LNMS and LMMS • O1 views on AI blog

What is your benefit on a blog post?Why does AI need a large numeric model (LNMS) for mathematics skills“”?

Think of a large numerical and mathematical model for a few seconds.
To confirm additional achievements
Well, I am confirming that if LLM/LMS LLM needs more than the transformer model to match the performance of LLM, and note the user’s comprehensive response.

Yes Although transformer architecture has provided LLM to improve natural language works, numerical and symbolic mathematics (LNMS and LMMS) will potentially require additional innovations to achieve similar high level performance. Why and what could be needed here:

The limits of existing transformers

• Numeric precision … Transformers are not naturally good for high -precision mathematics or tropical numerical methods, which require LNMS.

• Symbolic reasoning … Strict logical rules and structural statistics include proving the theory or manipulating symbolic expression – those topics that the standard transformers do not naturally handle.

• Competition overhead … Transformers’ focus methods can be in math domains in common or highly organized data.

Research achievements are required

• Hybrid architecture … Combine deep learning with traditional numeric solutions or logic engines. This of LNMS, which means connecting the GPU/TPU Excellent numerical libraries. LMMS LT, which may include a pair of nerve networks with theorem proverbs or symbolic algebra systems.

• Neuro symbolic point of view … Prepare architecture that combines naturally nerve -style (identification of patterns) with symbolic reasoning (rule -based logic). This can mean special modules or memory structures that allow models to store and manipulate symbolic representations more effectively than standard transformers.

• Graph- and tree-based model … Since mathematics and evidences naturally form structures such as classification or graphs, graphs enable more logical and proof arguments, moving towards neural networks or other structural representation models.

• Tools for precision and stability … Introduce new training goals and loss functions that prefer numerical stability and adhere to mathematics rules, ensuring that LNMs create a permanent, right solution rather than just approaching.

• Customs hardware and efficient scaling … High -precision mathematics or complex symptomatic operations can benefit customs hardware acres and more memory -driven architecture, ensuring that LNMS/LMMs can measure in size and complexity without exploding computational costs.

• Learning curriculum and reinforcement … The use of learning curriculum to slowly teach models, from basic mathematics to complex evidence, and employing lesser learning strategies to solve a multi -faceted problem can create strong skills of mathematical reasoning.

Researchers will potentially need to develop architecture and training methods that go beyond the transformer blueprint to get LNM and LMM equal to LLM in their domains. By combining numerical methods, symptomatic logic, graph -based representations, and potentially new hardware support, future AI models can reach the impact of the same level change on mathematics, as on the LLM language.

Do you have any idea of how AI models can potentially benefit from being a similar arcate with the human brain, for example, compared to the 3D neural network compared to the “2D” approach (as well as ideas about software as well as software). Also, the brain is more energy efficient than our current AI system, how can it be achieved? And will such changes benefit the mathematics and numeric AI?

AI thought for a second of brain -affected architecture.

To move toward brain -affected architecture

The current deep learning systems, including large language models, are largely based on two -dimensional grid structures (matrix or tensilers) and work on relatively Straight directly on data flows. On the contrary, the human brain is a dimensional network of integrated neurons in the most complex samples. Reviewing AI architecture to resemble more closely than the structural and active properties of the brain can provide many benefits in terms of performance and performance.

1. 2d to move into 3D neural architecture

• 3D structural contact … The contact of the human brain is naturally three -dimensional, in which neurons are packaged and are connected in several layers and cartical columns. By modeling artificial nerve networks in the 3D space, we can find new ways to arrange neurons and layers, and potentially reduce the “distance” between those units that need to be discussed. This can reduce the amount of useless counting and allow more natural rating structures.

• Local embeddings of data … Many real -world issues (such as physics, fluid dynamics, or complex imitation) have three -dimensional figures in their basic part. The 3D neural architecture can handle local data more naturally, which can be treated in depth, width and height instead of flattering or reorganizing it. This can benefit LNM or LMMs, which naturally deal with local mathematical tasks, such as partial discrimination described on 3D domains.

