Artificial Intelligence (AI) continues to transform the way we process and analyze information. More businesses now rely on breakthroughs in this field for their daily operations. How do AI systems make these critical decisions?
How AI makes decisions is better understood by looking at the different variations of Artificial Intelligence. For example, Machine Learning algorithms make decisions by evaluating past experiences. Natural Language Processing systems rely on an interpretation of a human language.
This article will take a closer look at how the various AI systems function and make decisions, how these decisions are helping in the world of business, and the overall accuracy.
Building an AI System
When building an AI system, developers carefully reverse-engineer human capabilities and traits and transfer them to a machine. Combined with the computational powers available today, these capabilities mean that machines can now accomplish more than humans can dream.
Some of the steps and much of the hard work of selecting an AI platform, acquiring datasets for training, preparing the data, choosing and training the algorithms, choosing a particular programming language, model building, deployment, and the ongoing job that it was built for are all aspects of building an AI system.
How Do AI Systems Arrive at Decisions?
As we mentioned above, AI systems make decisions in different ways. The variant of AI is what determines the overall processing pathway.
- Cognitive Computing (CC): Algorithms make decisions by looking at objects, images, speech, and text from a human standpoint. They try to copy the functions of the human brain to a large extent before delivering a decision.
- Computer Vision (CV): Algorithms make their decisions by breaking down an image into distinct parts and then studying the different parts as closely as possible. The system compares the new data to previous observations to make a decision.
- Deep Learning (DL): Systems make their decision by processing data in layers, which allows them to infer, classify, and predict outcomes.
- Machine Learning (ML): Systems make their decisions by identifying patterns in past data and drawing inferences based on this experience. They’re one of the most efficient AI systems that don’t require any human interference. Thus, they’re able to save businesses time and money while making impactful decisions.
- Natural Language Processing (NLP): Systems make a decision by decoding the language a user communicates by text or speech. Once the data is processed, the system responds as accurately as possible based on its interpretation of the language.
- Neural Networks (NN): AI functions like human neural networks by deciding by capturing and comparing the relationship between a set of variables. It processes the variables almost like a human brain.
How Are AI Systems Helping Businesses With Their Decisions?
These are just a few examples of how AI decisions are helping businesses to achieve better results overall. Whatever industry you’re operating in right now, it’s almost a given that you can find processes that will be immediately improved by AI, which is why more brands are tweaking budgets for the purpose every year.
AI software providers are accelerating digital transformation. Many AI companies are building AI applications more efficiently and cost-effectively. These companies are using AI applications for supply network optimization, predictive maintenance, energy management, fraud detection, sensor network health, across the board to even Building Automation Systems (BAS) that are controlling our environments.
Below are a very few basic examples of how AI systems are helping businesses to improve their bottom line:
The current business climate is more customer-centric than ever, which has led to more complexities in decisions that will affect customers overall. Businesses need to devote more time towards understanding the desires and needs of their customers and work hard towards ensuring those needs and desires are met. This requires constant monitoring of fluctuations in customer behavior.
AI modeling and simulation methods simplify the acquisition of customer persona insights, which makes it easier for businesses to predict the behavior of their customers overall. AI systems are helping businesses make better marketing decisions through real-time forecasting, trend analysis, and data gathering.
Improved Customer Relationship Management
Buyer persona models put together by AI systems make it easy for organizations to identify a consumer’s lifetime value, as well as simplify the managing of multiple data points. CRM is a complex process that requires efficient control and management of different factors simultaneously.
Humans running the process have to deal with decision fatigue from time to time. Algorithms don’t have such problems, so they deliver faster and better results in this regard.
Smarter Hiring With AI
AI can simplify the hiring process for businesses by looking at past hiring practices that worked best and repeat them. Basic details like knowing where you found the right candidates and knowing how you can reach out to them again can make a world of difference. AI can also help businesses find leads in unexpected places, figure out the type of communication that appeals to specific candidates, and more.
Hiring top talent is a challenge for most businesses, but it’s more challenging for smaller businesses. The bigger brands have the networks and resources to find talent, and most candidates love the opportunity to work with a readily identifiable name.
These companies also have highly-equipped HR desks that can effortlessly move a perfect-fit candidate from first-round interviews to onboarding. Small businesses are at a massive disadvantage overall, except for when they have embraced AI in their operations.
Is AI-Decision Making Always Accurate?
AI systems can improve future outcome predictions with their decision-making. However, sat this point, AI’s are not always able to predict which outcomes are better overall and which ones to choose, because even the best AI systems may not cover all of the rewards, risks, and hidden costs. Also, there are many decisions that will benefit from human judgment.
