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.

There’s tonnes of data being generated every minute of our life. Thereafter, the data is provided to a machine learning model so that it is calculated and analyzed thousands of times to make progressive decisions and recommendations. With no human assistance, machine models learn, identify patterns, and make accurate and efficient decisions removing any possibility of human error. Abundance data availability, affordable data storage and processing marks the expansion of this prospective technology. This is also the reason that motivates companies to build compelling machine learning models that are able to analyze bigger and complex data in less time with improved results. 

Countless industries like healthcare, government, marketing and sales, e-commerce, social media, transportation, logistics, manufacturing, etcetera are embracing the technology as the best way to walk the path of expansion and development. They understand the value that it can bring to business of all sizes.

75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10% (Source: Forbes)

Making informed decisions 

This is a very crucial aspect for business organizations and they rely on good information available at the right time for that. Business organizations take advantage of ML technology and put other intelligent technologies to use to extract large data sets to integrate them into everyday processes and develop actionable predictions. This futuristic technology in the 20th century offers improved business models and services.

You may also read Benefits of AI wearable devices for healthcare industry

AI-driven personal assistants reduce man’s efforts

These computer systems perform tasks based on human commands. Machine learning here uses natural language processing (NLP)  to program a system to process and assess the human language. The speech or text recognition is looked into that allows humans to communicate with computers in their own language and the computers to understand the command and act on it and perform likewise.

Machine learning improves logistics 

ML improves logistics (a business that runs on data) by enhancing the processes like buying raw materials, manufacturing, selling the end product, shipment, etc. It allows the service providers to study large frameworks of data making its management system intelligent and improved. From optimizing equipment costs, and increasing transparency across the supply chain, accounting for unusual activity, this technology is proving productive. 

Maintaining manufacturing for companies 

Predictive analysis and ML apps let businesses save and make money. It stores data, analyses it, predicts outcomes, generates insights, and brings solutions for the business. There are specific algorithms that can also analyse the working of a machine and when it requires maintenance and this avoids unexpected machinery problems and shutdowns.

According to a report, the global machine learning market was valued at around USD 1.58 billion in 2017 and is expected to reach approximately USD 20.83 billion in 2024, growing at a CAGR of 44.06% between 2017 and 2024.

Source: Globe News Wire

Analysing customer’s data

Now think of it this way, let’s say a company has 1 petabyte of data which is trust me a lot and calculatively, it is humanly impossible for one person to process all of this data without any errors. So, by using a machine learning software, the company will be able to analyze this large volume of unstructured data in less time and without errors. Now you tell me, won’t this give the company a competitive edge over the others?   

Business competition is overwhelming and for any business to do better in the market and earn profits, it is important for them to understand their customers and their ever-changing needs. There is large amounts of data to make the process a tad bit easier. And, when it comes to processing that data, machine learning comes next to help with the process. 

Customer’s data like purchasing patterns, preferences, demographics, their reviews, complaints, and more are analyzed to make predictions and machine learning will explain all the data that can be used to build business strategies and increase business by providing better customer experience. This can be achieved by building individual customer profiles, improving user interface and user experience and optimizing search results on search engines.

57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support.

Source: Forbes

Cybersecurity

Unidentified threats that are capable of causing harm to any business should be recognized and taken care of and having said that, machine learning in business is capable of doing so and within no time.

“If not now then when?” is the question here. It’s high time we discontinue manual data entry. There are errors and duplication of data likely to be found with a manual approach. Also, let’s not forget about the increased time consumption the process demands. Machine learning and predictive algorithms encourage to make the data entry process less arduous and also reduces the chances of glitches.

Financial Analysis

Investors, financers, traders, stock marketers use ML algorithms to analyse the market and make desired profits. Algorithm trading to make better trade decisions, fraud detection and prevention, portfolio management, loan underwriting and credit scoring are all taken care of with the use of developed machine learning systems. Oodles of data are accessed, studied, different parameters are adjusted to improve the overall experience by providing accurate insights and calculative predictions, streamlined processes, reduced risks, and better-optimized portfolios.

