Eps 5: Search Behaviour Artificial Intelligence
Artificial intelligence (AI) receives attention in media as well as in academe and business.
Invited talk at the summit on machine learning meets formal methods.
Tore Pedersen was partially supported by the project Oslo Analytics funded by the IKTPLUSS program of the Norwegian Research Council.
Host
Troy Kennedy
Podcast Content
If you are interested in anything we discuss or have questions about the development of artificial intelligence, please contact the SPD parliamentary group.
As AI tools offer companies a range of new features, the use of artificial intelligence raises ethical questions, because AI systems, for better or worse, reinforce what they have already learned. This is problematic, because the machine learning algorithms that underlie many of the most advanced AI tools are inherently biased in the data they are provided for training. The potential for machine-learning distortions is inherent and must be monitored, regardless of what data is used to train the AI program.
Currently, only 37% of retail organizations use AI in their production systems. If you want to use machine learning as part of the real world, you have to incorporate ethical aspects into your AI and try to avoid prejudice.
Retailers are used to being reactive, so changing how they work will not be an easy task. This is probably because many still do not fully understand how artificial intelligence could help them reach customers.
Moreover, brands and marketers are forced to obtain results from existing methods of marketing campaigns. The ability to predict trends will help them optimize their marketing strategies, such as the use of social media and other digital marketing tools.
With the ability to analyze massive data through machine learning, marketers "predictions are within reach. Samuel has invented play and proved that artificial intelligence is possible. The term "machine learning" was first invented by Dr. Lee Samuel, professor of computer science at the University of California, Berkeley, to describe self-learning - learning without being programmed by humans.
Perhaps this assumption worked well in the past, but now we have data - insights gained through artificial intelligence algorithms.
As more and more customer data is collected and processed, predictive analysis will enable brands to generate insights and take action. It is already possible to take social media-based customer sentiment analyses and make predictions for the coming months. We can do this much more accurately than humans and even make long-term predictions.
To get the most out of big data, AI becomes a valuable tool, and companies use AI and machine learning to optimize the profitability of businesses. The technology can detect patterns in data much faster than humans, so smart computers can learn customer behavior to help companies deliver better products and services.
Dashboards and reporting are probably one of the most popular applications of AI and machine learning for big data, and that's probably because it's so useful for business management.
With the help of a well-trained AI, marketers can rely on assumptions, assumptions and data to predict customer behavior more accurately and even better in the future. Marketers can use data on customer sentiment collected through social media and listening tools to identify patterns that can help them predict consumer behavior months in advance. For marketers, it's pure gold to predict what your customers need and get it right.
King also recommends that marketers experiment with AI before they understand the technology and use it to create value. Marketers don't have much time to figure out the innovation and understand how to make it value.
Some competitors have already introduced artificial intelligence technologies such as machine learning and deep learning. But most companies have not yet begun to adopt AI, according to King, due to a lack of understanding of AI.
At the same time, the majority of us are very quick to implement and it is a constant need for marketers to understand what consumers want and need, ideally before they even do.
Artificial intelligence could make this task much easier, especially with the advent of deep learning. As a subset of AI, deep insights have the potential to change the future of marketing by helping companies predict consumer behavior. Telecommunications companies are also harnessing the power of artificial intelligence to process and analyze vast amounts of big data to gain actionable insights, provide a better customer experience, improve operations, and increase revenue through new products and services.
Artificial intelligence applications in the telecommunications industry use advanced algorithms to search for data patterns, enabling telecommunications companies to detect and predict network anomalies and proactively resolve problems before customers are adversely affected. AI is a key component in helping CSPs build self-optimizing networks that enable operators to optimize network quality based on traffic information, regions and time zones. With 20.4 billion connected devices in use worldwide by 2020, more and more C-SPs are preparing to recognize the value of artificial intelligence to the telecommunications industry.