Dec. 1, 2020
What is an AI model exactly, and how do you train AI models? Why are all AI models not created equally? Better training, using the right mix of algorithms and frameworks, and even setting the right business requirements, that’s how we achieve the highest performance when developing and deploying an AI model for computer vision.
Artificial intelligence models have shown significant power to grow and improve businesses. According to a 2017 survey by BCG and MIT Sloan Management Review, 84 percent of businesses say that AI will enable them to gain or maintain a competitive advantage. What’s more, research firm Markets and Markets predicts that the AI market will grow to a $190 billion industry by 2025, with an annual growth rate of 37 percent.
An AI (artificial intelligence) model is a program that has been trained on a set of data (called the training set) to recognize certain types of patterns. AI models use various types of algorithms to reason over and learn from this data, with the overarching goal of solving business problems. There are many different fields that use AI models with different levels of complexity and purposes, including computer vision, robotics, and natural language processing.
As mentioned above, a machine learning algorithm is a procedure that learns from data to perform pattern recognition and creates a machine learning model. Below is a sampling of just a few simple machine learning algorithms:
to the same centroid. This mean value then becomes the cluster’s new centroid. We repeat the algorithm until it converges, i.e. the position of the centroids does not change.
AI and machine learning algorithms are fundamentally mathematical entities, but can also be described using pseudocode, i.e. an informal high-level language that looks somewhat like computer code. In practice, of course, AI models can be implemented with any one of a range of modern programming languages. Today, various open-source libraries (such as scikit-learn, TensorFlow, and Pytorch) make AI algorithms available through their standard application programming interface (API).
Finally, an AI model is the output of an AI algorithm run on your training data. It represents the rules, numbers, and any other algorithm-specific data structures required to make predictions about unseen test data.
The decision tree algorithm, for example, creates a model consisting of a tree of if-then statements, each one predicated on specific values. Meanwhile, deep neural network algorithms create a model consisting of a graph structure that contains many different vectors or weights with particular values.
The concept of artificial intelligence has been around for centuries, perhaps stretching back as far as ancient Greece with the story of the sculptor Pygmalion and his creation Galatea. However, it wasn’t until the 1950s that the true potential of AI was explored. On August 31, 1955, the term “artificial intelligence” was coined in a proposal for a “2 month, 10 man study of artificial intelligence”. The workshop, which took place in July and August 1956, is generally considered the new field’s official birthdate.
In the 1980s, AI research rapidly grew thanks to greater availability of both funds and algorithmic tools. David Rumelhart, Geoffrey Hinton, and Ronald Williams published “Learning representations by back-propagating errors” in October 1986, proposing “a new learning procedure, back-propagation, for networks of neuron-like units.” Backpropagation forms the foundation of the neural networks that make up today’s cutting-edge deep learning AI models.
However, it wasn’t until the 21st century that many milestones in artificial intelligence were achieved, and AI models could truly flourish. Progress in the 1980s was halted largely due to the need for large volumes of data and immense computing power, both of which were unavailable from existing technology. Thanks to Moore’s law, technological advancements in the 21st century have made high-powered AI models available to the masses.
Creating the right AI models that solve real-world business problems starts with having a deep understanding of and familiarity with your desired business goals and requirements. In the early phases of any AI project, all key stakeholders need to discuss the project objectives and the data they have available to train the model. At this stage, businesses will often conduct an exploratory analysis with various statistical techniques and visualizations to understand their data more effectively.
Next, data needs to be combined, transformed, and cleansed in order to be ready for your AI models. Don’t underestimate the time this can take: according to a 2018 survey, data scientists spend 60 percent of their working hours on cleaning and preparing data. Feature engineering is another key practice at this stage, in which you attempt to determine the data attributes that are most significant and useful for your model. Performing feature engineering requires a data scientist’s expertise, as well as possibly the input of domain experts who are familiar with the type of data you’re using.
The final step before starting AI training is to select the right algorithm. With hundreds of AI and machine learning algorithms to choose from, selecting the right model often also involves considering several requirements: model performance, accuracy, interpretability, scalability, and compute power, among other factors. Of course, since there will always be trade-offs to make, there’s no such thing as the “perfect” algorithm, and many projects will experiment with multiple algorithms to see which one gives the best results for their use case.
Once you have selected and trained the model, you can use it to reason over and make predictions about data that it hasn’t seen before. Any data set used for AI training should be split up into three distinct parts: a training set for training the model, a validation set for tuning the model’s parameters, and a test set for testing the model’s performance on unseen data.
In the past several years, AI models have advanced by leaps and bounds, radically transforming the business landscape. This has made many organizations look for powerful, mature AI platforms that can help them achieve their goals—platforms like Chooch AI.
Speed, accuracy, flexibility, and scalability are at the essence of Chooch AI’s services, with solutions in industries including geospatial, security, media, healthcare, hospitality, banking, retail, and more. Chooch AI is a complete visual AI platform that produces end-to-end deployments for the cloud and edge devices. AI models built using the Chooch platform are able to process any imagery, from visible light to electro-optical and X-rays, sourced from sensors and platforms.
Chooch generates highly accurate models called perceptions, which are groups of related AI models generated together with a group of algorithms. This technique is also called ensemble modeling. The idea is that by taking the majority vote of an ensemble of algorithms, you can get more accurate results than taking the output of a single model, which may have had problems during the training process (e.g. poor initialization or incorrect parameters).
Whether you’re interested in object detection, video annotation, facial authentication, or any other cutting-edge application, Chooch will help you achieve your business goals effectively. Sign up and start your trial of the Chooch AI platform for free, and follow our blog for more updates on artificial intelligence.