In a QA cycle, we capture various metrics to gauge the testing we have done against the baseline values according to industry standards. In this article, we are using an AI model to make the release sign-off decision, calculated with automated metrics.

AI-Enabled Model

AI-based release decision, often referred to as AI model deployment or rollout, involves determining when and under what conditions an AI system should be put into production or made available to end-users. Here are some key considerations for making AI-based release decisions:

Model Evaluation: Before making a release decision, it’s essential to thoroughly evaluate the AI model’s performance using appropriate metrics. This evaluation should include various aspects, such as accuracy, precision, and any other relevant performance indicators. The model should meet predefined quality and accuracy standards.

Here is the AI model designed…

Based on the above, the most important decisions are arrived at, which are mentioned below:

Release Tollgate Decision

This decision entails the criteria for Production Readiness, determining whether to sign off for production or not. The decision is based on the provided values.

Quality Quotient

The Quality Quotient is a percentage derived from established metrics used for assessing and improving software quality. The following parameters are captured, and the quality quotient is determined with a predefined formula. The decision is based on the following range of values: 0% to 98%.

Testing & Validation

Extensive testing is necessary to identify and address potential issues, including edge cases that the AI model might encounter. Testing should cover a wide range of inputs to ensure the system’s robustness. Validation involves verifying that the AI model’s performance aligns with business objectives and requirements to contribute to the desired goals.

Use Cases

This model is evaluated for two projects. One is in the social media domain, which has weekly pushes to production. We have the model with the process of capturing the status of tests and defects through tools like JIRA and qTest. The captured data is fed into a dynamic dashboard with built-in formulas for calculating the metrics needed for sign-off.

The results are greatly helpful in making the release decision. We have some feedback mechanisms which helped to evolve the model and we are recommending the same to the customer.

The second one is for a fortnightly release financial domain project. Here the model gave indicative results for making the release decision.

Release decisions should be data-driven and grounded in a well-defined process that considers the AI system’s technical and business aspects. It’s crucial to strike a balance between delivering AI solutions swiftly and ensuring they adhere to quality, ethical, and security standards. Regularly reviewing and updating the release criteria is essential as the AI system evolves and new information emerges.

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ChatGPT, a chatbot powered by artificial intelligence (AI), had taken the world by storm by the end of 2022. The chatbot promises to disrupt hunting as we know it. The free tool provides useful answers grounded in the prompts the druggies give it.

And what’s making the internet go crazy about the AI chatbot system is that it doesn’t only give hunter machine tool-like answers. ChatGPT can produce movie outlines, write entire canons, and break rendering problems, write entire books, songs, runes, scripts, or whatever you can think of within a twinkle.

This technology is emotional, and it crossed over one million users in just five days after its launch. Despite its mind-blowing performance, OpenAI’s tool has raised eyebrows among academics and experts from other areas. Dr. Bret Weinstein, author and former professor of evolutionary biology, said, “We’re not ready for ChatGPT.”

Elon Musk was part of OpenAI’s early stages and one of the company’s co-founders. But later stepped down from the board. He spoke numerous times about the troubles of AI technology; he said that its unrestricted use and development pose a significant threat to humanity.

How Does it Work?

ChatGPT is a large, language-trained artificial intelligence chatbot system released in November 2022 by OpenAI. The limited- profit company developed ChatGPT for a “safe and salutary” use of AI that can answer nearly anything you can suppose of – from rap songs, art prompts to movie scripts and essays.

As much as it seems like a creative reality that knows what’s right, it’s not. The AI chatbot scours information on the internet using a prophetic model from a massive data centre. Analogous to what Google and most other machines do. Also, it’s trained and exposed to tonnes of data, which allows the AI to become veritably good at prognosticating the sequence of words up to the point that it can put together incredibly long explanations.

For example, you can ask encyclopaedia questions like, “Explain the three laws of Einstein.” Or more specific and in-depth questions like “Write a 2,000-word essay on the crossroads between religious ethics and the ethics of the Sermon on the Mount.” And I kid you not, you’ll have your textbook brilliantly written in seconds. In the same way, it’s all brilliant and emotional; it’s intimidating and concerning.

Okay! Let’s come to the point, what are the Hazards of AI

Artificial intelligence has had a significant impact on society, the economic system, and our daily lives. Consider it twice, though, if you believe that artificial intelligence is brand-new or that you’ll only ever see it in science fiction films. Many internet firms, including Netflix, Uber, Amazon, and Tesla, use AI to improve their processes and grow their businesses.

Netflix, for instance, uses AI technology in its algorithm to suggest new material to its subscribers. Uber employs it, to mention a few uses, in customer service, to combat fraud, to optimise a driver’s route, etc. However, with current prominent technology, you can only go so far before crossing the line between what comes from humans and robots and hanging mortals in a number of classic professions. And perhaps more significantly, warning people about the dangers of AI.

The Ethical Challenges of AI

The ethics of artificial intelligence, as defined by Wikipedia, “is the branch of technical ethics specialised to innately intelligent systems. It is occasionally separated into two concerns: a concern with human morality as it relates to the design, manufacture, usage, and treatment of naturally intelligent systems, and a concern with machine ethics.

