
Luke Mahoney (MLabs)
Project OwnerGrant manager and advisor to the MLabs AI/ML team
Milestone Release 1 |
$37,500 USD | Pending | TBD |
Milestone Release 2 |
$40,000 USD | Transfer Complete | 27 Dec 2024 |
Milestone Release 3 |
$37,500 USD | Pending | TBD |
Milestone Release 4 |
$35,000 USD | Pending | TBD |
No Service Available
Neural Search is a new way to incorporate Monte Carlo Tree Search into neural networks. Unlike similar techniques, Neural Search builds the exploration and optimization of the search into the layers of the network. The search itself is learnable, and provides us with a means to develop guided search algorithms trained on data. We have demonstrated that the algorithm can learn robust solutions to search problems. We propose further development of the approach to large scale problems, and to demonstrate the effectiveness of the algorithm by training it to play Go, a standard achieved AlphaGo.
Total for the project is $150K over 11 months, and it will be resourced by 3 senior MLabs members.
We will provide a novel and efficient technique to create a performant Go engine for users of the SNET platform to play against.
Our team excels in AI innovation and research, leveraging decades of combined experience in deep learning, generative AI, and reinforcement learning. Led by Iindustry veterans, we have a proven track record in developing robust algorithms for real-world applications. Furthermore, our team members comprise a wide variety of deep expertise, meaning we have the ability to deliver innovative and impactful solutions in a cost effective manner.
New AI service
To offer players of Go and SNET service.
User input.
Go board moves.
MLabs LTD
Search dominated AI before the Deep Learning revolution. But current Deep Learning architectures cannot incorporate search as an intrinsic part of their models.
Search is still used as a driver in many Deep Learning applications such as Natural Language Processing to evaluate multiple options for sentence completions, or to drive Reinforcement Learning models by running trained deep learning models multiple times and sampling from their action space. This is the approach used to generate playouts in Alpha Zero and similar for playing strategic games.
In these applications the model relies on a quite separate search process: search has not been trainable nor introduced as a component in neural network architectures. The search space, the transition functions, and (to some extent) the fitness functions need to be designed extrinsically and are applied at the top level of model deep learning architectures. This usually requires running the full forward pass through the network as many times as there are branches in the search
We have developed a new deep learning component called Neural Search capable of learning to search in the latent space of a neural network. It is capable of discovering a useful transition function between states, and a fitness function to evaluate the best state in the search.
Having learnable search inside the latent space of a neural network has multiple benefits:
The neural network can evaluate multiple branches of computation and choose the optimum one, without running the full forward pass multiple times
The neural network can use fewer parameters: instead of having to encode all possible branches in its parameters, it can learn a transition function and a corresponding fitness function, and then compute possible sequences of states on the fly
The neural network can reason: it can learn to generate options and evaluate them
The Neural Search component is trainable via gradient descent and can be plugged into any deep learning architecture as a function between layer vectors of the same size (latent states).
We assume a generic encoder-decoder architecture, consisting of the following steps
h = Encoder(x)
h’ = NeuralSearch(h)
y = Decoder(h’)
Neural Search is defined via three components:
Transition Function: a neural network taking a state as input and generating a parametrized probability distribution over the next states. For example: it could return the element-wise mean and standard deviation of a Gaussian distribution, the element-wise lower and upper bounds of a uniform distribution, or the probabilities of a multinomial distribution
Sampling Function: a function that takes as input the parameters of a distribution over states and generates a fixed number of sampled states
Fitness Function: a function that takes a state and computes a scalar value defining the fitness of the state. Higher values correspond to higher fitness
Neural Search executes a randomized beam search over the state space by generating at each step a number of candidates and choosing/sampling the best states to retain based on the fitness function.
During training, we sample from the generated next_states using their fitness scores. A temperature hyperparameter is used to control how uniform or greedy the choices for the candidates are.
