The City of London Corporation, London and Partners and Microsoft have launched an AI Innovation Challenge, where participants will vie to spot and stop cybercriminals using fake identities and audio and visual deepfakes to commit fraud. With the increase of such events and the ubiquity of GenAI models, Nvidia, the multinational AI chip-maker, is increasingly becoming the modern-day Standard Oil. Nvidia’s chips can be found in just about all areas of economic activity, from education to medicine and in nearly all financial and professional services.
With its growing usage, its potential for fighting cybercrime increases, given its ability to analyse vast amounts of data rapidly, decipher patterns, and ultimately lead to higher fraud detection rates and greater trust in and securitise customer services. Banks in the United Kingdom lead the way in AI adoption, particularly as some 90 percent of them have already onboarded generative AI models to their asset portfolios.
Participants of the AI Innovation Challenge have until 26 July 2024 to register for the competition, which is scheduled for six weeks between September and November. The final event promises to be a display of fraud detection and other cybersecurity innovations developed during the course of the competition.
The surge in US stock prices, driven by enthusiasm for AI, draws comparisons to the dot-com bubble two decades ago, sparking concerns over inflated valuations. The S&P 500 has reached new records, climbing more than 50% from its October 2022 low, while the Nasdaq Composite has surged over 70% since the end of 2022. A few massive tech stocks, including Nvidia, are leading this rally, reminiscent of the ‘Four Horsemen’ tech stocks of the late 1990s.
Despite the impressive gains, some analysts caution that today’s tech stocks are more financially robust than their dotcom-era counterparts. However, fears persist that the AI-driven surge might end in a crash similar to the dotcom bust, which saw the Nasdaq Composite plummet nearly 80% from its March 2000 peak, and while some companies like Amazon thrived post-bubble, many did not recover.
Current tech stock valuations, while high, are more grounded in solid earnings prospects rather than speculative growth, a key difference from the dot-com era. For instance, Nvidia trades at 40 times forward earnings estimates, far lower than Cisco’s 131 times in 2000. Although the S&P 500’s price-to-earnings ratio of 21 is above its historical average, it remains below the peak levels of the late 1990s. Nonetheless, investors remain cautious, wary of metrics becoming overly stretched if economic growth continues and tech stocks keep climbing.
In the evolving landscape of marketing and advertising, the integration of generative AI presents both promise and challenges, as highlighted in a recent Forrester report. Key obstacles include a lack of AI expertise among agency employees and concerns over job obsolescence. Also, the human factor poses a significant hurdle that the industry must address urgently to fully harness the potential of genAI.
The potential economic impact of genAI on agencies is profound. Seen as a transformative force akin to the advent of smartphones, genAI promises to redefine creativity in marketing by combining data intelligence with human intuition. Agency leaders overwhelmingly recognise it as a disruptive technology, with 77% acknowledging its potential to fundamentally alter business operations. However, the fear of job displacement among employees remains palpable, exacerbated by recent industry disruptions and the rapid automation of white-collar roles.
To mitigate these concerns and fully embrace genAI, there is a pressing need for comprehensive AI literacy and training within agencies. While existing educational programmes and certifications provide a foundation, they are insufficient to meet the demands of integrating AI into everyday creative processes. Investment in reskilling and upskilling initiatives is crucial to empower agency employees to confidently navigate the AI-driven future of marketing and advertising.
Industry stakeholders, including agencies, technology partners, universities, and trade groups, must collaborate to establish robust training frameworks. In addition, a concerted effort will not only bolster agency capabilities in AI adoption but also ensure that creative workforce remains agile and competitive in an increasingly AI-centric landscape. By prioritising AI literacy and supporting continuous learning initiatives, agencies can position themselves at the forefront of innovation, delivering enhanced value to clients through AI-powered creativity.
A recent study has revealed that ChatGPT and similar large language models (LLMs) are highly effective in launching cyberattacks, raising significant concerns in the cybersecurity field.
Researchers Richard Fang, Rohan Bindu, Akul Gupta, and Daniel Kang tested ChatGPT-4 against 15 real-life ‘one-day’ vulnerabilities, finding that it could exploit these vulnerabilities 87% of the time. These vulnerabilities included websites issues, container management software, and Python packages, all sourced from the CVE database.
