Machine Learning – Quantilus Innovation http://email.quantilus.com AI Driven Innovation Mon, 17 Feb 2025 18:58:07 +0000 en-US hourly 1 https://wordpress.org/?v=6.2.6 http://email.quantilus.com/wp-content/uploads/2023/03/Quantilus-FavIcon-1.svg Machine Learning – Quantilus Innovation http://email.quantilus.com 32 32 The Role of Artificial Intelligence in Automating Business Processes http://email.quantilus.com/article/the-role-of-artificial-intelligence-in-automating-business-processes/ Mon, 08 May 2023 16:07:17 +0000 https://quantilus.com/?p=18612

The Role of Artificial Intelligence in Automating Business Processes

Prefer to listen instead? Here’s the podcast version of this article.

Artificial Intelligence (AI) has emerged as a game-changing technology transforming various aspects of business operations. AI is a technology that enables machines to learn from data, identify patterns, and make decisions without human intervention. It is increasingly used to automate business processes, leading to increased efficiency, reduced costs, and improved customer experience. In this article, we will explore the role of AI in automating business processes and how it is revolutionizing how organizations function.

Business Process Automation (BPA) refers to using technology to automate repetitive and manual tasks within an organization. BPA aims to streamline business processes, reduce errors, and increase efficiency by automating routine tasks traditionally performed by humans. With the advent of AI, BPA has become more advanced and intelligent, enabling organizations to automate even complex tasks that were previously difficult to automate.

 

Role of AI in Business Process Automation

AI transforms BPA by enabling machines to learn from data and make intelligent decisions without human intervention. Here are some of the ways AI is leveraged to automate business processes:

  • Predictive Analytics: AI-powered predictive analytics can analyze vast amounts of data to predict future outcomes. Predictive analytics can automate decision-making processes like predicting customer behavior or identifying fraud. Predictive analytics is used in various industries, including finance, healthcare, and retail, to automate decision-making processes. For example, in the finance industry, predictive analytics is being used to identify fraudulent transactions in real-time. In healthcare, predictive analytics is used to identify patients at risk of developing chronic diseases. In retail, predictive analytics forecasts demand and optimize inventory levels.
  • Natural Language Processing: Natural Language Processing (NLP) enables machines to understand and interpret human language. NLP is used in customer service to automate tasks such as responding to customer queries and complaints. NLP can automate tasks such as customer service, where chatbots can understand and respond to customer queries without human intervention. For example, Mastercard is using NLP to automate its customer service chatbot. The chatbot can understand and interpret customer queries and provide relevant real-time responses. Utilizing an AI-powered chatbot has led to significant cost savings for Mastercard and improved customer satisfaction.
  • Image and Video Recognition: AI-powered image and video recognition can automate tasks such as quality control, where machines can analyze pictures and videos to identify defects or anomalies. In manufacturing, transportation, and healthcare industries, photo and video recognition is used to automate quality control, inspection, and diagnosis tasks. For example, General Electric uses image recognition to automate its quality control processes. The system can analyze images of components to identify defects and anomalies, leading to improved quality and reduced costs.
  • Machine Learning: Machine learning enables machines to learn from data and make intelligent decisions. Machine learning automates tasks such as data entry and extraction, where devices can learn to identify and classify information from documents. It is used in various industries to automate data entry, fraud detection, and recommendation systems. For example, Netflix uses machine learning to provide personalized recommendations to its users. The system analyzes user data to identify patterns and preferences and recommends content based on those patterns.
  • Robotics Process Automation: Robotics Process Automation (RPA) involves using software robots to automate tasks such as data entry, invoice processing, and customer service. Robotics Process Automation (RPA) is used in finance, healthcare, and manufacturing industries to automate routine tasks such as data entry, claims processing, and inventory management. RPA can be enhanced with AI to enable machines to make intelligent decisions. For example, Blue Prism uses RPA to automate its claims processing system. The system can extract data from various sources, validate it, and process claims without human intervention. Utilizing RPA has led to significant cost savings and improved efficiency for Blue Prism.

 

Benefits 

AI-powered BPA offers numerous benefits to organizations, including:

  • Increased Efficiency: AI-powered BPA can automate routine tasks, increasing efficiency and productivity. This can free up human resources to focus on more complex tasks that require human intervention. For example, in the manufacturing industry, AI-powered systems automate quality control processes. These systems can identify defects and anomalies in real-time, improving efficiency and productivity.
  • Reduced Costs: By automating tasks previously performed by humans, organizations can reduce labor costs and increase profitability. For example, JPMorgan Chase implemented an AI-powered system that can process legal contracts in seconds, reducing the time to process a contract from hours to seconds.
  • Improved Accuracy: Machines are less prone to errors than humans, improving accuracy and fewer errors in business processes. For example, in the healthcare industry, AI-powered systems are being used to analyze medical images and identify diseases such as cancer at an early stage, improving the quality of diagnosis, treatment, and outcomes.
  • Enhanced Customer Experience: AI-powered BPA can improve the customer experience by enabling organizations to respond to customer queries more quickly and accurately. For example, in the retail industry, AI-powered chatbots can provide personalized recommendations based on their browsing and purchase history, increasing customer satisfaction and loyalty.
  • Competitive Advantage: Organizations that adopt AI-powered BPA can gain a competitive advantage by improving efficiency, reducing costs, and enhancing the customer experience. For example, in the finance industry, AI-powered systems can analyze market data and provide insights into investment opportunities which helps organizations make better investment decisions.

