Virtualitics’ End-to-End AI Platform allows companies to simplify the process of embedding AI into the flow of everyday decision making.

Pasadena, CA– March 16, 2022: – Today, Virtualitics Inc, an advanced analytics and predictive AI company, announced the launch of the Virtualitics AI Platform that aims to help enterprises and government agencies make reliable business decisions, faster with ready-to-use AI that can be understood–and used–by analysts and business stakeholders alike.The Virtualitics AI Platform supports the full end-to-end AI workflow and makes it easy for businesses to embed in their operations and take action.

  • Explore – Discover relationships within complex data sets with patented AI-generated networks and multidimensional, 3D visualizations.
  • Predict – Identify key trends, predict future outcomes, and test out scenarios that measure impact with advanced, no-code AI and Machine Learning techniques.
  • Prescribe – Deliver confident, consistent, and effective decision-making with AI-generated recommendations so people can take the next steps.
  • Act – Go from informed decision, to action, in a single move with integrations between AI-powered recommendations and business systems that carry them out.

“Enterprises today struggle to implement AI successfully and embed it into the flow of work. One of the big reasons for this is because business users and analysts can’t understand the output of AI applications and therefore don’t trust it. Virtualitics AI Platform solves this problem by making AI both understandable and accessible to anyone in the business.” said Michael Amori, Founder and CEO of Virtualitics.

For more information register for the upcoming webinar, From Magical to Practical: AI that Works for the Business.

About Virtualitics
Virtualitics, Inc. is an advanced analytics company that helps enterprises and governments make smarter business decisions, faster with ready-to-use AI that can be understood–by analysts and business leaders alike. Our AI platform allows organizations to rapidly process complex data into powerful multi-dimensional graph visualizations, and predict future business outcomes with clear, explainable no-code AI modeling. Virtualitics puts AI into use across the enterprise with enhanced analytics that’s easily integrated into the flow of work. Our patented technology is based on over 11 years of research at the California Institute of Technology and NASA Jet Propulsion Laboratory. 

Machine learning is a branch of artificial intelligence and computer science that uses data and algorithms to imitate the way that humans learn. The “learning” aspect of machine learning refers to the system’s ability to improve its accuracy and knowledge set over time based on experience.

Machine learning has a lot of practical applications, but one of the most powerful ways this technology is being used today is in big data analytics.

Applying machine learning algorithms to generating, aggregating, and analyzing large and complex datasets provides several key benefits, including:

  • Improving process efficiency
  • Reducing need for human intervention
  • Scaling for big data applications
  • Performance monitoring for sensors in remote, hazardous, or unmanned locations


How Machine Learning Is Currently Being Used in the Energy Sector

Data analytics in general, and machine learning specifically, is proving to be a valuable asset in the growth and management of the energy sector. 

With a widespread shortage of skilled labor, increased connectivity and reliance on smart technology, and a push for more sustainable and cost-effective energy sources, machine learning will play an integral role in shaping the future of the energy industry.

Here are seven ways the energy sector is harnessing the power of machine learning as part of a data analytics strategy to drive performance improvements and increase ROI:

Predictive Maintenance

Machine learning makes predictive maintenance possible by analyzing historical data and real-time data across multiple sources to predict which systems and parts are likely to fail and when. Creating AI-driven predictive models to monitor the condition of equipment allows maintenance teams to proactively schedule repairs and replacement of vulnerable components and systems.  

By addressing potential problems before they occur, predictive maintenance helps reduce the number of failures and breakdowns, which increases system availability, cost savings, and customer satisfaction. 


Grid Management

One of the big challenges of managing power grids is that power generation and power demand need to be equivalent at all times. Otherwise, utilities risk blackouts from insufficient energy on one end and wasted capacity on the other. Machine learning can help maintain balance and increase resilience, especially for renewable energy grids. 

For example, machine learning algorithms can identify changes in usage patterns, which allows utilities to quickly redirect stored energy to areas where it is needed most while decreasing load in regions with lower demand. 


Demand and Load Forecasting

Machine learning algorithms make it possible to analyze a variety of influencing factors from disparate sources, such as historical demand, temperature, time, wind speed, weather patterns, and day of the week. 

