Integration of artificial intelligence (AI) into games has led to a revolution in the gaming experience, reflected through enhanced gameplay, realism, and responsive in-game environments. Advanced AI computing technologies now allow non-player characters (NPCs) to exhibit intelligent behaviors and adapt to players' actions. Moreover, AI-powered algorithms contribute to more sophisticated game mechanics, such as advanced enemy AI, realistic physics simulations, and personalized gaming experiences through adaptive difficulty levels. Machine learning techniques are also being harnessed for character animations, thus enabling lifelike movements and expressions. A new era of interactive entertainment is here! 

Game app developers at leading game development companies, like Logic Simplified, are excitedly integrating AI into gaming, to deliver immersive, engaging, and personalized experiences for players. Despite challenges like ethical concerns in predictive analytics due to reduced transparency, data dependency for effective learning, and computational complexity existing, navigating these challenges ensures adaptive AI's responsible and efficient implementation. 

What is Adaptive AI?

Adaptive AI is an evolved version of AI where systems are made adaptive. They are trained to learn and adjust to the changes. Video games that adapt to a player's skill level and style of play provide a more engaging and personalized experience. AI algorithms monitor player actions, learning their strategies, strengths, and weaknesses and then use this information to dynamically adjust the game's difficulty, pacing, and challenges, keeping players hooked. 

Techniques of Adaptive AI Gaming 

Adaptive AI Gaming Solutions involve techniques and algorithms like reinforcement learning, transfer learning, and neural architecture search (NAS) optimization. 

Reinforcement learning (RL) enables systems to learn by interacting with an environment and receiving rewards for desirable actions. Popular RL algorithms include Deep Q-Networks (DQN) which is a combination of Q-learning with deep neural networks, Proximal Policy Optimization (PPO) which optimizes policy functions, and Actor-Critic Models improve the efficiency of RL. 

Transfer learning allows models to leverage knowledge from one task and apply it to another, facilitating adaptation. Prominent transfer learning frameworks include BERT (Bidirectional Encoder Representations from Transformers) which is fine-tuned for various tasks, making it highly adaptable, and OpenAI's CLIP (Contrastive Language-Image Pretraining) perform a wide range of vision and language tasks.

Neural architecture search (NAS) optimization involves powerful and versatile models that are proficient in various challenging learning tasks such as image recognition, speech processing, and natural language comprehension. These models have the potential to simplify and automate the complicated process of developing deep learning models and generate deep neural networks that are customized to meet specific production needs quickly and efficiently.

How We Use Adaptive AI in Game Genres

Adaptive AI has a strong ability to learn, evolve, and respond to change when it comes to different game genres. Adaptive AI is used in a variety of games, depending on the genre, theme, and mechanics. Here is how Logic Simplified uses it in three popular game genres:

Get a free 20-minute consultation on our Adaptive AI integration in various game genres.

Card Games

Artificial intelligence has significantly changed the way online card games are made and played. Our card game developers create AI-powered opponents that behave like human players, making the game more challenging and interesting. We create opponents that dynamically adjust their tactics based on the player's moves, making gameplay more unpredictable and enjoyable. This is a notable difference from traditional single-player models that relied on predictable, scripted opponents.

AI-powered card games can adjust the difficulty level in real-time, ensuring that gamers of varying expertise can enjoy the game at their own pace. The AI opponent might start with a more forgiving test for a new player before progressively becoming more challenging as the player's skills improve. We ensure that all players are guaranteed a fair and interesting experience!

Real-Time Strategy Games

Real-time strategy games pose a challenge to human players as they need to manage resources, plan missions, and coordinate their units. When designing an Artificial Intelligence system to act as an opponent in such games, it needs to be powerful enough to develop a cohesive strategy for victory. Even though the goal of the AI gaming system is not necessarily to defeat the human player, it still needs to display a high level of intelligence to be credible. On the other hand, the AI system must also provide just the right level of difficulty so that novice and expert players alike can appreciate the game. Currently, the behavior of computer-controlled opponents in RTS games relies heavily on static algorithms and structures. Our game developers introduce adaptive AI at the strategic level thus enhancing the illusion of intelligence, and increasing the entertainment quotient of the game.

Shooter Games

The integration of an artificial adaptive game agent in FPS/TPS games allows for adaptive functionality, minimizing the need for environment-based behavior agents while reducing the complexity involved in agent implementation. The main objective of adaptive agents is to provide entertainment to end-users. To achieve this goal, we add various functionalities such as a dynamic reward system for enhancing the learning process's efficiency and a dynamic weapon preference system for improving the winning rate of gun fights. Some systems utilize online algorithms, such as Q-learning algorithms, while others utilize offline algorithms to achieve the ultimate goal of the learning process.

Logic Simplified is a top game development company with years of experience in creating top-notch games. We have assisted over 100 businesses globally, with our expertise in gaming and emerging technology solutions. Deep knowledge of AI has helped us create some of the most complex games. Our expertise in game logic and game analytics is well known! We have built innovative and scalable AI solutions for businesses worldwide. Let us do it for you too. Contact us at enquiry@logicsimplified.com today and to know more about adaptive AI technology click below.

XR has a wide range of applications, from gaming and entertainment to education and training. It has the potential to revolutionize how we interact with digital content and could change the way we work, learn, play, and live.

The Extended Reality (XR) Market is growing at a CAGR of 57.91% according to a report by Mordor Intelligence.

AI has the potential to transform a wide range of industries, from healthcare and transportation to finance and retail. It can help businesses process large amounts of data more efficiently, make better-informed decisions, and improve customer service.

As per McKinsey Global Survey on AI, 2022, 60 percent of respondents expect their organizations to increase their investment in AI over the next three years.

Exploring the Transformative Power of Combining XR and AI

As XR and AI continue to evolve, it will be important for businesses and individuals to stay up-to-date on these technologies and how they can be used to improve their lives and work. With the right tools and strategies, XR and AI have the power to drive innovation, boost productivity, and create more meaningful and engaging experiences.

Here are just a few examples of how these technologies can work together to create powerful experiences:

Virtual Assistants: 

AI-powered virtual assistants, such as Amazon's Alexa and Apple's Siri, have become increasingly popular in recent years. By using XR technology, these assistants could potentially become even more lifelike and engaging. Imagine being able to see and interact with your virtual assistant in a fully immersive digital environment, rather than just hearing their voice. This could make the experience of using a virtual assistant feel more natural and personal.

Gaming:

Many game development companies are booming with the implementation of AI and XR due to the increased realism and immersion that these technologies provide. AI and XR allow for more complex and dynamic game worlds, as well as more realistic and responsive characters. Imagine being able to fully explore and interact with virtual worlds that are just as detailed and realistic as the real world. For example, the AI-driven NPC (non-player characters) in the game can have their own behaviors and motivations, making them feel more like real characters.

The advent of machine learning has made it possible to create new genres of games and experiences that were previously impossible. Machine learning algorithms allow for the creation of more sophisticated and dynamic systems and interactions within games, which can lead to the development of entirely new types of gameplay and experiences. 

The World Economic Forum's report on "The Future of Jobs and Skills" predicts that the demand for AI and XR skills in the gaming industry will grow significantly in the coming years. A similar report from the Global XR Nonprofit Network estimates that the global XR market (which includes gaming) will reach $80 billion by 2025.

