Category: Technology

Top Five Reasons Why You need to Implement Automation in 2023

If something can be automated, it should be. Automation can bring faster performance, greater accuracy, and solid returns on investment. If these benefits are certain, then it is worth trying. To illustrate, let us consider a simple example. Before currency counting machines, bank staff had to count notes manually, which was tedious and prone to errors. Today, the same task can be done faultlessly in no time, allowing cashiers to focus on customers and more essential tasks.


Now imagine the world of difference that automation can make in factories and offices. Here are five reasons why you should consider automating your business:

1. Timesaving

According to a report, business process automation can save more than 50% of jobs and 30% of their time, which can be spent on more significant activities. Even automating document processing and paper approval can save the equivalent of five working days.

2. Cost-effective

By automating tasks, you can lower your labor costs and save on essential resources that manual labor demands.

3. Greater productivity

When repetitive tasks are automated, employees can focus on more critical work. For example, web designers can monitor project deadlines and progress with an automated system, allowing them to concentrate on their main job.

4. Better customer service

Automation can improve customer service by providing accurate and faster information, recording customer interactions, and allowing instant replies to emails or live chat queries.

5. More efficiency

Automation can help organizations become more organized and efficient. For instance, a retail outlet can use an automated inventory system to replenish stocks faster and timelier.


Of course, there are many more reasons to automate your business, such as scalability, compliance, and auditing. So why wait? Start considering the various aspects of your business and let machines and software make your life easier in areas that can benefit from automation. You could start with something as simple as replacing the jars in your water cooler faster.

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Gokul Solai March 14, 2023 0 Comments

Low-Code Development Set To Grow Further in 2023

In recent years, low-code development has gained significant momentum due to the growing demand for automation and digitization across industries. Experts predict that the worldwide market for low-code technologies will see a 19.6% increase in 2023, reaching a value of US$ 26.9 billion, with this growth expected to continue until 2026. The increasing number of business technologists, hyper-automation organization-wide, and composable business tasks drive this trend.


Low-code development allows non-technical individuals to create software without extensive knowledge of programming. This has proven particularly useful for companies with limited IT resources which may struggle to recruit skilled tech professionals to develop innovative apps. Low-code application platforms help companies become less reliant on IT, providing fast access to business-sensitive information to primary stakeholders and enabling IT teams to create, implement, and offer technologies that facilitate digital transformation goals with minimal coding experience.


The adoption of low-code development is rising, with businesses utilizing low-code development software for prompt application delivery and highly personalized mechanized workflows. The low-code development market is expected to grow by 25% this year, reaching nearly US$ 10 billion, with low-code application platforms (LCAPs) accounting for most of the market share.


Two key factors driving the growth of low-code development are hyper-automation and composable enterprise. Hyper-automation is a growing trend due to the need for operational optimization, an expanding skills gap, and rising financial challenges. This year’s expenditure on hyper-automation enabling software is predicted to be over US$ 720 billion, with LCAPs, CADP, RPA, etc., playing a crucial role in process automation, integration, intelligence, and more.


The composable enterprise is another trend that is driving the growth of low-code development. Composable enterprises require improved reuse of packaged business capabilities (PBCs) already used for agile application development, creating personalized user experiences for the latest workflows and processes. The software created this way is agile and resilient, creating and recreating modular components and PBCs that can be adapted to changing business requirements.


In conclusion, low-code development is set to grow even further in 2023 as it provides a valuable solution for companies looking to become more agile and innovative in a rapidly changing business environment. While the concept of low code has been around since the 90s, it has gained popularity in recent years, and its usefulness is expected to increase as time passes. Low-code development has proven to be a boon for fledgling IT companies and tech startups, and as hyper-automation and composable enterprise continue to gain traction,low-code growth is poised to become an essential tool for companies across all industries.

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Gokul Solai March 14, 2023 0 Comments

We Must Shift to Digitally Underpinned, Learning Centered Education Systems

Here’s the problem:


  • The US continues to lag behind other countries in several education metrics.
  • The educational dichotomy created by socioeconomic and geographical issues is increasing.
  • Even Students with a college degree are having difficulty finding jobs.
  • Remote work and Outsourcing have decreased the number of onshore jobs.

What’s the cause?


For the most part, the education system has been the same for 50 years. We are still using books in classrooms, and the shift towards consuming this information via iPads and computers does not qualify as innovation or modernization.

