Amazon Web Services (AWS) and NVIDIA have announced a significant expansion of their strategic collaboration at AWS re:Invent. The collaboration aims to provide customers with state-of-the-art infrastructure, software, and services to fuel generative AI innovations.
The collaboration brings together the strengths of both companies, integrating NVIDIA’s latest multi-node systems with next-generation GPUs, CPUs, and AI software, along with AWS technologies such as Nitro System advanced virtualisation, Elastic Fabric Adapter (EFA) interconnect, and UltraCluster scalability.
Key highlights of the expanded collaboration include:
Introduction of NVIDIA GH200 Grace Hopper Superchips on AWS:
AWS becomes the first cloud provider to offer NVIDIA GH200 Grace Hopper Superchips with new multi-node NVLink technology.
The NVIDIA GH200 NVL32 multi-node platform enables joint customers to scale to thousands of GH200 Superchips, providing supercomputer-class performance.
Hosting NVIDIA DGX Cloud on AWS:
Collaboration to host NVIDIA DGX Cloud, an AI-training-as-a-service, on AWS, featuring GH200 NVL32 for accelerated training of generative AI and large language models.
Project Ceiba supercomputer:
Collaboration on Project Ceiba, aiming to design the world’s fastest GPU-powered AI supercomputer with 16,384 NVIDIA GH200 Superchips and processing capability of 65 exaflops.
Introduction of new Amazon EC2 instances:
AWS introduces three new Amazon EC2 instances, including P5e instances powered by NVIDIA H200 Tensor Core GPUs for large-scale generative AI and HPC workloads.
Software innovations:
NVIDIA introduces software on AWS, such as NeMo Retriever microservice for chatbots and summarisation tools, and BioNeMo to speed up drug discovery for pharmaceutical companies.
This collaboration signifies a joint commitment to advancing the field of generative AI, offering customers access to cutting-edge technologies and resources.
Internally, Amazon robotics and fulfilment teams already employ NVIDIA’s Omniverse platform to optimise warehouses in virtual environments first before real-world deployment.
The integration of NVIDIA and AWS technologies will accelerate the development, training, and inference of large language models and generative AI applications across various industries.
The UK has published the world’s first global guidelines for securing AI systems against cyberattacks. The new guidelines aim to ensure AI technology is developed safely and securely.
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.
Inflection, an AI startup aiming to create “personal AI for everyone”, has announced a new large language model dubbed Inflection-2 that beats Google’s PaLM 2.
Inflection-2 was trained on over 5,000 NVIDIA GPUs to reach 1.025 quadrillion floating point operations (FLOPs), putting it in the same league as PaLM 2 Large. However, early benchmarks show Inflection-2 outperforming Google’s model on tests of reasoning ability, factual knowledge, and stylistic prowess.
On a range of common academic AI benchmarks, Inflection-2 achieved higher scores than PaLM 2 on most. This included outscoring the search giant’s flagship on the diverse Multi-task Middle-school Language Understanding (MMLU) tests, as well as TriviaQA, HellaSwag, and the Grade School Math (GSM8k) benchmarks:
The startup’s new model will soon power its personal assistant app Pi to enable more natural conversations and useful features.
Thrilled to announce that Inflection-2 is now the 2nd best LLM in the world! 💚✨🎉
It will be powering https://t.co/1RWFB5RHtF very soon. And available to select API partners in time. Tech report linked…
Inflection said its transition from NVIDIA A100 to H100 GPUs for inference – combined with optimisation work – will increase serving speed and reduce costs despite Inflection-2 being much larger than its predecessor.
An Inflection spokesperson said this latest model brings them “a big milestone closer” towards fulfilling the mission of providing AI assistants for all. They added the team is “already looking forward” to training even larger models on their 22,000 GPU supercluster.
Safety is said to be a top priority for the researchers, with Inflection being one of the first signatories to the White House’s July 2023 voluntary AI commitments. The company said its safety team continues working to ensure models are rigorously evaluated and rely on best practices for alignment.
With impressive benchmarks and plans to scale further, Inflection’s latest effort poses a serious challenge to tech giants like Google and Microsoft who have so far dominated the field of large language models. The race is on to deliver the next generation of 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.
