Tag: artificial intelligence

Top 5 ways to implement artificial intelligence in mobile app development

Posted on by vallish

Artificial intelligence is no longer considered a futuristic technology but a requirement of the present. Many industries are always taking full advantage of AI; unsurprisingly, the software and app development domain top the list. Mobile app development companies cannot be lenient with adopting new technologies. It is because new changes in technologies are often platform supported that mandates the adoption to create secured and useful applications. According to Statista, mobile apps benefit from artificial intelligence in several ways. From predictive text and voice assistance to location-based app suggestions, a lot can be achieved using AI. Fierce competition demands the adoption of artificial intelligence in mobile app development.

That’s what this article will explore. We dive deeper into how most successful app development companies integrate AI into their products.

1. Chatbots 

Chatbots are everywhere. WhatsApp support for businesses, Facebook Messenger bot for social connections, and recently, Microsoft’s Bot Framework for enterprises and more. Now, chatbots can use NLP or Natural Language Processing to provide app users with suggestions, assistance, and services. An easy way to incorporate AI in mobile app development is through chatbots. You can implement complex capabilities, too, with chatbots, such as speech recognition and cognitive abilities. All these features make conversations more human-like, improving the end-user experience.

Siri and Alexa are the best examples of how Amazon and Apple apps use chatbots to offer services, suggestions, and assistance.

2. Facial recognition 

Facial recognition has become a powerful feature to implement in apps with better camera technology. Primarily used for security and recognition applications in conjunction with other information, facial recognition also has use in other apps. Improving image processing algorithms and cheaper hardware makes it possible to build myriad applications using facial recognition. Facebook, Snapchat, and Google Photos, all these apps use facial recognition to sort data, recommend interests, and offer filters. If your app is in the messenger category, you can boost its camera and photo filter capability using facial recognition. Some other ways to use this AI technology in mobile apps are text translation, image recognition, and information processing.

3. Recommendations

Suppose you are familiar with the OTT platforms like Netflix, Prime Video, and Hulu. In that case, you probably know how their streaming recommendation feature helps them to keep hundreds of thousands of customers engaged. These mobile apps use artificial intelligence to empower their recommendation algorithms. You can use AI and Machine Learning to augment your app’s decision-making capability. 

These technologies give a mobile app the tools necessary to recommend products, services, or shows based on the user’s age, gender, location, and preferences.

Recommendations based on artificial intelligence are much more relevant and have high conversion rates than those randomly offering suggestions. Almost all big brands use this AI feature in their mobile apps to cross-sell and upsell products. If your app is in the shopping or entertainment category, we recommend using AI.

4. Better app reasoning 

Today, mobile apps can do almost anything with the right technology. Given the capabilities of new artificial intelligence algorithms like deep learning and reinforcement learning, developers can make their applications phenomenal.

When you implement AI-based app reasoning, it makes your mobile application a lot smarter. Based on previous user data, it can determine what information the user needs at a given time and provide appropriate options. For instance, the Google Glass app can help you find the available parking spot by using the surrounding data about cars and available space. Most modern car GPS navigation systems rely on AI to find the best route. Another example is Microsoft’s Cortana digital assistant, which can learn new tricks with the AI chatbot Zo.

5. Sentiment analysis

Sentiment analysis might be the most complex implementation of AI in mobile app development. It primarily involves deciphering what people think about a particular entity which can be a brand, product, person, or idea. Some mobile apps already use sentiment analysis to provide feedback to brands about their customers’ perspectives of their products and services. 

Another use of this AI implementation is to decide which are the most frequently used features. It helps optimize the app and remove any unwanted or unnecessary features. Mobile development companies are increasingly using sentiment analysis to offer apps that help businesses make better decisions based on customer satisfaction levels. An example of such an app is AppBot which helps developers do sentiment analysis of their product reviews.

Conclusion 

We assume that you now have a clear understanding of how AI can enhance the quality of your mobile applications. Implementing the above-listed artificial intelligence concepts in your application requires a more profound understanding, irrespective of mobile app development frameworks. As the future of mobile development will depend on AI greatly, it is essential to explore opportunities for advanced mobile app development.

Talent500 is the platform on which Fortune 500 companies and global startups build their remote mobile development teams. Join our elite pool of mobile developers and work with some of the best engineers in the world.

