Tech Legal Outlook 2023 Mid-Year Update: Riding the wave of generative AI

In March 2023, the Italian data protection authority (Garante) blocked ChatGPT’s processing of personal data (effectively blocking the service in Italy) until ChatGPT complies with certain remediations required by the authority. In April 2023, the Spanish data protection authority (AEPD) initiated its own investigation. It is likely other data protection authorities will follow – the European Data Protection Board (EDPB) has since launched a task force on ChatGPT. European data protection authorities are concerned with the use of personal data in AI systems, including to train it, and questions around lawful processing, transparency, data subject rights and data minimisation in particular. If life is moving fast for generative AI technology, the legal landscape for generative AI is also moving fast. November 2022 saw a US class action against Co-Pilot claiming that its training process had breached open source licence terms.

generative ai landscape

Generative AI large language models use pre-written content on the Internet to formulate their responses (although ChatGPT currently uses the Internet up to September 2021, which comes with its own host of problems). These roles continue to be vital for your business, for the key reason that AI can only respond to a human-given prompt. AI alone cannot think outside of the box, fact-check itself or assess the quality of its work – this still falls to your talented humans.

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The rise of generative AI is being fuelled by billions of dollars of investment and continued technology advances, and its capability is expected to grow exponentially. In addition, Senate Majority Leader Chuck Schumer has announced an early-stage legislative proposal aimed at advancing and regulating American AI technology. The current text of the EU AI Act specifically covers generative AI, by bringing ‘general purpose AI systems’, those which have a wide range of possible use cases (intended and unintended by their developers) in scope.

He is passionate about data science and has championed data analytics practice across start-ups to enterprises in various verticals. As a thought leader, start-up mentor, and data architect, Anand brings over two decades of techno-functional leadership in envisaging, planning, and building high-performance, state-of-the-art technology teams. For instance, firms that are market leaders could improve their performance even further if they can properly implement generative AI to support value-adding or differentiating areas of their business. Conversely, competitor firms could close the gap on market leaders if they can implement generative AI faster and more effectively, for example, to reduce time to market, increase margins or improve customer service. We’re exploring the development of a toolkit of text-to-image generative AI processes designed to generate maps that can be used in many different applications across our VFX work.

Exploring the AI Landscape: A Cross-Sector Perspective

This data-driven approach revolutionises how organizations create content that truly resonates. While generative AI offers exciting creative potential, it also raises unsettled questions around copyright law that create risks for marketers exploring these technologies. As we figure out the copyright issues surrounding generative AI, a recent Drum article summed up the situation well.

generative ai landscape

Importantly, generative AI will also transform content insight and strategy through powerful analytics. Rather than intensive manual analysis, generative algorithms can rapidly uncover genrative ai buried patterns and insights from customer data. Startup companies are pioneering this smart data analysis to optimise content strategy based on consumer behaviour and preferences.

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Consideration should also be given to establishing clear and appropriate accountability lines throughout the company up to senior management, and having in place people with the right skills, expertise, experience and information to support and advise. Recruitment, talent pipeline management and staff training will be aspects to consider in planning for effective AI risk management. As part of any AI procurement your company would also need to understand its responsibilities regarding system use and configuration, the supplier’s business continuity plan and how the unavailability of that platform would affect your business.

Founder of the DevEducation project

Dun & Bradstreet Partners with Google Cloud on Generative AI – Destination CRM

Dun & Bradstreet Partners with Google Cloud on Generative AI.

Posted: Wed, 30 Aug 2023 04:00:59 GMT [source]

The availability of open-source libraries and frameworks has made it easier for startups to develop and deploy generative AI models. At its core, Generative AI is an AI subset that crafts new, previously non-existent data. Visualize this as a friendly duel between two AI networks, both continually bettering and refining the data they produce. Those who have followed Google since its inception will be aware of the increasing need to monetise the search engine results page (SERP) by increasing the percentage of paid clicks.

