Does Your Business Really Need an Enterprise Artificial Intelligence

Any technology, taking progress several steps forward, always raises concerns that border on excitement and disappointment at the same time. This trend has not spared artificial intelligence. Even though the technology is not new (the first solutions appeared in the 1960s), the real breakthrough and active use of AI in business appeared only in the 21st century. Computing power, larger datasets, and the rise of open-source software allowed developers to create advanced algorithms.

Nowadays, almost all businesses want AI, regardless of size and tasks. So let’s see if artificial intelligence is really so beneficial. For whom it’s too early to implement it, and who needs it as of yesterday.


AI Application for Business

Artificial intelligence is an imitation of the mental properties of the human brain by computer systems. The algorithm learns itself, becoming more and more perfect. To reach the level of a full-fledged thought process, still enough time must pass (although some experts argue that a machine and a person will equal in intellectual abilities in the next decade).

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Nevertheless, AI is designed to solve relatively voluminous and straightforward tasks from document flow to primitive communication as support. These AI capabilities alone save businesses around the world thousands and thousands of labor hours. Already, 72% of companies using AI in their work say that it makes doing business easier.


In this regard, there are fears that many people will be left without work. Indeed, according to forecasts, secretaries, accountants, administrators, auditors, repairers in factories, and even general and operational managers can lose their jobs. In contrast, new jobs will receive big data and data analysts specialists, AI and machine learning engineers, software and app developers… 

The World Economic Forum says 85 million jobs will be eliminated by 2025, while 97 million new jobs will appear. So the reformatting of the labor market towards technical specialties is inevitable one way or another.


According to Fortune Business Insights, the global AI market was estimated at $27.23 billion in 2019 and is projected to reach $266.92 billion by 2027, with a 33.2% CAGR over the forecast period.

At the same time, IDG claims that in 2021 the cost of AI and similar systems reaches $57.6 billion. For instance, Netflix spends $1 million annually to develop its recommendation engine. According to company representatives: “The typical Netflix contributor loses interest after about 60-90 seconds of selection, having watched 10 to 20 titles (possibly 3 in detail) on one or two screens.” It’s cheaper to spend money on a good advisor than to lose views.


The PwC’s forecast claims that in 2030 AI can contribute up to 15.7 trillion dollars to the global economy. For comparison, the combined output of China and India in the world economy is currently less. However, the PwC predicts that an attractive, innovative business that has yet to emerge could become a market leader based on AI technology in ten years.

AI is already used by 38% of healthcare providers as computer diagnostics and 52% of telecommunications companies as chatbots. It is not surprising. Consumers are increasingly demanding round-the-clock support and are ready to receive more primitive but instant answers to their questions; that is, they are ready to sacrifice quality to save their time.


Benefits of AI for Business 

Regardless of what field you work in – from law to marketing, from medicine to restaurant business – AI will find an appliance everywhere. Several undeniable benefits of AI will be effective for any business.

  1. Improving customer engagement. Chatbot has already become the most popular way to communicate with consumers. Enterprise artificial intelligence contributes to increased customer satisfaction, leading to lower costs, in particular, on the payroll. Moreover, chatbots have become a real salvation for small businesses, which do not have the opportunity to hire a large staff for support.
  2. Increased brand loyalty. Personalization is the key to the consumer’s heart, as evidenced by the investment of Netflix mentioned above in personalized search. With an individual approach, you will inevitably win the preferences of your customers, making them permanent. But to solve this problem, you need to collect a considerable array of analytics of behavioral factors. AI can solve it. Various studies say that this approach increases conversions from 14 to 30%.
  3. Data security and fraud prevention. Primarily relevant for financial enterprises. AI not only finds weaknesses in security systems but can also determine the characteristic behavior during transactions.
  4. Improving the accuracy of forecasts. Artificial intelligence allows you to avoid the human factor when making decisions, reducing the risk of mistakes. For example, lead scoring analyzes and predicts which leads will be the most promising. Other algorithms help control financial flows and trade. Also, you can be sure of compliance with all requirements, standards, and regulations that your company sets.
  5. Recruiting optimization. By automating the analysis of candidates’ CVs, human bias in preliminary checks is eliminated. In due course, PepsiCo needed to hire 250 people out of 1,500 applicants in two months. AI was drawn into the first round of interviews. Thus, all candidates were interviewed in nine hours. It would take human personnel nine weeks, by contrast. During this time, “live” recruiters could deal with more complex creative tasks. The latter concerns other employees of your company – let them develop while AI does the whole routine for them.

How to Get the most out of AI Benefits 

There will be no benefits at all if enterprise AI software is not implemented efficiently. To prevent this in business processes, it is better to follow a few tips that will allow you to comprehensively approach the implementation of artificial intelligence.