How do we fix this?


Change doesn’t occur overnight, but we can go digital very quickly. We are introducing Intelligent Automation technologies such as Robotic Process Automation is currently the path of least resistance.

The best way to picture RPA is to think about the automobile industry. Twenty-five years ago, most of the work was physical. As such, we designed robots to perform this manual work. Today, a lot of the work is digital. We have created digital robots, software tools that perform redundant, repetitive, mindless work that must be done, but no one wants to do it.

We first think of RPA as supplemental education and then as a vocational education pathway.  Our education system’s current focus for additional education is still geared toward creating a manual or service-based jobs — it has been like this since WW2, and it is imperative to shift our goals.

Here are a few ways we can introduce this change within one calendar year


  • We can provide personalized attention to each student: 

We have Robo advisors for investing; we can communicate with children via digital tutors. The digital tutors spend more time with a student, the more individualized the care gets. Using sentiment analysis, chatbots can identify when students are frustrated with an assignment and can either provide help by resources or point them to a tutor/teacher/knowledge forum. This sentiment can be tracked to see if a student is enjoying class more and provides metrics on customizing a curriculum to students. Chatbots can also supervise keystrokes and serve as a watchdog for internet students.


  • Create summer and after-school programs.

 We can incorporate RPA education into current curriculums. Students can start making money directly out of high school by developing robots. They can also design robots for particular tasks and sell them in a marketplace.


  •  Create new digital jobs.

RPA is a growing industry. Currently, over 40 per cent of companies use some form of intelligent automation. By 2025, that number will be close to 100%. Since well-paying jobs are hard to come by even with an advanced degree, we would train students on how to design robots to perform tasks. This is the future of America and gets children involved with AI, Machine learning, intelligent algorithms, all in a constructive and fun way.

The time is now!


This isn’t a pipe dream. Apple initiated the early adoption of coding into the curriculum, and many students have a head start. We can quickly do the same with Robotic Process Automation. Let’s raise a call to action to get this done this year!

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Gokul Solai March 28, 2022 0 Comments

AI Cannot Think for Us, But It Can Work for Us

The Symbiotic Relationship Between Humans and AI


When we say AI thinks “like a human,” we really mean that it thinks “similar to a human.” There are many key differences between our biological neural networks, and artificial neural networks.
In our last article, we discussed the similarities in neural networks which are modeled after human brains and how that benefits our data training and labeling needs. However, we did not talk about what makes this technology different from how we work.


AI Is an Imitation

Artificial intelligence may never think in the same way humans do, as AI is not “thinking” at all. There are inherent differences between human intelligence and machine-learned intelligence that will prevent this, such as the inability to think uniquely, or past what the system has been taught to do. 


In fact, a top researcher of Artificial Intelligence wants people to stop referring to AI as “Machine Learning” in general. Michael I. Jordan, a professor at the University of California, Berkeley, asserts that most AI systems are nowhere near the capabilities of humans, as they are simply showing the most basic levels of pattern recognition skills. 


That being said, these differences do not inhibit the technology. While the terms artificial intelligence and machine learning can be a bit overused, AI can certainly be very intelligent and has the capacity to learn. Although there are some processes that AI cannot do better than humans, there are also processes where technology will beat us every time; and these things are improving at a rapid rate. 


AI Is an Extension of Its Creator

A U.S. federal judge recently ruled that AI cannot be classified as an inventor on a patent. Rather, the creator or owner of the AI is listed as the inventor. After all, the owner of the AI was the one tweaking algorithm, overseeing the data labeling and training.


The purpose of AI in the present and future is not to replicate human intelligence or replace the role of humans. The purpose of artificial intelligence is to create a self-teaching system that enhances the capabilities of humans.


The Future of AI?

So, humans and AI will always be distinguishable from one another. (Sorry Bladerunner fans.) But we’re still curious. What does the future of AI look like? 


AI is already used to create self-driving vehicles, analyze vast quantities of data, diagnose and treat illnesses, enhance customer retail experiences, and much more. 


Many experts predict that these uses will be expanded and improved upon, so much so that AI will be able to overcome the obstacles created by the biological limitations of humans. For example, AI could conduct long-range space expeditions to colonize other entities within our solar system without risking human lives, or even fight climate change using atmospheric drones. In fact, Microsoft has already begun using artificial intelligence to fight climate change through their “AI for Earth” program.