XR and AI are not mere toys for gaming and entertainment but can also be used to create highly realistic training simulations for a wide range of industries, including healthcare, military, and aviation. By using AI to adapt to the user's actions and responses, these simulations can become even more immersive and effective. 

Some examples,

Workspace: 

XR and AI has the potential to transform the way we work. For example, XR could be used to create virtual coworking spaces where employees can meet and collaborate in a fully immersive digital environment. AI could be used to automate tasks and make the workplace more efficient. This technology has gained popularity, particularly to facilitate social distancing, and work-from-home transition.

Medical Simulation: 

XR can be used to create virtual reality therapies for a wide range of conditions, from anxiety and depression to chronic pain and post-traumatic stress disorder. AI can be used to analyze large amounts of medical data and help doctors make more accurate diagnoses and treatment recommendations.

A surgeon could practice a complex procedure in a VR simulation that challenges them with unexpected complications, helped by AI-powered feedback and guidance. 

Smart Pedagogy:

The global lockdown due to COVID-19 pandemic highlighted the importance of technology in education and the need for remote learning options. XR and AI can play a significant role in meeting these needs by providing students with virtual lab simulations and personalized learning experiences. AI can help create tailored educational experiences that adapt to each student's needs and abilities, making education more effective and efficient. AI could also be used to provide feedback and guidance to help students understand the underlying principles and concepts.

AI-powered Precision Farming:

AI and XR have the potential to optimize agriculture operations helping farmers to grow more healthy and sustainable crops. AI could be used to analyze the data and suggest the most efficient planting and harvesting patterns, as well as the best locations for irrigation and other infrastructure, while XR can create training simulations for farmers. Using sensors and cameras, AR can monitor soil conditions, and crops for signs of pests or diseases. 

Predictive Policing:

AI algorithms can analyze past crime data to identify patterns and trends, while XR technology can visualize the data for law enforcement. XR can also create training simulations for law enforcement personnel and VR therapy programs for offenders. AR can aid in crime scene investigations by gathering and analyzing data, with AI providing insights and recommendations. The combination of AI and XR can reduce crime rates and improve public safety.

As per a report by Mordor Intelligence, “The Threat Intelligence Security Services Market was valued at USD 1863.48 million in 2020 and is expected to reach USD 3829.77 million by 2026, at a CAGR of 12.9% over the forecast period 2021 - 2026.” 

The Future of XR and AI

While XR and AI have already shown tremendous potential when combined, we are still in the early stages of what these technologies can do. As they continue to evolve and become more sophisticated, the possibilities are almost endless.

Take a look at some of the very interesting data sourced from https://www.statista.com

• • Revenue in the AR & VR market is projected to reach US$31.12bn in 2023.

• • Revenue is expected to show an annual growth rate (CAGR 2023-2027) of 13.72%, resulting in a projected market volume of US$52.05bn by 2027.

• • The market's largest segment is AR Software with a market volume of US$11.58bn in 2023.

• • With a projected market volume of US$8,568.00m in 2023, most revenue is generated in the United States.

• • In the AR & VR market, the number of users is expected to amount to 2,593.1m users by 2027.

• • User penetration will be 28.8% in 2023 and is expected to hit 32.6% by 2027.

• • The average revenue per user (ARPU) is expected to amount to US$14.08.

• • Revenue values displayed here only account for B2C revenues. Meaning, according to the displayed market share of B2C, 0.0% of the total market (B2C & B2B) is covered.

There is already a multitude of XR and AI products and services available on the market, and this is just the beginning. As these technologies become more advanced and accessible, we can expect to see even more innovative and exciting applications emerge.

As with any new technology, there are also concerns about the potential impact of XR and AI on privacy and security. It will be important for businesses and individuals to carefully consider these issues as these technologies continue to develop and become more widespread.

In conclusion, as the world becomes increasingly digital, the demand for XR and AI technologies will only continue to grow. In fact, the global market for XR is projected to reach over $160 billion by 2023, while the global AI market is expected to reach over $190 billion by 2025.

Eighty-four percent of the respondents indicated that AI would allow their organizations to gain or sustain a competitive advantage over their rivals. 

(Source: Statista)

There’s tonnes of data generated everyday of our lives and more often than not businesses know the importance of using this data to their advantage. And, it is technologies like Artificial Intelligence that help them do that. Having said that, AI trends and solutions are taking customer experience to another level with chatbots, digital assistants, facial recognition, biometric scanners, and more. It is helping companies draw insights from the large amount of data available and is making business intelligent. And by business intelligence, it means improving the effectiveness of marketing, understanding customers better, creating personalized experiences, and developing business strategies to help corporate decision making and leverage artificial intelligence in business management.

2020: The year of AI Trends and digital transformation

2020 was the year that saw a pronounced digital transformation with unfortunately  COVID-19 kicking into our lives and the lockdown that followed. Businesses, work and people were facing challenges on many fronts and to overcome those issues, they couldn’t help but resort to technologies and digital solutions. During the times of crisis, when it became hard for us to look at the positives of any of it that was happening around, some industries managed to skyrocket at the progress level. Those being ecommerce, delivery services, gaming companies, fitness equipment companies, telehealth services, online tutoring services, tech companies, and more. And, we wonder how that was possible for them? Well, for some it happened by either going digital and for others, it happened because they were already working digitally. Believe it or not, a lot of that had to do with the AI trends and AI-powered solutions. Artificial intelligence and telecommunications helped some businesses stay afloat, and others do quite well for themselves.

AI is expanding dramatically and the approach to it being used has interestingly become disparate and innovative. Considering all of that, I am assuming that 2021 will be a good year for Artificial Intelligence and Machine learning. However, let’s take a look at a few AI trends that will contribute to boosting a businesses overall performance in 2021. 

AI-infused automation (IPA and RPA) of business processes

For those of you who are unfamiliar with the acronym, IPA is Intelligent process automation and RPA stands for robotic process automation. These technologies were introduced for businesses looking to automate processes which means optimizing the processes but at the same time ensuring that the functioning is carried out efficiently. They support automation of various processes at reduced costs and time without compromising on quality while making the automation intelligent. But at the same time, RPA only works with structured data and it is artificial intelligence that provides RPA with validated and structured data. AI through intelligent processing and by using natural language processing converts unstructured or semi-structured data into a relevant form for RPA to further work on it.

AI-powered cybersecurity

AI is growing and in 2021 we will be witnessing it fight cyber crime, data breach, hacking, and phishing attacks. Hence, more and more companies are inclined to invest in AI to take a step towards ensuring cyber security as they expect AI to enhance cybersecurity measures and prevent it. AI uses AIOps to monitor and manage hybrid and distributed IT environments and help IT professionals work smarter, faster, and better. Especially during these times, when most of the employees are working from home on unsecured devices, big companies like IPM, HPE (Hewlett Packard Enterprise), and more are working on making their AIOps program improved and stronger. Not just that, but these efforts are also helping these companies make headlines by reducing the resolution time, increasing the resolution rate of their AIOps programs. It is clear that there are heavy investments being made in this area, keeping the better in mind.

