Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) powered products and services are available now - from self-driving cars to personal digital assistants - and we are just getting started.
Artificial Intelligence is not the future, it's here today.
Artificial Intelligence (AI) powered products and services are available now - from self-driving cars to personal digital assistants - and we are just getting started. AI allows machines to learn and apply knowledge, enabling them to accomplish complex tasks. Examples of what AI products can do include:
- Drawing conclusions and making predictions from data
- Analyzing images and video (computer vision)
- Working with text and speech (natural language processing)
AI can be used in a huge variety of ways, and is an exciting development. It allows for us to create systems that can react and respond to the world.
Models of Machine Learning
Data touches every aspect of our lives. Machine learning enables us to teach computers to understand and make use of the insight that they provide.
Oak-Tree Is a Leader in Artificial Intelligence
Oak-Tree has been developing data driven applications which leverage AI for over a decade in healthcare, real-estate, finance, education and training, web and e-commerce, and government markets.
How Is AI Used?
AI is used within healthcare to help nurses and doctors triage patients and cut down on unnecessary visits to clinics or hospitals, streamline workflows resulting in billions in savings, and interpret medical images looking for signs of disease. AI in finance to improve customer experience, automate cumbersome decision making tasks, and monitor for fraudulent transactions.
Mapping the AI Landscape
Artificial intelligence is a broad field of technology. How do you know in which direction you should go? What is the general process to understanding how to use AI in your industry and organization?
How Does Artificial Intelligence Work?
Like human learning, AI works by taking in large amounts of data, processing it through algorithms that have been adjusted by past experiences, and using the patterns found within the data to improve decision making.
Data Drives the Education of Models
Data and classical analytics, supplemented by "feature engineering" produces the input for machine learning models. The ability to track nearly every facet of modern life as a "datapoint" means that machine learning models have a huge variety of sources to learn from. Big data sets improve the quality of education that AIs receive. For many organizations, learning how to capture and use their data is the first step toward the adoption of AI.
Classical Machine Learning
Classical machine learning models make use of statistical techniques to explain "variance" in data distributions. Examples of classical machine learning algorithms include linear regression, k-Nearest Neighbor, learning vector quantization, self-organizing maps, and support vector machines.
Deep Learning
Deep learning is a branch of machine learning that uses computer simulations called artificial neural networks. Deep learning differs from classical machine learning in the way that the models are trained and the amount of data required. Deep learning models represent one of the cutting edges of AI, and are capable of achieving accuracy beyond classical models.
AI and Medical Imaging
The promise of medical AI is a future where the "wisdom contained in the decisions made by physicians and outcomes of patients should inform the care of each patient." How do we get there?
Need help with a Machine Learning or AI project?
Speak with an AI/ML expert at Oak-Tree. Working with you, we will create a custom solution that is tailored for your field and your targets.
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