Artificial intelligence also has huge potential in future workplaces. AI could be used as a decision-making tool for companies, helping improve customer service systems and support management positions. To learn more about how technology like RPA and AI are already improving the workforce, read our blog The Factory Metaphor.


Again, AI will work alongside human workers, assisting us where we fall short, but never taking over completely.  In the future, we see a world where humans and Artificial Intelligence evolve together and AI has a metaphorical hand in most aspects of life. But unique decision-making and problem-solving will always set us apart from our robot counterparts.


If you are interested in implementing technology like AI, RPA, or ML into your workflow, contact us. Together, we can improve the way we work alongside artificial intelligence and drive the future of this technology.

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ZYPE Digital Agency November 2, 2021 0 Comments

Avoid and Eliminate Automation Debt Without Losing Forward Momentum

When we hear the word “debt” we tend to recoil, and for good reason. In both our business and personal lives, debt can be an inhibitor of growth and sustainability. In terms of technology and automation, the same is true. But technical and automation debt are not something that should stop your organization from remaining future-focused. Just like a college student incurs debt in pursuit of gaining a better career in the future, your company should handle its technical and automation debt with a forward-looking approach, too. 


What Are Technical Debt and Automation Debt?

When comparing technical debt and automation debt, one stands out as much more inhibiting than the other. Technical debt is often unavoidable and does not necessarily stop progress. Automation debt, on the other hand, is avoidable and can slow or even halt organizational growth.


Technical Debt

Technical debt is immaturity in technology created by anything from rushed timelines to “Band-Aid” fixes that cause future issues with compatibility, performance, or strategic alignment. But technical debt, like college debt, is often a necessary evil. 


Sometimes, waiting to roll out a new technology until it is perfect is not an option, especially because spending too much time on a “perfect” solution often means that the solution will become irrelevant by the time it’s ready for launch. The fact is that no technology is perfect and there will always be bugs—as long as you understand those risks and are prepared to deal with them, the debt you incur as a result is not inhibiting growth but enabling you to keep moving forward.


Still, it is important to note that not all technical debt is equal and that some types of technical debt can become a burden on your organization. In 2009, software development expert Martin Fowler created a quadrant that helps to weigh the risks and rewards of taking on technical debt and it still holds up today. Here is what it looks like:



The main idea behind this quadrant is to avoid reckless automation debt, the left side of the quadrant. Technical debt that is reckless can cause unforeseen issues or issues that cannot be ignored and will therefore halt progress, if not today, then surely down the road.


In contrast, prudent and deliberate technical debt is the best kind of technical debt to have because it implies that you have a plan moving forward while also keeping up the momentum of your business’s technological growth.


Automation Debt

Put simply, automation debt is all of the opportunities for automation that your organization misses. Any process that can be automated, but is not, creates debt. Since automation debt is the non-existence of something rather than the existence of issues, like technical debt, it is easy to accumulate a lot of automation debt without knowing you are doing so.


All of those missed opportunities for automation are also missed opportunities for advancement. In the long run, automation debt can be more damaging than technical debt because it implies a lack of movement.


But that does not mean you shouldn’t worry about technical debt. Technical debt can sometimes lead to automation debt because poor technology limits the capabilities of future automation tools. As a result, companies decide not to automate until the technical debt is resolved. Often, they end up never automating at all.


Odds are that if you are automating processes, you have both automation debt and technical debt. But there’s no need to panic, reducing/eliminating debt and avoiding it in the future is more than possible. It starts with implementing a future-focused strategy along with sustainable solutions.


Avoiding Debt from the Outset – Future-Focused Strategy

Although both technical and automation debt is a normal and somewhat expected part of your automation journey, there are preemptive measures that you can take to reduce the occurrence of debt and make your journey much smoother.


1. Create a Comprehensive and Realistic Timeline

This could be said about all forms of automation, technology, or new business processes: a good timeline will yield good results. When implementing automation tools into your business, it is crucial that you leave space for iterations, updates, and listening to team feedback. This is time that is well spent at the beginning of implementation and will reduce any technical debt that may have resulted from a rushed update or unrealistic timeline.