 

 

Trending technologies to add in your resume in 2022

Posted on by dikshant

In all likelihood, 2022 is going to be a monumental year for the world of technology. A plethora of new technologies is taking charge and redefining the digital space at a truly phenomenal pace. Last year marked an exponential growth in people building web and mobile applications for different purposes by using cross platforms such as React Native, Flutter, etc. Apart from that, AR/VR technology has also been rapidly developing, especially with the growing interest of companies such as Facebook, Google, Amazon, and Microsoft in this field. This era-defining makeover is continuously transforming all facets of our lives including the way we communicate, interact with our surroundings, and especially how we function in a professional environment.

With all these changes taking place, there is one pertinent question that pops up in the mind of every professional working in tech-related fields: What are the technologies worth exploring to improve employment prospects in the coming years? Here are some suggestions that will add value to your resume

Machine learning & artificial intelligence

Machine learning and Artificial Intelligence can be applied to different industries in various ways to improve efficiency and optimize processes that previously required human input and supervision. Machine learning in particular is a field of study that gives computers the ability to learn without being explicitly programmed. It has a lot of potential to help companies solve real-world problems and reduce costs. Additionally, the emergence of these technologies has led to the creation of new roles.

With the pandemic, many companies have begun implementing AI, especially in customer-facing processes. Chatbot usage has also grown as customer support teams are stretched thin. These technologies will continue to play a pivotal part in business transformation and recruitment, and 2021 is already seeing the need for highly specialized skills in these areas.

Data science & analytics

The growth and use of data are changing the game for every business. From finance to IT, and healthcare, big data is driving change in how markets operate. In simplest terms, Data Science is the use of machine learning, data, and statistics to get insights and make predictions about the future. Data scientists are the ones leading the way to the future for the corporate world, by helping companies understand their data and use it to make informed decisions.

According to LinkedIn, ‘data science specialist’ as a top “in-demand jobs” in 2022. Also, with the rise of other groundbreaking technologies like AI, Machine learning, Cloud Computing, and Automation, it has become the responsibility of data scientists to stay up-to-date with upcoming trends that can help in achieving faster and better business outcomes.

Blockchain technology

Blockchain is a new and revolutionary way of data storage on the internet. Data is essentially stored in packets called blocks. Each block of data is linked to the previous block, which makes it tamper-proof. It promises to change the way we use the Internet by making almost anything of value sharable without powerful intermediaries standing between the participants in a transaction. Hence Blockchain technology is quickly becoming a must-have skill set across industries.

Currently, It’s primarily used by Bitcoin and many other crypto-assets such as NFTs, but understanding how it works is not only vital for your future career in tech-related fields, but also for a consumer of digital content.

5G

5G technology is set to transform the way we use mobile networks as it promises a higher speed of connectivity and capacity. Companies are pumping lots of investment into the research and development of this technology. One of the remarkable features of 5G is that it enables you to connect multiple devices that require high network bandwidths simultaneously. This fact alone will be a catalyst for so many emerging technologies such as autonomous vehicles, enhanced mobile broadband, the Internet of Things, etc. Such a seismic shift in day to day life of the average person will create avenues for a wide range of new employment opportunities that are based on this technology.

So which one of these technologies should you master and add to your resume?  It would be a good idea to pick what suits you based on various factors such as interest level, past experience, and a willingness to learn. Also, it depends on job roles that require a combination of different skills.  Another approach would be to do your personal research and identify areas of application for all these technologies in your field of choice. Then choose the one that has the highest probability of taking over your field in the near future.

Trying to stay ahead of the curve in finding remote tech job opportunities in the current job market? Explore exciting options from across the globe with Talent500. Visit our official website to see the latest job updates.

How AI is changing DevOps

Posted on by jagriti

While humans are capable of many unthinkable and often impossible feats, there are limitations. One particular area is in data handling, its management, analysis, and interpretation. With the sheer magnitude of incoming raw data, thanks to rapid digitalization, adoption of automation, and several other such factors, relying on human resources alone is inefficient. In a field like DevOps, where high efficiency, accuracy, and speed are all underpinning elements, it seems only natural that artificial intelligence models and technology are a perfect fit. 