We’re still in the early stages of AI, so as the industry improves, machines might be able to make decisions on their own, delivering better outcomes compared to decisions made with human input.
For now, it’s best to keep in mind that AI systems are only as effective as the information they’re relying on, which is why there are still a few risks with relying on them completely. As technology improves and machines pay attention to humans making decisions, the results might improve. In the meantime, there are still lots of chinks that need ironing out.
Please see some of our other AI-related articles like “Smartphone AI: Can Smartphones Utilize Cognitive Simulation?” and “Smartphone AI: Can Your Mobile Phone Really Become Self-Aware?”
Pioneering Work in Evolutionary Genetic AI
Back in the early 2000’s prior to founding a company named NaturalMotion, Torsten Reil was researching for a PhD in Complex Systems at Oxford University’s Zoology department. They created Morpheme, which was the first graphically authorable animation engine
With his biology background Torsten started coding computer simulations with biologically modeled nervous systems, which created animated characters used in realistic games and movies. They did not use an AI controller but a pattern generator.
Torsten’s study of biology was instrumental in making natural-looking animated people. The thought process of building an AI human, with virtual bones, muscles and a nervous system was groundbreaking. These computer simulations of neural networks based on genetic algorithms programs used natural selection to evolve their own means of movement.
Using AI to Make AI
Fast Forward to now; and for a while human-designed algorithms are giving way to machine-learned ones. Many AI systems are teaching themselves from scratch. As we have discussed in other articles OpenAI developed bots that learned to play hide and seek by themselves in a virtual environment in 2018.
For years AI researchers have worked on creating algorithms to imitate human intelligence with Neural Networks that emulate evolution. Today Machine Learning is used on its own, to train itself in many cases from scratch without rules.
The potential for such evolutionary systems in intelligence and “species” growth is infinium. If AI is truly evolving within and beyond the current human made constructs and has unlimited time to train itself. Who is to say what will evolve from its own intelligence equivalents and beyond; where that will stop once it has transgressed those parameters.
AI-Generated Decisions from data is still just the beginning. Companies like Open AI and Google DeepMind are at the forefront of taking all these self-learning techniques and let the AI create the next step. Ai making its own decisions in essence. We actually don’t know the current potential for such behavior.
Today many of the Bots and Agents utilized in training and simulating the human body and brain may not be the most efficient engine to a future AI. When we watch a video of self-trained AI Algorithm agents learning to walk after a thousand tries then later you see them jumping through the air like a Ninja. Or when we watch the latest fluid simulations or bouncing Jello or the things that humans take for granted it is misleading as to the complexity to the average person.
Bots or Agents abilities in games or simulations overcoming obstacles will only increase with time and complexity. If you are giving an AI eighty years of training in one year as it works 24 hours per day 7 days per week it is like Neo in the Matrix plugging in his brain to a training simulation.
Current Limits of Artificial Intelligence Training
The current limits on training AI decision making is the Human intelligence bottleneck, energy, and data. Over time, the AI agents are continuing to learn limited only by its data acquisition. The AI will always take some form of its improved version forward to the next level. There will of course be dead ends in this process.
AI improvement through millions of future generations of trial and error will most likely produce a new variety of more efficient energy and data utilization. It is anticipated that this will create and generate new technologies and further advance the Human race.
With future self-generating AIs evolving to meet evolutionary challenges just as from a biological sense humans eventually evolved to be bi-pedal. What will be the most efficient and least cost prohibitive way to evolve for AI?
Artificial Intelligence Improving
Artificial Intelligence is improving dramatically daily but is only as good as the information it is given depending on the circumstances.These systems will continue to improve as we continue to see amazing results with Reinforcement Learning and self learning of AI systems.
Processes that involve decisions based on structured data work well in AI systems. Their decision-making is advanced enough to be a part of a workflow as a main processor of data. They’re not as prone to human bias, assuming that they’re not working with biased data in the first place.
It is likely with the current trajectory of AI systems it will surpass these limitations. With AI using multiple combined techniques, systems, agents, and then inventing or evolving its own evolution.
Artificial General Intelligence and Artificial Super Intelligence Decision Making
It is unlikely that the egocentric human race will ever stop long enough on the road to AI evolutionary transgression to stop and figure out or talk about our road to an overwhelmingly powerful AI.
AGI or Artificial General Intelligence is when the technology becomes self aware and equal to human intelligence and we certainly do not have a clear definition of this yet. ASI or Artificial Super Intelligence is when the technology goes far beyond human capabilities.
A new kind of intelligence and the rapid development of AI that can train itself also raises questions about how well we can control its growth. AI is already creating designs and techniques that people have not thought of. It is likely that a future ASI will be impossible to understand. Is it harder to guarantee that they will not behave in undesirable ways?