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- 🤖 Flexible Evolution: EU's Approach to General Purpose AI Rules
🤖 Flexible Evolution: EU's Approach to General Purpose AI Rules
PLUS: The Rise of Multimodal AI Models
Hyrise AI
December 12, 2023
Welcome, AI Enthusiasts.
European Union lawmakers reached a political deal on regulating artificial intelligence (AI), granting the Commission powers to adapt AI regulations over time to match technological advancements.
The evolution of AI has led to multimodal AI, capable of processing various data types simultaneously, mirroring human cognition and offering versatile applications across sectors.
In today’s issue:
🤖 Flexible Evolution: EU's Approach to General Purpose AI Rules
🦾 The Rise of Multimodal AI Models
🛠️ 3 New AI tools
💻 Custom prompts ChatGPT and DALL-E 3
🤖 3 Quick AI updates
Read time: 4 minutes.
LATEST HIGHLIGHTS
Image source: Unsplash
To recap: European Union lawmakers reached a political deal on regulating artificial intelligence (AI), granting the Commission powers to adapt AI regulations over time to match technological advancements. The law classifies high-risk AI models as "general purpose" and includes a threshold for compute power, setting it at 10^25 floating point operations (FLOPs). The Commission will have authority to update this threshold and introduce new benchmarks in collaboration with an AI oversight body. Companies developing such AI models must self-assess against the FLOPs threshold and comply with transparency requirements, including watermarking generative AI outputs. The EU AI Act will take a phased compliance approach, with strict prohibitions on certain AI uses applying within six months and high-risk rules for AI models in 24 months, expecting full implementation by 2026.
The details:
AI Regulation Evolution: The EU's AI law allows the Commission to adapt regulations over time, keeping pace with technological advancements in the field. This includes setting a threshold for high-risk AI models based on compute power, initially set at 10^25 floating point operations (FLOPs), with provisions to update and refine this threshold in collaboration with an AI oversight body.
Self-Assessment by AI Developers: Companies developing high-risk AI models, termed as "general purpose," are responsible for self-assessing whether their models meet the defined FLOPs threshold. This assessment determines whether they fall under regulations aimed at mitigating systemic risks, including transparency obligations such as watermarking generative AI outputs.
Phased Compliance and Implementation: The EU AI Act follows a phased compliance approach, with strict prohibitions on specific AI uses coming into effect within six months of the law's enactment. Regulations for high-risk AI models are expected to be fully operational within 24 months, aiming for complete implementation by 2026, allowing time for companies to adapt and comply with the evolving rules.
Here is the key takeaway: Generative AI (GAI) is playing a significant role in revolutionizing the electric vehicle (EV) industry. It is helping to accelerate the development of efficient batteries, improve EV range and charging speed, and provide predictive maintenance insights. This technology has the potential to address critical challenges in the EV industry and fuel innovation, making electric vehicles more practical and appealing to consumers.
Image source: Splash
In Summary: The evolution of AI has led to multimodal AI, capable of processing various data types simultaneously, mirroring human cognition and offering versatile applications across sectors. Key advancements in natural language processing, image analysis, and speech recognition have shaped this technology, promising expanded applications while emphasizing the need for ethical standards and privacy safeguards in its deployment.
Key points:
Multimodal AI Evolution: Multimodal AI represents a leap forward by processing diverse data types simultaneously, akin to human sensory integration.
Technological Underpinnings: Advancements in natural language processing, image analysis, and speech recognition are pivotal in developing multimodal AI systems.
Exemplary Models: ChatGPT and Google Gemini showcase cutting-edge multimodal AI capabilities, integrating varied data types like text, code, audio, and images.
Diverse Applications: Multimodal AI revolutionizes industries like healthcare, retail, education, and security with applications spanning diagnostics, personalized services, education transformation, and advanced surveillance.
Our thoughts: As a tech writer, the evolution of multimodal AI represents a fascinating convergence of diverse data types, pushing the boundaries of AI capabilities. The integration of natural language processing, image analysis, and speech recognition into cohesive systems is a testament to the evolving technological landscape. Models like ChatGPT and Google Gemini showcase the immense potential of this field, promising innovative applications across various industries. However, I'm attentive to the challenges—technical complexities, ethical considerations, and privacy concerns—that require careful navigation for responsible and beneficial deployment of these advanced systems.
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AI DOJO
ChatGPT
Christmas Footer: "Generate a festive email footer for the holiday season, incorporating Christmas-themed graphics and warm greetings suitable for professional correspondence."
DALL-E 3
Christmas Footer: "Create a series of decorative Christmas-themed footers suitable for email signatures, incorporating elements like snowflakes, ornaments, and warm seasonal greetings."
QUICK BYTES
AI and blockchain, both disruptive forces in today's world, converge to revolutionize several industries. Supply chain management benefits from their merger, enhancing transparency and optimizing logistics. Healthcare sees advancements in diagnostics, personalized treatment plans, and efficient data management. The pairing bolsters data analytics and security, offering robust data integrity and aiding predictive analytics. Moreover, it enables decentralized AI marketplaces and fortifies security systems against adversarial manipulation. However, challenges like interoperability, scalability, ethical concerns, and legal complexities persist, hindering their seamless integration. Despite obstacles, the future holds immense potential for these technologies, promising a transformative impact across various sectors, reshaping economies, and fostering trust, transparency, and reliability.
A French startup, Spore.Bio, is revolutionizing food factory cleanliness by using Generative AI for real-time detection of harmful microbes. Their device swiftly identifies pathogens on surfaces by comparing them with a database derived from typical microbes found in such facilities. This innovation significantly cuts down testing time—claiming near real-time results compared to the usual 5-20 days from labs. The company recently secured €8 million in pre-seed funding led by London's LocalGlobe VC, alongside several other investors. Founded in 2023 by industry veterans, including ex-Nestlé CEO Amine Raji, Spore.Bio faces competition from PathogenDX in the US, which has raised $11.6M for its alternative solutions.
Relevance AI is changing the game by enabling businesses, regardless of size, to create custom AI agents with its low-code platform. This Australian startup raised $10 million in a Series A round, totaling $13.2 million in funding. Their platform empowers companies to automate tasks, boasting 6,000 sign-ups and 250,000 completed tasks in just three months. Targeting sectors like sales and support, Relevance AI offers AI Tools and AI agents that streamline workflows, such as the Business Development Representative (BDR) agent tailored for sales teams. Their vision? By 2025, every team will have at least one AI agent; by 2030, full-fledged AI teams will support them. With plans to expand its U.S. presence and team, Relevance AI's focus remains on task-based outcomes, aiming to revolutionize how businesses delegate work and automate repetitive tasks across various industries.
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