“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.

The AI & Big Data Expo stood out for its great mixture of speakers, not only targeting people working within data, but making the topics feel completely accessible to somebody like me, who isn’t a data scientist by background. As the CEO of an infrastructure charity, I know our beneficiaries don’t necessarily work closely with data, or hold it at the forefront of their minds, so it was very interesting to see how AI and big data impacts a diverse range of different sectors, and how they employ and deploy different strategies to work with its new challenges. 

I especially enjoyed the talks focused on ethics and governance, which resonate with our beneficiaries and the challenges that they face. What’s interesting is that there seems to be a real drive to ensure that ethics is baked into AI strategies moving forward. It’s very heartening that ethics is being talked about at this early stage – that it isn’t being ignored as it may have been when past technologies developed as quickly.

One talk tackled governance, and how governments are still playing catch up. There seems to be an overarching feeling that AI has to be regulated, but whether the regulation that people want is possible is the next big question. Can it be regulated, and how? Is the EU act going to work well, and is the legislation in the US going to be effective, or will it be watered down? What systems do you use, and therefore what do you endorse? Is this the right thing to do, is this the right way to deploy this sort of power, and what would the fallout be if we did?

As a charity working to serve other charities, safeguarding is a huge area of concern for us, so it’s good to know that the mainstream is also thinking about transparency. AI providers and tools have not yet done enough to flag the potential risks for third sector organisations that, for example, routinely handle sensitive data about vulnerable individuals. This could result in a number of issues for charities using AI for the first time, not least data breaches. We have already seen the misuse of AI to replace services that are still necessarily led by humans – in one example, a chatbot that replaced a manned helpline gave people with eating disorders dangerous dieting advice. Tech leaders and governments must take the lead by demonstrating responsible approaches and creating frameworks around safeguarding and risks. There will always be bad actors in this space, but there seems to be a ‘coalition of the willing’ that wants to ensure AI is continually safe, not just for those with enough resources to create their own safeguarding.

As these debates continue and the technology develops apace, it’s so important that there are spaces in which the third sector can be heard alongside private or statutory organisations. At the AI & Big Data Expo, we were able to showcase our work as a representative voice, and garner enthusiasm in the ‘data for good’ movement. We made some fantastic connections with others, as a result of realising how aligned our overarching missions are. Testament to that was the enthusiasm of our audience, asking our wonderful volunteers Adam and Alvaro tons of questions, and chatting to us in person afterwards. We are thrilled to have been part of these conversations.

Finally, we want to say a big thank you to the organisers for the opportunity to get stuck into a cross-sector event like this. We’re looking forward to the next one!

To find out more about DataKind UK and how you can support our vision of a strong, thriving third sector that embraces data science to become more impactful, visit our website.

The post Ethics, governance and data for good at the AI & Big Data Expo appeared first on AI News.

Zheng first explained how over a decade working in digital marketing and e-commerce sparked her interest more recently in data analytics and artificial intelligence as machine learning has become hugely popular.

At Canon, Zheng’s team focuses on ethically integrating AI into business by first mapping current and potential AI applications across areas like marketing and e-commerce. They then analyse and assess risks to ensure compliance with regulations.

Canon is actively mapping out AI applications and assessing risks, as Grace explained, “to align with regulations such as the EU legislations.”

As founder of Kosh Duo, Zheng also provides coaching to help businesses scale up through the use of AI marketing and data-driven approaches. She coaches professionals on achieving greater recognition and rewards by leveraging AI tools as well.

A key challenge she encounters is misunderstandings around what AI truly means – many conflate it solely with chatbots like ChatGPT rather than appreciating the full breadth of machine learning, neural networks, natural language processing, and more that enable today’s AI.

“There’s a lot of misconceptions, definitely. One of the biggest fears, as I touched on, is the very generic understanding that GPT equals AI,” says Zheng. “[Kosh Duo] provides coaching services to businesses to scale to the next level using AI marketing and data-driven approaches.”

When asked about trends to watch, Zheng emphasised the need for continual learning given how rapidly the field evolves. She expects that 2024 will be an “awakening year” where businesses truly grasp AI’s potential and individuals appreciate the need to evaluate their current skillsets.

The interview highlighted the transformative but often misunderstood power of AI in business and the importance of developing specialised skills to properly harness it. Zheng stressed that with the right ethical foundations and coaching, AI and machine learning can become positive forces to drive growth rather than something to fear.

Watch our full interview with Grace Zheng below:

(Photo by Benjamin Davies 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. 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: Ethical AI integration and future trends appeared first on AI News.

