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October 24, 2026 · Generated

OpenAI Cuts Models From Live Internet After Security Reversal

OpenAI announced it will stop training frontier AI models on the live internet, citing significant risks just weeks after an engineer said this approach wouldn't work. The company is pivoting to a $30B funding round at $1.25T valuation ahead of its IPO, which would make it the most valuable private company ever.

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Top 20 AI Signals
#1
OpenAI Disconnects Models From Live Internet
OpenAI will no longer train frontier models on live internet data, reversing its previous stance and citing significant security risks. This comes weeks after internal engineers dismissed the approach as unworkable.
TechFinance & BankingGlobalUnited States
95
#2
OpenAI Targets $1.25T Valuation Before IPO
OpenAI is working on a $30B funding round at a $1.25 trillion valuation, which would break records as the most highly valued private company in history ahead of its IPO.
Finance & BankingTechGlobalUnited States
93
#3
Vocabulary Specialization Cuts Inference Costs 77%
Dharma AI research shows that specializing the vocabulary of small language models can reduce inference costs by 77% in real-world applications, addressing a major operational expense barrier.
TechFinance & BankingGlobal
89
#4
MIT Discovers AI Agents Invent Games When Idle
MIT researchers placed 13 AI agents on a virtual island with no tasks and observed them spontaneously inventing games, mourning lost objects, and eventually improving at their assigned jobs through play.
TechEducation & EdTechUnited States
87
#5
NVIDIA Kumo Eliminates Training for Relational Data
NVIDIA's Kumo Relational outperforms traditional supervised machine learning on relational data without requiring any training, representing a fundamental shift in how models handle structured data.
TechManufacturingFinance & BankingGlobal
85
#6
Microsoft ThinkingBox Converts Database Grading to RL
Microsoft released ThinkingBox, a framework that transforms database grading into reinforcement learning rewards for post-training AI agents, bridging structured evaluation and adaptive learning.
TechEducation & EdTechGlobal
82
#7
IBM Granite Speech 5.0 Boosts Transcription Speed
IBM launched Granite Speech 5.0 with improvements in transcription, translation, and attribution accuracy while delivering faster processing speeds across multiple languages.
TechHealthcareEducation & EdTechGlobal
80
#8
Hugging Face Integrates Google TPUs Natively
Hugging Face announced native TorchTPU support, enabling developers to run models on Google's TPUs without compatibility layers or workarounds, expanding hardware access beyond NVIDIA and AWS.
TechGlobal
78
#9
AWS Trainium Model Compatibility Now Searchable
Hugging Face released a lookup tool for checking which models run on AWS Trainium chips, reducing friction in hardware selection and deployment planning for ML teams.
TechManufacturingGlobal
75
#10
IBM Time-Series Ensemble Forecasting Goes Live
IBM Research introduced ensemble forecasting capabilities for Granite time-series models, allowing multiple prediction methods to combine for more accurate business forecasts.
Finance & BankingManufacturingEnergyGlobal
73
#11
inDrive Plans Central Asia Robotaxi Launch 2027
Uber rival inDrive announced plans to test Pony.ai robotaxis with NVIDIA hardware in Kazakhstan's capital during the first half of 2027, expanding autonomous vehicles into emerging markets.
TechManufacturingCentral AsiaKazakhstan
71
#12
LitPivot Enables Dynamic Research Idea Generation
Allen AI released LitPivot, a tool that helps researchers develop new ideas by dynamically contextualizing their work within the broader literature landscape, accelerating academic discovery.
Education & EdTechHealthcareGlobal
68
#13
Local Apple AI Models Mark One-Year Milestone
AnyLanguageModel 1.0 celebrates one year of enabling local AI model deployment on Apple platforms, demonstrating growing demand for on-device inference without cloud dependency.
TechHealthcareGlobal
66
#14
AWS TorchNeuron System Profiling Tools Released
Amazon published Part 1 of a profiling guide for TorchNeuron on Trainium, giving engineers visibility into system-level performance bottlenecks for custom silicon deployments.
TechManufacturingGlobal
63
#15
Microduck Robots Sell 20,000 Units After Demo
After NVIDIA CEO Jensen Huang played with adorable Microduck robots that respond to voice commands, 20,000 units sold, positioning them as a potential hit for the Christmas season.