 

Challenges 

While AI-powered BPA offers numerous benefits, there are also some challenges that organizations must overcome, including:

  • Data Quality: One of the critical challenges in implementing AI-powered business process automation is ensuring that the data used to train AI models is of high quality. Poor data quality can lead to inaccurate predictions and decisions. For example, an AI-powered system introduced on partial data in the healthcare industry could lead to incorrect diagnoses and treatment recommendations.
  • Integration with Legacy Systems: Integrating AI-powered systems with existing systems can be challenging. Legacy systems may not be compatible with new AI technologies, leading to integration challenges. For example, in the manufacturing industry, integrating AI-powered quality control systems with existing production systems can be a challenge.
  • Security: AI-powered systems that use sensitive data must be secure and comply with privacy regulations. Organizations must ensure that AI-powered systems are secure and comply with relevant laws. For example, an AI-powered system that analyzes patient data in the healthcare industry must comply with HIPAA regulations.
  • Ethical Concerns: AI-powered business process automation raises ethical considerations that must be considered. Organizations must ensure that AI systems are developed and implemented ethically. For example, in the finance industry, an AI-powered system that automatically approves loans based on specific criteria may result in discrimination against certain groups.
  • Talent Gap: Developing and implementing AI-powered systems requires specialized skills and expertise. Organizations must have access to talent with data science, machine learning, and natural language processing skills. There is a shortage of skilled talent in these areas, making it challenging for organizations to implement AI-powered business process automation.

 

Artificial Intelligence has become a game-changing technology that is transforming various aspects of business operations. The role of AI in automating business processes has become more advanced and intelligent, enabling organizations to automate even complex tasks that were previously difficult to automate. The benefits of AI-powered BPA include increased efficiency, reduced costs, improved accuracy, enhanced customer experience, and competitive advantage. However, organizations must overcome data quality, privacy, and ethical concerns. With the right approach, AI-powered BPA can revolutionize how organizations operate and deliver value to their customers.

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]]> The Best of Emerging Tech from CES 2021 http://email.quantilus.com/article/the-best-of-emerging-tech-from-ces-2021/ Fri, 22 Jan 2021 21:10:01 +0000 http://3.87.238.128/?p=5530

The Best of Emerging Tech from CES 2021

Prefer to listen instead? Here’s the podcast version of this article.

Last week we showcased some of the best product innovations generating buzz out of the virtual Consumer Electronics Show (CES) this year. While products usually make the highlights of the show, the spotlight shines brightest on the technology that powers them.


Most illuminating is how emerging technology evolves in the context of unchartered, innovative applications. Emerging technology trends such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT), and blockchain is behind some of the game-changing tech that could impact life and work in the near future.


Voice Technologies

Voice technologies are one set of AI and ML that is constantly providing more ways for people to communicate with each other. For example, mobile translation apps or Google Translate are able to help users navigate a different language in almost any situation. However, usually, these apps can’t always handle translating a full conversation correctly.


TimeKettle announced M2 Translator Earbuds at CES 2021. The software utilized for these earbuds differs from the normal translation app because each person with one earbud can select their desired language and engage in a full conversation.


The software also has Voice Activity Detection (VAD), so voice pick-up and translation are completely automatic. TimeKettle also utilizes Bluetooth 5.0, updates the earbuds for more languages or new features on its own, and translations can occur in areas without an internet connection.


In terms of how much space the voice technology can hold, the earbuds provide access to 40 languages, over 90 accents, and 12 servers. By TimeKettle’s estimations, they give translation access to 95% of the world population.


In addition, the earbuds have adaptive noise cancellation and use an algorithm to improve the quality of anyone’s voice input. Lastly, the product can also act as headphones for phone calls and music. The M2 Translator Earbuds are currently available.


The voice technology TimeKettle announced is another example of a technology that can help remove language barriers for businesses. And consumers shopping in different countries could even become more active shoppers when being able to start a conversation with a business owner who speaks a different language.


AI-Powered Diagnostics

MediWhale announced a diagnostic device powered by AI and it can predict eye diseases and cardiovascular diseases in just a few minutes.

The health monitoring device is powered by AI that uses an ophthalmic camera in addition to medical records. This device analyzes images and makes decisions and diagnoses by comparing images with an integrated database of systemic diseases.


One benefit of the “DrNoon” device is that it’s a cheaper and faster test to run than the average than a heart CT. Also, there is no common risk of radiation exposure that a CT might have.


MediWhale wants to provide a new biomarker for heart disease: “Our goal is to analyze the fundus image through AI and to present the fastest way to detect the disease through it.”


MediWhale claims “DrNoon” can achieve a 95 percent accuracy in identifying eye diseases and 84 percent accuracy for cardiovascular diseases.


