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How is Conversational AI Improving Customer Experience?

Conversational AI

The Conversational AI allows the program to be a part of human-like interactions. This set of technologies empower the applications to send automated replies. It is yet another example of the exponential rate of innovations happening in the artificial intelligence field.

As a result, businesses are investing in conversational AI technologies like Chatbots to serve customers round-the-clock. Although the benefits of using this advanced technology are innumerable, you need to answer certain questions while assessing a conversational AI solution.

Conversational AI is Still Evolving

We are still undergoing the phase of revolution wherein innovators are bridging the gap between the artificial and natural interactions among humans and computers. Constantly, developers are empowering Conversational AI technologies to decipher human actions and mimic human-like conversations.

According to research, the Conversational AI market size is expected to reach US Dollars 15.7 billion by 2024. This clearly depicts the interest of investors in this technology and gives a sign of a lucrative future scope for businesses.

The incorporation of context, relevance, and personalization after deciphering various languages and tones is the end goal of this set of technologies. Chatbots are integral components of these technologies. Consequently, they undergo continual enhancements.

Conversational AI is not the Same as Traditional Chatbots

What do you like more, scripted TV shows or reality shows? Traditional chatbots are the scripted ones and Conversational AI chatbots are the non-scripted ones. The former one works with scripted dialogues whereas the latter one works with the context.

When scripted traditional chatbots are created, developers feed the dialogues with proper keywords. The bots are able to respond with the most appropriate reply out of the many replies added to their memory.

When a user sends a particular text, the chatbot identifies the keywords and sends in the scripted replies. This adds tons of burden on the owner of the chatbots. Hence, they update the conversations to make them look realistic.

The traditional scripted chatbots are not able to converse in real-time with users by understanding the context of the whole conversation. As a result, this compromises the customer services of the businesses.

This particular loophole is looked after by the chatbots powered by conversational AI. They hold the capability to engage in any dialogue after grasping the context of the whole conversation. They do not follow a script because they have in-built conversational capabilities in the software. Let’s understand how they work in detail.

Work Process of the Conversational AI

Conversational AI works with a combination of technologies. With the integration of advanced technologies, Conversational AI performs the function of interacting like humans. Here are the steps involved in the work process of these technologies:

1. Accept the Inputs

The first step involved in the functioning of Conversational AI is to accept the inputs from users. These inputs can be in the form of text or speech. If the inputs are in the written form, text recognition technology is applied. On the other hand, if inputs are spoken phrases, then voice recognition technology is applied.

2. Comprehending

Text and voice recognition is done with AI technology natural language understanding (NLU). After the application reads the inputs, the user intent is understood before forming any kind of response. Usually, businesses can use conversational AI for comprehending responses in various languages. In a nutshell, this is one of the most difficult steps in the work process of a chatbot.

3. Creating Response

In this step, the Natural Language Generation (NLG) is used to create responses in a language that humans understand. After deciphering the intent of the human, dialog management is used to create responses. Finally, it converts the computer-generated responses into human-understandable language.

4. Delivering Response

Finally, the response created in the previous step is shared with the users in the expected form. Either the system delivers it as a text or conducts the production of human speech artificially. Are you able to recall the voice of Alexa or Google Assistant? They generate their responses by following this process only.

5. Learn from Experience

Conversational AI also has provisions for improving their responses for future interactions by learning from their experiences. By accepting suggestions, the application learns to deliver better responses in future conversations.

Technologies used in Conversational AI

The Conversational AI platforms use a set of technologies at the right times to complete the work process. All these technologies are empowered by Artificial intelligence. Let’s understand these technologies in brief.

1. Automatic Speech Recognition (ASR)

The application interprets the spoken phrases by deploying this technology. Adding to this, it converts the speech into texts for the app. Voice assistants like Alexa, Google Assistant, etc. use Automatic Speech recognition.

2. Advanced Dialog Management

This technology helps in forming the response to the conversational AI app. Dialog management arranges this response for the next technology. Further, converts it into something which humans can understand.

