While decades ago the use of this technology was primarily seen in marketing and retail, in 2024, industries like healthcare, transportation, logistics, and many others are actively adopting beacons. This smart solution not only streamline business operations and helps gain more clients by providing real-time insights but also make a meaningful difference in patient care. For example, in healthcare, providers can use beacons to remind patients to take their medications and track the locations of patients in emergency rooms and large medical facilities.

Let’s take a look at what the global market has to say about the rise of beacon technology.

Market statistics from 2016 to 2026 show that this wireless technology is set to usher in a new era of digitalization. In 2016, the beacon technology market was valued at 519.6 million U.S. dollars, and it’s projected to grow at a staggering CAGR of 59.8%, reaching approximately 56.6 billion U.S. dollars by 2026. That’s an incredibly promising outlook.

If you’re seeking a technology that can help you reach more clients, improve customer experiences, and deliver greater value — all in a cost-effective way — beacon technology is the way to go. To simplify your journey, we’ve compiled a guide that covers everything you need to know about beacons.

In this article, you’ll discover:

• What is beacon technology
• How does it work
• The many types of beacons
• How they benefit companies across industries
• Pros and cons of the devices
• How technology would evolve

Let’s get started!

What is Beacon Technology?

Beacon devices are at the heart of beacon technology.

Most beacons are small computers, roughly the size of a standard Wi-Fi router.

Part of indoor and outdoor positioning systems and Internet of Things (IoT) networks, beacons use proximity technology to detect human presence nearby and trigger preset actions to deliver informational, contextual, and personalized experiences.

Where Did Beacon Technology Begin?

The concept of beacons is as old as, for example, hand watches. Like lighthouses guide ships and show where the land is, beacon devices provide information and navigation to smartphone users. Beacon technology offers a new context for an old concept.

As we know it now, the beacon technology was introduced by Apple alongside a dedicated communication standard.

Called iBeacon, it was soon followed by Google’s Eddystone. The location beacon, which is rough twice the size of Apple’s Wi-Fi router Airport was a typical beacon device in the first years since 2013.

In a short time, beacon technology spawned many startups and new beacon types. Unlike other new technologies like Augmented and Virtual Reality, beacons were straightforwardly effective and had a low price tag. On top of that, they provided new, exciting ways to engage customers in a non-intrusive way. As a result, many established retail companies quickly adopted beacons and started reaping their benefits.

How Do Beacons Work?

When a user walks past an area where a positioning system or IoT network with beacons is set up, the nearest beacon sends a code with a message to their mobile device. Then, the beacon recognizes the message pops up as a notification on a user’s mobile device with a third-party or branded mobile app.

You need 3 things to make a beacon-based system work:

• At least one more beacon device
• Mobile app
• User’s permission

Beacon technology offerings usually consist of a software development kit (SDK), back end management tools, and beacon devices. So, you will need software engineers to set up your positioning system, more so if beacons are part of a vast IoT network that includes other smart devices too.

As for the user permission, your users make the final decision on whether they will receive beacon-based notifications and use your positioning system. Beacon technology works only with a smartphone and mobile app, so users can simply opt for turning off Bluetooth. There are also people who use their smartphones without keeping Bluetooth on all the time.

To overcome this obstacle, you can ask your users to turn on Bluetooth for the full customer experience, or even excite your users by notifying them about the beacon-based positioning system, presenting it as an innovative solution, and explaining what benefits it holds for them.

As a result, beacon technology appears as less intrusive than the similar RFID (radio-frequency identification) and other engagement tools, enabling a user experience where a user has a choice to opt out.

BLE as Bluetooth Beacon Technology

BLE beacon technology relies on Bluetooth Low Energy (BLE) to transmit data and send out messages at regular intervals. BLE is a power-efficient Bluetooth technology developed for the Internet of Things applications and devices like beacons. With BLE, a beacon needs only one coin cell battery every three years, and the technology is 60 to 80 percent cheaper than the classic Bluetooth.

Types of Beacons

There is a wide array of beacons available for positioning systems of any type and scale. They differ in size, battery life, use cases, and level of resistance to exogenous factors. Here are the common types of beacons:

• Standard beacon (devices the size of a Wi-Fi router or smaller) for proximity solutions and indoor tracking.
• Portable/small beacon (the size of a credit card or big sticker) for asset tracking and proximity solutions.
• USB beacon (small, portable, quick to deploy) — beacons the size of a flash drive for asset tracking and proximity solutions.
• Video beacon — devices plugged into the back of a screen to deliver contextual visual information (for example, a user approaches a display in a food joint and shows personalized offers based on usage of the beacon-enabling app) as well as for digital signage.
• AI beacon. A machine learning-driven device that can detect different movements and gestures.
• Sticker beacon — the smallest of beacons, used for asset tracking.
• Parent beacon (the size of big Wi-Fi routers) is used to track other beacons, gather data and store it in the cloud, and more.
• Dedicated beacon — devices resistant to exogenous factors (dust, water, shattering, antistatic and UV) for indoor tracking and proximity solutions in harsh environments.

Most of the aforementioned beacon types emerged in the years since the inception of beacon technology to accommodate the business needs of different companies, so new ones may come into being in the future, giving companies new options to engage customers.

What Are Beacons Used For?

Beacon technology can improve the user experience both at the front and back end of almost any company, helping to streamline operations and gather data, drive sales, and deliver unique user experiences. Still, the retail and marketing industries benefit from beacons the most, as they are almost tailor-made for the two.

Front & Back End of Business

At the front end, for example, at a retail store, you can pair beacons with a custom app to notify shoppers about limited in-store offers, deliver personalized promotions, and provide navigation at vast retail locations.

At the back end, beacons are as effective. You can set up a beacon-based indoor positioning system at a warehouse to simplify and speed up the indoor navigation in its bigger areas for new employees and representatives of third parties. Paired with an enterprise software development solution, beacons also allow for indoor personnel tracking to improve on-premises security as well as asset tracking.

For instance, an employee of a large enterprise needs a new laptop. The system administrator chooses one from the list of available laptops as a sticker beacon on it shows its location.

Beacons Elevate Marketing

Today, companies and their marketing teams mostly rely on emerging wireless technologies to cater to and engage customers. Small and hidden beacons have a smaller appeal, but they can have a big impact.

In a standout example, RedBull used beacon stickers to monitor the movements of audience members at a music event. First, the devices collected different types of data. Then, scientists used it to create algorithms and deep neural networks to understand the movements and energy levels of audience members.

The results were visualized on the walls of a separate room at the venue. RedBull then went further, allowing the audience to take their individual information home as a souvenir, embroidered on a scarf. This is also an example of how beacon technology can be used successfully without a mobile phone app.

Other Uses in Marketing:

• A new avenue for driving sales. You can pair beacons with mobile to deliver highly personalized location-based ads.
• One more way to collect data. Beacons can gather customer data that will help you create better, more personalized experiences, as well as gain additional insights into customers’ behavior.
• New meaningful experiences for customers. Like RedBull’s beacon stickers, some types of beacons give a lot of room to experiment and develop creative solutions that your customers will remember for a long time.

  Beacons are used in retail settings to send personalized promotions, discounts, and advertisements to customers’ smartphones when they are near a specific product or within a store. Retailers use beacons to guide customers through stores, helping them find products or providing information about sales and special offers.

Other industries that can leverage beacon technology:

• Healthcare ((patient tracking and safety, and equipment management)
• Hospitality (navigation, gamification, smart hotel rooms)
• Sports (navigation on big arenas, proximity marketing)
• Logistics & warehousing (asset tracking, security)
• Transportation (navigation, passenger guidance, vehicle tracking, transit efficiency)
• Smart home development (beacons can provide navigations to guests at vast properties; security)
• Construction (geofencing perimeters for projects in the middle of nowhere)
• Events (gamification, navigation, schedule notifications)
• Education (interactive learning, navigation)

Video beacons are the go-to example when it comes to showcasing the high value of beacon technology. For example, you can use video beacons to deliver contextual information and provide navigation at a hotel or resort. Such experiences are fresh, save time, and imbue the customer journey with small but memorable moments.

Beacon Technology: Pros and Cons 

Beacon technology has a wide array of solid business benefits and only a few disadvantages.

Pros:

• Low cost. Standard models are small and made of low-cost materials like plastic, making beacons more cost-effective than other state-of-the-art technologies.
• Easy to adopt. Most beacon devices are quick to deploy. Some, like USB beacons, are as easy to install as a computer mouse. Deploying beacons at scale is also easy with parent beacons.
• Tailored-made customer experiences. Beacon technology allows companies send personalized notifications and ads.
• Reliability and scalability. Beacons are dependable devices: unlike GPS, they use Bluetooth, so thick walls or underground areas will not block their signals. More so, some beacon suppliers offer beacons that are reinforced against exogenous factors.
• Vast outreach. All modern smartphones support beacon technology (your users need iOS 7 or Android 4 or higher installed on their devices), so you can reach out to virtually anyone, anywhere.
• Easy implementation. The majority of beacon technology offerings come with a software development kit (SDK) and back end management tools, making it easy to integrate a beacon solution with existing applications.

Cons:

• Users may opt-out. Most beacon-based solutions require user consent. Today, the flow of content from all sides seems endless, and even if you use only a few apps daily, you still get a lot of notifications. In the face of content overload, some users can simply turn off Bluetooth on their smartphones, preferring to miss out on your beacon-based experience, even if it is exciting and non-intrusive.
• A mobile app is a must. In most cases, beacons work only when paired with a mobile app. There are workarounds like third-party applications, but if you want to deliver a smooth experience your customers will definitely enjoy, you will at least need to add a feature to your application.
• Creating a beacon solution is not ubiquitously easy. The use cases of beacon technology are straightforward in the retail and marketing industries. Beacons can also be used effectively by hotels and companies that develop smart room solutions. In other cases, you should get creative or hire a software engineer with expertise in beacon-based or geofencing solutions, and this will take time, more so if your company is a medium-sized business or enterprise that needs a highly scalable solution.

Conclusion: The Future of Beacons

According to Global Market Insights, the beacon technology market is projected to surpass $25 billion in 2025.

Back in 2018, Google started sending out free beacons to small businesses across the U.S., helping them stand out to nearby customers. It was a game-changer, and since then, beacons have only gotten smarter. By 2019, Estimote introduced the AI beacon, pushing the boundaries with features like contextual awareness and real-time data processing.

Now, in 2024, beacons are everywhere. They’re guiding travelers through busy airports and train stations, optimizing public transit with real-time vehicle tracking, and providing a non-intrusive, spot-on way to engage with customers in a world overloaded with content.

Taking all this into account, it is safe to assume that more and more business locations will have beacons installed in them in the coming years. Although long-term projections might not reflect reality in the end, there are no visible hurdles on the way of large-scale beacon technology adoption. After all, beacons demand little to invest, are easy to set up and manage regardless of business scale, and bring clear, straightforward benefits to business across industries. Why not leverage them?

At Intellectsoft, we empower companies and their workforce with cutting-edge transformative solutions and data-driven insights. Are you and your organization ready to shift the mindsets and get the most out of innovations?
Talk to our experts and find out more about the custom solutions and how your business or project can start benefiting from them today!

With over 17 years of experience, Intellectsoft has been a trusted partner for businesses in industries like Fintech, Healthcare, Construction, Hospitality, Logistics, and eCommerce. Our global team of tech experts is dedicated to delivering solutions that fit your needs perfectly — whether you’re creating a new product, upgrading a system, or adopting the latest technologies.