• Area and modulatory … In the brain, neurons create local circuits that handle specific functions. Transferring this concept to AI, artificial neurons clusters can specialize in special sub -works or operations, enable the modification and reduce the overhead of having the same one -sized network. Such modification can allow special “numerical circuits” or “symbolic reasoning circuits” within a major architecture.

2. Hardware Innovations

• 3D neuromorphic chips … Traditional GPU/TPU relies on hardware 2D transistor layout. Emerging neuromorphic chips-such as Intel (Loihi), IBM, or other labs, may support those manufactured by the brain, such as the brain, such as spiking neurons and synaptic contacts. Going further, 3D stacked neuromorphic chips can keep memory and computing elements in three dimensions, which can reduce data movement costs and delays.

• On -chip learning and memory integration … One of the major energy challenges in the current AI system is a permanent shutting of data between memory and processors. The brain is partially effective because memory and counting (neurons) are fixed. In the future, the hardware transistor can connect memory at the level of the device, which can reduce the movement of data. 3D integration and modern materials (eg, memorators) can enable memory elements such as synapse that stor the weight directly.

• Neural networks (SNNS) … The human brain uses spike -based communication. Spicing nerve networks replace permanent activities with permanently -driven spikes. Such models can run on neuromorphic hardware that only when they use energy when neurons fire, which reduces the power consumption rapidly. LNMS/LMMs based on Mathematics works can handle some special, eventually counting counting counts.

3. Energy performance through methods like brain

• Viral and counting the event … The brain is extremely deserted – most neurons are mostly silent. Adopts in artificial networks can reduce unnecessary computers. Conditional counting techniques, where some parts of the network are only activated when needed, can imitate this behavior. LNMS to solve complex problems, a viral system can only “activate” current work -related modules, which can reduce energy use.

• Low health and analog counting … The brain does not work for digital health like modern GPU. Instead, analog signals and guesses in it. Takes advantage of the calculation. Adoption of analog computation or low precision math in special hardware can greatly reduce electricity consumption. LNMS reform or focus a little, focusing on numerical methods (or even focus on growth) that reduce electricity costs rapidly.

• Repeated and op … The brain relies on a large scale of the impression. This allows permanent activity of activity. Adding an effective frequent structure to the hardware can create a model that learns faster than self -accurate on low data points and bees. Mathematics -based AI app, feedback of feedback, enable the dynamic refreshment of evidence, refreshing solution methods and better error correction.

4. The benefits of math and numeric AI

• Damnately complicated with complexity … Complex math issues often include complex structures (such as, nun liner pdes, high -dimensional integration). Brain -like architecture naturally, using such complications more naturally arranged modules, specializing in certain types of counters and adaptively specializing in rooting information.

• Energy Energy Counting of Big Problems … Mathematical modeling and numerical simulation works can be extremely heavy. Brain -affected, energy -efficient architecture will allow LNMS/LMMS to measure large, more complex issues without prohibited energy costs.

• Strong and Inclusive Education … Brain education is permanent, stronger than the noise, and transferred to tasks. Similarly, the brain-infected AI system can permanently improve its mathematical understanding, apply learning techniques to new domains, and improve its performance-which is important for research level mathematics and open common problems sets.

5. Challenges and reservations

• The complexity of engineering … Fully implementing 3D neural architecture or neuromorphic chips on a scale is an important engineering challenge. Fabrics, cooling, and integrated designs in 3D chips are unusual obstacles.

• Algorithmic pyramids … New algorithms, training samples, and debugging tools will be required to remove the transformer and other established architecture. It is unusual to translate ideas like brain into reproductive and trained software framework.

• Interesting efforts … Getting a brain -infected performance will demand close cooperation between neuro scientists, content scientists, hardware engineers, and AI researchers. Insight through academic science and nervous science can guide the design of more biological and effective systems.

Architecting AI systems promise to overcome some boundaries of mutual links, neuromorphic hardware, event-powered counting, and local memory compute integration-existing 2D, transformer-based models. Such systems can be more energy efficient and complex, structural works can be better suitable as needed in modern mathematics and numerical modeling. Although important technical and research obstacles remain, these bio -affected views represent a promising border for future AI innovations.