3. Natural Language Processing (NLP)

Conversational AI uses natural language processing along with its two subsets. The first one is Natural language Understanding which understands the meaning as well as the intent behind any text. It can decipher texts shared in multiple languages as per the programming.

Both chatbots, as well as voice assistants, use this technology. After ASR, voice apps apply NLU. The second one under the NLP technology head is Natural Language Generation. Conversational AI uses this in the last stage of the work process by Conversational AI.

It creates the responses by converting the computer-generated replies into a language that is understandable for humans. This technology deploys dialog management to conduct this task seamlessly.

4. Machine Learning (ML)

Machine learning is great at understanding a set of data. In conversational AI also, machine learning is used to understand the interactions that have happened over time. Also, ML identifies better responses to these interactions.

Therefore, it understands user behavior and guides the app to create better responses. Humans also join machine learning in this task and together make the Conversational AI app a better interactor for customers.

Benefits of Using Conversational AI for Better Customer Engagement

Businesses are struggling for quite a long time to improve their customer engagements. As a consequence, conversational AI tools like Chatbots have become an integral part of websites and apps. Hence, the developers are working hard to incorporate conversational AI in their solutions.

Conversational marketing has become a proven corporate strategy for millions of businesses operating across various domains including healthcare, tourism, education, etc. Let’s find out what exactly can Conversational AI do to empower customer engagement:

1. Never-ending Scalability

Contrary to human customer support executives, Conversational AI can provide solutions to as many customers as possible at one time. Therefore, you can scale up your operations to any limits. Moreover, it can provide human-like interactions around-the-clock without any interruptions.

2. Acts as a Supportive Wing

In an organization, teams work together towards achieving organizational goals. Conversational AI technologies work with human experts and take their burdens away. They do those tasks which are humanly not possible at the same consistency as that of Conversational AI. This leaves room for human experts to entertain customers only when required.

3. Reduces Cost

Investing in conversational AI solutions might seem an added expenditure to you. But in the long run, the functions it performs reduces your cost. You will not have to pay employees for all the shifts to satisfy customers with real-time conversations. These applications prove to be immensely cost-effective for businesses.

4. Offers Data Insights

As mentioned above, machine learning understands the past experiences and interactions to improve your Conversational AI potential for future interactions. This allows businesses to get an insight into the data.

Hence, you will be able to know your customers’ preferences, behavior, and requirements. Furthermore, you can utilize this data for various other purposes to improve your plans and strategies.

5. Improves Productivity

The primary reason for investing in conversational AI solutions should be the need to improve productivity. It enhances overall productivity with uninterrupted, credible, and prompt customer services.

24×7 support and human-like interactions decrease the risk of losing customers. Hence, conversational AI is capable of providing better customer engagement and ultimately a rise in customer retention rate.

Leverage Conversational AI in Omni-Channel Approach

Investing in conversational AI might seem lucrative after reading about its work process and benefits. Before taking the final call, make sure to identify the channels where you are going to leverage this technology.

When it comes to the customer experience journey, we need to take care of many gateways. With conversational AI solutions, you can provide live chats, social media interactions, messaging on various platforms like Whatsapp, SMS, etc., as well as emails.

Therefore, businesses are using the omnichannel approach. Under this approach, they use multiple engagement channels and offer a seamless and intuitive customer experience. It allows businesses to offer their customers a proactive engagement and prompt responses.

Conclusion

Across the world, businesses are deploying high-end artificial intelligence technologies. This, in turn, offers business solutions to enhance the engagement of customers. Therefore, we can these technologies to offer an improved experience to your users. Conversational AI holds the potential to strengthen customer and business relationships. All you need is to explore it efficiently!

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Is Your Business Ready for Artificial Intelligence?

business ready ai

If you haven’t implemented an artificial intelligence (AI) solution into your business yet, you may feel like you’re missing the boat. And in many ways, I’d agree with you. But is your business ready for artificial intelligence?

