#synthetic data

[ashaitech]

The Rise of Synthetic Data in the Age of AI

Synthetic data is artificially generated data that is used to train machine learning models. It can be used to supplement or replace real-world data, and it has a number of advantages over real-world data.

#ai #ai generated #artificial intelligence #bias #synthetic data #age of ai

[shireen46]

Synthetic Data: Description, Benefits and Implementation

The quality and volume of data are critical to the success of AI algorithms. Real-world data collection is expensive and time-consuming. Furthermore, due to privacy regulations, real-world data cannot be used for research or training in most situations, such as healthcare and the financial sector. Another disadvantage is the data’s lack of availability and sensitivity. To power deep learning and artificial intelligence algorithms, we need massive data sets.

Synthetic data, a new area of artificial intelligence, relieves you of the burdens of manual data acquisition, annotation, and cleaning. Synthetic data generation solves the problem of acquiring data that would otherwise be impossible to obtain. Synthetic data generation will produce the same results as real-world data in a fraction of the time and with no loss of privacy.

Visual simulations and recreations of real-world environments are the focus of synthetic data generation. It is photorealistic, scalable, and powerful data that was created for training using cutting-edge computer graphics and data generation algorithms. It is highly variable, unbiased, and annotated with absolute accuracy and ground truth, removing the bottlenecks associated with manual data collection and annotation.

Why is synthetic data required?

Businesses can benefit from synthetic data for three reasons: privacy concerns, faster product testing turnaround, and training machine learning algorithms.

Most data privacy laws limit how businesses handle sensitive data. Any leakage or sharing of personally identifiable customer information can result in costly lawsuits that harm the brand’s reputation. As a result, one of the primary reasons why companies invest in synthetic data and synthetic data generation techniques is to reduce privacy concerns.

Any previous data remains unavailable for completely new products. Furthermore, human-annotated data is an expensive and time-consuming process. This can be avoided if businesses invest in synthetic data, which can be generated quickly and used to develop reliable machine learning models.

What is the creation of synthetic data?

Synthetic data generation is the process of creating new data as a replacement for real-world data, either manually using tools like Excel or automatically using computer simulations or algorithms. If the real data is unavailable, the fake data can be generated from an existing data set or created entirely from scratch. The newly generated data is nearly identical to the original data.

Synthetic data can be generated in any size, at any time, and in any location. Despite being artificial, synthetic data mathematically or statistically reflects real-world data. It is similar to real data, which is collected from actual objects, events, or people in order to train an AI model.

Real data vs. synthetic data

Real data is measured or collected in the real world. Such information is generated every time a person uses a smartphone, laptop, or computer, wears a smartwatch, accesses a website, or conducts an online transaction. Furthermore, surveys can be used to generate real data (online and offline).

In digital contexts, synthetic data is produced. With the exception of the portion that was not derived from any real-world occurrences, synthetic data is created in a way that successfully mimics the actual data in terms of fundamental qualities. The idea of using synthetic data as a substitute for actual data is very promising because it may be used to provide the training data that machine learning models require. But it’s not certain that artificial intelligence can solve every issue that arises in the real world. The substantial benefits that synthetic data has to provide are unaffected by this.

Where can you use synthetic data?

Synthetic data has a wide range of applications. When it comes to machine learning, adequate, high-quality data is still required. Access to real data may be restricted due to privacy concerns at times, while there may not be enough data to train the machine learning model satisfactorily at others. Synthetic data is sometimes generated to supplement existing data and aid in the improvement of the machine learning model.

Many sectors can benefit greatly from synthetic data:

1. Banking and financial services

2. Healthcare and pharmaceuticals

3. Internet advertising and digital marketing

4. Intelligence and security firms

5. Robotics

6. Automotive and manufacturing

Benefits of synthetic data

Synthetic data promises to provide the following benefits:

Customizable:

To meet the specific needs of a business, synthetic data can be created.

Cost-effective:

In comparison to genuine data, synthetic data is a more affordable solution. Imagine a producer of automobiles that needs access to crash data for vehicle simulations. In this situation, acquiring real data will cost more than producing fake data.