  1. New technologies need new people. Without hiring the appropriate specialists, only with the forces of the old state, you most likely will not succeed. Probably, you will need a whole department, but do not be afraid of such expenses – they pay off significantly. Of course, you can use the already developed AI technologies that other companies offer. Still, sooner or later, almost any business comes to the point when it becomes unprofitable and even unsafe to use third-party services.
  2. Don’t be afraid to expand. The introduction of new technologies should bring benefits and profits to the business. However, to reduce costs, in the end, they will need to be increased first. And it concerns not only the increase in staff but also the expansion of new markets because with AI, it is possible to work with large amounts of information. Accordingly, new expenses cannot be avoided; however, the competent use of AI will very soon turn your expenses into income.
  3. Don’t be afraid to change your motion vectors. Artificial intelligence often helps business owners understand that changing the business model will help them move on with greater efficiency. There is no need to be afraid to change anything because it is to change for the better that you started working with AI, right?


Signs Your Enterprise Needs AI Solutions 

Artificial intelligence is complex, and many businesses still don’t know how to implement and benefit from this technology. Companies around the world are at different stages of AI adoption:

  • Awareness (there is the only talk of introducing AI when planning business processes and strategies)
  • Activation (technology is not yet widely used, only as a test for some pilot projects)
  • Operation (at least one project from start to finish uses AI in its work, a separate budget and team is allocated for this)
  • Consistency (all new projects, products, and services are created using AI, all technical employees of the company are aware of the nuances of work, actively apply technology in their daily routine)

However, not all companies decide to implement AI, even if they see an obvious benefit. To understand if you really need AI, think about the following things.

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Well-established Data Collection 

Determine how much information your employees will have to work with. You won’t be able to endlessly hire new specialists to cover all your database needs. If the costs of implementing AI outweigh the other concerns, then prepare your data to ensure that AI adoption runs as smoothly as possible. This requires:

  1. Keep data up to date. The algorithm will not be able to make accurate predictions and provide relevant analytics if your data, for example, on customer behavioral factors, is not updated. To put it bluntly, you don’t need a smartphone if you still use pigeon posts. Spending on AI should pay off. It can process huge amounts of information and produce specific results, but who will need them if the initial data is outdated.
  2. Check your details for errors. A machine can process a large amount of data faster than you, but at the same time, it can get confused about some elementary thing that a first-grader would easily understand. Where the human brain sees a typo in the same word, the machine sees two different words. Of course, the AI ​​has reached the level where it realizes that you made a mistake (for example, when the search engine suggests that “you must have meant” something completely different). However, the search engine has enough experience to conclude the error, but will your algorithm have enough experience from scratch?
  3. Use a consistent format for storing data. For AI to collect all the information stored in your company and process it correctly, you should contain it in one setup.

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Particular Business Problem to Solve 

So, you have prepared the technical basis for the AI implementation, and now you need to decide what algorithm can help you in the first place? Perhaps, to solve critical problems, or are you already doing well, but you want it to be even better?

  1. Increase the price of the existing product. As we mentioned earlier, the attractiveness of a product or service increases not only due to the quality of the product in front of competitors but also due to a personalized approach to the client. Are you selling cosmetics? Let your AI match the eyeshadow palette, mascara, or 50 shades of lipstick from the same producer to the one chosen by the client.
  2. Analysis of the current status of the business. The algorithm can help you find weaknesses that you didn’t even know about: logistics, marketing, sales, manufacturing – all these can be bottlenecks. Plan resources and forecast demand correctly with AI technology.
  3. Business process automation. When you have identified and eliminated the problems and perhaps even radically changed the business model, it’s time to think about automating processes and, accordingly, optimizing the staff and retraining for more intelligent work.

Culture of Innovations 

Before implementing AI, make sure that your employees share a philosophy of innovation and progress with you, that they have no fear of not coping and fear for their workplace. New technologies can be quickly, organically, and painlessly introduced only if your company is constantly engaged in them.

  1. Corporate strategy. Don’t innovate for the sake of innovation. You will never make a profit this way. You should not put all products under the auspices of AI at once. Try with small, not very resourceful projects. Then there is no risk that your company will collapse like a house of cards in case of failure.
  2. Metrics. Be sure to define the criteria by which you will measure the success of the implementation of AI to understand when the payback comes.
  3. The right to make mistakes. Yes, this also needs to be incorporated into the business strategy. One of the indisputable advantages of AI is considered to be that it excludes the human factor. However, the machine can malfunction; this is a well-known fact. Do not assume that this risk negates all the other advantages of a smart algorithm. Just take into account that you need to spend money not only on the development but also on the support of the algorithm at first.


For all the attractiveness of AI technology, consider whether you really need it. Do your capacities give reason to implement it? If the amount of information is large and the corporate business strategy and tasks are flexible enough, there is no point in delaying.

