At A Glance
The time isn’t far when you’ll say, “Siri, read out Claim Genius’s latest blog to me while I get ready for work.” And Siri will comply, without any hiccups.
We’ve moved well beyond automation towards hyper-automation. There’s auto insurance, damage detection, online shopping, entertainment suggestions, injury prediction, matchmaking, medicine, security, and other fields.
What people don’t understand is ‘AI’ isn’t something tangible which you can set-up and get going. There are allied technologies associated with AI that make this level of automation possible.
Whatever area of application you are looking to get into, it is worth learning the ropes of these allied technologies so you can model your AI to your specific needs.
6 Technologies Associated with AI You Should Know
Machine Learning (ML)
Machine learning is a technology using which machines can learn from past experiences. This is the technology which raises AI to human-level intelligence. To learn, AI algorithms depend on datasets, which are groups of related data bound together with mathematical rules. Datasets help artificial intelligence with finding out similarities in data and map input to output.
Deep Learning (DL)
This is a subfield of machine learning, but stands out in the way it uses data parsing algorithms. Where machine learning is more dataset intensive, deep learning relies more on structured layers called artificial neural networks. ANNs are the technological equivalents of the neurons present in the brain.
A deep learning AI is closer to the human being than a ML-based AI. This is because deep learning algorithms can start thinking for themselves. ML algorithms still need a ‘guiding hand’ if you want to scale up or change your area of applicability. Deep learning can surpass these barriers and can effectively learn new things without major assistance.
Natural Language Processing (NLP)
People can lock horns all they want when it comes to the “man vs. machine” debate. AI can do most jobs humans can but one thing that’s never going away is communication. Through the many revolutions we’ve gone through, people have always longed to communicate with each other. Now, communications happen across geographical and cultural barriers. That AI felt the same need was but natural.
So, how can AI understand human communication?
The answer is Natural Language Process. NLP is the ability of AI to understand the syntax and semantics of written or verbal communication. NLP can understand the meaning of words, can interpret words within context, and learn new words all on its own.
Learning Resources: Deep insights into NLP.
Computer Vision (CV)
Until now we’ve discussed cognizance and communication. The only major processing capability now missing from AI suite to achieve human level of skill is vision. AI has bridged that gap using computer vision.
Much of the content we absorb today for work or entertainment is digital. AI needs to understand data in the form of images; static images or moving images (multimedia). It needs to differentiate between two objects of an image and label every object it identifies. CV is achieved using another one of the technologies associated with AI: image recognition.
Image Recognition (IR)
Image recognition works by analyzing an image in its entirety and breaking it down into data points. It stores these data points into its organically growing database and thus learns to identify even more objects on its own.
Reading resources: Understand image recognition in-depth.
Pattern Recognition (PR)
Consciously or not, we absorb the world around us as a series of patterns. Your daily routine is a pattern. The way you react to things is a behavioral pattern. Architectural marvels around the globe are majestic patterns repeated systematically. The examples are endless.
AI thrives when it is modelled along the same principles of learning. Using pattern recognition, artificial intelligence can find out similarities in the datasets it is fed. This allows it to throw away the crutches of supervision and stand on its own.
Reading resources: exploring pattern recognition.
Data Mining & Predictive Analysis
All the technologies associated with AI discussed thus far have been applicative areas of one foundation block: data. Data is the bedrock of artificial intelligence.
Reading Resources: How better quality data translates to better AI output.
Sometimes, patterns in a data are not obviously visible. They have to be excavated. Data mining or knowledge discovery in databases (KDD) is the process of finding out patterns in the data which were unknown. AI uses these patterns to find out relationships between data entities.
Using these relationships, AI can form rules which it then uses to classify right or wrong, input to output.
But it doesn’t stop there. Data mining just teaches AI. Now it has to generate some output, some magic of its own. This magic comes out in the forms of predictions.
Yes, artificial intelligence can effectively predict the future. These predictions are not flashes or visions from another “spiritual plane” but pure data driven decisions that we as humans couldn’t have made. This is truly one area where AI doesn’t just match human capacity, but surpasses it.
How Claim Genius Innovates the Use of Artificial Intelligence
Because we understand the various technologies associated with AI, we know just where to introduce our AI to supercharge the auto insurance and damage inspection process.
Using our photo and video image recognition, GeniusPreinspect gives underwriters confidence while GeniusClaim automates the claim process allowing automotive claim professions to make real time claim decisions.
Contact us today to learn how you can automate your processes.
