The arrival of Artificial Intelligence and its practical implementation in industries has paved the way for unlimited opportunities.

The term artificial intelligence (AI) first came up in 1965. AI has become the latest technology, incorporated in different industries across the globe. This technology, in turn, is a great combination when accompanied with techniques to enhance business operations.

Artificial Intelligence essentially means the development, theory, and execution of computer-based electronic systems.

These systems boost the ability of machines in the execution of tasks; tasks otherwise requiring human intervention. AI brings a boost to the profitability of businesses within a range of 38 percentage points.

Artificial Intelligence enhances efficiency and leads to a reduction in the overall costs. The primary objective of Artificial Intelligence techniques is to facilitate the machines to perform intellect-heavy tasks performed by humans.

AI technology is getting increasingly accepted in several areas with the common objective of achieving a reduction in redundant tasks, and attain a better level of performance.

AI Update:-

With time, AI has made its presence felt in all essential areas of work. With the pace AI adoption is happening in different fields, AI can rightly take up more positions of interest several times.

AI is present in self-driven cars, human resource planning, store management, and in the areas of medicine and science. AI has provided great results for all these jobs.

AI-Enabled Retail Outlet

The incorporation of an AI-based retail store in Kochi, India, dramatically shows the impact of AI. In this store, Artificial Intelligence is utilized to cut down repetitive jobs, demanded otherwise from the employer.

There are a lot of tasks monotonous in nature, having the potential to hamper the efficiency and cost utilization ability of human resource. This is a massive reason as to why AI has been such a significant influence on a global scale.

Benefits of this AI update:-

• The AI-based retail store in Kochi is entirely devoid of human interruption while the customers indulge themselves in shopping.
• The involvement of AI in this system sees automation to such an extent that the full payment process concludes by e-wallet. The entire store is cashier-free, making the whole shopping experience automated for the customers.
• The fact that a salesperson is absent in the store led to an experience of lesser interruption for the customers.

The purchase amount gets automatically deducted from the customer’s e-wallet. Here, a significant benefit that surfaces is related to the ease of purchase and reduction in time consumption.

The use of AI in retail stores has led to a noticeable decrease in the time invested in shopping. Long queues for payment get substantially avoided as the amount has a direct deduction from the e-wallets.

Customer Manual:-

• The customer is required to download the “Watasale” app, through which a QR code gets produced and scanned while a customer will enter the store.
• The store works with sophisticated camera technology and facial recognition. The shelves of the store have embedded cameras for keeping track of the items picked up.
• The algorithms then take care of the payment procedure based on the information transmitted through the cameras.
• All the customer has to do is download an app, scan the QR code, go in the store, shop on their own, and that is all because the store is free from cashiers and has hi-tech cameras working in tandem with the whole e-wallet, the needful gets automatically done at the time of payment.

AI has brought specific projections for the upcoming trends of retail in India for estimation through store visitor analytics. The store visitor analytics use the concepts of AI and machine learning to make a note of footfalls in the store.

AI For Forecasting Wind Farm Output

With the increasing need for renewable sources of energy, the world is making efforts for obtaining reliable sources of energy generation.  Wind farms are a great source of such renewable energy. However, there is a significant issue that wind farms have been facing lately.

Google has included Artificial Intelligence into this matter. In this case, AI is used to make accurate predictions and detailed calculations. The accuracy of these forecasts is an essential determinant for operators to meet the requirements.

The Benefits of AI in Wind Farm Output Calculation

• Future demands of wind power generation require assessment and analysis much before for staying prepared with the essential strategies and workload. AI is serving as a prospective solution to this issue by ensuring quick calculations and predictions.
• The system devised by Google can make accurate 36-hour predictions within the final output. This is considerably impressive, without a doubt.
• The system is beneficial as energy sources can be scheduled to produce extraordinary power at a particular time. Also, machine learning, when included in this system, aids in the enhancement of the value of wind energy generated.

Amidst the continuous optimization of the model, Google is claiming to have increased the value of wind energy by around twenty per cent so far. The upcoming optimization and work done on the system are targeting even higher returns in coming time.