The post What Are Beacons and Why They Matter for Your Company appeared first on Intellectsoft Blog.

With generative AI, that future is already here.

This technology is laying the groundwork for entirely novel business ventures and strategic approaches and has been heralded as a generational technological innovation. 

The global generative AI market is growing exponentially. Currently standing at an impressive $44.89 billion, it’s on track for significant expansion across multiple industries, including retail, banking, insurance, and marketing teams. 

The question now becomes, is the industrial revolution of our time? Or is it another blip in the business product life cycle? This article looks at how AI has become the future of business transformation and why it’s here to stay.

What is Generative AI?

We’ll let you in on a little secret. The introduction to this article was written entirely by generative AI (don’t worry, the rest of this article is 100% written the good old-fashioned way). 

Generative AI is most widely known by the public at large for its ability to create the written word and the production of stunning images from a mere text prompt. Types of generative AI like ChatGPT and Gemini have become household names. 

Just like ‘Google’ changed how we search online, or how ‘Facebook’ redefined the way we market our products, they’re changing the very face of business itself.

But beyond writing a few lazy articles for college students or creating interesting artwork, what exactly is generative AI?

Generative AI refers to a type of Artificial Intelligence that can generate completely new and unique content, from texts and images to music and even code. Generative AI learns how to do this after being trained on a set of human-generated data in a process known as ‘Machine Learning,’ or ‘ML’ for short. 

ML and deep learning allow AI to learn patterns and features from data, enabling it to generate new content based on what it has learned from human datasets. 

Once fully trained, it can ‘generate’ new content without explicit human input.

Why It’s Important

For businesses, the generative AI market translates into turning the cogs of creativity without constant human oversight. Picture the freedom to design processes that run themselves while your team focuses on strategy over routine tasks. 

It means allocating your resources with laser precision and making informed decisions based on foresight rather than hunches.

Generative AI is catapulting companies into a future brimming with uncharted potential, offering tools not only for survival but also for reimagining what’s possible in commerce and service offerings.

For business, embracing generative AI is like opening doors to rooms unknown: vital if staying ahead of the curve isn’t merely an aspiration but a business imperative.

How Does Generative AI Work?

Generative AI technology uses numerous sophisticated neural network architectures and algorithms to learn from truly massive amounts of human-generated data and create new and unique content. 

These architectures work side by side and generally include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), transformers, and foundation models. For those interested in how they work together, we’ll explore them in more detail below.

Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or “GANs,” are like a high-stakes game between two rival chess players. One player, the generator, develops new data points and creates fresh pieces to put into play. 

The other player is the discriminator. It has an eagle eye for detail and judges whether those newly minted data points look authentic when squared against true data.

The two go back and forth, refining moves until what they’re producing could pass for genuine at first glance. This dynamic duo has found its niche in business operations where creating something new yet convincingly real can be quite valuable.

Variational Autoencoders (VAEs)

Think of VAEs as diligent librarians. They excel at organizing information into more manageable, compact forms while ensuring that nothing critical is lost in the process. 

When you need them to find or reconstruct particulars from this condensed library to create something new (perhaps a document that’s never been written before), they’re on it with remarkable accuracy. 

Here, you can see why their potential in fields where every detail matters, such as finance and healthcare, is so high.

Transformers and Foundation Models

Transformers pay close attention not just to the words spoken but also how they relate to each other for a full grasp of meaning. That level of discernment is invaluable for machines meant to understand human language nuances and translate or respond accordingly.

Meanwhile, foundation models are like multitasking prodigies, initially nurtured on diverse data diets. These adaptable systems readily shift gears across various jobs without missing a beat. 

Crafting eloquent chatbot responses, translating languages seamlessly, and generating articles and reports with finesse all streamline customer interactions perfectly.

Real-World Applications of Generative AI in Business

Practical use cases for these models stretch across multiple industries and offer tangible benefits. 

• GANs play a significant role in making virtual worlds within video games and VR spaces more captivating, elevating the user’s enjoyment. 
• VAEs contribute to e-commerce by refining how online shops suggest items that shoppers might like. 

And when it comes to transformers, they’re behind the scenes of those smart chatbots replying swiftly and helpfully on many customer service platforms.

When a business brings these sophisticated generative AI tools into its fold, They have the potential to completely change and refine existing processes for the better. 

Businesses typically witness marked improvements in efficiency. Products and services feel more tailored to individual needs, and there’s a noticeable leap forward in creativity across different sectors of their business.

Benefits of Generative AI

Generative AI is reshaping the way industries operate, introducing levels of efficiency and scalability previously out of reach. Picture your business not just keeping pace but setting the tempo in a rapidly shifting market.

Let’s consider what these systems can do for you. They refine workflows to such an extent that “streamlined” seems like an understatement—they’re practically redefining process optimization. As your company expands, so does the prowess of generative AI. It adapts seamlessly, ensuring growth doesn’t become a bottleneck.

But here’s where it gets truly interesting: Customization.

Generative AI grants businesses the golden opportunity to craft products and services with precision, meeting each customer on their terms. In a day and age where customers rate personalization above even the products themselves, isn’t that exactly where any successful enterprise wants to be?

Impact on Industries

Let’s talk about how generative AI is making a mark across various sectors:

In healthcare, this technology acts like a turbo-charged assistant in drug research. It zips through the data on molecular interactions that used to take an age to process. For patients, think of it as having a guardian angel who develops treatment plans suited just for them while boosting recovery and health outcomes.

Switch gears to retail, where shopping becomes less about sifting through endless aisles and more tailored-to-you showcases. Generative AI sifts through heaps of customer data quicker than any human could, predicting what buyers might want next before they even click ‘search.’

Over in the manufacturing industry, imagine designing new products not over months but days with the help of 3D models churned out by AI—getting these innovations onto shelves swiftly while also keeping warehouses smartly stocked by anticipating demand curves with minimal manual meddling.

The entertainment and media realm keeps viewers glued because generative AI learns what tickles their fancy. It’s a matchmaker between audience preferences and content offerings, which means stickier subscription numbers. Industry giants like Netflix have been implementing this tech for quite some time now with incredible results.

Lastly, fintech businesses find a sidekick in AI. Tedious tasks like punching numbers are automated, while risk calculations get sharper. Plus, these models act almost clairvoyant when peeking into market trends so firms can make smarter investment calls.

Business Value Contribution

Generative AI is shaping the operational dynamics of businesses, serving as a catalyst for creativity and efficiency.

Imagine the possibilities when time-consuming creative tasks are streamlined by smart systems that learn and improve. Suddenly, your team can fast-track new products from concept to customer’s hands without breaking a sweat.

Take design, for example. Now, powerful software tools infused with AI capabilities let you whip up designs at lightning speed. You get instant feedback, you adapt immediately, and what used to be an endurance race against competitors becomes more like a sprint.

The brilliance of this technology extends beyond speed: Think about diving into oceans of data that would otherwise drown mere mortals in analysis paralysis. 

With generative AI, insights come not just faster but sharper, too, enabling you to stay ahead of market trends and really ‘get’ what your customers yearn for before they’ve fully articulated it themselves.

By 2030, forecasts suggest that AI-augmented decision support won’t just be a ‘should have.’ It will define business strategies across industries, with nearly half of all derived value hailing from these smarter systems, helping humans make bolder moves confidently.

Common Generative AI Use Cases

The effects of generative AI have already been significant across key industries, transforming traditional business operations with its innovative applications.

Here are a few of the common practical applications:

Content Creation

Perhaps no industry has felt the impact of Generative AI as much as content creation. Here, AI streamlines crafting written pieces, images, and various multimedia content. It’s a boon for marketing teams aiming to churn out captivating material with greater efficiency.

Customer Support

Whether they know it or not, around 88% of customers have already interacted with an AI-powered chatbot. Advanced chatbots and virtual assistants powered by this technology can field customer queries round-the-clock. This enhancement in service comes with the diminished need for human intervention. 

Security

The realm of cybersecurity benefits from simulated attack scenarios concocted by generative AI, serving as drills that bolster protective measures. It’s cheaper than penetration testing and unlike its human counterpart, the simulated attacks are relentless.

Predictive Maintenance

Foreseeing equipment failures before they occur is now possible thanks to AI capabilities. A tool for preventing unnecessary expenses and disruptions in industrial environments.

Cutting-Edge Diagnostic Systems

In healthcare, professionals can make more accurate diagnoses by analyzing patterns extracted from data collections, significantly advancing patient care. Perhaps one of the more well-known examples is the Cyto-AiSCAN AI, which is used to detect cancer. Interestingly, its original application was identifying pastries in a Japanese bakery.

Automated Financial Advisory

Personalized financial guidance is now available through AI technology. Clients receive advice based on thorough data analysis that can enhance investment strategies for both individual and corporate portfolios.

Choosing Your Tech Partner To Integrate Generative AI Into Your Business

Selecting the ideal technology collaborator is pivotal for a smooth and successful integration of generative AI into your enterprise. Intellectsoft distinguishes itself as an authoritative provider of robust AI development services.

For more than a decade, Intellectsoft has steered clients through the intricate process of digital revolution. Recognizing the essential role that AI plays in bolstering business profits is second nature to us. 

Our knowledgeable developers are at your service, offering bespoke solutions meticulously developed to meet your unique needs.

Explore our services and discover the transformative impact generative AI can have on your operations: Intellectsoft AI Development Services.

Final Thoughts

Tech trends can be fleeting, yet there’s a sense that generative AI has staked its claim in the digital terrain with more permanence than Google+ or the Facebook phone ever managed. 

Weaving this technology into your business framework might just unlock innovative strategies and ensure your offerings maintain their edge in an ever-competitive market.

Generative AI development stands at the forefront of change, regardless of industry boundaries. Consider it an industrial revolution 2.0—one that promises to redefine how we approach our businesses and markets.

As adoption grows, expect to witness new industries emerging from this technological boon. Here at Intellectsoft, we’re not waiting for tomorrow. We’ve already embraced what feels like the next chapter.

Is your enterprise prepared for what’s unfolding? Reach out to our specialists today and discover how seamless integration can be under expert guidance.

The post Generative AI Explained: What It Means for Your Business appeared first on Intellectsoft Blog.

The game development industry even birthed a new movement known as Creative Coding. While some sources define it solely as creating digital art through code, the gaming world expands this concept significantly. Creative Coding is actively practiced at game development conventions and incorporated into the curriculum of software engineering schools. It represents a unique blend of art, design, and scientific principles. This approach transcends conventional thinking, fostering the creation of extraordinary experiences where the physical and digital realms converge.

Game Dev coders endeavor to bridge the gap between the digital and physical worlds by enriching digital elements with the ability to interact with real-world environments. This allows digital information to become “embodied,” responding to the viewer, dynamically altering the context, and establishing intricate forms of interaction and behavior. Let’s see how AI and its disruptive tech counterparts like VR are currently transforming the gaming industry.

So, What is AI in Gaming?

Artificial intelligence (AI) in games extends beyond the boundaries of traditional interactive experiences. These systems learn about players’ behavior during gameplay, going beyond pre-programmed responses, which allows them to dynamically adapt and provide an optimal gaming experience tailored to each individual.