With the ability to compare analytics from many sources and weather models, utilities can make more accurate predictions about future load and demand requirements, which reduces the amount of capacity they have to hold in reserve “just in case.”


Reduced Energy Consumption

Smart metering, a technology that uses machine learning to track energy usage patterns over time, is an effective way to reduce the amount of wasted energy and save money. Machine learning algorithms can analyze data down to the device level and identify which business systems, appliances, and even recurring activities consume the most energy so steps can be taken to improve efficiency and reduce waste.

The Future of Machine Learning in the Energy Sector

The energy sector is at a crossroads, with reliance on fossil fuels slowly giving way to increased usage of renewable energy sources. As this transition plays out, artificial intelligence and machine learning technology will play an integral role in several key use cases.


Reliable Renewable Energy Forecasts

Machine learning algorithms can accurately predict the amount of electricity a wind turbine or other renewable energy source can generate during a given time period. This knowledge makes it possible for utilities to forecast supply versus demand with a high level of confidence.


Drone/Image-Based Damage Detection

Drones are revolutionizing how utilities monitor and detect damage to transmission and distribution infrastructure in remote or dangerous regions. However, the increased volume of images generated by drone-based detection makes non-AI-augmented review cost- and resource-prohibitive.

Machine Learning Is Powering Growth and Sustainability in the Energy Sector

The energy sector is in transition, and machine learning is helping organizations streamline the process. Smart technology, including sensors and IoT devices, are ushering in the big data era, and generating plants, utilities, and renewable energy providers are harnessing the power of AI to make that data usable.

The healthcare industry generates massive amounts of data. Unlocking its value can, however, be challenging. Although the healthcare industry shifted to digitization years ago with electronic health records (EHRs), there are still many obstacles, including exchanging data and compliance regulations. Machine learning in healthcare expands and touches many areas, deriving powerful insights and predictions from data models.

With machine learning, the healthcare industry has the potential to improve operations, reduce costs, drive better patient care, understand public health holistically, and do much more.

Applying Machine Learning in Healthcare

So how does healthcare use machine learning? First, it’s important to define machine learning.

Machine learning is the use and development of computer systems that can learn and adapt by using algorithms and statistical models to analyze data patterns.

The process applies those algorithms to structured and unstructured data, uncovering interrelationships, anomalies, and trends. Algorithms learn over time as they ingest more data, imitating human learning and improving their accuracy.

This technology can provide many advantages to various fields of healthcare.


Precision Medicine

A common use of machine learning in healthcare is precision medicine, which describes the prediction of a treatment protocol’s likelihood of success based on patient attributes.

One example is a study conducted on the treatment of acute myeloid leukemia. The researchers included data from patients with the disease to determine how and why pathologically similar cancers react differently to the same drug regimens. They also incorporated multiomic information. The study concluded with the assertion that this method performed better than other approaches.

What the researchers learned in this scenario allowed for future patients to receive more personalized care. With this use case, clinicians were likely able to save or prolong the lives of many patients.



One of the most effective areas of machine learning in healthcare is diagnosis. Many cancers and genetic diseases are hard to detect, even with modern science. IBM Watson Genomics is an example of this, combining cognitive computing with genome-based tumor sequencing.

It’s also proficient at diagnosing diabetes, one of the most deadly and prevalent chronic diseases. IBM Watson Genomics can detect diabetes early so treatment can begin much sooner, resulting in better outcomes for patients.


Radiology Analysis

Another avenue for machine learning in healthcare is through radiology. There are several applications for machine learning in diagnostic imaging, including:

  • Optimized order scheduling and patient screening, which can reduce the risk of patients missing care
  • Intelligent imaging systems, leading to decreased image time and unnecessary imaging and also improving positioning
  • Automated detection and interpretation of findings for various cancers and other diseases with faster processing speeds and the ability to detect anomalies beyond what the human eye can see
  • Postprocessing, including image segmentation, registration, and quantification
  • Automated clinical decision support and examination protocoling



EHRs are the standard houses for all medical records. Although this technology has many benefits, not all clinicians find it to be an efficient or effective tool. Now, machine learning is addressing these issues. For example, algorithms support clinician use by offering clinical decision support, image analysis automation, and telehealth technologies integration.


