Forecasting versus Predicting
Traditional forecasting, using historical observations estimate future business metrics usually fails to deliver the precision and agility required.
The past does not equal the future for asset performance management in seemingly chaotic environments. Yet traditional, historical forecasting attempts to shape trend lines, force moving averages, and smooth out variability. These methods miss a key point: the variability in future metrics must be accurately measured and represented with detail—not smoothed and flattened away.
Over-simplifying assumptions in traditional forecasting create models of components and assets that do not age, process that never change, and environments that are certain. For clear, accurate decision support, predictive modeling must capture complicating factors in detail. Yet, traditional forecasting is too restricted to meet this obligation.
These traditional, historical methods include many time series forecasting techniques and related models that are severely limited when applied to complex systems: exponential smoothing, moving average, Bayesian networks, trend models, segmentation, regression, cross-sectional forecasting, extrapolation, queuing theory analysis, etc.
In simple systems that reach steady state like modeling the lunch rush through a cafeteria, or predicting the number of tellers needed at a bank, traditional forecasting can work. In a controlled, sterile academic classroom, these models play a role in demonstrating simplified systems. However, when mission readiness, diminishing budgets, operating capacity, and corporate bottom-lines are at stake, traditional forecasting must be replaced by
precise and mature predictive models and that yield efficient and effective prescriptive actions
Rigorous predictive analysis must be applied rather than limiting our approach to unfocused, historical data mining that merely identifies interesting correlations. Complete, evolving, iterative, and time-dependent decision analysis can deliver accurate views of the future.
Some literature on AI
Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring "intelligence" are often removed from the definition, a phenomenon known as the AI effect, leading to the quip, "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.
The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.
AI applied to Finance
Machine learning has had fruitful applications in finance well before the advent of mobile banking apps, proficient chat bots, or search engines. Given high volume, accurate historical records, and quantitative nature of the finance world, few industries are better suited for artificial intelligence. There are more uses cases of machine learning in finance than ever before, a trend perpetuated by more accessible computing power and more accessible machine learning tools (such as Google’s Tensorflow).
Today, machine learning has come to play an integral role in many phases of the financial ecosystem, from approving loans, to managing assets, to assessing risks. Yet, few technically-savvy professionals have an accurate view of just how many ways machine learning finds its way into their daily financial lives.
Here follows an high brief list of current uses and applications:
Fields of use AI
Machine Learning algorithms already have a wide variety of employs:
- medicine : recognize recurrent paths in diagnostics;
- images recognition : recognize paths;
- unmanned guided vehicles: make decisions;
- web marketing : profile customers and tailoring ads;
- streets mapping : read street names and house nrs;
- mobile phones : tailoring apps;
- digital cameras : optimize photography parameters
List of applications
Typical problems to which AI methods are applied:
- Optical character recognition
- Handwriting recognition
- Speech recognition
- Face recognition
- Artificial creativity
- Computer vision, Virtual reality, and Image processing
- Photo and Video manipulation
- Diagnosis (artificial intelligence)
- Game theory and Strategic planning
- Game artificial intelligence and Computer game bot
- Natural language processing, Translation and Chatterbots
- Nonlinear control and Robotics
Other fields in which AI methods are implemented:
- Artificial life
- Automated reasoning
- Biologically inspired computing
- Concept mining
- Data mining
- Knowledge representation
- Semantic Web
- E-mail spam filtering
- Behavior-based robotics
- Developmental robotics (Epigenetic)
- Evolutionary robotics
- Hybrid intelligent system
- Intelligent agent
- Intelligent control