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Three key AI trends to watch in 2023

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Three key AI trends to watch in 2023

Three important AI trends have already started and will not develop until 2023.

The AI ​​market continued to grow and mature in 2022. Although venture capital funding has cooled after a historic 2021, we are starting to see AI move from conceptual use cases to business cases. of use deployed by companies around the world. As the new year approaches, several trends have started to emerge and will continue into 2023. Here’s what to expect in the coming year.

Trend #1: Responsible AI is moving from principles to practice

Conversations about responsible or ethical AI have been a hot topic for some time, but now we see these practices moving from concepts to tools and best practices used as part of the daily AI workflow. Horror stories about biased algorithms grabbed the headlines, but far less discussion took place about how to put safeguards into production systems. Part of the problem is that the data we have reflects actual patterns of inequality and discrimination. Lack of representation affects training data, leading to biased AI design and deployment practices. Although technology is part of the problem, it is also part of the antidote.

By helping practitioners make sense of structured and unstructured data, natural language processing (NLP) models can paint a more complete and accurate picture of enterprise data – from healthcare to finance and beyond. beyond – helping models learn and improve over time.

Pairing advanced technology with emerging legal frameworks around AI is a big step in the right direction. The AI ​​Act, for example, is a unique EU law proposal to govern the risk of AI use cases. Similar to GDPR for the use of data, the AI ​​Act could set a global baseline standard for responsible AI and aims to become law in the spring. This will have a big impact on businesses around the world using AI.

Trend #2: Generative AI is becoming a bright spot

Large language models and processing power have improved over the past few years, giving way to major advances in generative AI. This allows machines to understand audio, text and images to produce content from speech to writing, drawing and now even video. We have all seen DALL-E create realistic images and drawings from a natural language description. Features like these are now moving from simple to look at to real-world business use cases, with dozens of companies already offering solutions to help writers, designers, and marketers. Write your blog in a third of the time, because part of it is written for you. Instead of searching through the image bank, type something to get a fresh, newly created image.

Other exciting developments have taken place in speech synthesis. New tools improve communication between call center agents and customers in real time, at scale. New AI models can paste objects into photos and add realistic lighting and textures. This kind of realism has potential beyond art to reproduce 3D objects, which could have applications ranging from gaming and the metaverse to industrial machinery and architecture. DALL-E is just the start; more realistic human sounds and images produced by AI are in our immediate future.

Trend #3: The gap between humans and machines is narrowing

In 2019, NLP models could finally answer (some) reading comprehension questions as well as (some) humans. There was a time, not too long ago, when GPT-3 couldn’t perform simple mathematical equations because it didn’t understand the semantics. Now, algorithms understand not only math and physics, but also the emotional components of a story, common sense, jokes, sarcasm, and other features of human language. Within a few years, AI models can now far outperform math students, including multi-step proofs and “explain your work” problems. This is happening in all walks of life and the gap between humans and machines is narrowing.

Much of what humans know – physics, economics, law, politics – exists as data in hundreds of languages. Although machines aren’t quite ready to replace humans in these areas, they learn much faster and more accurately than the average person. Essentially, the machines are getting smarter and we are not. The good news is that more people will have access to the right data to make the right decisions. The bad news is that, as with most revolutions in history, AI will also be used in nefarious ways to make money, scam, and spread fear. We have to weigh the good and the bad, and with advances in AI ethics, we are heading for the good.

One last word

It’s an exciting time for AI, and 2023 will be no different. As Responsible AI finds its way into practice and Generative AI continues to develop, we can expect many new, exciting and innovative use cases soon. As a society, we will have to choose between the good and the bad and help people adapt to this rapidly changing world.

About the Author

David Talby is Chief Technology Officer at Pacific AI, helping fast-growing companies apply big data and data science to solve real-world problems in healthcare, life sciences, and related fields. David has extensive experience building and operating web-scale business and data science platforms, as well as building distributed, agile, world-class teams. Previously, he worked for Microsoft’s Bing Group, where he led Bing Shopping’s business operations in the US and Europe, and worked at Amazon in Seattle and the UK, where he built and led distributed teams that helped evolve Amazon’s financial systems. David holds a Ph.D. in computer science and master’s degrees in computer science and business administration.

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