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San Francisco-based AI startup Anthropic has unveiled Claude 2.1, an upgrade to its language model that boasts a 200,000-token context window—vastly outpacing the recently released 120,000-token GPT-4 model from OpenAI.
The release comes on the heels of an expanded partnership with Google that provides Anthropic access to advanced processing hardware, enabling the substantial expansion of Claude’s context-handling capabilities.
Our new model Claude 2.1 offers an industry-leading 200K token context window, a 2x decrease in hallucination rates, system prompts, tool use, and updated pricing.
With the ability to process lengthy documents like full codebases or novels, Claude 2.1 is positioned to unlock new potential across applications from contract analysis to literary study.
The 200K token window represents more than just an incremental improvement—early tests indicate Claude 2.1 can accurately grasp information from prompts over 50 percent longer than GPT-4 before the performance begins to degrade.
Claude 2.1 (200K Tokens) – Pressure Testing Long Context Recall
We all love increasing context lengths – but what's performance like?
Anthropic reached out with early access to Claude 2.1 so I repeated the “needle in a haystack” analysis I did on GPT-4
Anthropic also touted a 50 percent reduction in hallucination rates for Claude 2.1 over version 2.0. Increased accuracy could put the model in closer competition with GPT-4 in responding precisely to complex factual queries.
Additional new features include an API tool for advanced workflow integration and “system prompts” that allow users to define Claude’s tone, goals, and rules at the outset for more personalised, contextually relevant interactions. For instance, a financial analyst could direct Claude to adopt industry terminology when summarising reports.
However, the full 200K token capacity remains exclusive to paying Claude Pro subscribers for now. Free users will continue to be limited to Claude 2.0’s 100K tokens.
As the AI landscape shifts, Claude 2.1’s enhanced precision and adaptability promise to be a game changer—presenting new options for businesses exploring how to strategically leverage AI capabilities.
With its substantial context expansion and rigorous accuracy improvements, Anthropic’s latest offering signals its determination to compete head-to-head with leading models like GPT-4.
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.
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In the wake of the generative AI (GenAI) revolution, UK businesses find themselves at a crossroads between unprecedented opportunities and inherent challenges.
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:
AI experts don’t stay jobless for long, as evidenced by Microsoft’s quick recruitment of former OpenAI CEO Sam Altman and Co-Founder Greg Brockman.
Altman, who was recently ousted by OpenAI’s board for reasons that have had no shortage of speculation, has found a new home at Microsoft. The announcement came after unsuccessful negotiations with OpenAI’s board to reinstate Altman.
I deeply regret my participation in the board's actions. I never intended to harm OpenAI. I love everything we've built together and I will do everything I can to reunite the company.
Microsoft CEO Satya Nadella – who has long expressed confidence in Altman’s vision and leadership – revealed that Altman and Brockman will lead Microsoft’s newly established advanced AI research team.
Nadella expressed excitement about the collaboration, stating, “We’re extremely excited to share the news that Sam Altman and Greg Brockman, together with colleagues, will be joining Microsoft to lead a new advanced AI research team. We look forward to moving quickly to provide them with the resources needed for their success.”
I’m super excited to have you join as CEO of this new group, Sam, setting a new pace for innovation. We’ve learned a lot over the years about how to give founders and innovators space to build independent identities and cultures within Microsoft, including GitHub, Mojang Studios,…
The move follows Altman’s abrupt departure from OpenAI. Former Twitch CEO Emmett Shear has been appointed as interim CEO at OpenAI.
Today I got a call inviting me to consider a once-in-a-lifetime opportunity: to become the interim CEO of @OpenAI. After consulting with my family and reflecting on it for just a few hours, I accepted. I had recently resigned from my role as CEO of Twitch due to the birth of my…
Altman’s role at Microsoft is anticipated to build on the company’s strategy of allowing founders and innovators space to create independent identities, similar to Microsoft’s approach with GitHub, Mojang Studios, and LinkedIn.
Microsoft’s decision to bring Altman and Brockman on board coincides with the development of its custom AI chip. The Maia AI chip, designed to train large language models, aims to reduce dependence on Nvidia.
While Microsoft reassures its commitment to the OpenAI partnership, valued at approximately $10 billion, it emphasises ongoing innovation and support for customers and partners.