Some studies show that nearly 99% of companies are investing in AI in some way, shape or form. AI isn’t a “will we, won’t we” type of technology. AI will be the de-facto standard, much like an operating system or software, it will be embedded into every business technology in the not so distant future.

But that doesn’t mean you should just jump on the bandwagon for fear of falling behind. There are a lot of considerations to take into account before even dipping your toes in the AI water — or to carry through on my first analogy, to ensure you aren’t putting the cart (or wagon) before the horse.

Proper Planning of AI Implementation.

AI projects fail because of backlash due to a lack of proper planning and scoping. To ensure a successful artificial intelligence initiative, businesses need thoughtful preparation.

Take into consideration things like ensuring that AI doesn’t exist in isolation but is integrated into broader business processes are key to success.

What Questions Should You be Asking?

Plus, before rolling out any AI initiative, you need to ask a number of important questions.

Questions like what is the business opportunity? And do you have the resources you need to implement process transformation? Are there security implications?

What data do you need to solve the problem and what will you need to acquire it?

And maybe most important, are there any ethical implications for implementing an AI solution?

To help you get clear on these questions and more, here are a few things you must consider before seeking out an AI solution or hiring a team of machine learning engineers to build something in-house.

Understand what artificial intelligence is good at, and what it isn’t.

The question may seem trivial, but a lot of organizations we talk to don’t understand what problems are good and not good machine learning problems. Artificial intelligence is not a solve-all so make sure the problem you’re trying to find a solution for is appropriate.

Some common tasks AI is great for includes forecasting, anomaly detection, object detection, pattern detection, auto-generation, enhancement and reconstruction.

Have a well-defined problem

You need to consider what is the problem and why you are trying to solve it. If the scope is too broad, your initiative will quickly fail. For example, pathology of a whole-body offers too many variables but focusing within one body part is much better and will warrant better results.

Keep your scope narrow and build from there.

Identify the performance criteria for AI

Like any well-defined business initiative, before you begin, you need to identify what success looks like. Are you hoping to achieve greater accuracy than a human could achieve? Are you hoping to simply automate a task to save time?

Good performance criteria for an AI initiative will define performance on a narrow criterion with a given percent accuracy rate.

Determine the team and technology capability

Does your organization have the technical ability to work with AI? Currently, there are 300,000 machine learning engineers available and several million open positions.

Machine learning experts can earn as much as football players. Working with AI often requires understanding arcane mathematical and computer science concepts that most software engineers simply don’t have.

Finally, do you have the right tools to create and support artificial intelligence and machine learning processes?

Understand the long-term impacts

As I mentioned, the challenge with bottom up projects is that they often fail because of a lack of political will in organizations.

AI is simply not understood by most people in the organization and even framing a business argument for deploying AI is not always clear.

Obviously, a clear understanding of ROI will help but even this isn’t enough because in the end, like any other technology deployment, the ROI has to be compared to other non-AI alternatives.

Lastly, it is likely that AI will displace individuals. In one of the companies I worked for, we developed an AI solution that resulted in a 60% reduction in engineering issues for a very expensive manufacturing process.

Obviously, this would have had a significant impact on the business but in the end, after two years, the solution still did not gain as much traction as we would have desired because it would have entailed the elimination of an entire team.

Training data for machine learning

Do you have the data you need to effectively train a model? Plus, is that data accessible?

Artificial intelligence governance

Developing AI is only part of the process. Can you deploy and support the AI in production, deprecate it, or determine if the AI is performing to specs? Do you have a mechanism to enable broad deployment and management or the people to perform the work needed?

Few organizations have a complete strategy for how the AI is to be used or managed by their business. For example, a simple question of whether to deploy the AI into the cloud, on-premise, or deploy to the edge is not always clear.

Finally, is your AI solution “future-proofed.� If changes in technology or capability occur – how easily can the organization adapt?

Once you’ve gone through these set of questions and considerations, you’ll be ready to take on an AI solution (AI Dynamics, Inc, Bellevue, WA) or kick off an AI initiative within your organization. And that’s when the fun really begins.

