Appen’s 2023 AI Data Predictions

What We Predict are the Biggest Changes for the Future of AI and Data

We live in a society that’s ever changing, always wanting more, always wanting to see what the next big thing will be. To kick off 2023, we tapped some of our executives and thought leaders to share their predictions of what the year has in store for the future of AI and Data. Sujatha Sagiraju, Erik Vogt, and Jen Cole share their insights on what will have the greatest impact to the industry in 2023.  

Their predictions fall into four main topics: 

  • Generative AI 
  • Speed & Scale
  • Synthetic Data
  • Automotive 

 Generative AI will Change the Way We Work 

Generative AI has taken the world by storm as people are using the technology to create intricate works of art. This technology also extends to text, and people are having articles being written in almost no time.  

SVP and General Manager of Enterprise Sales Jen Cole focuses on “the accessibility of generative AI and how it will enable non-artists…to incorporate original art into PowerPoints.” Art takes skill and training to master—a luxury many don’t have. The lack of time or talent to create desired or needed creative is one factor that make this new form of artificial intelligence so appealing. Another is budget. Commissioning impactful creative for an internal presentation, as mentioned in Jen’s comment, could be costly with a low return on investment. Leveraging generative AI to quickly fill in gaps in a business presentation saves both time and money for executives. 

Speed & Scale will Drive Business 

According to our experts, speed and scale will be a primary focus for businesses in 2023. Jen predicts “businesses will prioritize AI initiatives that drives measurable efficiencies,” while Appen Chief Product Officer Sujatha Sagiraju comments,  

“For as long as businesses have leveraged AI, executives have been focused on prioritizing one of two things: speed to deploy AI or quality of AI data. These two have not been mutually exclusive things in the past, which has led to fundamental problems in how companies build, scale, deploy, and maintain their AI systems. In the future, however, companies should no longer find themselves in a position where they are sacrificing speed for quality or vice versa.”  

By focusing on both speed and scale, companies will see results sooner with stronger performing machine learning models that leads to efficient results from the completed projects.  

To avoid the problem of speed and scale not working in conjunction, Sujatha anticipates, 

We will see companies continue to deploy solutions that help them both source quality data and scale AI systems more efficiently and effectively than ever before. Technology, combined with human oversight to help spot areas of improvement along the way, will help merge speed and quality and help companies make their AI moonshot goals a reality in the coming year. 

“There is a huge, missed opportunity when it comes to not utilizing external vendors. External vendors are a powerful partner. Oftentimes, organizations try to create and deploy the AI model themselves, and quickly find that they lack good quantities of data, so they go to a cheaper source and end up with low-quality data. Even if an organization has access to clean, large-scale data relevant for the model, working with big data is time-consuming and requires experience. 

“What these companies should have done is found an external vendor who can offer them high-quality data that enables high-performing models. Outsourcing helps to cut costs, achieve quick turnaround times, and helps boost automation and focus on other key components like human-in-the-loop (HITL) practices. In 2023, there will be a clear shift to more and more companies looking to outsource for data for the AI lifecycle to help scale effectively and efficiently.” 

Privacy and Edge Cases will be In High Demand 

“Identity: Privacy will continue to increase in importance when handling real-world data,” says VP of Enterprise Solutions, Erik Vogt. 

“Expectations to protect people during data collection as well as model outputs will broaden as regulations kick in and privacy advocacy continues to raise awareness. This includes both how and what data is collected as well as how people are impacted by these systems. Increased interest in systematic bias will drive demand for platform evaluation and performance monitoring solutions. 

“Fidelity: General purpose models such as LLMs (Large Language Models) are providing solid functionality so demand for collecting data to fill in under-performing use cases is set to increase, especially for lower frequency events and the data needs to be realistic, so carefully targeted data or specifically generated synthetic data will be in ever greater demand. 

“Edge Cases: Organizations are continually inventing and experimenting with innovative and narrowly focused AI use cases and as a result we are seeing increasing common requests to collect very uncommon datasets. All these use cases are going to help solve specific edge cases that are vastly beyond the AI use cases that frequently come to mind.” 

Synthetic data creates artificially generated datasets, so data is naturally free of personally identifiable information (PII). Synthetic data can also generate data quickly at scale so edge-case data can be generated without time or safety constraints. We foresaw the need of synthetic data in our 2022 predictions and partnered with Mindtech to bring synthetic data to our customers.  

Automotive Drivers and Passengers will Become Even More Reliant on Seamless AI Features 

While we may not be quite at the flying car stage of autonomous mobility, Sujatha and Jen have some predictions about how the automotive industry will advance even further than it has this year.  

Jen believes trust in autonomy will continue to grow. “Many people today complain about automated driver assistance systems that don’t work well and often end up being an annoyance – expect more people to start liking and seeking out driver assistance systems as the technology gets better and more effective in new cars.” 

Sujatha predicts that there will be advances in safety and the consumer experience overall.  

“In the next year, AI will make large strides in safe driving technology for autonomous vehicles. The European Union launched the General Safety Regulation in June 2022, which mandates safety technologies such as distracted driver protection, lane-keeping systems, advanced emergency braking and pedestrian collision warning, to be included as standard in new vehicle types. The EU expects to save 25,000 lives and avoid 140,000 serious injuries by 2038. 

“Innovation in AVs will not be exclusive to safety, though. As AI spending in the automotive industry increases, there is an opportunity for AI to improve the consumer experience inside the vehicle. Manufacturers are already using AI to create features like in-car voice assistance and Tesla’s Autopilot, which allows you to summon your vehicle from its parking space. In the next year, we will see improvements within the in-cabin experience geared toward comfort, such as auto-adjusting seats, automatic sun glare protection and more personalized infotainment systems.” 

Excited to see how these predictions turn out? Be sure to check out our 9th Annual State of AI and Machine Learning report this summer where we’ll discuss trends of the industry and see how these predictions stack up! 

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