Appendix - GPT-4 responses

Explain what 'data gravity' means.

Data gravity is a concept in the field of data management and cloud computing that refers to the tendency of data and applications to attract each other and aggregate in a centralized location. The term was first coined by Dave McCrory in 2010. As the volume of data grows, it becomes increasingly difficult and expensive to move, making it more likely that applications and services will be built or migrated closer to the data itself. This phenomenon has several implications:

1. Performance: Applications and services perform better when they are closer to the data they need to access, as latency is reduced and data transfer speeds are increased.
2. Cost: Transferring large volumes of data across networks can be expensive, especially in cloud environments where data transfer costs are often billed separately. By minimizing data movement, costs can be reduced.
3. Security and compliance: Data gravity can help organizations meet regulatory requirements, as sensitive data can be stored and processed in specific locations, making it easier to comply with data residency and privacy regulations.
4. Data ecosystem: As data attracts more applications and services, a rich data ecosystem is formed, which can lead to the development of new products, insights, and innovation.

In summary, data gravity refers to the natural attraction between data and applications, which leads to the centralization of data and the development of data ecosystems that provide performance, cost, security, and compliance benefits.

Please explain LSTM TimeStep neural network to a web3 developer

Hello, web3 developer! I assume you're familiar with concepts like blockchain, smart contracts, and decentralized applications (dApps). Let's now delve into the world of recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) TimeStep neural networks.

An LSTM TimeStep neural network is a type of recurrent neural network (RNN) designed to handle time-series or sequence data. It's particularly useful for processing and predicting data points that depend on previous data points in the sequence, which is a common problem in many real-world scenarios like natural language processing, financial market predictions, and speech recognition.

LSTMs solve the issue of vanishing or exploding gradients, which are common problems in training traditional RNNs. They achieve this through a unique memory cell structure that stores and retrieves information over long sequences.

Here's a high-level explanation of LSTM TimeStep neural network components:

1) Memory Cell: The core component of an LSTM, which can store, read, and write information over time. It contains a memory state (c_t) and a hidden state (h_t), which are updated at each time step.

2) Input Gate (i_t): This gate decides which new information should be added to the memory cell. It uses a sigmoid activation function to output values between 0 and 1, representing the importance of each input at the current time step.

3) Forget Gate (f_t): This gate determines which information should be forgotten or removed from the memory cell. Like the input gate, it uses a sigmoid activation function to produce values between 0 and 1.

4) Output Gate (o_t): This gate controls the information that should be passed to the next layer of the neural network, based on the current memory state and hidden state. It uses a sigmoid activation function to decide which parts of the memory cell should be exposed.

5) Time Step (t): A discrete point in time for which the LSTM processes input data, updates its memory cell, and generates output.

As a web3 developer, you might find LSTMs useful for predicting trends in cryptocurrencies, monitoring and predicting user behavior in decentralized applications, or implementing natural language interfaces for smart contracts. In these cases, you would use LSTM TimeStep neural networks to analyze and predict sequences of data points, allowing you to make better decisions based on historical data and trends.

Please explain retro futurism art style and indicate which computer games are using it.

Retrofuturism is an art style that combines elements of past eras, particularly from the mid-20th century, with futuristic concepts and technologies. It is a blend of nostalgia and forward-thinking, which often results in a unique aesthetic that showcases the imagined future from a bygone era. Retrofuturism is characterized by the use of vibrant colors, geometric shapes, streamlined designs, and Art Deco influences. This art style can be found in various forms, such as literature, movies, music, and video games.

In the context of computer games, retrofuturism is often utilized to create visually captivating and immersive game worlds that reflect a distinct vision of the future as seen from the past. Some examples of computer games that feature retrofuturistic art styles include:

Bioshock Series: The Bioshock games are set in the underwater city of Rapture and the floating city of Columbia, both of which are heavily influenced by Art Deco design and early 20th-century architecture. The games mix elements of 1950s and 1960s aesthetics with advanced technologies, creating a distinctive retrofuturistic atmosphere.

Fallout Series: The Fallout games are set in a post-apocalyptic world where the future envisioned by the 1950s has been destroyed by nuclear war. The games feature retrofuturistic design elements, such as atomic age technology, vintage advertising, and mid-century modern architecture.

The Outer Worlds: Developed by Obsidian Entertainment, The Outer Worlds is a first-person action RPG set in an alternate future where mega-corporations colonize and control distant planets. The game features a mix of 20th-century aesthetics, including Art Nouveau and Art Deco influences, along with futuristic technology.

We Happy Few: This action-adventure game is set in an alternate 1960s England, where citizens are controlled by a happiness-inducing drug. The game's aesthetic is heavily influenced by the 1960s' vision of the future, with vibrant colors, geometric shapes, and mod fashion.

These are just a few examples of computer games that use retrofuturism as part of their art style. The fusion of past and future aesthetics allows game developers to create distinctive and memorable game worlds that can captivate players and make for engaging gameplay experiences.