Image Credit: Michael Dziedzic; Unsplash

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How Can AI and ML Transform the Way We Read and Understand Data?

ai and ml understand data

Today’s business is ruled by data and data-driven understanding. How you understand the data and interpret the data into business decisions has a direct impact on your business conversion and growth. For a more precise understanding of data, today we have artificial intelligence (AI) and Machine Learning (ML) technologies on our side. No doubt, these technologies that mimic human reasoning can positively transform businesses and their strategies.

We need to understand the impact of AI and ML technologies have in shaping our understanding and capability to interpret data.

Data-Driven Personalization

Any business understands the importance of communicating with customers individually. Yes, thanks to the very nature of digital interfaces that opened up the tremendous scope of individual preferences and choices, your business communication must take into account the preferences of individual customers. The increasing importance of addressing individual choices for business conversion has forced many companies to focus on data-driven personalization measures.

Not only the large businesses but also the startups and small businesses increasingly understand the importance of having access to the relevant data for meeting the needs of visitors. AI can dig the available user data deeper and fetch out relevant patterns and insights that can be further utilized for data-driven decision making personalization. AI can also help to scale up such personalization efforts for every individual user.

Stop the churn rate.

A superb example of how AI can allow personalization in business operations can be found in the case of Starbucks. The global coffee chain brand designed 400,000 different types of emails created based on the data of individual preferences, tastes, and choices. Such well crafted personalized communication can help brands to create more engaging communication and conversation for business brands. The brand actually AI to decipher the volumes of data corresponding to customer preferences and choices.

Data collection and data-centric.

When it comes to smaller businesses and little startups, such as AI-based data collection and data-centric personalization may be a little expensive. But small businesses can embrace similar approaches to create very specific data-oriented marketing campaigns with short duration to boost business conversion and customer engagement. Such AI-powered data-driven campaigns can also help to lift the brand image of any company.

Generating Sales Leads from Data that’s Understood

For the B2B segment, business conversion highly depends on generating new leads. The B2B companies also need to depend heavily on tracking contact data and reaching out to them effectively through lead generation funnel. Most marketers agree to the humongous range of challenges B2B-based businesses face in doing this. This is where AI can play a great role in streamlining the process of lead generation through intelligent automation.

Artificial Intelligence (AI) powered lead generation and contact tracking solutions have the capability to make an analysis of the customer base along with important trends and emerging patterns. These trends, patterns, anomalies, characteristics, and various attributes can deliver important insights for optimizing websites and web apps. Thanks to AI-based optimization insights a website can venture to use better programming language, tools, features, and UI elements to generate more leads.

Analytics and you.

On the other hand, AI-based business data analysis can work hand in hand with big data analytics. This sophisticated and highly incisive approach to data utilization can easily help to discover ideal customers for a business. The interactions of users on web pages and corresponding data can be analyzed by B2B brands with the help of AI tools to produce the most relevant as well as actionable insights.

Analytical activities.

To make things easier for the businesses, AI, and machine learning technology for such analytical activities are now spotted in most of the leading analytics solutions across the spectrum. Simple Google Analytics can also offer highly result-oriented and precision-driven reports. Such technologies can easily know about the shortcomings and loopholes behind the decreasing motivation of traffic and readings of business conversion fallout.

Great analytics tools.

There are also great tools like Finteza that uses AI technology for monitoring website traffic on a continuous basis besides checking other crucial issues and irregularities. These tools can also improve your data security since by detecting bad traffic they automatically point out the vulnerabilities in the web app.

Poor web traffic often results in DDoS attacks, manipulation of website cookies, and hackers or malicious programs impersonating computer bots. An AI-based lead generation solution can also reduce these security vulnerabilities.

Optimizing the User Experience (UX)

AI optimizes the scope of personalization in a data-driven manner and that is portrayed as the principal useless of AI in dealing with data. But AI is also highly effective in optimizing the web design and improving the user experience (UX).