Quicker to produce:

It is possible to produce and assemble a dataset considerably more quickly with the right software and hardware because synthetic data is not gathered from actual events. This translates to the ability to quickly make a large amount of fabricated data available.

Maintains data privacy:

The ideal synthetic data does not contain any information that may be used to identify the genuine data; it simply closely mimics the real data. This characteristic makes the synthetic data anonymous and suitable for dissemination. Pharmaceutical and healthcare businesses may benefit from this.

Some real-world applications of synthetic data

Here are some real-world examples where synthetic data is being actively used.

Healthcare:

In situations where actual data is lacking, healthcare institutions are modeling and developing a variety of tests using synthetic data. Artificial intelligence (AI) models are being trained in the area of medical imaging while always maintaining patient privacy. In order to forecast and predict disease patterns, they are also using synthetic data.

Agriculture:

In computer vision applications that help with crop production forecasting, crop disease diagnosis, seed/fruit/flower recognition, plant growth models, and more, synthetic data is useful.

Banking and Finance:

As data scientists create and develop more successful fraud detection algorithms employing synthetic data, banks and financial institutions will be better able to detect and prevent online fraud.

Ecommerce:

Through advanced machine learning models trained on synthetic data, businesses gain the benefits of efficient warehousing and inventory management, as well as an improved customer online purchase experiences.

Manufacturing:

Companies are benefiting from synthetic data for predictive maintenance and quality control.

Disaster prediction and risk management:

Government agencies are using synthetic data to predict natural disasters in order to prevent disasters and lower risks.

Automotive & Robotics:

Synthetic data is used by businesses to simulate and train self-driving cars, autonomous vehicles, drones, and robots.

Synthetic Data Generation by TagX

TagX focuses on accelerating the AI development process by generating data synthetically to fulfill every data requirement uniquely. TagX has the ability to provide synthetically generated data that are pixel-perfect, automatically annotated or labeled, and ready to be used as ground truth as well as train data for instant segmentation.

Final Thoughts

In some cases, synthetic data may be used to address a company’s or organization’s lack of relevant data or data scarcity. We also investigated the methods for creating artificial data and the potential users. Along with a few examples from actual fields where synthetic data is used, we discussed some of the challenges associated with working with it.

When making business decisions, the use of actual data is always preferable. When such true raw data is unavailable for analysis, realistic data is the next best option. However, it should be noted that in order to generate synthetic data, data scientists with a solid understanding of data modeling are required. A thorough understanding of the actual data and its surroundings is also required. This is necessary to ensure that, if available, the generated data is as accurate as possible.

#machinelearning #artificialintelligence #synthetic data #dataannotation

[aitalksblog]

Unlocking the Potential of SLMs: A Look at Microsoft's Orca 2

(Images made by author with MS Bing Image Creator) As AI research delves into building smaller, more efficient language models, a key challenge emerges: to equip them with the reasoning and comprehension abilities of their larger counterparts. While learning from powerful “instructor” models like GPT-4 offers significant benefits, “student” models often fall short when faced with complex tasks…

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#enhanced reasoning #Large Language Models (LLMs) #Orca #Small Language Models (SLMs) #student model #synthetic data

[gamesatwork]

e438 — We will always have Paris

VR, Quest 3, photorealism, artists using Nightshade for data poisoning to protect their works, code hemophilia from recursive training on synthetic data, Dutchification and much more!

Photo by Rodrigo Kugnharski on Unsplash Published 30 October 2023 Michael, Michael and Andy get together for a lively discussion on VR, AI, another virtual museum and end on a high note with “a touch of Dutch” applied via generative AI.   Starting off the episode with VR, the co-hosts explore a couple of articles dealing with the new Quest 3 headset and ways of working with it.  The TechCrunch…

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#ai #code hemophilia #data poisoning #Dutchification #nightshade #Paris #photorealism #quest 3 #recursive training #synthetic data #uncanny valley #vr

[andrewtaylor]

As the financial services industry becomes more and more digital, large amounts of diverse datasets are needed to fulfill the demands of running innovation programs. One of the critical challenges in this context is the handling of bank-specific and personal data and its processing in innovation initiatives. Synthetic data offers a solution to this conundrum.

