Why Business is Ready for AI-Powered Solutions
Award-winning artificial intelligence solutions use machine learning, natural language processing, and knowledge graphs to solve the world’s most critical problems.
SparkCognition’s comprehensive AI platform includes automated model building, knowledge graphs, natural language processing, and other artificial intelligence components that combine to solve critical problems and everyday challenges for your business.
What is artificial intelligence?
Many definitions exist for the term “artificial intelligence.” These range from various descriptions of machines having the ability to perform tasks typically performed by and at least as well as humans to more precise definitions tied to specific methodologies and implementations. In truth, artificial intelligence (AI) is a broad, dynamic, constantly evolving domain fueled by innovative, interdisciplinary research across academia, and the public and private sectors.
At SparkCognition, we take a pragmatic view over a purist one similar to Gartner’s definition, which states that “AI applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and to take actions.” By extension, AI includes technologies like deep learning, natural language processing, knowledge representation, and computer vision, and can be applied today to solve the world’s most critical problems. With our award-winning AI solutions, our customers are able to analyze, optimize, and learn from data, augment human intelligence, drive profitable growth, and achieve operational excellence.
90% of all data was created within just the last two years.
Why now for AI
Artificial intelligence (AI) is hardly a new technology, spanning nearly 70 years since its inception. However, AI has exploded within the last 10 years, with AI technologies like deep learning, natural language processing, and computer vision reaching performance thresholds that have enabled them to become integrated into our everyday lives.
This AI explosion is due to three big factors: continued exponential growth in computing power, related exponential growth in the creation and availability of digital data, and new advances in AI algorithms and architectures. Together, these factors are driving the sixth cycle of disruptive innovation, one that will accelerate faster than previous technology cycles, leaving less time for organizations to ramp up and giving leaders sustainable competitive advantage over laggards.
3 ways practitioners benefit from AI applications
USING ADVANCED ANALYTICS
Using advanced analytics that enable more accurate and faster pattern recognition to unlock new insights and find better ways to achieve successful outcomes.
AUGMENTING PERFORMANCE
Augmenting the performance of human workers with new forms of machine-driven decision intelligence.
AUTOMATED WORKFLOWS
Automating workflows and directly taking actions to accelerate and optimize operations.
Reasons for AI acceleration
Increasing Computing Power
Thanks to Moore’s Law, computing power has been increasing exponentially over the past four decades and is still accelerating with concepts like Neven’s Law and quantum computing, giving us the ability to analyze terabytes of data at a fraction of the cost.
The Abundance of Available Data
There is a need for AI to manage and understand the wealth of data created in just 2021. Tools like machine learning are able to leverage these large data sets to train and then be applied to new problems.
AI Research Breakthroughs
The advent of deep neural networks and deep learning has greatly expanded the capacity of AI. By mimicking neurons in the human brain, these technologies solve complex, nonlinear problems in new and innovative ways.
Machine Learning
Machine learning is a powerful branch of AI that enables a computer program to automatically learn from historical data and improve its performance through experience without the need for explicit programming by a human. Machine learning algorithms are designed to automatically find patterns in historical data and make inferences from new data to perform a number of tasks across a wide range of domains.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
Supervised Learning
Supervised learning is a form of learning by example, or function approximation, and consists of two stages. In the first stage, also known as the training phase, a given supervised learning algorithm is applied to an input training data set that has been previously tagged or labeled. In this way, the training set consists of a set of sample inputs each mapped to predefined output (the label).
During the training process, the supervised learning algorithm learns the inherent relationship between inputs and outputs, resulting in a model that will be used in the next stage to perform tasks like classification, regression, or forecasting.
In the second stage, the trained model is put to use, taking new data as input to generate some form of prediction in a process known as inference. These predictions can then be used to drive some form of actionable result.
Supervised learning has many applications but comes at a cost–it requires labeled training data to build a model. Such data may not exist or requires expensive, manual methods to procure.