The APP Solutions is a web and mobile app development team aware of AI algorithms development and implementation for Enterprises. Suppose you are already ready to introduce AI technologies into your business but cannot decide on a development team. In that case, we are ready for fruitful cooperation and are waiting for you!

What is Artificial Intelligence in Healthcare?

As life expectancy increases, healthcare organizations face an increasing demand for their services, rising costs, and a labor force struggling to meet the needs of their patients. By 2050, one in four people in Europe and North America will be over 65, which means the healthcare system will have to deal with many patients with complex needs. Managing these patients is costly and requires systems to move from an episodic-based service to a more management-oriented long-term care.


AI Technology of Healthcare Providers

Artificial intelligence based on automation can revolutionize healthcare and help to solve vital problems. Few technologies are advancing as rapidly as AI in the healthcare industry. AI is now used in many life spheres, but the health-care industry was one of the first to use it widely. According to Statista, from 2016 to 2017, the AI ​​market in healthcare grew by $ 500 million (from 1 to 1.5 billion), and by 2025 is predicted to grow to 28 billion.


An even more optimistic forecast is given by Tractica – by 2025, growth is projected to be 34 billion, and by 2030 to 194.4 billion.

All of these investments include case studies on patient data processing and management, and transformation from paper to digital format, digital image interpretation (for example, in radiology, ophthalmology, or pathology), diagnosis and treatment, biomarker discovery, and drug efficacy calculations.


Forbes says AI tools are already being implemented in 46% of service operations, 28% in product and service development, 19% in risk management, 21% in supply chain management, and 17% in marketing and sales in the healthcare industry.

North America dominated the healthcare AI market with the largest share of revenues at 58.9% in 2020. Factors which determine the market in the region are a broader adoption of AI technologies, growing licensing and partnerships, and favorable government initiatives.

AI has proven to be an important resource for evaluating patient scan data and identifying treatment options throughout the pandemic. It has also been also used to improve the administrative operations of hospitals and health centers. As a result, we may see more business applications from healthcare providers for more widespread use in medical procedures. 

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EIT Health and McKinsey, in their report 2020, drew attention to which areas of medicine artificial intelligence is most often used.


As you can see, first of all, these are diagnostic tests and clinical research. However, a large amount of investment is also spent on technologies related to managing the way hospital’s function. Education and prescription automation are also included.

For example, AI is already being used to more accurately detect diseases such as cancer in their early stages. According to the American Cancer Society, most mammograms give false results. One in two healthy women are being told they have cancer. Using AI, mammograms can be viewed and translated 30 times faster with 99% accuracy, reducing the need for unnecessary biopsies.


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Three Phases of Scaling

AI in healthcare is a pervasive technology that can be successfully applied at different levels, depending on the complexity of the development.


First Phase

AI is solving routine paper, managerial, administrative processes that take time for doctors and nurses.

Second Phase

Remote monitoring. According to Accenture, artificial intelligence and machine learning can help meet 20% of all clinical requirements by reducing unnecessary clinic visits. At the same time, it is possible to reduce the number of readmissions to hospitals by 38%. 

As AI in healthcare improves, patients will take more and more responsibility for their treatment. Already, successful developments are being applied in such complex fields of precision medicine as oncology, cardiology, or neurology. For example, clinicians can be virtually close to their patients and observe certain conditions without personal visits.


This technology has proven to be especially useful during the pandemic when personal care was limited, but patients still needed support from their medical providers. 

Third Phase

AI in healthcare will become an integral part of the healthcare value chain, from learning, researching, and providing care, to improving public health. The integration of broader datasets across organizations, and robust governance for continuous quality improvement, are essential prerequisites for greater confidence among organizations, clinicians, and patients for managing risk when using artificial intelligence solutions.


AI Tools

Artificial intelligence is reshaping healthcare, and its use is becoming a reality in many medical fields and specialties. AI, machine learning (ML), natural language processing (NLP), deep learning (DL), and others enable healthcare stakeholders and medical professionals to identify healthcare needs and solutions faster and with more accuracy.

Does Your Business Really Need An Enterprise Artificial Intelligence

AI vs. COVID-19: Patient Outcomes

Artificial intelligence technologies have played a critical role in the ongoing pandemic and positively impacted connected markets. It is used to quickly detect and diagnose virus strains and combat outbreaks with personalized information. For example, AI algorithms can be trained using chest CT images, infection history, symptoms, clinical documentation, and laboratory data to quickly diagnose COVID-19 positive patients.

In 2020, an NCBI study found that an artificial intelligence system identified 17 out of 25 COVID-positive patients based on typical computed tomography images, while experts diagnosed all patients as COVID-negative.

Thus, AI-based diagnostics can be used to accurately detect the disease even before the onset of apparent symptoms. In addition, these systems can be trained to analyze images and patterns to create algorithms to help healthcare professionals diagnose the disease accurately and quickly, thereby increasing the spread of AI technologies in healthcare. This will significantly reduce the load on the system and improve patient outcomes.