2. Align with IT

When creating your timeline, it is also greatly important to make sure that you are aligned with your IT team. Make sure they have the capacity for implementing on time and give them sufficient cushion for any unexpected issues that may arise and become technical debt if not addressed. 


3. Choose the Right Processes to Automate at the Right Time

The first two items in this list are mainly about avoiding technical debt, but process selection is the best way to preemptively avoid automation debt. Creating a comprehensive timeline with an aligned team will be ultimately fruitless if that time is spent on automating the wrong process at the wrong time.


Before you even begin creating a timeline, make sure you have assessed not only the most important places for automation but also the ideal times to implement. Read our recent blog on Dev Ops and Agile to learn more about not only choosing the right processes but also implementing them at the right time.


Eliminating the Automation Debt You Already Have – Sustainable Solutions

Chances are that you have already begun your automation journey and have therefore already gained some technical and automation debt. There’s no need for concern, the preemptive measure listed above are still valid when bringing on a new technology. And there are several things you cal do after the fact to get you out from under the technical and automation debt that may be holding you back.


1. Quick Delivery with Proper Investment and Competent Team

One way to reduce automation debt is to speed up the delivery of your technology. However, as we stated previously, rushed timelines pose the threat of creating more technical debt. But that doesn’t mean that quick delivery is impossible or inherently debt-heavy. With sufficient money, resources, and people, speeding up your automation implementation is more than possible. Remember, this is not the place to cut corners. If you are going to make an investment in automation, make it a good one.


2. Citizen Development

Previously we talked about the importance of aligning with your IT team and making sure they have the capacity to perform the tasks at hand, but for some companies, waiting for IT to have the capacity could mean waiting forever. That’s where citizen development comes in.


Don’t make employees wait for when IT has the capacity—give them the resources to start implementing automation tools themselves. With several low-code or no-code automation platforms on the market, limited IT capacity is no longer an excuse for automation debt.


3.  Zero/Low Dependencies

Many organizations recognize that they have technical debt and have concerns about how those flawed technologies will affect the technologies that are coming down the pipeline. Sometimes, technical debt does become an issue here, but there are several technologies on the market today that have low dependencies or no dependencies. This means that these technologies do not require other technology in order to operate.


Zero and low dependency technology can eliminate some technical dept by taking the place of old software. It also helps avoid automation dept because it allows your automation plan to move forward regardless of past mistakes.


Stay Focused on the Future

Automation is all about improvement, advancement, and growth. During your journey from RPA to hyperautomation, if you find you are not improving, not advancing, and not growing, it may be time to take inventory of your debt and revisit your strategy.


Contact us for more information on how to eliminate and avoid automation debt in order to create a culture of automation focused on continuous improvement.

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ZYPE Digital Agency September 21, 2021 0 Comments

How We Helped Chicago Public Schools Get On The Internet 25 Years Ago (And Everyone Thought We Were Crazy)

By: Gokul Solai, Co-Founder Solai & Cameron Technologies and Novatio Solutions


Solai & Cameron Technologies, Novatio Solution’s parent company, was started 25 years ago, near the dawn of the internet. Technology was starting to change with the increasing usage of the web. We saw an opportunity for schools to be able to use the internet for classroom work and research. The problem: schools weren’t able get reliable internet access at that time, especially building-wide systems for multiple users at once.

Being born and raised in Chicago, our parent company Solai & Cameron Technologies knew that a lot of these Chicago schools were older and had structural issues that impacted technology installation. This meant that traditional ways to provide the internet weren’t plausible.

Crazy Idea: Go Wireless

Upon brainstorming internet access solutions for Chicago Public Schools, we had this crazy idea. We decided to give the school’s wireless networks. This doesn’t seem all that unusual now, but wireless internet was not common in the late 90’s. We had many skeptics.

Back then, cell phones could barely keep signals, so who was to say an entire school building could rely on a similar concept.

With our background in new technology application, we created a solution and provided a pilot to a single Chicago Public School to demonstrate the concept in action. Not only did it work, but as a result, we provided all Chicago Public Schools with wireless networks, and they still use a much of those services today!

25+ Years Of Experience Beats Competitors

Today’s AI technology and the companies that implement them are less than five years old. Many of them lack experience and are learning at the same time as their customers. When new technology comes along, because of their inexperience, they are unable to quickly adapt.