Medical Research

In situations where large amounts of health data are available, machine learning can also play a key role in determining trends in public health. Such data must be anonymized to ensure compliance. Crowdsourcing is one way of collecting medical data in order to analyze it. Findings from this could be significant, especially with the world still being in the midst of a pandemic.

Why Healthcare Entities Haven’t Fully Adopted Machine Learning

With all the possibilities that machine learning brings to the table, why isn’t every healthcare organization embracing it?

The key challenges are data governance, data silos, data scientist hiring and training, data integrity, compliance, and costs. Not every organization can launch machine learning initiatives. It seems out of reach for many, but new platforms are making it easier and more cost-effective.

Machine Learning in Healthcare Made Easier with 3D Visualizations

It is possible to overcome some of the biggest obstacles for healthcare to realize the power of machine learning. New platforms gather and clean the data, ingest it, and deliver visualizations. Data scientists aren’t necessary, which means it’s more accessible and actionable.

Machine learning offers substantial opportunities to categorize and analyze data, something every company wants to do. As a branch of AI, machine learning occurs by applying algorithms to structured and unstructured data to find interrelationships, anomalies, and trends. Its conception was to imitate how humans learn, and these algorithms actually become “smarter” over time, improving accuracy.


The Origins of Machine Learning

The term originated from Arthur Samuel of IBM conducting a research project on checkers. The checkers master Robert Nealey lost to a computer Samuel built way back in 1962. When thinking about the achievements of machine learning nearly a quarter into the 21st century, this seems trivial, but everything has a beginning. In the decades since the computer’s chess victory, machine learning has become more mainstream and critical in big data analysis. 


How Machine Learning Works

The early stages of machine learning involved theories of computers recognizing patterns in data and learning from them. Since that time, the process has become more complex. The basics are the same—computers learn how to think as humans do. Now, its application enables companies to transform processes, assigning tasks that only humans could complete in the past to machines. 

The process corresponds to algorithms, and they have three main parts:

  • Decision process: Based on input data, which may be labeled or not, algorithms produce an estimate of a data pattern. 
  • Error function: This serves as the means to evaluate the prediction of the model. If known examples exist, an error function can compare the two to assess accuracy.
  • Model optimization process: If the model can fit better into the training set’s data points, weights can adjust to reduce the discrepancy between the known example and the model estimate. The algorithm repeats this evaluation and optimization process, updating weights autonomously until reaching a threshold of accuracy. 


Machine Learning Methods and Use Cases

So what are the practical applications of machine learning? Can you apply it to any process? In theory, yes—but a large amount of data is necessary for the learning to occur. 

There are three categories of machine learning methods:

  • Supervised learning: This method uses labeled datasets to train algorithms to classify data or predict outcomes accurately. It can help organizations solve real-world problems at scale, such as predicting customer lifetime value and understanding consumer behavior. It can also offer recommendations to users (e.g., eCommerce, YouTube).
  • Unsupervised learning: This approach works with data that isn’t labeled. Algorithms attempt to discover “hidden” patterns without human intervention. In finding similarities, opportunities can become apparent. Use cases include customer segmentation and fraud detection.
  • Semi-supervised learning: The final type of machine learning is a hybrid of the others. It uses a smaller labeled data set to guide classification from a larger unlabeled dataset. Applications include speech analysis and classifying large groups of text documents. 


The Challenges of Machine Learning

Implementing machine learning into your company isn’t as simple as having data. The first challenge is collecting and aggregating data into a single source from legacy systems or other siloes. Overcoming that challenge can be a significant hurdle. When you do, there will likely be more hindrances, including infrastructure requirements, because it requires considerable processing power. 

Companies also avoid adopting machine learning because of the time it takes and the costs involved. If the process is too slow, it won’t be possible to react to the insights derived while they’re still valuable. 

The single biggest obstacle is attaining the right talent. Data scientists are an expensive and in-demand resource. 

When they see all the adoption challenges converge, most companies decide not to use machine learning. A Census Bureau report found that only about 9 percent of U.S. firms employ it. For those that do, around 90 percent of machine learning models never make it into production. 

However, new platforms can alleviate many of these challenges so companies can realize the benefits of machine learning. 