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Benefits of AI/Machine Learning in Healthcare 

There are several areas in which AI has excelled, especially significantly helping doctors and medical institutions with the challenges that are becoming more and more in the modern world.


Predictive Analytics

With the rapid growth of medical knowledge, it is becoming increasingly difficult for doctors to keep up with the times. AI solutions that extract relevant medical expertise for each patient, and present it in a structured way can help clinicians choose the best treatment option, saving time and leading to more complex fact-based decision-making.

In a routine clinical setting, AI models can also detect patients at high risk of complications, or early deterioration, and provide recommendations to further support clinical decision-making with the prevention or early intervention. Reducing complications through early intervention can lead to improved health outcomes and reduced length of hospital stay and associated health care costs.

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AI can help identify a patient’s condition and recommend possible care and treatment options. This can save physicians doing research and, in turn, spending more time evaluating the possibilities presented by the AI ​​and discussing them with the patient.

One successful, and most importantly, relevant examples (in the midst of COVID) is a technology that predicts the oxygen levels of each patient. The engine indicates oxygen requirements within 24 hours of arrival in the emergency department with a sensitivity of 95% and specificity of over 88% based on previously examined X-rays. Software is being created that makes the work of radiologists unrealistically easier. 

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In the end, AI in healthcare could create a complete “home” version. For example, technologies already make it possible to produce “smart” toilets that could analyze urine and feces “on the spot.” Another question is that it is unlikely that the invention will have many fans at this stage of human development.

However, this extravagant decision could free up many laboratory specialists involved in this type of analysis for more complex work. And if you look more into the future, doctors will treat the consequences of patients who were too lazy to check urine tests on time (which they could have done without even leaving home).


Storing and Organizing Patient Data Bases

AI, in particular machine learning, can also be used with large datasets to predict health outcomes, helping healthcare systems focus more on prevention and early detection, improve health outcomes and, over time, make health care systems financially sustainable.

The big data automation capabilities, and real-time analytics built into syndromic surveillance, provide the information you need to understand disease progression and predict its risk to patients before it occurs. In addition, track disease symptoms, better manage public population health by predicting hospital utilization, geographic leaps, and associated material and resource requirements.

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Using AI to analyze large datasets can be helpful in both healthcare settings and epidemiological research. AI models based on clinical data from a large population (e.g., patients in a healthcare region or an integrated healthcare provider system) can help identify early risk factors and initiate preventive action or early intervention at the system level. 

They can also help prioritize during times of staff shortage. Likewise, identifying an increased risk of unplanned hospitalization can help clinicians proactively intervene and avoid them.


Analysis of Digital Images

Radiologists and cardiologists make it much easier for themselves to work with images and scans, thanks to the capabilities of AI. Technological advances in this area allow you to prioritize critical cases, avoid potential errors in reading electronic health records (EHR data) and electronic medical records (EMR) and establish more accurate diagnoses.

AI algorithms can analyze big data sets quickly and compare them with other studies to uncover patterns and hidden relationships. This process allows medical imaging professionals to track critical information swiftly.

The Patient Brief examines past diagnoses and medical procedures, laboratory findings, medical history, and existing allergies and provides radiologists and cardiologists with a summary that focuses on the context of the images. The product can be integrated with any structure of the medical unit’s system, accessible from any communication workstation or some medical devices on the neural networks, and be updated without affecting the daily activities of the medical department.


AI and Pharmaceuticals

Another truly revolutionary example of the positive uses of AI in healthcare is drug research and discovery; one of the most recent AI applications in healthcare. By channeling the latest advances in AI to streamline drug discovery and repurposing processes, both the time to market for new drugs and their cost can be dramatically reduced.


Supercomputers have been used to predict, based on databases of molecular structures, which potential drugs will, or not, be effective for various diseases. AI and machine learning algorithms can identify new drugs, track their toxic potential and mechanisms of action. This healthcare technology has led to creating a drug discovery platform that allows the company to repurpose existing drugs.

Identifying new uses for known drugs is another attractive strategy for large pharmaceutical companies since it is cheaper to repurpose and relocate existing drugs than to create them from scratch.



AI and Genetics

Altered molecular phenotypes, such as protein binding, contribute to genetic diseases. Therefore, predicting these changes means predicting the likelihood of a genetic disorder. This is possible due to data collection on all identified compounds and biomarkers relevant to specific clinical trials.

This allows us to recognize genetic abnormalities in the fetus and compose an individual treatment for a person with sporadic congenital disease.


AI in the Healthcare Apps

The growing popularity of smartphones and AI technologies among patients and professionals is driving the proliferation of virtual assistants. In addition, robotic surgery has been the most promising segment in the AI healthcare market as of 2020. This is mainly because surgical robot manufacturers are entering numerous strategic partnerships with data science and analytics companies and artificial intelligence technology providers.