Finally, when the maximum search depth is reached, the fitness function is used to compute an attention score for each state in the beam and returns a convex combination of them.
class NeuralSearch:
transition_function
sampling_function
fitness_function
beam_width
max_width
max_depth
temperature=1.0
def forward(x):
return search(x)
def search(x):
candidates = [x]
for _ in range(max_depth):
next_dist = transition_function(candidates)
next_states = sampling_function(next_dist, max_width)
next_states_fitness = fitness_function(next_states)
if train:
candidates = sample_categorical(
next_states,
softmax(next_states_fitness/temperature),
n_samples=beam_width
)
else:
candidates = topk(
next_states,
next_states_fitness,
k=beam_width
)
return softmax(fitness_function(candidates)) @ candidates
We have thus far used a transition function which defines a probability distribution over the following states, and mandates the use of sampling to get those next states. This turns the transition and sampling functions into a one-to-many mapping.
We have selected this approach as opposed to the alternative formulation of using a one-to-many network directly as the transition function. This is because a one-to-many network is only feasible for a small number of following states, and requires many more parameters to solve the same task.
A further benefit of sampling is that we can dynamically change the number of following states without affecting the network architecture, and possibly without the need for retraining, since all the samples are independently drawn from a single probability distribution. This can allow us to increase the amount of search that the network does for a specific sub-task. It therefore has the ability to quickly race through the obvious parts of a problem, and slow down to think harder about the difficult parts - all under internal network control.
The one key drawback to a randomized transition function is that the whole network is non-deterministic. We have not found this to be a problem thus far, and indeed other neural network algorithms with randomized steps, such as Variational AutoEncoders, seem to work well, even with constraints on the distribution parameters.
We have trained our Neural Search architecture on a number of small scale problems to establish proof of concept. We are satisfied that the innovation can successfully learn the components of a search module within a neural network. To evaluate feasibility we have trained Neural Search networks to perform both classification and puzzle solving tasks, in addition to improving the inference efficiency of GPT models.
In the classification applications:
Since beam search applies the same function at each step, the final path from input to output was very similar to a recurrent neural network. Stabilization measures were needed to successfully train the model. We found that adding Layer Normalization at each step appropriately controlled the process
The broader the width of the search, the better the classification accuracy. This meant that the neural search was not collapsing to a single deterministic path, and the more exploration it performed the better the final results, for the same number of parameters and the same search depth
In the maze solving tasks:
We used Reinforcement Learning to provide feedback of successful strategies, and were able to learn the transition and fitness functions without the need for labeled training data
Neural Search was able to solve mazes using fewer parameters than a dense MLP trained on the same task. For larger problems this more parsimonious architecture has profound implication for generalization and inference efficiency
For GPT models:
Adding a search module to each transformer block allowed us to reduce the total number of transformer blocks, from 6 to 4 (with 25% fewer parameters), while preserving the same accuracy on the validation set. Training time was unaffected
Using Neural Search as part of the transformer model deterred overfitting, and opened up the capability for a far richer set of sequence selection behaviors. Learning such behaviors could provide GPT-based natural language generation to be more finely controlled
We are satisfied that Neural Search provides a robust algorithmic approach to such problems, and will scale to applications with search trees which are infeasible to solve using traditional approaches and deterministic search algorithms. Furthermore, we have clear evidence that Neural Search can solve such large scale problems more efficiently than architectures which use Deep Learning networks in conjunction with bespoke search modules (e.g. Monte Carlo Tree Search).
We compose a feasibility analysis - see above - and research similar applications in the SNET market place. Given the history of AI applications and consumers of SNET, we believe our application will be superficially successful - e.g. in terms of API calls - while representing a novel technical approach.
Our Rigel project from DFR3 - currently on track with already 2 milestones delivered.
https://proposals.deepfunding.ai/graduated/accepted/e5b9301b-f1e3-4198-b6f3-66ee3484b311?id=weaknesses&commentId=1694189367893
Our Website
https://mlabs.city/
New reviews and ratings are disabled for Awarded Projects
Overall
I know Mlab and what they have achieved. Obviously, Mlab has a certain reputation in the community, which contributes to a significant plus point for feasibility. But that does not mean that the team presents the members' names lightly. This sketchiness makes it impossible for newcomers to fully appreciate the possible transparency that the team has (no specific records name of any member - 1 account pending)
Mark Florisson is a well-known Python contributor who has made significant contributions to high-performance, compiler-related libraries such as Numba and PyPy.