The quick diagnostic technology MediWhale is working on can ultimately lead to hospitals or urgent care centers have more resources to use when dealing with an influx of patients.


Machine Learning Cybersecurity

ArcRAN announced at CES 2021 a machine learning algorithm that can help enterprises and governments to quickly respond to potential cyber threats and attacks. ArcRAN has pushed in the past the Smart City concept, which is the idea of utilizing an urban area with new electronic methods to provide services to citizens or collect data.


The newest solution from ArcRAN is committed to protecting software and hardware applications that are used in any smart facility or building and public areas. ArcRAN integrates its solution with IoT security detection APIs for tracking and scans local networks for unauthorized devices and behavior.


Their IoT Honeypot gains information and motives or tactics of attackers. Also, it can lure attackers into interacting with fake systems. 5G security is also utilized by the company both locally and privately.


Technologies relying more and more on 5G security could mean more cities adopting 5G to help assist the building of a true Smart City concept.


Blockchain Traceability

Numbers Protocol was one of 100 start-ups to be featured at CES 2021 from Taiwan. The company leverages blockchain to create The Numbers Capture app that can deal with debunking AI deep fakes and misinformation.

The app records birth information of a piece of media content and can generate a unique signature for that piece of content. Blockchain is then used to record the birth information, as well as any changes that happen to that piece of content in the future.


The Numbers Capture app also has a “PostCapture” function to create digital assets ensured by blockchain. So, for example, this function allows a way for an original image or asset to have reinstated value and security. The asset can still be shared and transferred to a new owner, but blockchain creates a level of security to prevent an asset from being tampered with.

This blockchain security technology can create a safer outlet for media outlets or even governments to release information and keep that information from being tampered with.


One-Click-Through AI Framework

IoT companies are always looking for smart implementations that can lead to faster optimization and operation of new technology.


Avalanche Computing announced at CES an AI framework that is meant to increase production speed and provide a smart implementation that requires one-click-through.


This means that Avalanche Computing’s AI framework can retrain another company’s AI model without having to change any algorithms. The framework would reduce AI project costs and deployment to other devices could occur in just one command.


From smart manufacturing to medical AI, to construction, Avalanche Computing hopes to not only increase manufacturing speed, but also add predictive maintenance, defect detection, and supply chain analysis.

New AI frameworks that are more interchangeable can lead businesses across industries to increase productivity, especially IoT companies, and save time on these types of projects in the future.

Emerging tech trends continue to play their role in revolutionizing business and enterprise in any industry and for any size of business. CES 2021 seemed to be no different from CES 2020 in continuing the ongoing trend in new technology that can assist with security, speed, and production.

Want to dive deeper into the best of emerging tech in 2021? Contact us at info@quantilus.com for a consultation and learn more about what Quantilus has to offer here.

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]]> Gartner’s Top Nine Tech Trends of 2021 http://email.quantilus.com/article/gartners-top-nine-tech-trends-of-2021/ Fri, 18 Dec 2020 21:59:44 +0000 http://3.87.238.128/?p=5261

Gartner’s Top Nine Tech Trends of 2021

Prefer to listen instead? Here’s the podcast version of this article.

A new year means new technology and advancements. With a seemingly endless amount of technology and innovations being created daily, it can be challenging for businesses to pinpoint which technologies are worth the investment. In 2020, the impact of the COVID-19 pandemic resulted in immense disruption for organizations, influencing technology adoption and requiring agile strategies. Global research and advisory firm, Gartner, recently released their predictions for the upcoming year’s top tech trends that CIOs and executives need to consider. Many of these trends highlight how organizations can maintain continuity amid changing or unforeseen conditions moving forward. Gartner’s tech trends for 2021 are as follows… 

 

  1. Internet of Behaviors (IoB)
  2. Total Experience
  3. Privacy-Enhancing computation
  4. Distributed cloud
  5. Anywhere Operations
  6. Cybersecurity mesh
  7. Intelligent composable business
  8. AI engineering
  9. Hyperautomation

 

Continue reading to explore each trend and find out their benefits. 

 

1) Internet of Behaviors:

The Internet of Behaviors, also referred to as IoB, utilizes collected user data to influence behaviors. This data is mined from various sources, including actions on a company’s website, social media profiles, computer vision, sensors or RFID tags, and more.  

 

An example of this is setting up computer vision on work devices. Computer vision can analyze employees’ faces and detect if they are wearing a face mask. If they are not wearing a face mask, it can remind the employee and alert this information to other stakeholders in the company.

 

Accordingly, there are ethical implications with the IoB as it collects data on users and attempts to change their behaviors. The data collected can also have varying levels of intrusiveness due to the technology being able to capture a wide variety of personal information. Laws on the IoB vary widely, which will play a critical role in adopting and advancing this tech trend.

      

2) Total Experience:

Total experience links the customer, user, and employee experiences together using multi-experience. Multi-experience utilizes various technologies to engage and interact with consumers. Therefore, total experience focuses on leveraging different technologies to create a more seamless process for customers, users, and employees. 

