Is fake data the real deal when training algorithms? – The Guardian
Youre at the wheel of your car but youre exhausted. Your shoulders start to sag, your neck begins to droop, your eyelids slide down. As your head pitches forward, you swerve off the road and speed through a field, crashing into a tree.
But what if your cars monitoring system recognised the tell-tale signs of drowsiness and prompted you to pull off the road and park instead? The European Commission has legislated that from this year, new vehicles be fitted with systems to catch distracted and sleepy drivers to help avert accidents. Now a number of startups are training artificial intelligence systems to recognise the giveaways in our facial expressions and body language.
These companies are taking a novel approach for the field of AI. Instead of filming thousands of real-life drivers falling asleep and feeding that information into a deep-learning model to learn the signs of drowsiness, theyre creating millions of fake human avatars to re-enact the sleepy signals.
Big data defines the field of AI for a reason. To train deep learning algorithms accurately, the models need to have a multitude of data points. That creates problems for a task such as recognising a person falling asleep at the wheel, which would be difficult and time-consuming to film happening in thousands of cars. Instead, companies have begun building virtual datasets.
Synthesis AI and Datagen are two companies using full-body 3D scans, including detailed face scans, and motion data captured by sensors placed all over the body, to gather raw data from real people. This data is fed through algorithms that tweak various dimensions many times over to create millions of 3D representations of humans, resembling characters in a video game, engaging in different behaviours across a variety of simulations.
In the case of someone falling asleep at the wheel, they might film a human performer falling asleep and combine it with motion capture, 3D animations and other techniques used to create video games and animated movies, to build the desired simulation. You can map [the target behaviour] across thousands of different body types, different angles, different lighting, and add variability into the movement as well, says Yashar Behzadi, CEO of Synthesis AI.
Using synthetic data cuts out a lot of the messiness of the more traditional way to train deep learning algorithms. Typically, companies would have to amass a vast collection of real-life footage and low-paid workers would painstakingly label each of the clips. These would be fed into the model, which would learn how to recognise the behaviours.
The big sell for the synthetic data approach is that its quicker and cheaper by a wide margin. But these companies also claim it can help tackle the bias that creates a huge headache for AI developers. Its well documented that some AI facial recognition software is poor at recognising and correctly identifying particular demographic groups. This tends to be because these groups are underrepresented in the training data, meaning the software is more likely to misidentify these people.
Niharika Jain, a software engineer and expert in gender and racial bias in generative machine learning, highlights the notorious example of Nikon Coolpixs blink detection feature, which, because the training data included a majority of white faces, disproportionately judged Asian faces to be blinking. A good driver-monitoring system must avoid misidentifying members of a certain demographic as asleep more often than others, she says.
The typical response to this problem is to gather more data from the underrepresented groups in real-life settings. But companies such as Datagen say this is no longer necessary. The company can simply create more faces from the underrepresented groups, meaning theyll make up a bigger proportion of the final dataset. Real 3D face scan data from thousands of people is whipped up into millions of AI composites. Theres no bias baked into the data; you have full control of the age, gender and ethnicity of the people that youre generating, says Gil Elbaz, co-founder of Datagen. The creepy faces that emerge dont look like real people, but the company claims that theyre similar enough to teach AI systems how to respond to real people in similar scenarios.
There is, however, some debate over whether synthetic data can really eliminate bias. Bernease Herman, a data scientist at the University of Washington eScience Institute, says that although synthetic data can improve the robustness of facial recognition models on underrepresented groups, she does not believe that synthetic data alone can close the gap between the performance on those groups and others. Although the companies sometimes publish academic papers showcasing how their algorithms work, the algorithms themselves are proprietary, so researchers cannot independently evaluate them.
In areas such as virtual reality, as well as robotics, where 3D mapping is important, synthetic data companies argue it could actually be preferable to train AI on simulations, especially as 3D modelling, visual effects and gaming technologies improve. Its only a matter of time until you can create these virtual worlds and train your systems completely in a simulation, says Behzadi.