As mentioned on our team page, Ibrahim Abdelghany has over 10 years of industry and research experience.
Mark Bedworth has over 40 years of experience, but unfortunately not much of a public profile.
Overall
I want to know clearly about the specific division of tasks for each member but I can't see that. This is also a relatively important factor in feasibility. In addition, I was a bit disappointed when I saw the names of the team members' profiles. It doesn't show anything worth talking about.
TrucTrixie, thanks for the review.
Ibrahim Abdelghany and Mark Bedworth worked together on the early conceptual stages and will specify and finalize the design with input from Mark Florisson. Ibrahim will lead the implementation and will be assisted by a new team member, Banashree Sarma. Banashree is an AI researcher currently pursuing her PhD at the Indian Institute of Technology.
Overall
Feasibility
Technically a relatively complex project. It is very useful that the proposal draws the picture of the architecture against the past and current context. The differences and improvements to related solutions are logically explained. Proposal itself provides comprehensive information and sufficient details to get a grasp of what is meant to be done. It is a great foundation that the basics of the solution are already prototyped and there is some evidence on the potential performance. The building blocks of the system are well known and solid. Of course, the ultimate capability becomes obvious only after the implementation.
Viability
MLabs has implemented really complex projects and earned a solid reputation in the space. The proposal gives sufficient information about time expectations, human resources allocated to the project and financial sustainability. The company itself is well resourced from the perspectives of both mental and financial capital. The proposing entity has not yet (as far as I know) launched services on the marketplace and even though there is no reason to expect any difficulties it is good to note that many new projects have faced delays due to technical integration challenges. All things considered, with this team the project is very viable.
Desirability
Many long-existing AI algorithms have a lot of potential, arising not only from new hardware but also from new insightful combinations such as presented in this proposal. Go has become one of the kinds of industry standards to demonstrate the performance of different AI architectures and implementations. It gives some very objective performance metrics and grabs attention of industry participants. According to my knowledge, this is a novel solution bringing something new to the table by using sound and tested building blocks.
Usefulness
This is a way to unite powerful methods that have previously been used more or less independently. Potential real world applications beyond Go may be plenty. It would also be great for the SingularityNET marketplace to introduce this design solution to the industry. In the best case scenario, the successful service will span several new solutions based on the design choices pioneered in this project.
Thank you for your positive review
Overall
This proposal has a new approach called Neural Search which is an important technological breakthrough in incorporating Monte Carlo Tree Search into neural networks, promising to improve performance in applications that require complex searches. In addition, the team proposing this project has extensive experience in the field of innovation and artificial intelligence research, giving them confidence that they can produce innovative and useful solutions. Promising initial trial results also provide evidence that this approach has the potential to be an effective solution in a variety of contexts.
Testing conducted primarily on small problems results in limitations in understanding how this approach will perform on a larger scale. Additionally, implementing Neural Search requires deep technical understanding and careful parameter tuning. it is worth trying to conduct large-scale testing, develop the tools and guidelines needed for implementation, and conduct further research on the use of manually controlled parameters.
Overall, this proposal promises to be a significant contributor in the development of decentralized AI platforms. By addressing the challenges faced through appropriate solutions, this proposal has the potential to pave the way for new solutions in the development of search algorithms integrated with neural networks.
Overall
Budget and costs are distributed relatively evenly across 4 milestones. This makes it quite difficult for me to choose the key milestone as the quintessence of this proposal. Perhaps milestones 3 and 4 have the most technology integration. In fact, every milestone is important, but the prominence of milestones 3 and 4 is clearly visible. I want the team to explain more about the technology aspect of these two milestones.