 

Gartner’s tech trends report provided an example of a telecommunications company improving its total experience by interlinking both customer and employee in-store experiences to enforce social distancing guidelines. Specifically, the telecommunications company leveraged its consumer-facing app for scheduling appointments, geo-tracking, and push notifications to manage the customer’s in-store experience in a safe, orderly way. Simultaneously, employees in the store leveraged digital kiosks to service the customer as well as personal tablets to co-browse customers’ devices, thus avoiding the need to touch consumers’ devices.

 

So instead of perfecting the customer, user, and employee experiences independently, they should be thought of as one experience and flow harmoniously into each other. Meanwhile, integrating various technology solutions into these experiences will help automate tasks, improve efficiencies, guide customers and staff, maintain social distancing guidelines, engage customers, and more. 

      

3) Privacy-enhancing computation

Privacy-enhancing computation aims to protect personal information in the following ways:

  • Provide a safe environment for sensitive data to be processed and analyzed. 
  • Perform processing and analytics in a decentralized manner. 
  • Transform data and algorithms before processing or analytics.

 

Privacy enhancing computation is becoming increasingly important because as teams become more geographically widespread and more people require access to sensitive information, the demand for digital and online protection also grows. Privacy enhancing computation also allows for better collaboration while not compromising security. 

 

4) Distributed cloud

The purpose of distributed cloud is to have cloud providers implement their services near their customers. Perks of having cloud services physically closer to customers include reducing low-latency, decreasing data costs, and complying with laws that require data to remain in a specified geographical location. Additionally, businesses are not accountable for managing their cloud when using distributed cloud, which can be an expensive and elaborate responsibility.

 

5) Anywhere Operations

Anywhere operations is an IT model that allows businesses to operate…from anywhere. This means employees can work remotely and companies can operate from nearly anywhere in the world, and still complete their tasks. However, this does not mean that employees and businesses must always be remote. 

 

Anywhere operations require businesses to become digital-centric. This means digital processes and operations should be the default for organizations. Therefore, business is less likely to be interrupted due to things such as social distancing guidelines and inclement weather. Lastly, physical stores still have a place in business, but the stores should adopt digital processes like contactless payment. Anywhere operations minimizes downturns in business due to foreseen and unforeseen circumstances (such as COVID-19).

 

6) Cybersecurity mesh

An increase in remote workers equates to more opportunities for online security to be compromised. Cybersecurity mesh allows users to safely access digital information, files, and assets securely from anywhere geographically.  

 

To create a cybersecurity mesh, companies need to take security measures outside of the ‘normal’, physical location where information was originally intended to be accessed…which in most cases is the company office(s). To do so, businesses should implement scalable cloud models that set user identity as the security parameter. This way, employees can securely access company information and assets anywhere around the globe without worrying about data leaks. 

 

7) Intelligent Composable Business

Successful businesses can quickly adapt their processes, operations, and model based on data. Therefore, intelligent composable business means as technology continues to transform, it is vital that companies:

 

  • Have greater access to all pertinent information.
  • Gather more insightful findings from the information.
  • Adapt their model based on their findings.

 

Furthermore, autonomy will be essential in helping companies quickly adapt so outdated processes do not delay businesses. Find out more information on automation by reading trend nine below. 

 

8) AI Engineering

Gartner’s findings reveal that only 53% of artificial intelligence projects make it from prototype to production. Therefore, many AI projects appear to lack resources, scalability, and internal company support. 

 

That’s where AI engineering comes into the equation. The foundation of AI engineering is made of three pillars – DataOps, ModelOps, and DevOps. Thus, AI engineering is essentially lifecycle management for artificial intelligence projects. More planning, strategy, team-members, financial backing, etc., increases the likelihood of AI projects making it to production. Therefore, giving the AI projects a chance to make a profit. Also, it’s important to note that once an AI project does come to production, it is easier for the product to evolve as new technology and findings are revealed. 

 

9) Hyperautomation

As Gartner’s top tech trend of 2020, hyperautomation landed another spot on this year’s list. Hyperautomation is automating anything in a business that can be automated. If processes are left un-automated, companies are likely operating less efficiently than they could be and are therefore missing out on profit. Working at less than optimal efficiency also makes organizations more susceptible to falling behind competitors. 

 

Conclusion

Businesses in all industries can implement these tech trends, not just companies in the technology sector. Furthermore, companies can cherry-pick which trends they wish to implement in their business. However, it is essential to note that technology tends to be more efficient and successful when combined with other technologies. 

 

Which tech trends do you think will be the most vital during 2021? Let us know in the comments below. Interested in learning more about how technology can help your business? Contact us at info@quantilus.com for a consultation to learn how we can help.

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]]> Machine Learning: The Brain Behind Self-Driving Cars http://email.quantilus.com/article/self-driving-cars/ Mon, 26 Oct 2020 13:55:24 +0000 http://3.87.238.128/?p=5281

Machine Learning: The Brain Behind Self-Driving Cars

Machine Learning: The Brain Behind Self-Driving Cars

Prefer to listen instead? Here’s the podcast version of this article.