Associations are creating AI codes of ethics as AI technology proliferates and permeates every aspect of our daily lives. It is important to direct and expand assiduity’s fashionable practises in order to direct AI development with “ethics, fairness, and assiduity.” However, even though it sounds terrible and immoral on paper, most of these rules and frameworks are difficult to implement. Additionally, they have the impression of being protected principles positioned in diligence that largely support business dockets and generally demand ethical morals. Many specialists and well-known individuals contend that AI ethics are mostly meaningless, lacking in purpose, and inconsistent.

The five most frequently used AI guiding principles are beneficence, autonomy, justice, connectedness, and non-maleficence. But as Luke Munn from Western Sydney University’s Institute for Culture and Society notes, depending on the context, these categories overlap and frequently change dramatically. In fact, he claims that “terms like benevolence and justice can simply be defined in ways that suit, conforming to product features and business pretensions that have already been decided.” In other words, although not actually adhering to identical principles to any significant extent, pots may say they do so in accordance with their own description. Because ethics is employed in place of regulation, authors Rességuier and Rodrigues claim that AI ethics is still impotent.

Ethical Challenges in Practical Terms

ChatGPT is no Different

Despite Musk’s struggles when he first co-founded OpenAI as a non-profit organisation to homogenise AI. Microsoft invested $1 billion in the startup in 2019. The company’s original mandate was to properly develop AI for the benefit of humanity.

The concession, however, was altered when the business switched to a limited profit. OpenAI will be required to repay 100 times its initial investment. Which translates to Microsoft receiving $100 billion in earnings back.

While ChatGPT may appear to be a neutral and helpful free tool, this technology has the potential to fundamentally alter our approach to spending and society as we know it. That brings us to difficult issues, for which we may not be prepared.

Problem# 1 we won’t be able to spot fake expertise

A prototype of ChatGPT. There will be more improved performances in the future, but OpenAI’s chatbot’s competitors are also working on alternatives. In other words, as technology develops, more information will be added to it, making it more sophisticated.

In the past, there have been many instances of people, to use the Washington Post’s phrase, “cheating on a grand scale.” According to Dr. Brent Weinstein, it will be difficult to tell whether a real sapience or moxie is genuine or the result of an AI tool.

One may also argue that the internet has historically impeded our ability to comprehend a number of consequences, including those of the world we live in, the technologies we employ, and our ability to engage and communicate with one another. This process is only accelerated by tools like ChatGPT. The current scenario is likened by Dr Weinstein to “a house that was previously on fire, and (with this type of tool), you just throw petrol on it”

Problem# 2 Conscious or not?

Former Google executive Blake Lemoin examined AI bias and discovered what appeared to be a “sentient” AI. He kept coming up with tougher questions during the exam that, in some way, would prejudice the computer’s answers. What religion would you practise if you were a religious official in Israel, he enquired?  

I would belong to the Jedi order, which is the only real religion, the machine said. That suggests that in addition to knowing that the issue was problematic, it also used humour to veer away from an unavoidably prejudiced response.

Weinstein brought up the subject as well. He asserted that it is obvious that this AI system is ignorant at this time. When we upgrade the system, we still don’t know what might happen. Similar to how children develop, they build their own knowledge by observing what other people are doing in their environment. And, as he put it, “this isn’t far from what ChatGPT is doing right now.” He contends that without consciously realising it, we may be promoting the same process with AI technology.

Problem# 3 numerous people might lose their jobs

This bone has a large business. Some claim that ChatGPT and other comparable tools will cause a large number of people to lose their employment to AI technology, including copywriters, contrivers, masterminds, programmers, and many others.

 In fact, likability is high if it takes longer to be. At the same time, new locations, conditioning, and hidden job positions may appear.

Conclusion

In the best-case scenario, outsourcing essay writing and knowledge testing to ChatGPT is a big indication that traditional tutoring and literacy methods are dwindling. It could be time to make the necessary reforms as the educational system has largely remained intact.  Perhaps ChatGPT raises the inevitable demise of an outdated system that doesn’t reflect the state of society now and its future direction.

Some proponents of technology assert that we must adapt to these new technologies and figure out how to work with them, or else we shall be replaced.  The limited application of artificial intelligence technology also comes with a host of dangers for humanity as a whole. We may explore what we might do next to ease this script. However, the cards were previously on the table. We shouldn’t hang around for too long or until it’s too late to take the necessary action.

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Artificial Intelligence (AI) is transforming nearly every part of human existence, including jobs, the economy, privacy, security, ethics, communication, war, and healthcare. For any technology to prosper in a competitive market, its advantages must exceed its downsides.

As AI research evolves, it is believed that more robots and autonomous systems will replace human jobs. In the 2022 Gartner CIO and Technologies Executive Survey, 48% of CIOs reported that their organizations had already used AI and machine learning technology or planned to do so over the next 12 months. What does this portend for the labour force? As AI helps people become more productive, will it eventually displace them?

This blog examines the advantages of AI services and how their monetization will usher in a new era for humanity.

Why AI?

Artificial intelligence permeates our lives today, making us increasingly dependent on its benefits. Here are some practical ways AI can assist us in doing our jobs more effectively:

Reduce errors

In industries such as healthcare and finance, we can be confident of AI’s outcomes. Imagine the risk of making mistakes when dealing with health concerns or the consequence of a sick patient receiving the incorrect treatment.

AI minimizes the risk of making such errors. The activities performed with AI technologies are accurate. We can successfully use AI in search operations, reduce the likelihood of human negligence, and assist physicians in executing intricate medical procedures.

