Category Archives: investing

investing

The Electric Vehicle Revolution – a primer

Notes from my discussions with multiple industry analysts and reading many reports.

There are multiple changes happening with autombiles:

  1. Move to software-defined cars for “features” to be bought as subscriptions
  2. Move to electric vehicles from internal combustion engines
  3. Autonomous car technology
  4. Electrification of the charging network
  5. The growth of China as the leading EV and automobile producer (vs. Japan and Germany in the last 4-5 decades

The challenges:

  1. Availability of raw materials – Nickle, rare earths, etc.
  2. Supply chain security
  3. Affordability

In 2022, electric vehicles (EVs) took 11% global market
share (up from 6.5% in 2021), expecting 20% penetration by 2025
and the most aggressive for 100% by 2030.

Globally, we now expect battery electric vehicles (BEVs) to reach 40% penetration by 2030, and xEVs to reach 80% by 2035.

EV forecast calls for battery capacity to rise from ~600GWh in 2022 to 2,700GWh by end of 2030.

China has seen new energy vehicle (NEV) adoption become increasingly consumer-driven, with the country blowing past NEV penetration targets — the government sought to achieve ~20% penetration by 2025; it saw 23.5% in the first eight months of 2022. Chinese buyers seem to
favor domestic EVs over foreign (German and Japanese) brands though, leaving foreign OEMs potentially playing catch-up with domestic brands.

China represented close to 40% of the global battery
equipment market in 2021, up from 10%+ during 2016-19. Explosive growth over the past three years has been driven by sharply increasing investment in new energy industries.

Range anxiety over the switch from ICE to EVs has long been cited as a common impediment to greater EV adoption.

Artificial Intelligence, investing

An introduction to Vector databases

In this post I will try to answer the questions:

  • What is a vector database?
  • Why use a vector database?
  • What are the benefits of using a vector database?
  • Types of Vector databases
  • How to choose a vector database
  • Use cases for vector databases

What is a vector database?

Vectors are mathematical representations of features or attributes. Each vector has a certain number of dimensions, which can range from tens to thousands, depending on the complexity and granularity of the data.

A vector database is a type of database that stores data as high-dimensional vectors.

Why use a vector database?

There are several reasons why you might want to use a vector database, including:

  • To store and manage large amounts of unstructured data.
  • To perform similarity searches on large amounts of data.
  • To build machine learning models.
  • To improve the performance of your applications.

What are the benefits of using a vector database?

There are many benefits to using a vector database, including:

  • High performance: Vector databases are designed to perform similarity searches on large amounts of data quickly and efficiently.
  • Scalability: Vector databases can be scaled horizontally to handle large amounts of data.
  • Flexibility: Vector databases can store and manage a variety of data types, including text, images, and audio.
  • Ease of use: Vector databases are easy to use and manage, even for users with limited database experience.

Types of vector databases

There are many different types of vector databases available, each with its own strengths and weaknesses. Some of the most popular vector databases include:

  • Milvus: Milvus is a vector database developed by Tencent AI Lab. It is designed for high-performance similarity search on large-scale vector data.
  • Pinecone: Pinecone is a vector database developed by PineconeDB. It is designed for storing and managing large amounts of unstructured data.
  • Vespa: Vespa is a vector database developed by Yahoo!. It is designed for high-performance search and analytics on large-scale text data.
  • Weaviate: Weaviate is a vector database developed by Weaviate. It is designed for storing and managing large amounts of vector data.
  • Vald: Vald is a vector database developed by MemSQL. It is designed for high-performance search and analytics on large-scale data.
  • Gsi: Gsi is a vector database developed by Google AI. It is designed for storing and managing large amounts of vector data.

How to choose a vector database

When choosing a vector database, there are a number of factors to consider, such as:

  • The size and type of data you need to store: Some vector databases are better suited for storing large amounts of data, while others are better suited for storing smaller amounts of data. Some vector databases are better suited for storing text data, while others are better suited for storing images or audio data.
  • The features and functionality you need: Some vector databases offer more features and functionality than others. For example, some vector databases allow you to build machine learning models, while others do not.
  • Your budget: Vector databases can range in price from free to thousands of dollars per month. It is important to choose a vector database that fits your budget.

