A Guide to Real-World Artificial Intelligence and Machine Learning Use Cases

Machine learning and artificial intelligence are driving major changes in the global economy.

Artificial intelligence and machine learning will drive industry 4.0

This article examines the ways in which companies in various sectors of the economy are adopting artificial intelligence (AI) techniques. However, before looking at the sectors affected, it is important to note that the underlying drivers that are fueling the growth in influence and reach of machine learning in sectors of the economy will only increase. as we go along. Indeed, Big Data is only getting bigger, faster data speed, the availability of cheaper data storage and the arrival of powerful graphics processing units (GPUs) to allow the deployment of Deep algorithms. Learning. Additionally, new research in deep learning and other areas of machine learning will continue to emerge in real-world production over the next few years, leading to new opportunities and applications.

The DLS team firmly believes that the advent of 5G around 2021 will be a transformative and revolutionary moment in human history. The improved speed of 5G over 4G will allow technologies that struggle with latency demands today, such as virtual reality and autonomous systems, to operate with real-time efficiency.

It will be an intelligent Internet of Things (IoT) world at the edge (i.e. on the device) where data is processed where it is generated and deep learning models can run on the device itself rather than on a remote cloud. server. This will prevent an autonomous agent such as a robot or a vehicle from waiting to receive a response from a remote server before it can take action.

We believe that this world will lead to the creation of new opportunities and businesses that do not exist today. AI techniques such as Convolutional Neural Networks (CNN), Long-Term Memory Networks (LSTM), Deep Reinforcement Learning, and Generative Antagonist Networks (GAN) will play an important role in this world alongside traditional methods of machine learning.

Sectors of the economy affected by artificial intelligence

Financial services: Use cases here include applications related to regulatory technology, such as using face detection and verification to “Know Your Customer” from documents. This is fed by CNN.

Financial firms can also use AI to extract text from financial documents such as corporate documents and documents containing personal information such as driver’s licenses and passports (optical character recognition (OCR) with LSTM) .

In the field of investment management, AI can be applied to replicating and tracking passive portfolio ETF indices (evolutionary genetic algorithms). Fraud detection is powered by CNN and Gaussian mixing models. Additionally, financial services firms are applying machine learning techniques to credit risk for lending and counterparty risk assessment decisions with linear regression, random forests, gradient amplification, and cross networks. neurons. Trader Oversight is implemented using variational encoders for anomaly detection. In the retail industry, there are plenty of opportunities to apply AI to areas such as face-to-face payments (using a CNN).

Assurance: AI has enormous potential in this financial services sub-sector. Examples include automating the claims verification process whereby, for example, if the policyholder made a claim for a car that was insured under the policy, a CNN can be used to verify that the brand and the model are the same as those of the insurance policy. Chatbots have been used as a virtual agent to converse with the end user. In addition, variational autoencoders can also be used to check for the presence of outliers.

Distribution sector: This industry is likely to be heavily impacted by AI and machine learning, both on the e-commerce side and the physical side of the business. The e-commerce side generates large sets of unstructured data which can be used to generate meaningful information which in turn enables personalization and recommendation engines. Examples include the use of collaborative filtering for recommendations, CNNs for clothing detection and classification, and natural language processing (NLP) for descriptive text extraction. Retailers have significant cash costs with inventory, and machine learning techniques can help them optimize inventory management to reduce waste and costs. Additionally, areas like visual search (using CNN) allow for cutting edge personalization with the client. We’ll likely see virtual stylists, fueled by deep learning techniques combined with NLP, emerge to allow retailers to offer combinations to complete the look. Facial payment is likely to take off in the future as well. Physical robots powered by AI (Deep Reinforcement Learning) will emerge to optimize supply chain management in warehouses and possibly even in the future with automated delivery and customer service.

Marketing: Much of this is covered in the retail above. We’ll likely see marketing as the first industry to be transformed by AI, as it has lower regulatory barriers compared to other industries and large datasets available. Hyper personalization with customer segmentation and targeted marketing highly relevant to the user as the future of marketing. NLP techniques help us extract the vast amount of unstructured information present in social media for automated lead generation.

Health care: This industry is likely to be the most transformed by AI over time. Machine learning will have a major impact in areas such as medical imaging (CNN), electronic healthcare data mining (NLP), robotic surgery using deep reinforcement learning with 5G. Application of machine learning techniques to portable devices allowing us to process sensor data to enable preventive health care. Additionally, fields like precision medicine are fueled by techniques like variational autoencoders that improve patient outcomes. Drug discovery and regenerative medicine dramatically reduce the time required for clinical trials and shorten the time to market for new drugs.

Transport: It is a sector that is facing fundamental changes with self-driving cars and drones becoming a reality. 5G will enable connected cars to become an experience pod powered by augmented reality and virtual reality for those inside. In addition, areas such as multi-agent reinforcement learning will allow automated cars to communicate with each other and with the environment around them, thus providing a solid foundation for communication with well-defined standards. This will allow safe autonomous driving. In addition, autonomous drone technology will be able to provide logistical support to search and rescue teams in areas of remote or difficult terrain, and help farmers automate irrigation and harvesting.

Security: This is an area that will benefit greatly from the adoption of machine learning techniques such as CNNs for face detection and recognition, behavioral recognition in videos, and aiding in the detection of suspicious activity in matters. cybersecurity.

Manufacturing and industry: The manufacturing sector will be revolutionized by the arrival of Industry 4.0 which will give rise to autonomous agents such as robots capable of automatically analyzing faults and, thanks to the application of deep reinforcement learning, will allow manufacturing precision at much higher speed and scale as well as improved efficiency in supply chain management.

The above is not exhaustive and it should be noted that other sectors such as travel and tourism will be heavily influenced as hotels, airliners, and car rental companies will all adopt various forms of business. machine learning. Agriculture will see an increasing adoption of autonomous systems. Education will also see the increased use of AI to help teachers. The construction industry will benefit from the deployment of large-scale construction robots and machine learning techniques to assist architects at the design stage.

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