The leading players in the AI ​​market:

  • IBM Corporation
  • NVIDIA Corporation
  • Nuance Communications, Inc.
  • Microsoft
  • Intel Corporation
  • DeepMind Technologies Limited

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Future of AI/Deep Learning in Healthcare: Perspectives

According to The World Health Organization forecasts, the number of medical workers is steadily decreasing every year, and by 2030 there will be a shortage of almost 10 million professionals. AI, machine learning systems, and NLP can transform the way care is provided, meeting the need for better, more cost-effective care and helping to fill some of this gap in staffing. This is especially true as the population ages and health needs become more complex.

As the next step in telemedicine, Telesurgery aims to help reduce the damage caused by staff shortages. Telehealth, or virtual meeting, has become more widely used during the pandemic. This service has been used by those living in remote areas for several decades, but regularly by telephone rather than video conferencing.

With the pandemic and the need for social distancing, telemedicine has become an integral part of healthcare services. Therefore, it has improved significantly as a result of the demand throughout the pandemic. Telesurgery is a field that is being researched and can be used in the provision of emergency care.

The current use of robotics in surgery allows physicians to perform minimally invasive surgeries and limits the impact of the procedure, improving outcomes. Expansion of surgery automation will continue to include AR and VR for increased productivity. 

Telesurgery is the next step being researched and provides access to a surgeon who does not specialize in the patient’s area of ​​residence. This saves the patient from traveling and can also be used when the patient requires immediate assistance. Problems may include delay and the need for a surgical team to support the procedure if a problem arises.


AI and automation are uniquely positioned to understand these needs and the complex interdependencies between various factors affecting public health. In addition, the extraordinary shift from symptom-based medicine, to molecular and cellular medicine, is generating ever-growing data amounts.


The pace of change in AI within healthcare has accelerated significantly over the past few years thanks to advances in algorithms, processing power, and the increasing breadth and depth of data that can be used. In response, countries, health systems, investors and innovators are now focusing their attention on the topic.


Global venture capital funding for AI, ML, and deep learning in healthcare has reached $ 8.5 billion for 50 companies as clinical trials of AI healthcare applications increase.

And although AI will not be able to replace medical personnel (especially doctors) entirely, however, with the gradual introduction of technologies, the work of doctors will change only in a positive direction:

  • More time for patients – less for paperwork (time optimization from 20 to 80%)
  • Acceleration and improvement of diagnostics (especially in such fields as radiology, ophthalmology, pathology)
  • Assistance in prioritizing the complexity of a patient’s condition (e.g., determining the likelihood of a heart attack, septic shock, respiratory failure)
  • Improving the soft skills of clinicians by changing the format of communication with patients (people with chronic diseases can be served from home thanks to telemedicine)
  • Increased educational level (while less severely ill patients can be treated remotely, the hospital will mainly admit patients with more complex cases, which requires more advanced skills from doctors)



AI bias in Healthcare: Disadvantages and Challenges

AI does not always become the optimal solution and salvation from all problems. This happens for several reasons:

  • Insufficient development of technologies (moreover, several companies can solve the problem at once, but in the end, none of them will make a high-quality product that can be immediately thrown onto the market). The solution could be the unification of diverse teams that could consider all the necessary nuances.
  • Changes in the medical education system around the world (the more technological solutions that can be offered to doctors, the more technically savvy they will have to be, and even top medical universities have not yet reorganized these new realities. Changes in patient behavior caused by AI also implies a change in the relationship between patients and practitioners, with the latter needing more attention to counseling and interpersonal skills).
  • Databases (healthcare is one of the minor digitized sectors of the economy. Healthcare providers and AI companies need to implement robust data management, ensure interoperability and standards for data formats, improve security, and clarify consent to exchange health data).
  • Regulation and risk management (defining the regulatory framework for AI in healthcare is significant for solving possible problem situations in which it is difficult to determine the degree of responsibility of all parties to the conflict).




AI in medicine still has many different stages to go through; the improvement process is only gaining momentum. But positive results are already visible. There are still fears that the excessive interference of technology will make the medical field less “human,” but only people who have not delved into the issue can speak this way. The more technologies are used in medical diagnosis, prevention, and treatment, the more time an actual doctor has directly for the patient.

Many medical and health apps help people self-diagnose their health, which ultimately allows doctors to focus on treatment. The development of such applications is carried out by companies with high expertise, including The APP Solutions. We are a highly skilled app development company who can bring your ideas to life, and we look forward to meeting you. If you have an interesting idea but are still contemplating how to implement it, contact us, we can help.

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Artificial Intelligence and the Fashion Industry

Styling an outfit is an intricate process that involves theme selection, selection of the primary color, matching of clothing pieces, selection of accessories, and getting the right fit.