Previously, we have explored machine learning and its impact on small businesses. However, machine learning is integrated in many aspects of life. Let’s switch gears and explore the applications of machine learning in the context of our lives as consumers. The automotive industry commonly uses artificial intelligence to optimize the design and manufacturing of vehicles released to the market. One area where machine learning plays a critical role is in the advent of self-driving cars. 

A handful of tech and car companies are currently racing to have their self-driving cars on the road first. The self-driving cars being developed rely on advanced technologies, including machine learning, to power these cutting-edge vehicles. So, before the day comes that you look in the rearview mirror and notice you are surrounded by self-driving cars, let’s go over the technology that propels these innovative cars.

 

Machine Learning to Drive Decisions

Machine learning is at the center of self-driving cars. For a self-driving car to drive on the road, it utilizes specially designed hardware and software. The hardware continuously collects data from the surrounding environment, while the software takes the collected data and sorts it. The software then relinquishes the data to machine learning algorithms to process the data and make decisions.

With machine learning algorithms, the more data the algorithm is exposed to, the more intelligent the algorithm becomes. This is because the algorithms take what they learn from prior experiences, draw conclusions based on the data, and then perform the action that will result in the best possible outcome.

 

What Data Does Machine Learning Look At?

Machine learning algorithms in self-driving cars are fed data and process it in real-time. The machine learning algorithms analyze data, including everything from recognizing upcoming stop signs to identifying deer on the side of the road and cars braking ahead. The vehicle then interprets this data and decides the best course of action. 

Also, it is important to note that machine learning is already utilized by certain cars on the market. For example, lane detection uses machine learning to monitor the lanes on the road. When machine learning detects the car is drifting out of its lane, it will either notify the driver or actually steer the car so it remains in the correct lane. 

 

Breaking It Down Further

We can further break down the use of machine learning in self-driving cars into smaller segments. These segments are object detection, object recognition, and movement prediction. When machine learning algorithms in self-driving cars analyze data, the first step is to recognize an object. Once an object is detected, the algorithms then need to determine what exactly the object is. Lastly, the algorithm needs to determine if the object will potentially get in the car’s path.

For example, if the car recognizes an object and then determines it is a gas station on the side of the road, the car knows it can continue driving since the store will not move into its path. However, if the vehicle recognizes an object and then determines it is a kid riding a scooter, the car decides that the kid may ride out in front of the vehicle, and the car will slow down.

 

Machine Learning for Prescriptive Alerts

Machine learning in cars is also used for the next level of predictive maintenance. Currently, most drivers experience notifications when their vehicle requires maintenance, such as the check engine light illuminating on the dashboard. However, machine learning takes it a step further by discovering budding issues before they happen. Additionally, machine learning can also provide prescriptive alerts. The purpose of prescriptive alerts is “to produce outcome-focused recommendations for operations and maintenance.” 

Essentially, prescriptive alerts determine when something is going to be an issue and then provides the solution, all before the issue actually arises. Examples of prescriptive alerts include notifying drivers when to change tires, replace brakes, and fix cooling systems. Prescriptive alerts and maintenance are extremely beneficial to the shipping industry and truck drivers. This is because drivers will not have to stop mid-trip to fix these issues which would delay the shipment, but instead can have their truck maintained ahead of time to avoid delays. Prescriptive alerts and maintenance are still new but are thought to be the next level of predictive maintenance.

 

The Dilemma with Self-Driving Cars

One of the most significant issues with self-driving cars is making sure they collect enough real-world experience and data on public roads. It is a catch-22 because on the one hand, self-driving cars need to be on public roads to collect data and be exposed to real-world situations to improve accuracy and safety. However, allowing self-driving cars on public roads also puts the public’s safety at risk by exposing them to the emerging technology.

 

Conclusion

Self-driving cars are not expected to be widely available and legal to the public for years to come. The leading companies currently building and testing these vehicles have predominantly already passed their initial predictions of when the cars will be ready. Therefore, they are still perfecting the technology behind the wheel (if there even is a wheel). However, without machine learning, self-driving cars probably would not have made it this far in their development. 

Machine learning is vital to the success and advancement of self-driving cars and is central to the vehicles’ operation. As advancements in machine learning are made, not only will self-driving cars improve, but so will countless other aspects of the publics’ everyday lives. From eCommerce to marketing and healthcare, machine learning will be innovating life as we know it. 

Interested in developing more about machine learning and how it can apply to your business? Contact us at info@quantilus.com for a consultation and learn more about what Quantilus has to offer here.

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]]> 3 Ways to Implement Machine Learning and AI into Small Businesses http://email.quantilus.com/article/3-ways-to-implement-machine-learning-and-ai-into-small-businesses/ Wed, 26 Aug 2020 21:49:51 +0000 http://3.87.238.128/?p=5583

3 Ways to Implement Machine Learning and AI into Small Businesses

Prefer to listen instead? Here’s the podcast version of this article.

Earlier this month, John Giannandrea, Apple’s head of artificial intelligence (AI), gave insight into how Apple is leveraging machine learning (ML) within their iOS and the future of machine learning at Apple. Anything from language translation to only sorting photos on your phone into pre-made galleries is made possible with machine learning. These various types of machine learning applications are becoming more and more common and essential.