Role of Artificial Intelligence in the Gaming Industry

The likely reason why AI in the gaming industry strives, particularly in gaming, might be attributed not only to the fact that it is a lucrative market worth billions of dollars. The roots of this interest might stem from the deeply human nature of wanting to gain an advantage in a competitive field to achieve goals. Remember the Game Theory? It is a mathematical study that drives strategic decision-making among individuals and bigger entities like companies or even entire societies. 

Despite the games being traditionally related to entertainment, scientists are working on some serious applications of AI by training their models in the gaming setting. Possible real-world scenarios include military training, education, driving training, medical training, and mental health therapy. 

What’s more, game dev is at the forefront of software development innovation at scale. For instance, IEEE’s annual conference is a platform for presenting research papers. The latest findings are highlighted by the gaming giant EA:

A case study [for automated testing] is Battlefield V, which requires testing of 601 different features amounting to around 0.5M hours of testing if done manually. This corresponds to ~300 work years.

In response to these challenges, researchers are training Reinforcement Learning (RL) and other models, thus striving to rise beyond the limitations of scripted bots (while manual testing becomes increasingly obsolete). Additionally, developers spend thousands of hours on routine tasks like programming characters’ gestures, which could be automated with AI, freeing their time for more interesting tasks.

In this light, integrating AI in gaming and testing appears not just desirable but essential.

How is AI Used in Gaming?

Gaming industry seems to be leading the race in AI creation and evolution. The market has flourished with AI algorithms that are actually working in cloud gaming platforms and scaling on an ongoing basis.

Non-Player Character (NPC) Behavior

Older-generation NPCs are known for their unnatural body language, which results from their lack of responsiveness. AI engineers have figured out multiple ways of enhancing NPC behavior to make games more enjoyable. For example, smart NPCs were implemented in Unreal Engine. According to the original paper behind the research, scientists developed an algorithm based on the use of finite state machines and fuzzy state machines. Finite state machines adjust the NPC behavior based on a set of states (patrolling, chasing, etc.) that are triggered by events (seeing the player, hearing a noise). Fuzzy state machines allow for smooth and nuanced transitions between states based on fuzzy logic (spectrum of variables as opposed to binary logic). Additional models observed in smart NPCs are artificial neural networks that let them learn from experiences and genetic algorithms that let the behaviors evolve over time.

Procedural Content Generation

Machine learning algorithms can generate new levels, items, and quests. This can help keep games fresh and interesting for players, who will always encounter new content. Procedural simply means that developers do not let AIs go haywire while only letting them generate new content based on the predetermined set of rules. This helps them avoid unethical content generation and propagation. Generative adversarial networks work as an improvement mechanism where a generator and a discriminator work “against” each other, which results in more realistic and creative content.

Cheating Detection

AI can be used to detect cheating players. This can help to keep games fair for everyone. They do this by employing anomaly detection to identify and isolate deviating patterns. Supervised machine learning algorithms can be trained on cheating data and non-cheating data so that AI can understand the difference.

Dynamic Difficulty Adjustment

Similar to dynamic online exams where the questions adjust to the learner’s knowledge level, AI in the gaming industry can adjust a game’s difficulty based on a player’s skill level. This can help ensure that the game is challenging enough to keep it interesting, yet at the same time not frustrating. First of all, key metrics to track are identified, and tracking mechanisms are activated. For example, these could be time to complete a level, number of enemies killed, weapons used, etc. Then, the model of the player’s skill level is created. This model can then be used to predict how the player will perform on future tasks and adjust the difficulty accordingly.

Personalized Gaming Experience

Content recommendations that are familiar to us from most of the video and music content apps have also been implemented in cloud gaming. When a user logs into a game, they might be recommended items, heroes, and quests that they are more likely to enjoy. Furthermore, the whole game world can adjust based on the user’s preferences, including the landscape, quests, and AI companions. For example, No Man’s Sky introduced the possibility of creating a whopping 18 quintillion open worlds that are modified based on the player’s experiences.

Enhanced Graphics with AI Upscaling

Cloud gaming lets you play high-end games on devices that wouldn’t normally be powerful enough. However, the data stream from the powerful remote servers can be hefty, requiring good internet bandwidth. Here’s where AI upscaling comes in, offering a win-win situation for both cloud gaming services and players. The remote server renders the game at a lower resolution, reducing the data required for transmission, while the AI model analyzes the incoming lower-resolution image and predicts missing details that would be needed to get a high-resolution experience. Finally, the missing details are generated, and the upscaled imagery is delivered right to your laptop or mobile device.

Major Trends in AI Gaming

Below, we will describe in more detail the key AI trends in the gaming industry.

Generative AI

One of the most ubiquitous, this type of AI in gaming is much more than just text-based chatting. Yes, it can elevate dialogues, but it can also adapt gestures and tone of speech to what the character is saying. This can be used to create more realistic and believable game worlds and characters. When developing open worlds and personalized storylines, game creators rely on generative AI in procedural content generation and speech-to-gesture animation, which frees developers from the overwhelming amount of tedious routine jobs.

AR/VR Technology

This can be used to create more immersive gaming experiences. For example, AR could be used to overlay game elements onto the real world, or VR could be used to create a completely virtual world for players to explore. Technology-wise, hardware quality has skyrocketed over the last few years. Major brands are offering truly immersive experiences with photorealistic image quality, eye motion tracking, body motion tracking, spatial audio, and even sensors that enable tactile sensations and feedback. 

BCI-Enabled Games

Speaking of body signal tracking, some EEG headset manufacturers claim that they implemented neural interfaces for AI in gaming that allow users to play with the power of their minds. Big players like Valve (the owner of Steam) are actively researching the niche, but the progress could be faster. Meanwhile, famous Twitch streamers are trying to play games with their DIY BCI headsets, while critics think it’s a joke because EEG headsets can’t tell meditation state from the hustle of a busy convention space. 

Multiplayer Games

Cloud-based gaming revamped the whole industry, allowing users to play multiple games on a single platform regardless of their device’s processing power. Additionally, it responds to a growing trend of multiplayer games. With an estimated 1.86 billion PC gamers worldwide, the ability to come together in a game is what fuels the industry’s growth. To spice it up a little, developers have something more to offer.

AI Opponents

Smart NPCs are fun, but how about smart opponents? Of course, we are all familiar with playing “against the computer,” which has been historically challenging. However, AI in gaming aims to make this experience more adaptive and exciting. For example, case-based reasoning systems store past experiences and use them to make decisions in new situations. In a game, a case-based reasoning system could be used to allow an AI opponent to learn from its mistakes and adapt its behavior accordingly.

How to Use AI in Game Development?

AI in Game Programming

Sometimes boilerplate code is what you need. If the unit of writing is destined to be reused without any changes to it, then why write it every time by hand? AI can automate the creation of such basic backend code for common functionalities like basic enemy movement or environmental interactions. This frees up developers to focus on more complex backend logic specific to the game.

AI in Game Design

AI in game development is still evolving, but it holds promise for assisting in the development process, not necessarily replacing it entirely. Multiple use cases require continuous optimization and automation. Luckily, AI is specifically good at that kind of stuff.

For example, you want to optimize server performance. You can employ AI algorithms that analyze player behavior and server load to suggest resource allocation and server infrastructure adjustments. This can help maintain smooth gameplay and scalability for a large player base.

Furthermore, AI in gaming can help you monitor server activity and predict potential issues like outages or bottlenecks. This allows developers to take preventative measures and ensure a stable gaming experience.

AI in Game Testing and Debugging

Machine learning algorithms can analyze vast amounts of gameplay data to identify potential bugs and glitches, allowing programmers to address them before release. As discussed above, AI can potentially mitigate the issue of the overwhelming amount of testing scenarios, even in an automated setting.

QA engineers strive to deploy AI that can control virtual testers to explore the game world, identify areas where players might get stuck, and uncover potential balance issues. This reduces the manual effort required for thorough testing.

After AI playtesting, algorithms can employ data-driven analysis to review the collected data and pinpoint problematic areas or unbalanced mechanics, allowing designers to refine the game.

Benefits and Disadvantages of Using AI in Games

AI in gaming offers a double-edged sword. On the bright side, it can create sprawling open worlds, enrich interactions with NPCs, and render stunning visuals. It also proves to be valuable in AI developers’ pipelines, automating simple repetitive tasks and performing QA on massive amounts of gaming material.

However, content generation might need help to rise beyond simple tasks. Procedural content, though vast, can be unpredictable and repetitive. For example, open worlds might offer generic, repetitive patterns, while human characters might have three hands or two heads.

Training AI in game development requires significant computational resources, potentially increasing gaming prices and amplifying the unnecessary carbon footprint. Additionally, ethical concerns linger as AI may struggle to distinguish between intended and unintended actions. For instance, in content moderation, a chat joke might be interpreted as hate, and the user will be banned, while a player who is displaying toxic behavior during the quest will stay in the game.

The Future of AI in the Gaming Industry

Future applications of AI in gaming involve building on new hardware capabilities. With the rise of edge AI devices, this technology is being brought closer to the sources of collected data (like blood pressure, eye motion, etc.). If that is complemented with VR/AR, brain-computer interfaces, and quantum computing, the games will probably become even better than real life. 

The increased opportunities for open worlds and character development in virtual realities will allow for social interactions and creative exploration. However, ethical concerns about such an escape from the real world are rising proportionally to AI gaming’s popularity.

Summing Up

Overall, AI in the gaming industry enables enhanced player experiences and software scalability at an unprecedented pace, so it seems like we will see it becoming mainstream soon. Some applications of AI in gaming are purely practical (like AI testing in QA), while others are quite far-fetched (like BCI). One thing we know for sure is that the implementation of AI requires a lot of research and experimentation. An iterative approach can fuel this technology’s evolution because, as they say, “A journey of a thousand miles begins with a single step.”

Intellectsoft collaborates with various companies exploring AI, from early-stage startups to giants like Sony. You can talk to our experts and obtain insights about your idea or an ongoing project absolutely free of charge — just hit the button below to schedule your spot.

The post AI in Gaming: Transforming and Reshaping the Industry appeared first on Intellectsoft Blog.

Building AI software is about designing human-centered solutions. This article guides you through a thoughtful and real approach that breaks the mold of boilerplate, formulaic methods.

Having been navigating the wild world of IT since 2007, we’ve seen the good, the bad, and the downright misleading when it comes to building AI. This guide aims to cut through the noise and dive into the practical trenches of crafting real-world AI solutions that solve problems, not chase trends.

How to Create an AI Software: Major Steps

Planning Your AI Software (Defining Business Goals)

All in all, building AI is about mimicking human intelligence, i.e., learning, logical reasoning, making decisions, and, ultimately, solving problems. Machine Learning is usually a starting point for many businesses in the AI journey because they want to learn from vast amounts of data and build optimal solutions that go beyond the limitations of human error and cognitive capacity. Then, if ML models begin to learn and self-optimize effectively without human facilitation, there is a possibility of them becoming true AI.

This perspective sounds a little intimidating, but it is considered a North Star for the majority of businesses today, regardless of whether IT is a part of their value proposition or not. McKinsey reports that over 40% of respondents will increase AI investments. Peter H. Diamandis, a future-focused serial entrepreneur, puts it very clear: “In 2023, more than 1 in 4 dollars invested by VCs in US startups went to an AI-related company.”