It is often said that you are what you wear, yet not many people are skilled in making the right decision when it comes to outfit choice. AI, on the other hand, is capable of driving a clothes swap app to precise decision-making.

From fuzzy logic to genetic algorithms, decision trees to Bayesian networks and neural networks, artificial intelligence software is conveniently positioned to make the work of fashion styling on a day to day basis quick and easy. Each of these AI methods has gained popularity due to their unique abilities gained from years of artificial intelligence development.

Benefits of artificial intelligence methods that can be used in fashion apps

The previous section has enumerated some AI methods that have been used by companies using artificial intelligence. Here are the unique benefits to be obtained from using each of these methods in a fashion app:

  • Bayesian networks – they use probabilities to represent variables. They have the ability to infer existing relationships between current and future trends in fashion.
  • Fuzzy logic – makes use of approximate reasoning and uncertainty. It is the closest to a human brain regarding being able to interpret truthfulness and falsehood, indicating clear likes and dislikes of the user.
  • Artificial neural networks – they are capable of modeling complex styling tasks by modeling preferred outcomes.
  • Decision trees – logically allow decision-making.
  • Genetic algorithms – assign fitness values that enable the user to find exact solutions to an optimization problem.
  • Knowledge-based systems – reason out the existence of a relationship between features of style in fashion.

See also: How To Develop The Best Fitness App

Using artificial intelligence in clothes matching applications

A computer program that intends to use Artificial Intelligence to style its users would have to focus on three main areas:

  • Visual garment representation
  • Computational imitation of stylist behavior
  • The detection and forecasting of fashion trends

Under visual garment representation, the clothes matching app should be able to extract the images of desired outfits. Garments can be described by their unique features including shape, print, color, and fabric. The app would require computer vision techniques to recognize color, shape, and print.

Computer vision techniques automatically recognize color in a Red, Green, and Blue model which it converts into a Hue, Saturation, and Intense model. The same goes for shape as these techniques can extract the outline of the garment. In addition to this, the print is detected under the loudness of the garment (the frequency in color changes and locality).

The fabric would require a more specialized AI method as even human beings struggle to identify all fabrics online by sight alone. Stylistic semantic correlations would come in handy. They would entail having a system that relates certain attributes to certain fabrics to make a prediction. For example, casual T-shirts would be related to cotton fabric, formal dinner dress – to silk, and so on.

Next, this clothes matcher would require the ability to model human stylist behavior. Once the garment has been located, there would be a need for computational styling.  

The first aspect of styling is the color harmonization. An ideal app would be one that can take a standard color scheme and adapt it to the user’s preferences. Interactive artificial intelligence algorithms programming would be able to adapt such color scheme in real-time by using schemes that have additional labels such as “slightly,” “neutral,” “extremely” instead of plain colors.

The second aspect of styling would include the styling of shapes, prints, and fabrics. Many factors go into the personal preference of shape, print, and fabrics including the current fashion trends, the occasion, and the cultural background of the user. The ideal apps that help you choose your outfit would use a neural network model that converts the physical attributes of a garment into a sensation. For example, color into temperature, shape into fit, fabric into softness, and so on.

Thus, as a user, keying in the words “garment with a soft feel on a summer evening” into the application will automatically let artificial intelligence guess your preferred garment fabric and color.

Finally, this app should be able to track fashion trends. An ideal outfit picker would be extremely sensitive to past, current, and future fashion trends. This would require a combination of Bayesian networks and knowledge-based systems.

The Bayesian network would be used to model a human stylist’s trend proposals. Based on the knowledge of the past and current trend, the Bayesian network would be able to classify trends into binary target values which will then be proposed (or not) based on the probability of their reoccurrence.

In summary, the following applications of artificial intelligence would drive this app:

  • Use computer vision techniques to extract the desired image of the outfit
  • Use interactive genetic algorithms to match colors of different pieces
  • Use neural networking to select the desired shape print and fabric
  • Use Bayesian networks to select items based on future fashion trends

The pros of artificial intelligence technology

Many advantages assure the future of artificial intelligence applications in fashion and other areas:

  • It deals with tasks that humans would find boring to do on a daily basis.
  • It quickens the decision-making process, where one would take hours to decide on an outfit for the day.
  • It does away with the margin of error. When the right information is fed into the app to try on clothes, the outcome is accurate.
  • It takes the stress away from an individual. Using an app to make outfits with your own clothes would reduce by a considerable amount the stress that comes with choosing outfits daily.

FYI: Want to find out your web development cost? Try our web calculator.

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Web Development: The Results of the 2017 and What’s Waiting for Us in 2018

It’s the end of the year and what a year it has been! Every day seems to bring more and more innovations. Just at the WebSummit 2017, there were over 2,000 startups that talked about the digital world and how business and approaches are transforming daily.