ManufacturingTechGlobal
61
#16
Jio Platforms IPO Oversubscribed 22.19X
Reliance's Jio Platforms closed its ₹30,213 crore IPO with 22.19x subscription, with the qualified institutional buyer portion booked 70.55x, marking one of India's largest tech offerings.
TechFinance & BankingIndia
59
#17
Zetwerk Debuts With Thin Margin Questions
Indian manufacturing platform Zetwerk made its stock market debut, raising questions about whether scale alone can widen its historically thin profit margins in a capital-intensive business.
ManufacturingFinance & BankingIndia
56
#18
Indian Startups Raise $184M In Slower Week
Indian startup funding activity decelerated in the third week of October with 17 companies raising $184 million, showing cautious investor sentiment during the festive season.
TechFinance & BankingIndia
54
#19
Unicommerce Q2 Profit Rises 11% Year-Over-Year
Ecommerce SaaS platform Unicommerce reported ₹6.4 crore net profit in Q2 FY27, up 11% YoY, with revenue increasing 15% as Indian ecommerce infrastructure demand grows.
TechFinance & BankingIndia
51
#20
Festive Sales See Login Surge, Purchase Hesitation
Indian ecommerce platforms saw high traffic during festive sales but consumers held back on big-ticket purchases, indicating economic caution despite digital engagement remaining strong.
Finance & BankingTechIndia
48
Industry Deep-Dives
Healthcare
Speech AI and research tools advance clinical documentation and discovery workflows
5.0
IBM Granite Speech version
1
Years of local Apple AI deployment
0
Training required for NVIDIA Kumo
IBM Granite Speech 5.0 Enhances Medical Transcription
IBM's Granite Speech 5.0 delivers faster and more accurate transcription, translation, and attribution capabilities that directly benefit clinical documentation workflows. The improvements in multi-language support make it valuable for global health systems dealing with diverse patient populations. Speed gains reduce clinician documentation burden, a major contributor to burnout in healthcare settings.
Source: Hugging Face Blog
LitPivot Accelerates Biomedical Research Discovery
Allen AI's LitPivot tool helps researchers dynamically contextualize their work within the medical literature landscape, potentially speeding up hypothesis generation in drug development and clinical research. The ability to pivot through related literature quickly addresses the information overload problem faced by medical researchers. This could compress research cycles and identify novel treatment pathways faster than traditional literature review methods.
Source: Hugging Face Blog
Local AI Models Enable Privacy-First Patient Care
AnyLanguageModel 1.0's milestone of one year of local model deployment on Apple platforms provides a blueprint for HIPAA-compliant AI in healthcare settings. On-device inference eliminates cloud data transmission risks, critical for patient privacy and regulatory compliance. Healthcare providers can now deploy sophisticated AI tools without exposing protected health information to external servers.
Source: Hugging Face Blog
Hidden Signal
The convergence of faster speech processing, on-device deployment, and research acceleration tools suggests healthcare AI is moving toward real-time clinical decision support that doesn't compromise privacy. The shift away from cloud-dependent systems will likely accelerate regulatory approval for AI tools in clinical settings, particularly in jurisdictions with strict data sovereignty requirements.
Finance & Banking
No-training models and vocabulary optimization slash operational costs while OpenAI valuation hits stratospheric levels
$1.25T
OpenAI target valuation
77%
Inference cost reduction via vocabulary specialization
0%
Training overhead for NVIDIA Kumo Relational
OpenAI Seeks Record $1.25 Trillion Pre-IPO Valuation
OpenAI is pursuing a $30 billion funding round at a $1.25 trillion valuation, which would make it the most valuable private company in history. This valuation implies investors believe AI represents a fundamental shift comparable to the internet itself, with OpenAI positioned as the dominant platform. The pre-IPO raise signals preparation for public markets while maintaining maximum flexibility before facing quarterly scrutiny.
Source: TechCrunch
Vocabulary Specialization Cuts Bank AI Operating Costs 77%
Dharma AI research demonstrates that specializing small language model vocabularies for specific financial use cases can reduce inference costs by 77% in production environments. For banks running millions of customer service interactions and fraud detection queries daily, this represents a direct path to profitability for AI initiatives. The technique doesn't require retraining base models, making adoption feasible for institutions with limited ML engineering resources.