Let’s say you decided it’s time to roll up your sleeves and start the AI project. As we mentioned, you can’t go wrong with problem-solving. Identify areas where other AIs currently struggle and work on engineering solutions. You might also consider making custom AI on demand (for example, in healthcare or construction). That would be smart because of the specificity of datasets and relationships between hyperparameters. However, this approach is based on scarcity (create something exclusive that no one else will have), – which is extremely hard to scale. On the other hand, if you aim at creating some core algorithm that gracefully resolves complexities that everyone else is having, you might achieve the true North Star of your business sooner than you know. 

Alternatively, consider the reverse psychology principle if you don’t like to go with the flow. For example, if everyone is working on AI, what impact does it have on our use of data and electrical signals? Maybe we can make data storage or transmission more efficient. You need an idea that will truly stand out to get your foot in the door of venture investments. Sometimes, instead of pursuing a vague mission of making the world a better place, choosing a niche and sticking with value creation for that particular AI niche is more beneficial.

To put it simply, imagine AI companies like different floors in a giant building, each adding value in a unique way.

• The common area (Applications) is filled with companies creating finished products like apps. It can be tough for them to stand out from the crowd, just like competitors selling similar products in a store.
• The middle floor (Infrastructure) holds the tools that help AI engineers build these applications, kind of like the workshop where tools are made for different projects. Can you create a library or a framework? Go for it!
• The boiler room (Models) is where you boldly go, while no man has ever been there before. These are the special ingredients (parameters) and secret recipes (formulas) that make AI tools work their wonders. The primary focus is on advanced mathematical operations and complex logic between them.
• Foundation, pillars, ceiling (Hardware) This level includes the powerful machinery that runs everything, similar to the power plant that keeps the whole building functioning. While important, working on this floor can be challenging, just like building and maintaining a power plant requires a lot of resources.

Once you decide on the right way to go, it’s time to move on to the next step.

Data Collection and Preparation

A lot of novice AI makers are tempted by vast amounts of public data that they can get absolutely free. However, it might be useful to look beyond those common sources. Instead, what if you collect niche industry data? This strategy might result in more accurate predictions because your model will not be distracted by noise, and you won’t have to spend so much resources on cleaning your data. Try to diversify your data sources: smart devices, citizen science initiatives, and likewise pools of information with a wealth of real-world scenarios might be more valuable than just downloading public data that everyone else is likely to use for building their models. If you have no idea where to take this data, aim at partnering with subject matter experts, researchers, and organizations that are collecting this data and are interested in enhancing their insights.

Implement a pipeline that preserves its initial state and tracks modification to ensure data integrity. This might be based on the blockchain of version control systems. This will help you eliminate bias and errors down the line.

Finally, you need to make sure that the data you collect is interpretable. Use techniques like feature importance analysis and counterfactual explanations to understand how data points to influence your model’s decisions. This can help identify potential biases and ensure responsible AI development.

AI Model Selection and Development

The selection of an AI model depends on the business value that you decide to pursue. After crafting a business case and project charter, your software engineering stakeholders will proceed with outlining a network architecture, overarching logic of the solution, technical roadmap, and tech stack. 

Finding experienced software folks who code in specialized AI-oriented libraries might be challenging. If you are a novice startup, you might not possess that experience yourself. If you need to move fast, the viable solution here is to gather brilliant software developers and embark on a learning journey. 

To get started with coding, you need to decide which of the available AI architectures you are going to use to achieve your business objectives. 

If you have never programmed neural networks before, keep calm! Just think of them as colleagues in your company:

Convolutional Neural Networks (CNNs)

• Imagine these as image processing experts. They’re like programs that break down images into smaller pieces using multiple “filters.” By analyzing these pieces, they can identify objects, classify images, and even segment different parts within an image.

Recurrent Neural Networks (RNNs)

• Think of these as business intelligence specialists. They excel at handling data that comes in order, like text or time series. They have a “memory” that allows them to remember past information and use it to understand the current data point better. This makes them perfect for tasks like analyzing text sentiment, predicting future patterns, or understanding trends over time.

Generative Adversarial Networks (GANs)

• Imagine these as a developer and product owner. They consist of two parts: a “generator” and a “discriminator.” The generator tries to create new data based on existing knowledge, while the discriminator tries to distinguish real data from the generated one and give feedback on what is acceptable and what is not. This review process pushes the generator to become better at creating data.

Autoencoders

• Think of these as storage optimization experts. They’re like programs that learn to represent complex data in a simpler way. They achieve this by compressing the data into a smaller form (a “latent representation”) while still capturing the important features. This compressed data can then be used for other tasks like extracting informative features for further analysis or detecting anomalies in datasets.

Transformers

• These are like customer support experts in the world of AI, particularly for tasks involving text. They use a special mechanism called “attention” that allows them to focus on specific parts of a text sequence, just like you might focus on certain keywords in a sentence. This helps them understand complex relationships between words, making them powerful for tasks like machine translation, analyzing emotions in text, and summarizing large amounts of text data.

By understanding particular AI roles in simple terms, you can manipulate their functionality more easily without getting lost in their mathematical complexity.

Training and Evaluation

AI training is not a one-off activity. In fact, it requires multiple iterations of fine-tuning the algorithm. This doesn’t mean that the algorithm you built was wrong or buggy. On the contrary, fine-tuning is quite beneficial, provided that it gives valuable improvements with every iteration. 

To boot, some interesting findings are only discoverable in the process of experimentation. You wouldn’t have known them otherwise. If additional robustness is needed, use augmentation to increase dataset diversity. Examples of fine-tuning-worthy parameters include: Regularization Strength, batch size, learning rate/schedule, decay rate/schedule, number of hidden layers, and dropout rate. 

Testing the Model

While algorithms consistently outperform humans in tasks like predicting employee success and optimizing supply chains, people struggle to trust them due to a phenomenon called “algorithm aversion.” This lack of trust stems from our inability to understand the inner workings of algorithms and learn from their mistakes, unlike human advisors. 

Studies show that simply demonstrating an algorithm’s ability to learn through its past performance significantly increases user trust and preference for the algorithm compared to humans, even when both have the same success rate. Additionally, even implying an algorithm’s potential for future learning with terms like “machine learning” can boost user acceptance.

On a technical level, by employing a comprehensive testing strategy and embracing XAI techniques, QA specialists can ensure the development of reliable, trustworthy, and user-friendly AI software.

A testing routine could look something like this:

1. Test preparation:
   • Identify the desired user experience.
   • Establish quantifiable metrics.
2. Data preparation:
   • Evaluate data quality and diversity.
   • Test for data poisoning and manipulation.
   • Implement data augmentation techniques.
3. Running functionality testing:
   • Test core functionalities.
   • Run scenario testing.
   • Stress test the system.
4. Ensuring explainability:
   • Utilize explainable AI (XAI) techniques.
   • Test for fairness and bias.
5. Testing integration and security:
   • Test integration with other systems.
   • Perform security testing.
6. User testing:
   • Involve real users to gather feedback.
   • Conduct A/B testing against humans and/or non-AI software.

Integration and Deployment

Relying on cloud infrastructure might be efficient, but to go even further, consider deploying lightweight AI models on edge devices closer to data sources. As a result, you can communicate a lot of interesting value props, like reducing latency, improving data privacy by keeping sensitive data localized, and increasing system resilience in case of network disruptions.

Before fully deploying the AI, you can also try to experiment by running it in a “shadow mode” alongside existing systems. This allows you to observe its performance in real-world scenarios, compare its outputs to human decisions, and refine the model or decision-making logic based on insights gleaned from this shadow phase.

Last but not least, ensure ethical AI deployment. Go beyond the technical level by creating policies that address possible bias and unethical usage. Propose detection and mitigation strategies for unacceptable content. For example, implement human-in-the-loop to monitor the output. You might also include environmental considerations like designing efficient architectures that minimize computational requirements and memory footprint.

Best Practices to Develop AI Software from Scratch

If you’ve gone this far in this blog post, you already know that ensuring efficient and reliable AI software development requires a multifaceted approach. To ensure you can keep track of multiple initiatives when you build AI software, adopting version control systems is a good idea. This allows for tracking different model architectures, hyperparameter configurations, and training runs, simultaneously ensuring that ethical concerns are met. 

Secondly, containerization with tools like Docker is a valuable strategy. It packages your code and dependencies into a self-contained unit, streamlining deployment across various environments and guaranteeing consistent execution. It is also easier to revert to more successful images or to go back and see why less successful experiments didn’t work.

Finally, proactive problem identification is key. A robust logging system captures information about model training, inference, and system errors. This data proves invaluable for debugging, performance analysis, and pinpointing potential issues before they escalate in production. 

Common Challenges in Developing AI Software

Scarce or Biased Data

This consideration is like a double-edged sword. If you feed your model with too much diverse data, it might be difficult for it to learn, which will lead to errors. On the contrary, if you have too little data or biased data, the model will make errors, too. Unfortunately, there is no uniform solution when you decide how to build AI software. You need to experiment and fine-tune your algorithms for your particular use cases.

Lack of Model Explainability

To effectively debug the model, you need to be able to trace the reason for problems that arise. However, many AI models come to their conclusions in mysterious ways. That is why developers strive to create Explainable AI (XAI), where it will be possible to understand how the model came to a particular outcome.

High Costs of Training and Deployment

Training an AI model might require you to go wild with those CPU cores. As a result, infrastructure and computational costs skyrocket, and it becomes difficult to achieve a substantial ROI. When designing a technological roadmap, consider the future need for optimizing resource allocation.

After-Release Fears

Integrating AI models into production environments holds a certain degree of risk when building AI software. The erroneous output might be offensive to certain groups of people or even plainly wrong. That is why AI developers can’t afford just to let AI go wherever it wants to go. Instead, they need to set up continuous monitoring, logging, and incident response routines. 

AI Software Solutions: Success Stories

While AI models are still regarded as a technology of the distant future, some businesses have already embraced their power and are now enjoying benefits.

JP Morgan Chase

One of the world’s oldest chain of banks, recently started using an anti-fraud AI model. OmniAI works as a helper of human data scientists, helping them extract valuable insights from vast varieties of unstructured information. The result is quite promising: the company claims to have reduced fraudulent transaction attempts by 80%.

Duolingo

Duolingo Max uses ChatGPT 4 to personalize the learning experience. They also host English language proficiency exams that automatically adapt to the knowledge level of the student while taking the exam. While using the app, learners can ask Duo to explain why the previous answer was wrong in the chat window. Additionally, they can practice simulated real-world conversations by chatting with AI.

Future Trends in AI Software Development

While generative AI is on top right now, emerging trends bring those models closer to edge devices. As intelligence gets closer to the data source, it becomes easier to create AI software for correct decision-making. Such models reduce latency and dependence on third-party cloud processing, thereby enhancing user experience. Some examples include developing AI for autonomous vehicles or industrial process control.

Development tools like low-code and no-code platforms are emerging as well, empowering software developers with a broad range of skill sets to contribute to creating and training AI models. This trend makes AI more accessible and democratizes the market.

Wrapping Up

Building AI software is currently a top trend in the IT industry. Companies that offer proprietary insights receive generous funding and explore ways of improving AI output. However, there are still some substantial risks to be addressed when it comes to collecting relevant data, maintaining efficient learning, mitigating ethical concerns, and optimizing infrastructure costs. 

Intellectsoft offers 15+ years of experience in custom software development, system architecture, and team augmentation. Talk to our experts today to discover how you can improve your AI development pipelines, conduct efficient project management, and meet your business goals.

