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Officials will have to prioritize whether to experiment with cutting-edge technologies or wait to see the results of other implementations, Airbnb’s Chief Technological Officer G Officer Vanja Josifowski told VentureBet’s Transformer 2021 Virtual Conference. Most businesses – even the big ones – have limited resources, so they have to decide what technologies to invest in and what to expect.
In particular, the decision is to use state-of-the-art technologies in critical areas and to avoid experimental or emerging technologies in all other areas, Joshibowski said.
“It’s the hardest part of my job because I want to hire the best and brightest people, but then I want to divide that ability into areas that will provide a business impact,” Josyfowski said. “In some cases, [we] Avoid using state of the art unless we feel we will be compensated. “
Josifowski and Marshall discuss some innovative trends in artificial intelligence (AI). If Josifovsky said, “If we look at what’s happening today, some amazing technologies are coming,” Josifovsky said, such as Graph Neural Network, Transformer Models, and Language Dells.
Graph neural network
Josifowski predicts that the graph neural network will be a major trend in 2021. At its core, the learning paradigm is a different way of structuring data, such as sequencing data such as images and text. However, the use and structure of the information needed to make the model work can be rigorous. Graph neural networks, in contrast, allow more flexible architectures as they define the architecture of the data model.
“Graph Neural Networks is the next iteration that allows us to use more information more naturally in the Deep Learning Framework,” Josifowski said. “I think they’ll open up a whole new area, where you’ll be able to apply the easier deep learning pattern over a whole different set of data.”
Pinteret has used this model to create a recommended feature, and Berber created a fraud detection model, for example.
Although it is “a remarkable technological breakthrough,” working with a large language model could happen very soon, Joshifowski said. Being able to measure these models is a relatively new concept, but the challenge is to find the data to train the model. However, he added that predictions are needed to use models in the production process. There are good examples of using models to generate text and webpages. This is a good place to use the models because they are “not mission complicated”, Joshifowski said. In contrast, machines such as self-driving cars will not initially fit this type of function.
Language models do not currently work with chatbots, Joshibowski believes they will in the future.
Center for innovation?
In the early years, Academic AI was a hub of innovation and research, with large companies developing some proprietary technologies, Josefowski said. Over time, waves of innovation in AI came from big companies like Google, Amazon, Microsoft and Facebook. As many technologies become commodified, Jos Sifowski predicts a second shift, this time to smaller, independent companies. In areas such as storage and cloud infrastructure management, well-resourced companies will provide infrastructure to allow smaller players to develop AI.
The center of gravity will shift from large companies to a few independent companies, Joshivowski said. “We will see a complete ecosystem of companies that [have] Has now developed and will shape the future. “
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