As Altman and Brockman embark on leading Microsoft’s advanced AI research team, the industry will be watching closely to see what the high-profile figures can do with Microsoft’s resources at their disposal. The industry will also be observing whether OpenAI can maintain its success under different leadership.
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Ahead of this year’s AI & Big Data Expo Global, Umbar Shakir, Partner and AI Lead at Gate One, shared her insights into the diverse landscape of generative AI (GenAI) and its impact on businesses.
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.
Amdocs has partnered with NVIDIA and Microsoft Azure to build custom Large Language Models (LLMs) for the $1.7 trillion global telecoms industry.
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.
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.
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Kinetic Consulting, the leading boutique consulting company providing business growth consultancy, has released macky.ai, the first AI business consulting platform that offers any organisation an easy, non-prompt-based AI consulting solution for up to 55 different business categories. The platform is powered by OpenAI’s artificial intelligence technology.
What is Macky AI?
The Macky AI platform overcomes some of the key hurdles preventing the mass adoption of AI in a business environment. No training is required for employees to begin taking advantage of the consulting platform. No knowledge is required on how to prompt the AI to get the right output or determine if the output is suitable. The hard work of determining which prompt to use or if the output is suitable has already been determined by the software’s creator, Kinetic Consulting. Platform users are asked a maximum of three questions to generate the desired output.
The creators of the Macky AI software have curated the types of everyday requirements of key departments in a business and what type of suitable output can be generated from a generative AI solution. An example may be generating something as simple as a job description for a new employee or something more complex, such as creating a new business process or reengineering an existing one. These types of requests are often requested for consultants to perform. Macky AI aims to reduce the cost of everyday consulting needs for companies so they can empower their employees to complete these tasks without the need for costly consultants.
By freeing up the costs paid for these lower-level activities, companies can now divert effort and funds to develop higher-value business initiatives, such as business roadmaps and growth strategy plans. These higher value and more complex business requirements will remain better suited for traditional consulting. The Macky AI platform is unique because it also provides its users with traditional consultants for more complex needs. The ability for an organisation to have the best of both worlds, all on one platform, is made possible on Macky AI. The future of consulting will be the augmentation of AI and human consultants.
Macky AI provides new consulting options for SMEs
A 2023 report by the OECD[1] on the outlook of SMEs in OECD countries highlights that the majority are currently operating in highly challenging environments. The report cites that SMEs have been greatly impacted by the COVID-19 pandemic, rising geopolitical tensions, high inflation, tighter monetary and fiscal policy, and supply-chain disruptions. Retaining and attracting staff has also become a major issue for SMEs in OECD countries. According to a Future of Business Survey, it is reported to be the second most pressing challenge faced by SMEs that are older than two years in the first quarter of 2022[2]. Many SMEs have also depleted their cash reserves during the pandemic and now find it challenging to raise capital for their business to fuel the rising costs of goods and services and the capital required for digital transformation projects.
Outside of the OECD, we find the importance of a thriving SME ecosystem even more critical. In the Gulf region, SMEs contribute even more to the economy than their counterparts in OECD countries. Within the UAE, for example, SMEs represent 94% of the companies and institutions operating in the country, contributing more than 50% to the country’s GDP. SMEs account for 86% of the private sector’s workforce. Operating extensively throughout the rest of the GCC, SMEs employ 80% of Saudia Arabia, 43% of Oman’s workforce, 57% of Bahrain, 23% of Kuwait, and 20% of Qatar.
The importance of having healthy and thriving SMEs is recognised as the primary pillar of strength for any economy. The challenging environment and rising cost of capital make it difficult for SMEs to afford traditional consulting. Ironically, this is the time when consulting is most needed to help SMEs navigate, transform, and thrive again. Macky AI gives SMEs affordable access to consulting services using artificial intelligence. The AI business consulting platform provides an on-demand service for key business challenges, such as analysing a profit and loss statement to identify cost savings and developing a 12-month marketing plan to increase sales.