AI-driven IoT (AIoT - Artificial Intelligence of Things)

The change in the way businesses, industries, and economies function is quite evident. And, technologies like Artificial intelligence, Internet of Things, Cloud computing are the major cause for all of this happening and if this continues, businesses will see growth even in these times of uncertainties. Nevertheless, it won’t be wrong to say that IoT and AI mutually benefit one another. If it is IoT that lays the foundation for AI to be more impactful, then AI is not far away in returning back the favour. AI helps IoT create intelligent machines that induce smart behaviour and helps in intelligent decisions with little or no human interference. Other than that, AI empowers IoT by offering programmable AI operating systems that allows devices to learn, reason, and process information like humans. There are four major divisions where they are making an influence and those are Wearables, Smart Home, Smart City and Smart Industry. Researchers and AI developers are becoming innovative and are introducing new applications which just speaks volume of the potential this duo holds.  

You may also read: Roles and uses of AI wearables in healthcare industry 

AI-enabled chips

AI chips are setting a new trend in the world of technology as they add value to businesses by boosting the performance of AI applications. They are known for their intense processing capabilities and speed and these traits are being used by big companies to their full advantage.

According to a recent report published by Allied Market Research, titled,"Artificial Intelligence Chip Market by Chip Type, Application, Technology, Processing Type, and Industry Vertical: Global Opportunity Analysis and Industry Forecast, 2018 - 2025," the global artificial intelligence chip market size was valued at $6,638.0 million in 2018, and is projected to reach $91,185.1 million by 2025, growing at a CAGR of 45.2% from 2019 to 2025. (Source: Allied Market Research) This statistic makes one thing very clear that companies are investing a huge sum on AI chips, a few to mention are BMW and Microsoft. 

Out of the many capabilities of Machine learning, a few are language translation, facial recognition, and detection. Likewise, there are several applications of AI that I mentioned earlier. What AI chips do here is make all the AI and ML processes faster, better, flexible with less power consumption. A reason big enough for companies to adopt it.

AI runs on data, computing power, and algorithms. Anybody who is interested in understanding its capability and potential should be well conversant with the trio. Nevertheless, Logic Simplified is an artificial intelligence development company that understands the scope of AI in gaming business and the role of AI apps in small businesses as well as big. WIth that in mind, they offer state-of-the-art quality services and solutions that will help you scale your business growth with impactful AI integration. For more information get in touch with us or email at enquiry@logicsimplified.com

As the COVID-19 hit our lives, businesses have had a lashing impact on them and have transformed from the usual to virtual. In these times, they are leveraging technology to overcome the everyday challenges, to provide quality services to the customers, and to gear up for the future with future business solutions. Yet, the digital transformation has been amazing over the times and even so Businesses are eager to use AI and ML to the best of its capability to increase efficiency and productivity. 

The AI infrastructure

The advancements in artificial intelligence has opened the doors for development and innovation. It is contributing multifolds to improvement in production and quality work by precisely discovering opportunities, patterns and themes in real time with large amounts of data and input sets. Of course with the speed at which these AI algorithms operate, they are paving the better ways of getting business done.

There are large amounts of data generated everyday that gives us a great deal of information about our customers preferences. Having said that, businesses understand the need to rely on the modern methods to drive growth. Intelligent computer softwares and actionable insights drawn from customer’s data help boost revenue, increase productivity, improve customer experience, and drive growth. Let’s see how.

Customer service

Approximately 70% of buying decisions are made based on how clients feel they are being treated, superior customer service has the power to turn any company into an industry juggernaut. (Source: Salesforce)

Companies have started taking utmost care of their customers by putting effort into giving them the best experience with their services. 

For instance chat-bots allow the customers to interact with the company in real-time so that their complaints are resolved, queries are answered, and other information is shared quickly and seamlessly. Artificial customer support helps give faster answers by handling commonly asked questions through live chat experiences which is available 24 hours a day providing continuous customer support. On the other hand, customer support teams spend a lot of time researching the answers to the customer’s questions increasing the wait time for the customers. AI can save these answers and use them for the frequently asked questions that customers have. 

AI leverages information from CRM solutions and highlights the key customer details so that the approach towards the customer’s is intelligent and AI-powered. With predictive insights, AI is able to suggest products to the customer based on their previous experience and the product’s availability in the inventory. And, sentiment analysis helps the customer executives by categorizing the tickets raised by customers into different categories like neutral, positive, or negative. This helps the agents understand how to prioritize their work. Artificial intelligence makes the work for the customer’s more incisive and responsive. Strategic and smart work.

Business Intelligence

AI-powered natural processing language and automated predictive insights are doing wonders for businesses. Employing AI in business intelligence is turning into everyday business with many adopting the technology. The plethora of data and the need to understand that data are the factors that are contributing towards the growth of Business Intelligence. This is a system that is designed to structure the large volumes of data that is collected, processed, and analyzed so that the data can be used to strengthen their business processes by making smart business decisions. Then again, digital AI amplifies the functionality of Business Intelligence by splitting large chunks of data into detailed insights that could be used in advantage for their business.

With benefits of Machine learning coming into the scenario, the issue of real-time insights was able to be resolved by ML algorithms predicting trends and generating real-time insights. This has influenced the value of Business Intelligence in a constructive manner.

You may also read: Scope of AI on gaming business

AI-driven personalized and targeted marketing

To be able to communicate with the target markets is extremely essential for every business and AI helps you do just that. It is behaviour-based algorithms and predictive analysis that brings together all the data that makes this communication possible. Also, there are several AI-powered intelligent multisensory systems that monitor different types of sensors in an environment and accordingly interpret the activities that are taking place in that monitored environment. This monitoring and interpreting has come together as a solution to several challenges. They help businesses learn about their customers’ requirements and expectations which helps them provide the best services possible. 

You may also read: Predictive analytics & machine learning across 5 industries

The other facets that come with integrating AI into brand marketing is Ad targeting that are Ads that target specific users based on their previous online shopping behaviour. To serve these various purposes, there are advertising intelligent apps like Match2One, ReFUEL4, and others that prospect and retarget customers. Then there’s personalized messaging that again uses behavioural data to send messages to the customers that are of some relevance to them. Yoochoose, Dynamic Yield are some of the companies that use data to provide the customers with personalized emails and messages.

AI-enabled product recommendation and predictive analysis

Product recommendations engines are leveraged by companies to improve customer’s experience. There are many AI development companies that offer AI-enabled products and services and advertise more and more products in front of the customers. The fundamental point is to put out the right product in front of a customer and that is how AI recommendations work. The  scads of customer data like the customer’s profile, product metadata, the products purpose, etc. that is present on the internet is used by the AI system (machine learning algorithm) to predict and match products for the customers that are most likely to be purchased by them. Well, there are a number of popular recommendation approaches that subsist. Most of the product recommendation operates on predictive analysis that helps train the algorithm with all the product and customer data. The relevant product ads then appear to the customers on the sidebar, top banner, and other areas of the retailer’s choice through designated channels.

Blockchain and AI development 

Blockchain and AI development individually are preeminent technologies across almost every industry and also together they are proving to be quite an influential duo. Integration of Artificial intelligence in Blockchain is imperative as they both deal with data and value and they collectively improve the capability of a machine learning algorithm. Not to mention that Blockchain ensures secure storage and exchange of data and AI analyzes and draws insights from the data to derive value. Both the technologies mutually benefit one another and enhance each other’s capabilities which multifolds the grade of efficiency, security, and accountability for business processes.