Overall
The project aims to build the process of exploring and optimizing searches right in the neural network layers that opened a new approach in the field of artificial intelligence. However, the project faces great challenges in applying this method to large -scale issues and the standard that Alphago has established in the field of neuroscience. The training of algorithms for large amounts of data and complex calculations, this is still a significant challenge. Therefore, although the development potential is very large, the project needs to prove more efficiency and the ability to apply widely to truly succeed.
Thank you
Overall
i think it is feasible but need to watch out and fine-tune the Neural Search components, handling complex search spaces, and optimizing computational resources for large-scale problems making sure not to affect the project
The team seems to be qualified for this
Thank you for the feedback.
Overall
i think this similar project is done by AlphaGo, the milestone is already doen by alphago, so why do you want to create that one since its was already done and avaialable? its my question, however the general feasibility viability and usefullness was good, i dont think it needs to be redone again,
good luck with the voting.
Thank you for your comment. This project is meant to highlight the capability of neural search compared with state-of-the-art algorithms. Alpha-Go is one such algorithm that we will compete with to show the benefit of neural search in reasoning tasks.
The state-of-the-art is to use neural networks to evaluate branches and to use an external tree search algorithm to explore possible paths. With the tree search on the outside, the neural network needs to be run once for every path being considered. Neural search internalises the tree search so that the network can learn the tree, the fitness of particular moves, and heuristics for selecting promising strategies for exploration. It does so in a single forward pass.
Neural search is a novel addition to the fundamentals of neural networks which improves training capability, inference speed and quality of search. It applies to a very broad range of problems. We have chosen to pit it against AlphaGo to demonstrate its ability to finesse an existing benchmark which is considered to be at the pinnacle of what can be achieved with neural network-driven search.
Overall
Usefulness
This project offers a new and improved way to combine searching techniques with neural networks. It can help AI systems make better decisions more quickly, which is useful for various applications like playing complex games (e.g., Go), processing natural language, and solving puzzles. By making search an integrated part of the neural network, it streamlines processes and can significantly enhance AI performance.
Desirability
The ability to create smarter, more efficient AI systems is highly desirable. This project promises to make AI systems more capable and versatile, which is appealing to both developers and users. The potential to improve AI performance in popular applications, like strategic gaming and language processing, makes it an attractive prospect in the AI community.
Viability
The project appears viable, given the expertise of the team and the solid proof-of-concept results. They have already demonstrated the effectiveness of Neural Search on small-scale problems, which is a positive indicator. However, there is always some uncertainty when scaling up to larger, more complex problems. The project's success will depend on continued development and testing.
Feasibility
The team has outlined a clear plan and has the necessary skills and experience. They have considered potential challenges, like the need for training stabilisation measures, and proposed solutions. The feasibility is rated slightly lower due to the inherent risks of developing and scaling new AI technologies, but the groundwork and initial successes are obvious
Summary
The Neural Search project is promising, with significant potential to advance AI capabilities. It addresses a clear need in the field and offers a novel solution that could lead to more efficient and intelligent AI systems. The project is well-planned and backed by a capable team, making it a strong candidate for success.
Thank you for considering our project, Ayo.
Overall
By directly integrating search algorithms into neural network designs, MLabs LTD's Neural Search solution solves a major gap in the present AI landscape and is both unique and technically sound. The project is a great contender for funding because it is ambitious and supported by a highly skilled crew. Nevertheless, a more thorough examination of competitors and risk mitigation techniques might be beneficial for the project.
Technically, the project is doable and has defined deliverables and milestones. A full grasp of the problems and potential solutions is demonstrated by the in-depth description of the Neural Search architecture and its possible uses. Then, if the plan included more precise information about potential risks and how to mitigate them, it would be stronger.
The viability of the project is high given the extensive experience of the team in AI and machine learning. The budget and timeline seem reasonable for the scope of work. More detailed information on the market potential and adoption strategies would enhance this rating.
The desirability of the project is very high, as it promises to bring a novel approach to AI by embedding search capabilities within neural networks. This can significantly enhance the performance and efficiency of AI models, making it a valuable addition to the SingularityNET ecosystem.
The project has great potential, especially for applications like strategic planning and gaming that call for intricate decision-making and optimization. The suggested Go engine will provide an engaging example of the technology's potential.