The study utilised a detailed prompt with 1,056 tokens and 91 lines of code, including debugging and logging statements. The research team noted that ChatGPT-4’s success stemmed from its ability to handle complex, multi-step vulnerabilities and execute various attack methods. However, without the CVE code, ChatGPT-4’s success rate plummeted to just 7%, highlighting a significant limitation.
The researchers concluded that while ChatGPT-4 currently stands out in its ability to exploit one-day vulnerabilities, the potential for LLMs to become more powerful and destructive is a major concern. They emphasised the importance of the cybersecurity community and LLM providers collaborating to integrate these technologies into defensive measures and carefully consider their deployment.
Amazon has recently hired the co-founders and several team members from AI startup Adept in a strategic move to bolster its AI capabilities. Adept’s CEO David Luan and other key employees have joined Amazon. At the same time, the startup will continue to operate independently, with Amazon paying a licensing fee to use some of its technology to automate business functions.
The recruitment is similar to Microsoft’s earlier hiring of Inflection AI’s team, which has drawn regulatory scrutiny. Adept, valued at over $1 billion, has already named a new CEO. Amazon’s recruitment of Adept’s team signals its ambition to advance AI agent tools, an area of focus for major tech labs. The company is also working to update its Alexa voice assistant with generative AI for more complex and responsive interactions.
At Amazon, Luan and others will report to Rohit Prasad, who leads the company’s artificial general intelligence efforts. Previously head of Alexa, Prasad has integrated researchers across Amazon to enhance AI model training. He stated that these new hires will significantly contribute to Amazon’s pursuit of achieving AGI.
The Center for Investigative Reporting (CIR), known for producing Mother Jones and Reveal, has sued OpenAI and Microsoft, accusing them of using its content without permission and compensation. The lawsuit, filed in New York federal court, claims that OpenAI’s business model is based on exploiting copyrighted works and argues that AI-generated summaries threaten the financial stability of news organisations by reducing direct engagement with their content.
CIR’s CEO, Monika Bauerlein, emphasised the danger of AI tools replacing direct relationships between readers and news organisations, potentially undermining the foundations of independent journalism. The lawsuit is part of a broader legal challenge faced by OpenAI and Microsoft, with similar suits filed by other media outlets and authors.
Why does it matter?
Some news organisations have opted to collaborate with OpenAI, signing deals to allow the use of their content for AI training in exchange for compensation. Despite OpenAI’s argument that its use of publicly accessible content falls under ‘fair use,’ CIR’s lawsuit highlights the financial and ethical implications of using copyrighted material without proper attribution or payment, warning of significant impacts on investigative journalism and democracy.
Two companies that benefited the most from AI average, Nvidia and Microsoft, are the most exposed to antitrust investigations for AI monopolies. Regulatory authorities have shifted their approach, acting quickly against potential monopolistic practices instead of taking years to intervene.
Notable investigations include the US Department of Justice examining Nvidia’s alleged anticompetitive behaviour in the GPU market and the Federal Trade Commission (FTC) probing Microsoft’s $13 billion investment in OpenAI and strategic staff acquisitions from Inflection. The UK’s Competition and Markets Authority (CMA) is also investigating, particularly concerned about the over 90 partnerships tech giants have formed with large language model developers since 2019, potentially stifling competition.
Politically, there’s a risk that excessive intervention could be seen as stifling innovation, particularly in the face of global competitors like China. Regulators must balance fostering competition with enabling innovation, ensuring that the rise of generative AI, which promises significant technological upheaval, does not result in a market dominated by a few powerful players.
In a groundbreaking shift for the academic world, AI now contributes to at least 10% of research papers, soaring to 20% in computer science, according to The Economist. This transformation is driven by advancements in large language models (LLMs), as highlighted in a University of Tübingen study comparing recent papers with those from the pre-ChatGPT era. The research shows a notable change in word usage, with terms like ‘delivers,’ ‘potential,’ ‘intricate,’ and ‘crucial’ becoming more common, while ‘important’ declines in use.