The future of consulting
Business consulting, like most industries, is undergoing a period of disruption. Technological advances, such as artificial intelligence, are accelerating the delivery of consulting in the future. Critics of the technology may highlight how AI is not 100% accurate in its outputs and is prone to error, so it should not be used. This argument is fundamentally flawed because even human-based consulting is prone to errors. All outputs delivered by human or AI consultants should be checked for accuracy. The advancement of generative AI technology has reached a point where it is now highly useful in a business or education environment.
AI technologies should be embraced rather than resisted if they are fit for purpose. Macky AI is designed to be specifically for business-related needs, and even in the open question section of the platform, the AI has been programmed not to answer questions that are not business-related. The objective of restricting it for business purposes only is to ensure that if employers give it to their employees, it will not be used for personal needs.
“As advancements in AI evolve, we need to accept that it will become a natural part of how we interact with things, get answers to our questions, and help solve complex problems. The future of consulting will be an augmentation between AI and human consultants. This is the inevitable evolutionary path. The percentages of AI usage versus human is unknown at this stage. However, I am 100% confident it will not be all traditional human consulting for much longer. Macky AI is the first step towards bringing AI into the workplace in a controlled environment for a specific business purpose. By empowering SMEs with affordable consulting outputs for business tasks, we are also helping SMEs overcome everyday business challenges and thrive in the future. Macky AI is designed to democratise consulting, making it accessible to all organisations regardless of size.” said Joe Tawfik, founder of Macky AI.
The hype surrounding generative AI and the potential of large language models (LLMs), spearheaded by OpenAI’s ChatGPT, appeared at one stage to be practically insurmountable. It was certainly inescapable. More than one in four dollars invested in US startups this year went to an AI-related company, while OpenAI revealed at its recent developer conference that ChatGPT continues to be one of the fastest-growing services of all time.
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 Department of Homeland Security’s (DHS) Cybersecurity and Infrastructure Security Agency (CISA) has launched its inaugural Roadmap for AI.
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.
The hype surrounding generative AI and the potential of large language models (LLMs), spearheaded by OpenAI’s ChatGPT, appeared at one stage to be practically insurmountable. It was certainly inescapable. More than one in four dollars invested in US startups this year went to an AI-related company, while OpenAI revealed at its recent developer conference that ChatGPT continues to be one of the fastest-growing services of all time.
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 (left), 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 the AI & Big Data Expo. 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 Department of Homeland Security’s (DHS) Cybersecurity and Infrastructure Security Agency (CISA) has launched its inaugural Roadmap for AI.
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.
Quantum AI is the next frontier in the evolution of artificial intelligence, harnessing the power of quantum mechanics to propel capabilities beyond current limits.
GlobalData highlights a 14 percent compound annual growth rate (CAGR) increase in related patent filings from 2020 to 2022, underscoring the vast influence and potential of quantum AI across industries.
Adarsh Jain, Director of Financial Markets at GlobalData, emphasises the transformative nature of Quantum AI:
“Quantum AI represents a transformative advancement in technology. As we integrate quantum principles into AI algorithms, the potential for speed and efficiency in processing complex data sets grows exponentially. This not only enhances current AI applications but also opens new possibilities across various industries.
The surge in patent filings is a testament to its growing importance and the pivotal role it will play in the future of AI-driven solutions.”
Kiran Raj, Practice Head of Disruptive Tech at GlobalData, highlights that while AI thrives on data and computational power, the inner workings of the technology often remain unclear. Quantum computing not only promises increased power but also potentially provides greater insights into these workings, paving the way for AI to transcend its current capabilities.
GlobalData’s Disruptor Intelligence Center analysis reveals significant synergy between quantum computing and AI innovations, leading to revolutionary impacts in various industries. Notable collaborations include HSBC and IBM in finance, Menten AI’s healthcare advancements, Volkswagen’s partnership with Xanadu for battery simulation, Intel’s Quantum SDK, and Zapata’s collaboration with BMW.
Raj concludes with a note of caution: “Quantum AI offers the potential for smarter, faster AI systems, but its adoption is complex and demands caution. The technology is still in its early stages, requiring significant investment and expertise.
“Key challenges include the need for advanced cybersecurity measures and ensuring ethical AI practices as we navigate this promising yet intricate landscape.”
GitLab is empowering DevSecOps with new AI-powered capabilities as part of its latest releases.