Maybe the development of generative AI poses a risk to certain industries, requiring businesses to take a public anti-generative AI stance. Other companies may want to buy some breathing space to better assess and understand the risks and formulate better informed policy or guidelines. The level of explicability – or “explainability” – required or expected depends on the type of activity, the relevant legal jurisdictions of deployment, the recipient of the explanation and the nature of the AI used. For example, the EU GDPR contains transparency requirements regarding use of personal data, and specific requirements regarding fully automated decisions with legal or similarly significant effects on a data subject. There are, in particular, legal and reputational risks in relation to any customer receipt of AI output that has not been identified as such, or misleading statements relating to AI. China’s emerging laws relating to AI also include labelling requirements for certain AI-generated content.

While traditional AI and generative AI have distinct functionalities, they are not mutually exclusive. Generative AI could work in tandem with traditional AI to provide even more powerful solutions. For instance, a traditional AI could analyze user behavior data, and a generative AI could use this analysis to create personalized content. AI adds further complexity to written and visual humanitarian storytelling and documentation. In a recent and rather spectacular example of failure, Amnesty International controversially used AI to generate images to protect Colombian protesters from possible state retribution. Google debuted its Search Generative Experience last month, integrating ChatGPT style answers directly into the search engine results page, replacing featured snippets for informational queries.

AI can analyze patient data, clinical trials, and
scientific literature to predict drug responses, identify potential adverse
effects, and personalize treatment plans. This helps in optimizing drug
dosage, improving patient stratification, and reducing the risk of adverse
events. AI enables the rapid design of new molecules with desired properties,
allowing for the generation of drug candidates that are more potent,
selective, and less toxic. Machine learning algorithms can learn from vast
libraries of existing compounds to generate novel chemical structures with
optimized drug-like properties. This represents a shift in attitudes towards government oversight, acknowledging the need for responsible AI practices. Many companies have already embraced responsible AI, although perceptions of its effectiveness may differ between leaders and frontline employees.

Innovate UK BridgeAI: Digital Catapult Workshops

Video generation and manipulation through AI have opened doors to new forms of entertainment, education, and advertising. AI can create realistic animations, enhance video quality, generate 3D models from 2D images, and even create entire scenes or movies. Like with images, deepfake technology can also be applied to videos, leading to potential misuse. Generative AI, at its most basic level, is an artificial intelligence model that has been trained to create new content. It’s a powerful tool that has the capacity to revolutionise several industries but content marketing especially given its ability to create many content formats.

One of the most exciting aspects of generative AI is its ability to produce novel and creative content. For example, generative AI can be used to generate realistic simulations of natural disasters, helping insurance companies assess risk and develop better policies to protect their customers. Unlike traditional AI models that rely on pre-programmed rules or algorithms, generative AI systems learn from vast amounts of data to generate new outputs that imitate human-like creativity. These systems utilise complex algorithms and neural networks to produce realistic images, texts, music, and even entire virtual worlds.

The core argument is that while the capabilities of generative AI for content are powerful and transformative, blind reliance without human creativity, strategy, ethics and oversight poses risks. Many of the generative AI tools available for content marketers today are applications built on top of an LLM or a ‘general purpose AI’. These new content marketing tools use the API provided by the LLM to create a bespoke service for marketers. Like many other industries, the content marketing industry is set to become massively disrupted by AI or, more specifically, generative AI. When ChatGPT was launched on 30 November last year, it sent shockwaves across the marketing industry and the wider world.

This approach allows physicians to make
data-driven decisions and focus on providing targeted therapies, maximizing
patient outcomes and minimizing adverse reactions. AI Algorithms can identify existing drugs that could be repurposed for new
therapeutic indications. By analyzing vast datasets and uncovering hidden
relationships, AI helps in finding new uses for approved drugs, saving time
and resources compared to traditional drug discovery approaches. However, they are often plagued by high costs, long durations,
and complex data analysis.