In fact, DevOps is now a critical piece of the enterprise IT environment, and both AI and ML are readily adopted to increase efficiency. Around 75% use AI and ML for testing and in some cases, this has caused an uptick in new code released by nearly 10X! Considering the nature of the job, it would be near-impossible to do it without the use of advanced technologies. Moreover, AI improves several key DevOps practices and allows teams to leverage their potential to the maximum. It reduces waste by handling large computing scenarios that would otherwise overwhelm human-led operation cycles.

However, recently AI’s application in the DevOps space has brought about rapid change. Several new advancements are afoot, and you should know about them to stay ahead. Read on to know how AI is transforming DevOps.

Improved security and tracking capabilities

A key prowess of AI and ML models is their ability to analyze volumes of data and do it at remarkable speeds with accuracy. As such, its application in tracking and security is undeniable. In a DevOps setting, this advantage comes in handy when analyzing threats and usage data for better optimizations. Since models can be designed to inspect and track user data at every touchpoint, DevOps teams can leverage to deliver a better user experience simply based on data collected by modules doing the heaviest lifting. 

On to security, improved tracking capabilities can pinpoint areas in systems that are most vulnerable to breaches. AI better defense against DDOS attacks and can even lend a hand in fraud detection. Since AI models can be designed to spot unusual data patterns and can do this in real-time, tracking and stopping fraudulent acts is a lot more reliable.

Increased reliance on automation 

DevOps inherently includes complex systems, run on distributed application environments and operational models. Keeping up and effectively absorbing information isn’t a simple undertaking, especially if it is completely manual. While DevOps does bring some level of automation to the table, AI betters it in every way. With AI, DevOps professionals can leverage the full power of automation to streamline tedious tasks in the operational cycle that were otherwise error-prone due to human involvement.

In fact, this reliance on AI-backed automation has gone so far as to institute systems that can self-heal without requiring external intervention. Essentially, AI can not only jump into the driver’s seat but can get the team there too! However, stubborn DevOps teams are resilient to this level of independent function, but it is only a matter of time until it becomes standard practice.  

Swifter, more reliable pattern and anomaly detection

Complex application systems bring with them another problem area — error tracking and analysis. For instance, in an IoT environment with several microservices in use along with its numerous touchpoints, pinpointing failures with accuracy and speed simply isn’t possible. There are troves of raw data to go through and AI models can easily handle these without nearly as much effort. ML and AI models can be designed to look for abnormalities that trigger failure events and even suggest optimizations. 

Naturally, when exposed to such analysis over time, these models can be trained to recognize patterns and employ predictive analysis. This lends itself to anomaly detection, which can be vital to DevOps cycles as teams can then address these directly in the development pipeline and ensure they make it out to the live version. 

Better and refined access to data

An ever-present challenge to DevOps is the lack of access to complex data streams. With data gushing through every stream and data point in the system, professionals often struggle to keep up. AI models can address such a problem and deliver refined data from various sources and collate them for easy access. In fact, data trapped in siloed operations can also be harnessed to ensure that insight are meaningful.

Enhanced resource management

Certain AI tools lend themselves to non-technical business users, normally in charge of operations. With AI, data mapping and integration can be simplified, and this frees up the IT department for more high-value tasks. In fact, with better data integration, teams are in a better position to innovate, customize, optimize, and grow as the mundane and uninspiring tasks are no longer taking away time and effort. While it may seem like it could result in reduced collaboration, this isn’t the case. Unburdened by technical challenges, the focus is shifted to more big-picture undertakings, which often involve teams across departments to collaborate.

The integration of AI into DevOps is the natural cycle of advancement within the field of software development. There was a time when the traditional development life cycle distinguished development from operations, but in just a few years, it evolved into what we now know as DevOps. Similarly, a new acronym, AIOps, is taking hold and in this new model, the ‘Developer’ is no longer part of the equation. Is this the future of AI? It may still be too soon to tell, but developers would do well to upskill and branch out into other fields that work in line with this technology.

Those with skills that can leverage AI and drive development are sure to find themselves amongst the changemakers in the industry.

If you fit the bill and are looking to work at the bleeding edge of technology at some of the best Fortune 500 companies in the world, sign up on Talent500. The platform can help you take the next crucial step in your career and lay the foundation for your dream career amongst the best of the best.

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