Edges of Innovation - #4

I can't believe it's already the 4th edition of Edges of Innovation. This edition is focused on #GenAI as news are pouring almost every day. I forecasted a few months back that the pace of innovation in the Gen AI space would be increasing and it is, although I still believe we're at the beginning of it. Of course, it's not too late to put your heads down on it and one of my goals with Edges of Innovation is to help you with this.

This week, I'm giving you some advice on how to structure a Gen AI project with the BRAIVE framework, look into Open Source LLMs to avoid being locked down by proprietary models like ChatGPT, come back on the changes ahead when it comes to work, explain why Gen AI is revolutionizing marketing and what you can do with this, and finally, offer a perspective on Moore's law and the doubling of computing power every two years that does not seem to stop.

I wish you a great read and a wonderful week!

BRAIVE: The Quintessential Framework for AI Success in Business

In the rapidly evolving landscape of artificial intelligence (AI), businesses face the challenge of integrating AI into their operations effectively. Microsoft released a?white paper?that captures the essential approached to AI in the enterprise. From this approach and our experience in delivering AI projects, we've distilled a straightforward framework, BRAIVE, representing the essential components for achieving AI success: Business Strategy, Resources & Technology, AI Approach and Experience, Values, Ethics & Governance, and Employees, Organisation & Culture.

Here's a proven approach to AI deployment in any organisation, from any industry and any size.

1. Business Strategy (B):?

-?Where AI Meets Purpose: Start by aligning AI initiatives with core business objectives. It's about understanding how AI can enhance customer experiences, streamline operations, or drive innovation.

-?Actionable Insight: Regularly review your business objectives and reassess how AI can support these goals.

2. Resources and Technology (R):?

-?The Tech Foundation: An AI-ready infrastructure is vital. This includes choosing the right platform and determining whether to build or buy AI applications.

-?Actionable Insight: Conduct a technology audit to assess readiness for AI integration and identify areas for development or investment.

3. AI Approach and Innovation (AI):?

-?AI Beyond Tech: It's not just about technology; it's about how AI can solve real-world problems, enhance customer experiences and bring innovation at the core of the business.

-?Actionable Insight: Foster a culture of innovation where AI solutions are regularly brainstormed and tested.

4. Value, Ethics & Governance (V):?

-?Navigating the AI Maze: Establish strong governance frameworks to manage data privacy, ethical considerations, and compliance.

-?Actionable Insight: Develop a comprehensive AI policy, ensuring all stakeholders are aware of and adhere to these guidelines.

5. Employees, Organisation and Culture (E):

-?The People Factor: The success of AI is as much about people as it is about technology. This includes leadership support, team structures, and fostering a culture of AI literacy.

-?Actionable Insight: Invest in continuous learning and development programs to build AI competence across your organization.

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The BRAIVE framework provides a holistic approach to AI integration, ensuring not just the adoption of AI technologies but their effective and ethical application in business. By embracing this framework, organizations can navigate the complexities of AI, driving innovation and value creation.

The journey of AI integration is continuous. It demands agility, a willingness to learn, and adaptability to evolving technologies and market demands. If you want more information about BRAIVE and to devise a strategy to embrace it and unlock the full potential of AI in your business, DM. I'll be happy to have a thorough discussion on AI in your organisation.

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Open-Source Large Language Models, Embrace a Fair Future

Open-source Large Language Models (LLMs) like LLaMa 2 and Claude 2 are revolutionizing the AI industry, offering unique advantages to businesses that embrace them. Here's my personal analysis of why companies should consider open-source LLMs, instead of closed solutions like OpenAI or Bing, and how they can leverage these models, including utilizing AWS Bedrock for LLaMa 2, to enhance their operations.

Why Opt for Open Source LLMs?

1. Democratization of AI Technology: Open-source LLMs democratize AI, making it accessible to a broader audience, including researchers, developers, and businesses. This accessibility fosters innovation and collaboration in the AI community.
2. Tailored Solutions: Open-source models allow companies to customize and fine-tune AI solutions for their specific needs. This flexibility is crucial in addressing unique business challenges and objectives.
3. Cost-Effectiveness: By using open-source LLMs, businesses can save on licensing fees associated with proprietary models. This cost advantage is particularly beneficial for startups and smaller companies with limited budgets.
4. Community Support and Collaboration: Open-source projects benefit from a vibrant community of developers and users who contribute to the improvement and evolution of the models.

