DeepSouth AI

An innovative system that utilizes neuromorphic computing to develop an AI application with multiple functions.

While incorporating traditional elements of programming, our system is fundamentally built on a range of advanced supervised and unsupervised training algorithms.



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Neuromorphic Computing

Neuromorphic computing is a process that mimics the human brain's structure and functionality, using artificial neurons and synapses to process information.

Using artificial neurons and synapses, neuromorphic models simulate how our brain processes information, allowing them to solve problems, recognize patterns, and make decisions more quickly and efficiently than conventional computing models.

A Neuron, also called a node, is a basic computational unit that processes inputs and produces an output.

A Synapse is a connection between two neurons. Unlike the von Neumann model, where processing units and memory are separate, the neuromorphic computing model integrates memory and processing units in the neurons and synapses.

Instead of encoding information as numerical values in binary format, neuromorphic computing uses Spikes as inputs, where the timing, magnitude, and shape of these spikes encode numerical information.

Parallel Processing

Every neuron and synapse functions autonomously and simultaneously, working alongside others. By distributing tasks among many neurons and synapses, neuromorphic systems avoid overburdening any single processing unit with too many tasks.

Unified Memory and Processing

Memory and processing functions are unified within a single component, where both neurons and synapses are responsible for data processing and storage tasks. This unification helps to overcome von Neumann bottlenecks.

Scalability

Neuromorphic systems possess an intrinsic ability to scale. Upgrading neuromorphic systems can be straightforwardly achieved by programming additional virtual neurons and synapses.

Event-Driven Computation

Neuromorphic systems become active only when there is a need for computation and the necessary data is present. Neurons and synapses in these systems are engaged solely when there are spikes, or data events, to process.

Stochasticity

The autonomous functioning of neurons and synapses in neuromorphic systems leads to a level of unpredictability. This randomness is not just a challenge but also a feature that closely mimics the probabilistic behavior of biological neural networks.

Integrating Neuromorphic Computing with Artificial Intelligence

Integrating neuromorphic computing with artificial intelligence is mainly achieved by employing neuromorphic algorithms, which include:

  • Spiking Neural Networks (SNN)
  • Advanced algorithms



Spiking Neural Networks (SNN)

This group of algorithms trains artificial intelligence systems by tuning the states and parameters of artificial neurons and synapses, facilitating learning new behavior by achieving new homeostasis.

SNN algorithms leverage the plasticity nature of ANN systems.
Plasticity is the ability of a neural network to quickly change its predictions in response to new information. It is essential for the adaptability and robustness of artificial intelligence systems.

We utilize the following algorithms within the SNN framework:

  • Spike-Timing-Dependent Plasticity (STDP)
  • Backpropagation-based direct training schemes
  • Suxpervised temporal learning
  • ANN-to-SNN conversion strategies

Advanced Algorithms

Additional algorithms for integrating neuromorphic computing principles into advanced artificial intelligence systems include:

  • Reservoir computing (LSM)
  • Genetic algorithms

Reservoir computing is characterized by the use of a sparsely interconnected SNN, serving as the reservoir.

This reservoir is randomly structured but maintains two essential properties: input separability, ensuring different inputs lead to different outputs, and fading memory, which prevents signals from propagating indefinitely, causing them to diminish over time.

Genetic algorithms constantly adjust the system using available data to carry out new functions. These methods refine artificial intelligence systems by modifying their parameters, neurons, and synaptic thresholds.

DeepSouth AI Technology

DeepSouth AI achieves exceptional computational capabilities and efficiency by employing a combination of neuromorphic computing algorithms. These algorithms form the basis of its multifunctional capabilities; text and voice control, visual interpretation, and a sophisticated chat engine, along with various other AI-driven functions.

  • AutonomousAI

    Autonomous AI is designed to transform how users interact with their computing devices. By integrating advanced voice recognition and natural language processing, this AI function enables users to operate their computers and devices through both voice and text commands. The system's core capability lies in its ability to interpret and autonomously execute instructions.

    For example, when a user says, "play my favorite music on YouTube," the AI autonomously opens a web browser, navigates to YouTube, and selects music based on the user's historical preferences and data on the platform.

  • VisualAI

    Deep South's AI leverages sophisticated neuromorphic algorithms to analyze visual inputs. This enables the AI to provide real-time, accurate descriptions of images and videos, either in text or through voice narration.

    Whether it's a still image, video or a live stream, the AI evaluates the content and delivers in-depth narratives of the visuals it processes. AI-powered visual interpretation demonstrates its versatility through a range of practical applications that seamlessly integrate into different areas of everyday life and work.

  • ConversationalAI

    The primary function is to simulate a conversational partner that can understand and respond to a wide array of queries and commands. From providing information and answering questions to aiding in complex problem-solving scenarios, this function is designed to be both a source of knowledge and a companionable interface.

    Initially powered by OpenAI's API, the system will evolve, incorporating a diverse range of AI technologies such as Grok, Google's Gemini, and SpikeGPT. This expansion will empower users with the choice to select the AI that best aligns with their specific needs.

Tokenomics

  • Token Ticker: $SOUTH
  • Native Network: Ethereum
  • Total Supply: 1,000,000
  • Tax Structure: 5% Buy/Sell
CHART BUY


$SOUTH was stealth launched on the Ethereum mainnet without any presale. 5% of the tokens are allocated for the team, locked in linear vesting for 60 days. 15% of the tokens are allocated for ecosystem supply, locked in linear vesting for 180 days. The remaining tokens are allocated for liquidity.

Liquidity lockup time is for a period of 1 year.

The smart contract's ownership remains unrenounced, with plans to lower taxes once there is sufficient funding for product development.

  • Utility

    As a web-based application, it will require users to connect their digital wallets for access. The specific amount of $SOUTH tokens required to access the application will be revealed.

    With an increase in the market cap, we also plan to introduce a subscription model. Payments will be converted into $SOUTH tokens and partially allocated to a token buyback and burn program.

  • Smart Contract

    The smart contract we employ is structured to guarantee that any alterations made by the owner can not limit or stop trading for the investors.

    ● Owner cannot mint new tokens.

    ● Owner cannot pause trading.

    ● Owner cannot blacklist wallets.

    ● Owner cannot set a max transaction limit lower than 0.1% of circulating supply.

    ● Owner cannot set a max wallet limit lower than 1% of circulating supply.

    ● Tax cannot be higher than 10% buy or sell.