You may also read about the Development Sets in Machine Learning

Machine learning has become an integral part of all businesses and that is because entrepreneurs understand its value and how it can work in great ways for them, if used to its full potential. The flourishing internet, the booming online presence along with the outsized number of connected devices, will make businesses reliable on algorithms to bring solutions to their expansive problems. This discipline of artificial intelligence will increase performance and enhance security with a substantial booth in ROI and revenue. The key is to know how to take advantage of ML to solve business problems effectively.

Be that as it may, Logic Simplified, an artificial intelligence development company in Dehradun, India has aspiring and focused machine learning programmers that are innovative and share their experience with their admirable work. The real-world cases of machine learning are heard by many if not all and we as a team walk slowly but with heavy steps to bring to the world the unheard and unseen with our unparalleled work in the field of artificial intelligence and machine learning. To know more, drop in your queries at enquiry@logicsimplified.com

Surveys of machine learning developers and data scientists have shown that the data collection and preparation steps can take up to 80% of a machine learning project's time. 

Source: SearchEnterpriseAI

A machine learning model creation step involves training the model and then testing it. It starts with an idea, according to which the raw data is collected for the model and then data processing for AI and ML algorithms takes place which converts the data into a form that can be used by the model to learn. Once the model is built to solve a specific problem, it is tested until the model gives satisfying results.

Building a model can be time consuming and asks for the right approach and methods. For that, it is essential to understand the requirements that are expected of the model and the problem that is trying to be solved.

First things first, the data collected is prepared for use by employing algorithms and appropriate techniques. It is distributed into three categories - structured, unstructured, and semi-structured data. The model is thereafter trained by using the good quality and prepared data. This exhaustive process involves selecting the right technique, hyperparameters, algorithm, whereupon configuring and tuning hyperparameters, identifying the appropriate features for best results, finally testing and evaluation of different versions of models for optimum performance and whether it meets the objective. The evaluation is done applying the validation technique and using a validation dataset which determines the model’s capability of performing once ready. Operationalizing the model is what comes next which involves measuring and monitoring its performance. Then finally what is left is to make the model adjustable so that it works best in all circumstances and iterations are also made to attain the desired results.

The technique

The Hold-out cross validation technique is a cross validation technique used to  build a computational machine learning model and it divides the algorithm into three variant subsets on which the training, tuning, model selection, and testing is carried out. Those three sets are Training set, Validation set, and Testing set. As the name suggests, the machine learning algorithm is trained using a training dataset, the trained model is validated using the validation or development dataset, and the testing dataset tests the trained and validated model. 

Based on the algorithm and the type of data the model consumes, Machine Learning has two basic types of important learning methods - Unsupervised and Supervised. Supervised learning follows predictive data analysis while on the other hand unsupervised learning works on finding data patterns.

The dataset that does carry weight

The development set is a significant dataset in the process of developing a ML model and it forms the basis of the whole model evaluation procedure. A machine learning algorithm has two parameters -  model parameters that define individual models and hyperparameters define high-level structural settings for algorithms. The development set is used to select the parameters, tune them and then use them to choose the best model of a training algorithm. Nevertheless, it also helps in avoiding or minimizing overfitting and simultaneously controls the learning rate. 

It is the quantity and quality of the dataset that determines the picking of the best performance model and it’s precision. Development sets develop machine learning solutions and help one find the best model of all the different models. It allows one to choose the number of layers (Depth), neurons per layer (width), activation function (ReLU, ELU, etc.), optimizer (SGD, Adam, etc.), learning rate, batch size, and more in the algorithm.

60-20-20 rule of thumb

The size of the dev set is 20% of the whole and that sums up to a large amount of data which is used for training and teaching the model more diverse features. The three sets that are the training set, development set and test set split the algorithm into the ratio of 60 : 20 : 20.