Devbasrahtop, thank you for the thoughtful review.
Overall
Hi Luke, interesting Git.
I'll give you a detailed review of the project
Feasibility
Your project demonstrates exceptional feasibility. The comprehensive feasibility analysis you provided, along with the successful proof of concept in classification, puzzle solving, and GPT model enhancement, strongly supports the technical viability of your approach. The clear outline of your architecture and algorithm further reinforces your preparedness to execute this project effectively.
Viability
The viability of your project is high. The potential applications of Neural Search in various domains, such as natural language processing and reinforcement learning, indicate a significant market opportunity. While the market for Go-playing AI might be niche, the broader implications of your technology could attract considerable interest from researchers and industry professionals alike.
Desirability
Your project is highly desirable. The ability to integrate search directly into neural network architectures is a groundbreaking concept with the potential to revolutionize how AI models are designed and trained. Your approach addresses the limitations of current deep learning architectures by offering a more efficient and elegant way to incorporate search capabilities.
Usefulness
The usefulness of your project is undeniable. By enabling neural networks to learn and optimize search processes internally, you're opening up new possibilities for developing more intelligent and adaptable AI systems. The potential for Neural Search to improve performance, reduce computational costs, and enhance reasoning capabilities across various applications makes it a highly valuable tool for the AI community.
In summary, your Neural Search project is exceptionally feasible, highly viable, extremely desirable, and incredibly useful. It has the potential to make a significant contribution to the field of AI and drive innovation in numerous domains. I wholeheartedly encourage you to pursue this project, as it represents a significant leap forward in AI research and development.
I am the designer of Transcendence Platform. We made a proposal for AI-enabled holograms, aiming to create a holographic virtual assistant for various applications, such as customer care, emotional support, advertising, and entertainment. By combining advanced AI with holographic technology, we enable natural and empathetic interactions that enhance the user experience. I invite you to watch it and give me your opinion. Thank you very much. Check it out here: https://deepfunding.ai/proposal/revolutionizing-assistance-3d-holographic-ai/
Nicolas, thank you for your thoughtful and positive review. Will checkout your proposal. Bests.
Overall
Mlab is a well-known team in the community. The problem they solve is that current Deep Learning architectures cannot incorporate search as an intrinsic part of their models. They have developed a new deep learning component called Neural Search capable of learning to search in the latent space of a neural network. But the information they give is quite detailed.
They should determine the start and end times of important milestones. This will help make the proposal more detailed. Additionally, they should also allocate more detailed budgets based on milestones. Additionally, they should define detailed indicators to measure outputs.
Thanks for the review, Tu.
Overall
This proposal focuses on an ideation phase for a conversational AI system to manage complex projects in sustainable communities. Here's an analysis of its viability for securing funding for a full proposal:
Strengths:
Weaknesses:
Recommendations for Strengthening Viability:
By addressing these weaknesses and incorporating these recommendations, the proposal can increase its chances of securing funding for the full project.
Additional Considerations:
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary)
This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user's assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary)
This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
This milestone represents the required reservation of 25% of your total requested budget for API calls or hosting costs. Because it is required we have prefilled it for you and it cannot be removed or adapted.
You can use this amount for payment of API calls on our platform. Use it to call other services, or use it as a marketing instrument to have other parties try out your service. Alternatively you can use it to pay for hosting and computing costs.
$37,500 USD
This work task comprises the development of the baseline reinforcement learning loop for the neural search algorithm and the AlphaGo-style model we will compare our results with. Risk is low - we have already tested the basic approach to reinforcement learning but need to implement a full-scale version which is tightly integrated with the neural search. We will train a neural network to play Go using the AlphaGo architecture.
Tested implementation of neural search with reinforcement learning, and a working version of an AlphaGo-style neural network.
$40,000 USD
This work task comprises the development of the Neural Search model to play Go using the reinforcement learning framework developed in the previous work package. The algorithm will be used to train a number of neural networks with different architectures. The sensitivity to hyperparameters will be evaluated, and a systematic approach to design established.