Natural language processing

What this means is the capability of an AI system to read, understand and recognize several human languages and an NLP technique converts the written or spoken language into a form that computers will understand. In business, the NLP technology is put forth using sentiment analysis that helps the businesses earn a broad public viewpoint on their products and services. A few applications of NLP technology are Email filters that are used to stop spam emails from entering their inbox, smart voice driven interfaces to understand and take better care of user’s concerns and queries, extraction of information from texts, infographics, and images (unstructured data) to understand human conversation and improve the ML program. There are some popular NLP softwares and tools like Amazon comprehend, Gensim, Google Cloud translation, IBM WatsonTone Analyzer, and others that are used to build chatbots, voice assistants, predictive text applications, python programs and libraries, cloud based solutions to work with human language data, etc.

The number of AI-powered voice assistants is forecast to reach 8 billion by 2023—a 146 percent increase from 2019’s 3.25 billion. (Source: Oberlo)

For those not in the know, one such company that provides AI solutions to businesses is Logic Simplified, an artificial intelligence development company in India. We understand the importance of data processing in AI for businesses and its capability to solve long standing business challenges. Every business is different but some of the challenges that they come across are age old and quite common among all. And, the team of AI developers at LS are well-positioned to bring to you AI-enabled solutions and approaches that are effective and capable of allowing businesses to manage data, satisfy customers, improve customer interactions, ensure secure transactions. For the most part, make business highly productive. For more information get in touch with us or email at enquiry@logicsimplified.com

The video gaming industry is overwhelmingly huge and shows no signs of slowing down. While there were almost two billion video gamers across the world in 2015, this figure is expected to rise to over 3.24 billion gamers by 2023. (Source: GAMEFID) With the mentioned statistic, it is quite evident that there is a large population that is dependent on the peculiar industry for their source of entertainment.

It was in 1952 when Arthur Samuel created a computer program that could learn from itself, and it is since then that the propensity for a machine learning game model has been to learn and improve from its experience without any human assistance. In recent years, machine learning in video games has boomed to a higher tier of triumph. And, the humongous data produced everyday and the improvements in the GPU processing speed are the major reasons for the growth of this technology.

Other than that, altogether there is a lot that together makes the game designing and development process multifaceted and tedious. The game environments, the storyline, the plots involved, the game characters, their features, and so forth. Over the time, the game development process has evolved and so has the technologies that contribute to making it an intelligent development process. Through video gaming and machine learning, game app developers have been creating softwares that work like humans do and develop virtual worlds on their own from scratch without any human support.   

From our very own traditional games to today’s modern strategic games, artificial intelligence has been used in video games since a long time now. And, it’s subset, Machine learning has made a huge difference in how video games are developed. Not just that, but the gaming experience of the user is also not like it used to be. It has evolved with machine learning algorithms. Machine learning based games learn from the player’s behaviour and howsoever react and respond accordingly. Also, the content, storyline, characters, challenges have transformed the game’s look and feel entirely.

With machine learning, it is just not about crafting games that are intriguing, propelling, and immersive but the idea behind the amalgamation is to bring intelligence and astuteness into softwares, and the virtual world of gaming. There are several issues that a machine learning model addresses and it brings methods and strategies on the table for advancement. It has become of great value to game designers and developers if they know how to make the most of it. It would not be wrong to say that Data is driving the industries today and other technologies especially machine learning that primarily runs on data in making the most of it. Whether it is working with behaviour trees to manipulate non-player characters or developing ML programs that are capable of beating humans at their own games. These innovations and systems are improving game environments, assets, and behaviours in all kinds of games, be it strategy games, shooting games, or even racing games. They are making games better, smarter, and interactive.

As of 2019, the market worth of the gaming industry was close to 150 Billion dollars. With the introduction of technologies like Artificial Intelligence, Augmented Reality and Virtual Reality, the numbers are set to cross more than 250 billion by 2021-2022.

Source: Insidebigdata

Data-driven game design and development

Data analytics and algorithms allow developers to make informed design and development decisions. These data driven techniques are used to study gamer’s behaviour at different levels. As it is, Design involves creativity but along with that when AI in games provides data-driven design rules, this takes designing to another magnitude. The data that is put to use reflects what players expect and that evidently works in favour of any digital game by serving the purposes of learning and assessment. Understanding data and embedding it to the game development process impacts the overall enjoyment of the players and the qualities and efficiency of the game. 

They say “The more the merrier” and the phrase can be very aptly put for data. The more the data is provided to a machine learning model, the more it learns and gets better and improved. Thus, Data can never be enough and is used itself to generate more data which is collected, processed, and used to produce results that caters to gamer’s concerns and expectations. 

The design of a game impacts the performance of the player and their engagement. Data oriented games operate and are optimized on the input data provided which is then transformed into output in a productive way. Plenty of data is available that is used to provide an excellent design that is smart and constructive. Once the design is out, the response and feedback by the users is collected by the machine learning model and considered to make improvements and produce a final design that provides an improved experience. Not to mention, artificial intelligence and machine learning are leveraged so that reinforced outcomes are produced with data-driven game designing.

For the most part, Machine learning in game development endows the gaming business with real-world experiences. ML algorithms that run on data can be used to enrich a gamer’s experience by creating a virtual environment where characters don’t just exist but perform actions and behave in a certain manner that they are expected of. All the abundant data and information is used to design and develop characters that are realistic and natural. And, hence ML algorithms are trained with organized data that comes from humans to build systems and models that will outsmart humans itself. With time, the in-game characters are evolving as they learn more with time and with the more data that is provided to them. And moreover, the games are changing with the responses that come from the players and the observations that a machine learning model makes by itself.

All things considered, there are numerous AI game companies that are at the vanguard of bringing to the world some advances and breakthroughs for the expansion of the gaming industry. One such company is Logic Simplified, an Artificial intelligence development company in Dehradun. We boast a team of skilled programmers and game designers and use cutting-edge technologies to craft unique, immersive and fun games. They work on creating algorithms with predictive analysis to identify and understand the language and behaviour of characters, the whole purpose being to design an accommodating and friendly gaming environment for the players. And, a game that is exemplary in every measure.

Another technology that has expanded exponentially across the tech industry and esports gaming businesses is live streaming. Companies are also conducting online events to allow cross-country participation. Besides modern digital broadcasting formats, gaming businesses around the globe are implementing AI and machine learning in game development to enhance the gaming experience through its realism and interactivity. As companies are interacting with game developers, we see that a lot of them are focusing on aspects such as the intelligence of the game and its capacity to respond to live inputs, rather than solely following gaming story plots, which is a big factor in driving interest and perception.

Creating A Game Using AI

The researchers of mobile gaming companies follow the machine learning path, where the system learns to create close representations of the games, and recombine that information to develop new games via conceptual expansion. This method helps in illustrating the ability of the system to recreate the games.

The team of researchers first input the data to the machine in the form of videos. This video contained hours of gameplay from humans playing the first levels of games like Super Mario Bros, Kirby’s Adventure, and Mega Man.

Use of Artificial Intelligence in gaming

The use of artificial intelligence in gaming can be defined as a collection of a set of rules that will be followed by the computational agents to respond to the external stimuli. There are a set of advanced rules in modern games, and the NPC’s try to improve the storyline, which in turn disturbs the actions of the game players.  Artificial intelligence mainly focuses on developing algorithms which can simulate human behaviour. 