#synthetic data

[nextbrainai]

Looking for powerful synthetic data generation tools? Next Brain AI has the solutions you need to enhance your data capabilities and drive better insights.

#synthetic data generation #synthetic data generation tools #synthetic data

[zoetech]

#synthetic data

[innonurse]

A novel machine-learning algorithm has produced an atlas of pediatric cancers

- By InnoNurse Staff -

A new platform created at The Hospital for Sick Children (SickKids) in Canada classifies every known major childhood cancer, allowing clinicians and researchers to detect particular cancer types more quickly and correctly.

Read more at SickKids

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Other recent news and insights

Ascertain's predictive AI preeclampsia algorithm demonstrates the company's continuous commitment on femtech (Fierce Healthcare)

Study: In robot-assisted surgery, synthetic data can outperform actual data (Johns Hopkins University)

An 'electronic tattoo' conceived at UT Austin can sense when you are stressed (The Dallas Morning News)

#cancer #oncology #ai #artificial intelligence #canada #data science #pediatrics #ascertain #predictive analytics #femtech #women health #preeclampsia #pregnancy #hypertension #robotics #surgery #synthetic data #medtech #health tech #tattoo #digital health #wearables #stress #biotech #biotechnology

[osrcnetwork]

Our work with synthetic data is important for three reasons: privacy, product testing, and training machine learning algorithms. We have established a partnership with replica analytic to work on this subject.

#Synthetic data #data augmentation #developing #data de-identification

[sql-unicorn]

New blog post: How to use SyntheticData in Azure Synapse Database Templates

#azure synapse #synthetic data

[idol--hands]

#data soong #lore soong #st tng #star trek comics #datalore #soong family #androids #lt commander data #star trek the next generation #data star trek #lore star trek #lore tng #data tng #peanut brother sandwich #data x lore #commander data #synthetic commanders #artifical intelligence #cybernetics #brothers of destruction #star trek tng brothers #star trek tng #star trek data #lore’s ego is my favorite #my edit #twins #data looks less than thrilled 😆

[lesless]

I really feel absolutely normal until like the day after socializing a lot & then I begin to reflect & start to think that my friend’s autistic girlfriend might have been right about me being a little autistic lmaoooo

#me #I don’t think the benefits outweigh the trouble of getting assessed #but I will say I’ve taken the diagnostic quiz more than a few times & scored higher than I expected #it’s in key areas mostly but maybe I don’t see how I’m coping in other ways #extremely touch sensitive & can’t wear synthetics #I can touch a shirt & accurately guess the fabric content #I do dishes & cleaning with organic rubber gloves on #I take things super literally #I have a mental list of social shortcuts so I can interact more seamlessly #a histamine intolerance #I love routines #I carry earplugs bc I can’t handle loud noises #but mostly I notice my differences when I socialize a lot #& when I don’t socialize seamlessly I get a lot of anxiety about how I communicate or the information I share #as though if I have enough data points I can ‘fix’ how I interact with others #which is not a concept that I think a lot of people worry about?? #maybe they do?? #everything went well yesterday but I still caught myself analyzing my interactions #like I’m preparing for the next mission lol #idk IS that normal? what do you think?? #basically I met her & out of nowhere she asked me if I am autistic #I said I didn’t know #she was quiet for a few moments watching me wash dishes #& then she said ‘you are autistic’ #lol peer reviewed #oh and I LOVE TO ORGANIZE INFORMATION!! #it’s like my favorite thing to do

[fortes-fortuna-iogurtum]

"look over at Data... there's an aura around him."

"well of course! he's an android."

"but... you say that as if you think that's what we all see."

"don't you?"