Titled “A Framework for U.S. AI Governance: Creating a Safe and Thriving AI Sector,” the main policy paper proposes leveraging current US government entities to regulate AI tools within their respective domains.

Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, emphasises the pragmatic approach of initially focusing on areas where human activity is already regulated and gradually expanding to address emerging risks associated with AI.

The framework underscores the importance of defining the purpose of AI tools, aligning regulations with specific applications and holding AI providers accountable for the intended use of their technologies.

Asu Ozdaglar, deputy dean of academics in the MIT Schwarzman College of Computing, believes having AI providers articulate the purpose and intent of their tools is crucial for determining liability in case of misuse.

Addressing the complexity of AI systems existing at multiple levels, the brief acknowledges the challenges of governing both general and specific AI tools. The proposal advocates for a self-regulatory organisation (SRO) structure to supplement existing agencies, offering responsive and flexible oversight tailored to the rapidly evolving AI landscape.

Furthermore, the policy papers call for advancements in auditing AI tools—exploring various pathways such as government-initiated, user-driven, or legal liability proceedings.

The consideration of a government-approved SRO – akin to the Financial Industry Regulatory Authority (FINRA) – is proposed to enhance domain-specific knowledge and facilitate practical engagement with the dynamic AI industry.

MIT’s involvement in AI governance stems from its recognised expertise in AI research, positioning the institution as a key contributor to addressing the challenges posed by evolving AI technologies. The release of these whitepapers signals MIT’s commitment to promoting responsible AI development and usage.

You can find MIT’s series of AI policy briefs here.

(Photo by Aaron Burden 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 MIT publishes white papers to guide AI governance appeared first on AI News.

The guidelines were developed by the UK’s National Cyber Security Centre (NCSC) and the US’ Cybersecurity and Infrastructure Security Agency (CISA). They have already secured endorsements from 17 other countries, including all G7 members.

The guidelines provide recommendations for developers and organisations using AI to incorporate cybersecurity at every stage. This “secure by design” approach advises baking in security from the initial design phase through development, deployment, and ongoing operations.  

Specific guidelines cover four key areas: secure design, secure development, secure deployment, and secure operation and maintenance. They suggest security behaviours and best practices for each phase.

The launch event in London convened over 100 industry, government, and international partners. Speakers included reps from Microsoft, the Alan Turing Institute, and cyber agencies from the US, Canada, Germany, and the UK.  

NCSC CEO Lindy Cameron stressed the need for proactive security amidst AI’s rapid pace of development. She said, “security is not a postscript to development but a core requirement throughout.”

The guidelines build on existing UK leadership in AI safety. Last month, the UK hosted the first international summit on AI safety at Bletchley Park.

US Secretary of Homeland Security Alejandro Mayorkas said: “We are at an inflection point in the development of artificial intelligence, which may well be the most consequential technology of our time. Cybersecurity is key to building AI systems that are safe, secure, and trustworthy.

“The guidelines jointly issued today by CISA, NCSC, and our other international partners, provide a common-sense path to designing, developing, deploying, and operating AI with cybersecurity at its core.”

The 18 endorsing countries span Europe, Asia-Pacific, Africa, and the Americas. Here is the full list of international signatories:

• Australia – Australian Signals Directorate’s Australian Cyber Security Centre (ACSC)
• Canada – Canadian Centre for Cyber Security (CCCS) 
• Chile – Chile’s Government CSIRT
• Czechia – Czechia’s National Cyber and Information Security Agency (NUKIB)
• Estonia – Information System Authority of Estonia (RIA) and National Cyber Security Centre of Estonia (NCSC-EE)
• France – French Cybersecurity Agency (ANSSI)
• Germany – Germany’s Federal Office for Information Security (BSI)
• Israel – Israeli National Cyber Directorate (INCD)
• Italy – Italian National Cybersecurity Agency (ACN)
• Japan – Japan’s National Center of Incident Readiness and Strategy for Cybersecurity (NISC; Japan’s Secretariat of Science, Technology and Innovation Policy, Cabinet Office
• New Zealand – New Zealand National Cyber Security Centre
• Nigeria – Nigeria’s National Information Technology Development Agency (NITDA)
• Norway – Norwegian National Cyber Security Centre (NCSC-NO)
• Poland – Poland’s NASK National Research Institute (NASK)
• Republic of Korea – Republic of Korea National Intelligence Service (NIS)
• Singapore – Cyber Security Agency of Singapore (CSA)
• United Kingdom – National Cyber Security Centre (NCSC)
• United States of America – Cybersecurity and Infrastructure Agency (CISA); National Security Agency (NSA; Federal Bureau of Investigations (FBI)

UK Science and Technology Secretary Michelle Donelan positioned the new guidelines as cementing the UK’s role as “an international standard bearer on the safe use of AI.”