LLaMa 2: A Case Study

Meta’s LLaMa 2 represents a significant leap in AI-driven interactions. Its features include:

• Diverse Training Data: LLaMa 2’s training data is extensive and varied, providing a comprehensive understanding and performance.
• Safety-Centric Design: The model is designed to minimize misleading or harmful content, making it a reliable choice for businesses.
• Versatility: LLaMa 2 is optimized for different platforms, including AWS, Azure, and Windows, broadening its application scope.

Claude 2: A Fantastic Example

I really like?claude.ai?and Anthropic’s Claude 2 showcases advancements in AI capabilities, offering:

• Extended and Coherent Responses: Claude 2 is designed for detailed and intuitive interactions, beneficial for customer service and other business applications.
• High Academic and Reasoning Capabilities: Claude 2’s performance in exams like the Bar and GRE indicates its proficiency in generating complex content, useful for a range of business applications.

Both LLMs are available through AWS Bedrock. Amazon Bedrock, a fully managed service offering high-performing foundation models from leading AI companies. On November 13, Amazon announced the availability of?Meta's Llama 2 Chat 13B model. This integration marks Amazon Bedrock as the first public cloud service to offer a fully managed API for Llama 2, a next-generation LLM. Llama 2 has been pre-trained on 2 trillion tokens from online public data sources, consuming a considerable 184,320 GPU/hour for training, equivalent to over 21 years of a single GPU's effort.

What Does This Mean For Your Business?

1. Simplified Access: You can now access Llama 2 Chat models on Amazon Bedrock without managing the underlying infrastructure. This simplifies the adoption process, making advanced AI technologies more accessible to businesses, regardless of their size or technical expertise.
2. Enhanced Capabilities: Amazon Bedrock provides a range of generative AI applications, maintaining privacy and security. This opens up new possibilities for businesses to innovate and enhance their customer interactions, data analysis, and automation processes.
3. Cost-Effectiveness: The integration reduces the need for significant investments in hardware and specialized AI training, democratizing access to cutting-edge AI technologies.
4. Scalability: Amazon Bedrock's managed service ensures scalability, allowing businesses to expand their AI capabilities as they grow, without worrying about the complexities of scaling the technology.

The Open Source Advantage

Open-source LLMs like Llama 2 and Claude 2 represent a shift towards more collaborative, transparent, and innovative AI development. This approach fosters community-driven improvements, wider accessibility, and potentially faster advancements in AI technologies.

If you want to know more about Bedrock and Llama 2, read the details from AWS?here?or DM me for a chat on how to leverage Generative AI Open-Source models on hybrid clouds.

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Unleashing the Future: Generative AI's Radical Transformation of Knowledge Work

In an era where ChatGPT has reached 100 million monthly users faster than any other internet application in history, the transformative impact of Generative AI on knowledge work is undeniable. As industries from healthcare to finance invest billions into adopting these technologies, the potential for efficiency and productivity gains is immense. A recent article from the Harvard Business Review (HBR), entitled?How Generative AI Will Transform Knowledge Work?contributed to anchor my deep belief that we are now passed an inflection point where life (and work) will never be the same. I'm here revisiting this article for the non-subscriber of HBR to provide some thought-provoking ideas on how work is reshaped as we speak.

Rethinking the Role of Knowledge Workers

Knowledge work, characterized by cognitive processing and specialized skills, is undergoing a fundamental shift with Generative AI. While it's expected to automate some tasks, its true power lies in enabling knowledge workers to devote more time to meaningful, complex work, thereby enhancing both performance and productivity.

The New Paradigm: Enhancing Cognitive Abilities

Generative AI is reshaping the way we manage the deluge of digital information, reducing cognitive load and freeing up mental capacity for higher-value tasks. This shift is evident across various industries, from law firms like Allen & Overy using AI to streamline legal processes to marketing departments automating content generation.

Boosting Creativity and Critical Thinking:

Generative AI fosters a new level of creativity and critical thinking. It prompts a wider range of questions and ideas, leading to more innovative and effective solutions. The tool's storytelling capabilities are particularly notable in strategy formulation, although it may be less effective in implementation due to limited contextual understanding, although this may change soon with fine-tuning.

Sharing and Growing Knowledge

AI technologies not only generate but also disseminate knowledge. For instance, Morgan Stanley's implementation of AI in wealth management has been transformative, making a wealth of knowledge accessible to advisors, enhancing client interactions.

AI as a Mentor and Learning Tool

Generative AI's role extends to training and education. It acts as a mentor, providing feedback and guidance. This is seen in environments like call centers, where AI-assisted training leads to improved productivity and employee satisfaction.