You Don’t Need to Be a Data Scientist to Operationalize Machine Learning  

New technology platforms can do the heavy lifting for you, from gathering and cleaning the data to ingesting and delivering visualizations. Anyone can transform this complex process into actionable insights.

Data is everything in today’s business environment. The problem is there is just so much of it. Every application we use, every interaction we have, and every system we access generates and consumes data. 

The information held within this data can be used to drive business decisions, improve performance, and increase efficiency, but before we can extract value from the data, we first have to understand what it is telling us.

In today’s era of Big Data, it’s almost impossible to draw out the relevant insights using traditional data analytics. In order to make the data useful and actionable, many organizations are turning to artificial intelligence (AI) routines and machine learning technology to power their analytics.


The Role of Artificial Intelligence and Machine Learning in Data Analytics

Traditional data analytics involves a group of very specialized data scientists spending days, weeks, or even months examining and manipulating data, looking for patterns and relationships that can be used for business intelligence.

The process is tedious and time-consuming, and often not very effective. With the introduction of artificial intelligence (AI), organizations can analyze exponentially larger volumes of data in a fraction of the time, with far less intervention from skilled data experts required. 

Machine learning (ML), a subcategory of AI, applies algorithms to structured and unstructured data looking for interrelationships, anomalies, and trends. AI routines and multi-dimensional visualizations are then used to render the results in a format that is highly understandable by anyone.

This “AI understandability” and visual modeling is crucial in today’s enterprises. Many of the key stakeholders and decision-makers who rely on the data insights are non-technical professionals, such as C-level executives, marketing directors, facility operators, and fleet maintenance managers.


Common Challenges of Adopting Artificial Intelligence and Machine Learning

Although there are many benefits to implementing AI and machine learning solutions to assist with data analytics, there are also inherent challenges. Here are four common roadblocks organizations encounter when taking on AI and ML adoption:

1. Legacy Systems

Data analytics has become a business imperative in essentially every industry. However, not all of these industries have kept pace with technological advances. 

Organizations that operate with a significant number of legacy systems in place will encounter integration and compatibility issues with current AI platforms. Older technologies use different programming languages, frameworks, and configurations that simply don’t play well with today’s flexible cloud- and IoT-driven solutions.

To alleviate some of these issues, consider modernizing legacy systems prior to investing in AI and ML analytics tools, to help ensure all systems are compatible and integrate easily.

2. Data Quality

We’ve all heard the adage, “Garbage in, garbage out.” Machine learning models don’t know the difference between good and bad data. Machine learning precedent is set using whatever data you tell it to use. If that data is inaccurate, out of date, or otherwise poor quality, your analytics will not yield good results. 

To increase the quality of your machine learning training dataset, employ a human to do a thorough review of the training data to ensure it is clean, complete, and consistent. 

3. Knowledge Gaps

Although some AI-driven data analytics solutions are designed for a broader audience, every organization needs access to knowledgeable data scientists and analysts. However, as with most technical roles today, there is a significant lack of trained machine learning professionals actively looking for work. In fact, the shortage is so severe that a recent RELX survey found that 39 percent of respondents aren’t using AI because they don’t have the technical expertise to do so. 

Organizations that are unable to staff needed analytics and data professional positions can partner with a managed services provider on projects that require a higher level of technical expertise.

4. Siloed Operational Knowledge

In manufacturing and utilities, facilities data is often siloed by department, which makes identifying interrelationships among data sources difficult. Without access to all datasets, both structured and unstructured, AI and machine learning capabilities are inefficient and won’t generate actionable insights.

Creating a “single pane of glass” for data analytics enables AI routines to provide full visibility into which variables are having the biggest impact on the target data. In turn, this allows data, operations, and maintenance teams to work together on a holistic solution to performance issues.


Tactics to Overcome Obstacles to Machine Learning and AI Adoption

Take the next step in supercharging your data analytics by implementing machine learning and AI solutions. Watch this on-demand webinar, ML Model Explainability Using 3D Visualizations, to: 

  • Gain a deeper understanding of your machine learning models
  • Discover use cases for business analysts who need to utilize 3D visualizations to explore complex models
  • See how using Virtualitics API enables data scientists to fit our 3D visualizations into their existing process when it comes to creating and refining models