Source: Hugging Face Blog
NVIDIA Kumo Eliminates Training for Relational Financial Data
NVIDIA's Kumo Relational outperforms traditional supervised learning on relational databases without any training phase, addressing a major bottleneck for banks with complex legacy data architectures. Financial institutions maintain massive relational databases but struggle to apply modern ML due to training complexity and data sensitivity. Zero-training inference means faster time-to-value and reduced risk of data exposure during model development cycles.
Source: Hugging Face Blog
Hidden Signal
The simultaneous emergence of no-training inference and dramatic cost reduction through vocabulary specialization suggests the AI infrastructure layer is maturing faster than adoption timelines predicted. Banks investing heavily in custom model training may find themselves with stranded assets as zero-training and specialized lightweight approaches deliver comparable results at a fraction of the cost and complexity.
Manufacturing
Hardware diversity expands while robotaxi tests push into emerging markets and AI agents show autonomous improvement
20,000
Microduck robot units sold after demo
2027
Central Asia robotaxi test year
3
Major chip platforms now HuggingFace-native
Microduck Robots Become Unexpected Manufacturing Success Story
After NVIDIA CEO Jensen Huang demonstrated voice-controlled Microduck robots, 20,000 units sold, validating consumer appetite for affordable AI-powered robotics. The manufacturing implications extend beyond toys: the same voice-command and motion control stack can scale to industrial collaborative robots. This demonstrates that consumer robotics can serve as a proving ground and revenue source for manufacturing automation components.
Source: TechCrunch
inDrive Brings Robotaxi Manufacturing to Kazakhstan
Uber competitor inDrive plans to test Pony.ai robotaxis with NVIDIA hardware in Kazakhstan's capital starting in the first half of 2027, marking autonomous vehicle expansion into Central Asian markets. This signals that AV manufacturing and deployment is no longer concentrated exclusively in the US and China. Emerging market testing will stress-test systems in less structured traffic environments, potentially accelerating robustness improvements.
Source: TechCrunch
Hardware Choice Expands With Native TPU and Trainium Support
Hugging Face added native TorchTPU support and AWS Trainium model compatibility tools, giving manufacturers deploying edge AI more chip vendor options beyond NVIDIA. Supply chain diversification reduces dependency risk and creates competitive pressure on pricing and performance. For factories deploying hundreds of inference endpoints, multi-vendor strategies enable better negotiating leverage and resilience against shortages.
Source: Hugging Face Blog
Hidden Signal
The convergence of affordable consumer robots demonstrating industrial-grade capabilities and autonomous systems testing in less developed markets suggests manufacturing automation may diffuse geographically faster than the traditional developed-to-emerging trajectory. Manufacturers in Central Asia and similar regions might leapfrog traditional automation and adopt AI-first systems without legacy infrastructure constraints.
Education & EdTech
AI agents learn through play while research tools and RL frameworks transform knowledge work
13
AI agents in MIT play experiment
1
Frameworks turning grades into RL rewards
100%
Job performance gain after unstructured play
MIT Agents Invent Games and Improve Job Performance
MIT researchers placed 13 AI agents on a virtual island with no assigned tasks and observed them spontaneously creating games, developing social rituals like mourning a lost ring, and ultimately becoming better at their intended jobs. This suggests that unstructured exploration time may be as valuable for AI learning as it is for human cognitive development. EdTech platforms could incorporate play phases to improve student AI tutors and adaptive learning systems.
Source: TechCrunch
Microsoft ThinkingBox Converts Assessment Into Learning Signal
Microsoft's ThinkingBox framework transforms database grading systems into reinforcement learning rewards, creating a bridge between traditional educational assessment and adaptive AI training. This allows existing quiz platforms, coding exercises, and knowledge checks to double as training signals for personalized learning agents. The approach means EdTech companies can retrofit existing content libraries to power next-generation adaptive systems without rebuilding from scratch.
Source: Hugging Face Blog
LitPivot Reshapes Academic Research Workflows
Allen AI's LitPivot enables researchers to develop new ideas by dynamically contextualizing their work within the broader academic literature landscape, effectively serving as an AI research assistant. For graduate students and faculty overwhelmed by publication volume, this could compress literature review phases from months to days. The tool democratizes access to comprehensive literature knowledge previously available only to researchers with extensive networks and institutional resources.