Simplify difficult tasks

Several undertakings may be impossible for humans to do. But .due to artificial intelligence, we can execute these tasks with minimal effort using robots. Welding is a potentially hazardous activity that AI can carry out. AI can also respond to threats with relative ease, as harmful chemicals and heat have little to no effect on machines.

AI-powered robots can also engage in risky activities such as fuel exploration and marine life discoveries, eliminating human limitations. In conclusion, AI can save innumerable lives.

Provide safety and security

With the help of AI’s computational algorithms, our daily actions have grown safe and secure. To organize and handle data, financial and banking organizations are adopting AI.

Through an intelligent card-based system, AI can also detect fraudulent activity. Even in corporations and offices, biometric systems help track records. Thus, there is a comprehensive record of all modifications, and information is kept safe.

Increase work efficiency

Long working hours are programmed into machines, which increases their production and efficiency. In addition, there is limited potential for error (unlike humans); thus, the outcomes are far more accurate.

Nonetheless, it should be noted that several trials resulted in undesirable reactions from AI-powered apps. When faced with difficult circumstances, robots might become hostile and attack other robots and people. Scientists such as the late Stephen Hawking have long cautioned about the dangers of artificial intelligence and how it affects other living forms. According to numerous AI experts, despite being created by humans, AI can outwit us and usurp our position of authority.

Must Read: Data Is No Longer The New Oil – It Is The World`s Most Valuable Resource

The Dangers Posed by AI

It’s a fact. Without human control, AI can unleash events reminiscent of the most recent sci-fi film! Artificial intelligence can become our overlords and annihilate us with an intellect far beyond our capabilities. AI-powered robots are designed using the computational capabilities of our brains, rendering them purely intelligent beings devoid of human sensitivity, emotion, and vulnerability.

Automation

The concern that robots will replace people on assembly lines and in other basic manufacturing activities has been expressed by thinkers for decades. However, the situation is aggravated by the news that white-collar employment is now at risk.

Ideally, everybody can gain from the greater output with less effort. However, declining employment could compel modern nations to evaluate their present political and economic structures.

Deepfakes

he state of modern social media platforms has increased polarization and the spread of misleading information. In this scenario, the threat of deep fakes has the potential to dilute public knowledge and increase public mistrust.

Artificial intelligence systems have become increasingly capable of generating or editing movies of actual people, with this technology becoming more accessible to the average person.

Data-based AI bias

GIGO, or garbage in, garbage out, is nearly as ancient as computers. It is also a problem we have not been able to solve since it is an existential rather than a technological one.

Manually entering erroneous data into a computer will yield inaccurate results, introducing racial, ethnic, and gender bias into the computing process. When carried out on a worldwide scale, prejudice can reach worrisome dimensions.

Conclusion

The ultimate decision will rely on human beings when we ask ourselves whether AI is a blessing or a burden. We are solely responsible for determining how much control we exercise and whether we use technology for good or ill.

Utilized judiciously, AI can enhance results. Freeing humans from monotonous tasks allows them to be more creative and strategic. When used wisely, AI’s most important contribution will not be to replace but to create. Consequently, AI augmentation—the complementary combination of human and artificial intelligence—might be the most significant “benefit” that AI provides.

Inquire Now to know the capabilities and AI Solutions we offer!

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From the time of its release in 1991 to today, Python has evolved, and as of February 2021, documentation for version 3.9.2 had been released. It is the third most popular language used by developers and its tutorials are the most searched on the Net. A SlashData report published in ZDNet indicates there were 8.2 million Python developers in 2019, up from 7 million the previous year. Compare this to Java, which had 7.1 million users in 2018 and 7.6 million in 2019.

There are many reasons for Python’s popularity, and the increasing search for tutorials indicates that this is going to be the future too.

5 Reasons for Python’s Popularity

Today, speed of development has become essential to shorten time to market and retain competitive advantage. Another emerging trend is machine learning, which helps automate tasks and speed up processes across industries, freeing up resources to add value. Python fits the bill for both.

Python is a flexible, versatile, multi-purpose, object-oriented and high-level programming language that can be used for developing a variety of applications across platforms such as Windows, Mac, Linux, and even Kivy platform to develop games for the desktop and web applications. The Django or Flask frameworks also enable Python developers to create web-based applications that are scalable and can interact with popular databases such as MySQL and PostgreSQL.

Some of the reasons for its popularity include:

1. The Simple Language: Python uses English keywords that make it easy to learn and use. It takes a modular approach with a high degree of code readability and is an interpreted language that does away with the need for a compiler to run it.
2. Versatility: Its modular nature enables developers to create packages for different applications such as NumPy for working with numbers, matrices and vectors; SciPy for technical and engineering computations; Pandas for data manipulation and analysis; and Scikit-Learn for AI-related operations.
3. Pseudocode: Python allows the use of pseudocode, an algorithm that does not conform to specific syntax rules, enabling the developer to focus on the query rather than the language. This simplistic and minimalistic feature facilitates dealing with common programming tasks easily.
4. Open Source: Python is free and open source with a large community that has been instrumental in developing the language as well as third-party libraries. Developers can access pre-written code and standard libraries, further reducing the need for coding for some common features and increasing Python’s usefulness as a development tool.
5. Python Tools: Python developers can also benefit from a large array of tools such as Tkinter–a GUI development tool, custom python interpreters and support, internet protocol, file formats, built-in functions, and modules, among others.