This kind of thinking is gaining ground in the autonomous vehicle industry, where synthetic data is becoming instrumental in teaching self-driving vehicles AI how to navigate the road. The traditional approach filming hours of driving footage and feeding this into a deep learning model was enough to get cars relatively good at navigating roads. But the issue vexing the industry is how to get cars to reliably handle what are known as edge cases events that are rare enough that they dont appear much in millions of hours of training data. For example, a child or dog running into the road, complicated roadworks or even some traffic cones placed in an unexpected position, which was enough to stump a driverless Waymo vehicle in Arizona in 2021.
With synthetic data, companies can create endless variations of scenarios in virtual worlds that rarely happen in the real world. Instead of waiting millions more miles to accumulate more examples, they can artificially generate as many examples as they need of the edge case for training and testing, says Phil Koopman, associate professor in electrical and computer engineering at Carnegie Mellon University.
AV companies such as Waymo, Cruise and Wayve are increasingly relying on real-life data combined with simulated driving in virtual worlds. Waymo has created a simulated world using AI and sensor data collected from its self-driving vehicles, complete with artificial raindrops and solar glare. It uses this to train vehicles on normal driving situations, as well as the trickier edge cases. In 2021, Waymo told the Verge that it had simulated 15bn miles of driving, versus a mere 20m miles of real driving.
An added benefit to testing autonomous vehicles out in virtual worlds first is minimising the chance of very real accidents. A large reason self-driving is at the forefront of a lot of the synthetic data stuff is fault tolerance, says Herman. A self-driving car making a mistake 1% of the time, or even 0.01% of the time, is probably too much.
In 2017, Volvos self-driving technology, which had been taught how to respond to large North American animals such as deer, was baffled when encountering kangaroos for the first time in Australia. If a simulator doesnt know about kangaroos, no amount of simulation will create one until it is seen in testing and designers figure out how to add it, says Koopman. For Aaron Roth, professor of computer and cognitive science at the University of Pennsylvania, the challenge will be to create synthetic data that is indistinguishable from real data. He thinks it is plausible that were at that point for face data, as computers can now generate photorealistic images of faces. But for a lot of other things, which may or may not include kangaroos I dont think that were there yet.
Excerpt from:
Is fake data the real deal when training algorithms? - The Guardian
- Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction - Nature - July 6th, 2025 [July 6th, 2025]
- Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm... - July 6th, 2025 [July 6th, 2025]
- A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs - Nature - July 6th, 2025 [July 6th, 2025]
- Ultrabroadband and band-selective thermal meta-emitters by machine learning - Nature - July 4th, 2025 [July 4th, 2025]
- Machine Learning is Surprisingly Good at Simulating the Universe - Universe Today - July 4th, 2025 [July 4th, 2025]
- Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in... - July 4th, 2025 [July 4th, 2025]
- Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis - Nature - July 4th, 2025 [July 4th, 2025]
- Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers - Nature - July 4th, 2025 [July 4th, 2025]
- Enhancing game outcome prediction in the Chinese basketball league through a machine learning framework based on performance data - Nature - July 4th, 2025 [July 4th, 2025]
- A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques -... - July 4th, 2025 [July 4th, 2025]
- Machine learning for Parkinsons disease: a comprehensive review of datasets, algorithms, and challenges - Nature - July 4th, 2025 [July 4th, 2025]
- Cervical cancer prediction using machine learning models based on routine blood analysis - Nature - July 4th, 2025 [July 4th, 2025]
- Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach - Nature - July 4th, 2025 [July 4th, 2025]
- Predicting car accident severity in Northwest Ethiopia: a machine learning approach leveraging driver, environmental, and road conditions - Nature - July 4th, 2025 [July 4th, 2025]