User Behavior

AI achieves this optimization and improvement by analyzing user behavior and interaction data and user feedback. Machine learning programs particularly can play a very effective role in learning from user behavior and adjusting various interactive elements accordingly.

AI and ML programs running behind the scene basically collect a lot of data corresponding to real user behavior so that real-time feedback about shortcomings and improvement needs can be communicated to the business owners. An ML-based program can also bring instant tweaks to the UX attributes for better engagement.

Another important thing in this respect that needs to be explained is the great role of AI in improving the efficiency of A/B tests. In the A/B testing process the AI and machine learning can deliver the most important insights about user demands and preferences to take further enhancement measures for UI and UX.

The most important aspect of AI in making an impact over A/B testing is that it leaves no scope for vague assessment or guessing. The data-driven insights guiding the A/B testing is more possible now as website cookies provide clear insights concerning user behavior.

Based on such insights the landing pages can reduce form fields as per user interest and preferences.

Biometric Data Pushing for Enhancements

Biometrics data corresponding to direct interactions with a web app can help developers and marketers with a lot of actionable insights. There are many advanced online services right now available in the market that can help to understand and decipher website data.

Biometrics data coupled up with AI and machine learning technology opened up new possibilities for improved user experience.

Among these available services for data interpretation mostly take the help of a combination of both artificial intelligence and machine learning. These sophisticated solutions can easily track the eye movements of the users.

In addition, some of these services can also track facial expressions to assess user responses in different contexts. These services can extract the most organic kind of user data and generate the most valuable insights that can be used for UX design and performance optimization of websites.

Conclusion

As the trends stand, from this year onward the AI and ML-based data analytics and data-centric optimization of business apps will have more dominance. Thanks to these two technologies, there will be the least guesswork for all design, development, and optimization decisions.

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Machine Learning and Exception Management – A Logistics Tech Game-Changer

Exception management with machine learning

There has been a lot of talk about machine learning in logistics management. The idea is simple: optimize, infer, implement and repeat. Here is: machine learning and exception management — a logistics tech game-changer.

What is included in the different pillars of logistics management?

A system optimizes the different pillars of logistics management that include order planning; vendor performance management; fleet capacity optimization (management); dispatch management; in-transit shipment tracking; and delivery management.

Next, the system infers the points or bottlenecks within these pillars (logistical processes) which can be fixed, improved, or enhanced. These inferences or analytics are then ‘implemented’ back into the logistics set-up. The learning mechanics start back from optimization. Over-time the system evolves and improves all the connected logistics management processes. This is machine learning in logistics management.

What is exception management in logistics?

A logistics exception (issue) is a deviation from planned or expected process execution. Here are a few examples.

  • Shipment loads aren’t mapped properly to available fleet options (creating capacity-mismatches and loading/dispatch delays).
  • In-transit shipments are detained at a spot for more than two hours (or are violating service level agreements with speeding or harsh braking).
  • Consignees didn’t receive all the SKUs (stock-keeping units) as per the initial purchase order.

Every transportation management system (TMS) involves some or many human touchpoints. A person supervises these system or process interactions (touchpoints). This can be anything from checking the shipment assignment schedule and ensuring that the handlers are following the planned loading patterns. Similarly, many other touchpoints work to ensure that the gap between plans and ‘actuals’ is minimal.

The goal of exception management is to minimize this gap between planned and on-ground results. Overall, the machine-learning aspect of exception management induces accountability and efficiency within the company’s and logistics network’s culture. This can be with the supervisors, warehouses, freight forwarders, logistics service providers, consignees (distribution points), etc.

 

6-stages of machine-learning enabled exception management system.

The 6 stages are Discovery, Analysis, Assignment, Resolution, Records, and Escalation.

Discovery:

It detects and reports issues or anomalies within the processes. This can be through temperature sensors (cold-chain logistics), real-time movement tracking, order journey tracking (in-scan and out-scan of each SKU), etc.

Analysis:

It analyses and processes the issue or exception as per protocols (or learnings). It categorizes and pushes ahead all exceptions – either to an assignment or to an escalation.

