Trends in Artificial Intelligence and its impact on organizations
Trends in Artificial Intelligence and its impact on organizations
Every day advances in artificial intelligence force us to reflect on its present and future impact on our organizations. Gartner estimates that by this year 85% of CIOs will be implementing Artificial Intelligence programs through external services, own developments or turnkey solutions and according to the Boston Consulting consultancy, more than 50 Billion smart devices will be connected to systems of Artificial Intelligence in 2025.
All the technology giants from Alibaba, Google, IBM, SAP, Microsoft to Amazon seek to be the leaders and pioneers in Artificial Intelligence services by offering their customers new and better products and services.
It is one thing to listen to analytical platform experts and researchers forecast the great impact that Artificial Intelligence will have in the future of BI and analytics platforms, and another to observe how large business solutions manufacturers such as SAP, SAS, Microsoft , IBM, etc., are beginning to integrate these AI technologies into their business portfolios.
Today we must have the basic knowledge and concepts as well as understand the implementation strategies of these new technologies to help our organizations face the new market demands.
Artificial intelligence is a set of techniques and technologies that operate automatically, aimed at imitating the way human beings think, reason and carry out actions. They are applied to the solution of optimization, prediction, diagnosis and planning problems. Systems built for artificial intelligence are called intelligent agents or agents, these have multiple properties, such as autonomy, learning, ubiquity, proactivity and if multiple agents work, properties such as cooperation, communication, coordination, planning are generated.
Intelligent agents intend to transform the way in which systems built by human beings operate in such a way that they: operate autonomously (without human intervention), thus requiring fixed real-time data acquisition systems and / or or ubiquitous; that they operate with a specific purpose, so they must have a way of measuring their results; that they operate cooperatively, allowing multiple agents to perform simple tasks unitarily but their power in being found in the way they are connected; that they learn automatically from past experiences in such a way that the same system is fed back to make decisions automatically.
These techniques and technologies are changing the way workers carry out their activities, how organizations relate to their customers and suppliers, how the state serves citizens and how citizens relate to their rulers, how companies can automate each one of machines in the field of your business, in such a way that it is now possible to: know the behavior of each of the actors in a ubiquitous way; massively capture structured and unstructured data, learn the behavior of each of these actors from the captured data; know the risk levels of each of these actors; know the risk levels in the operation of their equipment.
This is possible thanks to the great computing capacities of parallel computing (GPUS), thanks to small IoT devices, thanks to the multiple algorithms of machine learning and optimization and mainly to the trained human resources that can work with these techniques and technologies.
At the end of the course, participants will be able to:
- Understand what artificial intelligence is, its techniques and applications.
- Understand the concepts of data, information, knowledge
- .Differentiate the properties of information systems and intelligent systems.
- Understand how pattern recognition contributes to the machine learning process.
- Understand the machine learning process.
- Differentiate the multiple types of machine learning models.
- Identify machine learning algorithms.
- Know the applications of machine learning in the financial and non-financial field.
- Know the life cycle of machine learning projects.
- Identify the necessary knowledge that a professional must demonstrate to be pro-efficient in machine learning.
- .INTRODUCTION TO ARTIFICIAL INTELLIGENCE.
Natural intelligence, human senses, understanding and comprehension. Artificial intelligence, sensors, data acquisition. Data Information, knowledge. Memorization and learning. Patterns and behaviors. Sensors and signals. Background / history. Artificial intelligence techniques. Subsets of IA (reasoning, planning, NLP, ML). Current context: state of the art, applications.
- INTRODUCTION TO AUTOMATIC LEARNING.
Definition of machine learning. Statistical generalization. Classification models, regression models, clustering models, association model, sequencing models. Patterns, typical behaviors.
- TECHNIQUES OF AUTOMATIC LEARNING.
Approaches to machine learning: supervised, unsupervised, reinforced and deep learning (neural networks). Descriptive, predictive and prospective models.
- DATA SCIENCE AND DATA ENGINEERING.
Data engineering and data science. Cloud computing, data science and statistics and interrelations with machine learning. Definition of Big Data, importance and interrelation with machine learning.
- TECHNOLOGIES FOR THE IMPLEMENTATION OF AUTOMATIC LEARNING.
Parallel computing, GPUs: Nvidia and AMD. IoT, Internet of things. AUTOMATIC LEARNING in the cloud. Machine learning and machine self learning.
UTILITY OF MACHINE LEARNING IN BUSINESS.
Trends of artificial intelligence in the world. Importance of machine learning in the current context. How machine learning can help business strategy and casuistry. Professional changes as a result of the introduction of AI. Adaptation of organizations to new technologies.
- AUTOMATIC LEARNING PROJECTS.
The life cycle of machine learning projects. Data collection, data sources, unstructured data, data reliability, data volume. Preparation of attributes, identification of explanatory attributes. Data labeling, data labeling plan, is automatic labeling possible? Data preparation: identification (structured and unstructured) and governance (rules and privacy). Training of algorithms and machine learning systems: representation, evaluation and optimization. Optimization of machine learning models. Deployment of the models.
- APPLYING AUTOMATIC LEARNING IN ORGANIZATIONS.
Applications of machine learning for the prediction of risk events. Machine learning applications for automatic diagnostics.
Considerations before applying to an organization: Focus on human resources. Focus on business problems. Focus on collecting and labeling the data. Focus on the customer. Execution of a pilot: stages (identification of the opportunity for improvement, data collection, preparation of attributes, evaluation, go to production). Determining the best learning model: selection algorithms, model validation indicators. Example and experiences of applications of machine learning in Peruvian companies: proposal, development, risk management and results.
The course is aimed at students of the last cycles or recent graduates of Engineering, Administration, Finance, Economics and related careers, Technology Managers, Systems Analysts, Database Administrators, Process Analysts, BI Researchers and any other interested .
The specialization course is offered in virtual mode and lasts 1 month , with a total of 24 academic hours, 12 hours of online videoconferences with the specialist (3 hours a week) and 12 hours of videos. multimedia and upon completion and approval with a minimum grade of 11 you will be able to obtain your certificate .
The student will receive for each module recorded videos of the topics, readings, files of the classes in Power Point and tasks to be developed that will allow the student to be evaluated in the module and guide him through the training. The hours of the sessions are coordinated at the beginning of the course.
Continuous communication with the tutor will take place virtually through consultation forums, chats, videoconferences and email in the virtual classroom. Through these means the participant will be able to express their difficulties, concerns and suggestions regarding the development of the subject and report their activities.
The follow-up of the course will be carried out by means of a specialized direct tutoring in each module, through the virtual platform and other virtual means of communication. The teacher supports the participant in his progress, encouraging him in his advancement and in carrying out the proposed activities.
DURATION OF THE PROGRAM
The specialization course has a minimum duration of 4 (four) weeks of study, with a total of 24 academic hours.
The Specialization course has a total cost of: USD 280.00 dollars.
The investment includes the right of registration, registration and certification of the NGO LPI Italy with the endorsement of our sponsors. Additionally, user manuals and access to the virtual classroom of the course will be delivered.
Payment must be made via deposit or bank transfer.
At the end of the specialization course, the participant will be issued and sent their respective digital certificate issued by: Limitless Power of Information LPI Italy, sponsored by IT Latino Spain, which is credited with a total of 24 academic hours. The diploma does not indicate the virtual modality of study.