The post How to Build an AI Software: A Comprehensive Guide appeared first on Intellectsoft Blog.

ChatGPT has gained a reputation as a transformative tool that reshapes certain aspects of our development processes. IT professionals are using it to automate and streamline specific tasks, such as assisting in writing test cases or providing quick solutions to common coding problems. On the business side, ChatGPT assists in rapid prototyping and ideation phases, where its ability to generate coherent, context-aware text can significantly speed up the initial stages of development. However, its effectiveness is constrained by the scope of its training and its lack of deep, project-specific context. It can suggest but not validate, nor can it understand the nuanced requirements of complex, custom software solutions.

Intellectsoft’s Center of Excellence (CoE) is an innovation hub where our technical teams conduct experiments and test new approaches. 

This interview covers our expert’s perspective on the use of AI, specifically focusing on GPT (Generative Pre-trained Transformer) models and their applications, limitations, and impacts on software development. 

Let’s dive right in! What are your plans for contributing to the TechBridge project?

Yevhen Kulinichenko: My plans for contributing to the TechBridge project are strategically focused on leveraging our technological expertise to maximize the project’s success while ensuring alignment with our broader business objectives. Firstly, we intend to deploy a cross-functional team of our best engineers and developers, who will work closely with the TechBridge project stakeholders. This team will not only bring in-depth technical knowledge but also a profound understanding of agile methodologies to ensure rapid and efficient project development.

A key aspect of our contribution will be the integration of advanced technologies like AI and cloud computing to enhance the project’s capabilities. For instance, we plan to utilize AI algorithms to analyze and optimize data flow, ensuring efficient processing and storage of data. Cloud computing resources will be leveraged for scalable infrastructure, ensuring the project can adapt to changing demands seamlessly.

Moreover, we are committed to fostering an environment of innovation and collaboration. We’ll encourage our team to engage in creative problem-solving sessions and think-tank workshops with other TechBridge participants. This collaborative approach is designed to not only contribute our ideas but also to learn from the diverse perspectives and expertise within the TechBridge community.

Lastly, we recognize the importance of sustainability in technology. Therefore, our involvement will also focus on developing sustainable and environmentally friendly technology solutions. We’ll explore and implement green computing practices to minimize the environmental impact of our technological contributions.

In summary, we plan to contribute a blend of technical excellence, innovative thinking, collaborative engagement, security-conscious practices, and a commitment to sustainability to the TechBridge program. We aim to help set a foundation for its long-term success and growth.

You mentioned AI implementation as a focus area. Can you expand on that?

Yevhen Kulinichenko: We want to build on the experience that we already have. In 2023, we conducted experiments on improving software development processes by implementing generative AI assistance. Results are still being finalized, but we’ve identified dozens of scenarios where ChatGPT can be safely utilized to streamline delivery and facilitate problem-solving. 

As we delve deeper into the utilization of ChatGPT in software development, we’re uncovering its profound potential in augmenting our coding processes. For instance, when dealing with boilerplate code, ChatGPT can swiftly generate templates for commonly used code structures in various programming languages like Python or JavaScript, significantly reducing repetitive tasks. We’ve experimented with using it to draft initial versions of RESTful API endpoints, where it considers standard practices and even suggests appropriate HTTP methods and response codes. 

We’ve also started leveraging ChatGPT for debugging assistance. By describing a bug in natural language, the model can suggest potential causes and solutions, drawing from a wide range of similar issues it has learned from. This doesn’t replace a developer’s expertise but acts as an additional tool to accelerate problem-solving. Thus, while ChatGPT is a game-changer in certain aspects of software development, it works best as a complement to human skills, enhancing productivity and quickly highlighting some basic blind spots or standard reusable patterns. However, it still cannot handle the complex logic, style, or requirements.

Can ChatGPT handle tasks requiring senior-level expertise?

Yevhen Kulinichenko: ChatGPT is limited in creating novel solutions. It works by learning from vast amounts of text data to generate plausible word combinations. However, it neither invents nor actually comprehends ideas. It is still a mathematical algorithm that arranges words according to numeric probabilities. There is no independent consciousness that is inherent to human intelligence. ChatGPT’s utility is rather in quick recommendations based on existing knowledge.

How does ChatGPT compare with development environment tools like PyCharm?

Yevhen Kulinichenko: That’s an interesting question. Indeed, Integrated Development Environments (IDE) like PyCharm offer tools like code completion, debugging, and context-aware refactoring options that consider the entire project structure. What’s more, It can adapt to the style and requirements of the codebase it’s working with.

Meanwhile, ChatGPT’s understanding of software languages and programming concepts is broad but can be superficial. It lacks the ability to understand the deeper context of a software project, such as project-specific architectures, dependencies, or the state of the current codebase. It can assist in generating code snippets, providing documentation references, or even suggesting solutions to coding problems. Still, it’s limited to the data that it’s trained on and doesn’t attend to the entire code structure and its state in real time like IDEs.

Are there any concerns about the misuse of ChatGPT?

Yevhen Kulinichenko: Yes, there’s a risk of misuse, especially if ChatGPT provides information based on public data that could be harmful. The potential for misuse of technologies like ChatGPT forms a crucial part of our risk assessment and management strategy. There’s always a possibility of inappropriate information being fed into AI models with malicious intent.

For example, in the software development realm, we can talk about the potential for intellectual property theft or inadvertent leakage. ChatGPT, when fed with sensitive data, could incorporate this information into its outputs, leading to data breaches. As a result, proprietary code or business logic could be exposed. So, for now, my recommendation is to refrain from trying to fix the code in ChatGPT or copy-pasting any information that might be considered sensitive. And since chatGPT 4 can now crawl the web, make sure your websites and social media accounts are properly protected as well.

Moreover, there’s the risk of psychological dependency on AI. Over-reliance of IT professionals on ChatGPT for code generation or problem-solving could lead to a degradation of fundamental skills. I think it’s vital to use such tools to augment, not replace, human expertise, ensuring that core development skills are retained and nurtured within the team.

Finally, what’s your perspective on the future of ChatGPT and AI in collaborative programs like TechBridge?

Yevhen Kulinichenko: Looking to the future, I anticipate significant advancements in tools like ChatGPT, particularly in their integration with more and more traditional software development tools and environments. We’re likely to see more sophisticated versions that offer better contextual understanding and can interact dynamically with existing codebases, potentially offering more nuanced and targeted suggestions, which will help us quickly scale and efficiently maintain global digital infrastructures. 

These advancements could lead to models that not only assist in code generation but also play a role in code review processes, offering intelligent insights based on coding best practices and even specific project history. Furthermore, as AI models become more adept at understanding and generating human languages, they will become invaluable in bridging communication gaps between technical and non-technical stakeholders, facilitating a clearer understanding of project goals and requirements. This evolution will require a shift in how we perceive these tools, not just as assistants but as integral components of our software development ecosystems, enhancing both efficiency and creativity.

The post The UK-Ukraine TechBridge: Intellectsoft Creates A Work Group To Build A Global Digital Infrastructure appeared first on Intellectsoft Blog.

AI in the FinTech Market

Fintech is an extensive concept that encompasses the implementation of various digital technologies in the financial services industry. These days, artificial intelligence, Big Data, blockchain, cloud computing, and the Internet of Things are the top five technologies that represent the leading trends in FinTech. Among them, AI arguably has the most diverse implementations thanks to its crucial role in data analysis and process automation.

One of the beneficial features of artificial intelligence is its ability to effectively cooperate with other digital technologies. Such cooperation greatly enhances the range of tasks that AI can perform. So, thanks to this feature, artificial intelligence has become a versatile and invaluable tool for the purposes of security, customer service, audit, and many other aspects of FinTech. 

The Role of Artificial Intelligence in FinTech

These days, companies of all sizes seek ways to optimize business processes with the help of digital solutions. As a part of digital infrastructure, artificial intelligence plays an essential role in many financial processes. Of course, the ultimate role of AI-powered solutions is to increase the profitability of companies through a range of benefits and business opportunities. 

The recent survey of 500 financial services professionals conducted by Statista revealed the most acknowledged benefits of implementing AI in the respective industry. Here are the results of the survey and some other applicable advantages in the context of FinTech:

1. Better customer experience.
2. Improved operational efficiency.
3. Lower costs of running a business.
4. Gaining a competitive advantage.
5. Access to accurate predictions and analysis.
6. Discovery of new business opportunities.
7. Improved risk management.
8. Better security of assets and property.

These and other benefits offered by digital technologies have revolutionized the financial sector. Even the oldest banks started investing in FinTech and shifted their operations from traditional “pen-and-paper” manual approaches to automated methods.

Opportunities Presented by AI in FinTech

Thanks to the constant development of artificial intelligence, AI development companies keep devising new ways to implement it in FinTech.

Customer Experience. AI-driven chatbots are typically the first thing that comes to mind when considering the implementation of artificial intelligence in FinTech and other industries. However, having an AI-based personal wealth manager that analyzes your income and spending behavior is a financial service that has a prominent future. Artificial intelligence can also be successfully used for offering personalized investment advice that considers numerous variables like the client’s age, location, financial history, education, interests, risks, trends, and so on.

Risk Management. Artificial intelligence is vastly beneficial for every financial institution as a risk management system. The AI algorithms can gather and analyze the client’s financial history to make better decisions on whether they are eligible for specific loans. Credit scoring and automated loan processing empowered with FinTech AI or machine learning models can significantly minimize and even eliminate the risks associated with loan approval.

Fraud detection. Fintech companies need custom AI-empowered fraud detection software for successfully fighting cases of account botting like creating bot networks and fake accounts, online payment fraud, identity theft, and others. Here’s everything you’d like to know about fraud detection with machine learning and AI.

Innovation in Services and Products. Artificial intelligence algorithms boost the efficiency and quality of market research, customer surveys, trend analysis, etc., to create new offers that better fit customer demands. Additionally, AI takes significantly less time to generate multiple results and recommendations based on the analyzed data.

Market Expansion. AI in the FinTech market is widely used for more efficient and reliable market research, customer surveys, and trend analysis to expand the business reach with fewer or zero risks. AI-driven forecasting helps banking, financial, and other institutions to open offices in new locations with a favorable business climate.

Regulatory Compliance. Custom AI systems vastly increase the quality and effectiveness of anti-money laundering (AML) and know-your-customer (KYC) guidelines. The system verifies customer documents by comparing them against various internal and third-party registries within minutes and helps the manager or the automated system make better-quality decisions on each client.

Automation of operations. Despite the fact that the implementation of artificial intelligence reduces the need for staff, it won’t replace qualified specialists. On the contrary, it only helps to accelerate processes like decision-making and data analysis. Thus, it serves as an assistant rather than a full-scale employee replacement. Most importantly, AI systems eliminate human errors that may result in money or reputation loss.

Enhanced security of computer networks to prevent breaches and data leaks is also one of the perks of using artificial intelligence in FinTech companies. The AI-based system effectively detects anomalies when employees perform actions like logging in, requesting specific information, etc., as well as protects the entire company’s infrastructure from malware.

AI and IoT for the security of offices and branches. Surveillance, face recognition, behavior analysis, and other security things are already widely and successfully used in FinTech and many other industries. Automated security protocols are designed and used to prevent and neutralize armed robberies and thefts, which is especially important when the company deals with enormous sums of money and the sensitive personal data of thousands of clients. 