“Just weeks after we brought world leaders together at Bletchley Park to reach the first international agreement on safe and responsible AI, we are once again uniting nations and companies in this truly global effort,” adds Donelan.

The guidelines are now published on the NCSC website alongside explanatory blogs. Developer uptake will be key to translating the secure by design vision into real-world improvements in AI security.

(Photo by Jan Antonin Kolar on Unsplash)

See also: Paul O’Sullivan, Salesforce: Transforming work in the GenAI era

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 Global AI security guidelines endorsed by 18 countries appeared first on AI News.

Paul O’Sullivan, Senior Vice President of Solution Engineering (UKI) at Salesforce, sheds light on the complexities of this transformative landscape, urging businesses to tread cautiously while embracing the potential of artificial intelligence.

Unprecedented opportunities

Generative AI has stormed the scene with remarkable speed. ChatGPT, for example, amassed 100 million users in a mere two months.

“If you put that into context, it took 10 years to reach 100 million users on Netflix,” says O’Sullivan.

This rapid adoption signals a seismic shift, promising substantial economic growth. O’Sullivan estimates that generative AI has the potential to contribute a staggering £3.5 trillion ($4.4 trillion) to the global economy.

“Again, if you put that into context, that’s about as much tax as the entire US takes in,” adds O’Sullivan.

One of its key advantages lies in driving automation, with the prospect of automating up to 40 percent of the average workday—leading to significant productivity gains for businesses.

The AI trust gap

However, amid the excitement, there looms a significant challenge: the AI trust gap. 

O’Sullivan acknowledges that despite being a top priority for C-suite executives, over half of customers remain sceptical about the safety and security of AI applications.

Addressing this gap will require a multi-faceted approach including grappling with issues related to data quality and ensuring that AI systems are built on reliable, unbiased, and representative datasets. 

“Companies have struggled with data quality and data hygiene. So that’s a key area of focus,” explains O’Sullivan.

Safeguarding data privacy is also paramount, with stringent measures needed to prevent the misuse of sensitive customer information.

“Both customers and businesses are worried about data privacy—we can’t let large language models store and learn from sensitive customer data,” says O’Sullivan. “Over half of customers and their customers don’t believe AI is safe and secure today.”

Ethical considerations

AI also prompts ethical considerations. Concerns about hallucinations – where AI systems generate inaccurate or misleading information – must be addressed meticulously.

Businesses must confront biases and toxicities embedded in AI algorithms, ensuring fairness and inclusivity. Striking a balance between innovation and ethical responsibility is pivotal to gaining customer trust.

“A trustworthy AI should consistently meet expectations, adhere to commitments, and create a sense of dependability within the organisation,” explains O’Sullivan. “It’s crucial to address the limitations and the potential risks. We’ve got to be open here and lead with integrity.”

As businesses embrace AI, upskilling the workforce will also be imperative.

O’Sullivan advocates for a proactive approach, encouraging employees to master the art of prompt writing. Crafting effective prompts is vital, enabling faster and more accurate interactions with AI systems and enhancing productivity across various tasks.

Moreover, understanding AI lingo is essential to foster open conversations and enable informed decision-making within organisations.

A collaborative future

Crucially, O’Sullivan emphasises a collaborative future where AI serves as a co-pilot rather than a replacement for human expertise.

“AI, for now, lacks cognitive capability like empathy, reasoning, emotional intelligence, and ethics—and these are absolutely critical business skills that humans need to bring to the table,” says O’Sullivan.

This collaboration fosters a sense of trust, as humans act as a check and balance to ensure the responsible use of AI technology.

By addressing the AI trust gap, upskilling the workforce, and fostering a harmonious collaboration between humans and AI, businesses can harness the full potential of generative AI while building trust and confidence among customers.

You can watch our full interview with Paul O’Sullivan below:

Paul O’Sullivan and the Salesforce team will be sharing their invaluable insights at this year’s AI & Big Data Expo Global. O’Sullivan will feature on a day one panel titled ‘Converging Technologies – We Work Better Together’.

The post Paul O’Sullivan, Salesforce: Transforming work in the GenAI era appeared first on AI News.

From addressing the spectrum of use cases to navigating digital transformation, Shakir shed light on the challenges, ethical considerations, and the promising future of this groundbreaking technology.

