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.

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.

Data is what that most businesses of today rely on to make critical decisions. But, is just having piles of data available at your disposal enough to be worth your salt? Naaah! The secret sauce is the way you do data processing and analysis to get structured and meaningful information so as to actually be able to act on actionable insights. Even using the new revolutionary technologies such as Artificial Intelligence (AI) and Machine Learning (ML) for smart decision making and driving business growth are like flogging a dead horse without applying the right data processing techniques. This is what today’s businesses are learning, though, slowly. That said, I am making an attempt to help you understand the importance of data processing in ML and AI algorithms so that they can do correct analysis and furnish you with information you can comprehend and use to bring that proverbial midas touch in your way of doing business.

When done right, data processing teaches ML and AI algos to work as intended

After you extract data that can be in structured, semi-structured, and unstructured form, you transform it into a usable form so that ML algorithms can understand it. But, what’s more critical here is the relevance. If the data itself is not relevant, you can’t expect from your ML algorithms to learn what would eventually make them  smart and bring value to your business.

Phases of Data processing

The graphic above explains and simplifies the phenomenon of data processing for machine learning algorithms through sequential steps, elaborated below - production of actionable motive being the sole purpose of this procedure.

1. DATA SELECTION

This step involves collecting data from available sources that are trustworthy and then selecting the highest quality of the whole. In this case, remember that less is more because the focus here has to be on quality and not quantity. The other parameter to take into consideration is the objective of the task.

2. DATA PREPROCESSING

Preprocessing here means getting the data into a format that the algorithm will understand and accept. It involves -

• Formatting - There are different formats in which data could be found, such as a proprietary file format and a Parquet file format, to name a few. Data formatting makes it convenient for learning models to effectively work with data.
• Cleaning - At this step, you remove the unwanted data and fix the instances of missing data by removing them also.
• Sampling - This step is essential to save time and memory space. You need to understand that instead of picking the whole dataset, you can use a smaller sample of the whole that will be faster for exploring and prototyping solutions.

3. DATA TRANSFORMATION 

Lastly, the specific algorithm you are working with and the solution that one's looking for influence the process of transformation of preprocessed data. After you upload the dataset in the library, the next step  is the Transformation process. A few of the many are mentioned below.

Scaling: Scaling means the transformation of the value of numeric variables in a way that helps it fit in a specific scale like 0 - 1 or 0 - 100. This procedure ensures the data we receive has similar properties, and no odds, thus makes the outcome meaningful.

Decomposition: This process uses a decomposition algorithm to transform a heterogeneous model into a triple data model. The transformation rules here will categorize the data set into structured data, semi-structured data, and unstructured data. Subsequently, we can pick the category that suits our model's ML algorithm.

Data Aggregation Process (DAP): The raw dataset is aggregated through an aggregator with the purpose of locating, extracting, transporting, and normalizing it. This process may undergo multiple aggregations to bring up aggregated data, which may either be stored or carried out further for other operations. This process directly impacts the quality of the software system.

4. DATA OUTPUT & INTERPRETATION 

In this, meaningful data is obtained as an output in various forms as one prefers. It could be a graph, video, report, image, audio, etc. The process involves the following steps:

• Decoding the data to an understandable form, that earlier was encoded for the ML algorithm.
• Then, the decoded data is communicated to various locations that are accessible to any user at any time. 

5. DATA STORAGE

The final step of the entire process is where data or metadata is stored for future use.   

Difference between a regular computing program and AI

Let’s take you through a simple example:

Let’s say, an AI is given marks of 10 students in a class (1, 3, 5, 6, 8, 9, 12, 7, 13, 100). Based on that, I ask it a question, "How will you rate the overall class on a scale from A-E (based on slabs like 0-20 is E, 21-40 is D and so on)?".