AI Use Cases in FinTech

Nothing proves the validity of a concept better than successful real-life implementations. Below are a few examples showcasing the use of artificial intelligence in leading financial enterprises.

AI Assistants and Robo-Advisors

These customer-oriented solutions may take the form of a separate mobile or web application, but more often, they are integrated into a more complex online banking app. Usually, they communicate with customers via in-app chats, mobile notifications, or SMS. These FinTech AI assistants enable personalized approaches to improve customer experience and cover many vital issues, from account security to financial advice.

Many banks employ such multifunctional chatbots as their 24/7 customer support. Among them is Bank of America with its AI assistant Erica available for customers via a smartphone. Capital One offers Eno, a chatbot available both on the bank’s website and inside a mobile app. A standalone service Cleo is an AI-powered personal finance assistant available as a mobile app. 

Automated Fraud Detection Systems

For obvious reasons, financial enterprises do not specify the details of their security systems. However, most of them prefer to announce the implementation of AI solutions to prevent fraudulent activities. This way, FinTech companies attract clients and partners and send a warning to potential lawbreakers. For example, both PayPal and Mastercard use data processing systems that employ AI and ML for real-time detection of possible fraud attempts and other suspicious activities. 

AI Tools for Regulatory Compliance

Similar to the previous type of AI solutions, many FinTech companies employ the same technologies to meet applicable legislation. Many states enforce strict rules regarding data protection, anti-money-laundering (AML) measures, know-your-customer (KYC) processes, and other programs that are compulsory for the finance industry. To comply with such laws and standards, providers of financial services use AI analytics combined with machine learning and Big Data technologies. For example, JP Morgan has an AI Research team that creates synthetic datasets for further use in AML, fraud detection, and other programs.  

Challenges and Ethical Considerations

Though AI-powered fintech solutions have proven their reliability and improvement potential, many experts are concerned about related ethical issues and challenges. These problems slow further development and limit the scope of AI applications in FinTech. 

• Issues regarding data privacy and security. First of all, AI involves gathering and storing large amounts of data, which requires adequate security. Additionally, FinTech AI solutions are allowed to access financial information, so they can be used as a proxy by hackers to steal this data.
• Machine logic vs. fairness. AI decisions can be biased due to the flaws in algorithms and learning strategies. For example, when calculating credit scores or authorizing loans, AI may be discriminatory due to its reliance on statistical methods rather than a personalized approach to every specific situation.  
• Regulatory challenges. As digital technologies improve, governments pass more laws to regulate the use of sensitive information and enforce transparency and accountability. Providers of financial services must ensure that their AI solutions comply with the growing list of rules and regulations.
• Less human employment. Artificial intelligence performs most tasks faster and better than human workers. Some may see it as “job stealing,” which presents an ethical issue considering regular surges in the unemployment rate. 

Future Prospects of AI in FinTech

Numerous use cases prove that FinTech and AI are already inseparable, and the bond between them will continue to grow. The ongoing improvement of artificial intelligence is the hard requirement for this technology to remain efficient in all its current applications and discover new beneficial implementations. Below are several promising trends that will shape the future of AI in FinTech.

Improvement of Natural Language Processing

One of the current tasks of engineers is to simplify communication between AI and people. To engage in productive conversation, computers must learn to interpret human languages more accurately, take the context into consideration, and remember previous dialogues with every particular user. As one example, the development of NLP technologies will bring new capabilities for AI chatbots thanks to better speech recognition.

Focus on Deep Learning

To improve artificial intelligence, machine learning methods also have to evolve. Deep learning involves using multi-layer neural networks to analyze information, similar to thinking processes in a human brain. This is vital for financial services since data processing is the core task of AI in FinTech.

Further Automation of Financial Services

One of the ultimate business tasks of AI is ensuring high efficiency and profits for FinTech companies. To continue this trend, companies will increase the level of automation using bots integrated into the corporate infrastructure.

More Accurate Predictive Analysis

As a result of improved machine learning, Artificial intelligence will be able to perform more complex analysis and make more weighted decisions. This is especially beneficial for robo-advisors, risk management, and other financial aspects that depend on accurate predictions.

Closer Cooperation with Blockchain Technology

Further advances in AI and blockchain will improve automated secure transactions with high transparency. This is beneficial for every implementation that involves regular payments, which is vital in our era of subscription-based services.

Final Thoughts

Artificial Intelligence has already transformed the entire financial industry across the globe and continues empowering it with new enhancements. The automation of crucial processes, security increase, improved data analysis and forecasting, and better customer experience via personalization are only a few advantages from the long list for every fintech company that has already adopted AI or is considering it.

Being a company with over 15 years of experience in the Internet Technology market, we have delivered more than 600 digital solutions of different types and complexity in FinTech, healthcare, construction, logistics, eCommerce, and many other industries. 

Intellectsoft provides solid digital solutions to companies of all sizes – startups, SMBs, and enterprises. Thus, here you can get your custom solution for business needs – from a simple AI chatbot to complex enterprise artificial intelligence software.

To learn more about our software development expertise, check out the most prominent cases we have already delivered to our clients, including Fortune 500 enterprises. 

So, what can we do for your business together with artificial intelligence? Our AI-driven bespoke solutions include:

– AI chatbots and apps;

– Cloud-based AI models;

– Visual information processing;

– Deep learning and neural networks;

– Data generation and augmentation;

– Predictive ML models;

– Raw data and data analysis management;

– AI enterprise apps, and a lot more.

If you want to start empowering your business and future with AI FinTech, get in touch with us and tell us your requirements, goals, and needs. As soon we analyze and estimate the information you give us, our solutions advisor will reach out to you with our custom solutions and recommendations designed specifically for your case. Let’s make your business benefit from everything artificial intelligence offers.

The post AI in FinTech: Role, Opportunities and Use Cases appeared first on Intellectsoft Blog.

It is evident that human capabilities are not enough to fight this problem, so people need a more resourceful companion, like AI and ML technologies. They have shown great potential in detecting behavior patterns and specific features that are characteristic of fraud. The global investments in AI in fraud management are growing every year and are expected to exceed $57 billion in 2033. 

Understanding Machine Learning and AI in Fraud Detection

Artificial intelligence is a general concept of simulating human thinking processes with computer technologies. Within this concept, there are several aspects, and machine learning is one of them. ML focuses on training AI based on given data sets without manual reprogramming. Usually, machine learning is performed using one of the three approaches: supervised, unsupervised, and reinforcement.

Deep learning is a subset of ML that focuses on improving automation by creating a neural network that resembles the structure of a human brain. As opposed to traditional ML approaches, deep learning algorithms can process unstructured information and improve their accuracy using such processes as backpropagation and gradient descent. 

AI trained on specific historical data can become a powerful instrument for such complex tasks as fraud detection. Given that many schemes of online fraud rely on bots, the automated detection provided by AI and ML technologies is a very fitting solution with excellent capabilities of scaling and improving its performance.

Principal Fraud Types Detected by Artificial Intelligence

Defining even the approximate number of fraud schemes and tactics across all industries is challenging since new variants appear every day. Here are the three most popular categories of fraud associated with online activities.

Account Botting: Creating Fake Accounts and Bot Networks

Fake accounts are the modern-day plague of the Internet. Billions of fake profiles are automatically created and linked together, forming massive networks on popular social media and other relevant websites. For example, every quarter, Facebook removes from 1.3 to 2.2 billion fake profiles. Thanks to proactive measures, such as using AI for fraud detection, LinkedIn was able to detect and remove 44.7 million fake profiles during registration in the second half of 2022.

Online Payment Fraud: Card-Related Crimes

Bot networks are often used to perform brute-force attacks. One of the purposes of such attacks is to test stolen credit card information and determine whether it is still valid. This activity is usually characterized by large amounts of low-value orders. Machine learning fraud detection can identify and prevent such automated purchase attempts before they harm businesses and cardholders.

Identity Theft: Account Hijacking, Automated Credential “Guessing”

As another implementation of bot networks, account theft can be performed using brute-force methods similar to those described in the context of credit card fraud. Bots can try to input credentials from stolen databases or pick passwords based on the vocabularies of the most common variants, and so on. Again, AI solutions are innately good at identifying automated routines, and they can quickly adapt to new variants of such fraud.

Usually, the types of fraud mentioned above are the initial steps of complex schemes that involve other criminal activities, such as money laundering, investment scams, insurance scams, and so on.

How to Use Machine Learning for Fraud Detection?

Implementing Machine Learning for fraud detection can be broken down into a sequence of steps. To describe this process, we will use financial fraud as an example since it is one of the most popular types of scams encountered by businesses. However, with minimal adjustments, this scheme can be applied to fraud detection in other industries. For the sake of clarity, we will mostly refer to supervised learning as an example of using ML for fraud detection.

1. Providing Source Data

Teaching an AI involves feeding input information into a machine learning system to create a basic functional model that will be continuously improved. For supervised learning, source data must be pre-processed and structured. Specifically, it must be labeled as good when it refers to legitimate operations, and bad when it is related to known fraud. Unsupervised learning can discover patterns in unstructured information. 

2. Extracting Relevant Parameters

In the context of financial fraud detection, the most common parameters of source data are: 

• number and frequency of successive transactions;
• value of each transaction;
• payment system and other information related to credit cards;
• type of purchased product or service.

For example, when a popular fraud scheme involves ordering a specific product and then demanding a refund or chargeback due to alleged quality issues or failed deliveries, the known instances of such fraud will be broken down into individual parameters and analyzed as input data.

Another useful information for fraud detection with machine learning is user-related data, which includes:

• IP address ranges;
• detected use of VPN or other proxy services;
• hardware IDs, such as MAC addresses;
• various software-related data, such as name and version of OS, web browser, etc.

More input data requires more resources for processing but leads to a more accurate model capable of detecting more types of fraud.

3. Performing Rule Management

Source data analysis results in specific rules that define the fraud determination method. Basic rules involve one parameter: if it matches, an action will be marked as a potential fraud. For example, if a specific IP range is related to previous fraud, a rule will mark it as potentially unwanted. As a result, any IP address that belongs to the said range can be blocked automatically or flagged as a potential source of fraudulent activity.

However, most fraud detection algorithms use complex rules based on several parameters, which improves accuracy and reduces the probability of false positive triggering. Every parameter can be associated with a potential accuracy value and certain thresholds for triggering a relevant rule. In supervised learning, they can be reviewed and tuned by engineers after a predetermined number of cycles. 

4. Training, Evaluating, and Tuning the Model

A fraud detection model can run multiple times on the relevant historical data with the chosen set of rules to make decisions. More runs or cycles improve the accuracy of the model and make it find more patterns. Testing the “real-world” performance of the model on previously unknown data is the vital stage of creating an AI-powered fraud detection solution. It allows engineers to review and finalize the rules and customize the model according to the specific features of the particular business and industry. Then, specialists must seamlessly integrate the ML model as a custom solution into the client’s digital infrastructure. 

