare system and reduce waiting times. However, only 20 percent have more confidence in AI than humans in detecting food contamination issues.

The study also highlighted a pressing need for education, as 65 percent of respondents felt patients should be informed about the use of AI tools in diagnosis or treatment. 37 percent of respondents expect to use AI regularly in medical settings by 2030.

Craig Civil, Director of Data Science and AI at BSI, said:

“The magnitude of ways AI can shape the UK’s future means we are seeing some degree of hesitation of the unknown. This can be addressed by developing greater understanding and recognition that human involvement will always be needed if we are to make the best use of this technology, and by ensuring we have frameworks that are in place to govern its use and build trust.

Now is the moment for the UK to collaborate to balance the great power of this tool with the realities of actually using it in a credible, authentic, well-executed, and well-governed way.

Closing the confidence gap and building the appropriate checks and balances can enable us to make not just good but great use of AI in every area of life and society.”

60 percent believed consumers needed protections regarding AI technologies. The study also revealed that 61 percent of Britons are calling for international guidelines to ensure the safe use of AI. This demand reflects a global sentiment, with 50 percent of respondents highlighting the need for ethical safeguards on patient data use.

Harold Pradal, Chief Commercial Officer at BSI, commented:

“AI is a transformational technology. For it to be a powerful force for good, trust needs to be the critical factor. There is a clear opportunity to harness AI to drive societal impact, change lives, and accelerate progress towards a better future and a sustainable world.

Closing the AI confidence gap is the first necessary step, it has to be delivered through education to help realise AI’s benefits and shape Society 5.0 in a positive way.”

The study’s findings are a call to action for the UK, urging collaboration and the establishment of frameworks to govern AI’s use.

The UK Government, recognising the importance of safe AI implementation, is set to host a global AI Safety Summit at the historic Bletchley Park on 1-2 November 2023. BSI is an official partner for the much-anticipated event.

(Photo by Suad Kamardeen on Unsplash)

See also: UK reveals AI Safety Summit opening day agenda

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 Digital Transformation Week.

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

The post BSI: Closing ‘AI confidence gap’ key to unlocking benefits appeared first on AI News.