Errors

While the model is computationally trained, there are chances of error arising, just like in any other process. And here, the error value on the training set is called Bias while the difference between the error value on dev set and training set is called Variance. And, error is analysed by identifying  Bias and Variance. 

To choose the best model that aligns with the needs of the objective, it is necessary that there are reduced possible errors in the process. The different sort of errors that arise in the path (bias-variance trade-off) are the training error and development error. Focusing on the latter, it is measured by analysing the divergence from the value predicted. 

Different data is spent to train and test your model by feeding it to the algorithm. If you use the same data to train and test the model, in that case the model could be overfit and then the model could perform well on the training data subset but poorly on the test data and vice versa.

The development error should be the lowest so that the model comes out good. Taking that into account, the errors are analysed time and again until they are reduced to minimal. This paradigm is used to pick the best model (algorithm) that can later be used to find accuracy on the test set. It is the development set that is used to choose and tune the AI model. 

A model’s performance should have low bias and variance. And, Cross validation is a common technique that is used to balance the Bias and Variance of a model. It contributes to achieving a stable estimate of the model performance. If the dataset is not split appropriately, it can lead to extremely high Variance of the model performance. Cross-validation techniques can also be used when evaluating and mutually comparing more models, various training algorithms, or when seeking for optimal model parameters. The model must work in training as well as validation and should not be overfitted. And, a validation set or development set can be taken as a part of the training set that helps find the accuracy and efficiency of the algorithm.

Logic Simplified, a machine learning and artificial intelligence development company in India has developers with qualities that are looked for, like precision, accuracy,  someone who understands the ML ecosystem well and has the capability to build machine learning models that meet the interests of the industries and create diverse possibilities and opportunities for people. 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, and we will get back to you shortly to provide you with high-end Artificial Intelligence and Machine Learning solutions.

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 

In this section, I am discussing a few studies that have been very successful in early prediction of certain preventable diseases. The higher accuracy of prediction results has set high hopes for the fast emerging field of healthcare with machine learning.

A Targeted Real-time Early Warning Score for Septic Shock

Septic shock is a condition that occurs when there is a severe infection in the body and often leads to life-threatening low blood pressure or organ damage. Doctors usually keep patients at high risk of septic shock in Intensive Care Units (ICU) and monitor them with the help of medical equipment. If anything goes wrong, nurses and doctors get a warning through sensors. However, it’s often too late for doctors to intervene by the time such a warning is heard. So, a team of scientists built a machine learning system based on datasets of patients admitted to ICUs, including medical, surgical, and cardiac units, between 2001 and 2007. Using a supervised learning model and regression algorithms, they developed a targeted real-time early warning score (TREWScore) to estimate the number of hours until the onset of septic shock for patients. The model looked at 54 potential features that were based on routinely available measurements in the Electronic Health Record (EHR).

In the end, the machine learning model identified 63.8% patients with a median of 7.43 hours before any sepsis-related organ dysfunction. The TREWScore made the prediction in real-time with 85% accuracy and gave doctors a good enough time to intervene and stop the shock before it even happens. The prediction model produced great results and showed how healthcare businesses can adopt machine learning solutions to detect lethal diseases in advance and avoid many premature deaths.

Also read the future of artificial intelligence in Game Development.

Prediction of Hospitalization Due to Heart Diseases

This study was targeted at predicting heart-related hospitalizations by using patient-specific EHR data from Boston Medical Center. The ML algorithms processed and learned from the data of patients with at least one heart-related diagnosis or procedure record, along with 200 medical features, such as demographics, visit history, problems, medications, labs, procedures and limited clinical observation. The study used five machine learning algorithms: Support Vector Machines (SVM), AdaBoost with Trees, Logistic Regression, Naïve Bayes Event Model, and K-Likelihood Ratio Test (K-LRT). The scientists randomly divided the patients into a training set and a testing set so as to train each model and then test it for predicting heart-related hospitalization of each patient in the target year.