Tested implementation of neural search for playing Go.
$37,500 USD
This work task comprises the further development of the Neural Search model to play Go. Lessons learned from the previous work package will inform changes to the underlying neural search algorithm and how it is architected into the solution. The setting of hyperparameters will be further investigated.
Tested implementation of advanced version of neural search for playing Go.
$35,000 USD
Reviews & Ratings
New reviews and ratings are disabled for Awarded Projects
Overall
I know Mlab and what they have achieved. Obviously, Mlab has a certain reputation in the community, which contributes to a significant plus point for feasibility. But that does not mean that the team presents the members' names lightly. This sketchiness makes it impossible for newcomers to fully appreciate the possible transparency that the team has (no specific records name of any member - 1 account pending)
Mark Florisson is a well-known Python contributor who has made significant contributions to high-performance, compiler-related libraries such as Numba and PyPy.
As mentioned on our team page, Ibrahim Abdelghany has over 10 years of industry and research experience.
Mark Bedworth has over 40 years of experience, but unfortunately not much of a public profile.
Overall
I want to know clearly about the specific division of tasks for each member but I can't see that. This is also a relatively important factor in feasibility. In addition, I was a bit disappointed when I saw the names of the team members' profiles. It doesn't show anything worth talking about.
TrucTrixie, thanks for the review.
Ibrahim Abdelghany and Mark Bedworth worked together on the early conceptual stages and will specify and finalize the design with input from Mark Florisson. Ibrahim will lead the implementation and will be assisted by a new team member, Banashree Sarma. Banashree is an AI researcher currently pursuing her PhD at the Indian Institute of Technology.
Overall
Feasibility
Technically a relatively complex project. It is very useful that the proposal draws the picture of the architecture against the past and current context. The differences and improvements to related solutions are logically explained. Proposal itself provides comprehensive information and sufficient details to get a grasp of what is meant to be done. It is a great foundation that the basics of the solution are already prototyped and there is some evidence on the potential performance. The building blocks of the system are well known and solid. Of course, the ultimate capability becomes obvious only after the implementation.
Viability
MLabs has implemented really complex projects and earned a solid reputation in the space. The proposal gives sufficient information about time expectations, human resources allocated to the project and financial sustainability. The company itself is well resourced from the perspectives of both mental and financial capital. The proposing entity has not yet (as far as I know) launched services on the marketplace and even though there is no reason to expect any difficulties it is good to note that many new projects have faced delays due to technical integration challenges. All things considered, with this team the project is very viable.
Desirability
Many long-existing AI algorithms have a lot of potential, arising not only from new hardware but also from new insightful combinations such as presented in this proposal. Go has become one of the kinds of industry standards to demonstrate the performance of different AI architectures and implementations. It gives some very objective performance metrics and grabs attention of industry participants. According to my knowledge, this is a novel solution bringing something new to the table by using sound and tested building blocks.
Usefulness
This is a way to unite powerful methods that have previously been used more or less independently. Potential real world applications beyond Go may be plenty. It would also be great for the SingularityNET marketplace to introduce this design solution to the industry. In the best case scenario, the successful service will span several new solutions based on the design choices pioneered in this project.
Thank you for your positive review
Overall
This proposal has a new approach called Neural Search which is an important technological breakthrough in incorporating Monte Carlo Tree Search into neural networks, promising to improve performance in applications that require complex searches. In addition, the team proposing this project has extensive experience in the field of innovation and artificial intelligence research, giving them confidence that they can produce innovative and useful solutions. Promising initial trial results also provide evidence that this approach has the potential to be an effective solution in a variety of contexts.
Testing conducted primarily on small problems results in limitations in understanding how this approach will perform on a larger scale. Additionally, implementing Neural Search requires deep technical understanding and careful parameter tuning. it is worth trying to conduct large-scale testing, develop the tools and guidelines needed for implementation, and conduct further research on the use of manually controlled parameters.