The difference between a regular computer program and AI is the same as the two men in this saying, "Give a man a fish and he'll eat for a day. Teach a man to fish, and he'll eat for his lifetime.” The first man is like a regular program that does not learn on its own and will give an output only on providing input data. Still, on the other hand, AI is the man you teach "how" once, and then it learns and  improves on its own and gives the desired output with rules and methods of what to do with certain kinds of data and how. The way it learns is ML (Machine Learning or Machine Intelligence).

A regular program may take an average of 10 marks and rate it based on that. Nevertheless, an AI will be able to identify the outlier (100 in this case) and then give us the answer, which clearly shows the world of difference between the two computer programs and how Artificial Intelligence gets ahead of all with the help of Machine Learning.

GIGO

We train a machine learning model based on the output we expect from it. And, the data that we provide to the AI algorithm determines this. If the data provided is inappropriate, then the information it would give us would be worthless. The strict Logic that computers work on is the compatibility between the input and the output. The quality of data provided (input) determines the quality of information we will receive (output). In other words, Garbage in, garbage out (GIGO). 

Logic Simplified has done much work in Artificial Intelligence and Machine Intelligence, and understands the critical role and importance of data processing. Being the driver for different other technologies, we know that AI and ML will impact the future of every industry and humans in many expected and also unexpected ways. Let our AI programmers help you make an impact in your world - to ensure enhanced productivity, escalating profits, reduced time consumption, enhanced security throughout the process, prevention of unauthorized access, and so much more - by making your systems smarter.

What we see in video games is just a fraction of what AI is currently. It is not that AI hasn’t evolved to the desired level, but then the game designers have been holding themselves back from using AI to their true potential. Many game developers are hesitant towards building advanced AI into their games as they fear losing control over the user experience. 

Let’s assume you pick up a new game and start playing it, would you like to get defeated over and over again? Nope, right. Video game players want to engage with something that is designed as per their intellectual capacity so that they can learn and improve over time. Artificial intelligence game developers don't intend to create an unbeatable environment for the player, but to maximize the participation of players over long time periods.

The AI mostly falls on two factors that are pathfinding and finite state machines. Pathfinding is how to get from point A to point B in the simplest way possible. A finite state machine is where a non-playing character can be in different possible states and move between them.

Story of AI till now

There is a vast difference between AI used in general and the AI used in video games. The AI designed to play a game at a superhuman level is developed a lot differently. In 1997 IBM’s DeepBlue system beat, the Russian Chess champion Gary Kasparov and AI, has come a long way since then.

Google bought DeepMind in 2014 for more than 500 million dollars, Facebook has its own AI research division, and there is also Elon Musk’s OpenAI company that works on AI research. The game development companies today are focusing on teaching the software on how to play different games; these include Chinese games such as Go and classic advanced games such as Dota 2. 

The main focus behind developing the AI is not to provide dynamic and realistic game experience but to push the boundaries of software intelligence to the most extreme. The goal is that by providing the software in the gaming environment, we can understand how the machines adapt to more complex tasks and execute them. 

Today most modern edge realistic games don’t revolve advanced AI but instead create a complex environment where the results are unexpected and take place in random order. There are also instances from viral clips of video games that indicate that there is a big possibility that one player experiences a totally different thing compared to the other player. This kind of AI builds a real-life system but doesn’t result in groundbreaking outcomes in game development. 

Data Analysis and AI

AI is growing at a rapid rate, not only in the virtual gaming industry but also across all the other technical fields in the existing sectors. The software developers should prepare themselves to work with machine learning to develop AI-enabled software tools. 

Consumers are now very tech-savvy, and they have access to a lot of information at their fingertips. Due to this, the game developers now can’t rely on old methods and game development principles; there is a need for them to step up their game. Machine learning plays a huge part in AI, and soon, it will become a gold standard to develop industry-level software designs. 

Current role of AI in video games

In all the video games, the AI is used to enhance the user experience of the player. Machine learning uses all the accumulated data to create a more realistic and immersing environment in the video game. But to achieve this, the AI needs not few but an abundance of information. Data is a sensitive property, and it just can’t be handed over to anyone. This is the reason why machine learning hasn’t been developed industry-wide yet despite its endless possible applications.