Wide spectrum of use cases

Shakir highlighted the wide array of GenAI applications, ranging from productivity enhancements and research support to high-stakes areas such as strategic data mining and knowledge bots. She emphasised the transformational power of AI in understanding customer data, moving beyond simple sentiment analysis to providing actionable insights, thus elevating customer engagement strategies.

“GenAI now can take your customer insights to another level. It doesn’t just tell you whether something’s a positive or negative sentiment like old AI would do, it now says it’s positive or negative. It’s negative because X, Y, Z, and here’s the root cause for X, Y, Z,” explains Shakir.

Powering digital transformation

Gate One adopts an adaptive strategy approach, abandoning traditional five-year strategies for more agile, adaptable frameworks.

“We have a framework – our 5P model – where it’s: identify your people, identify the problem statement that you’re trying to solve for, appoint some partnerships, think about what’s the right capability mix that you have, think about the pathway through which you’re going to deliver, be use case or risk-led, and then proof of concept,” says Shakir.

By solving specific challenges and aligning strategies with business objectives, Gate One aims to drive meaningful digital transformation for its clients.

Assessing client readiness

Shakir discussed Gate One’s diagnostic tools, which blend technology maturity and operating model innovation questions to assess a client’s readiness to adopt GenAI successfully.

“We have a proprietary tool that we’ve built, a diagnostic tool where we look at blending tech maturity capability type questions with operating model innovation questions,” explains Shakir.

By categorising clients as “vanguard” or “safe” players, Gate One tailors their approach to meet individual readiness levels—ensuring a seamless integration of GenAI into the client’s operations.

Key challenges and ethical considerations

Shakir acknowledged the challenges associated with GenAI, especially concerning the quality of model outputs. She stressed the importance of addressing biases, amplifications, and ethical concerns, calling for a more meaningful and sustainable implementation of AI.

“Poor quality data or poorly trained models can create biases, racism, sexism… those are the things that worry me about the technology,” says Shakir.

Gate One is actively working on refining models and data inputs to mitigate such problems.

The future of GenAI

Looking ahead, Shakir predicted a demand for more ethical AI practices from consumers and increased pressure on developers to create representative and unbiased models.

Shakir also envisioned a shift in work dynamics where AI liberates humans from mundane tasks to allow them to focus on solving significant global challenges, particularly in the realm of sustainability.

Later this month, Gate One will be attending and sponsoring this year’s AI & Big Data Expo Global. During the event, Gate One aims to share its ethos of meaningful AI and emphasise ethical and sustainable approaches.

Gate One will also be sharing with attendees GenAI’s impact on marketing and experience design, offering valuable insights into the changing landscape of customer interactions and brand experiences.

As businesses navigate the evolving landscape of GenAI, Gate One stands at the forefront, advocating for responsible, ethical, and sustainable practices and ensuring a brighter, more impactful future for businesses and society.

Umbar Shakir and the Gate One team will be sharing their invaluable insights at this year’s AI & Big Data Expo Global. Find out more about Umbar Shakir’s day one keynote presentation here.

The post Umbar Shakir, Gate One: Unlocking the power of generative AI ethically 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.

Viewed as a crucial step in the broader governmental effort to ensure the secure development and implementation of AI capabilities, the move aligns with President Biden’s recent Executive Order.

“DHS has a broad leadership role in advancing the responsible use of AI and this cybersecurity roadmap is one important element of our work,” said Secretary of Homeland Security Alejandro N. Mayorkas.

“The Biden-Harris Administration is committed to building a secure and resilient digital ecosystem that promotes innovation and technological progress.” 

Following the Executive Order, DHS is mandated to globally promote AI safety standards, safeguard US networks and critical infrastructure, and address risks associated with AI—including potential use “to create weapons of mass destruction”.

“In last month’s Executive Order, the President called on DHS to promote the adoption of AI safety standards globally and help ensure the safe, secure, and responsible use and development of AI,” added Mayorkas.

“CISA’s roadmap lays out the steps that the agency will take as part of our Department’s broader efforts to both leverage AI and mitigate its risks to our critical infrastructure and cyber defenses.”

CISA’s roadmap outlines five strategic lines of effort, providing a blueprint for concrete initiatives and a responsible approach to integrating AI into cybersecurity.

CISA Director Jen Easterly highlighted the dual nature of AI, acknowledging its promise in enhancing cybersecurity while acknowledging the immense risks it poses.

“Artificial Intelligence holds immense promise in enhancing our nation’s cybersecurity, but as the most powerful technology of our lifetimes, it also presents enormous risks,” commented Easterly.