Managing Risks and Encouraging Innovation

With millions globally using Generative AI, the potential for innovation is enormous, but so are the risks. Errors, biased decisions, and privacy concerns are real challenges. Companies must establish clear policies, encourage responsible experimentation, and celebrate successes to harness the potential of AI while mitigating risks.

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The advent of Generative AI in knowledge work is a call to action. Rather than waiting for externally imposed changes, we can harness these tools now for our benefit. By understanding and mitigating the associated risks, we can transform how we work, learn, and innovate. The future of knowledge work with Generative AI is not just a possibility; it's a present reality we can shape for the better.

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Revolutionizing Marketing with Generative AI - Insights and Strategies for Tomorrow's Marketers

The marketing landscape is on the cusp of a revolution, thanks to the emergence of generative AI. This powerful technology is not just a futuristic concept; it's here, and it's reshaping how we create and perceive marketing content. The research paper "The Power of Generative Marketing: Can Generative AI Reach Human-Level Visual Marketing Content?" offers groundbreaking insights into this development, demonstrating how AI-generated images can rival and even surpass human-made content in quality, engagement, and effectiveness.

Study Overview

The paper presents three rigorously conducted studies with over 17,000 human evaluations of more than 1,500 synthetic images produced by 13 text-to-image diffusion models. These studies explore:

1. The?perceived quality and realism?of AI-generated versus human-made images in various marketing contexts.
2. The?engagement levels?of synthetic images on social media.
3. The?effectiveness of AI-generated images?in real-world banner ad campaigns.

Key Findings

• Quality and Realism: In many cases, AI-generated images were indistinguishable from or superior to human-made counterparts, especially in product design and social media content.
• Social Media Engagement: Certain AI models produced images that matched or exceeded the engagement levels of human-created visuals.
• Click-Through Rates: Impressively, synthetic images in banner ad campaigns outperformed traditional images, with up to a 22% higher click-through rate.

Implications for Marketers and Businesses

This research underscores the immense potential of generative AI in marketing. Not only can it significantly reduce costs and time associated with content creation, but it also levels the playing field, enabling smaller businesses to produce high-quality visuals that compete with larger enterprises. However, the choice of AI model is crucial, as different models yield varying results.

Practical Applications

1. Integrating AI into Marketing Strategies: Businesses should consider incorporating generative AI into their social media campaigns, product design, and online advertising. It offers a rapid, cost-effective method to produce diverse and appealing visuals.
2. Choosing the Right AI Model: Select models based on your specific needs—some are better for realism, while others excel in creativity. Continuous experimentation and adaptation are key.

Future Outlook

As AI technology advances, we can expect even more sophisticated and nuanced applications in marketing. However, this also brings ethical considerations to the forefront. Marketers must use this technology responsibly, ensuring authenticity and transparency in their AI-generated content.

Generative AI is not just a tool; it's a game changer for marketing. Its ability to create high-quality, engaging content quickly and cost-effectively opens up new horizons for creativity and efficiency. As marketers, we must embrace this technology, experiment with its capabilities, and integrate it thoughtfully into our strategies. By doing so, we can stay ahead in an increasingly competitive and fast-paced digital world.

Advice for Marketers

1. Start Experimenting Now: Don't wait. Begin exploring generative AI tools and understand how they can fit into your marketing mix.
2. Focus on Quality and Authenticity: Use AI to enhance your brand's message, not replace it. Authenticity still reigns supreme in marketing.
3. Stay Informed and Adaptable: The field of AI is evolving rapidly. Keep abreast of the latest developments and be ready to adapt your strategies.
4. Balance Innovation with Ethics: Always consider the ethical implications of using AI-generated content. Transparency with your audience is crucial.

Generative AI is more than just a technological advancement; it's a new canvas for creativity and a strategic asset for smart marketers. The future of marketing is here, and it's powered by AI. Let's embrace this change and harness its potential to create more engaging, effective, and innovative marketing campaigns.

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Navigating Beyond Moore's Law - The Future of Semiconductors and AI

For over five decades, Moore's Law has been a beacon in the semiconductor industry, predicting a doubling of transistors on silicon chips every two years. This observation, made by Intel co-founder Gordon Moore, has been instrumental in the evolution of digital technology, AI, and deep learning. However, as the limits of silicon lithography are approached, the industry faces new challenges and opportunities.