Artificial Intelligence and The Future

Artificial Intelligence is a concept on the brink to reform every industry and the working and structure of every business. Spheres like inventory management, medical examinations, and prior predictions also get streamlined beautifully with AI.

Also, the involvement of AI-powered robots in retail stores is a prospective boon, expected soon by the store owners in retail management.

Besides this, bot chats, blockchain, and machine learning are some of the related areas that are under works.

Furthermore, right from the management of inventory, workforce, movement of materials in the organization to technical analysis, experts are working on making AI useful in every possible way.

Artificial Intelligence and the various concepts surmounting it are indeed capable of reforming a large part of our daily lives and businesses.

If there is any idea you have in mind, or any assistance required, do get in touch with us.

This new AI-based Internship Recommendation System can be a Game Changer

Technology has become synonymous to simplification. There is no denying that all the gadgets and tech have made our day to day life easy and simple. It keeps getting better, as technology is advancing and evolving in leaps and bounds. The world has very much entered the age of Artificial Intelligence with voice-controlled smart assistants like SIRI, Google Assistant, Alexa, Bixby, Cortana, and the likes. These allow us to enjoy the perks of a personal assistant without having to keep one on the payroll.

There is still a lot of work going on AI. Techies are trying to implement it in all the sectors and a new idea has emerged that speaks of an AI-based internship recommendation system. It has been developed by the researchers at Universitas Pendidikan Ganesha, Indonesia, and the tech world is already buzzing with excitement.

This AI based recommendation system can assign or get the best internship placements for the students matching their knowledge, skills, and goals.

The Internship Dilemma

Students spend years studying and acquiring degrees, all the while building their resume step by step for the ultimate goal of getting the dream job. Many take up different courses to forge their skills. All these add up to a strong resume to woo the recruiters. Now, we all know that the rite of passage from being a student to a full-time employee is getting an internship.

An internship is where you learn the tricks of the trade from your predecessors in the field. It is a great way to get into the groove of a working individual leaving the student life behind. That’s why every student aims to land a good internship in the final stages of their education. The experience not only helps them to build a career but it also gives a boost to the resume.

Besides that, it also helps in understanding whether a certain line of work will suit one well or not. It often happens to people who get a job without first doing an internship. After a few days, they might feel that the sector or work is not suitable for them. It is easy to leave an internship but not when it comes to resigning from a job. Hence, the end result is a bad start to the career, which can either slow a person’s growth or derail them from their initial goal eventually. So, the career chart should have three stages i.e. student then intern and finally a professional or employee.

Though an internship is very important, getting one becomes a hassling process. Not all universities guide their students or provide them with internship placements. For average students, it takes a lot of net searching, going to job fairs, etc to finally land a good internship. Even then one might find the place not suitable for him/her. In such a scenario the AI tech developed by Universitas Pendidikan Ganesha can be of a blessing to students to land the perfect internship that matches their profile.

Let AI get You an Internship Placement

The researchers from this University in Indonesia came up with a brilliant idea to solve the internship dilemma of students. They started to work on a recommendation system that will help the students in their graduation year to find the best-suited internship placement for them. It is totally AI based that makes it all the more interesting.

The system developed by them utilizes a recurring artificial neural network (ANN). It is called the Elman neural network. Their system’s job is to assess the student test results and use it to choose the internship placement which is the best match for their skills.
The students are required to give two tests. In the first one, they have to give sufficient information about their grades, knowledge, skills, goals, and likes. The second test is known as the Inventory Personal Survey. Here they have to answer a few questions that will reveal their behavior and overall attitude.

The researchers explained that the students need only to take the test and answer the questions. After that, the results of the test gets processed and analyzed by the artificial neural network or ANN.

The Development Phase

In the development phase, the university researchers ran many tests on the system. Sample information was collected from graduate students who wanted to apply for an internship after studies were over. The system was trained and tested in various ways with the help of the data collected from the students.

After running the assessments they were able to come up with great results. The system developed was able to achieve a 95 percent accuracy level. In all those cases the students have suggested internship placements that were ultimately given to the particular students by the University using the usual manual methods.