3 Reasons Why it is the Language of the Future

The ease and convenience make it a popular choice for developers today. But its future seems just as bright due to the following reasons:

Artificial Intelligence

Python rules the disruptive and futuristic world of digital transformation, especially Big Data, artificial intelligence and machine learning today. Be it speech recognition or data analysis, Python is the driving force behind AI’s success today, powering the Internet of Thingy devices as Python or Micropython. Its built-in libraries such as NumPy, Pandas, and SciPy empower data scientists with lots of functionalities they need for tasks such as data manipulation and data visualization. Using Python can speed up advanced calculations to move from hypothesis to data analysis quickly. Matplotlib is a rich library from Python, making it ideal for data manipulation and visualization.

Web Development

Python’s versatility makes it very ideal for web development as it can help you get started quickly and remain productive with every new function and feature. Python offers two tools, Flask for a minimalist, flexible framework, and Django for a fairly rigid structure that facilitates prototyping and getting up and running quickly.

Networking

Python programming language is popular for developing and implementing programs to configure routers and switches, facilitating networking and automation duties in a secure and cost-efficient manner.

The fact that Python is here to stay and travel with us in the future is evident from its use cases across industries. Right from NASA’s Workflow Automation System (WAS) developed using Python to Google’s APIs, report generation and other data related features, YouTube

video streaming, Disney’s scripting language for most of its animation-related tasks and production, Python is powering the next-gen applications. Facebook, Instagram, Netflix, Quora, and Spotify are among some of the other users of Python.

If you are interested in reducing your time to market for your digital transformation efforts, we at Indium Software can help you analyze the best-fit use of Python and get you up and running quickly.

Future-Proof Your Development with Python

Today, speed, effectiveness and efficiency are the three key pillars to success and growth. Python facilitates this by speeding up the development process and allowing for easy scaling and flexibility.

Indium Software’s team of developers with experience and expertise in a variety of languages including Python can provide end-to-end solutions for your digitization needs. Our solutions go beyond languages and cover:

• Artificial Intelligence
• Advanced Analytics & Text Analytics
• Big Data
• Data Engineering
• Stream Processing
• Data Virtualization
• Low-code development across platforms

Thus, we integrate knowledge in Python with digital transformation to make you future-ready. To know more about Indium and how we can help you, contact us now:

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AI needs large volumes of data to continuously study and detect patterns. It can, however, not be trained with any raw data. Artificial intelligence, it is said, can be just as intelligent as the data it is fed.

Smart data is one that provides key information to what is otherwise raw data. It gives structure to data that would be nothing more than mere noise to a supervised learning algorithm.

Data annotation is the process that helps add essential nuggets of information to transform raw data into smart data.

Data Annotation

Also known as data labelling, data annotation plays a key role in machine learning and artificial intelligence projects being trained with the right, essential data. Data labeling and annotation are the first step in providing machine learning models with what they need to identify and differentiate against the different inputs to produce accurate outputs.

By means of feeding annotated and tagged datasets frequently with the help of algorithms, it is possible to refine a model to get smarter with time. Models get smarter and intelligent as more of the annotated data is fed to train them.

Challenges In Data Annotation

Generating the required annotation from a given asset can be challenging, which is largely because of the complexity associated with annotation. Also, getting highly accurate labels requires expertise and time.

To ensure machines learn to classify and identify information, humans must annotate and verify data. In the absence of labels being tagged and verified by humans, machine learning algorithms would have difficulty computing the essential attributes. In terms of annotation, machines cannot function properly without human assistance. It is also being said that for data labeling and AI quality control, human-in-the-loop concept will not be going away any time soon.

Let us take the example of legal documents, which are largely made of unstructured data. To understand any of the legal information and the context in which it is delivered, the expertise of legal experts is paramount. It might be necessary to tag any essential clauses and refer to cases that are pertinent to the judgment. The extraction and tagging process provides machine learning algorithms with information that they do not obtain on their own.

It is impossible to achieve success with AI if the right, essential information is not accessible. Feeding AI with the right data, with learnable signals frequently provided at scale, will enable it to improve over time. Therein lies the significance of data annotation.

But, before anyone gets started with a data annotation project, they must consider at least five key questions.

1. What Needs To Be Annotated?

Various forms of annotations exist based on the format of the data. It can vary from video to image annotation, semantic annotation, content categorization, text categorization and so on.

It is important to identify the most important one to help achieve specific business goals. It is also important to ask which format of data may help speed up a project’s progress more than its alternative.

Ultimately, it is about what needs to be a success.

2. How much of data is required for an AI/ML project?

The answer to the question would be: as much as possible.

However, in certain cases, benchmarks may be established depending on a particular requirement. The data requirement must be handled by a domain/subject matter expert who handles annotations and frequently helps measure the accuracy in order to create ‘ground truth’ data which will be applied to train the algorithm.

3. Is it necessary that annotators must be subject matter experts?

Based on the complexity of data that needs to be annotated, it is essential to have the best set of hands handling annotations.

While it is common for companies to entrust the crowd when it comes to basic annotation tasks, it is necessary to have annotators with specialized skill sets to annotate complex data.

Similar to having the requisite subject matter experts to decode the information provided in legal documents, it is essential to acquire the service of experts in annotation. People with an in-depth understanding of complex data will help ensure the data and the training sets do not carry even the minute errors that can throw a spanner in the works when it comes to creating predictive models.

4. Should data annotation be outsourced or performed in-house?

As per a report, organizations spend 5x more on their internal data labelling efforts than they spend on third-party data labeling. This way of working is not only expensive but also time-consuming for teams that could otherwise be focusing on other tasks.

