Ai And Machine Learning For Data-driven Insights In Digital Transformation Projects – Artificial intelligence and all its subcomponents are one of the most interesting and potentially transformative of all the current emerging technologies that Transforma Insights is tracking.

There are many ways to define and classify AI. One of the most interesting is the Artificial Intelligence/Machine Learning/Deep Learning triumvirate, which looks at generalizations. Artificial Intelligence (AI) is often considered an umbrella term for many different types of activities, all aimed at human intelligence. The most commonly discussed subfield is machine learning (ML), which specifically involves applying complex algorithms and statistical methods to existing data to make inferences or predictions. An important subfield of ML that a lot of new research is focusing on is deep learning, which uses a combination of big data and neural networks that try to mimic human behavior. pressure, for example by using additional studies.

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Another categorization refers to the type of intelligence that is developed. Artificial intelligence, for example, seeks to create the ability to perform various tasks based on independent decisions. Artificial intelligence, on the other hand, often tries to do a specific job well. The definitions of “strong” and “weak” AI are somewhat ambiguous.

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The third useful categorization deals with methods in the form of specific methods for applying AI in the form of Supervised ML, Unsupervised ML and Reinforcement ML Learning and their associated algorithms, with increasing next level in the form of Interactive Learning.

To date, most AI applications have focused on machine learning and ML in particular: telling the machine what tasks to do and suggesting the best way to do it. This is easy to use, easy to understand, and easy to use. Not surprisingly, most AI success stories today are focused on time-consuming, but also simple tasks. These promise the fastest return on investment. Good examples include analyzing legal documents or reviewing medical images.

Unsupervised learning involves analyzing unstructured and/or unstructured data to create a foundation for understanding the data. The machine is not told to achieve its goals, and does not have to be what the goals are. Instead, it is more or less unleashed on the data set, only the instructions about the final goal, which itself is only the vague goal of the data set not created necessary. The main goal of unsupervised ML is to find patterns that they may not have seen before and group the data. An example of a project would be a project around customer segmentation, where ML is presented with data about customer behavior and asked to identify some differences or differences that allow segmentation of buyers.

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Motivational learning is characterized by trial and error. He decides based on past experiences, eg. such as trying an action and failing, and often trying and failing a million times to decide which way is best to make the “gift” he got accepted by his comments. The purpose of the support tool is to create a framework for future work. A good example is a game, where AI teaches itself by playing and gradually improves its performance.

The most widespread is deep learning (DL). The principle of DL is that the algorithms are presented with a lot of data and then asked to decide for themselves how to classify or respond to what they see, perhaps achieving a goal specifically. Perhaps the most important research area for DL is software research to enable traffic management. The parameters in which AI must work can be diverse. Therefore, training that makes people and use a lot of information is very necessary.

AI has many components, including hardware, software, data and instructions. The following table summarizes the main points:

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At the bottom of the pile, and especially relevant to deep learning, is information. Deep learning requires big data. The bigger the better. So, there is too much data to make a difference for an AI company. This of course leads to competition for access to the largest and best data. Big data is generally reserved for hyperscale companies like Amazon, Google and Microsoft. Others have acquired data-rich companies, such as the Air Force Base in the case of IBM. Innovation in deep learning AI capabilities will be difficult to achieve for companies without access to massive amounts of data. However, there are many niches where there are special information owners.

AI requires efficient processes to work efficiently. So far, GPUs (Graphics Processing Units) have been adapted for deep learning, and a new class of “AI accelerators” has emerged. This is a class of multicore processors with massive parallel functionality as well as more computer power and efficiency. An interesting example is Google’s Tensor Processing Unit (TPU), designed for neural networks. It provides high volume calculation with low precision, i.e. H. it can process data very quickly, but may not have the number of GPU, which is good for most AIs. Other capabilities that need to be leveraged for effective use of AI include High Bandwidth Memory (HBM), memory-on-chip (such as TPU), new non-volatile memory, low latency networking , and MRAM (Magnetoresistive Random access memory).

Algorithms are the engine of machine learning. A data scientist chooses a particular type of algorithm depending on the process he is involved in. Just as different categories of supervised learning, unsupervised learning, reinforcement learning, and deep learning are suitable for different categories of applications, the specific algorithm type is chosen depending on the data used.

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The framework exists to make building AI applications faster and easier. These serve as standards and guidelines for the development, training, implementation, deployment and management of various AI applications. Notable examples are Keras, PyTorch and Tensorflow.

AI software platforms enable the use of AI for data science and other tasks. They help non-technical professionals to use pre-built resources for data processing, training, and evaluation of models to speed up the deployment process. Platform features include, for example, a choice of tools for developing ML algorithms and sometimes access to expert knowledge from the ML platform provider. Examples include Amazon ML/SageMaker, Google AutoML, H2O.ai Q and OpenML.

There are thousands of different AI/ML capabilities. In the current intelligence market, we at Transforma Insights are trying to measure the space. Below are some important uses:

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RPA uses IT-based activities that were previously done manually by people, monitor their progress and repeat them by intelligent workers. Often this is caused by bots that track human actions and target them. Today, RPA focuses heavily on brownfield replication, i.e. H. the automation of existing processes, while the future path is to more greenfield automation of processes. Transforma Insights announces a special Robotic Process Automation 101 training course for leading the RPA market.

Perhaps the most common use of AI (not least in the context of intelligent consumer speech) is concerned with the understanding of human speech and writing to provide access more advanced in other applications (such as AI or non-AI). This can vary from translating commands to a smart speaker to analyzing and interpreting product opinions in customer reviews. The ultimate goal is to improve the ability to create a bot that passes the “Turing test”, i.e. H. seems to react as people think.

Much of AI deals with image recognition and operations, often in the form of simple tasks such as identifying a picture of a cat or detecting a car parked in a restricted area. Behavioral analysis is a complex step and involves the interpretation of images, usually video streams, to understand the behavior of the people (in general) being observed. This can be used to identify suspicious behavior, track employees for security reasons, or even for loan processing, as in China.

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There are many ways to improve business operations and potentially make significant profits. This can include business processes, systems, operations, transportation, logistics, human resources, and many more. The goal is to improve (and sometimes change) human decision making.

Financial services are among the first to adopt AI due to the availability of large, accurate and comprehensive data and the need for efficient operations and potential ROI. In addition to the use of AI for basic tasks such as credit scoring and data analysis (such as credit cards), the use of AI in the financial industry is now expanding, in financial transactions in general.

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