According to the researchers, the AI based recommendation system can identify both training and the testing data. This observation was based on the results of all the tests run by them in the development phase. They further clarified that the system can recommend internships like administration jobs, networking, software, etc to students who already in the lookout for internships based on their education and skills.

It is Just the Beginning

This AI based recommendation system can have great use to the management and staff of Universitas Pendidikan Ganesha. It can help them provide internships to students who are seeking it in a faster and more efficient way. It is also highly beneficial to the students as they will get the best places to learn the job from.

To see whether the system works with the same efficiency for a larger population of diverse students, the researchers will have to run more tests and study it further. For that, they will need a huge training dataset.
As of now, they have only used the system to get placements for students who are in the field of informatics. However, they believe that the technology can be further extended to other streams as well.

Nonetheless, this new AI-based internship recommendation system can be a game changer in the field of education and placements. That will be beneficial to both the institutions as well as the students.

If you have any unique idea as such that needs AI based recommendation system, please reach out to us. GoodWorkLabs has a dedicated team of experts to deliver such power-packed solutions for all kind of businesses.

Artificial Intelligence is a part of our day to day lives.

Advancement in the field of AI might be the latest buzz in the tech world but AI in itself is not the new kid in the block. The first instances of AI can be found back as late as the 1960s. It was during this time that researchers and experts of cognitive sciences and engineering first started to work on a smarter and more responsive technology.

The idea was to create a computing language that like humans could learn, reason, sense and perform. With the advancement in AI, a subfield came to the forefront which we call the ML or Machine Learning.

It developed as researchers started to use numerical strategies coordinating standards from optimization computing and statistics thus teaching the programs to perform the jobs naturally by processing the data at hand.

Since then a lot has happened in the field of AI, especially in recent years. Artificial Intelligence is involved in our day to day lives. Some of the notable works remain to be gaming and transportation sector being driven by computer vision and planning and phone-based conversational apps that operate through speech processing. Besides that, we have also seen significant progress in works like language procession and knowledge representation as well.  

In this write-up, we will focus on the advances made by AI and Machine Learning in the Medical field. We will discuss the various ways in which we can use ML in that respect.

ML FOR DIAGNOSIS

There is a lot of scope for ML in medical practice especially when it comes to the diagnosis of the patient. Experts in the field believe that the medical imaging sector will have a significant impact. For example, ML algorithms that can naturally process 2 or 3-dimensional scans to confirm the condition and follow up with the diagnosis. Often these algorithms use deep learning to influence the image data to undertake the respective tasks. Deep learning is of great use in the field of ophthalmology. Recently a healthcare automation company named as the IDx developed a software that can scan images to detect signs of diabetic retinopathy. It is cloud-based software that has already received a green signal from the FDA (US Food and Drug Administration). This kind of software can be of great help in places which are low on resources and yet have a bulk load of complex imaging data to process.   Deep learning based software has also proved to be helpful in radiology as well.

DISCOVERING DISEASE SUBTYPES

The classification and description of diseases and their subtypes that are used today are solely based on the symptoms related observations that were recorded centuries ago. With the advancement in technology, the time has come to opt for a more data-driven approach for classification and diagnosis of diseases.

Some researchers have been working in this respect for diseases like allergy and asthma. They assessed the data from the Manchester Asthma and Allergy Study (MAAS). After analyzing they were able to recognize novel phenotypes of childhood atopy. They have further their research and identified clusters of component-specific IgE sensitization through hierarchical cluster analyses. This according to them will be able to detect the risk of childhood asthma more efficiently.

Experts believe that there is ample scope of using the same data-driven technology to aid in the diagnosis of other diseases as well. Using Machine Learning to detect new actionable disease subsets will be instrumental in the advancement of precision medicine.    

ML CAN REDUCE MEDICATION ERRORS BY DETECTING ANOMALIES

Fluctuating healthcare costs, morbidity, and mortality, all are the by-products of the wrong medication or rather medication errors. All these errors are identifiable through expert chart reviews, the rules-based approach of EMR screening, and use of triggers and audits of events. But all these are faced with a number of hurdles such as time consumption, suboptimal specificity, and sensitivity, high expenses, etc.