Researchers are leveraging LLMs for editing, translating, simplifying coding, streamlining administrative tasks, and accelerating manuscript drafting. However, this integration raises concerns. LLMs may reinforce existing viewpoints and frequently cite prominent articles, potentially leading to an inflation of publications and a dilution of research quality. This risks perpetuating bias and narrowing academic diversity.
As the academic community grapples with these changes, scientific journals seek solutions to address the challenges as the sophistication of AI increases. Trying to detect and prevent the use of AI is increasingly futile. Other approaches to uphold the quality of research are discussed, including investment into a more solid peer-reviewing process, insisting on replicating experiments, and hiring academics based on the quality of their work instead of quantity, promoted by public obsession.
Recognizing the inevitability of AI’s role in academic writing, Diplo introduced the KaiZen publishing approach. This innovative approach combines just-in-time updates facilitated by AI with reflective writing crafted by humans, aiming to harmonize the strengths of AI and human intellect in producing scholarly work.
As AI continues to revolutionize academic writing, the landscape of research and publication is poised for further evolution, prompting ongoing debates and the search for balanced solutions.
G42, an ambitious AI company based in the UAE, is positioning itself as a central player in transforming the UAE into an AI powerhouse while aiming to diversify its economy away from hydrocarbons. Founded six years ago and state-backed, G42 has set its sights on regional and global influence through strategic collaborations and innovative technological advancements. Unlike other AI firms focused on developing large language models (LLMs) like ChatGPT, G42 prioritises building the infrastructure for the AI economy and creating real-world applications in key sectors such as healthcare and energy.
Recently, G42 has been active in forming partnerships and securing investments, including deals with OpenAI and Cerebras to construct a supercomputer and with AstraZeneca to manufacture innovative medicines in the UAE. A significant highlight is a $1.5 billion investment from Microsoft, underscoring confidence in G42’s potential. Additionally, G42 is pursuing global ventures to extend Emirati influence, such as enhancing Kazakhstan’s energy grid and developing data centres and digitising services in several African countries like Angola, Gambia, Kenya, Senegal, and Zambia.
However, G42 faces challenges, including local competition from entities like the Advanced Technology Research Council’s AI71 and Equinix. Regionally, Saudi Arabia is also advancing in AI by building the Middle East’s most powerful supercomputer and collaborating with IBM. Geopolitics add complexity, as G42 had to cut ties with Huawei to secure Microsoft’s investment.
Why does it matter?
In summary, G42 is taking a significant role in the UAE’s AI strategy, focusing on infrastructure and practical applications. Its high-profile partnerships and financial backing underline its strategic importance, yet it must navigate competition, geopolitical intricacies, and the challenge of making generative AI profitable.
SoftBank Group has launched a joint venture called ‘SB TEMPUS Corp.’ with Tempus AI, a leader in AI and precision medicine. The joint venture aims to provide precision medicine services in Japan by applying the expertise and technology that Tempus has accumulated in the US. That includes Tempus’ AI-enabled platform, which works to make diagnostics more intelligent and support healthcare providers in making more informed decisions. The goal is to provide personalised, data-driven therapies to patients, with the aim of helping them live longer and healthier lives.
A key focus of the joint venture will be collecting and analysing siloed and unstructured medical data, such as molecular, clinical, pathological, and medical imaging data. By leveraging AI to analyse this data, the joint venture aims to contribute to the advancement of pharmaceutical research, including clinical and drug discovery research and the proposal of treatment plans more suited to individual patients. That approach is expected to reduce side effects and enhance the effectiveness of medications, marking a significant step towards personalised medicine.
To help as many people suffering from cancer as possible, SB TEMPUS plans to establish collaborations with cancer genomic medicine hospitals and Japanese hospitals, medical facilities, pharmaceutical companies, biotech ventures, medical device companies, cancer insurance companies, and testing companies.
Why does it matter?
The collaborative network will support the provision of better diagnosis and treatment for patients, ensuring that they benefit from personalised, data-driven therapies. Also, the joint venture aligns with SoftBank’s corporate philosophy of ‘Information Revolution—Happiness for everyone.’
