Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minutePC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Unlearn.ai announced a $12 million Series A equity financing on April 20, 2020, led by 8VC. Existing investors DCVC, DCVC Bio and Mubadala Capital Ventures also participated, taking the company’s reported total funding above $17 million. The company said it would use the money to develop machine-learning-generated “digital twins” that could supplement or reduce conventional control-arm enrollment in clinical trials—not replace real patients or entire studies.
What Unlearn announced in April 2020
The financing was an equity Series A, not a grant, debt facility, acquisition or public-market investment. VentureBeat reported that 8VC led the round and that 8VC principal Francisco Gimenez joined Unlearn’s board. The announcement said the company planned to work with pharmaceutical companies, initially concentrating on neurological diseases such as Alzheimer’s disease and multiple sclerosis. VentureBeat’s April 20, 2020 report put cumulative funding at more than $17 million after the round.
| Financing detail | What was reported |
|---|---|
| Announcement date | April 20, 2020 |
| Round | $12 million Series A equity financing |
| Lead investor | 8VC |
| Other participating investors | DCVC, DCVC Bio and Mubadala Capital Ventures |
| Reported funding after the round | More than $17 million |
| Board change | 8VC principal Francisco Gimenez joined Unlearn’s board |
The investment thesis addressed a familiar clinical-development bottleneck: trials need control groups, but finding eligible participants can be slow, costly and especially difficult in slowly progressing or rare diseases. People assigned to placebo or standard care also take on trial visits and procedures without receiving the experimental treatment.
What “digital twin” meant in this clinical-trial context
In Unlearn’s 2020 usage, a digital twin was a machine-learning-generated, longitudinal virtual medical record intended to estimate how a particular real participant might progress under a control condition.
Recommended Free Tools
#1 Best Overall
- Easily store and access 2TB to content on the go with the Seagate Portable Drive, a USB external hard drive
- Designed to work with Windows or Mac computers, this external hard drive makes backup a snap just drag and drop
- To get set up, connect the portable hard drive to a computer for automatic recognition no software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
A simplified example is:
- A real participant enters a randomized trial and provides baseline demographic, clinical and laboratory information.
- The model uses those characteristics and historical trial data to estimate that participant’s likely control trajectory at future visits.
- Investigators compare the participant’s observed outcome with the model’s predicted control outcome as part of a prespecified analysis.
- If the method is valid for that disease, endpoint and trial design, the sponsor may need fewer concurrently enrolled placebo or conventional-control participants.
This is a statistical forecast, not a conscious virtual patient or a physical simulation of an entire human body. The output may contain predicted demographics, laboratory results, biomarkers, clinical endpoints and other longitudinal disease-progression measures, but it is not automatically a complete substitute for every clinical measurement. Treated participants, real-world safety monitoring, informed consent and clinical oversight remain necessary.
How the proposed system was built
Unlearn described disease-specific models trained on historical clinical-trial datasets involving thousands of patients. The company’s earlier work included restricted Boltzmann machines (RBMs), the open-source Paysage package and a hybrid architecture called a Boltzmann Encoded Adversarial Machine, or BEAM. These unsupervised-learning methods generated virtual patients and associated medical records.
The technical objective was not simply to produce an average patient. The company said its approach attempted to preserve distinct patient distributions rather than blending unlike groups into one population average. For a trial sponsor, however, architecture is secondary to whether forecasts are calibrated and transportable to the new study’s population, endpoints, time horizon and standard of care.
Rank #2
- Easily store and access 5TB of content on the go with the Seagate portable drive, a USB external hard Drive
- Designed to work with Windows or Mac computers, this external hard drive makes backup a snap just drag and drop
- To get set up, connect the portable hard drive to a computer for automatic recognition software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
What evidence existed in 2020?
The case study described in the 2020 coverage used the Coalition Against Major Diseases Online Data Repository, an Alzheimer’s disease dataset of approximately 5,000 patients. It included about 18 months of measurements and roughly 50 variables, including components of the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog) and the Mini-Mental State Examination.
Free tools Windows power users keep installed
One-click scans. No signup required.