AI developers for games must dedicate a lot of their time and resources to investigate the possibilities that AI could offer. For video game programmers that have a passion for creating and innovating new things, the only limitations are money and time.

What stands for AI in the future?

In order to truly make some ground making innovations in the AI, game app developers and many tech companies are now moving away from the pressure of the commercial gaming industry and big studios and designing new games. This is what lays the groundwork for authentic AI-powered gaming experiences that revolve around the devices powered with machine learning. 

We can expect that in the near future, AI development for games would work hand to hand with game designers and developers to create art assets, design multiple levels, and even design video games from scratch. AI can provide you with experiences that keep on changing and never grow old.

AI in video games may sound as a new innovation, but one of the very first attempts to use game AI had been made in 1950s when Arthur Lee Samuel, an American pioneer in the field of computer gaming and AI, built a self-learning Checkers-playing program. AI has come a long way since then, from IBM’s Deep Blue that defeated a reigning world chess champion, Garry Kasparov, on 11 May 1997 to Google’s AlphaGo AI Go player that defeated the world’s best human Go player.

However, the future of AI in video games is not just to outsmart humans, but to generate a user experience that is better and more unique.

Before we proceed further to understand how AI is a boon to video games, let’s understand in a nutshell what AI basically is.

Artificial Intelligence is a science that makes a computer program or a machine capable of thinking, learning and solving problems the way human brain does. The sole reason why “Artificial” is used in “Artificial Intelligence” is that such intelligence is not acquired naturally as we humans do, but by using learning algorithms that assess vast amounts of data and make logical sense out of it to behave or respond intelligently like humans. Machine learning (ML) is a subset of AI that uses certain algorithms to learn and make smart decisions.  

Facebook’s image recognition, Amazon’s shopping recommendations, Apple’s Siri and Netflix’s personalized video streaming service are some of many examples of AI people come across in their daily lives.

As far as AI development for games is concerned, you can think of F.E.A.R, The Last of Us, Far Cry 2 and First Person Shooter (FPS) like Call of Duty: Black Ops II. Let’s dive deeper into this.

How AI was used In those Games

If we talk about F.E.A.R, the reaction from enemies is not predictable at all. The game AI makes them capable of reacting to each other’s situations and learning from their mistakes and never repeating them. As a result, the players need to keep devising new strategies and never sit in the same position. Many video game companies are now looking to hire AI game developers as AI and ML in game development are quickly gaining ground to meet the expectation of today's modern gamers.

In The Last of US, Ellie is a companion AI of Joel, the player’s character. She accompanies and supports Joel throughout most of the game. All her moves, dodging style, taking cover, runtime cover, combat performance, fire rate and accuracy look natural and believable, making the game awe-inspiring.

The enemy AI of Far Cry 2 amazed players with its brutality and unforgiving nature. The players never saw such a chaotic and unpredictable AI behavior before. Even the veteran players hard a very time to win the game.

Call of Duty: Black Ops II displays one of the best AI bots behaviours. The commendable AI algorithm enables each bot in the game to use different tactics, like running, gunning, knifing, camping and drop-shooting/jump-shooting.

How AI enhances User Experience and makes Video Games Better?

Makes Non Player Characters (NPCs) Smarter

One of the best uses of AI that Artificial Intelligence game developers make  is controlling the behavior of NPCs. The games without AI often become boring after playing for sometime as they become easy to beat due to their predictable behavior. The real fun of playing video games comes from competing with NPCs that react in unpredictable way and surprise you.

Imagine an FPS game in which enemies are capable of analyzing their environments so that they can find what’s important for their survival or take actions that preempt your intelligent moves to increase their chances of victory. Not only this, what if they can learn from their own actions and are able to take cover, recognize sounds and patterns, communicate with each other and maneuver in a way you never saw or predicted before. Having new experiences despite playing such a game several times keeps players excited and enticed to keep coming back, isn’t it? An expert artificial intelligence game development company can help you build such games that player love to play for long long hours.