Assignment:

It matches the exception with the right person or department (best-suited to resolve the exception on time).

Resolution:

It tracks the speed and effectiveness of the person’s (assignee) resolution. It moves the ‘resolution’ through multiple criteria and validations before satisfactory ‘completion’.

Records:

It records and analyses each exception right from discovery to resolution. The system processes these records to throw-up insights or best-practices for future applications.

Escalation:

This is an important aspect of dynamic exception management. The system constantly tracks each issue within the system.

  • If at the analysis or resolution stage, the supervisor (or system) deems the issue – critical or complicated, then it’s escalated through special ‘analysis’ and resolution. It mostly includes people with different skill-sets or authority.
  • If the system detects that an issue hasn’t been resolved in its time-frame, it’s again escalated.

Through these 6-stages, the system constantly weeds-out inefficiencies from within itself. It helps propagate a more transparent, accountable, agile, and responsive culture. Furthermore, it helps reduce errors and delays, which, in turn, improves profit margins. A few new-age TMS start-ups, like Fretron, are trying to capture market share using this 6-stage exception management.

Real-world applications of escalation management in logistics

Let’s consider a real-life use-case for an exception management system (EMS) – a fast-growing retailer in India focusing on Tier-2 and Tier-3 cities.

Their biggest challenge was an unorganized logistics (vendor/freight forwarder) network and weak city infrastructure. Even though the retailer had opted-in for total logistics automation, they still weren’t able to implement it to the full extent. The client was looking for a tech-enabled process and culture change.

Let’s take vendor performance management as an example.

  • The EMS helped cut down discrepancies in billing and settlements. A single synchronized TMS was able to track each order (at the SKU level) as it moved through crates, pallets, trucks, cross-dockings, and final delivery. The out-scan could automatically highlight all the missing items.
  • The EMS would process the information and mark the exact point of deviation where the item went missing. This helped with issue resolution and also to plug these operational gaps. It cut down invoice-level disputes and hastened the settlements.
  • The EMS enabled fast and error-free invoicing which incentivized the carriers and freight forwarders to work in a more organized fashion. Through an iterative learning process, the system improved upon itself. It brought a higher degree of transparency and accountability within the logistics ranks (in the company).
  • On the back of machine learning-enabled EMS, the company was able to deliver on-time value (better shelf choices) for its end consumers.

Conclusion: Exception management, in logistics, is a game-changer

EMS successfully bridges the gap between tech-induced efficiency and on-ground employee efficiencies. It’s especially effective in unorganized or traditional markets that are riddled with such ‘exceptions.’

If machine-learning backed EMS is used in the right manner, many mid-level companies can scale fast and improve their outlook within the next five years. At this time of COVID-19, scaling faster may be the only option to save your company.

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How Machine Learning Will Impact the Future of Software Development and Testing

Machine learning (ML) and artificial intelligence (AI) are frequently imagined to be the gateways to a futuristic world in which robots interact with us like people and computers can become smarter than humans in every way. But of course, machine learning is already being employed in millions of applications around the world—and it’s already starting to shape how we live and work, often in ways that go unseen. And while these technologies have been likened to destructive bots or blamed for artificial panic-induction, they are helping in vast ways from software to biotech.

Some of the “sexier� applications of machine learning are in emerging technologies like self-driving cars; thanks to ML, automated driving software can not only self-improve through millions of simulations, it can also adapt on the fly if faced with new circumstances while driving. But ML is possibly even more important in fields like software testing, which are universally employed and used for millions of other technologies.

So how exactly does machine learning affect the world of software development and testing, and what does the future of these interactions look like?

A Briefer on Machine Learning and Artificial Intelligence

First, let’s explain the difference between ML and AI, since these technologies are related, but often confused with each other. Machine learning refers to a system of algorithms that are designed to help a computer improve automatically through the course of experience. In other words, through machine learning, a function (like facial recognition, or driving, or speech-to-text) can get better and better through ongoing testing and refinement; to the outside observer, the system looks like it’s learning.

