Cocoa's growth is higher than Nvidia
Interesting trends
Forget Nvidia, check out cocoa beans!
The significant appreciation in cocoa bean prices by 124% can be attributed to a complex interplay of factors affecting the global cocoa market. Key drivers include supply constraints, adverse weather conditions, diseases, geopolitical tensions, and rising global demand.
Supply Constraints: The International Cocoa Organization highlighted that supply was a major factor driving bullish prices, with concerns over supply deficits due to unconducive weather conditions and diseases like black pod disease and swollen shoot virus exacerbated by excess rains. Additionally, geopolitical tensions affecting freight rates have introduced further cost pressures on the already high cocoa prices.
Weather Conditions and Diseases: West Africa, responsible for two-thirds of the world's cocoa harvest, has faced production challenges including diseases affecting plantations and adverse climatic conditions. The La Niña weather pattern, followed by concerns over El Niño, has led to excessive rainfall and dry conditions, respectively, damaging cocoa plantations and affecting crop prospects. Diseases exacerbated by these conditions, such as the swollen porpoise virus and brown rot disease, have further impacted production.
Global Demand and Supply Dynamics: The global demand for cocoa has seen a considerable increase, particularly as economies recover from the COVID-19 pandemic slowdown. This resurgence in demand, against the backdrop of reduced supply from major producers like Ivory Coast and Ghana due to adverse weather, diseases, and other factors, has led to stock deficits and contributed to the price surge. Notably, Ivory Coast halted the sale of future contracts for the 2023/24 cocoa cycle, signaling concerns over meeting demand.
Impact on West African Economies: The price surge presents both challenges and opportunities for West African countries, the heartland of global cocoa production. Governments in the region have responded by increasing the guaranteed producer prices to farmers, aiming to improve livelihoods and address the structural challenges within the cocoa value chain. However, the rising production costs, exacerbated by factors such as land availability and increased labor costs due to disease management, have not been fully accounted for in cocoa bean pricing. This misalignment has implications for sustainable farming practices and the economic stability of cocoa-dependent economies.
Sustainability and Market Volatility: The cocoa market's volatility is further compounded by sustainability challenges. The sector has seen growth in voluntary sustainability standards aimed at enhancing farmers' livelihoods and promoting more sustainable production practices. However, climate change and its impact on suitable cocoa-growing regions, along with political and economic unrest, continue to threaten the sector's sustainability. These factors underscore the need for comprehensive strategies to address both immediate and long-term challenges facing the cocoa market
Customer Analysis
Beyond multichannel and omnichannel, now please welcome optichannel
The article from MarTech delves into the evolution of customer engagement strategies, marking a transition from multichannel and omnichannel approaches to what is termed as the optichannel approach. This nuanced shift underscores not just the need for presence across multiple platforms (multichannel) or a seamless experience across these platforms (omnichannel), but the imperative to strategically select the best channel for communication based on customer data and preferences (optichannel).
This commentary underscores the significance of understanding and implementing the optichannel approach as a means to not only meet but exceed customer expectations. In the digital age, where customer data is abundant, the ability to analyze and utilize this data to make informed decisions about where and how to engage customers is paramount. The optichannel approach is essentially about precision and personalization at scale. It recognizes that not all channels are created equal for every customer or every type of message. Some messages may be more effectively communicated through email, while others through social media or even traditional mail, depending on the target audience's preferences and behaviors.
The move towards optichannel reflects a broader trend in marketing and customer engagement towards deeper personalization. It's an acknowledgment that the customer journey is not just multi-faceted but also unique to each individual. By leveraging data to understand these journeys and preferences, businesses can create more meaningful and impactful interactions. This approach does not merely aim to increase customer satisfaction on a superficial level but seeks to build deeper relationships and loyalty by demonstrating an understanding of and respect for the customer’s preferences.
Moreover, the optichannel approach offers an opportunity for more efficient allocation of resources. By focusing efforts on the channels that yield the highest engagement or conversion rates for a given audience segment, businesses can optimize their marketing spend and potentially achieve a higher return on investment. This efficiency also means that customers are less likely to be bombarded with irrelevant messages on channels that they do not prefer, reducing the risk of message fatigue or brand disengagement.
In conclusion, the shift towards an optichannel strategy represents a mature phase of digital marketing, where data-driven insights enable a more sophisticated and customer-centric approach to engagement. This strategy is not without its challenges, notably in the realms of data analysis and integration, requiring robust systems and expertise. However, the potential rewards in terms of customer loyalty and business performance make it a compelling approach for businesses willing to invest in understanding and catering to their customers at a deeper level.
Worth considering if you are a data driven entrepreneur. Check out the original article on MarTech.
Collaborative learning with large language models
The Google Research Blog discusses a novel approach to enhance large language models (LLMs) through social learning, where models learn from each other using natural language. This method, inspired by human social learning, involves models sharing knowledge in a privacy-conscious way to improve task performance without sharing sensitive data. The research tested this approach on tasks like spam detection and math problems, showing that models could teach each other effectively through synthetic examples and instructions, maintaining data privacy. This innovative strategy could lead to more efficient and secure collaborative learning among AI models
Social learning for LLMs
- The authors extend the concept of social learning, originally described for humans, to LLMs.
- In this framework, a student LLM learns to solve a task from multiple teacher LLMs that already know the task, without directly sharing the teachers' private data.
Synthetic examples
- As an alternative to sharing original data, the authors propose a method where teacher models generate new synthetic examples for the task and share them with the student.
- The generated examples are sufficiently different from the original ones to preserve privacy while still enabling comparable performance to using the original examples.
Synthetic instruction
- Another approach is to have the teacher models generate instructions for the task, which the student model can follow.
- Providing generated instructions effectively improves performance over zero-shot prompting, reaching accuracies comparable to few-shot prompting with original examples.
- The effectiveness of generating instructions versus generating examples depends on the specific task.
Memorisation of private examples
- The authors adapt the Secret Sharer method to quantify how prone the social learning process is to leaking information from the teachers' private data.
- Results show that the student model is only slightly more confident in the "canary" examples the teacher used compared to similar ones that were not shared, indicating that the teacher uses its data to teach without simply copying it over.
Conclusion
- The proposed framework allows LLMs to transfer knowledge through textual communication while maintaining data privacy.
- Future work includes improving the teaching process, such as adding feedback loops and iteration, and investigating social learning for modalities other than text.
For more details, visit the Google Research Blog.
Interest in Graphene OS has surged 133% in the past 2 years
GrapheneOS has become a topic of interest for its strong privacy and security focus, offering an Android-based OS without Google apps or a default app store, suggesting alternatives like F-Droid for app installation. Its straightforward installation process contrasts with a potentially less seamless user experience due to the absence of Google's smart features and reliance on Google apps. It also gained attention with the introduction of AphyOS by Apostrophy AG, a subscription-based service built on GrapheneOS, enhancing its visibility and relevance in discussions about mobile operating system privacy and security
Privacy Focus: GrapheneOS prioritizes user privacy, excluding Google services and apps to minimize data sharing.
Security Enhanced: Offers hardened security features beyond standard Android, making it resilient against attacks.
Open Source: Its open-source nature allows for transparency and community contributions to its development.
Google Services Alternative: Supports installations of privacy-respecting app stores like F-Droid instead of the Google Play Store.
Installation: Designed for straightforward installation on compatible devices, but may present a learning curve for users dependent on Google's ecosystem.
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