 

However, a common misperception of AI and machine learning is that these advanced and sophisticated technologies are only for big brands with budgets that allow for experimentation and implementation.

A small business owner reading a report like Gartner’s 2019 survey of CIO’s would find that although 37% of organizations have installed some form of AI or machine learning, most of the CIO’s interviewed were from large brands. This type of survey may further intimidate small and medium-sized business owners into thinking the growing age of AI and machine learning isn’t ready for their company yet. But, you’d be surprised by how easily small companies can adopt cutting-edge technology without having to rely on an extensive budget.

 

Marketing

For small businesses, ML software as a service can be a great tool to utilize, especially in consumer and B2B marketing spaces. In fact, 40% of marketers prioritize AI and machine learning more than any other department and consider them critical to their success. 

 

One familiar and accessible ML tool marketers at small businesses can leverage is a chatbot tool for their website. Chatbots now utilize natural-language processing (NLP) that can interact conversationally with website visitors and collect information like preferences visitors have as they browse a website.

 

Chatbot tools like Botsify also allow for integration with several services and offer an easy interface to help customize your company’s brand into their templates.

 

In terms of ML digital marketing tools, a chatbot is just one of the many corners that can be explored. Also, consider implementing machine learning marketing applications for email marketing, ad targeting, voice search, or predictive analysis to help create your campaigns reach new, multiple touchpoints.

 

Security

Like marketing, cybersecurity is witnessing a fast-growing trend with investments into machine learning tools to help protect their own company and customers. ABI Research estimates ML, AI, and big data spending will increase to $96 billion by 2021.

 

Machine learning technology can track users’ patterns and make assessments of these patterns, such as the iPhone creating galleries from related pictures, as previously mentioned. This technology can be applied for security responsibilities by implementing security algorithms into something like your mobile application.

 

If your business relies on the consumer making financial transactions, it’s the company’s responsibility to keep any entered information secure.

Biocatch, for example, utilizes behavioral biometrics, a machine learning technology that tracks user behavior within an application as another form of security. Behavioral biometrics can identify when a different person uses an application by the way they move around the app or the way they type.  

Sometimes even just adding a single line of code can improve your mobile application’s security and add another form of authentication working in the background.

 

Accounting

Another aspect of any business that requires managing a ton of data, repetitiveness, and predictably is the accounting department, which means machine learning for accounting tasks is a relationship that makes too much sense to not explore.

 

The future of accounting is heavily entangled with AI and ML, and back in 2018, a report suggested tasks like taxes and payroll would eventually become fully automated. In 2020 machine learning is now applied to generating and processing invoices, even including specific requirements with each task.

 

Xero, a New Zealand-based accounting software company, provides a cloud accounting software that creates and processes invoices for small businesses. The machine learning aspect in the software enables the creation of invoices based on past behaviors for customers.

 

Whether you leverage machine learning through marketing, security, or accounting, the decision ultimately depends on where you possibly see the value and ROI of any machine learning tool. For a small-to-medium-sized business, where that value is found can vary.

 

More and more companies are tapping into or at least exploring how machine learning can impact their business as the machine learning market is expected to grow at 42% CAGR by 2024. While adoption of AI and ML may be daunting, machine learning models are proving to show increasing value to any business size.

 

Interested in implementing ML or AI in your business process? Contact us at info@quantilus.com for a consultation and learn more about what Quantilus has to offer here.

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]]> AI Explained: Understanding the Basics of Artificial Intelligence http://email.quantilus.com/article/ai-explained-understanding-the-basics-of-artificial-intelligence/ Sat, 01 Aug 2020 21:04:18 +0000 https://quantilus.com/?p=9260

AI Explained: Understanding the Basics of Artificial Intelligence

Prefer to listen instead? Here’s the podcast version of this article.

Many people have seen The Terminator and know what happens if you’re busy playing video games instead of preparing for SkyNet. That world’s artificial intelligence isn’t too far from what’s available today—but how, exactly, can AI be explained for everyone to understand?

 

Let’s start with its history. In 1956, Marvin Minsky and John McCarthy, who coined the term AI, described it as a task performed by a machine or program that—were a human to do the same task—would require at least some intelligence to complete. The definition has evolved somewhat in the past 68 years, but generally all AI systems include the following behaviors which we associate with human intelligence:

 

  • Planning
  • Learning
  • Reasoning
  • Knowledge representation
  • Perception
  • Problem solving
  • Manipulation
  • Social intelligence
  • Creativity
 

Two types of artificial intelligence

There are two types of AI: narrow AI and general AI. It is the narrow AI that permeates our world today, in all fields from medical to mechanic, financial to engineering, and everything in between. General AI is still a ‘pipe dream’ that computer engineers are working to creating. Most experts say we’re still a decade or two away from achieving true general AI because of its complexity.

 

Narrow Artificial Intelligence

Most computers use narrow AI. They’re intelligent systems that know how to conduct specific tasks without having been explicitly programmed to do so. Apple’s Siri is a perfect example. So is Amazon’s Alexis, Google’s new virtual assistant, and IBM’s Watson supercomputer.