The recent GitLab 16.6 November release includes the beta launch of GitLab Duo Chat, a natural-language AI assistant. Additionally, the GitLab 16.7 December release sees the general availability of GitLab Duo Code Suggestions.
David DeSanto, Chief Product Officer at GitLab, said: “To realise AI’s full potential, it needs to be embedded across the software development lifecycle, allowing DevSecOps teams to benefit from boosts to security, efficiency, and collaboration.”
GitLab Duo Chat – arguably the star of the show – provides users with invaluable insights, guidance, and suggestions. Beyond code analysis, it supports planning, security issue comprehension and resolution, troubleshooting CI/CD pipeline failures, aiding in merge requests, and more.
As part of GitLab’s commitment to providing a comprehensive AI-powered experience, Duo Chat joins Code Suggestions as the primary interface into GitLab’s AI suite within its DevSecOps platform.
GitLab Duo comprises a suite of 14 AI capabilities:
Suggested Reviewers
Code Suggestions
Chat
Vulnerability Summary
Code Explanation
Planning Discussions Summary
Merge Request Summary
Merge Request Template Population
Code Review Summary
Test Generation
Git Suggestions
Root Cause Analysis
Planning Description Generation
Value Stream Forecasting
In response to the evolving needs of development, security, and operations teams, Code Suggestions is now generally available. This feature assists in creating and updating code, reducing cognitive load, enhancing efficiency, and accelerating secure software development.
GitLab’s commitment to privacy and transparency stands out in the AI space. According to the GitLab report, 83 percent of DevSecOps professionals consider implementing AI in their processes essential, with 95 percent prioritising privacy and intellectual property protection in AI tool selection.
The State of AI in Software Development report by GitLab reveals that developers spend just 25 percent of their time writing code. The Duo suite aims to address this by reducing toolchain sprawl—enabling 7x faster cycle times, heightened developer productivity, and reduced software spend.
Kate Holterhoff, Industry Analyst at Redmonk, commented: “The developers we speak with at RedMonk are keenly interested in the productivity and efficiency gains that code assistants promise.
“GitLab’s Duo Code Suggestions is a welcome player in this space, expanding the available options for enabling an AI-enhanced software development lifecycle.”
Google has announced the expansion of its partnership with Anthropic to work towards achieving the highest standards of AI safety.
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.
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.
OpenAI has been grappling with a series of distributed denial-of-service (DDoS) attacks targeting its API and ChatGPT services over the past 24 hours.
While the company has not yet disclosed specific details about the source of these attacks, OpenAI acknowledged that they are dealing with “periodic outages due to an abnormal traffic pattern reflective of a DDoS attack.”
Users affected by these incidents reported encountering errors such as “something seems to have gone wrong” and “There was an error generating a response” when accessing ChatGPT.
This recent wave of attacks follows a major outage that impacted ChatGPT and its API on Wednesday, along with partial ChatGPT outages on Tuesday, and elevated error rates in Dall-E on Monday.
OpenAI displayed a banner across ChatGPT’s interface, attributing the disruptions to “exceptionally high demand” and reassuring users that efforts were underway to scale their systems.
Threat actor group Anonymous Sudan has claimed responsibility for the DDoS attacks on OpenAI. According to the group, the attacks are in response to OpenAI’s perceived bias towards Israel and against Palestine.
The attackers utilised the SkyNet botnet, which recently incorporated support for application layer attacks or Layer 7 (L7) DDoS attacks. In Layer 7 attacks, threat actors overwhelm services at the application level with a massive volume of requests to strain the targets’ server and network resources.
Brad Freeman, Director of Technology at SenseOn, commented:
“Distributed denial of service attacks are internet vandalism. Low effort, complexity, and in most cases more of a nuisance than a long-term threat to a business. Often DDOS attacks target services with high volumes of traffic which can be ’off-ramped, by their cloud or Internet service provider.
However, as the attacks are on Layer 7 they will be targeting the application itself, therefore OpenAI will need to make some changes to mitigate the attack. It’s likely the threat actor is sending complex queries to OpenAI to overload it, I wonder if they are using AI-generated content to attack AI content generation.”
However, the attribution of these attacks to Anonymous Sudan has raised suspicions among cybersecurity researchers. Some experts suggest that this could be a false flag operation and the group might have connections to Russia instead which, along with Iran, is suspected of stoking the bloodshed and international outrage to benefit its domestic interests.
