Five ways to increase the effectiveness of artificial intelligence projects


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Artificial intelligence is being talked about everywhere these days, giving the impression that it is being implemented in almost every company. But in fact, this is not so, because many of them often have problems. AI, in one way or another, will affect almost every brand and every industry. Those companies that do not adapt to the new changes will be out of the competition. It is not enough just to master the thinking, which will help the company to enter into a business, the balance of which is tilted more towards artificial intelligence. You need to prepare for real challenges and obstacles that may hinder your AI product to the next level. Setting the Right Metrics for AI It must be emphasized that there is no need to set high expectations for AI. Startups have intentionally exaggerated the capabilities of their AI project. It has been repeatedly reported that contractors Microsoft, Facebook, Google and Apple have been exploiting the audio recordings of their users. These cases can be explained by the fact that modern artificial intelligence systems are not intelligent enough and do not yet comply with the level of requirements that these companies impose on them. As a result, the tech giants are forced to apologize and mitigate AI issues with the help of their employees. Setting realistic expectations is key to ensuring the success of any AI project. Setting goals is just as important. Agree on what needs to be improved As with every particularly new and inflated technology, a common mistake is to create an AI project without identifying the core of the problem. It is also worth paying attention to the power of artificial intelligence. As a rule, most, if not all, business processes consist of a number of simple but time-consuming tasks, such as defining keywords in documents. These works are of little value compared to the human resources involved in carrying them out. Time-consuming routines are ideal targets for AI automation. Decide what data to collect Although data is the source of the functionality of any AI application, it is reckless to collect it at random. Data must meet three main criteria: Extraction (sampling) of data must not infringe anyone’s legal rights. Companies must comply with confidentiality rules and restrictions on receiving data; IT managers need to understand the value of data and know how to use it; The cost of data extraction and processing should not exceed the potential income from its use. In addition, the reliability of the data and how it is stored is important, as attackers sometimes target the underlying AI data system, exposing the algorithms to errors, distortions, or missing red flags. Companies need to control the data entering the system, filter out unverified units or fraudulent cases. By using the blockchain, companies can effectively track who accessed or altered data, allowing them to shut down erroneous data and identify the root cause. Testing and Retraining Except for a few professionals, few people understand how AI works, so not everyone will dare use it to run mission-critical applications. To some extent, the risks can be reduced by comprehensive testing, which ensures the accuracy of the predictions. Many organizations run comparison tests over a period of months, comparing the results of tested AI models with the actual results, and modifying computer algorithms to improve the results. AI testing is essential, because an algorithm that has performed well in the lab may behave very differently in a commercial environment. AI is a method of trial and error, as what seemed to be a good idea at first glance often turns out to be of little use in the real world. This is why fast repetition is key to automation. To address this issue, you have to pay attention to the importance of retraining the AI ​​model. There are cases where vulnerabilities in AI solutions do not appear until they are released. One of the biggest problems is the error compensation in the algorithm. Any data, by its nature, reflects human bias, so it can skew results. Constant testing and retraining will help correct misconfigurations. Testing AI development automation, retraining AI models, as well as cleaning up data, and extracting features are all time-consuming. To solve this problem, experts borrow process automation techniques from traditional software developers. DevOps focuses on continuous delivery, leveraging on-demand IT resources, and automating code testing and deployment. DataOps offers the same improvements to data analytics. Simply put, DataOps automates every step of AI training and development by quickly resolving problems as they arise. DataOps solves problems early in the data lifecycle by checking them at every stage of pipeline delivery. If any irregularities occur during this process, the data analysis team will be the first to know them via automatic alert. Machine learning can be used to identify irregularities. Conclusions AI is supposed to be at the forefront of digital transformation, and many have high hopes for it, but CIOs should not forget that this is a new technology. On this basis, there is reason to believe that it does not always meet the expectations placed on it or, even worse, can cause problems. CIOs must be realistic about the capabilities of the technology and be prepared for the challenges associated with implementing and maintaining an AI project. Written by Helen Wilson, Professional Content Writer Helen Wilson is a professional content writer, who also provides assistance with physics homework. Her main areas of specialization are information technology and technology. It is also developing in other fields such as psychology and self-development. .


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