🚀 Welcome welcome! This week, we're zooming into major legal shake-ups in AI 🧠: UK's landmark neural network patents, (more) U.S. AI certifications in court, Europe's AI Pact, and the global "secure by design" AI agreement. Let’s dive in!

On the docket today:

• UK High Court Approves Patenting of Artificial Neural Networks

• U.S. Court of Appeals Proposes AI Certification for Legal Filings

• European Commission Launches AI Pact

• 18 Countries Unveil Agreement to Make AI “secure by design”

• BONUS: Juicy Snippets from IAPP's AI Governance Course

• Hilarious Meme

  New here? → Subscribe

UK High Court Approves Patenting of Artificial Neural Networks

The UK has now made it possible to patent artificial neural networks (ANNs) and their training methods. This significant development, resulting from a recent ruling by the English High Court, marks a pivotal change in the UK's approach to AI technology and its legal protections.

 Key Points:

1. Significant Legal Change: The English High Court has ruled that the training of artificial neural networks (ANNs) and ANNs themselves can now be patented in the UK. This overturns a previous decision by the UK Patent Office (UKIPO).

2. Background of the Case: AI Venture Studio Time Machine Capital Squared (TMC2) and its subsidiary, Emotional Perception AI Ltd (EP AI), led this initiative. EP AI filed a patent in 2019 for a technique enabling ANNs to align outputs more closely with human semantic perception.

3. Challenging Outdated Legislation: The initial patent application was rejected due to old legislation from the 1970s. This decision showcases the need to update laws to reflect advancements in AI and computer-implemented inventions (CIIs).

4. Impact on the AI Industry: This ruling is seen as a major breakthrough for the UK's AI industry, enhancing its global leadership and attractiveness for investment in AI innovation. It is especially significant for sectors like banking and markets, improving capabilities in natural language processing, economic and financial crime detection, and sentiment analysis.

5. Implications for Patent Law and AI Development: The decision acknowledges the distinct technical complexities involved in training and running ANNs. It aligns UK law with contemporary AI development and implementation, ensuring better protection for AI-based patents.

6. Industry Reactions: Experts and leaders from TMC2 and the AI industry have lauded the ruling, noting its potential to transform the AI sector and affirm the UK's position as a leader in AI innovation.

The UK High Court's decision to allow patenting of ANNs and their training is a landmark change.

Will it impact your practice? If so, how?

U.S. Court of Appeals Proposes AI Certification for Legal Filings

The 5th U.S. Circuit Court of Appeals has proposed a new rule that would require lawyers to certify their use or non-use of artificial intelligence (AI) tools like ChatGPT in drafting legal briefs. Lawyers appearing before the court, including those representing themselves, must certify that any AI-generated filings have been reviewed for accuracy in citations and legal analysis. Non-compliance could result in sanctions or filings being stricken.

This proposal, open for public comment until January 4, follows incidents of AI misuse, including a case in New York where lawyers faced sanctions for submitting a brief with fictitious citations from ChatGPT. Similar policies have been adopted in some Texas courts, highlighting the growing concern over AI use in legal proceedings.

If you were to submit comments on this proposal, what would they be?

European Commission Launches AI Pact 

The European Commission has introduced the AI Pact, a scheme aimed at supporting companies in preparing for the upcoming AI Act. This initiative encourages early adoption of the AI Act's measures, focusing particularly on the rapid technological advancements and widespread adoption of AI systems. Key aspects of the AI Pact include:

• Early Implementation: Companies are encouraged to voluntarily outline their readiness and plans for compliance with the AI Act, emphasizing the development and use of trustworthy AI.

• Industry Commitments: Participants in the AI Pact will make pledges detailing their actions towards compliance, which can be gradual and aim for higher levels of ambition over time. These pledges will be collected and published by the Commission to enhance visibility and credibility.

• Participation: The Pact is open to key industry players from both the EU and non-EU regions, forming a community for the exchange of best practices and raising awareness about the AI Act's principles.

• Benefits for Participants: The Commission will aid participants in understanding and adapting to the AI Act, allowing them to share knowledge, build internal processes, and demonstrate their commitment to trustworthy AI. This gives front-runners a chance to test and share their solutions, offering a first-mover advantage.

• Next Steps and Involvement: The Commission has started a 'call for interest' for organizations to actively participate in the AI Pact. Post-adoption of the AI Act, the Pact will be officially launched, inviting organizations to publicly share their pledges.

Companies are encouraged to join this initiative, sharing and testing their internal guidelines for trustworthy AI within the AI Pact community.

Do you plan to advocate for your company's participation in the AI Pact to stay ahead in AI compliance and legal practices?

18 Countries Unveil Agreement to Make AI “Secure by Design”

The United States, the United Kingdom, and 16 other countries have recently unveiled an international agreement focused on developing "secure by design" artificial intelligence (AI). This 20-page document, while significant in promoting global cooperation on AI safety, is non-binding and lacks enforceable provisions, often referred to as having no "teeth." Key aspects of this agreement include:

1. General Recommendations: The agreement offers general guidelines for monitoring AI systems to prevent misuse and safeguarding data, but without legal enforcement capabilities.

2. Global Collaboration: Countries including Germany, Italy, Australia, Nigeria, and Singapore have joined this initiative, highlighting a growing international interest in standardized AI security practices.