#nevermind I'll just be over here weeping #of course geordi is the one who sees him like that #everyone else looks at data and even if they don't mean to even if they don't /want/ to #they see something less #a not-human. a not-living. he's mechanical he's synthetic he's not truly 'alive' in the way they are. #but geordi... he looks at data and he sees something /more/. he always has. and he doesn't know how not to. #for him data glows. #idk man I just get kinda emo about their friendship I guess T.T #gurt says stuff #star trek #st: the next gen

[moribundinstitute]

Exploring the Interdisciplinary Field of Thanatometrics: Measurement of Deaths

Death and dying are complex and multi-faceted phenomena that are affected by a wide range of social, economic, and environmental factors. Thanatometrics is a field of study that uses statistical methods to measure patterns, causes and factors related to death and dying. It is an interdisciplinary field that draws on methods and theories from a variety of fields such as demography, epidemiology, sociology, and criminology with the ultimate goal of obtaining accurate data and understanding death's patterns.

Accurate measurement of deaths is crucial in understanding the complex interplay of factors that lead to death. This includes the study of both proximate and ultimate causes of death. Proximate causes refer to the direct cause of death, such as a specific medical condition or injury, while ultimate causes refer to the underlying or indirect causes of death, such as social, environmental or behavioral factors that may have contributed to the proximate cause. By studying both proximate and ultimate causes of death, researchers can have a comprehensive understanding of the factors that lead to death.

Measuring deaths in relation to government policies is also an important aspect of thanatometrics. For example, government policies can lead to deaths in both proximate and ultimate ways. Rent control is an example of a policy that may lead to deaths indirectly. Rent control policies, which limit the amount landlords can charge for rent, can lead to a lack of affordable housing, forcing people to live in overcrowded and substandard conditions, increasing the risk of disease and death. On the other hand, the Polish Operation of the NKVD in 1940 would be considered as the ultimate cause of death, as it was a government action that led to the execution of thousands of people. These examples illustrate how government policies can have both proximate and ultimate effects on deaths, and it is important for researchers to consider these factors when studying death patterns.

Thanatometrics also includes a subfield, comparative thanatometrics, which looks at the death rates across different populations or regions. This subfield uses a variety of methods to compare death rates, such as standardized mortality ratios and comparative case-control studies. One of the methods used in comparative thanatometrics is synthetic control method, which allows researchers to construct a synthetic control group that mimics the death rates of a certain population before a certain event or intervention occurred. Synthetic control method is a useful tool to isolate the effects of an event or intervention on death rates. This allows researchers to track how death rates change over time and in response to different events or interventions, providing valuable insights into the factors that contribute to death.

The interdisciplinary nature of Thanatometrics and its goal of accurate measurement of deaths make it a useful tool for collaboration between researchers from different fields and for providing a unique perspective on the study of death and dying. It also allows researchers to understand the complex relationship between government policies and deaths, and how these policies can have both proximate and ultimate effects on death.

Despite the importance of the field, Thanatometrics is still a new and emerging field. As with any field, there are limitations and criticisms. One limitation is the difficulty in obtaining accurate data on deaths, and a potential source of bias, errors, and limitations in data collection and analysis. The field is also constantly changing as new data and methods become available, making it a dynamic and ongoing process.

In summary, thanatometrics is a valuable field that uses statistical methods to measure patterns, causes and factors related to death and dying. Accurate measurement of deaths and government policies relationship is crucial in understanding the complex interplay of factors that lead to death, and it allows researchers to make comparisons over time and across different populations or regions, and to inform policies and interventions that can improve public health and reduce deaths. Thanatometrics is an interdisciplinary field that draws on a variety of theoretical perspectives, methods, including synthetic control method, and aims to have a comprehensive understanding of death.

#Neologism #Thanatometrics #Death and dying #Proximate causes #Ultimate causes #Interdisciplinary research #Public health #Mortality #Synthetic control method #Comparative analysis #Data analysis

[andrewtaylor]

Synthetic data has become increasingly popular in recent years, particularly in industries such as finance, healthcare, and retail. It is used for a variety of purposes, including training machine learning models, testing software and applications, and conducting research and analysis

#synthetic data

[nextbrainai]

Synthetic data offers an innovative approach to training machine learning models without compromising privacy Discover its benefits and limitations in this comprehensive guide.

#Synthetic Data #Synthetic Data in Machine Learning #AI Generated Synthetic Data #synthetic data generation #synthetic data generation tools