Artificial Intelligence has come a long way since the initial applications the technology was developed for, and advancements in the field yet again show another added benefit in the form of detecting COVID-19. Artificial Intelligence applications have been used to detect COVID-19 in patients and distinguish the virus from other diseases such as phenomena and other lung diseases. According to Jun Xia from Shenzhen Second People's Hospital's Department of Radiology, a learning model can be used so AI can accurately differentiate COVID-19 from different types of lung disease, while detecting whether or not one is positive for the virus as well.
Furthermore, the use of AI is used within contact tracing models to detect where the virus is spread and how severe it is in different given areas. Applications can currently track where the virus is and algorithms are being tested to not only track the virus but also predict where it will spread as well. This not only gives us a better fighting chance against COVID-19 but future pandemics as well.
Finally, AI and machine learning are being used to accelerate medical drug treatment for COVID-19 to identify potential medications to help act as a treatment for the virus. BenevolentAI used machine learning techniques for this purpose to deduce that Baricitinib, a drug for rheumatoid arthritis, is a strong candidate to inhibit the progression of COVID-19 and is now in clinical phases to act as a treatment as a result.
AI and machine learning have come so far in such a short amount of time, and as a result are helping us deal with real-world problems in more ways than one.
There are many ways synthetic data can be used to help grow, strengthen, and rejuvenate your organization and many processes it handles, but here are five key ways in which synthetic data will be able to directly help you and your company!
1) Synthetic Data has a wide variety of use cases to help you out with. Synthetic Data is artificially generated and thus can be manipulated for production testing and model fitting in a plethora of ways. It can be used for machine learning, mathematical model fitting, model testing, and more!
2) Synthetic Data adds an extra layer of security to your data; because synthetic data is artificially generated, if there is a data leak, hack, or if something ends up going wrong, there will be minimal security risk and harm as the exposed data will not put any individual's private information in danger of being exploited. This factor is huge within the cybersecurity world and adds as an extra precaution just in case there is a breach in the system.
3) Synthetic Data is cost-effective. Synthetic Data is less expensive to generate than it is to buy real data in terms of both time and money. Furthermore, because you may need different types of data for different types of test, you'll need several different types of data to test with; this begs the question, wouldn't it be easier to generate each type on the fly as needed rather than stat testing, realize you need to collect more samples and pause testing until you have collected enough to continue?
4) Synthetic Data is great when it comes to threat detection. Synthetic data can reflect authentic patterns and behaviors for insider threat detection and user behavior in the models it is used to create. Furthermore, it can be used during performance testing to cover a variety of different scenarios which can lead to increased threat detection and strengthen an application or model's defensive capabilities.
5) Synthetic Data strengthens performance more than authentic data can. Synthetic data can be used to test models with quickly and efficiently so that data can be analyzed right after the data is plugged in. Moreover, it can be used to train models in ways models can't be trained when using authentic data; it can be generated to fill in for any missing data or used to predict different types of behavior based on reasonable machine learning, rather than leaving data empty or assuming what 'would' have been answered.
Enterprise Implementation Best Practices: Behavioral Threat Detection for Sexual Harassment
We have discussed that with technology currently available, you can combine commercial network traffic and synthetic data generation technologies to provide rich content that mirrors real-world network traffic with configurable threat patterns contained within the traffic data.
Imagine if you were responsible for implementing a solution for detecting and preventing sexual harassment within your system’s network. Would it not make sense to procure this solution in a fashion where vendors could be quantifiably evaluated based on your actual network and sexual harassment criteria? And the awarded contract would include these same metrics as Service Level Agreement (SLA) criteria so that you would know the solution was implemented and operating over time correctly?
For those of you operating on the buy side of the Enterprise consider implementation best practices where you are not only trusting what the vendor is telling you the system is doing, but also verifying and holding the vendor to its commitments.
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