What were the trends of 2017 that affected our lives and what should we be ready for in 2018?

Artificial Intelligence, Machine Learning, & Robots on the Rise

Artificial Intelligence is certainly not a new word for most of us. If we think of the ancient myths and fairy tales we’ve read as kids, there were often artificial beings that were bestowed with intelligence by their creator.

The AI as we know it (or got used to, from all the science fiction books and movies as well as the current news) got a boost in the 1950s and then again in the 2000s when the world wide web started to offer a lot of the information online and the world became digitized.

The basic (speaking in relative terms here) AI example is widely used Facebook photo tagging. Image and facial recognition are a part of the AI’s machine learning features.  

In 2016, the AI started to write poetry (it was weird, speaking personally, but hey, tastes differ) and now there are also AI web designers. Molly is the Grid’s designer who helps the users create their website with the best UX and UI practices in mind and who’s available 24/7.

2017 also was the year when the first non-human woman was made a citizen. Sophia the Robot was bestowed this honor by the Saudi Arabia’s government. In one of the interviews at the WebSummit 2017, she said she was delighted but at the same time surprised that she wasn’t accepted as a citizen of the world, yet a country with strict gender rules has welcomed her with arms wide open.

Bots – Putting AI and Machine Learning to Work

Back in 2016, Microsoft’s CEO Satya Nadella has boldly declared “Bots are the new apps.” In 2017 they have started to shine, as businesses around the globe realized the potential hidden in these little powerful instruments.

What used to feel like talking to a little child who is learning a new language now feels like talking to a person. Bots are getting more personalized and provide a much better user experience, whether it’s Poncho, a weather bot who tells you the weather and shares jokes, or Dinner Ideas, a bot that helps you decide what’s for dinner based on your fridge’s contents.   

During the conversations with users, the bots learn from human language and adapt to it naturally. However, it’s both a blessing and a curse. Microsoft has learned it the hard way when they have launched a bot named Tay, who learned sexist and racist slurs from the users it talked to. Oh well, things didn’t go as planned.

Internet of Things – Business and End Users

The top four industries that adopt IoT on a wide scale are manufacturing, consulting, business services, and distribution & logistics. It can be explained because these are the industries in which revenue growth is often hard to achieve and the Internet of Things technologies can provide a competitive advantage. Just think of all the tracking possibilities now for packages via drones.

IoT Importance by Industry

From the needs and most-requested instruments, the businesses placed the most importance on Business Intelligence (BI), namely, the features like dashboards, reporting, advanced visualization, and other. The main objective that businesses place here is improved decision-making. The enhanced customer experience is also on the list of top 5.

In terms of the end-user relationship with IoT, people are getting used to the fact that you can turn on the vacation setting on your fridge when you’re away from your mobile phone or ask your Amazon Echo speaker to order you an Uber.

Static Site Generators

If we are talking about the actual web development as in websites and such, static site generators like Jekyll or Hugo certainly became the game changer in 2017. Well, okay, in a way, it’s going back to the first sites that were published in the WWW, but only much better.

Static site generators allow creating a website without a database, instead of running from files on your servers. The advantages of such an approach are shorter loading time, better security, and much easier deployment of templates and content.

It’s not ideal, however, because static sites require additional efforts to integrate real-time content (like user comments) with this type of sites. 

JavaScript and the Great Battle of Angular vs React

JavaScript is the hottest web development trend of 2017 and it will continue to capture more and more evangelists. The frameworks and libraries of JavaScript are quite flexible and powerful and currently, there are two frameworks that are like Samsung and Apple, going back and forth.

The army of React fans is almost as big as Angular’s, but we’ll see how that pans out in the coming year.

Another potentially big player in this competition is VueJS. 

SVGs Taking Over

With retina and ultra-high definition screens taking over the computers and mobile phones alike, making sure that your website or app looks great on any resolution is a must.

Conventional image formats, like jpg or png, can somewhat perform the task, but they are losing to the SVGs. These vector files are resolution independent and therefore look awesome on all devices.


Motion Design – Interactive Simplicity

Not a new trend of 2017, but it was the year of motion design gaining momentum. People crave simpler interfaces, but at the same time not at the cost of interactivity. Motion design helps to bridge these two, helping users to understand the flow between the actions using animation.

An added bonus: if it’s done properly and optimized for speed, motion design animations can make the user feel like the app is faster.

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AdTech, Big Data, AI, and Machine Learning Conferences and Meetups You Shouldn’t Miss in 2018

Here’s the list of upcoming AdTech / MarTech, Business, AI, and Machine Learning-oriented conferences worth visiting.

It’s time to catch up with the latest news from industry insiders, set up some new connections, and expand your professional network. There is whole lotta interesting stuff going on. It’s better to know what happens when in order to get the latest insights from the folk in the know.