Benefits of Fraud Detection Using Machine Learning

Computers are excellent for processing and analyzing large amounts of information, so they are incredibly efficient in fraud detection. Here are a few advantages of using AI and ML for this purpose:

• Improved speed and efficiency. AI uses hardware capable of performing billions of instructions per second, which is far beyond human capabilities. With a sufficient amount of source data, artificial intelligence quickly learns to detect patterns characteristic of most frauds. This enables real-time detection and immediate response to fraud threats.
• Less human involvement. For such a responsible task as fraud detection, the decisions made by AI have to be double-checked and finalized by experts. Still, computers provide significant time savings by going through all the hard work and presenting results for manual verification. This benefit allows managers to relieve staff from monotonous and tedious activities and direct specialists to more creative and skill-demanding tasks. 
• Better accuracy. Thanks to the ability to process large datasets, AI and ML solutions can reach conclusions with higher degrees of certainty. Further learning under the supervision of human experts additionally enhances fraud detection by increasing the probability rate and reducing false positives.
• Higher learning potential. Again, AI’s excellence in processing information results in more advantages compared to human analysts. Data scientists just have to keep feeding new data to machine learning systems to enhance machine learning fraud detection models.
• Cost savings. Since AI and ML greatly outperform the capabilities of human staff, these technologies present a much more cost-effective solution to the fraud problem. Additionally, they have excellent scalability to conform to the changing demands of the business. Compared to staff management, increasing the scale of an AI solution is cheaper and involves less hassle than finding and hiring a new specialist for fraud detection.
• Round-the-clock availability. Unlike human staff, computers can work 24/7 and do not require holidays or weekends. Sure, the AI infrastructure is relatively complex and needs periodic maintenance, as well as hardware upgrades and software updates. However, these operations can be specifically planned and performed in stages to minimize downtime and ensure uninterrupted fraud detection with machine learning.

Though AI-based solutions require financial investments and the skills of many specialists to build and train learning models, all those efforts and resources eventually pay off. As AI learns and evolves, its beneficial effects become more powerful, and it gains more advantages over manual fraud detection. For example, artificial intelligence becomes more accurate and capable of functioning with less supervision.

Drawbacks of Machine Learning in Fraud Prevention

Though machine learning has proven its high efficiency in such demanding tasks as fraud detection, it also has several drawbacks. In some cases, they can have such a negative impact on the accuracy of conclusions made by Artificial Intelligence that manual analysis and human opinion would be preferable.

• The complexity of implementation. Teaching an AI-based solution, just like teaching students, requires time, skills, and knowledge of experts. The unsupervised approach to ML that relies on deep learning algorithms has gained popularity over the years due to its autonomous nature and drastically improved accuracy. However, many experts still root for a supervised approach that requires constant human involvement.
• Chance of malfunctioning and false triggering. When using AI-based analysis, there is always a possibility of a false positive result. In the context of fraud detection, this means that a legitimate activity is marked as illegal, which may result in restrictive actions against an innocent client. If an ML system is not aware that its decision is wrong, it can set off a chain reaction of further false detections that reduce accuracy and make AI choices increasingly unreliable.
• Some models lack transparency and control. Depending on the type, ML models may be more or less automated and accurate. For example, black box models generally offer better automation and accuracy but do not offer many options for specialists to interpret their results and tweak their rules. On the contrary, white box models are more transparent and linear, so they provide clearly interpreted relations between input variables, fraud detection rules, and output results.

ML and AI Use Cases in Fraud Detection

Banking and Finance

JPMorgan Chase. Being among the global industry leaders, JPMC has been actively using artificial intelligence for years. The company has incorporated ML into its processes aimed at anomaly detection. Their AI-based security system that comprises deep learning algorithms and big data allows detecting malicious payloads like phishing emails or Trojan activities from both external and internal sources aimed at employees.

Healthcare

Elevance Health. This health insurance company, formerly known as Anthem Inc., uses Google Cloud’s data analytics capabilities and AI-driven offerings to detect possible fraudulent claims. They use statistical models and algorithms for generating petabytes of synthetic data, including datasets of healthcare claims, medical histories, etc.

eCommerce

Shopify uses and promotes a NoFraud system – an AI-driven decision engine that can analyze multiple data points on each transaction to eliminate fraudulent activity related to every step of order processing. The system examines numerous order details like IP address, device history, email longevity, social media, and others to identify the individual making a transaction and the possibility of fraudulence.

Wrapping Up

Frauds make their schemes smarter and more sophisticated every few months. So, to always be at least one step ahead of them, you need to engage artificial intelligence and machine learning in your fraud detection and prevention systems. The advantages these technologies bring to the fraud detection field are enormous – enhanced accuracy, total availability, unlimited learning potential, speed and efficiency, and a lot more. Despite a few drawbacks, using machine learning and artificial intelligence for fraud detection helps to save confidential information, money, time, and reputation for companies and their clients.

Intellectsoft is a custom software development company that has been delivering complex digital solutions for over 15 years. We offer a wide range of services and solutions, including enterprise artificial intelligence software that includes but is not limited to:

– Neural networks and deep learning;

– Custom machine learning models;

– Cloud-based AI-models;

– AI chatbots and applications;

– Face and voice recognition;

– Data analysis management;

– Raw data management;

– Data generation and augmentation, and more.

Over the years, we have delivered more than 600 bespoke solutions for businesses of all sizes – from early-stage startups to Fortune 500 enterprises from many countries across the globe. Last but not least, we have an engineering workforce in 21 countries, and almost 30% of our employees are females.

So, if you’re looking for a tech team that can build a solid fraud detection solution for your business using machine learning and artificial intelligence, don’t hesitate to contact us. As soon as our team gets your requirements, we will analyze your information and offer you the most effective solution or recommendations, depending on what you need. Just get in touch with us, and let’s make your product 100% fraudproof.

The post Fraud Detection with Machine Learning and AI appeared first on Intellectsoft Blog.

Let’s get acquainted.

Understanding AI in Construction

We live in an age of digital advancements. That means industries worldwide are more than willing to harness the power of what we know as Artificial Intelligence (AI) to their advantage. As a result, the strategies to streamline operations and improve efficiency have never been more accessible, leading to highly productive, time- and cost-effective developments. 

The AI application prospects are particularly notable in the construction industry, traditionally reliant on manual labor and complex administration systems. Combined with machine learning and deep learning, an array of digital automatization, calculation, and control systems has been gradually reshaping the trade as we know it.

Source

But before delving into the benefits and use cases of artificial intelligence in construction, let’s deal with the essentials and clearly distinguish the concepts.

What is AI?

The primary characteristic of AI technology is its ability to process immense amounts of data within seconds and yield viable results in return.

Artificial intelligence is a data-driven, science-based technology. It analyzes, calculates, compares, identifies, and summarizes. Consequently, artificial intelligence is able to:

• Identify strengths and weaknesses
• Provide insights and make predictions
• Recognize or create patterns
• Optimize processes
• Add in architecture and building design
• Provide recommendations/propose solutions

In certain instances, properly applied AI in construction can even make decisions.

Machine Learning and Deep Learning Concepts

Machine learning is a subfield, a background process. It renders the machine able to learn “from experience,” eliminating the need to program it for each specific task separately. In simple words, more data to analyze means more developed AI capabilities. 

However, the method is more complex and requires trainers who work on algorithm development, facilitating computers to perform required tasks based on available data.

Deep learning is a part of that method. It employs digital “neural” networks to analyze and crack the data to improve further the software’s ability to handle complex tasks. These systems strive to emulate the human brain’s learning mechanism.

Source

Regarding artificial intelligence in construction, the best strategies involve a blend of machine learning, deep learning, and human control/execution. AI-driven algorithms can help optimize building design, energy efficiency, and maintenance. Meanwhile, smart buildings equipped with sensors and data analytics improve occupant comfort and reduce energy consumption. 

The future of construction lies in the implementation of AI, and it’s exciting to witness how its integration will shape building designs in the coming years.

What Are the Benefits of AI in the Construction Industry?

AI in construction has revolutionized the industry by bringing in smart building technology that holds immense application prospects in every sector, offering numerous advantages. Citing them all would produce a long read so that we will focus on the most significant examples instead.

Efficient Planning and Scheduling

AI can streamline project schedules by considering the weather, materials, and workforce availability. It can also predict potential delays and suggest adjustments, ensuring projects remain on track. That way, it provides invaluable assistance in planning and scheduling, leading to optimized resource allocation, reduced costs, and timely project delivery.

• In short, with the help of AI, construction managers can now ensure a smoother and more efficient completion of projects.

Enhanced Risk Management

Using artificial intelligence in construction helps managers identify potential safety hazards on building sites by analyzing data from sensors, security cameras, and other smart building devices. This data is processed by algorithms that, in return, detect perilous situations before they emerge and alert site managers in advance. AI can also help see flaws in building materials and designs, reducing the risk of costly rework or building collapse. 

• In short, by leveraging these capabilities, AI and construction companies can ensure safer and more efficient building environments.

Improved Resource Management

Artificial intelligence in construction allows for smart building design with superb efficiency and precision. This technology can optimize the resource distribution, from materials to labor and energy. AI can assist construction managers in recognizing potential resource shortages by analyzing data and making real-time adjustments, thus ensuring that the appropriate materials and personnel are available when needed.

• In short, AI allows for a streamlined construction process that saves time and resources while improving the overall quality of the project.

Quality Control

AI-powered visual inspection systems exhibit exceptional precision in detecting (thus preventing) potential problems. For instance, using AI-powered drones and cameras enables real-time monitoring of the construction to identify structural defects or material quality issues. 

• In short, AI in the construction industry guarantees that projects meet the highest quality standards, ultimately promoting customer satisfaction.

Cost Reduction

By leveraging AI technologies, construction companies can monitor and predict the performance of buildings, automate repetitive tasks, and optimize the use of resources such as electricity and water. By identifying and addressing inefficiencies, AI in the construction industry can help save money and improve project efficiency. As the technology continues to evolve, AI is poised to become an integral part of the construction industry, transforming how we build and sustain our environment.

• In short, by optimizing processes and resources, AI can help reduce construction costs and enhance productivity, making projects more financially viable.

Improved Efficiency

Smart building technology has made it possible to have real-time monitoring of various systems. That includes energy use, lighting, and temperature control. AI for construction processes the collected data to identify patterns and determine what areas need improvement. That way, building managers can optimize systems and avoid waste of resources. 

• In short, AI algorithms enhance overall operational efficiency, rendering projects more cost-effective.

Use Cases for AI in Construction

Let’s now explore the examples of artificial intelligence in construction and the most common practical applications.

Smart Building Design

AI can analyze data from smart sensors to design buildings characterized by energy efficiency, sustainability, and responsiveness to user needs.

Autonomous Machinery & Robotics in Construction

Robots driven by AI and equipped with sensors and cameras can perform repetitive or hazardous tasks, such as bricklaying or excavation, with precision and safety. Robotic arms and machines equipped with AI capabilities can work perpetually, reducing labor costs, enhancing efficiency, and expediting project timelines as a result.

Predictive Maintenance

Unforeseen machinery breakdowns can be financially draining and disruptive. AI’s predictive capabilities make it possible to optimize maintenance needs, ensuring timely interventions that minimize costly downtime.

Real-Time Project & Site Monitoring

To prevent means to predict. Real-time information on a project’s status can be provided via AI, allowing stakeholders to act fast and decisively.

In addition, AI-powered drones and cameras can perform round-the-clock monitoring of construction sites, scanning safety compliance and adherence to plans to ensure projects stay on track.

Automated Documentation

AI can automate the administration of construction documents, reducing administrative workload in return.