The situation once again highlights the ongoing challenges faced by organisations dealing with DDoS attacks and the complexities of accurately identifying the perpetrators.
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.
Explore other upcoming enterprise technology events and webinars powered by TechForge here.
Amazon is reportedly making substantial investments in the development of a large language model (LLM) named Olympus.
According to Reuters, the tech giant is pouring millions into this project to create a model with a staggering two trillion parameters. OpenAI’s GPT-4, for comparison, is estimated to have around one trillion parameters.
This move puts Amazon in direct competition with OpenAI, Meta, Anthropic, Google, and others. The team behind Amazon’s initiative is led by Rohit Prasad, former head of Alexa, who now reports directly to CEO Andy Jassy.
Prasad, as the head scientist of artificial general intelligence (AGI) at Amazon, has unified AI efforts across the company. He brought in researchers from the Alexa AI team and Amazon’s science division to collaborate on training models, aligning Amazon’s resources towards this ambitious goal.
Amazon’s decision to invest in developing homegrown models stems from the belief that having their own LLMs could enhance the attractiveness of their offerings, particularly on Amazon Web Services (AWS).
Enterprises on AWS are constantly seeking top-performing models and Amazon’s move aims to cater to the growing demand for advanced AI technologies.
While Amazon has not provided a specific timeline for the release of the Olympus model, insiders suggest that the company’s focus on training larger AI models underscores its commitment to remaining at the forefront of AI research and development.
Training such massive AI models is a costly endeavour, primarily due to the significant computing power required.
Amazon’s decision to invest heavily in LLMs is part of its broader strategy, as revealed in an earnings call in April. During the call, Amazon executives announced increased investments in LLMs and generative AI while reducing expenditures on retail fulfillment and transportation.
Amazon’s move signals a new chapter in the race for AI supremacy, with major players vying to push the boundaries of the technology.
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.
OpenAI has announced a slew of new additions and improvements to its platform, alongside reduced pricing, aimed at empowering developers and enhancing user experience.
Following yesterday’s leak of a custom GPT-4 chatbot creator, OpenAI unveiled several other key features during its DevDay that promise a transformative impact on the landscape of AI applications:
GPT-4 Turbo: OpenAI introduced the preview of GPT-4 Turbo, the next generation of its renowned language model. This new iteration boasts enhanced capabilities and an extensive knowledge base encompassing world events up until April 2023.
One of GPT-4 Turbo’s standout features is the impressive 128K context window, allowing it to process the equivalent of more than 300 pages of text in a single prompt.
Notably, OpenAI has optimised the pricing structure, making GPT-4 Turbo 3x cheaper for input tokens and 2x cheaper for output tokens compared to its predecessor.
Assistants API: OpenAI also unveiled the Assistants API, a tool designed to simplify the process of building agent-like experiences within applications.
The API equips developers with the ability to create purpose-built AIs with specific instructions, leveraging additional knowledge and calling models and tools to perform tasks.
Multimodal capabilities: OpenAI’s platform now supports a range of multimodal capabilities, including vision, image creation (DALL·E 3), and text-to-speech (TTS).
GPT-4 Turbo can process images, opening up possibilities such as generating captions, detailed image analysis, and reading documents with figures.
Additionally, DALL·E 3 integration allows developers to create images and designs programmatically, while the text-to-speech API enables the generation of human-quality speech from text.
Pricing overhaul: OpenAI has significantly reduced prices across its platform, making it more accessible to developers.
GPT-4 Turbo input tokens are now 3x cheaper than its predecessor at $0.01, and output tokens are 2x cheaper at $0.03. Similar reductions apply to GPT-3.5 Turbo, catering to various user requirements and ensuring affordability.
Copyright Shield: To bolster customer protection, OpenAI has introduced Copyright Shield.
This initiative sees OpenAI stepping in to defend customers and cover the associated legal costs if they face copyright infringement claims related to the generally available features of ChatGPT Enterprise and the developer platform.
OpenAI’s latest announcements mark a significant stride in the company’s mission to democratise AI technology, empowering developers to create innovative and intelligent applications across various domains.
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.
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