 

These systems simulate a human being’s knowledge and cognitive ability within specific parameters. The systems can include self-driving cars and spam filters. Why? Because the systems use pattern recognition, natural language processing, machine learning, and data recognition to make decisions.

 

Narrow AI, in addition to telling you a joke or the weather, has a host of applications. These systems can identify inappropriate content online or in emails, respond to customer service requests, read video feeds from drones, organize and coordinate business/personal calendars, analyze data to make predictions, and more.

 

General Artificial Intelligence

This AI—also referred to a human-level, strong, and superintelligence AI—can understand and reason within its environment, just like a human. Think Data from Star Trek: The Next Generation, or Hal from 2001 A Space Odyssey.

 

It’s “strong” because this AI will be stronger than us humans and “general” because we’ll be able to apply it to all problems. However, it’s nearly impossible to create a computer that can think abstractly, innovate, or plan. Experts agree that it’s really difficult—at this point still impossible—to teach a computer how to invent something that doesn’t exist.

 

AI is gaining strength—it can produce more accurate predictions about the data it’s fed. That DeepMind algorithms can win more games and transfer learning from one game to another is another indication that AI is growing stronger.

 

Whole Brain Architecture Approach

Dr. Hiroshi Yamakawa, Director of Dwango AI Laboratory, is one of the world’s foremost authorities on AI. He says that currently, AI can solve particular issues or address specific problems. His organization is using the Whole Brain Architecture Approach which is an engineering-based approach to “create a human-like artificial general intelligence (AGI) by learning from the architecture of the entire brain.” This AGI uses the human brain’s hard wiring as a model to integrate machine learning modules and artificial neural networks. He theorizes that the WBAI will be achieved by 2030 and will help to find solutions for global problems that include environmental, food, and space issues.

 

Still a journey to achieve artificial general intelligence

Computer scientists continue to work to develop an actual AI that can think like a human. We’ve seen the “results” of such successes in Terminator, I, Robot, A.I. Artificial Intelligence, Ex Machina, Blade Runner, and many other sci-fi books movies and books.

 

But the reality is that even the world’s best machine learning engineers, with access to millions of dollars, are struggling to build a general AI product. Nearly $15.2B of capital venture was given to AI startups in 2017 and over 45,000 research papers on AI have been published since 2012. Read this article to learn more about what’s propelling the recent surge in general AI development.

What’s next?

The closest thing we’ve got today to general AI is machine learning (ML). This term describes feeding vast amounts of data into a computer system which then extrapolates it to carry out a specific task—like Facebook’s algorithms that can recognize faces from your contacts list or Waze and Google Maps, that can analyze traffic speeds and plot alternate routes. And there are many other examples of machine learning, a growing field designed to create machines that are faster and more accurate.

How does machine learning work?

In a nutshell, this subset of AI uses statistical techniques to enable a computer to learn without explicit programming. According to Dr. Yoshua Bengio, from the University of Montreal, machine learning uses data, observations, and world interactions to provide computers with acquired knowledge which then facilitates the computers’ ability to accurately generalize to new settings.

ML groups a variety of algorithms by learning style or similarity in form or function. These algorithms include representation, evaluation, and optimization—their goal is to provide computers with the “skill” to interpret never-before-seen data and apply it to new situations.

The field of ML and data science continues to grow, but while these mathematical concepts can be implemented into real world applications, this so-called deep learning isn’t real intelligence… yet. It’s a type of mathematical optimization that does have limits. The “thinking” is limited to specific domains and the intelligence depends on the training dataset (so humans are still in control). It’s difficult to use it within constantly-evolving, dynamic environments and can’t be used for control problems—only classification and regression. And to ensure the greatest accuracy, it requires huge datasets.

Will we ever achieve true artificial intelligence?

It’s hard to say. Sixty-two years after its inception, we’re still working to achieve true AI. Weak AI systems make more and more decisions as scientists and engineers develop ways to gather, quantify, and feed more data into more algorithms.

And we must, caution Phil Torres, an Affiliate Scholar at the Institute for Ethics and Emerging Technologies, consider the human element—as AI develops, it’s incumbent upon those in the field to program human values into algorithms. After all, he says, “If we suddenly decided, as a society, that we had to solve the problem of morality—determine what’s right and what’s wrong, and feed it into a machine—in the next 20 years… would we even be able to do it?”

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]]> Why is Machine Learning Important and How Will It Impact Business http://email.quantilus.com/article/why-is-machine-learning-important-and-how-will-it-impact-business/ Sat, 01 Aug 2020 18:46:38 +0000 https://quantilus.com/?p=8776

Why is Machine Learning Important and How Will It Impact Business

Prefer to listen instead? Here’s the podcast version of this article.

It’s a bit difficult to narrow down one specific definition of machine learning (ML), because you’ll get a different explanation depending on whom you ask.

 

Nvidia defines it as “the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.” McKinsey&Company agree with Nvidia, saying that ML is “based on algorithms that can learn from data without relying on rules-based programming.” Stanford suggests that ML is “the science of getting computers to act without being explicitly programmed.”