Overall, this proposal promises to be a significant contributor in the development of decentralized AI platforms. By addressing the challenges faced through appropriate solutions, this proposal has the potential to pave the way for new solutions in the development of search algorithms integrated with neural networks.
Overall
Budget and costs are distributed relatively evenly across 4 milestones. This makes it quite difficult for me to choose the key milestone as the quintessence of this proposal. Perhaps milestones 3 and 4 have the most technology integration. In fact, every milestone is important, but the prominence of milestones 3 and 4 is clearly visible. I want the team to explain more about the technology aspect of these two milestones.
Overall
The project aims to build the process of exploring and optimizing searches right in the neural network layers that opened a new approach in the field of artificial intelligence. However, the project faces great challenges in applying this method to large -scale issues and the standard that Alphago has established in the field of neuroscience. The training of algorithms for large amounts of data and complex calculations, this is still a significant challenge. Therefore, although the development potential is very large, the project needs to prove more efficiency and the ability to apply widely to truly succeed.
Thank you
Overall
i think it is feasible but need to watch out and fine-tune the Neural Search components, handling complex search spaces, and optimizing computational resources for large-scale problems making sure not to affect the project
The team seems to be qualified for this
Thank you for the feedback.
Overall
i think this similar project is done by AlphaGo, the milestone is already doen by alphago, so why do you want to create that one since its was already done and avaialable? its my question, however the general feasibility viability and usefullness was good, i dont think it needs to be redone again,
good luck with the voting.
Thank you for your comment. This project is meant to highlight the capability of neural search compared with state-of-the-art algorithms. Alpha-Go is one such algorithm that we will compete with to show the benefit of neural search in reasoning tasks.
The state-of-the-art is to use neural networks to evaluate branches and to use an external tree search algorithm to explore possible paths. With the tree search on the outside, the neural network needs to be run once for every path being considered. Neural search internalises the tree search so that the network can learn the tree, the fitness of particular moves, and heuristics for selecting promising strategies for exploration. It does so in a single forward pass.
Neural search is a novel addition to the fundamentals of neural networks which improves training capability, inference speed and quality of search. It applies to a very broad range of problems. We have chosen to pit it against AlphaGo to demonstrate its ability to finesse an existing benchmark which is considered to be at the pinnacle of what can be achieved with neural network-driven search.
Overall
Usefulness
This project offers a new and improved way to combine searching techniques with neural networks. It can help AI systems make better decisions more quickly, which is useful for various applications like playing complex games (e.g., Go), processing natural language, and solving puzzles. By making search an integrated part of the neural network, it streamlines processes and can significantly enhance AI performance.
Desirability
The ability to create smarter, more efficient AI systems is highly desirable. This project promises to make AI systems more capable and versatile, which is appealing to both developers and users. The potential to improve AI performance in popular applications, like strategic gaming and language processing, makes it an attractive prospect in the AI community.
Viability
The project appears viable, given the expertise of the team and the solid proof-of-concept results. They have already demonstrated the effectiveness of Neural Search on small-scale problems, which is a positive indicator. However, there is always some uncertainty when scaling up to larger, more complex problems. The project's success will depend on continued development and testing.
Feasibility
The team has outlined a clear plan and has the necessary skills and experience. They have considered potential challenges, like the need for training stabilisation measures, and proposed solutions. The feasibility is rated slightly lower due to the inherent risks of developing and scaling new AI technologies, but the groundwork and initial successes are obvious
Summary
The Neural Search project is promising, with significant potential to advance AI capabilities. It addresses a clear need in the field and offers a novel solution that could lead to more efficient and intelligent AI systems. The project is well-planned and backed by a capable team, making it a strong candidate for success.
Thank you for considering our project, Ayo.
Overall
By directly integrating search algorithms into neural network designs, MLabs LTD's Neural Search solution solves a major gap in the present AI landscape and is both unique and technically sound. The project is a great contender for funding because it is ambitious and supported by a highly skilled crew. Nevertheless, a more thorough examination of competitors and risk mitigation techniques might be beneficial for the project.
