Technology, despite the revolutionary progress that it has enabled over the millennia, has never been short of doubting Thomas’s. Computers, for example, have become so closely integrated into our lives that we use them in our homes and we carry them around in our pockets. That has never stopped people from fearing computers and predicting doomsday scenarios. The anxiety rose to fever pitch as Artificial Intelligence (AI) and Machine learning (ML) became the talk of the town.
These new age technologies are poised to impact us in ways we can’t imagine. The topic fascinates and worries us in equal measure. That’s why people, even the least technology-savvy among us, need to understand what AI and ML are and how these technologies are poised to change our lives dramatically.
We requested IT experts from FirewallTechnical to break down the technical complexities, shed the jargon, and present a simplified guide to AI and ML so that a layperson could gain a better understanding of what these technologies are capable of doing. We share these valuable insights:
What Is Machine Learning and Artificial Intelligence?
It could be the weather, a population group, an industrial process, or something as mundane as an economic impact theory or cultural event. We study these events and gather an enormous amount of data. This data is minutely analyzed. When we analyze data, we focus on patterns of behavior that keep recurring. Then we apply advanced principles of computer science to analyze the behavior and predict what may happen in the future. The end result is a “statistical model” capable of predictive analysis. Artificial Intelligence and Machine learning are the technological breakthroughs that create statistical models and make predictive analysis possible.
Artificial Intelligence and Machine Learning: How Are They Different?
Artificial Intelligence
The word artificial means it was created by humans. Intelligence implies that the computer is programmed to ‘think’ on its own almost like (but not superior to) a human. AI is a complex cluster of algorithms that simulate human-like behavior in a computer. It’s a way of programming a computer to think on its own (mimic human intelligence) without human guidance at every step.
Machine Learning
Machine learning builds on the foundation of AI to develop programs that teach computers to gather and analyze data on their own and predict future behavior. In such a scenario, the machine is not merely programmed to do a defined task. The machine is actively ‘learning’ and changing its responses with experience. You can ask ten people for solutions to a problem and probably receive ten different answers. ML will analyze thousands of variables and predict the best outcome.
If AI infuses a machine with human-like intelligence, ML ensures that the machine thinks on its own and learns from experience to predict outcomes.
If Artificial Intelligence was a tree, ML applications would be the low hanging fruit. Both tree and fruit are mutually dependent and one can’t exist without the other. AI is the foundational technology, ML is its application. AI is the architect and strategic planner, ML is the builder and project manager.
Three Foundational Tasks That ML Is Geared to Perform
Having acquired a bird’s eye view of what these new technologies are, we move on to the three basic tasks that AI and ML are programmed to do in real life situations – Classification, Regression, and Ranking.
ML Classification:
Take facial recognition, for example. The observed image is matched with an existing database and quickly identified. Facebook is using this to tag pics of people known to you.
ML Regression:
This makes it possible to correlate numerical values with factors that tend to vary. For example, in real estate, ML can help you predict the value of your home after considering related factors such as market value of residential property, the age of the home, and depreciation.
ML Ranking:
This is a way of predicting an outcome within a certain set of parameters. For example, the likelihood of the New York Yankees topping the charts in Major League Baseball. Another example is Google’s search engine that ranks the most relevant result to a searcher’s query.
How ML Uses Statistical Models to Analyze Data and Predict Outcomes
We’ve already seen that statistical data gathering creates a machine learning model of how events pan out in the world. When data analysis gives you patterns of behavior, the same can be studied and what the future holds in store can be predicted. ML does this using datasets and functions.
- A dataset is a graph with rows and columns showing how variables relate to each other. For example, a row may show age-groups (young, middle-aged, senior, super-senior) within a targeted population, and the column indicates their preference for an herbal product.
- A function is a sequence of code that instructs a machine to perform specified tasks. The same functions can be used to manipulate data sets to yield a particular result.
In the population study, you could use functions (codes) to teach the machine to recognize patterns such as more middle-aged folks opting for herbal products as opposed to younger people. The machine “learns” the pattern and recognizes it when it is repeated elsewhere.
Like Human Learning, Machine Learning Follows Different Pathways
The statistical model lies at the heart of machine learning. Using these models, the ML application follows a steep learning curve imbibing “lessons” on the way to predicting behavior. Humans are said to follow at least seven learning styles – video, audio, language, and physical among others. The same is mimicked by AI and ML powered systems:
● Supervised Learning:
This is the ML way of linking a programming input to an output. For example, you can teach a program to learn the difference between good food and bad food. The computer can’t taste the food, so you explain through examples that certain foods are good, some are bad to enable it to recognize the difference. The Microsoft Virtual assistant, Cortana that trains a computer device to recognize speech commands is an example of supervised learning.
● Unsupervised Learning:
If it is cumbersome to modulate the machine ’s decision-making ability with endless examples, unsupervised learning takes over. Without referring to any known classification or category of data, the machine studies patterns and comes to its own conclusion. This is very useful when large data sets are being analyzed, and it becomes manually impossible to program responses covering every possible variation. NASA, for example, uses unsupervised learning to discover new stars that do not figure in an existing database.
How AI/ML principles impact us in daily life
Google’s search engine switched to AI and ML, creating a new algorithm called RankBrain. Previously, Google would deliver web results relevant to the keywords used by customers in web searches. Powered by AI and ML, Google’s new search engine studies the context in which the keyword has been used compares it with the customer’s past searches, studies the customer’s choices (rejection/acceptance), and delivers a result that satisfies the searcher’s intent.
The major difference is that the search engine adapts search results in the way the customer responds and offers improved results as the search intensifies. Google search ‘learns’ through experience to become more accurate at predicting the customer’s intent to give a better and personalized web experience.
Agreed, AI and ML Are a Big Deal, but How Do They Affect Me?
Social media and e-commerce giants need not engage you in long intimate conversations to get to know you better. Your web browsing habits, personal preferences, and product choices are already known to them. All they do is hand over the data to AI and ML to analyze. Patterns of behavior emerge that prompt company to reorient and personalize marketing strategies suiting individual preferences.
Machine learning is about to change the job landscape. The World Economic Forum predicts Data Analytics, AI and Machine learning specialists, and Operations managers as the top three in-demand job vacancies by 2020. Data entry, accounting and bookkeeping, and administrative roles are the top three jobs destined to become obsolete. Financial services will be worst affected because no human capability can match AI and Machine learning capacity to crunch big data and predict more accurate results.
Conclusion
Let’s put it in a nutshell. If you’re a developer, AI and ML unleash your potential to go where no man has gone before. If you’re a layperson, understanding and integrating the new age technologies into your lifestyle will mean the difference between survival and extinction. Agreed, AI can’t offer solutions for everything, but it is better to be aware of how AI inspired changes are reshaping our universe.
We should respond positively when a new technology promises to change the globe as we know it. Forget doomsday. Already, AI and IoT (Internet of Things) is merging with blockchain to save the environment. The same technology mergers are revolutionizing supply chain management in industry. The benefits of AI and ML are so awesome we ignore this revolution only to our disadvantage.