The prediction results of all the algorithms were quite similar, but AdaBoost with Trees was the most accurate with 82% accuracy. The predictions made by machine learning algorithms enabled the doctors to identify which patients will be hospitalized in the following year. This greatly helped doctors take preventive measures to avoid emergency admissions in the future and deaths in many cases. Such an early intervention not just saved the lives of so many patients by early detecting a preventable disease, but also significantly cut down financial costs of public hospitals by treating patients before they came in for an emergency. The false alarm rate was under 30%, which, according to the researchers, was the result of reaching the prediction limitations of their data sets. This points towards the importance of quality of Big Data that has immensely grown in the healthcare sector and data processing in AI and ML algorithms. Nonetheless, the study proved to be a very good indicator that algorithm approaches can scale to far more monitored patients and much more effectively than human monitors.

Also read why machine learning in game development is turning out to be the next frontiter of gaming industry.

Alzheimer’s Disease Prediction

Alzheimer’s is one of the major public health concerns worldwide with no cure available so far. However, this machine learning study helped predict Alzheimer’s disease five years before diagnosis, which led the patients to use medication much earlier and delay it further. The scientists used data from two Alzheimer’s Disease Neuroimaging Initiative studies which focussed on 5-year longitudinal outcomes of clinical, cognitive and biomarker tests of 562 subjects with mild cognitive impairment (MCI). The researchers used an unsupervised learning model and applied a multi-layer clustering algorithm to discover homogenous clusters based on baseline and prognostic characteristics. They also tested for sex differences within clusters. The results helped researchers identify two major clusters among all patients: Rapid decliners and slow decliners. The algorithm predicted with high accuracy that rapid decliners will have Alzheimer’s in the next 4-5 years. This allowed early medical intervention and delayed the onset of the deadly disease in rapid decliners. Slow decliners’ cognitive score also declined but not as fast as of rapid decliners’, so it was not a big worry. The study results were very promising and we are highly likely to see more machine learning applications coming up in the future to help beat one of the worst nemeses of the healthcare world.

Conclusion

There are some other machine learning studies as well, but I have mentioned just three above to give you an idea of the potential that machine learning applications hold for future healthcare by predicting preventable diseases early. The advantage is increased life expectancy as doctors get good enough time to treat a preventable disease before it becomes too late and expensive to save lives. Wearable devices and Internet of Things (IoT) will be one of the biggest enablers. IoT data collection from numerous devices and sensors will significantly help machine learning applications process humongous amount of data and make accurate disease predictions so that doctors can intervene in time and saves lives. Blockchain & AI development is another great mix of two revolutionary technologies that will also improve healthcare for much better. Nanobots may seem like a sci-fi technology for now but continuous advancements in the making are likely to bring them to reality in the near future. Nanobots can travel across the body through blood vessels and generate vital data from everything it sees in the human body. Digital twins is also a very interesting technology that is expected to become widespread in healthcare. It allows to create a simulation of a person’s body on a server and let machine learning algorithms learn from it to help doctors make correct decisions about what may go wrong with the body and what precautions to take. Machine learning has emerged as a revolutionary technology that more and more healthcare businesses will adopt in the future to detect preventable diseases and increase the average lifetime of humans.

Are you also looking forward to developing a machine learning application for your healthcare business? Logic Simplified can help!

Why Choose Logic Simplified to build Machine Learning Applications for Healthcare?

