Solutions
- Information Technology
Artificial Intelligence has had a positive impact on the way the IT sector works; in other words, there is no denying the fact that it has revolutionized the very essence of the space. Since the IT sector is all about computers, software, and other data transmissions, there is a relatively important role Artificial Intelligence can play in this domain.
Data security is of the utmost importance when it comes to securing confidential data. Government organizations, as well as private organizations, store tons of customer, strategic, and other forms of data, which need to be secured at all times. Through the use of algorithms, Artificial Intelligence can provide the necessary security and help to create a layered security system which enables a high-security layer within these systems. Through the use of advanced algorithms, Artificial Intelligence helps identify potential threats and data breaches, while also providing the necessary provisions and solutions to avoid such loopholes.
Artificial Intelligence uses a series of algorithms, which can be applied directly to aid programmers when it comes to writing better code and overcoming software bugs. Artificial Intelligence has been developed to provide suggestions for coding purposes, which increase efficiency, enhance productivity, and provide clean, bug-free code for developers. By judging the structure of the code, AI can provide useful suggestions, which can improve the productivity and help to cut downtime during the production stage.
The benefit of automation is that almost every piece of work can be done without human intervention. Through the use of deep learning applications, organizations can go a long way in automating backend processes, which help enable cost savings and reduce human intervention. AI enabled methods improve over time as the algorithms adjust to enhance productivity and learn from mistakes.
Often, the hosting server is bombarded with millions of requests on a day to day basis. The server, in turn, is required to open web pages which are being requested by the users. Due to the continuous inflow of requests, servers can often become unresponsive and end up slowing down in the long run. AI, as a service, can help optimize the host server to improve customer service and enhance operations. As IT needs progress, Artificial Intelligence will be increasingly used to integrate IT staffing demands and provide seamless integration of the current business functions with technological functions even making critical business decisions.
- Medical
Without doubt, artificial intelligence (AI) is the most discussed topic today in medical imaging research, both in diagnostic and therapeutic. For diagnostic imaging alone, the number of publications on AI has increased from about 100–150 per year in 2007–2008 to 1000–1100 per year in 2017–2018. Researchers have applied AI to automatically recognizing complex patterns in imaging data and providing quantitative assessments of radiographic characteristics. In radiation oncology, AI has been applied on different image modalities that are used at different stages of the treatment. i.e. tumor delineation and treatment assessment. Radiomics, the extraction of a large number of image features from radiation images with a high-throughput approach, is one of the most popular research topics today in medical imaging research. AI is the essential boosting power of processing massive number of medical images and therefore uncovers disease characteristics that fail to be appreciated by the naked eyes.
Machine learning, as a subset of AI, also called the traditional AI, was applied on diagnostic imaging started 1980’s.12 Users first predefine explicit parameters and features of the imaging based on expert knowledge. For instance, the shapes, areas, histogram of image pixels of the regions-of-interest (i.e. tumor regions) can be extracted. Usually, for a given number of available data entries, part of them are used as training and the rest would be for testing. Certain machine learning algorithm is selected for the training to understand the features. Some examples of the algorithms are principal component analysis (PCA), support vector machines (SVM), convolutional neural networks (CNN), etc. Then, for a given testing image, the trained algorithm is supposed to recognize the features and classify the image.
Two challenges need to be resolved before AI can be more widely implemented in medical imaging research. First, how to organize and pre-process data generated from different institutions. Miotto et al stated in their breakthrough work “deep patient”—challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using electronic health records. They presented a novel unsupervised deep feature learning method to derive a general-purpose patient representation from electronic health record data that facilitates clinical predictive modeling.46 Authors have successfully derived patient representations from a large-scale data set that were not optimized for any specific task and can fit different clinical applications. However, their data are from one institution. Tackling data set from multiple institutions in fact is a much more challenging task. Even for the same procedure, different institution might implement differently. Patient cohorts might also be different. All these will need to be addressed when pre-process data for AI algorithm.
Second, on a policy or infrastructure level, how to encourage more image data sharing is also a challenge. Currently, image data sharing is very limited. HIPAA compliant is one concern, and lack of infrastructure is another. The medical data security needs to work with the emerging needs of data sharing. Corresponding infrastructure also needs to be built.
On the long run, how AI can become true “intelligent” at the human level is a key to the question if AI can replace human in medical imaging. Unlike pure quantitative task, the knowledge involved in medical imaging related decision making require life experience and philosophy. For the machine to behave in human level, there are not only challenges on data collection and algorithm development, but also on ethical regulations.
- Customer Service
AI can seamlessly automate service tasks, reducing valuable time off each customer support interaction. Pair with AI search functionality to guide agents to the information needed to resolve customer queries, improving the customer and human agent experience. Couple conversational AI with speech-to-text capabilities, driven by machine learning, to improve first contact resolution for voice interactions. Best of all: allow your human agents to focus on higher value work, all while reducing costs and improving customer satisfaction.
- Finance
There are many benefits of using AI in financial services. It can enhance efficiency and productivity through automation; reduce human biases and errors caused by psychological or emotional factors; and improve the quality and conciseness of management information by spotting either anomalies or longer-term trends that cannot be easily picked up by current reporting methods. These applications are particularly helpful when new regulations, increase senior management’s level of responsibility to review and consider higher-quality data from within the firm.