At Solai & Cameron, and now Novatio Solutions, we are aware of how technology impacts organizations and society. We’ve been there and worked through wave upon wave of new technology. Our shared experience allows us to deliver an organization-centric solution while being cognizant of all technology options out there — and ability to draw upon past successes and failures to the benefit of our clients.

Solai & Cameron keeps its partners ready for the next big thing. As we stay up on the latest trends, developments, and apply “crazy ideas” just like we did 25 years ago in Chicago, we avoid being limited in our thinking. We have a transformational experience for our clients and that’s what the success of our solution depends on. Our services extend beyond a five-year-old code base. The knowledge of our people and seasoning of our solutions is our difference.

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June 27, 2018 0 Comments

Laws, Ethics, and AI: Many Questions Without Clear Answers

The marriage of robots and ethics has been explored by philosophers, technologists, and even science fiction writers since long before robots even existed. Isaac Asimov’s “Three Laws of Robotics” first appeared in 1942 – predating today’s advanced artificial intelligence by half a century.

But as AI technology advances and intermingles with human life more and more, questions of laws and ethics have never been more relevant.

The Case of the Self-Driving Car

Modern Diplomacy writer Maksim Karliuk shows the debate on this subject is very real, offering the case of the Mercedes self-driving car in unavoidable car accident and human safety prioritization.

When representatives at Mercedes said their self-driving cars would prioritize the lives of passengers [not pedestrians], the German Federal Ministry of Transport and Digital Infrastructure answered that “such a choice based on a set of criteria would be illegal, and that the car manufacturer be held responsible for any injury or loss of life.”

Questions raised: Who decides ethical guidelines? How / do we define which lives are more important than others?

Self-driving Mercedes-Benz F 015 Luxury in Motion concept car. Source: Mercedes-BenzThe Case of Autonomous Weapons

The Guardian journalist Bonnie Docherty covered the United Nations’ meeting to address the creation and use of autonomous weapons, also known as “killer robots.” What made it so newsworthy was that the group had already met previously four times to discuss the same issue.

“Legally, ‘killer robots’ would lack human judgment, meaning that it would be very challenging to ensure that their decisions complied with international humanitarian and human rights law,” she reported.

“For example, a robot could not be preprogrammed to assess the proportionality of using force in every situation,” Docherty reported, “And it would find it difficult to judge accurately whether civilian harm outweighed military advantage in each particular instance.”

Questions raised: How do law makers and algorithm coders plan for complex, nuanced circumstances? What levels of force should robots be allowed to use?

Legal and Ethical Hope for AI

Although a challenge to navigate, panelists at IAPP Global Privacy Summit are starting to lay the groundwork for tackling artificial intelligence laws and ethics.

IAPP Global Privacy Summit 2018 Source: IAPP

Panelist and senior vice president of public policy at SIIA, Mark MacCarthy stated that regulating artificial intelligence can be tough in certain instances, while other times very obvious. A simple way to regulate the application of artificial intelligence, MacCarthy adds, is to look to laws already established.

“Using machine learning isn’t a get out of jail free card,” he says. “You can’t say, for example, ‘I’m using AI, so I don’t need to live up to fair lending laws.’”

Additionally, for guidance companies should look to SIIA’s ethical principles for AI. MacCarthy highlighted four principals at the 2018 summit:

  1. Rights: Participate in artificial intelligence applications that respect the law and human rights.
  2. Justice: Steer clear of artificial intelligence applications that target vulnerable groups.
  3. Welfare: Strive to use artificial intelligence to improve the welfare of all humans, so that all communities can benefit.
  4. Virtue: Use artificial intelligence in virtuous ways to help human beings.

While it’s not a complete solution, it’s a start. More dialogue like the one at the Global Privacy Summit is needed to work through these major questions.

Don’t be afraid of what artificial intelligence can do for your business! Connect with Novatio Solutions now.

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June 27, 2018 0 Comments

What are The Algorithms that Power AI?

It is not uncommon to hear the word algorithm thrown around the topic artificial intelligence. But what are these algorithms and what they do? Read on to find out.

AI algorithm defined

Generally, algorithms are calculations or commands used in mathematics or computer software. These formulas instruct data to solve a set of problems.