 

And Carnegie Mellon’s definition—also a favorite of many other experts in the field—states “the field of Machine Learning seeks to answer the question ‘How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?’”

 

Regardless of the definition you choose, at its most basic level, the goal of machine learning is to adapt to new data independently and make decisions and recommendations based on thousands of calculations and analyses. It’s done by infusing artificial intelligence machines or systems with the ability to learn from the data they’re fed. The systems learn, identify patterns, and make decisions with minimal intervention from humans. Ideally, machines increase accuracy and efficiency and remove (or greatly reduce) the possibility of human error.

The Importance of Machine Learning

The nearly limitless quantity of available data, affordable data storage, and growth of less expensive and more powerful processing has propelled the growth of ML. Now many industries are developing more robust models capable of analyzing bigger and more complex data while delivering faster, more accurate results on vast scales. ML tools enable organizations to more quickly identify profitable opportunities and potential risks.


The practical applications of machine learning drive business results which can dramatically affect a company’s bottom line. New techniques in the field are evolving rapidly and expanded the application of ML to nearly limitless possibilities. Industries that depend on vast quantities of data—and need a system to analyze it efficiently and accurately, have embraced ML as the best way to build models, strategize, and plan.


Industries that Use Machine Learning

Healthcare. The proliferation of wearable sensors and devices that monitor everything from pulse rates and steps walked to oxygen and sugar levels and even sleeping patterns has generated a significant volume of data that enable doctors to assess their patients’ health in real time. One new ML algorithm detects cancerous tumors on mammograms; another identifies skin cancer; a third can analyze retinal images to diagnose diabetic retinopathy.


Government. Systems that use machine learning enable government officials to use data to predict potential future scenarios and adapt to rapidly changing situations. ML can help to improve cybersecurity and cyber intelligence, support counterterrorism efforts, optimize operational preparedness, logistics management, and predictive maintenance, and reduce failure rates. This recent article highlights 10 more applications for machine learning within the healthcare industry.


Marketing and sales. Machine learning is even revolutionizing the marketing sector as many companies have successfully implemented artificial intelligence (AI) and ML to increase and enhance customer satisfaction by over 10%. In fact, according to Forbes, “57% of enterprise executives believe that the most important growth benefit of AI and ML will be improving customer experiences and support.


E-commerce and social media sites use ML to analyze your buying and search history—and make recommendations on other items to purchase, based on your past habits. Many experts theorize that the future of retail will be driven by AI and ML as systems become even more adept at capturing, analyzing, and using data to personalize individuals’ shopping experiences and develop customized, targeted marketing campaigns.


Transportation. Efficiency and accuracy are key to profitability within this sector; so is the ability to predict and mitigate potential problems. ML’s data analysis and modeling functions dovetail perfectly with businesses within the delivery, public transportation, and freight transport sectors. ML uses algorithms to find factors that positively and negatively impact a supply chain’s success, making machine learning a critical component within supply chain management.


Within logistics, ML facilitates the ability of schedulers to optimize carrier selection, rating, routing, and QC processes, which saves money and improves efficiency. Its ability to analyze thousands of data points simultaneously and apply algorithms more quickly than any human enables ML to solve problems that people haven’t yet identified.


Financial services. The insights provided by ML in this industry allow investors to identify new opportunities or know when to trade. Data mining pinpoints high-risk clients and informs cybersurveillance to find and mitigate signs of fraud. ML can help calibrate financial portfolios or assess risk for loan and insurance underwriting.


The future of AI and ML in this industry includes an ability to evaluate hedge funds and analyze stock market movement to make financial recommendations. It may render usernames, passwords, and security questions obsolete by taking anomaly -detection to the next level: facial or voice recognition, or other biometric data.


Oil and gas. ML and AI are already working to find new energy sources and analyze mineral deposits in the ground, predict refinery sensor failure, and streamline oil distribution to increase efficiency and shrink costs. ML is revolutionizing the industry with its case-based reasoning, reservoir modeling, and drill floor automation, too. And above all, machine learning is helping to make this dangerous industry safer.


Manufacturing. Machine learning is no stranger to the vast manufacturing industry, either. It’s accomplishing the goal of improving operations from conceptualization to final delivery, significantly reducing error rates, improving predictive maintenance, and increasing inventory turns.

Not unlike the transportation industry, ML has helped companies improve logistical solutions that include asset, supply chain, and inventory management. It also plays a key role in enhancing overall equipment effectiveness (OEE) by measuring the availability, performance, and quality of assembly equipment.


ML & AI: Here to Stay

Is all the hype surrounding machine learning really worth it? Most experts say “yes” – with this caveat: The key is understanding how to use it to meet each individual business’s challenges and goals. It’s clear, based on a significant volume of data and evidence, that machine learning and artificial intelligence are here to stay. The trick, however, is recognizing that ML and AI aren’t a magic spell that work for every situation.


Experts agree that it’s important to clearly understand the value that incorporating ML will bring to your business. If it’s negligible, the expense may not bring a significant enough return on investment (ROI). This article from Business highlights four questions to ask before you consider beginning an ML project.


If you’re interested in leveraging artificial intelligence and machine learning for your business, please contact us.

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