AI is considered an intelligence demonstrated by a machine, and it often uses ML as its foundation. It’s possible to have a ML system without demonstrating AI, but it’s hard to have AI without ML.

The Importance of Software Testing

Now, let’s take a look at software testing—a crucial element of the software development process, and arguably, the most important. Software testing is designed to make sure the product is functioning as intended, and in most cases, it’s a process that plays out many times over the course of development, before the product is actually finished.

Through software testing, you can proactively identify bugs and other flaws before they become a real problem, and correct them. You can also evaluate a product’s capacity, using tests to evaluate its speed and performance under a variety of different situations. Ultimately, this results in a better, more reliable product—and lower maintenance costs over the product’s lifetime.

Attempting to deliver a software product without complete testing would be akin to building a large structure devoid of a true foundation. In fact, it is estimated that the cost of post software delivery can 4-5x the overall cost of the project itself when proper testing has not been fully implemented. When it comes to software development, failing to test is failing to plan.

How Machine Learning Is Reshaping Software Testing

Here, we can combine the two. How is machine learning reshaping the world of software development and testing for the better?

The simple answer is that ML is already being used by software testers to automate and improve the testing process. It’s typically used in combination with the agile methodology, which puts an emphasis on continuous delivery and incremental, iterative development—rather than building an entire product all at once. It’s one of the reasons, I have argued that the future of agile and scrum methodologies involve a great deal of machine learning and artificial intelligence.

Machine learning can improve software testing in many ways:

  • Faster and less effortful testing. Old-school testing methods relied almost exclusively on human intervention and manual effort; a group of software engineers and QA testers would run the software manually and scout for any errors. But with ML technology, you can automate testing, conducting tests far faster, and without the need to spend hours of human time.
  • Continuous testing. Additionally, QA testers are only available for a portion of the time, and if you’re developing software continuously, this is untenable. A refined ML-based testing system can deploy continuous testing, constantly checking how your product performs under different conditions.
  • Consistent testing. If you conducted a test for the same product twice, are you confident in your ability to conduct the test exactly the same way, both times? Probably not; humans are notoriously inconsistent. But ML algorithms are built and executed to repeat the same processes over and over, reliably; you’ll never have to worry about consistency with a ML-based testing script.
  • Higher detection acuity. Modern ML-based validation tools are capable of picking up on UI discrepancies or anomalies that human eyes may not be able to discern. Is this UI element the right color? Is it in the right position? Visual bugs are sometimes easy to notice, but a refined ML-based “eyeâ€� can give you even more accuracy.
  • Multi-layer testing. ML testing also allows for multi-layer testing, without the need for a user interface. The right ML software testing system can be applied to application logs, including source code and production monitoring system logs.

While cognitive computing holds the promise of further automating a mundane, but hugely important process, difficulties remain. We are nowhere near the level of process automation acuity required for full-blown automation. Even in today’s best software testing environments, machine learning aids in batch processing bundled code-sets, allowing for testing and resolving issues with large data without the need to decouple, except in instances when errors occur. And, even when errors do occur, the structured ML will alert the user who can mark the issue for future machine or human amendments and continue its automated testing processes.

Already, ML-based software testing is improving consistency, reducing errors, saving time, and all the while, lowering costs. As it becomes more advanced, it’s going to reshape the field of software testing in new and even more innovative ways. But, the critical piece there is “going to.” While we are not yet there, we expect the next decade will continue to improve how software developers iterate toward a finished process in record time. It’s only one reason the future of software development will not be nearly as custom as it once was.

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The Value of AI-Based Visual Inspection in 2020

ai visual inspection

For over a decade, manufacturers have turned to automated solutions to improve their bottom line. Automation and machine vision are now being augmented and even replaced by AI. Here is the value of AI-based visual inspection in 2020.

Value of AI-Based visual inspection

Being replaced by AI is especially true when it comes to visual inspection. The use of AI-based visual inspection technology is transforming manufacturing’s ability to improve business operations.

