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Synthesis AI 2023 Predictions: Synthetic Data and Generative AI Will Simulate the World - What to Expect in 2023


Industry executives and experts share their predictions for 2023.  Read them in this 15th annual series exclusive.

Synthetic Data and Generative AI Will Simulate the World - What to Expect in 2023

By Yashar Behzadi, CEO and Founder of Synthesis AI

Artificial Intelligence (AI) continues to fuel innovation across industries, and the field is poised to heat up even more in the year ahead. With many predicting a recession, there will be a greater focus on faster R&D cycles and cutting costs from "data inflation" - the upward creep of expenses associated with collecting and labeling data for machine learning models. Additionally, the widespread adoption of generative AI is bringing ethical and privacy concerns to the forefront. 

Synthetic data will play a major role in combating data inflation and addressing consumer privacy issues in computer vision model development. A report from Synthesis AI found that 89% of decision-makers believe that synthetic data will be critical to the future of many industries and will lead to widespread change, indicating that these technologies will only continue to impact organizations in all sectors as we enter the new year.

Synthetic Data: The Key to Addressing Generative AI Ethical Concerns

Generative AI has dominated headlines, and the hype surrounding the technology is continuing to grow. Data remains the most critical aspect in building generative AI systems, but using real-world data poses ethical and privacy concerns. The use of real-world data is only becoming more challenging as individual countries and economic blocs implement a patchwork of regulations for data collection, data storage, and more.

Development teams will increasingly use synthetic data when creating ML models to limit bias and address privacy concerns associated with datasets collected from the real world. AI adoption is steadily rising, with over 55% of organizations indicating AI as a core function in 2021, up from 50% in 2020. As innovation only continues to increase in the space, it will be imperative for organizations to invest in the tools and technologies that help mitigate bias and ensure generative AI models are built in a more ethical and privacy-compliant way.

Ramping Innovation, Reducing Costs: How to Address Data Inflation in AI Development

Do more with less: It's the mantra of corporate America, especially during times of economic uncertainty. During cyclical downturns, it's important to remember that scaling back does not have to stifle innovation. Organizations will need to continue investing in the tools and technology required to advance their processes, products and services--but in a much smarter and more efficient way.

Over the past several years,  several factors have led to spiraling costs for machine learning training data. First, the hardware used for data collection has suffered from the same supply-chain challenges as the rest of the economy, with many specialized providers relying on a small number of niche semiconductor companies for chip components. Even as supply chain issues resolve over time, the fact remains that it's expensive to build and deploy customized hardware arrays for collecting data. Second, labeling data is a complicated task for humans, with no economies of scale. As computer vision systems increase in complexity, so, too, does the work required to label data for training them, which increases labor costs. And third, more and more limitations are being placed on the commercial usage of public datasets coming from academia as awareness of the problem of "data laundering" becomes more widespread. Limiting supply (by reducing commercial access to public datasets) while increasing demand (as more companies invest in ML model development), all other factors being equal, results in higher costs for labeled, real-world data.

Synthetic data provides an elegant solution for addressing all of these cost drivers. No specialized hardware is required for data collection. Synthetic data is labeled perfectly during the creation process, completely eliminating the need for human annotation. Finally, there are very few supply constraints with synthetic data - its availability is functionally limitless - and because it's typically owned and managed by the company that creates it, ML practitioners don't have to worry about whether their training data has been ethically sourced.

Generative AI Changed How We Create Pictures. Emerging 3D Generative AI Will Simulate the World

Generative AI enabled the creation of 2D images from text prompts, creating new opportunities for artistic expression. Over the next year, we'll see the technology spur further transformation as companies take these models one step further to generate 3D models. This emerging capability will change the way games are built, visual effects are produced, and immersive 3D environments are developed. For industrial uses, democratizing this technology will create opportunities for digital twins and simulations to train complex computer vision systems, such as autonomous vehicles.

There is no doubt that the momentum around AI and other technologies will continue to transform our lives. Each wave of AI innovation builds upon the last, and generative AI's opportunity to create virtual 3D worlds by simulation is no different.

Despite being nascent technologies only beginning to scratch the surface with enterprise adoption, synthetic data and generative AI holds great promise to disrupt the AI paradigm as we know it. As we enter 2023, the potential for synthetic data and generative AI is boundless.



Yashar Behzadi, CEO and Founder of Synthesis AI

Yashar Behzadi 

Yashar Behzadi is an experienced entrepreneur who has built transformative businesses in AI, medical technology, and IoT markets. Now the CEO at Synthesis AI, he spent the last 14 years in Silicon Valley building and scaling data-centric technology companies. His work at Proteus Digital Health was recognized by Wired as one of the top 10 technological breakthroughs of 2008 and as a Technology Pioneer by the World Economic Forum. Yashar has over 30 patents and patents pending and a Ph.D. in Bioengineering from UCSD.

Published Friday, December 23, 2022 7:30 AM by David Marshall
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