Logic Simplified, an artificial intelligence development company, based in Dehradun, India, helps businesses predict future outcomes and make informed decisions. With our competency in building ML algorithms and using data science, we can help you build ML applications for healthcare, such as detecting a preventable disease, personalized medicine, health information management and many more. We understand that machine learning is the future of healthcare and are eagerly looking for opportunities to help healthcare businesses adopt algorithmic and data approaches to save millions of lives. If you want to know in detail how exactly we can help you build an ML solution, please reach out to us at enquiry@logicsimplified.com, and our experts will get back to you shortly to hear all your requirements and offer a solution that fits the best. We are committed to bring human-like intelligence in machines and bring advantages to humankind! We also have a very experienced and talented team of game app developers which builds AI and ML solutions for video games, so you can also avail next-gen game development services from us and create a new gaming mania for millions of fans out there. 

Note: Predictive analytics does not mean the models always predict a future outcome accurately. It means that they make the best prediction based on data available and keep improving the accuracy with machine learning. 

Many businesses in the finance sector have now started using predictive analytics for churn prevention, demand forecasting, personalized offerings, customer insights to identify new marketing opportunities and fraud prevention. It can help finance businesses predict the extent to which customers will engage in a particular direct marketing promotion by harnessing historical data, such as customer information, their location, purchase history, how they responded to previous promotional campaigns, or their engagement rate. This way businesses can derive actionable insights for deciding which product to promote, when to promote and whom to target with high chances of conversion. Predictive analytics has already started impacting CRMs to improve relationships with customers and increase conversion rates.  

Predictive analytics can also help detect frauds as intelligent systems can monitor customer transactions and flag those that have anomalies in standard customer behaviour, such as transaction history and the geographical locations of those transactions. Customer service is another area where finance businesses can greatly improve upon using predictive analytics. For example, Bank of America and Fargos are using chatbots with natural language processing (NLP) capabilities to handle several individual customer queries. By building chatbots, insurance companies can allow customers to check when their next premium is due and also get quotes. Credit scoring, sophisticated evaluation of loan applications and personal finance management (PFM) are some other applications of predictive data analytics with machine learning that are increasingly getting popular in the finance sector.  More and more financial institutions will employ predictive analytics in the near future to improve customer experience in many ways.

There’s a wide scope of predictive analytics in the field of healthcare and medicine. Data processing in AI and ML algorithms paves the way to diagnose diseases earlier than a serious episode later, which will greatly avoid the risk of long-term health problems that are difficult and costly to treat. Researchers at the University of Pennsylvania Health System have developed a predictive analytics tool that uses machine learning and electronic health record (EHR) data to help identify patients highly likely to contract severe sepsis or a septic shock 12 hours before the onset of the condition. Senior author Craig Umscheid, MD, of the Hospital of the University of Pennsylvania, said, “The algorithm was able to do this.  This is a breakthrough in showing that machine learning can accurately identify those at risk of severe sepsis and septic shock.” Similarly, a Philadelphia-based healthcare system Penn Medicine is using predictive analytics to develop a prognosis score. The predictive analytics model helps clinicians generate a score based on 30 factors to determine a patient’s likely prognosis over the course of next six months, allowing to recognize dangerous patterns early and proactively treat patients.

Predictive analytics also makes medical imaging faster and accurate. For example, researchers at Stanford University studied a machine learning algorithm which they say is capable of doing chest X-rays in a matter of seconds to detect 14 types of diseases. CheXNeXt, as the algorithm called, can also triage the X-rays to identify patients who need urgent care with accuracy on a par with the readings of radiologists. Besides, predictive models using EHR data can also help doctors assess the risk of patients not showing up for a scheduled appointment. So, by using predictive data analytics that leverages machine learning, care providers can actually build numerous health applications to greatly lift patients’ experience and avoid many premature deaths.

Manufacturing industry already uses some sort of traditional analytics to understand their existing processes, but predictive analytics can help them forecast future performance. For example, predictive analytics models that rely on machine learning can bring the ability to monitor machinery by analyzing data generated through machine-to-machine communication and sensors in real time in order to assess efficiency and predict future breakdowns. IoT data collection and processing in ML algorithms will tell plant managers well in advance which machines are likely to experience a major malfunction, hence they will be able to repair or service such machines in time to avoid a dip in productivity because of some machines not working.