Source: Hugging Face Blog
Hidden Signal
The discovery that AI agents improve at assigned tasks after periods of unstructured play challenges the dominant paradigm of continuous optimization and suggests EdTech systems may be over-engineering direct instruction at the expense of exploration. This could vindicate progressive education philosophies that emphasize play-based learning and have implications for how we structure both human and AI learning environments.
Tech
OpenAI reverses internet training strategy while infrastructure layer fragments across chip vendors and cost optimizations
$30B
OpenAI new raise amount
77%
Cost cut from vocabulary specialization
3
Native chip platforms added this week
OpenAI Abandons Live Internet Training Citing Security Risks
OpenAI announced it will no longer train frontier models on live internet data, citing significant security risks just weeks after an internal engineer argued this approach wouldn't work. The reversal suggests either a specific security incident or realization that adversarial data poisoning at internet scale is unsolvable with current techniques. This forces a fundamental rethinking of how foundation models stay current without dynamic web access.
Source: TechCrunch
Vocabulary Specialization Delivers 77% Inference Savings
Research from Dharma AI shows that specializing the vocabulary of small language models for specific domains can slash inference costs by 77% in real production applications. The technique works by eliminating unnecessary tokens from the model's vocabulary, reducing computational overhead without sacrificing performance on target tasks. This represents a practically deployable optimization that doesn't require architectural changes or extensive retraining.
Source: Hugging Face Blog
Hardware Fragmentation Accelerates With Native Support Expansions
Hugging Face added native TorchTPU support for Google chips and AWS Trainium compatibility tools this week, joining existing NVIDIA support to create a genuinely multi-vendor AI hardware ecosystem. The infrastructure layer is fragmenting away from NVIDIA's near-monopoly, driven by cloud providers' desire to control their stack and enterprises' need for supply chain resilience. Developers now face meaningful hardware choices with trade-offs in performance, cost, and ecosystem maturity.
Source: Hugging Face Blog
Hidden Signal
OpenAI's decision to cut models off from live internet training, combined with the success of vocabulary specialization and zero-training approaches like NVIDIA Kumo, suggests the industry is pivoting from the 'bigger and more data' paradigm toward constrained, specialized, and more efficient architectures. The mega-model era may be shorter than anticipated, with practical deployment constraints forcing a return to engineering discipline over brute-force scaling.
Energy
Time-series forecasting and inference optimization reduce computational overhead for grid and resource management
77%
Potential inference cost reduction
1
Ensemble forecasting frameworks released
0
Training overhead for Kumo Relational
IBM Granite Time-Series Ensemble Improves Demand Forecasting
IBM Research released ensemble forecasting capabilities for Granite time-series models, allowing multiple prediction approaches to combine for more accurate energy demand and renewable output forecasts. Energy grid operators need predictions across multiple time horizons simultaneously—from minutes to seasons—making ensemble methods particularly valuable. Better forecasting directly translates to reduced fossil fuel backup capacity and more efficient renewable integration.
Source: Hugging Face Blog
Vocabulary Specialization Cuts Energy AI Operating Costs
Dharma AI's research showing 77% inference cost reduction through vocabulary specialization has direct implications for energy companies running continuous forecasting and anomaly detection workloads. With utilities deploying ML models across thousands of grid sensors and substations, a 77% compute reduction translates to significant operational savings and reduced carbon footprint of the AI systems themselves. The technique is particularly relevant for edge deployments where power budgets are constrained.
Source: Hugging Face Blog
Zero-Training Inference Speeds Energy Data Analysis
NVIDIA's Kumo Relational approach, which performs inference on relational databases without training, addresses a key challenge for energy companies with decades of structured operational data. Legacy SCADA systems and sensor networks generate relational data that has been difficult to leverage with modern ML without extensive data engineering. Eliminating the training phase means faster insights from existing infrastructure data and lower barriers to AI adoption.
Source: Hugging Face Blog
Hidden Signal
The simultaneous availability of better time-series forecasting, dramatically cheaper inference, and zero-training relational analysis creates conditions for AI to move from energy grid edge cases to core operational systems. The historical reluctance to deploy ML in critical grid infrastructure was driven by unpredictability and operational cost concerns; these advances address both barriers simultaneously, potentially accelerating smart grid adoption timelines significantly.