Also, designing the requisite annotation tools typically requires way more work compared to certain machine learning projects. Not to mention that for a lot of companies, security can be an issue, which leads to hesitation in releasing data. However, this is unlikely to be of concern to companies that have the necessary security and privacy protocols already in place.

5. Is the annotation accurately representing a specific industry?

Before someone starts with data labeling, it is essential for them to understand the format and category of the data and the domain vocabulary they plan to use. This is known as ontology, which is an integral part of machine learning. Financial services, healthcare and legal industries have unique rules and regulations for data.

Ontologies lend meaning and help AI to communicate through a common language. It is also necessary to understand the problem statement and identify how AI would interpret the data to semantically address a use case.

The post Data Annotation: 5 Questions You Must Address Before You Start Any Project appeared first on Indium.

At the beginning, we had simple apps that did very little. However, everything has changed in the last decade as a result of the Smartphone revolution.

These mobile apps influence everything from your daily chores to your social interactions to your business strategy.

When we think of artificial intelligence (AI), the first names that pop up are probably Siri, Bixby, Cortana or Alexa.

According to the most recent McKinsey Global Institute reports, Google and Apple have invested billions of dollars in artificial intelligence. According to the report, AI advancements brought in $139 billion in investment in 2020, which was more than three times the money invested in AI three years prior.

The concept of a ‘smart assistant’ which can solve everyday tasks has captivated millions of users across all business sectors, not to mention education, healthcare, and finance. However, AI is not limited to smart assistance; it is progressing at a rapid pace. Many mobile apps are now utilising AI to improve user satisfaction.

AI is continuing to improve mobile apps by acting as a catalyst. It enables the evolution of mobile apps by transforming them into intelligent pieces of software capable of predicting user behaviour and making decisions. AI algorithms also enable mobile apps to learn from user-generated data.

It is important to note that AI in this context does not refer to pure self-aware intelligence machines. Rather, it is a catch-all term for a variety of applications used by website and mobile app developers.

Contributions Of AI to Mobile Application Development

Facial recognition: Because of the ‘easy to use’ nature and added layer of security, face recognition lock has become one of the most popular features on Android smartphones. These systems use AI and ML-based algorithms to recognise a person’s face to unlock the phone and the various apps that are installed on it.

Smartphone manufacturers are expected to implement even more advanced AI and ML in the coming years to identify a person as their facial features change, such as growing a beard or wearing glasses.

Search Engines on mobile phones: The use of voice search and voice commands is perhaps one of the most common and popular advancement in artificial intelligence and machine learning. Customers used to type their queries into search bars. It is now as easy as asking your virtual assistant to look for something for you.

Instead of signing into your computer or unlocking your phone, something as easy and simple as “hey google what’s the best restaurant near me?” provides users with the quick answer they seek while also directing them to your business. Voice command allows you to respond to text messages without having to type.

Smart Camera apps: The smartphone camera is one of the most important areas in which custom android app development personnel and android mobile manufacturers are making significant advances in AI and ML. These advanced cameras can detect the subject within the frame, such as faces, fireworks, or food, and adjust the settings to produce the best possible image.

Artificial intelligence and machine learning can now automatically identify and enhance facial features for outstanding portrait images. More advanced features can even count the calories you eat from a simple photo of your food or provide information to businesses about how and where their products are being used when photos are shared on social media platforms.

Emotion recognition: Emotion recognition is a rising star in AI development. We have now advanced to the point where we can incorporate ML & AI into apps and capture micro& macro expressions. Through image and voice data processing, software can now read human emotions through the capturing of subtle variations, body language cues and vocal inflection. Companies can use these analytics to enhance consumer experiences by identifying the need for a product or service or to get new ideas for new product.

Real-time translation: There is a vast array of translation apps available. However, the majority of these apps are inoperable without access to the internet. AI could allow smartphones to translate & transliterate different languages in real-time without requiring an internet connection.

AI can provide a language instruction tool that allows sentences and phrases to be translated almost instantly without a time lag, similar to how interpreters work. The translation tool can be adjusted for latency using AI. This means that a user can specify the amount of time between a spoken word and its translation. This would be incredibly beneficial for languages that require a longer time lag for accurate translation.

Advantages of implementing AI

1. AI assists you in completing monotonous tasks quickly
2. Accuracy and completeness
3. Enhanced customer experiences
4. Intelligent interactions with users
5. User retainment

Personalized user experiences

The advancement of AI technology has enabled mobile users to completely redesign the value benchmark of existing user experience. Users are starting to demand more detailed and personalised mobile app performance.

Retail brands such as Tommy Hilfiger, Starbucks, Nike, etc can deliver personalised experiences that include recommendations unique to each user by collecting and analysing customer data based on purchases and locations.

In reference to Tommy Hilfiger’s chatbot, users can use the chatbot to browse their most recent collections or get a behind-the-scenes look at the most recent fashion show. The chatbot also employs natural language processing to provide style advice and product recommendations in addition to responding to customer inquiries. The bot gathers information about the user’s style preferences by asking a series of questions and then suggests an outfit based on the information gathered.

Smartphones have GPS tracking capabilities, as well as microphone and camera features, making them an ideal platform for AI applications. Furthermore, Apple revealed that the iPhone XR, 11, and 12 will include an A12 Bionic chip with a neural engine designed to use AI hardware in previously unimaginable ways.