AI is also used for Pathfinding in real-time strategy games. Pathfinding means that NPCs are adept at moving from one point on a map to another after analyzing the terrain, obstacles and possibly "fog of war”. AI provides enemies the ability to safely navigate in a dynamic environment without colliding with other entities. It also enables group navigation by allowing an NPC to collaborate with other NPCs.

A few games that have smart AI-powered NPCs are Tom Clancy's Splinter Cell: Blacklist, XCOM: Enemy Unknown and Halo: Reach.

AI Makes the Gaming World more Realistic

AI has a huge potential to improve visual quality of video games, making them appear more realistic and natural than ever before. Game environments and game characters can mimic the real world through deep learning and by using algorithms that make sense of the ever-growing amounts of game data. Video games look more realistic when NPCs behave like humans, be it walking, moving, running, expressing themselves or making a decision.

Combining AI with Virtual or Augmented Reality further opens the gates to add reality factor to video games. Pokémon Go, an augmented reality-based game, has already proved that immersive and interactive video games are the future, be it on mobiles, computers, Xbox or Playstation.

Real-time customization to Enhance Overall Gaming Experience

AI overhauls the overall gaming experience by real-time customization of scenarios. EA Sports’ FIFA 17 is a good example to understand how.

The game gives you one of the five player choices to pick for each position in your team. However, you have no idea what the chemistry between the players you have chosen for your team is. But don’t worry, the AI of the game is so designed that it automatically determines that for you and increases the chances of your team performing well.

Besides, the AI makes the game more interactive by boosting your playing experience. For instance, if you’re losing a game, it will encourage the fans to cheer for your team louder, so as to lift the morale of your team and make your players perform better. Such an ability of AI takes the overall gaming experience to an all new level.

Adjusts Difficulty Level as Per Player’s Ability

Another virtue of AI-designed video games is player-experience modeling, which means providing tailor made experience to players as per their level of expertise in real time. So, if a player is a noob, the AI will adjust the difficulty level to easy mode so that the player doesn’t get frustrated or exapserated for not being competent enough to progress in the game. On the contrary, if a player is an expert, the AI will make the game difficult so that the player doesn’t get bored or jaded. This ability of AI is called dynamic game difficulty balancing.

Crash Bandicoot, Archon: The Light and the Dark and Flow are some video games that use dynamic game difficulty balancing. Game AI can also determine player intent through gesture recognition which enables players to communicate and interact with video games naturally without any mechanical devices.

Procedural Content Generation

Game AI also enables game app developers to automatically generate creative content, like landscapes, items, levels, rules, automated music and quests. Employing procedural content generation in quest-driven games can automatically generate weapons and armor based on the player-character's level.

There are also many open world or survival games that use procedural content creation to create a game world from a random seed or one provided by a player, making each playthrough unique with high visual appeal. This technique has already been used in many video games, including Rogue, Elite, Diablo, Diablo II, Dwarf Fortress, etc.

Conclusion

The most beautiful part of AI in video games is incredible environment creation and presenting unpredictable scenarios by altering the flow and intensity of the gameplay, which makes gaming a lot more fun. There’s nothing better for a player than getting a satisfying and challenging experience, right? As the future unfolds, we will see more and more games with AI controllers to optimize user experience like never before. Besides, AI will also provide a testing ground to game developers to improve their code and design to finally build a game that rocks the game charts.

Logic Simplified is a top game development company which always believes in embracing new technologies to keep the pace with ever-changing market demands. And, AI is no different! You can hire game app developers from us to get advatage of our expertise in AI game development and build games that offer gamers personalized and highly interactive experiences. Please write to us at enquiry@logicsimplified.com to discuss your AI game idea, and we will get back to your shortly to tell you how we can help shape it into a reality.