The Current Landscape

Moore's Law, an observation rather than a strict law, has driven the impressive evolution of digital devices, particularly in improving the price-performance ratio of products like smartphones. Yet today, this law is at a critical juncture. The shrinking size of transistors is nearing atomic dimensions, challenging the very techniques that enabled their miniaturization. This deceleration affects not only the semiconductor industry but also the realms of AI and machine learning, which rely heavily on computing power.

Future Directions

Hardware Innovations

The industry is responding to these challenges with innovative hardware solutions. New manufacturing techniques, such as nanosheet field-effect-transistors (FET), are being explored to overcome the limitations of extreme miniaturization. Additionally, 3D chiplet architecture offers a promising avenue for advancement. Concurrently, there's a shift towards Software-defined Domain Specific Architectures (DSA), which includes GPUs, FPGAs, and ASICs. These architectures offer flexibility and energy efficiency by being reconfigurable in real-time.

Software and System Evolution

In the software domain, there's a pivot towards AI-driven high-performance computing (HPC). AI is transitioning from a user of HPC to a driver, merging supercomputers and cloud systems into a cohesive unit. New architectures like OmniX are being developed, employing SmartNICs (smart-network-interface-cards) to create secure, efficient systems. This marks a significant shift from traditional, CPU-centric designs.

Combining Technologies

Combining different technologies is another strategy being explored. For instance, integrating photonics with digital electronics could address the bandwidth bottlenecks in processors, enhancing speed and reducing latency. Neuromorphic photonics networks, inspired by the human brain, hold the potential to revolutionize chip design by replicating brain-like functions.

Open Hardware Movement

The open hardware movement, paralleling open-source software, is gaining momentum. It fosters collaborative and democratized hardware development, exemplified by organizations like the CHIPS Alliance, which is part of the Linux Foundation. This movement champions open alternatives to proprietary architectures, like RISC-V.

The Road Ahead

The future of computing beyond Moore's Law is not bleak but rather filled with opportunities. High-level domain-specific languages and architectures, open-source ecosystems, and agile chip manufacturing are paving the way for this new era. The focus is shifting towards cost, energy efficiency, security, and performance improvements. In the context of AI and GenAI (General AI), these advancements promise to enhance computational capabilities significantly, offering a brighter future for technology applications.

As we navigate the path beyond Moore's Law, the semiconductor industry is entering an exciting phase. Innovations in hardware and software, combined with the rise of open hardware initiatives, are setting the stage for a new golden age in computing and AI. This evolution holds vast potential for further advancements in technology and its applications.

Acronyms

• GPU (Graphical Processing Unit):?Specialized processors designed for rendering graphics and images, now increasingly used for parallel processing in AI and deep learning.
• FPGA (Field Programmable Gate Array):?Integrated circuits that can be configured by a customer or a designer after manufacturing – flexible and adaptable for various applications.
• ASIC (Application Specific Integrated Circuit):?Customized integrated circuits designed for a specific use or application, offering higher efficiency and performance for specialized tasks.
• CPU (Central Processing Unit):?The primary component of a computer that performs most of the processing inside a computer.
• HPC (High-Performance Computing):?Computing at the leading edge of processing capability, often used for scientific research, simulation, and large-scale computation.
• SmartNIC (Smart Network Interface Card):?Advanced network interface cards that incorporate additional processing and computing capabilities to offload tasks from the CPU.
• RISC-V (Reduced Instruction Set Computing - V):?An open standard instruction set architecture (ISA) that enables a new era of processor innovation through open standard collaboration.
• GenAI (General Artificial Intelligence):?A form of AI intended to perform any intellectual task that a human can, encompassing a wide range of cognitive tasks.

References

- WSJ: Pro Take: Going Beyond Moore's Law

- Thoughtworks: Moving Beyond Moore's Law

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Crazy, Exciting and Latest News

• Microsoft enters the foundry work with its first series of processor, Maia, an AI Accelerator chip designed to run cloud-based training and inferencing for AI workloads such as OpenAI models, Bing, GitHub Copilot and ChatGPT.
• AWS goes to Las Vegas for re:invent 2023, its partners meeting. Expect a series of announcements in the AI front...
• Eric Schmidt, Rodolphe Saadé and Xavier Niel join forces to create an AI lab in France.
• Apple launches a beta version of Object Capture, as sotware that transforms pictures into 3D objects.
• Learn more about Generative AI with free AWS courses: 8 hours of increbible knowledge to increase your understanding of #GenAI

Another week ahead with exciting things. Stay tuned for next, innovation and AI are hot topics these days... and if you have any topic you want me to address, just let me know in comments or DM! Have all a great week!

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