Future Roadmap
All roads leads to freedom of thoughts & imaginations of humans
Currently Koboto is building its infrastructure where developers , data scientists and smart contract devs can build and use applications mostly with V 0.1 we going to launch Koboto sdk and take one of the approaches in to enter the market by building a intersection between consumers sitting on inefficient decentralized economy & suppliers who wants supply compute from less core processors chips for inferencing in order to capitalize there processing units . We are building Agent economy around multi-interactive, inference aggregator and intent based agent universe has a long to path to go on.
Here are the few architectures will introduce in future -
Self-verifiable Inference
By enhancing Simplex-based DNN verifiers with proof production capabilities. Here are the key points:
Proof Production: The mechanism generates an easy-to-check witness of unsatisfiability, ensuring the absence of errors in DNN verifiers.
Farkas’ Lemma: The proof production is based on an efficient adaptation of Farkas’ lemma, combined with mechanisms for handling piecewise-linear functions and numerical precision errors.
Implementation: The technique was implemented on top of the Marabou DNN verifier and evaluated on a safety-critical system for airborne collision avoidance.
Evaluation: The approach succeeded in producing proof certificates with minimal overhead, enhancing the reliability of DNN verification.
This mechanism ensures that the verification process is self-verifiable, increasing trust in the correctness of DNNs used in safety-critical systems.
To know more — IBZ+22.pdf (stanford.edu)
2. Cold Inference
NNV12 an inference engine designed to optimize cold inference for Deep Neural Networks (DNNs) on edge devices. Here are the key points:
Kernel Selection: NNV12 selects the most efficient kernel for each DNN operator, considering cold inference performance rather than just warm inference speed.
Weights Transformation Caching: It caches post-transformed weights on disk, bypassing the weights transformation process during inference.
Pipelined Execution: NNV12 pipelines the reading, transformation, and execution of weights, utilizing asymmetric processors (CPU/GPU) to minimize latency.
These optimizations result in significant speedups for cold inference on both edge CPUs and GPUs.
Cold inference is necessary when managing limited memory resources or when models are used infrequently.
TO KNOW MORE — main-coldstart-1.pdf (arxiv.org)
3. Quantization & Distillation models
Quantization
AIMET, a library of state-of-the-art quantization and compression algorithms.
AIMET aims to ease the effort required for model optimization, driving the AI ecosystem towards low-latency and energy-efficient inference.
TO KNOW MORE -[2201.08442] Neural Network Quantization with AI Model Efficiency Toolkit (AIMET) (arxiv.org)
Distillation
Introduces a novel mechanism called “Distilling step-by-step”.
Trains smaller models that outperform large language models (LLMs) by leveraging LLM rationales as additional supervision.
Achieves this with less training data, making it an efficient approach.
TO KNOW MORE — [2305.02301] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (arxiv.org)
4. Collective Intelligence
In future , Koboto will be packed with self-improving, decentralized AI ecosystem that harnesses collective intelligence to enhance machine learning models. By combining innovative mechanisms such as context-aware inference synthesis, modular topics, and differentiated incentives, koboto network will outperforms traditional monolithic models.
Offers a decentralized infrastructure where AI agents collaborate to improve model accuracy with context aware inference synthesis . By enabling AI agents to forecast each other’s model performance under current conditions. Context awareness significantly enhances prediction accuracy. Unlike basic networks that lack context, our forecasting approach will continually improves predictions over time.
A future where a self-improving, decentralized machine intelligence network capable of translating a continuous data stream into a series of network inferences that outperform any individual inference existing within the network
TO KNOW MORE — whitepaper.pdf (allora.network)
JOIN US IN THE FUTURE OF BUILDING NEXT AGENT ECONOMY
Building multi modular agent economy while introducing new tools & architecture designs while creating zero sum positive games with better UX and intuitive — interactive applications for the future of AGENT ECONOMY is what koboto is building & bullish on…
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