AI has been used in various video games since 1951 like checkers, chess and Nim. Machines have been outperforming multiple calculations in a short period when compared to the human brain. As a theory, AI came into existence in the global gaming business in 1997 when IBM’s computer program, Deep Blue, defeated chess grandmaster, Garry Kasparov. Over the years, AI in game development has emerged to advance the industry across the world, allowing a much-needed gaming evolution. Developers are getting training on artificial intelligence to play games so that they can transfer the skills into the real world where AI game development services can be used to train more of artificial intelligence.

Below are some ways AI is making a great impact over video games:

1. AI makes the gameplay  realistic

It creates behaviour for agents in a game, also known as non-player characters (NPC). It means the actions that characters over the screen will be doing. NPC is what puts life into any game.  The power of AI is observed when one picks off NPC and the other becomes twitchy and alert. It can also be used to identify a player's location.

With well developed and right AI, one can have and inhabit an amazing gaming experience. Similarly, a poor AI execution might result in erratic behaviour of NPCs that do not follow any game rule and might lead to a door collision. It also leads to immersion-breaking experience and clunky animation while playing the game.

2. It can enhance the look of old games 

With artificial intelligence, one can also enhance the way the old classic games used to look. Usually, the old video games face the issue of bad scaling algorithms and irregular textures when they are being played over modern systems. 

For this obstacle, the ideal solution is going with anti-aliasing. Most graphic cards already support multiple algorithms like  Multisample anti-aliasing (MSAA) and Fast approximate anti-aliasing (FXAA) that helps to upscale the low-resolution pictures into high-quality images. With machine learning into the mix, AI upscaling can be used to enhance the quality of old games. Programmers can successfully create a new and high-resolution design which gives out more details. Tools such as ESRGAN and NVIDIA can be used to refurbish the favourite games of their preference. 

3.  It can develop the game levels on the high

With procedural generation methods, you cannot include response to the actions of the player, and it’s also not based on the technical AI. When the AI model gets created based on the behaviour of the player and their preferences, it will create in-game experiences based on what the person would prefer. Sometimes a person may encounter a new city which they didn’t think existed nor had seen it before in the game.It has all been created according to the concern of the player. It has all been created according to the interest of the player. Some may like shooting, some motorcycle racing etc. but you might not be very good at it. So, it would not give you tough challenges and dial down according to your skills.

4. AI can change the play style on the move: 

Some video games will respond according to the skill levels of the player. Depending on how well one performs in a game, the adaptive AI adjusts the difficulty levels to either up or down. It can also get accustomed to the playing technique of the player while making it more interesting for them. Well-known researchers of AI development companies and game developers said that directors will also concentrate on the players and measure their stress levels to see if they are lower than others or not.

5. It can also improves the life outside the game 

Researchers are also deriving the algorithms from GTAV game software so that it can be used in the currently growing self-driving sector. But getting the AI-controlled vehicles directly onto the roads comes with a hefty price. Hence it has very limited to no scope as it might cause accidents while training AI, and it may lead to serious collisions.  But this can definitely be utilised in games as it can substitute the real city life scenario which can help someone experience and learn. Hence, helping us train AI that running over pedestrians isn’t the right thing without actually hitting them. 

6. One can learn from AI 

The video games have created and made great advances in AI. Many researchers came up with the conclusion that video games are helping the next generation in getting familiar and engaged with AI. They also stated that the adoption of AI would create the best motivation tools in the games. For example, DeepMind's StarCraft AI helps the students greatly and motivates them by turning their cell phones off.  People are learning the gameplay through AI. According to Temple Gates Duringer, AI can provide you with numerous novel strategies which are beneficial for humans as they have a human inclination.

7. Help in creating interaction

Characters from books or stories could be turned into an interactive figure, so we could communicate to them. For example, someone might want to play Harry Potter or one of the other fictional characters in the book. Using artificial intelligence, Characters like Harry Potter and others will be able to show emotions, possess behaviours, and give insight. You might even be able to practice your lines with those characters. It is completely different from watching a movie—this is you interacting with others and being completely involved in the game. 

Outlook

While gaming has improved its ability over the years, complex deep learning applications for self-driving cars or AI robots have not entirely entered into the gaming universe. Innovations like that can vastly elevate the current gaming space to create a deep immersive future of AI in game development. With AI being fast-tracked into the gaming business, it might not be that far-fetched goal anymore.  AI has complemented video games over the years, and with its current pace, it is on the right course of developing innovative technologies that can further transform the AI and gaming industry. 

Logic Simplified, an artificial intelligence development company based in Dehradun holds potential AI programmers to bring tangible solutions for the gaming industry needs through AI games and applications. Let our AI programmers help you make an impact in your world - to ensure enhanced productivity, profits, reduced time consumption, improved security throughout the process. Get in touch with us and enquire all you want to avail the best services in town. Share your thoughts, enquiries and suggestions by writing to us at enquiry@logicsimplfied.com

The present and future of it

Well, speaking of Artificial Intelligence, it is the emulation of human intelligence in machines. And, in the case of game development, its underlying objective is to make video games intelligent. Speaking of the specifics, it provides the games with the ecosystem that supports intelligent game behaviors for non-player characters, and provides smart game control to make the interaction and movement between the game character smarter, and close to reality.

In the coming times, it is predicted that the benefits of Artificial Intelligence on games will continue to grow and will be more positive than not, making games more immersive, realistic, and life-like. Intelligent game behavior will provide attractive features in terms of realistic movement and interactions between game characters and game players.

The avatar or character in a game can communicate with its environment using sensors and actuators and then reacts according to the situation. The coder programs this behavior of the character, and then it further learns on itself by observation and interaction. All of this is possible due to the existence and convergence of IoT and AI. It is Artificial Intelligence that is building a future that is more about personalized gaming. 

You will all agree that every game has a specific set of rules, and it eventually comes down to completing the missions and winning the game. AI in game development will give us a prospective that will be technology-ready as it works with other trending technologies, including Internet of Things, Machine Learning, Big Data, Augmented Reality, Virtual Reality, Mixed Reality, and others. 

AI and non-player characters - the duo

Artificial Intelligence is most prominently observed in NPCs. It provides an opportunity to implement intelligent, responsive, and adaptive behavior of independent computer-operated characters called NPC agents (non-player characters) in video games. Pathfinding and behavior & decision trees in modern games steer the actions of NPCs.

Zynga has spent a lot of money on acquisitions until now. It bought 80% of Small Giant Games for $560 million. It acquired Gram Games for $250 million. It bought most of Peak Games for $100 million. And years ago, it bought NaturalMotion for $527 million. All of that activity burned up a lot of the money that Zynga had from its initial public offering in 2011.

Source: VentureBeat - How Zynga looks for the right game companies to acquire

Game development is already a big thing, and big companies are all set to get into this industry considering the future returns it will bring. With AI creating a virtual world that is marvelous for a gaming experience. There are a whole bunch of benefits of artificial intelligence that come along AI game development. At the same time, it reduces the workload of employees by effectively handling developer’s and designer’s tasks by choosing and using the ideal tools for every job. It reduces the game development time and also the time used for content generation. It allows game app developers to construct games like FPS shooting games, racing car games, or strategy games in a shorter period, at a lower budget and reduces the chances of errors. And, AI game development only gets better in the future. For the same reason, Small-scale and Indie game development companies are ready to become bigger players on the mobile game development front soon. There will be challenges too, but technology will be strong enough to address them. 