Unsupervised Learning
Unsupervised learning techniques are applied in scenarios involving unlabeled input data. In these cases, an unsupervised learning algorithm identifies patterns in the data without human oversight, inferring the inherent structure in the data on its own. In essence, unsupervised learning is about learning how to automatically organize the data in order to best describe it.
An essential task in unsupervised learning is clustering–dividing a data set into groups of similar objects. Unsupervised learning techniques are also well suited for anomaly detection and driving predictions based on detecting subtle changes encountered in a data source over time.
Reinforcement Learning
Reinforcement learning involves learning by trial and error. In this form of machine learning, software “agents” learn what actions to take in response to a reward-based mechanism applied during the training phase. The actions constitute the behavior the agent will take in response to its environment. The reward serves as a form of feedback, allowing the agent to learn over time the optimal policy to employ in the future when it’s put into use in a live environment.
A commonly cited metaphor can be found in Ivan Pavlov’s famous experiments in which he trained his dogs to salivate in response to hearing a ringing bell after previously conditioning them to associate its sound with receiving a reward (meat). From automated stock market trading to self-driving cars to robotic vacuum cleaners, reinforcement learning has been successfully applied in many daily areas of life, automating actions that were once solely taken by humans.
Machine Learning Features
Unsupervised: Anomaly Detection
Identify a behavior as unusual to determine if an asset or process is need of attention or maintenance before an event like a critical failure.
Unsupervised: Normal Behavior Modeling
Learn to recognize normal machine behavior and deviations from the norm based on time-series data of a machine’s operation.
Unstructured Analytics
Unlock unstructured data from a broad range of sources and streamline business decisions with deep learning.
Neural Networks
Artificial neural networks (ANNs) are a popular form of machine learning loosely inspired by the organizational structure of biological neural networks found in animal brains. In this structure, an input and output layer along with one one or more internal “hidden” node layers are interconnected sequentially. This organizational arrangement, coupled with the particular activation function implemented by each individual node, gives neural networks their power to learn with high accuracy, even from highly-dimensional input data sets.
Neural networks come in different flavors that are specifically well-suited for different tasks such as convolutional neural networks (CNNs), commonly used for computer vision tasks, and recurrent neural networks (RNNs), which contain feedback loops that make them well equipped for sequential learning tasks dealing with time-series data.
Deep Learning
As the depth of inner node layers deepens, so does its learning power, given sufficient training data. This is where the notion of deep learning is derived–when multiple hidden node layers are implemented between the input and output layers. The word “deep” refers to the number, or depth, of hidden layers in a neural network. Deep learning models have tremendous representational learning capacity and deliver outstanding performance for many tasks where the underlying data set is composed of hierarchically-related elements. During training, the hidden layers learn the important features of the input data set automatically, which is of tremendous benefit in situations where costly manual feature engineering would otherwise first need to be applied.
In this way, deep learning is able to solve many different types of complex problems, often with superhuman levels of competence, excelling in areas such as computer vision, natural language understanding, predictive maintenance, and difficult strategy games like chess and Go.
Knowledge Representation
Knowledge representation and reasoning (KRR) is a subfield of artificial intelligence that differs from the probabilistic forms of machine learning that have become predominant in AI applications in recent years. KRR approaches are a part of symbolic AI, affectionately known as “good old-fashioned artificial intelligence” by practitioners owing to its place as the dominant paradigm in early AI research history. It consists of concepts and techniques used to represent information about the world in a form that is usable by machines to solve complex problems and perform human-like semantic reasoning on a variety of tasks.
Knowledge graphs are data structures that are well suited for storing information about various entities and their relationships. Knowledge graphs have broad applicability across a large range of AI use cases because of their ability to facilitate inference and understanding. When used in combination with sub-symbolic AI techniques like deep learning, emerging research is showing the possibility for new levels of performance in reasoning and understanding beyond those offered by today’s solutions.
Natural Language Processing
Natural Language Processing (NLP) is another important branch of artificial intelligence in which machine systems are developed to understand, interpret, and manipulate human language. NLP has existed for quite some time, with work on automatic translation and similar projects dating back to the early 1950s using punch cards. However, NLP has progressed rapidly in the past decade thanks to the combined advances in big data and deep learning. This technology now not only allows us to talk to our phones, cars, and other devices in natural language, but unlocks huge potential business value across a number of domains.