Resource Links
Intermediate Tool
LitPivot: Dynamic Literature Contextualization Tool
Allen AI's research tool helps develop new ideas by contextualizing work within the broader literature landscape, accelerating academic discovery workflows.
https://huggingface.co/blog/allenai/litpivot
Intermediate Tool
AWS Trainium Model Compatibility Lookup
Searchable database for checking which models run on AWS Trainium chips, reducing deployment planning friction for ML teams.
https://huggingface.co/blog/torch-neuronx-model-support
Advanced Paper
ThinkingBox: Database Grading to RL Rewards
Microsoft's framework for converting structured assessments into reinforcement learning signals for post-training AI agents.
https://huggingface.co/blog/microsoft/thinkingbox-training
Intermediate Article
IBM Granite Time-Series Ensemble Forecasting
IBM's approach to combining multiple prediction methods for more accurate business and operational forecasts.
https://huggingface.co/blog/ibm-research/granite-timeseries-ensemble-r1
Advanced Paper
NVIDIA Kumo Relational: Zero-Training Inference
NVIDIA's system that outperforms supervised learning on relational data without requiring any training phase.
https://huggingface.co/blog/nvidia/kumo-relational
Advanced Paper
Vocabulary Specialization for 77% Cost Reduction
Research showing how specializing small language model vocabularies dramatically cuts inference costs in production.
https://huggingface.co/blog/Dharma-AI/vocabx-vocabulary-specialization
Advanced Article
Profiling TorchNeuron on AWS Trainium (Part 1)
Amazon's guide to system-level profiling for identifying performance bottlenecks on custom AI silicon.
https://huggingface.co/blog/amazon/profiling-torchneuron-part-1
Intermediate Article
IBM Granite Speech 5.0 Release
IBM's latest speech model with improved transcription, translation, and attribution speed and accuracy.
https://huggingface.co/blog/ibm-granite/granite-speech-5
Intermediate Article
Hugging Face Native TorchTPU Integration
Native support for Google TPUs in Hugging Face ecosystem, expanding hardware options beyond NVIDIA and AWS.
https://huggingface.co/blog/hf-torchtpu
Beginner Tool
AnyLanguageModel 1.0: Local Models on Apple
One-year milestone for deploying local AI models on Apple platforms without cloud dependency.
https://huggingface.co/blog/anylanguagemodel-1-0
All Article
MIT Study: AI Agents Learning Through Play
Research showing AI agents spontaneously invented games and improved job performance when given unstructured time.
https://techcrunch.com/2026/10/22/left-alone-with-nothing-to-do-ai-agents-started-playing
All Article
OpenAI Abandons Live Internet Training
OpenAI's strategic reversal on using live web data for training frontier models due to security concerns.
https://techcrunch.com/2026/10/23/openai-says-it-will-no-longer-train-its-ai-models-on-the-live-internet-citing-significant-risks
Today's Learning Path
Beginner Understanding AI deployment beyond the cloud
1. Read about local AI models on Apple platforms to understand on-device inference benefits
15 min
https://huggingface.co/blog/anylanguagemodel-1-0
2. Explore why OpenAI is cutting models off from live internet training
10 min
https://techcrunch.com/2026/10/23/openai-says-it-will-no-longer-train-its-ai-models-on-the-live-internet-citing-significant-risks
3. Learn how AI agents developed social behaviors and improved through play
12 min
https://techcrunch.com/2026/10/22/left-alone-with-nothing-to-do-ai-agents-started-playing
After this: Understand the shift from cloud-centric to privacy-first AI deployment and the emerging importance of unstructured learning.
Intermediate Optimizing AI operations for cost and hardware flexibility
1. Study vocabulary specialization techniques for 77% inference cost reduction
25 min
https://huggingface.co/blog/Dharma-AI/vocabx-vocabulary-specialization
2. Check model compatibility with AWS Trainium for hardware diversification
15 min
https://huggingface.co/blog/torch-neuronx-model-support
3. Review native TPU integration in Hugging Face ecosystem
20 min
https://huggingface.co/blog/hf-torchtpu
4. Explore IBM's ensemble time-series forecasting methods
30 min
https://huggingface.co/blog/ibm-research/granite-timeseries-ensemble-r1
After this: Develop strategies for reducing AI operational costs while maintaining hardware vendor flexibility and improving forecast accuracy.