With relation to artificial intelligence, algorithms are “a set of rules or instructions given to an AI, neural network, or other machines to help it learn on its own.”1

Whereas before it was a big deal if a computer could perform a task repeatedly, now computers are expected to perform, learn, and apply. The algorithms of the tech age, designed by software engineers, are created to facilitate this automated machine learning.

Basic algorithm types

While there are many algorithms currently used in AI, when it comes to machine learning, these three are the most used:

1. Regression

Variables are imputed to predict the value of a target. Regression codes are divided into linear and logistic regressions.

Example Uses: predicting retail sales, scoring insurance customers’ risk,

2. Decision Trees

These algorithms mimic the way humans make decisions. They basically draw a map of all possible paths along with results in each case.

Example Uses: automated customer communication, automated workflows

3. Clustering

What are The Algorithms that Power AI?

Data is grouped together with like data.

Example Uses: product recommendations, search engine results

Algorithms in the real world

Algorithms can be complex and sophisticated, but so is the pay off and results that these codes offer.

More than ever, with the proliferation of new technology like smartphones and tablets, people are finding that algorithms are helpful and impactful – they are used in the apps we depend on for driving directions, give us shopping recommendations, and search results. They simplify our choices, improve system efficiency, and even provide us inspiration.


Source: We Are Social

Want to know what our algorithms and digital workforce can accomplish for you? Contact Novatio Solutions to see what services of ours you can implement in your business.

1 Davis, Sarah, “28 Artificial Intelligence Terms You Need to Know,” AI DZone.


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January 17, 2018 0 Comments

There Are More Self-Driving Cars In Use Than You Think!

Look around. There may be self-driving cars speeding past you, right now. (At least in the testing phase.) Surprised? Here’s a rundown on which makers and tech firms are exploring self-driving cars.


BMW has plans to beat Uber in the ride transportation service with their self-driving car test in Munich. Their program is called ReachNow.


Nissan is partnering with Microsoft for their self-driving cars and are anticipating their release by 2020.


This auto dealer is focusing on self-driving cars, but with heavy direction toward making it low cost for buyers.


Volvo released 100 self-driving cars on public roads in Sweden this year. Their goal is to make them “death proof” by 2020.


Ford is tripling its self-driving car tests on public roads to 100 in total this year. Their aim is to have cars available for taxi services without steering wheels, brakes, and accelerators by 2021.

General Motors

GM has been testing across the nation in warm weather climates and climates with bad weather conditions such as snow.

Google, Fiat Chrysler, Honda

Google’s Waymo is launching their self-driving car in partnership with Fiat Chrysler and Honda.


Tesla is also joining the game to create assorted levels of automated driving and delivering alternative technology to self-driving cars.


Audi has established self-driving technology for autopilot, but only up to a certain speed. They will be the first company to provide this level of artificial intelligence in stop-and-go traffic.


While it has been lagging behind, Toyota has spent many of its research and development resources on catching up with others. It plans to have an a working self-driving car by 2020.


Originally, they announced a partnership to provide cars for Uber. However, an opportunity came their way with Bosch for self-driving technology. They may also possibly be in the market for self-driving freight trucks.


Baidu, China’s version of Google, is testing self-driving cars on their roads that require driver supervision, with a plan for shuttle service by 2018 and production by 2021. EasyMile’s self-driving busses have been released in Finland to shuttle a maximum of 12 riders each for a quick trip along the seaside.

Uber’s Self-Driving Intelligence

Uber has tested over 30,000 trips in Pittsburgh, Pennsylvania and Tempe, Arizona. They have even created their own artificial intelligence lab with the goal of perfecting the tech that self-driving cars depend on. In detail, Uber has taught a car to move the steering wheel, accelerate, maintain speed, and brake in all sorts of environments.

Retirement Communities

In one San Jose retirement village, vehicles have been implemented with sensors on each roof that cost $80,000 each! This small senior living community allows for live testing in controlled settings that will help programs scale for fleet operation and taxis.

The Machine And Driver Partnership

Many self-driving cars on the road right now require physical drivers to attend all vehicles as safety backups. While Uber was the United State’s first autonomous taxi, Lyft may have passed them in on-the-road advancements. Lyft has officially “promoted” their safety driver to the backseat of the vehicles, while Uber’s safety driver must still locate the driver’s seat.