Building Information Modeling (BIM)

BIM harnesses AI to create elaborate 3D visualizations of construction designs. This collaborative tool promotes informed decision-making, resulting in more accurate and efficient project execution.

Predictive Analytics

AI algorithms analyze historical project data to predict potential issues and recommend proactive solutions. Such an approach mitigates risks and aids in cost control.

Data-Driven Decision Making

AI empowers informed decision-making by processing profuse project data. Project managers can optimize resource allocation and identify areas for improvement, leading to better outcomes.

Future of AI in Construction: Challenges and Prospects

The potentials of AI in construction are virtually endless, starting from the investment decision. As we know, traditional project development requires a significant amount of time and labor, also involving numerous factors such as building codes, materials, and environmental conditions. All these fields can benefit from digital assistance. Machine learning algorithms can simultaneously analyze a variety of copious databases to identify the ideal structural design that would comply with all relevant aspects.

Undoubtedly, AI will soon bring more irreversible changes to the construction industry. But those changes do not render the human factor obsolete. On the contrary, a fusion of artificial and real intelligence and expert engineering is a beacon of hope that will guide modern society into the future, where the structures are more reliable, more sustainable, and more adaptable to fluid market requirements.

Source

According to analytics portals such as The Mordor Intelligence, “the artificial intelligence market in construction is expected to reach USD 9.53 billion by the next five years, registering a CAGR of 24.30% during the forecast period.“

Meanwhile, the Global News Wire forecasts that “the solution in Artificial Intelligence (AI) in Construction Market sales will account for more than 55% of total sales by 2030.” They also expect software industry leaders like Microsoft, Oracle, and IBM to assume a severe position in the race.

What About the Challenges?

While AI benefits the construction industry considerably, it is necessary to determine what obstacles stand on the road to success. Integrating AI construction technologies demands substantial investments, and many companies are still in the early stages of adoption. Moreover, machine learning and deep learning require a continual influx of fresh, accurate, and reliable data, which is not always accessible. 

The broad implementation field also implies various challenges, such as data privacy concerns, integration complexities, and the need for specialized skills.

And finally, the industry takes into account a growing concern about the impact of AI construction systems on the human labor market.

Nevertheless, the potential for innovation and efficiency gains far outweigh the hindrances, and the prospects are bright.

Conclusion

AI is set to revolutionize the construction industry by improving efficiency, reducing costs, and enhancing safety. With that established, all that’s due is to find a reliable partner with sound expertise in the field.

Intellectsoft, a leading technology solutions provider, is at the forefront of AI development for the construction sector. With a portfolio of successful projects, we offer cutting-edge solutions to transform traditional processes.

From optimizing project timelines to enhancing safety, Intellectsoft is guiding the way in providing innovative solutions for smart buildings. Our field encompasses everything from ERP, analytics, and inspection management to system integrations, project onboarding, and professional training.

In addition, we offer you the possibility to develop a tailored solution that integrates essential functions within your construction business processes. It will automate core processes, including HR, accounting, project management, and more, fostering seamless collaboration to maximize project efficiency and profitability.

The post AI in Construction: Transforming the Industry appeared first on Intellectsoft Blog.

Definition of AI and Its Various Applications in Software Development

Artificial Intelligence is one of the oldest concepts, originating from ancient times and myths. However, it was not until the 20th century when old ideas and crude prototypes got practical realization. Many prominent mathematicians, logicians, and computer scientists joined their efforts to define the concepts of “reasoning” and “learning” in the context of computers. The works of Alan Turing, Arthur Samuel, Tom M. Mitchell, and many others created the base for the progress of AI and ML technologies and computer algorithms and programming languages in general.

The generally recognized features that define AI are the ability to solve complex problems and the ability to generate information based on the source parameters. This way, researchers focus on the similarities between “human” thinking and “machine” thinking. Artificial Intelligence also should have the ability to learn, with human supervision or without it.

Thanks to the rapid progress of AI and ML technologies, they have been successfully integrated into many key areas of modern life. For example, the bond between AI and software development turned into two-way beneficial cooperation. This means that Artificial Intelligence has two major layers of interaction with software engineering.

1. It is an immensely useful tool that helps developers with numerous tasks. An AI paired with great computational power and data storage capabilities easily outperforms human abilities.
2. It is an object of software developers’ labor. Software engineers are often tasked to create an AI for specific purposes or to integrate an existing AI into a solution that is being developed.

Let’s take a look at both of these areas of interaction.

The Applications of Artificial Intelligence in Software Engineering

Software developers have mastered the art of training and customizing AI for various purposes. In general, state-of-the-art AI has unparalleled abilities for data mining, analysis, and classification. Those abilities also make Artificial Intelligence a great assistant capable of understanding requests and finding solutions. Here are a few examples of using AI technologies in software development:

Code Generation

Modern AI can write code based on given parameters. The results, on average, are quite satisfactory but not enough to replace a skilled programmer. However, specialized AIs and learning models are getting progressively better. It is just a matter of time until they become on par with junior-level specialists. This pace of progress should be a clear signal for software engineers to keep improving their skills.

The machine-written code still requires comprehensive checking from experienced specialists, especially regarding its security. In the near future, this tendency may change the role of software developers. It is possible that AI will perform the “writing”, and developers will switch to “editing” the code. This way, Artificial Intelligence will do the bulk of hard and mundane work, and software engineers will oversee this process.

Requirements Gathering

Developers often use Artificial Intelligence to compile a set of requirements for a future software product. It optimizes workflow and reduces the flow of documents associated with this task. AI proves to be extremely useful in demanding projects with complex requirements.

For example, Watson AI, developed by IBM, is especially useful for requirements management. In addition, it effectively assists in other stages of the software development life cycle. The use of AI eliminates or at least reduces the need for going through lots of paper and digital documentation manually.

Planning, Estimating, and Designing

Based on the gathered requirements, AI can take over the rest of the software development process. It is more efficient in tasks that require calculations, such as estimating costs and scheduling deadlines. It also can determine the development team setup and the number of required specialists to meet the deadlines within the set budget.

Of course, this process is closely supervised by managers. So, the role of AI here is to make rough drafts rather than final plans. It can also make suggestions regarding workflow optimization. However, humans still have the final word regarding AI advice.

Quality Control and Feedback Management

Artificial Intelligence can also assist developers by running error scans and looking for vulnerabilities in code. Software engineers can train AI using the existing guidelines and checklists for ensuring software security. Then, any particular development company can customize and enhance obtained machine learning models individually. The specialists can turn those models into intellectual property by training them on every software product created by that company.

AI is also invaluable for gathering feedback from users and testers in a convenient automated form. It can analyze reports and divide them into categories depending on the nature of feedback (positive/negative), the severity of a reported error (critical/major/minor), and other parameters. The resulting structured data will be much more useful for the purposes of QA than the bulk of assorted reports.

Areas and Industries That Use AI-powered Solutions

As was described above with reference to software development, specialists use AI for two general purposes. One purpose involves Artificial Intelligence performing tasks automatically with or without human supervision. The other purpose is to improve the productivity of human workers by assisting them and enhancing their abilities. Below are a few illustrative implementations of AI in different areas outside software development.

• Data mining. One of the main purposes of AI nowadays is to gather enormous amounts of raw data on the Internet. It is the task of web crawlers that are used in search engines, including Google, Bing, Baidu, and others. However, those and other interested companies, such as Meta, Twitter, Amazon, etc., are also collecting information about their users. Every user’s activity on the Internet is usually tracked by several companies simultaneously. The resulting data flow would be impossible without the help of Artificial Intelligence.
• Data analysis, classification, and statistics. AI is also responsible for analyzing user data mainly for the benefit of Internet corporations. It defines behavior patterns, habits, and preferences for every user account. The resulting data is used for targeted advertising and other types of personalized content. The latter is especially true in the case of social networks and streaming services.
• AI assistants in smart devices. Artificial Intelligence is often integrated into gadgets to make them more convenient and resourceful. The most common types are, of course, smartphones, tablets, and speakers. This is because they have the necessary hardware to listen to users and respond, preferably using AI-generated voice.
• Autonomous driving. Artificial Intelligence enables AI-assisted driving in the form of an autopilot that can be toggled on and off.  It also allows fully autonomous driverless operation of vehicles in certain environments.
• Gaming. One of the earliest showcases of AI was in the form of chess computers. Specialized versions of AI are also used for such table games as go, backgammon, and poker. Artificial Intelligence is widely used in the video game industry, both in single-player and multiplayer games. It controls enemies, NPCs, and bots in order to react to players’ actions.
• Writing and maintaining a dialogue. ChatGPT and its analogs are the hottest topics in all the tech news these days. They can hold written conversations with users and are often tasked to write letters, articles, and even scientific papers.
• Creating visual art. The combination of AI and ML gave several amazing tools for visual artists and designers. Midjourney, DALL-E 2, and Neural filters in Adobe Photoshop are the most popular examples of such instruments.

The Benefits of Artificial Intelligence for Developers

From the description above, it is clear that Artificial Intelligence and Machine Learning offer many advantages. Most of them can be effectively applied to benefit software development. Here are a few ways how AI improves the work of software engineers:

• Precise planning
• Predictable workflow
• Informed decision-making
• Accurate estimates
• Better security and QA
• Reduced workload

Overall, the main benefits of AI and ML in software development come down to higher efficiency and lower costs. Artificial Intelligence helps to automate many time-consuming processes. This allows managers to switch specialists from boring routine tasks to more creative and responsible assignments.

Additionally, these benefits offered by AI allow engineers to tackle more complex tasks that would otherwise be unfeasible or extremely costly. Ultimately, Artificial Intelligence enables the creation of more enhanced versions of itself. And those better versions will be able to solve even more difficult tasks, thus increasing the benefits.

Challenges of Using AI in Software Development

1. Higher demands for specialists’ skills. AI is a sophisticated tool capable of performing many tasks. However, to fully utilize its potential, it must be operated by a specialist with sufficient skills and experience. The main requirement is the ability to formulate all the input parameters with maximum precision. In other words, users must say exactly what they want to achieve and describe it in full detail.
2. Advanced tools require large computational power, storage space, and energy supply. A software development company that heavily relies on AI technologies has to invest in top-tier hardware. Better equipment usually means that AI would perform its tasks faster. In its turn, this would speed up the work of the development team and reduce the time to release the final product.
3. Extra licensing costs. Professional tools are not cheap, especially in the software development business. Most state-of-the-art AI and ML solutions involve a hefty price for their commercial use. However, the costs are reasonable and more than justified. AI-enhanced tools quickly pay for themselves, especially when they are used by skilled software engineers.

The Future of AI in Software Development

It is safe to admit that AI technologies are the present and future of mankind. They have been successfully integrated into both personal and professional aspects of our lives. This means more work for software developers who create and implement custom AIs.

The combination of Artificial Intelligence, software engineering and Machine Learning is growing more powerful as the technologies improve every day. Moreover, software developers can now use specialized AIs to create other AIs. The circle has come to a close. But it also created the foundation of the constant evolution of Artificial Intelligence, allowing engineers to create better versions of AI with improved abilities.

Though the use of AI simplifies the work of software developers, it also demands high levels of skill and knowledge. This is especially true when engineers need to integrate Artificial Intelligence into custom software solutions. Our team in Intellectsoft has enough know-how and experience to handle complex and challenging AI-related cases. We offer a range of AI development services for various industries. Contact us to bring the advantages of Artificial Intelligence to your software.

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