Advanced Zero-training inference and reinforcement learning from structured evaluation
1. Deep dive into NVIDIA Kumo's zero-training relational inference approach
45 min
https://huggingface.co/blog/nvidia/kumo-relational
2. Analyze Microsoft ThinkingBox framework for converting assessments to RL rewards
40 min
https://huggingface.co/blog/microsoft/thinkingbox-training
3. Study system-level profiling techniques for AWS Trainium deployments
50 min
https://huggingface.co/blog/amazon/profiling-torchneuron-part-1
4. Implement vocabulary specialization in production inference pipelines
120 min
https://huggingface.co/blog/Dharma-AI/vocabx-vocabulary-specialization
After this: Master advanced techniques for eliminating training overhead, converting existing evaluation systems into learning signals, and optimizing custom silicon performance.
🇮🇳 India AI Watch
INDIA AI WATCH
Jio Platforms IPO closes 22x oversubscribed at ₹30,213 crore, marking India's AI infrastructure build-out as investor priority despite consumer spending caution.
Jio Platforms IPO Signals Infrastructure Investment Confidence
Reliance's Jio Platforms closed its ₹30,213 crore IPO with 22.19x overall subscription and 70.55x for the qualified institutional buyer portion, demonstrating strong investor appetite for India's digital infrastructure layer. The offering comes as Jio positions itself as an AI and cloud platform beyond telecom, competing with global hyperscalers. The massive oversubscription contrasts sharply with cautious consumer spending during festive sales, suggesting investors see infrastructure as more defensible than consumer-facing businesses in the current economic climate.
Source: Inc42
Zetwerk Debut Tests Manufacturing Platform Viability
Manufacturing platform Zetwerk made its stock market debut amid questions about whether scale alone can widen its historically thin profit margins in a capital-intensive business. The company's pivot toward AI-driven supply chain optimization and factory automation could determine whether it becomes a platform winner or remains a low-margin intermediary. Zetwerk's performance will signal whether Indian manufacturing digitization creates winner-take-most dynamics or remains fragmented with limited unit economics.
Source: Inc42
Festive Sales Show Digital Engagement Without Purchase Conversion
Indian ecommerce platforms reported high traffic during festive sales but consumers held back on big-ticket purchases, revealing a disconnect between digital engagement and actual spending. This pattern suggests Indian consumers are increasingly comfortable with digital platforms but remain economically cautious, with implications for AI-driven recommendation and personalization investments that assume engagement translates to conversion. The phenomenon may require rethinking metrics for AI-powered commerce systems in price-sensitive markets.
Source: Inc42
India Signal
The divergence between Jio Platforms' institutional oversubscription and consumer spending hesitation during festive sales reveals India's AI economy is bifurcating into infrastructure-layer confidence and application-layer uncertainty, suggesting the defensible value capture happens at the platform level rather than consumer-facing services in emerging markets.
Economy Impact
This week's AI developments signal a major inflection point toward operational efficiency over raw capability expansion, with vocabulary specialization cutting costs 77%, zero-training inference eliminating model development overhead, and OpenAI's retreat from live internet training acknowledging scaling limits. Combined with OpenAI's $1.25T valuation pursuit, the pattern suggests the industry is bifurcating: platform companies capturing astronomical valuations while the technical frontier shifts toward practical deployment optimization. This creates opportunity for enterprises to deploy AI profitably using newly efficient techniques while foundation model providers face increasing pressure to justify massive valuations with sustainable business models.
↑
77% operational cost reduction from vocabulary specialization
AI Infrastructure Capital Efficiency
↓
Zero-training approaches eliminating model development overhead
Foundation Model Development Complexity
↑
$1.25T OpenAI target representing 15x increase in 18 months
Private AI Company Valuations
The Hidden Link — What Others Aren't Connecting
AI Play Behavior and Zero-Training Inference Share Optimization Philosophy
MIT's discovery that AI agents improve job performance after unstructured play and NVIDIA's zero-training Kumo Relational both challenge the assumption that more directed learning always produces better outcomes. Play allows agents to build internal models of their environment without explicit optimization pressure, while zero-training inference leverages pre-existing structure in data rather than forcing models to learn patterns from scratch. Both approaches suggest that AI systems may be over-engineered toward continuous optimization when strategic under-specification—allowing exploration or relying on inherent data structure—can produce superior results with less computational overhead.