- Sensormatic Solutions Adds Machine Learning to Shrink Analyzer - Ink World magazine - July 4th, 2025 [July 4th, 2025]
- Exploring the link between the ZJU index and sarcopenia in adults aged 2059 using NHANES and machine learning - Nature - July 4th, 2025 [July 4th, 2025]
- Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate... - July 2nd, 2025 [July 2nd, 2025]
- New insight into viscosity prediction of imidazolium-based ionic liquids and their mixtures with machine learning models - Nature - July 2nd, 2025 [July 2nd, 2025]
- Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application -... - July 2nd, 2025 [July 2nd, 2025]
- Advanced analysis of defect clusters in nuclear reactors using machine learning techniques - Nature - July 2nd, 2025 [July 2nd, 2025]
- Machine learning analysis of kinematic movement features during functional tasks to discriminate chronic neck pain patients from asymptomatic controls... - July 2nd, 2025 [July 2nd, 2025]
- Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above - BMC Geriatrics - July 2nd, 2025 [July 2nd, 2025]
- Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and... - July 2nd, 2025 [July 2nd, 2025]
- A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia - Nature - July 2nd, 2025 [July 2nd, 2025]
- Identifying ovarian cancer with machine learning DNA methylation pattern analysis - Nature - July 2nd, 2025 [July 2nd, 2025]
- High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction - Nature - July 2nd, 2025 [July 2nd, 2025]
- Sony and AMD want to focus on machine learning for the PS6 - Instant Gaming News - July 2nd, 2025 [July 2nd, 2025]
- How Machine Learning is Reshaping the Future of Sports Betting? - London Daily News - July 2nd, 2025 [July 2nd, 2025]
- An interpretable machine learning model for predicting depression in middle-aged and elderly cancer patients in China: a study based on the CHARLS... - July 2nd, 2025 [July 2nd, 2025]
- These Eight Projects Showcase the Power of Machine Learning on the Edge - Hackster.io - June 29th, 2025 [June 29th, 2025]
- Build Custom AI Tools for Your AI Agents that Combine Machine Learning and Statistical Analysis - MarkTechPost - June 29th, 2025 [June 29th, 2025]
- Check out these essential tips and trends for SEO in 2025 as AI and machine learning loom large - EdTech Innovation Hub - June 29th, 2025 [June 29th, 2025]
- Using machine learning to predict the severity of salmonella infection - Open Access Government - June 28th, 2025 [June 28th, 2025]
- How AI and machine learning are transforming drug discovery - Pharmaceutical Technology - June 28th, 2025 [June 28th, 2025]
- Capturing the complexity of human strategic decision-making with machine learning - Nature - June 26th, 2025 [June 26th, 2025]
- A framework to evaluate machine learning crystal stability predictions - Nature - June 24th, 2025 [June 24th, 2025]
- Machine learning revealed giant thermal conductivity reduction by strong phonon localization in two-angle disordered twisted multilayer graphene -... - June 24th, 2025 [June 24th, 2025]
- How AI and Machine Learning Are Powering the Next Generation of Pump Maintenance - Robotics Tomorrow - June 24th, 2025 [June 24th, 2025]
- Actuate Therapeutics Reports Positive Biomarker and Machine Learning Data from Phase 2 Elraglusib Trial in First-Line Treatment of Metastatic... - June 24th, 2025 [June 24th, 2025]
- Texas A&M Researchers Introduce a Two-Phase Machine Learning Method Named ShockCast for High-Speed Flow Simulation with Neural Temporal Re-Meshing -... - June 22nd, 2025 [June 22nd, 2025]
- Machine learning method helps bring diagnostic testing out of the lab - Medical Xpress - June 22nd, 2025 [June 22nd, 2025]
- Sebi proposes five-point rulebook for responsible use of AI, machine learning - The New Indian Express - June 22nd, 2025 [June 22nd, 2025]
- HAPIR: a refined Hallmark gene set-based machine learning approach for predicting immunotherapy response in cancer patients - Nature - June 20th, 2025 [June 20th, 2025]
- Machine learning boosts accuracy of point-of-care disease detection - News-Medical - June 20th, 2025 [June 20th, 2025]
- How AI and Machine Learning Are Transforming Food Poisoning Outbreak Detection - Food Poisoning News - June 20th, 2025 [June 20th, 2025]
- Evo 2 machine learning model enlists the power of AI in the fight against diseases - Medical Xpress - June 20th, 2025 [June 20th, 2025]
- Machine learning can predict which babies will be born with low birth weights - Medical Xpress - June 20th, 2025 [June 20th, 2025]