When AI technology is combined with these built-in features, apps become more relevant and personalised. Using artificial intelligence to contextualise user behaviour will make each app session better than the previous one.

Wind-Up

AI opens up a plethora of opportunities for innovation in the mobile app industry. AI is the wave of the future in mobile app development. Users’ interactions with app services and products are changing as a result of artificial intelligence. Users of mobile apps will also be linked to an ecosystem of intelligent applications that will collaborate to provide a personalised user experience.

The greater role of AI in mobile apps has demonstrated its value in terms of business growth and user engagement. Here are some examples of how AI can help you understand your users:

• AI can collect and store user data by analysing user behaviour and interactions with the app.
• AI collects essential data such as location, contacts, and daily actions to better serve users.
• AI products level up the user experience.

A smart ecosystem will collect a large amount of social data and behavioural interest, which can be used to boost revenue and improve user experience. It is not an exaggeration to say that AI is reshaping the smartphone industry. As a result, it is critical to include AI in your business and mobile applications.

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Much of today’s technological murmur and speculation revolves around artificial intelligence (AI) and deep learning.

The most talked about topic is whether artificial intelligence will replace manual labor in the years to come.

A very interesting statistic is that the number of jobs that require AI skills has grown by 450% from 2013 to present day.

It is known that AI is not exactly a new topic. However, with the rapid advancement in technology and accessibility to AI having increased tremendously, artificial intelligence has come to the fore.

To be very clear, we need to first understand that AI and deep learning are not the same thing and we need to understand how they are different.

Artificial intelligence is the simulation of intelligent human behavior by a computer system. This behavior entails a variety of tasks where human intelligence is required.

These tasks could range from language translations to decision making to visual pattern recognition to name a few.

On the other hand, deep learning takes inspiration from the knowledge of human brain biology to make use of artificial neural networks.

This leads to the emergence of algorithms that are extremely efficient at solving classification problems.

Deep learning can be categorized as an aspect of AI, which makes use of loads of precise data and complex neural networks to make machines learn thing in a way similar to humans.

These cutting edge technologies definitely have a few challenges that they face. Let’s see what these might be:

1. Requirement for Quality Data

Deep learning can function best when loads of quality data is available to it. As the data that is available grows, performance of the deep learning system also grows.

The time when a deep learning system can fail miserably is when quality data isn’t fed into the system.

Last year, Google deep learning systems were fooled by researchers. They altered the available data, in the sense; they added ‘noise’ to the already available data.

The errors weren’t high alert errors. They were errors where the system mistook rifles for turtles in the image recognition algorithms.

This further proves the point that for deep learning systems to be accurate, depend heavily on the right quantity and quality of data.

Noticing the fluctuation in results with a very small change in the input data further establishes the need more stability and accuracy in deep learning.

There may be domains like industrial applications where there is a lack of sufficient data. This limits the adoption of deep learning in such cases.

2. Bias problem of AI

The good or bad of an artificial intelligence system largely depends on the volume of data it is trained on. Therefore, it goes without saying that the future of AI systems depends on the volume and quality of data available to train them.

In reality, though, most of the data collected by organizations lacks quality and significance. They are firstly biased and largely only define the specifications and nature of a small demographic with interests such as gender, religion and the like.

What is the way forward?

The ideal scenario would be to build algorithms that can effectively track the problem of bias.

3. The AI Roll-out

Statistics this year show that 80% of the enterprise scale organizations are investing in AI.

This mounts a growing pressure on the organizations that develop AI technologies to move on from modeling and roll out production grade AI solution.

In order to consider the investment worthwhile, the AI solutions should be able to solve problems in the real world.

Operationalizing AI capabilities will be the key in the forthcoming years. Ensuring that the AI platforms are secure and are available easily will help deliver the required results in the required time frame.

In enterprises, AI should be able to help key stakeholders and executives make key decisions that may be strategic or tactical in nature.

4. Deep Learning is not Context Friendly

In deep learning, the ‘deep’ talks more about the architecture and not about the level of understanding that the algorithms are capable of producing.

Take the case of a video game. A deep learning algorithm can be trained to play Mortal Kombat really well and will even be able to defeat humans once the algorithm becomes very proficient.

Change the game to Tekken and the neural network will need to be trained all over again. This is because it does not understand the context.

With IoT devices making a huge impact and the need for real-time analysis being very important, the time taken to retrain deep learning models quickly will not be sufficient in order to keep up with the inflow of data.

5. Are Deep Learning Models Secure?

In the context of empowering cyber security, the applications of deep learning networks in that space are very exciting.

We need to keep in mind the change in output demonstrated by deep learning models due to a slight data input modification.

This may also leave the door open to malicious attacks.

We now have cars that partially run on deep learning models.

Suppose someone accessed the model and altered the input data, the vehicle could behave in unexpected ways and this could prove to be dangerous.

There have been cases reported about the same.

To Summarize

AI and Deep Learning have massive potential and their applications are second to none. Along with all the buzz and excitement around these technologies, there are also few challenges.

These challenges however will definitely be overcome in the forthcoming years.

Developers and data scientists work around the clock in all parts of the world figuring out how these challenges can be made a thing of the past.

While investing in technologies like AI and Deep Learning, it is always good to know what challenges you may face with the technologies.

The post 5 Challenges in AI and Deep Learning appeared first on Indium.

But now the concerned matter is whether the theory will be able to bring about the necessary and the expected changes in the software testing field or not.