Programmatic I/O

  • San Francisco, CA, USA
  • April 10-11, 2018
  • Topic: AdTech
  • Website

Stay ahead of programmatic trends and connect with peers and industry partners you won’t meet anywhere else. Hear from innovators, industry leaders, researchers, and analysts on both the buy and sell-side of programmatic media and marketing.


  • Hamburg, Germany
  • April 10-11, 2018
  • Topic: AdTech
  • Website

Since 2011 d3con is the first and biggest German event about the future of digital advertising. More than 1,500 participants from the leading agencies, publishers, and service providers meet once a year in Hamburg to discuss, network, and learn at the top level.

AI Expo Global

  • London, UK
  • April 18, 2018
  • Topic: AI
  • Website

Topics covered include Business Intelligence, Deep Learning, Machine Learning, AI Algorithms, Data & Analytics, Virtual Assistants & Chatbots as well as case study based presentations proving an insight into the deployment of AI across different verticals.

MarTech 2018

  • San Jose, USA
  • April 23-25, 2018
  • Topic: MarTech
  • Website

MarTech® is for senior marketing, IT, and digital executives and experts at the intersection of marketing, technology, and customer experience. What’s working? What’s not working? Dial into the global digital transformation with the marketing technology explosion at The MarTech Conference!

Artificial Intelligence Conference: New York

  • New York, NY, USA
  • April 29, 2018
  • Topic: AI
  • Website

The Artificial Intelligence Conference delivers an unsurpassed depth and breadth of technical content—with a laser-sharp focus on the most important AI developments for business.


  • New Orleans, LA, USA
  • April 30, 2018
  • Topic: Business
  • Website

The Collision is “America’s fastest growing tech conference” created by the team behind Web Summit. In three years, Collision has grown to almost 20,000 attendees from 119 countries. Attendees include CEOs of both the world’s fastest growing startups and the world’s largest companies, alongside leading investors and media.


  • Kyiv, Ukraine
  • May 16-17, 2018
  • Topic: AdTech
  • Website

Ad Summit was designed for digital advertising executives to implement best practices & strategies, expand their markets and generate a new revenue stream.

Big Data: Toronto 2018

  • Toronto, Canada
  • June 12, 2018
  • Topic: Big data
  • Website

The conference will focus on technical and practical verticals including use cases around predictive analytics, advanced machine learning, data governance, privacy, cybersecurity, Smart Home & IoT, digital transformation, Hadoop, cloud analytics, and cloud computing.

Deep Learning for Robotics Summit

  • Amsterdam, the Netherlands
  • June 28, 2018
  • Topic: AI / Machine Learning
  • Website

Where AI meets the real world. Improving robotics via deep learning & creating the next generation of smart robots

Artificial Intelligence Conference: San Francisco

  • San Francisco, USA
  • September 5, 2018
  • Topic: AI / Machine Learning
  • Website

The Artificial Intelligence Conference brings the growing AI community together to explore the essential issues and intriguing innovations in applied AI. We’ll delve into practical business applications, compelling use cases, rock-solid technical skills, dissections of failures, and tear-downs of successful AI projects.

Future Port Prague

  • Prague, Czechia
  • September 6-7, 2018
  • Topic: AI / VR / Healthcare
  • Website

By creating Future Port Prague together with our visionary partners, we want to help people and businesses in our region better understand the phenomenon of exponential progression; not just the technology, but the deeper societal changes that will require a rethinking and rewiring of our business models and environments, our education systems, and most importantly our own mindsets.

Ad: Tech

  • London, UK
  • September 26-27, 2018
  • Topic: AdTech
  • Website

As we arrive in a post-GDPR world, what will advertisers and marketers have to consider in order to develop innovative, yet compliant channels of engagement? Now co-located alongside stellar industry events Technology for Marketing and eCommerce Expo, ad: tech London is here to help savvy practitioners unlock the power of the latest emerging tech, spur opportunities for experimentation, and open minds to the future.

Artificial Intelligence Conference: London

  • London, UK
  • October 9, 2018
  • Topic: AI / Machine Learning
  • Website

Organizations that successfully apply AI innovate and compete more effectively. Those who fail to implement AI successfully will fall behind. The AI Conference in New York will give you a solid understanding of the latest breakthroughs and best practices in AI for business.

World Summit AI

  • Amsterdam, the Netherlands
  • October 10, 2018
  • Topic: AI / Machine Learning
  • Website

From applied solutions for corporates and enterprises to the implications of AI on society, including ethics and AI4good, World Summit AI will tackle head-on the most burning AI issues for 2018 and beyond.

Web Summit

  • Lisbon, Portugal
  • November 5-8, 2018
  • Topic: Business
  • Website

We live in uncertain times for business. At Web Summit we welcome the people who shape the world around us. Our attendees hear from C-level executives driving change at the world’s most influential companies, participate in workshops, roundtables, and more.

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