- Development and Validation of a Machine Learning Model for Identifying Novel HIV Integrase Inhibitors - Cureus - June 20th, 2025 [June 20th, 2025]
- IIT launches new online certificate programme in data science and machine learning for working profession - Times of India - June 20th, 2025 [June 20th, 2025]
- Calgary startup tackles referee abuse with microphones and machine learning - Yahoo - June 20th, 2025 [June 20th, 2025]
- New machine learning program accurately predicts who will stick with their exercise program - AOL.com - June 20th, 2025 [June 20th, 2025]
- Machine learning and generative AI: What are they good for in 2025? - MIT Sloan - June 4th, 2025 [June 4th, 2025]
- Researchers use machine learning to improve gene therapy - Stanford Report - June 4th, 2025 [June 4th, 2025]
- Machine learning for workpiece mass prediction using real and synthetic acoustic data - Nature - June 4th, 2025 [June 4th, 2025]
- Analyzing the Effect of Linguistic Similarity on Cross-Lingual Transfer: Tasks and Input Representations Matter - Apple Machine Learning Research - June 4th, 2025 [June 4th, 2025]
- Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury - Nature - June 4th, 2025 [June 4th, 2025]
- A machine learning approach to carbon emissions prediction of the top eleven emitters by 2030 and their prospects for meeting Paris agreement targets... - June 4th, 2025 [June 4th, 2025]
- Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance - Nature - June 4th, 2025 [June 4th, 2025]
- Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique -... - June 4th, 2025 [June 4th, 2025]
- Your DNA Is a Machine Learning Model: Its Already Out There - Towards Data Science - June 4th, 2025 [June 4th, 2025]
- Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning... - June 4th, 2025 [June 4th, 2025]
- Predicting long-term patency of radiocephalic arteriovenous fistulas with machine learning and the PREDICT-AVF web app - Nature - June 4th, 2025 [June 4th, 2025]
- How Machine Learning and Cascade Learning Open Doors of Advanced Automation - Supply & Demand Chain Executive - June 4th, 2025 [June 4th, 2025]
- New Hydrogenation Reaction Mechanism for Superhydride Revealed by Machine Learning - Asia Research News | - June 4th, 2025 [June 4th, 2025]
- AI experiences rapid adoption, but with mixed outcomes Highlights from VotE: AI & Machine Learning - S&P Global - June 4th, 2025 [June 4th, 2025]
- IIPE introduces online M.Tech in Data Science and Machine Learning for working professionals - India Today - June 4th, 2025 [June 4th, 2025]
- Introducing Windows ML: The future of machine learning development on Windows - Windows Blog - May 19th, 2025 [May 19th, 2025]
- Settlement strategies and their driving mechanisms of Neolithic settlements using machine learning approaches: a case study in Zhejiang Province -... - May 19th, 2025 [May 19th, 2025]
- MyWear revolutionizes real-time health monitoring with comparative analysis of machine learning - Nature - May 19th, 2025 [May 19th, 2025]
- Leveraging stacking machine learning models and optimization for improved cyberattack detection - Nature - May 19th, 2025 [May 19th, 2025]
- Predicting land suitability for wheat and barley crops using machine learning techniques - Nature - May 10th, 2025 [May 10th, 2025]
- AI and Machine Learning - Ribeiro Preto adopts Optibus to optimise public bus system - Smart Cities World - May 10th, 2025 [May 10th, 2025]
- Childrens Hospital Los Angeles Leads Development of First Machine Learning Tool to Predict Risk of Cisplatin-Induced Hearing Loss - Business Wire - May 10th, 2025 [May 10th, 2025]
- Google is using machine learning to help Android users avoid unwanted and dangerous notifications - BetaNews - May 10th, 2025 [May 10th, 2025]
- London School of Emerging Technology (LSET) Concludes International Workshop on Emerging AI & Machine Learning Innovation - Barchart.com - May 10th, 2025 [May 10th, 2025]
- Thermal performance, entropy generation, and machine learning insights of AlO-TiO hybrid nanofluids in turbulent flow - Nature - May 10th, 2025 [May 10th, 2025]
- Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning - Nature - May 10th, 2025 [May 10th, 2025]
- How AI and machine learning are supercharging video conferencing tools - European CEO - May 10th, 2025 [May 10th, 2025]
- The need for a risk-based approach to AI and machine learning in healthcare - Health Tech World - May 10th, 2025 [May 10th, 2025]
- Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus... - May 10th, 2025 [May 10th, 2025]