Unlearn reported predictions for measures such as word recall, orientation and naming, and said ADAS-Cog predictions remained accurate out to at least 18 months. The result was meaningful as a feasibility demonstration, but it was retrospective, disease-specific model-development evidence. It did not establish that the method would work across diseases, endpoints or confirmatory trials, nor that regulators would accept it in every setting.
| Question | What the Alzheimer’s case study did—and did not—show |
|---|---|
| Predictive accuracy | Reported ability to forecast selected Alzheimer’s progression measures in the historical dataset. |
| Calibration | Not fully established by the news report; predicted probabilities or intervals must match observed outcomes in appropriate validation. |
| Causal validity | Not automatically proven. Predicting a likely outcome is different from estimating what the same person would have experienced under control. |
| Trial operating characteristics | Type-I error, power and treatment-effect behavior require prespecified simulations and prospective evaluation. |
| Regulatory acceptance | Study-specific and not implied by the retrospective case study. |
Why sponsors might consider modeled control outcomes
- Less control-arm burden: A valid model could reduce the number of patients assigned to placebo or standard care.
- Recruitment potential: A higher probability of receiving the experimental therapy may make participation more attractive, while scarce eligible patients can be allocated more efficiently.
- Planning and power: Historical data and patient-level forecasts may improve simulations and sample-size decisions.
- Neurological-disease fit: Slowly progressing diseases such as Alzheimer’s can require long follow-up and difficult recruitment, making control-arm efficiency particularly valuable.
Those benefits are conditional. A model that is wrong for the target population, endpoint or treatment era can introduce bias rather than remove it.
Rank #3
- Easily store and access 1TB to content on the go with the Seagate Portable Drive, a USB external hard drive.Specific uses: Personal
- Designed to work with Windows or Mac computers, this external hard drive makes backup a snap just drag and drop. Reformatting may be required for Mac
- To get set up, connect the portable hard drive to a computer for automatic recognition no software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
Scientific and operational risks
Distribution shift
Performance can deteriorate when a new trial enrolls different demographics, uses changed diagnostic criteria, operates in different countries or care settings, studies another disease stage, or reflects an updated standard of care.
Endpoint mismatch
Accuracy for ADAS-Cog does not establish reliability for functional outcomes, safety events, imaging biomarkers, mortality, hospitalization or quality-of-life measures. Each endpoint needs its own evidence.
Missing data and dropout
Trial observations are rarely missing at random. Dropout may relate to severity, adverse events, treatment response or access to care. Treating missing observations as benign can bias a generated control trajectory.
Rank #4
- Easily store and access 4TB of content on the go with the Seagate Portable Drive, a USB external hard drive.Specific uses: Personal
- Designed to work with Windows or Mac computers, this external hard drive makes backup a snap just drag and drop
- To get set up, connect the portable hard drive to a computer for automatic recognition no software required
- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
- The available storage capacity may vary.
Counterfactual treatment assumptions
The required estimate is what a participant would have experienced under the control condition, not merely what a similar historical patient experienced. Historical studies may encode treatment effects, changing background care or selection differences that must be addressed.
Leakage, overfitting and subgroup performance
- Testing must use genuinely held-out patients or trials.
- Future visits must not leak into predictions for earlier time points.
- Model choices should not be repeatedly tuned against the test set.
- Confidence intervals require external calibration.
- Performance should be examined for older adults, underrepresented racial and ethnic groups, comorbid patients and unusually fast or slow progressors.
Operational controls
Inconsistent endpoint definitions, incompatible data schemas, poor source-data quality, site measurement changes and failure to update a model as care evolves can all undermine a digital-twin analysis. Synthetic records must not be treated as observed clinical measurements, and sponsors need an auditable account of which source data drove each forecast.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What regulators and statisticians would need to see
A technically impressive model is not automatically acceptable evidence. A sponsor would normally need a prespecified statistical analysis plan, disease- and endpoint-specific validation, simulations showing acceptable type-I error and power, transparent handling of missing data, subgroup analyses and a plan for model failure or drift. Early engagement with regulators is important, particularly for confirmatory studies.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Best Value
- [Upgraded Version] - This external hard drive features a mirrored logo stripe combined with a striped anti-slip design, and the rounded corners of the casing make it easier to grip. The stripes also have a heat dissipation function, ensuring stable and fast data transfer.
