For most, the term Artificial Intelligence evokes thoughts of futuristic technologies, of C-3PO and Asimov’s “I, Robot.” We think of walking, sentient robots that look like us – or at least we imagine they appear to like us.
What we don’t generally think of is a glorified speakerbox, or a supercomputer tasked to create recipes. But that’s where AI starts – with small steps and incremental advances in technology. These small steps gradually lead to giant leaps forward; think of a narrow canyon off in the distance: you’re pretty sure you can jump the canyon, but first you need to get there.
While Chef Watson and Amazon’s Echo may not be what automatically comes to mind when one mentions AI, it is the reality. Artificial Intelligence isn’t created by artificial bodies, it’s created by complex machine learning algorithms that increasingly result in smarter and smarter machines. At SDI we develop mobile apps for AI for humans, medicine, robotics and business.
While the aforementioned examples may not be AI in the truest sense – they’re not sentient (yet), they are the latest and closest step towards true sentience and machine intelligence. When a machine eventually becomes aware, it will happen instantaneously – one moment it will not be sentient and the next it will be.
But, we aren’t quite there. What we have before us today are techniques that can improve the quality, features, and usefulness of our existing technologies. AI machine learning algorithms helps technologies – like apps and websites -learn about the needs, preferences, and desires of its users.
Apps like Cortana, Siri, and Google Now are examples of machine learning at work. These digital secretaries are designed to learn from you, to offer increasingly better answers and results tailored to fit you. So, what are these machine learning tactics I keep mentioning?
Why don’t we take a closer look at top 7 algorithms use today.
1. Sentiment Analysis
Sentiment Analysis garners opinion-based information. Rather than gathering facts about a query, Sentiment Analysis focuses on gathering information about what other people have written on the topic. When users query about food, travel, fashion, and so on, they are going to want to read about opinions of trendsetters, not a collection of data sets. Sentiment Analysis is specifically designed to recognize subjective material and to return said material to the user. It basically teaches your website or app what people are thinking and teaches it how to differentiate between fact and opinion – a significant move towards true AI.
2. Latent Semantic Analysis (LSA):
LSA is similar to Sentiment Analysis but is meant to be complementary instead of a replacement. LSA is a vector oriented system that treats documents on the internet as individual vectors. Each vector is analyzed for content and, based on the content, is deemed relevant or not. For example, if you were to ask Siri “how to run a restaurant” an LSA model will break down individual documents based on the queried phrase and the keywords. The model will then return to the user the most relevant documents, determined by the frequency of the keywords. LSA filters out miscellaneous content that is irrelevant to the query.
3. Support Vector Machine (SVM):
A Support Vector Machine (SVM) separates data into binary categorizations. SVM will take a user query, for instance, “what does a tulip look like” and an SVM will search through data to determine what is and what is not a tulip. Specifically, SVM is the best tool for image categorization and outperforms competing models when it comes to multilinear binary classification.
4. Anomaly Detection
Anomaly Detection is a tool used to recognize and remove anomalous data from a data set. The basic idea is that it improves the accuracy of search results. Anomaly Detection can be used to eliminate outlying data before it is passed on to more sophisticated language detection systems, like LSA and Sentiment Analysis.
5. Reinforcement Learning
Reinforcement Learning is a goal-oriented approach to machine learning, where a program learns from interaction (with users) and problem-solving. It teaches a program to better understand its users and to learn from them. In other words, the heart of A.I. This is in direct contrast to other forms of machine learning where a program is told what the best option is, resulting in a much more limited, less helpful piece of software.
6. Structured Prediction
Structured Prediction helps programs to better understand whole sentences in a block of text. Structured Prediction excels at comprehending structured values, as opposed to discrete or real ones.
7. Bag-of-Words Model
The Bag-of-Words (BoW) is a feature of Natural Language Processing (NLP). BoW helps a machine determine what are the important words in any given text, regardless of syntax or grammar. It does this by tracking word frequency and providing a weight to each word, based on how often it appears. This helps improve a machine’s understanding of natural language and to better comprehend user requests.
To sum up, machine learning helps a program, whether it’s a program for an app, or for a website, to become better. With algorithms such as the above, our technology is getting ever closer to the depiction of Artificial Intelligence we see portrayed in popular fiction. One day, we will likely see true machine sentience, but in the meantime, this is as close as we can get.
Software Developers India has been using machine learning for years – ever since our Chief of Technology told us this was the future. We specialize in enterprise solutions for SMB and small projects for entrepreneurs. To learn more about how you can utilize machine learning algorithms in your project (including how it attracts investors and slows down the competition!) give us a call at 408.802.2885. Contact us today to get a free consultation with our Tech Director!