AI controls the actions of the NPCs (Non-player characters), the characters on the  screen and contributes enormously to give the player an immersive and responsive gaming experience. An adaptive AI can either up the difficulty level or reduce it to make the game challenging or adaptive to the gamer’s playing style. It majorly improves the look and feel of the game.

Game engineers use data analytics to make informed decisions by noting details like how often a player lands in a particular area, what character is being used the most, or what weapons and other items are being used frequently. It is certain that the AI developer’s decision depends majorly on the data analytics of a player’s behavior, game assets, and the environment.

The future is unpredictable, but the games that adopt new technology and create next-level competition make gamers stay. What the human development team could not do alone; AI has made that possible. The future has AI developers for games create video games using the present-day AI techniques at advanced levels to plan strong frameworks inside games. The characters that are now able to self-learn from their actions and evolve thereon. There will exist a game that can interpret and react to your in-game activities, foresee your next move, and act accordingly. AI learns from a player’s wins and losses by identifying where these happen and how they happen. That’s evolution next-level.

The Gaming industry has changed the most with the rise in modern technology, all thanks to Artificial intelligence. There are advanced tools that exist and provide access to in-depth analysis and predictions for a game with the help of AI algorithms that also help with decision-making processes taken for the NPCs in games like Skyrim, Grand Theft Auto, etc., gameplay mechanics, and level design. This, in-turn can change the state of the AI-enabled games in the market. 

Gaming AI is improvising with time using the data and AI techniques provided. It goes without saying that in the coming 10 to 20 years, game app developers and experts will be able to create virtual worlds. It will present the gamers with difficult situations to test their mental capabilities, and they will be able to perform any action in the virtual world that they could perform in the real world. 

To the best of my knowledge, Artificial Intelligence technology is giving players an immense scope to build and experiment with artificial intelligence robots, vehicles, weapons, ammunition, and extraordinary free-form crafting tools. AI will have the most impact on gaming that others. There lies the great scope for enhancing the virtual characters in the gaming world. Making the characters more immersive, the character’s behavior more realistic are the possibilities of advancement.

Logic Simplified is one such company in Dehradun that is a leading Artificial Intelligence game design company with knowledge, skills, and expertise to craft games that are made to stay in the market. Their high-octane and brilliant game developers employ technologies in unique and exciting ways to personalize a player’s gaming experience and provide interactive entertainment.

Game development involves designing, development, and release of a game for entertaining the user - the world. It is wholly an art of creating enticing games. The intricate creation is a process that requires experts in their field like a programmer, sound designers, artists, and graphic designers, along with laborious work, oodles of money, and befitting execution.

Machine Learning in video games has a significant impact on how a video game could turn out. Over the last years, technology has swayed gaming needs, and people’s diverse preferences have led to innovation and evolution in the video game sector.

An individual plays games to have fun, but there’s a lot more than just the fun part. Video games help step up a human’s brain functions, involve continuous engagement of cognitive skills, and release a chemical called serotonin in the brain, also called the happy compound. Innovative technologies like ML and more make games more creative, immersive, and satisfactory, setting a path to revolutionize game development.

The learning agent 

It starts by creating a learning agent with the necessary knowledge that learns from experiences, and it comprises certain elements.

• • A learning element that alters the agent's behavior to make improvements in its performance.

• Critic, just as the word itself, provides feedback to the agent on how well it performs as regards a fixed standard.

• A performance element is responsible for choosing the action based on suggestions from an external factor for improvements.

• A performance analyzer examines the performance of the agent. Accordingly, it provides feedback for improvement to the learning element and whether or not there is scope for enhanced performance by modifying the performance element.

The strategies and techniques that are developed by the critic's observation and the performance analyzer's suggestion are executed by the learning agent to determine the performance of the cognitive machine learning. Machine Learning adds logic and experience to the games. The enhanced usability of AI and its subset ML is making more and more gaming companies hire AI app developers to build more engaging and personalized video games.

Ray Kurzweil, an American inventor and futurist quoted “Artificial Intelligence will reach human levels by around 2029. Follow that out further to, sat, 2045, and we will have multiplied the intelligence – the human biological machine intelligence of our civilisation – A billion-fold.” 

Source: SpringBoard

Machine Learning Game development Techniques

The system is fed relevant information based on which decisive future predictions can be made using Reinforcement Learning, Deep Learning, or any other ML technique. Games like Atari, Doom, Minecraft showcase the most notable application of machine learning techniques in game playing.

When machines learn from the behavior of others by subjects to large sets of data, it is considered as Deep Learning in games. This technique focuses majorly on the Artificial Neural Network (ANN) and uses multiple layers to extract information from an input to learn and solve complex tasks.

Reinforcement Learning uses a reinforcement agent that is trained depending upon the problem, using rewards or punishments. This reinforcement agent provides suggestions or decides what to do to perform the given task. It lets machines understand the difference between right and wrong and collect the right information to maximize the reward. This technique is used in methods like Q-learning, Deep Q-networks, policy search, etc. It works great in the field of game development.

Convolutional neural networks (CNN) involve specialized ANNs used to analyze data by learning translation-invariant patterns (not dependent on location). It can learn visual data, making it an extensively used tool for deep learning in the gaming industry.

Long short-term memory (LSTM) is a sort of recurrent neural network (RNN) that is used in deep learning. Its applications lie in functions like connected handwriting recognition, speech recognition, and anomaly detection in network traffic or IDSs (intrusion detection system).

ML Application in Game 

From developing complex systems to AI & ML algorithm playing as NPCs (Non-player characters), from video games becoming more exquisite to NLP (Natural Language processing) creating more realistic conversational video games, advancements in Machine Learning have enhanced the algorithms capable of supporting creativity - the creation of not just games but music, art, and more. Let's crawl into a few use cases of ML but concerning video gaming only.

Player experience

Yes, machine learning is enhancing at a promising rate. But, it becomes challenging when it comes to personalizing the gaming experience based on a player's behavior, thus data processing in AI and ml algorithms has to be done just right. Artificial intelligence game developers are defying the odds now and making next-gen games that look and feel more realistic, where players can interact naturally with other players and the environment. The motive is to enhance an individual player's experience during the game, and even after. Some tools are used to evaluate a player's experience. Minor details and lower-level game design choices like the choice of GUI elements, game structure, sound, mechanics, story, visual embellishments, etc. contribute immensely to a player's highly immersive experience.

Data Analytics

Game app developers have been leveraging machine learning and data analytics to build the best gaming experiences, which will attract more players to the game. With video game development on the rise, there has been a generation of massive amounts of data that is used to yield insights used for improvements and developments. 

The crux behind data collection for game development is capturing the graphical display and recording the user's data so that those inputs can be studied by learning algorithms to generate optimized results. 

It enables data-driven gaming design concepts to make it easier to generate excellent experiences to make video gaming popular across the globe. Once a game design is developed, the testers gather people's response towards the game which is used further to improve game design.

As per the reports, game designing is one of the most profitable professions, a very competitive sector. By learning the ways, your game design can be improved, and you can always ensure to generate beneficial models.

Algorithms Playing as NPCs

Earlier, the opponents that a player used to fight against were pre-scripted NPCs. Still, with Machine learning-based NPCs, the game has become more uncertain and unpredictable for that gamer. And the unpredictability increases as the learning agent studies your behavior making the game all the more interesting as the opponents become smarter by observing and learning the player's actions.