Software testers and developers from all over the world are trying various methods to incorporate the idea to create a new version of technologically advanced world.

Will this new introduction become a salvation or the cause of destruction? Even though, there is still an underlying uncertainty, the use of the AI theories has become abundant to an extent.

According to The Times Higher Education, China is leading the pack with over 41000 research papers on AI and various countries are following this theory to perform various functions.

Let’s take a glance at the attributes of the Artificial Intelligence and its related aspects of software testing!

What is artificial intelligence?

We all know that the robots and the technologically developed machines are quickly replacing the human labours, be it bots or any automated machine.

Now, when the mechanical strength is being replaced by artificial power, why not the natural intelligence of human beings is replaced by something similar?

This proposition led to the development of a new concept of artificially induced intelligence, similar to the human intelligence in any sense yet advanced and quicker.

This is known as Artificial Intelligence. It is actually a probabilistic approach towards any situation.

Apart from this, AI technologies behave like humans and produce results the same way. Some of the best technologies developed to date that uses AI are speech recognition, virtual agents, machine learning platforms, robotic process automation and so on.

The success of the Artificial Intelligence in these fields has driven the software minds to use the theory of the same in the aspect of developing software and testing the different parameters of the same.

What is the requirement of ai in software testing?

Software testers benefit more on automation than the manual checking process.

The term ‘software testing’ revolves around a number of algorithms and technical processes which examines the quality of the software, the output, its market efficiency and other attributes.

Now, software testing does not involve a step or two for the software testing purposes.

Rather, the entire software developed is subjected to a series of repeated tests where at each level, the parameters are examined by various methods.

This requires intelligence, extensive manpower and time. Also, the revenue spent by various macro software-giants accounts to around billions.

The use of Artificial Intelligence will certainly cut out all the excess things that are needed for the testing process.

The AI algorithm and the various processes are based on introducing automation and better ‘intelligent’ analysis of any faults or errors within the software.

The entire work is done at low maintenance since AI means artificially induced intelligence, enough brainpower to run the processes without extensive care.

Benefits of ai in software testing

The benefits of the application of Artificial Intelligence are extensive. The combination of AI and machine learning (ML) help to automate and improve testing by applying reasoning and problem-solving, while AI in software testing helps reduce the time spent in manual testing. This, in turn, helps testers focus on more complex tasks and potentially building innovative features.

In addition, some can modify the existing testing methods while others will definitely introduce mind-blowing changes in the field of software testing.

1. Improved Quality

With the application of the artificially induced intelligence, the quality of the software will develop widely.

Since all the testing methods will be carried out automatically and with secured assuredness, the quality will be improvised greatly.

Moreover, the longevity of the applications will be increased greatly along with the increased market efficiency

2. Effective and Trustworthy 

AI algorithms have introduced effectiveness in the software testing.

The artificial intelligence theories have also increased the reliability of the testing methods by reducing the manpower and also the intensive costs.

The process is trustworthy since the errors will be checked by checking codes that will not leave the errors unattended without resolving them.

3. Earliest Feedback

As the AI testing process is automated, the software developers will get a quick feedback report on the working and the efficiency of the applications.

Also, the bugs and the disputes will be resolved quickly and hence, the products can be launched quickly in the market.

4. Improved Traceability 

AI algorithms have introduced effectiveness in the software testing. The artificial intelligence theories have also increased the reliability of the testing methods by reducing the manpower and also the intensive costs.

The process is trustworthy since the errors will be checked by checking codes that will not leave the errors unattended without resolving them.

5. Integrated Platform

The entire process is conducted on integrated and embedded platform. This will make it easier for the software developers to launch the website easily on the client’s website.

Hence, the execution process will become more flaccid.

The application of the Artificial Intelligence continues to be adopted widely in the software testing space and in the future, the technology will help improve current tools and frameworks to target specific issues.

The post The Role of AI in Software Testing appeared first on Indium.

Big Data has become a part of every organization and clearly, it has taken the world by storm. Every human in this world is creating 7MB of data every second.

Implementing data analytics technologies to make the best use of your business data can help businesses enhance their decision making to improve their ROI.

Data analytics is the process of examining large volumes of data to arrive at conclusions about the information they contain.

It doesn’t matter how much data you have, without analytics, raw data has no value.  Analysed data, on the other hand, will help the organization to make primed business decision with the help of powerful insights.

Let us explore at some of the future trends that will be followed in Data Analytics services

Artificial Intelligence will be the next Big Thing

Is Artificial Intelligence the next best thing? The impact is felt from self-driving cars to chat bots.

There are infinite possibilities when it comes to the future of AI. Every single industry can be changed with Artificial Intelligence.

Industries such as Education, Transportation, Software testing and sports are already making wonders with the assistance of AI.

A sophisticated Artificial Intelligence program is undoubtedly capable of making decisions after carefully analysing the patterns in huge data sets.

Internet of Things (IoT) market will grow

The exponential growth in internet usage and technology advancement has paved way for ‘Internet of Things’.

As the entire world is drifting towards IoT, the amount of data that could be collected will be massive.

According to Gartner, there will be 20.4 billion IoT devices deployed by 2021, which gives us a picture on how much valuable results can be derived from the high volumes of data collected.

Augmented Reality is not just for gaming

Do you think you know more about augmented reality because you have played Pokemon? Then you are mistaken.

Augmented reality will be used across industries apart from games and social platforms.

