2023 Artificial Intelligence Trends
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2 min read
Data Science & AI
Author: Dr. Chen Mingyu, Dr. Lin Shoude, Dr. Sun Min
Looking back on the second half of 2022, the world has been severely impacted by inflation, economic uncertainty and large-scale layoffs; at the same time, experts predict that all industries will face many challenges in 2023 , including supply chain issues and more intense competition for talent recruitment.
Although the economic outlook for 2023 does not look promising, as the bahamas mobile phone number list saying goes, "crisis is a turning point." For example, the COVID-19 epidemic has greatly accelerated the digital transformation of brands and changed the way they operate in the past. Appier found that brands are more actively adopting AI solutions under the impact of the epidemic, and we also expect that more brands will seek AI assistance during this period of uncertainty.
When general economic and other external market conditions are adverse, brands need to turn to internal resources for solutions. Appier observed that AI is quickly becoming an indispensable part of enterprises to strengthen internal resources, because AI solutions have been proven to drive more growth, predictable investment returns, and at the same time save operating costs. As brands become more conservative in marketing spending, the above two key advantages will become AI's best selling points in both the short and long term.
Appier will continue to improve AI solutions that can create good returns on investment. In the period of economic uncertainty in 2023, it will use AI to strengthen brand competitiveness and accelerate the results of digital transformation.
1 Transformer model: It is a deep learning model that uses self-attention mechanism . It learns the context and meaning between contexts by tracking the relationships in sequence data. Under this mechanism, it allows more parallel operations, processes all input data at once, and can detect data elements in a series that interact and depend on each other in subtle ways, even ambiguous data elements , thereby using more Less data enables more accurate identification.