Use cases for vector databases

Vector databases can be used for a wide variety of applications, including:

  • Image search: Vector databases can be used to build image search engines that can find similar images based on their visual content.
  • Product recommendations: Vector databases can be used to build product recommendation engines that can recommend products to users based on their past purchases and interests.
  • Text classification: Vector databases can be used to classify text documents into different categories, such as news, sports, or finance.
  • Natural language processing: Vector databases can be used to perform natural language processing tasks, such as sentiment analysis and machine translation.
  • Fraud detection: Vector databases can be used to detect fraud by identifying patterns of suspicious activity.
  • Drug discovery: Vector databases can be used to discover new drugs by identifying patterns in biological data.

Conclusion

Vector databases are a powerful new technology that can be used to store, manage, and search large amounts of unstructured data. If you are looking for a database that can handle the challenges of modern data, then a vector database is a great option.

investing

What is a vector database? As explained by ChatGPT and Google bard $GOOGL

Presented without commentary first is my screenshot.

Bard does not want to help
ChatGPT knows the answer

I am now really curious why Google Bard won’t answer the question. It obviously knows the answer. Or Google search does know something.

What I think happened is that over a series of questions, which Google got consistently wrong I responded with a series of thumbs down responses.

That resulted in “high accuracy” mode of operation. So, now I mostly get “no response”.

investing

How to write, publish, edit & distribute a book – A resource for book writers in 2023

Writing a book is a big amount of effort and commitment!

There’s a ton of steps involved, from brainstorming to publishing. Let me break it down if you are so inclined.

NightCafe AI generated

Writing a Book:

  1. Idea generation: Think of a topic you’re passionate about and come up with some killer ideas.
  2. Research: Look up some background info and gather facts that’ll help you develop your story and characters.
  3. Outline: Sketch out a rough draft of your book, including the main plot and chapters.
  4. First draft: Start writing your first draft. Don’t worry about being perfect, just focus on getting your ideas on paper.
  5. Revision: Read through your draft and make any necessary changes to improve the flow and structure.
  6. Beta readers: Get some feedback from beta readers on what they liked and what could be improved.
  7. Edit: Use the feedback to make any necessary edits to your manuscript.
  8. Final draft: After polishing your manuscript, give it one final read-through before it’s ready for publishing.
fotor. AI generated

Publishing:

  1. Self-publish or traditional: Decide whether you want to self-publish or try for a traditional publishing house. My publishing is Diamond Consulting, easy to work with. They helped with editing, proof reading, layout, cover design and distribution. The book is available now on Amazon, Barnes and Noble, Apple iBooks (more coming soon).
  2. Submit your manuscript: If you choose traditional publishing, submit your manuscript to agents or publishers who accept unsolicited submissions.
  3. Negotiate the contract: If your manuscript gets accepted, negotiate the terms of your publishing contract.
  4. Editing: Work with an editor to make any necessary edits to your manuscript. My editor was ok, not a great experience. Choose one that knows your space. Its okay to pay more for this.
  5. Proofreading: Have your manuscript proofread by a pro to ensure there are no errors. I had an awful experience with Fiverr for proof reading.
  6. Layout: Format your manuscript according to publishing standards.
  7. Cover design: Work with a graphic designer to create a dope cover that’ll attract readers. My graphic design HDMXPublishing (Hamaad) was awesome. Based in London, the guy was fast, reliable and consistent.
  8. Print and distribute: Once everything is complete, print and distribute your book.
  9. Consider all book formats: Ebook (ePub), Hard cover (if this is a publication people will cherish), Paperback (for folks that like paper in their hands) and Audio book.
  10. ISBN: I got an ISBN for the eBook from myidentifers.com ($295 for 10) but used Kindle ISBN (free) for the Amazon paperback version and Barnes & Noble ISBN (free) for the B&N paperback version. Every version of your book needs a different ISBN – audio, eBook, paperback and hardcover.

Editing and proofreading:

  1. Editing: Hire a pro to review your manuscript and give suggestions.
  2. Revision: Make revisions based on the editor’s feedback.
  3. Proofreading: Have your manuscript proofread to ensure it’s error-free.
Fotor: AI generated image

Layout and cover design:

  1. Layout: Format your manuscript according to industry standards.
  2. Cover design: Work with a graphic designer to create a sweet cover that’ll grab readers’ attention.
  3. Print and distribute: Once everything is complete, print and distribute your book.
  4. Think about size (5X8 is for Paperback Amazon and 5.25X8 for Barnes and Noble)