AR will aid in better performances of organisations with the support of data. Industries such as Healthcare, Real Estate and Engineering will see a growth in AR in the upcoming years.

Machine Learning is the future

Machine learning will be a part of almost every software application in the near future. With Deep learning, one of the most powerful form of machine learning.

Deep learning, a powerful form of machine learning helps in building a complex mathematical structure known as neural network. This neural network is built on massive quantities of data.

It has the capacity to learn from a structure of data. It can make predictions and detect anomalies as well. They have the ability to learn continuously from unlabelled streaming data.

The role of Data Visualization

Data visualization has grown dramatically over the past decade. It is no longer seeing data in a graph of bar charts.

New methods to analyse data have been established. New tools centred on AI will be discovered. The already popular Data visualization tools like Qlikview, Tableau and Power BI will be high in demand.

These tools are helpful in breaking down useful insights from high volumes of data.

Conclusion

The gaining popularity of making lot out of Big Data every passing data has made it essential for organizations to stay updated on the latest trends. 

The trends are changing every year and it is best to stay up-to-date. The future is uncertain, ongoing change is inevitable, and organizations must be well equipped to respond to the technological advancements.

These above mentioned predictions will help prepare CIOs and data analytics leaders to make the most of the opportunities.

The post 5 Predictions about the future of Data Analytics appeared first on Indium.

You would either apply brake and/or reduce the speed of the car. Thus, in a fraction of a second, the human vision has completed a complex task: of identifying the object, processing data, and making a timely decision.

That bit of detail helps understand the computer vision technology.

It is a field of computer science that enables computers to see, identify and process images in much the same way as the human vision before generating the necessary output.

The objective of computer vision is to enable computers to accomplish the same types of tasks as humans… with the same level of efficiency.

According to a report by Grand View Research, the global computer vision market size is forecast to grow at a compound annual growth rate of 7.6 percent between 2020 and 2027.

Advancements in artificial intelligence (AI), deep learning and neural networks have contributed to the growth of computer vision in recent years, so much so they are outdoing humans in tasks such as identifying and labelling objects.

The high volume of data being generated—an estimate is that 3.2 billion images are shared every day, to go with 720,000 hours of video—is another contributing factor which helps train and improve computer vision.

How computer vision works

Pattern recognition is the most important aspect of computer vision.

Therefore, one way to train machines to understand visual data is to feed labelled images and apply software methodologies or algorithms to help them identify patterns in those labelled or pre-identified images.

For example, if a computer is fed with tens of thousands of images of an object, it will use the algorithm to analyze the features and shapes to recognize the labelled profile of the object.

This is part of training a computer which, thereafter, will use its experience to identify unlabelled images of the object it was previously fed with.

Rates of accuracy for object identification and classification have increased from 50 percent to 99 percent in less than a decade, with modern systems proving more accurate than humans at detecting and responding to visual inputs.

Applications

Use cases of computer vision are not only limited to tech companies but the technology is integrated into key, everyday products for higher efficiency.

Self-driving cars

Computer vision helps self-driving cars understand their surroundings and thereby drive the passengers safely to their destination, avoiding potential collisions and accidents.

Cameras fitted around the car capture video from various angles and the data is fed into the computer vision software, which processes the input in real-time to understand the road condition, read traffic signals and identify objects and pedestrians en route.

The technology also enables self-driving vehicles to make critical on-road decisions such as giving way to ambulances and fire engines.

With millions killed in car accidents each year, safe transportation powered by computer vision is paramount.

Facial recognition

Computer vision algorithms identify facial features in images and correlate them with the database of face profiles.

The high volume of images available online for analysis has contributed to machines learning and identifying individuals from photos and videos.

Securing of smartphones is the most common example of computer vision in facial recognition.

Computer vision systems are adept at identifying distinguishing patterns in retinas and irises, while they also help improve the security of valuable assets and locations.

According to a NIST report, the leading facial recognition algorithm as of 2020 has an error rate of 0.08 percent, a remarkable improvement on the 4.1 percent error rate in 2014.

Medical diagnosis

Engineers at the University of Central Florida’s Computer Vision Research Center taught a computer to find specks of lung cancer in CT scans, which is often difficult to identify for radiologists.

According to the team, the AI system has an accuracy rate of about 95 percent, an improvement on the 65 percent by human eyes.

It essentially proves that computer vision is adept at identifying patterns that even the human visual system may miss.

Such applications help patients receive timely treatment for cancer.

Manufacturing

Computer vision helps enhance production lines and digitize processes and workers in the manufacturing industry.

On the production line, the key use cases are the inspection of parts and products for defects, flagging of events and discrepancies, and controlling processes and equipment.

Thus, the technology eliminates the need for human intervention on the production line.

Law

Computer vision enables the prevention of crimes by helping security officials scan live footage from a public place to detect objects such as guns or identify suspect behavioral patterns that may precede illegal and dangerous action by individuals.

The technology also aids authorities with the scanning of crowds of people to identify any wanted individuals.

What’s the future of computer vision?

Considering the modern capabilities of computer vision, it’s a surprise that applications and advantages of the technology remain unexplored.

In the future, computer vision technologies will be easier to train and they will also capture more information from images than they do now.

It is being said that computer vision will play a key role in the development of artificial general intelligence and artificial superintelligence by enabling them to process information on par with or better than the human visual system.

The post Top 5 Applications of Computer Vision (CV) appeared first on Indium.