3. Emphasis on Preventing Misuse: The primary focus is on ensuring AI systems are secure from the design stage, aiming to protect public safety and prevent potential abuses of AI technology.

4. Scope and Limitations: The agreement does not address complex issues such as the ethical applications of AI or data collection methods. Its non-binding nature means these areas remain largely unregulated and open for future policy development.

5. Contrast with European Efforts: Europe is advancing in AI regulation, with several countries advocating for self-regulation in foundational AI models. This approach contrasts with the more fragmented regulatory landscape in the U.S.

6. U.S. Regulatory Efforts: Despite efforts by the Biden administration to push for AI regulation, U.S. Congress has been slow in passing comprehensive laws. The White House's executive order aims to mitigate AI risks but lacks the legislative support to enforce widespread changes.

This agreement, while a step towards global collaboration, is another in a series of non-binding initiatives in the realm of AI governance. It raises a critical question: Is the increasing trend towards non-binding agreements sufficient to address the complex challenges posed by AI, or do we need more enforceable regulations to ensure safety and ethical standards?

What are your thoughts on this approach to AI governance?

Juicy Snippets from IAPP’s AI Governance Course

Ready for juicy AI governance insights without the hefty fee? I'm dishing out key takeaways from the $1,000 IAPP AI Governance course at no cost, all value! 🚀📚

For those that have been following over the last couple weeks, we’re still in Module 1 (we should be out of this module in two weeks). These notes include info on Robotics and AI.

Module 1: Robotics and AI

Fundamentals and Integration with AI

• Foundations of Robotics: Robotics combines elements of mechanical engineering, electrical engineering, and computer science to build machines that can perform tasks autonomously. The field has historically focused on creating robots that are programmed to execute specific, often repetitive tasks.

• Incorporating AI: The integration of AI into robotics allows for the development of machines that can learn from their environment and experiences, leading to significant improvements in their performance over time. This integration is essential for advancing the capabilities of robots beyond simple, programmed behaviors.

The Fourth Industrial Revolution: AI-Driven Robotics

• Industry 4.0: The term "Industry 4.0" represents the transformation of manufacturing and industry brought about by the introduction of digital technology and AI. In this new era, robotics empowered with AI capabilities are at the forefront, enabling advancements such as smart factories, where robots can communicate with one another and adapt to new tasks in real-time.

• Example of Application: In a large warehouse setup, AI-driven robotics might involve a fleet of robots that organize inventory, pack items, and manage logistics, all while continually optimizing their tasks without human intervention.

Machine Perception and Sensory Integration

• Enhancing Sensory Input: Machine perception seeks to endow robots with human-like sensory capabilities, allowing them to interpret and understand the world around them. This involves equipping robots with advanced sensors and AI that can process and analyze sensory data in a context-aware manner.

• Applications in Quality Control: In sectors such as food production, AI-enhanced sensory systems could dramatically improve the monitoring and control of food quality, analyzing produce through touch, smell, and taste sensors to ensure products meet health standards.

Robotic Process Automation (RPA) and AI Convergence

• RPA Defined: Robotic Process Automation involves software tools designed to automate a wide range of office tasks that are rule-based and repetitive. RPA bots mimic human actions on user interfaces, executing tasks like data entry, invoice processing, and routine customer service inquiries.

• AI Amplification of RPA: The integration of AI technologies such as machine learning and natural language processing enhances the scope of RPA. This allows RPA bots to handle tasks that require understanding of context or decision-making, thereby increasing the complexity of processes that can be automated.

Expert Systems: AI with Specialized Knowledge

• Expert System Overview: An expert system is a specialized form of AI that is programmed to simulate the decision-making abilities of a human expert in a specific domain. These systems are knowledge-intensive and rely on a rich database of domain-specific information.

• Key Elements of Expert Systems:

  • Knowledge Base: This component stores the domain-specific knowledge, often in the form of rules and facts, which the system uses to make decisions.

  • Inference Engine: Acting as the 'brain' of the system, the inference engine applies logical rules to the knowledge base to deduce new information or make decisions.

  • User Interface: This allows for user interaction with the system, where users can input queries and receive expert advice or solutions.

• Diverse Industry Usage: Expert systems have a broad range of applications, from assisting with financial decision-making in banks to supporting medical diagnosis in healthcare settings. Their role is to augment the expertise of professionals, providing them with high-level decision support.

Key Takeaways

• Robotics and AI Synergy: The convergence of robotics and AI is creating machines that are not only autonomous but also capable of learning and adapting, which is vital for tasks that require decision-making and problem-solving.

• Smart Automation: AI-infused robotics is central to the next wave of industrial automation, featuring machines that can communicate, collaborate, and improve their performance autonomously.

• Advanced Machine Perception: The integration of AI into robotic sensors is creating opportunities for robots to perform tasks that require nuanced sensory discrimination, far exceeding human capabilities in speed and accuracy.

• RPA Evolution: With the integration of AI, RPA is evolving from simple task automation to handling complex business processes that require cognitive capabilities.

• Expert Systems' Functionality: These AI systems encapsulate human expert knowledge and reasoning in a machine-accessible format, enabling them to serve as sophisticated decision-support tools in various domains.

Meme Of The Week:

That’s all for today!

Catch ya’ll on LinkedIn