- 【Ultra-thin and quiet】 - The motherboard adopts JMicron 578 noise-free solution, giving you a quiet working environment. Lightweight and portable size designed to fit in your pocket for easy portability.
- 【Ultra-Fast Data Transfers】 - Pairing this external hard drive with JMicron 578 solution USB 3.0 and USB 2.0 interfaces enables blazing-fast data transfer. It boasts theoretical read speeds of up to 125MB/s and write speeds of up to 103MB/s.
- 【Plug and Play】 - With no software to install, just plug it in and the drive is ready to use.The hard disk chip is wrapped with an aluminum anti-interference layer to increase heat dissipation and protect data.
- 【What You Get】 - 1 x Portable Hard Drive, 1 x USB 3.0 Cable, 1 x User Manual, Gift-type shell packaging ,Three-year manufacturer's warranty and free technical support services.
Exploratory planning, trial simulation and prospective deployment are different evidence stages. Success in one does not prove success in the next. The standard is higher when modeled controls influence a pivotal treatment-effect estimate than when they are used only to explore design options.
What Unlearn said it would do with the money
The 2020 announcement described plans to develop the digital-twin technology, collaborate with pharmaceutical companies and begin with Alzheimer’s disease and multiple sclerosis. The broader ambition—“a digital twin for every patient”—was a company goal, not a delivered capability or verified clinical outcome.
What has changed since 2020?
As of August 18, 2026, Unlearn describes a broader enterprise clinical-development platform with three areas:
| Area | Company description |
|---|---|
| Plan | Trial planning, literature and regulatory-precedent research, historical-data analysis and trial simulations. |
| Monitor | Trial monitoring and anomaly detection. |
| Analyze | Digital-twin-based trial analyses, including methods such as PROCOVA. |
On its current site, Unlearn reports approximate figures including a 33% control-arm reduction, more than four months of enrollment time saved and a 20% sample-size reduction in a listed planning context. It also ties a 33% control-arm reduction to a Phase 3 bapineuzumab analysis and says digital twins increased power from 80% to 90% on ADAS-Cog11 at 18 months using PROCOVA. These are company-reported claims; the page does not make them universal outcomes for every disease or trial. Their interpretation depends on the named study, endpoint, analysis method and whether the result was retrospective, simulated, prospective or operationally deployed. See Unlearn’s current platform information.
Who would buy this kind of platform?
This is a specialized business-to-business service for biopharmaceutical sponsors, CROs, academic trial networks and larger biotech companies—not a self-serve tool for individual patients. Unlearn directs prospects to contact the company, and no public standardized pricing was available as of August 16, 2026. A likely engagement would involve assessing historical data, testing endpoint compatibility, running simulations, preparing validation evidence and integrating the method into the sponsor’s statistical and regulatory strategy. Pricing may therefore be negotiated per study or program rather than sold as a published subscription.
Potential buyers should ask:
- Which diseases, populations and endpoints have prospective or externally validated evidence?
- What historical datasets and minimum sample sizes are required?
- How are missing data, drift, subgroup performance and model updates handled?
- What regulatory precedents and documentation are supplied?
- Can the analysis be fully prespecified, audited and integrated with the sponsor’s statistical analysis plan?
- What happens if the model underperforms in a key subgroup or fails during the trial?
Bottom line for the 2020 financing story
Unlearn’s $12 million Series A backed a credible attempt to make clinical trials more efficient by forecasting control outcomes from historical data. The important interpretation is narrower than “replacing human test subjects”: the proposed use was to supplement or reduce conventional control-arm enrollment while real patients, treatment groups and oversight remained essential. The decisive test is whether digital twins produce valid, reproducible and regulator-acceptable treatment-effect estimates in prospective trials—not merely whether they can generate plausible virtual records in a retrospective Alzheimer’s dataset.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