Google’s Waymo can hang its hat on being the first to test self-driving cars on public roads. While not currently transporting riders inside the car, they have had self-driving cars on public roads in Arizona since mid-October. They claim riders will soon be invited to join the cars for trips along with Waymo safety drivers. Their partnership and technology offerings extend to Lyft, Fiat-Chrysler, and Avis.

The Expense Of Self-Driving AI Technology

Lidar is used today through most cars in production today for driver-assistance. It is the widest-used self-driving technology and comes with the biggest price tag. Its production and application are delayed for every interested industry because wide-scale implementation would be so expensive.

Tesla states they can offer full self-driving without the use of lidar through the use of cameras and radar. Uber has decided to create their own AI solutions that involve deep learning artificial intelligence and cameras. This alternative to lidar is not standard, though, and it will take time for industries to put resources into research and development.

Meanwhile, GM and Ford, to compete with the self-driving future, have just purchased their own lidar companies.

Self-Driving’s Effect On Current Companies

Besides GM, Chevy, Tesla, and other makers, these new enhancements will create an upheaval in related industries. From delivery, to auto insurance, auto parts retail, parking, and even trucking, these industries will all have to adjust they way they do things and the products they offer.

Just like with any major change in technology, there will be an extensive financial cost during the transition. In the end, new developments will unlock different job skills, create less vehicle accidents, lessen fuel usage, and create more opportunities for companies to sell products.




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January 17, 2018 0 Comments

AI, AR, and VR in Manufacturing: The Next Tech Revolution

Technological advances in manufacturing have greatly changed the field (and the world) since the industrial revolution. Techniques like the assembly line, manufacturing machinery, and industrial robotics have allowed for faster, safer, and more prolific manufacturing.

The application of artificial intelligence, augmented reality, and virtual reality are sure to be the next phase of technological advancements in manufacturing. In-the-moment analysis, intelligent maintenance, in-the-moment ordering, and other developments will change the way we manufacture and use products in the future.


Assembly Instructions

Many manufacturing companies use PDF instruction manuals, printed out by customers. They are difficult to follow and need constant updating. From phones to planes, these products require thousands of parts to be assembled.

Augmented reality and AI enable the display of video instructions, with detailed drawings for assembling. Customers can work while following instructions via VR glasses or using a mobile device.

Maintenance and Troubleshooting

When it comes to maintenance, the currently process might be as follows:

  • Customer uses a manual to try to identify the issue
  • Unable to fix, customer contacts manufacturer
  • Manufacturer creates incident report or support ticket
  • Back and forth communication to try and resolve issue
  • If needed, customer is sent return or maintenance instructions; ships product to manufacturer
  • Manufacturer fixes and ships back or sends replacement product

This process is time-consuming, costly, and prone to human error. With AI paired with VR a customer can view a 3D display which overlays on the product and helps pinpoint problems. A remote support technician could also view this feed and help walkthrough fixes with the user.



As with most industries, manufacturing centers rely on a team of on-site experts for solving technical problems. In rare occasions, skilled techs are brought from offsite if the issue cannot be resolved by local maintenance workers. By using live 3D AR models, complex issues can be resolved between experts and local support more quickly, cheaply, and efficiently. The use of such augmented reality can reduce the time of repairs by 4 times.

This can even impact the area of training. Experts in their field see first hand what remote workers are engaging. They can then give step by step instruction for hands-on learning. Thus providing real time teaching with superior learning

Quality Assurance

For QA, adding cameras connected to AI on assembly lines would allow automated processes to be signaled. If the computer identifies an irregularity, the item will be immediately removed from the queue. Simultaneously, if continuous faulty items are noticed, replacements can be ordered automatically.

This type of in-the-moment diagnostics prevents billions in returned products, replacements, and forfeited sales. Updating a new part to a product can be cut from weeks to hours. This is done simply by having intelligent sensors identifying when new parts are required, placing a request with the design department, then printing a three dimensional model for testing.

Safety Inspections

When it comes to safety inspections, specification images from the supplier can be layered onto live photos in the field. Any errors are quickly identified using VR. Some inspections like this have been shortened from weeks to a matter of days.

Conclusion: Evolve to Compete

Ultimately, the benefit of artificial intelligence applications utilizing AR and VR compound worker’s capabilities and production. Thus creating augmented workers. A key to competing in the future of manufacturing is by implementing these digital processes. This is especially true as the competition increases and companies develop quicker operations.

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December 1, 2017 0 Comments