AI-based visual inspection relies on two of AI’s main strengths: computer vision and deep learning. Every AI system is built with the core capacity to perceive its environment (computer vision) and act on those perceptions (deep-learning).

As a result of deep-learning, AI adapts to a range of environments, making it useful across a multitude of industries. It has unlimited potential and can be developed rapidly to meet a manufacturer’s needs.

Concept of AI-based visual inspection

Well-trained human eyes can detect defects. A well-trained AI-based vision system can do the same — but with greater efficiency. Like a human eye, AI-based vision systems capture an image and send it to a central “brainâ€� for processing.

Like a human brain, an AI “brain� makes detailed meaning from the image by contrasting it against its existing knowledge.

AI-based vision systems are made of two integrated components. A sensing device acts as an “eye,� while a deep learning algorithm acts as a “brain.� The integrated system successfully mimics the human eye-brain ability to interpret images.

AI-based vision systems are more efficient than human eyes because the AI “brain� stores greater amounts of information.

Robust computational power can parse through available data at rapid speeds. The system can classify objects in both photos and videos and perform complex visual perception tasks.

AI-based vision systems can search images and captions, detect objects, and classify multi-media.

Thanks to deep learning-based visual processing, AI-based visual inspection systems can perceive cosmetic flaws and detect defects across general or conceptual surfaces (mobidev dot biz).

Benefits of AI-based visual inspection

1. Fast Implementation

Decades-old automated systems depend on defect libraries, lists of exceptions and complicated filters. The time it takes to accrue this information, clean it for accuracy, and re-implement it decreases its efficacy. It also wastes labor.

AI and deep learning do not require prolonged programming or tediously lengthy algorithms. AI-based visual inspection systems might be constructed by several quality engineers and a dataset of training images. The system learns rapidly and is integrated over several weeks.

2. Improved Analytics and Quality Control

Manufacturers can use AI to document inspection results and to assess product quality. Some overall process improvement initiative metrics that can be successfully tracked and correlated with concrete vision data include:

  • process recipes
  • equipment differences
  • component suppliers
  • factory location

In addition, inspection images and results can also be tracked and documented. These initiatives prevent future failure, which saves time and additional production costs. Applying deep learning-based machine vision across all initiatives and inspections helps manufacturers recognize and address defects early.

3. Labor Costs Reduction

AI solutions have higher rates of consistency than most expert human inspectors. Human inspectors must be trained and are only able to maintain a high degree of focus for 15-20 minutes at a time. Labor costs are incurred yearly and staff turn-over is an issue. For these reasons, AI-based vision inspections are more cost-effective than manual labor.

Use Cases

AI is increasing the competitiveness of manufacturers across every industry. Here are recent use cases from the aviation industry, semi-conductor manufacturing sector, and bio-science.

Alibaba has risen to meet healthcare challenges created by the coronavirus. Alibaba’s deep-learning-based visual recognition system is capable of detecting the coronavirus in chest CT scans at a 96% accuracy rate. The system accessed 5,000 COVID-19 cases and can provide a diagnosis within 20 seconds. Moreover, the system can differentiate between images of viral pneumonia and images of coronavirus.

Fujitsu Laboratories implemented an Image Recognition System at Fujitsu’s Oyama factory. The system ensures that parts are produced at optimal quality levels by supervising the assembly process. The system was so successful that Fujitsu implemented it across the entirety of the company’s production sites.

Airbus introduced an automated, drone-based aircraft inspection system in 2018. The system has improved the quality of inspections and reduced aircraft downtime.

GlobalFoundries is a leader in semiconductor manufacturing. The company designed a visual inspection system that detects defects in a scanning electron microscope (SEM) images. The system detects defects in a wafer map which then helps to determine the semiconductor device’s performance.

The use cases listed above reveal the extent to which AI is capable of automating many aspects of our lives. Although AI vision will never replicate human vision, the technology continues to classify information and advance in ways human eyes and brains cannot. And only humans might consider how to use this technology to get advantages.

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