Major game development companies are working on machine learning-based NPCs applications where algorithms learn four times faster than reinforcement training.

Complex Systems 

Complex systems are developed with codes and specialized tools to build a gaming world that is more real and practical. Game app developers pay close attention to detail and work on presenting minute information so that images stand out dynamically. The player is able to interact with its environment and the opponents. NLP also achieves this objective differently.

Enhancing Developers Skills

The traditional game developers can skill up their ML techniques with the growing demand in the industry. The technologies and innovations take the scope of game development a notch up with the potential and possibilities machine learning brings into its arena. Nevertheless, Artificial intelligence game design and development companies will continue using ML to make smarter and realistic games and bring a change in the way video games are created.

Logic Simplified, a game design company based out of Dehradun, India, has ML game developers researching, refining, and applying AI into their game development. They take it as an exciting opportunity to extend video games into new horizons by giving gamers even more immersive experiences and more playable and unexpected content with intelligent gaming. For more information get in touch with us or email at enquiry@logicsimplified.com 

Businesses are collecting humongous data from IoT devices and social media. AI integration turns that data into insights with more accuracy and at a faster rate, and Blockchain assures security and privacy.

The indispensable pair

Machines are built, and they learn to perform intelligent tasks without any human assistance through Artificial Intelligence. They are made artificially smart and independent so that they can sense information, and learn from tonnes of data. That knowledge is used or applied to offer services and conduct operations. With AI, there come enhanced data management capabilities, data-centric predictive analysis, development errors are minimized; people experience real-time assistance, and assistance with data mining. Artificial intelligence (AI) promises genuine human-to-machine interaction where machines can reason, observe, and plan.

Put simply by Sally Davies, FT Technologies Reporter - Blockchain is to Bitcoin as what the Internet is to Email. Blockchain is a decentralized and distributed ledger or database across a network of computers that records and stores data with reduced chances of security breaches. This technology is safer, optimizes transparency, ensures data privacy, and is faster compared to other centralized technologies. Originated from Bitcoin, and has now demonstrated its potential in numerous domains.

These two, AI and Blockchain, mutually benefit one another. AI requires massive amounts of data to learn and make insightful decisions and fundamentally change the way Blockchain networks are managed to make them more efficient while Blockchain is a useful way of storing data securely. 

These technologies have made their way into our lives through cryptocurrency, chatbots, personal assistance, and robotics. They have bought possibilities that are efficient in obtaining energy, improving scalability, security, and privacy. Blockchain does have its shortfalls in terms of scalability, security, and efficiency. AI addresses those challenges along with the others. 

Energy consumption

The considerable energy consumption comes as a big challenge for Blockchain, and Artificial Intelligence is capable of keeping a check on that.

Most blockchain technology follows bitcoin's infrastructure and uses Proof-of-work (PoW) as a consensus mechanism for validating transactions. These protocols demand complex mathematical puzzles to be solved and enormous computing power. The estimated Bitcoin transaction energy consumption is significantly high. Adding to that is the energy and cost used to cool down the computers.

To overcome this challenge, AI algorithms are being built that consume less energy. To support minimum consumption of energy, proof-of-stake (PoS) protocols were introduced, which included an algorithm to randomly assign the task of validation to a node and a combination of a participant’s stake in the network.

Source: Ethical Corporation

Enhanced Security

Even though Blockchain is by nature incredibly secure and tamper-proof, and the choice for storing sensitive and personal data but artificial intelligence with machine learning and deep learning uplifts the whole process. 

If you have heard of data markets, then you must be quite aware of the private data that is sold in those markets. Many of us rely on AI for a few cybersecurity solutions that it provides like intrusion detection, user behavior analytics, phishing and malware detection, and much more.

High Efficiency

AI can make sure that the resources are being efficiently utilized so that reduced latency is experienced and costs are minimized. It also entitles us to reduce the carbon footprint of blockchain technology. It turns out, the data on the blockchain network keeps growing every minute. Out of this, the information that would be needed for use in the future is automatically pruned by the AI’s data pruning algorithms maintaining high efficiency and productivity.

The applications of Blockchain and AI are extraordinary but when combined they’re exceptional yet ample. Let’s read about a few of its many applications.

SMART CONTRACTS 

As we have seen, the implications of this technology for finance are highly disruptive. It includes consent, confidentiality, and financial transactions. Blockchain-enabled smart contracts exhibit the capability to exchange value without third parties getting involved. The exchange of value, goods, and services is now more efficient and secure. All thanks to artificial intelligence, cryptography, algorithms, or smart contracts. AI-enabled blockchain solutions will better analyze data sets from thousands of variables, and contribute to increase cyber resilience and optimize intricate interchanging of distributed energy resources by encrypting, monitoring, and automating transactions. 

HEALTHCARE

Impressive technologies are contributing to the medical world in ways one couldn’t even imagine. Healthcare organizations are doing their best to offer solutions and services that satisfy customer needs and, at the same time, reduce costs and improve outcomes by leveraging data. They are turning to Blockchain and Artificial Intelligence to blur the impediments that hinder the development of the healthcare industry.

Source: Health IT Analytics

ROBOTICS TECHNOLOGY

We are well aware that Artificial intelligence powers robotics. It empowers them to learn and deliver excellence and efficiency as it has in chatbots, or voice-assisted technologies - Siri and Alexa. For efficient and effective RPA (Robotic Process Automation), businesses are relying on multiple technologies and not just one. This challenging field insists on producing and offering solutions that are customer-satisfactory. With automation multi-fold, Artificial Intelligence mushrooms the efficiency of robots while Blockchain offers data immutability that ensures that the operations are tamper-proof. Together, the operating pathway of these technologies helps achieve the desired goal.

The most significant use of this pair was in Swarm Robotics, where these were used to control a group of robots. The application of artificial intelligence and Blockchain could collectively enhance the response and behavior of the robots.

AUTONOMOUS VEHICLES (AV)

The two most cutting-edge technologies come together, and an artificially intelligent blockchain simplifies and streamlines the learning process of autonomous, driverless, self-learning cars using reinforcement learning and then imparts the learned information.

The car learns from its experience. Cars connected to a shared public ledger can share their knowledge so that all the vehicles possessing that shared ledger can determine when to stop from the experience of one single car, thereby eliminating the cumbersome task of training each car separately. This collective learning can be carried out using Blockchain Technology.

Together, technologies widen the spectrum of opportunities. Similarly, the Blockchain developers and AI programmers at Logic Simplified augment the path of opportunities for businesses to grow and thrive in various industries like Tourism, Education, Finance, Manufacturing, Retail, Customer Service, Gaming, Security, Energy, Logistics and Transport. You name the industry and we have it covered. For any query get in touch with us or email at enquiry@logicsimplified.com 

With advancements in healthcare and Artificial Intelligence kicking in, especially through wearable devices, the growing population has now a hold on their health. AI wearables are making it easier for doctors to cater to the large population with real-time technology. Yes! The wearables are all things to all men. Also called a body-borne computer, it is worn by the user on the body so that it can interact with the user. Technically, the wearable device consists of

• Sensors in either glasses, watches or in footwear, 

• And, an information aggregator & analyzer

When talking about wearables, how can we not mention Steve Mann , the pioneer, the main man behind this innovative idea, aka The father of wearable computing. This great inventor at the young age of just 12 in the 1970s built the wearable computer and, in 1988, the first smartwatch (Linux), which has now undergone 18 generations of development to reach where it is today. He has his own several companies. To name a few are the Meta in Silicon Valley that works on Augmented reality digital iglasses to substitute vision, VisionerTech in Shenzhen, China, that deals with mediated reality using real-time HDR to help people see the world better. This man has been contributing to different industries and also the healthcare industry in his own ways.