Apart from playing a role in keeping machines in good health, predictive analytics can prevent workplace injuries too, like by checking a machine remotely through sensors without any worker being near it, especially in hazardous locations. Predictive analytics can also analyze data of past incidents and based on their root causes, it can run potential scenarios and quantify risk factors. Other advantages in manufacturing include speeding up supply chain, demand forecasting, product or quality control, project management, reduced cost, plant uptime and many more. The possibilities that predictive analytics can bring to the manufacturing industry are huge and the sector is highly likely to adopt the practice in the next three years.  

Predictive data analytics can positively impact eCommerce by providing the ability to get unparalleled insight into customers. Driving on the back of machine learning, predictive data models can get insights into customers through data generated from  websites, mobile apps, social media, and the Internet of Things (IoT) in real time to run sentiment analysis and offer enhanced experiences. The ability to understand past behavior to promotional activities, purchase history, expectations, and desires helps create different profiles for customers and target them with personalized offerings. Besides, eCommerce owners, seeking expert Hyvä theme development services, can ensure that customers pay the highest price possible by analyzing data from customer activity, competitor pricing, available inventory, historical pricing, etc. Companies like Airbnb and Uber are already using predictive analytics to regularly do variable pricing in real-time.

Predictive analytics can also help eCommerce owners maintain the right inventory: neither overstocked nor understocked. By analyzing shopping trends, surge or dip in seasonal demand, historical patterns, and even political activities, predictive data models can accurately point towards which products need to be added to the stock to meet customer demands and which ones not to due to their low sales prediction. eCommerce owners can bring various other advantages to their way of doing business by using predictive analytics, such as fraud prevention, effective promotions, customer retention, correct recommendations, improved customer service and many more. Blockchain & AI development together can strongly address concerns related to data duplicacy, data security and frauds, which is why blockchain is now seen as the future decentralized apps.    

Education is one of the important sectors that will largely benefit from predictive analytics. For example, predictive analytics gives rise to adaptive learning. Teachers and lecturers can get insight into students’ academic records, engagement with online resources, current performance and learning gaps, and use it to customize the academic experience matching the potential of individual students for better learning. Educators can also perform better through feedback occurring more frequently.

Predictive analytics can also identify students highly likely to not reach standard attendance levels as early as their first semester or at risk of dropping out, thereby paving the way for college management to intervene and offer students help and support before it’s too late. Community colleges and other institutions of education can also effectively manage recruitment and enrollment using predictive analytics, which can statistically identify students most likely to apply, enroll and succeed based on various factors like geographic location, anticipated program of study, ethnicity, socio-economic status, high school grade point average (GPA), source of first contact and more. Education institutions can also narrow their marketing efforts by identifying and targeting specific high schools that have a high proportion of students most likely to enroll. More and more educational institutes are now  adopting predictive analytics to transform operations and pedagogical approach for good.

Conclusion

Many businesses now understand the significance of using predictive analytics and have already adopted the practice to be no left behind in the competition which has become so fierce today. Predictive analytics, machine learning, IoT and Big Data are the future as they automate digital systems to make smart decisions like humans, and there’s no reason why businesses of today should not embrace them. Are you also looking forward to build predictive analytics that can leverage machine learning to help you make informed decisions and drive new business growth? Yes! That’s fantastic! Logic Simplified can help you build smart systems that use machine learning to accurately predict future outcomes and greatly improve the way you perform daily business operations. We are an Artificial Intelligence development company caterting to several businesses, ranging from finance to manufacturing, retail, customer service, gaming, security, energy, logistics & transport and tourism & education. We also offer Big Data analytics and IoT app development services that help train machine learning algorithms to make accurate predictions. For any query related to how we can help you build predictive data analytics and machine learning solutions, please write to us at enquiry@logicsimplified.com, we will soon get our experts talking to you to get you started for a smart future of your business.

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.