Technically, the project is doable and has defined deliverables and milestones. A full grasp of the problems and potential solutions is demonstrated by the in-depth description of the Neural Search architecture and its possible uses. Then, if the plan included more precise information about potential risks and how to mitigate them, it would be stronger.
The viability of the project is high given the extensive experience of the team in AI and machine learning. The budget and timeline seem reasonable for the scope of work. More detailed information on the market potential and adoption strategies would enhance this rating.
The desirability of the project is very high, as it promises to bring a novel approach to AI by embedding search capabilities within neural networks. This can significantly enhance the performance and efficiency of AI models, making it a valuable addition to the SingularityNET ecosystem.
The project has great potential, especially for applications like strategic planning and gaming that call for intricate decision-making and optimization. The suggested Go engine will provide an engaging example of the technology's potential.
Devbasrahtop, thank you for the thoughtful review.
Overall
Hi Luke, interesting Git.
I'll give you a detailed review of the project
Feasibility
Your project demonstrates exceptional feasibility. The comprehensive feasibility analysis you provided, along with the successful proof of concept in classification, puzzle solving, and GPT model enhancement, strongly supports the technical viability of your approach. The clear outline of your architecture and algorithm further reinforces your preparedness to execute this project effectively.
Viability
The viability of your project is high. The potential applications of Neural Search in various domains, such as natural language processing and reinforcement learning, indicate a significant market opportunity. While the market for Go-playing AI might be niche, the broader implications of your technology could attract considerable interest from researchers and industry professionals alike.
Desirability
Your project is highly desirable. The ability to integrate search directly into neural network architectures is a groundbreaking concept with the potential to revolutionize how AI models are designed and trained. Your approach addresses the limitations of current deep learning architectures by offering a more efficient and elegant way to incorporate search capabilities.
Usefulness
The usefulness of your project is undeniable. By enabling neural networks to learn and optimize search processes internally, you're opening up new possibilities for developing more intelligent and adaptable AI systems. The potential for Neural Search to improve performance, reduce computational costs, and enhance reasoning capabilities across various applications makes it a highly valuable tool for the AI community.
In summary, your Neural Search project is exceptionally feasible, highly viable, extremely desirable, and incredibly useful. It has the potential to make a significant contribution to the field of AI and drive innovation in numerous domains. I wholeheartedly encourage you to pursue this project, as it represents a significant leap forward in AI research and development.
I am the designer of Transcendence Platform. We made a proposal for AI-enabled holograms, aiming to create a holographic virtual assistant for various applications, such as customer care, emotional support, advertising, and entertainment. By combining advanced AI with holographic technology, we enable natural and empathetic interactions that enhance the user experience. I invite you to watch it and give me your opinion. Thank you very much. Check it out here: https://deepfunding.ai/proposal/revolutionizing-assistance-3d-holographic-ai/
Nicolas, thank you for your thoughtful and positive review. Will checkout your proposal. Bests.
Overall
Mlab is a well-known team in the community. The problem they solve is that current Deep Learning architectures cannot incorporate search as an intrinsic part of their models. They have developed a new deep learning component called Neural Search capable of learning to search in the latent space of a neural network. But the information they give is quite detailed.
They should determine the start and end times of important milestones. This will help make the proposal more detailed. Additionally, they should also allocate more detailed budgets based on milestones. Additionally, they should define detailed indicators to measure outputs.
Thanks for the review, Tu.
Overall
This proposal focuses on an ideation phase for a conversational AI system to manage complex projects in sustainable communities. Here's an analysis of its viability for securing funding for a full proposal:
Strengths:
Weaknesses:
Recommendations for Strengthening Viability:
By addressing these weaknesses and incorporating these recommendations, the proposal can increase its chances of securing funding for the full project.
Additional Considerations:
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary)
This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user's assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses.
Overall (Primary)
This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively).
Feasibility (secondary)
This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding.
Viability (secondary)
This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?”
Examples:
Desirability (secondary)
Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference?
Example:
Usefulness (secondary)
This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?”
For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?).
For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things?
Examples:
MLabs is a blockchain and AI consultancy
Proposal Summary
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