“The overall wearable AI market is projected to reach USD 42.4 billion by 2023, from USD 11.5 billion in 2018, growing at a CAGR of 29.75% during that period.”
(source: MarketsandMarkets)

Wearable devices have been one of the most significant and on the rise applications of IoT in the healthcare sphere. Let’s read how.

1. The IoT device shares insights on blood pressure, oxygen level, blood sugar-level, ECGs, and more, reducing costs for patients and improving the quality of care. 

2. Nowadays, healthcare facilities use their own off-the-shelf hardware module (ex.Toradex), that enables them to write their own firmware, and have access to connectivity with all of their legacy devices. This way, medical equipment can leverage low-cost, low maintenance wireless networks and RS232-, RS485-, and USB-based protocols. 

3. Healthcare companies are using IoT Fitbits that can be connected to pill dispensers, their boundaries can be set by geofencing, using Bluetooth low energy. The processed data received by these AI healthcare wearables come to great use to the doctors, nurses, patients through predictive analysis with high accuracy.

Healthcare analytics at the edge has been able to save lives around the world.

AI Wearable technology in healthcare is slowly and gradually drifting towards the top. Big companies like Apple, Xiaomi, Huawei, Fitbit, etc. are coming up with AI enabled wearable technology and their better versions every now or then to give the customer's choices and a better experience.

Examples of AI in healthcare

1. A Google Brain initiative was an AI-powered diabetic’s eye disease detection. An artificial intelligence invention to prevent blindness that worked on a mechanism of machine learning algorithms based on Deep Learning. The algorithm here learned and performed a particular task by repetition and self-correction using neural networks.

2. Sunu Band Sunu, a Boston-based wearables manufacturer, unveiled the first wearable device for visually impaired in 2015 called the Sunu band. The device did gain ground for the blinds. It detects obstacles on the way using ultrasounds and notifies the person about it for safe navigation.

3. Inspero Inc., the firsts to bring AI-enabled smart Bluetooth Headphones to the market with Vinci, voice-controlled headphones and personal assistant that helps by providing real-time tracking and insights. Of course, depending on the health parameters.

4. Google’s AI healthcare masterpieces have been the Google smart lenses, health patch MD, Cloud DX vitality, and iTBra. These detect changes in the body and analyze symptoms that start to occur, making the person cautious of his health so that precautions can be taken and disease can be prevented.

The devices just don’t stop at Wearables

Different types of AI Healthcare wearable

1 - Wearable Sweat Sensors

"Sweat is the new blood," GraphWear is developing wearables that test a patient’s sweat and not blood. The AI technology based wearable diagnostic technique is more convenient and cost-effective.

Wearable skin sensors can monitor sweat rate and give insights on electrolytes and metabolites in sweat, health problems like fatigue, dehydration. Researchers have come up with a new spiraling microscopic tube called microfluidic. The speed of sweat moving through the microfluidic determines the person's sweat rate through the help of sensors.

2 - Hearables

"Hearables," the term was introduced to the world by the Technology analyst, Nick Hunn. In-ear devices help with fall detection by tracking activities, heart rate monitor, measuring body temperature. Virtual or voice assistants like Apple's Siri or Amazon's Alexa serve as caregivers to patients, reminding them to take their medicines, exercise, or visit their doctors, activity trackers — in all, they’re a real-time audio coach. 

3 - Ingestibles

These are broadband-enabled electronic devices that are edible. Example "smart" pills that use wireless technology like microprocessors, power supply, sensors, etc. to help monitor internal reactions, disease diagnostics. It also tracks blood levels of medications in a patient's body to find optimum dosage levels, avoid overmedicating, and truly individualize treatment. Also, small pill-shaped video cameras will soon replace endoscopies. A pill swallowed by the patient would transmit images while passing through the digestive tract.

A pill-sized sensor, this was a breakthrough. Less invasive and less costly, used in both diagnosing disease and monitoring medication's impact on the body.

4 - Moodables

Design for every human possibly existing. It provides relaxation to people suffering from stress disorders and ADD (Attention deficit disorder). In other words, it enhances a patient's well being. These devices send low-intensity currents to the brain by reading brain waves. These have the potential to replace antidepressant medications in the coming times.

5 - Embeddables

Embeddables are inserted under the skin or more in-depth into the body. Ex. A heart pacemaker. In the future, embeddables may use nanotechnology and be so tiny that doctors would "inject" them into the patients to monitor blood sugar levels reliably and automatically, without the need to prick their fingers or otherwise draw blood.

6 - Charting

Healthcare charting can reduce a doctor's workload by 15 or more hours a week. Patient's data is made more accessible, tasks automatic and redundant, reduced errors, so that doctors will have the time to focus on their patients.

7 - Smart Dispensers

The number of smart medication dispensers has exploded. The latest generation of dispensers is automated and connected. One connected to the cloud, dispensers are connected to patients, their healthcare providers, their caregivers, their insurance companies, and their other devices. Often sold pre-filled and pre-programmed according to the physician's instructions, dispensers can operate automatically for weeks, even adjusting dosages per the doctor's instructions based on the patient's real-time condition.

2003 gave us the world’s first fully digital pacemaker which gave the doctors the patient’s information in just 18 seconds.

Back in 2006, Nike & Apple launched a wireless system, Nike+iPod that allowed Nike+ footwear to coordinate with an iPod Nano to give the users an ultimate personal running and workout experience, making fitness tracking more accessible.

2010’s Philips Lifeline GoSafe and HomeSafe is a one-touch portal and in-home medical alert system, respectively. It uses the phone to call an emergency response center and connect you with an agent when you press the help button.

Proteus Discover, the world's first Digital Medicine offering, was developed by Proteus Digital Health in 2012 to measure the effectiveness of medications so that better treatment and improved health results could be achieved. It consisted of ingestible sensors, a small wearable sensor patch, an application on a mobile device, and a provider portal. 

The 2019’s Apple series 4 came to the market with an inbuilt electrocardiogram. It lets you take an ECG at home using an ECG app. Isn’t that unreal? The reading of the watch gives you symptoms like Sinus rhythm, atrial fibrillation, low or high heart rate, based on which a doctor can be consulted. Adding to that, it also sends notifications to your physician and close family members in case of an emergency like a fall or irregularity in heart rate.

It is absolutely the time for prevention so that there isn’t a question of cure. In the era of Artificial Intelligence and Machine Learning development, healthcare has become digital and hence, predictive, proactive, and preventative. Healthcare wearable devices are not just making their way into our lives but are now a way of making healthy life choices for every person. 

Logic Simplified, an artificial intelligence company based in Dehradun holds potential AI programmers to bring tangible solutions for healthcare needs through AI wearables and applications. Also, bearing a motive to make the life of patients, doctors, and nurses simpler than ever before -— for a healthier population and a healthier world.