“Our Roadmap for AI – focused at the nexus of AI, cyber defense, and critical infrastructure – sets forth an agency-wide plan to promote the beneficial uses of AI to enhance cybersecurity capabilities; ensure AI systems are protected from cyber-based threats; and deter the malicious use of AI capabilities to threaten the critical infrastructure Americans rely on every day.”

The outlined lines of effort are as follows:

• Responsibly use AI to support our mission: CISA commits to using AI-enabled tools ethically and responsibly to strengthen cyber defense and support its critical infrastructure mission. The adoption of AI will align with constitutional principles and all relevant laws and policies.
• Assess and Assure AI systems: CISA will assess and assist in secure AI-based software adoption across various stakeholders, establishing assurance through best practices and guidance for secure and resilient AI development.
• Protect critical infrastructure from malicious use of AI: CISA will evaluate and recommend mitigation of AI threats to critical infrastructure, collaborating with government agencies and industry partners. The establishment of JCDC.AI aims to facilitate focused collaboration on AI-related threats.
• Collaborate and communicate on key AI efforts: CISA commits to contributing to interagency efforts, supporting policy approaches for the US government’s national strategy on cybersecurity and AI, and coordinating with international partners to advance global AI security practices.
• Expand AI expertise in our workforce: CISA will educate its workforce on AI systems and techniques, actively recruiting individuals with AI expertise and ensuring a comprehensive understanding of the legal, ethical, and policy aspects of AI-based software systems.

“This is a step in the right direction. It shows the government is taking the potential threats and benefits of AI seriously. The roadmap outlines a comprehensive strategy for leveraging AI to enhance cybersecurity, protect critical infrastructure, and foster collaboration. It also emphasises the importance of security in AI system design and development,” explains Joseph Thacker, AI and security researcher at AppOmni.

“The roadmap is pretty comprehensive. Nothing stands out as missing initially, although the devil is in the details when it comes to security, and even more so when it comes to a completely new technology. CISA’s ability to keep up may depend on their ability to get talent or train internal folks. Both of those are difficult to accomplish at scale.”

CISA invites stakeholders, partners, and the public to explore the Roadmap for Artificial Intelligence and gain insights into the strategic vision for AI technology and cybersecurity here.

See also: Google expands partnership with Anthropic to enhance AI safety

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 DHS AI roadmap prioritises cybersecurity and national safety appeared first on AI News.

The collaboration between Google and Anthropic dates back to the founding of Anthropic in 2021. The two companies have closely collaborated, with Anthropic building one of the largest Google Kubernetes Engine (GKE) clusters in the industry.

“Our longstanding partnership with Google is founded on a shared commitment to develop AI responsibly and deploy it in a way that benefits society,” said Dario Amodei, co-founder and CEO of Anthropic.

“We look forward to our continued collaboration as we work to make steerable, reliable and interpretable AI systems available to more businesses around the world.”

Anthropic utilises Google’s AlloyDB, a fully managed PostgreSQL-compatible database, for handling transactional data with high performance and reliability. Additionally, Google’s BigQuery data warehouse is employed to analyse vast datasets, extracting valuable insights for Anthropic’s operations.

As part of the expanded partnership, Anthropic will leverage Google’s latest generation Cloud TPU v5e chips for AI inference. Anthropic will use the chips to efficiently scale its powerful Claude large language model, which ranks only behind GPT-4 in many benchmarks.

The announcement comes on the heels of both companies participating in the inaugural AI Safety Summit (AISS) at Bletchley Park, hosted by the UK government. The summit brought together government officials, technology leaders, and experts to address concerns around frontier AI.

Google and Anthropic are also engaged in collaborative efforts with the Frontier Model Forum and MLCommons, contributing to the development of robust measures for AI safety.

To enhance security for organisations deploying Anthropic’s models on Google Cloud, Anthropic is now utilising Google Cloud’s security services. This includes Chronicle Security Operations, Secure Enterprise Browsing, and Security Command Center, providing visibility, threat detection, and access control.

“Anthropic and Google Cloud share the same values when it comes to developing AI–it needs to be done in both a bold and responsible way,” commented Thomas Kurian, CEO of Google Cloud. 

“This expanded partnership with Anthropic – built on years of working together – will bring AI to more people safely and securely, and provides another example of how the most innovative and fastest growing AI startups are building on Google Cloud.”

Google and Anthropic’s expanded partnership promises to be a critical step in advancing AI safety standards and fostering responsible development.

(Photo by charlesdeluvio on Unsplash)

See also: Amazon is building a LLM to rival OpenAI and Google

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