NLP technology can take human language, break it down into individual pieces, categorize words or phrases, and then automatically derive the underlying meaning and relationships. More recently, advanced NLP techniques using deep learning have been applied to automatically learn grammatical rules, linguistic habits, and contextual patterns to create powerful pre-trained language models like BERT and GPT-3. These models can then be used to understand, analyze and even generate new text and speech and are powering new classes of AI-powered applications that never existed before.
NLP technology has enormous implications for businesses and organizations, enabling them to extract knowledge and insights from unstructured text sources (written and verbal) and leverage them for analysis and information retrieval. NLP can improve and drive high-value business decisions. This includes minimizing operational costs, reducing the risk of human error, capturing tribal knowledge, and gaining visibility and valuable insights that streamline workflows, optimize processes, and augment human decision-making.
To learn more, watch our video on the basics of Natural Language Processing.
Use NLP for:
Natural Language Analysis
Process text and speech for sentiment analysis, entity recognition, information retrieval, categorization, and pattern discovery.
Natural Language Generation
Respond to various requests or create structured information with natural human language.
Speech Understanding and Generation
Reliably converting voice data into text allowing for better processing.
Computer Vision
Computer Vision (CV) is a subdomain of artificial intelligence that enables computer systems to capture and interpret meaningful information from images and video data, training machines to understand the visual world much the same way as humans do. By applying machine learning models to visual data from the real world, machines can be taught to accurately identify and classify objects and make a decision or take some action based on what they “see”—like unlocking your smartphone when it recognizes your face or automatically steering your car out of harm’s way to avoid an accident.
Computer vision is almost as old as artificial intelligence itself, deriving from early interdisciplinary research spanning psychology, neurobiology, cybernetics, mathematics, and computer science over 60 years ago. Nearly from the beginning, computer vision systems have adopted a hierarchical approach to understanding visual information, similar to the optical systems employed throughout the biological world, building higher-level tasks like object recognition and semantic segmentation composed of lower-level ones like detecting edges and textures.
As noted above, deep learning is a form of representational learning that is particularly well-suited for image data sources—able to automatically extract hierarchical features with increasing complexity across its neural network layers. Like NLP, modern CV has benefited greatly from advances in computing, big data, and the rise of deep learning to achieve performance levels that often exceed humans across a variety of machine vision tasks. Computer vision has become prevalent in modern-day life, with use cases such as optical character recognition, machine inspection, medical image analysis, robotic vision, video surveillance, facial recognition, and human emotion analysis employed extensively in recent years.
Computer vision has enormous utility for businesses across industries, enabling them to generate insights and understanding from images and videos created by the billions of digital camera systems actively deployed worldwide today. Today’s top computer vision use cases are focused on driving operational excellence, cost reduction, and productivity improvements.
In the future, computer vision will merge with other AI systems to increasingly drive greater automation and process improvements, manage risk, ensure regulatory compliance and safety, and unlock visual analytics to make better decisions.
Use CV for:
Object Recognition
Visual Inspection
Video Surveillance
SparkCognition areas of research
SparkCognition has built a world-leading applied research group consisting of some of the top minds and leaders from academia and industry spanning the entire spectrum of artificial intelligence. Our research team has partnered with universities to push the science of artificial intelligence, resulting in numerous patents and groundbreaking solutions for our clients.
Example research areas by SparkCognition’s Applied AI Research Group:
- AI in Cyber Physical Systems
- Anomaly Detection Modeling
- Automated Model Building
- Deep Learning
- Natural Language Processing
- Reinforcement Learning
- Robotics/Autonomous Systems
- Threat Detection Modeling
- Computer Vision
- Seismic Imaging
SparkCognition’s artificial intelligence tools solve business’s most critical problems
SparkCognition has implemented advanced AI technology in machine learning, neural networks, natural language processing and knowledge graphs in some of the world’s largest organizations. These AI solutions provide better results than traditional approaches that continually self improve.