On the other hand, anomaly detection techniques that use ML start with developing a probabilistic model. This model will ascertain what is likely to happen in a given context by using historical data. By utilizing that model a new approach within a particular context will be shown as an anomaly if the probability of that happening is at a lower percentage. For example, the patient’s characteristics can be studied after the particular dose of a certain medication to understand the anomaly.   

This kind of technology is already in use. MedAware is a commercially used system that detects medication errors with the help of anomaly detection.

ML AUGMENTED DOCTOR

There is no denying that ML has great potential to alter the traditional rules and methods of clinical care. But one has to be absolutely sure about the technology used before implementing it. Using the wrong methods can be harmful and even be fatal to the patients.

Let’s take an example: Someone wants to foretell the risk of emergency admissions in hospitals by utilizing a model that is trained on past admissions information and data of patients with varied symptoms. Generally, admissions depend on the availability of beds in a medical center, medical insurance of the patient and the reimbursement. The trained model might be able to work out a population level planning of resource to use it for individual-level triage. But it can falsely identify a person and determine that he/she does not require admission. So the algorithm has to be fully tested and trained to avoid such mistakes.

Another downside of naive implementation of a deep learning algorithm in medical care is to acknowledge associations in the training datasets that are not completely related to clinical prediction. These are not even relevant externally. Methods that influence causal elements are less inclined to such overfitting. Faithful development of training datasets and various external approval efforts for each model can give some affirmation that ML-based models are legitimate. These developments need to be validated by medical data scientists so that there is absolutely no risk to the patients. ML can be used for medical care and can benefit many patients. So there is no need to avoid ML. The medical practitioners should learn to understand the idea and technology and use it for the improvement of patient care.

India’s most anticipated events of 2019 — General Elections of Lok Sabha is right here.

From political campaigning to social good, AI seems to have been actively used for data prediction & accuracy. On the other hand, New Zealand which will be hosting the election for Prime Minister in the year 2020. For this very election, Sam is the frontrunner. He has the right amount of knowledge on education, policy, and immigration and answers all related questions with ease. Sam also is pretty active on social media and responds to messages very quickly. When it comes to being compared with the other politicians; however, there is one huge difference- Sam is an AI-powered politician.

Sam is the world’s first Artificial Intelligence (AI) enabled politician developed by Nick Gerritsen, an entrepreneur driven by the motive to have a politician who is unbiased and does not create an opinion based on emotions, gender and culture.

This is just one of the many instances where AI is playing an increasingly crucial role in politics all over the globe. Political campaigns have been taking the help of AI for quite a long time now.

ARTIFICIAL INTELLIGENCE AND POLITICS

The most significant advantage of AI in politics can is its ability where it can accurately predict the future. Political campaigns make the use of machine learning, social media bots and even big data to influence the voters and make them vote for their political party.
Apart from just wins and losses on the political front, AI presents with more obvious implications in decisions and policy making. Reports claim that deep learning, an essential aspect of AI, can look after issues that relate to executing the schemes laid down by the government.

The technologies that use AI for social good are also on the rise since some time now. This is why the arrival of AI politicians is not very surprising. As to how big data and deep learning help it all out, we will be discussing it further below.

BIG DATA AND VOTER’S PSYCHOLOGY

With such a flurry of content on all social media platforms, it is understandable to get confused in determining which political leader is going to have the best interests of the nation at heart. You will be surprised to know that the leaders know how you think and also what you expect from them. Elections have a lot to do with psychology other than just indulging in political games.
While going through the Internet or mobile apps, you must have noticed that there is a pattern to the kind of videos which pop on your window. Some of these pop-ups are also related to the elections and candidates located within your vicinity. This pattern is backed up by reason.

The Lok Sabha election of 2019 may or may not play a decisive role in creating a bright future of India, but it is a witness to the fact that the use of technology is driving the people to act in a certain kind of way. It essentially is India’s big data election which is underway through several algorithms, analytics, and obviously, Artificial Intelligence.

Though they are not exactly visible in the election, they are more of the channels which are always present when it comes to tracing the online actions of voters, political messaging, customizing the campaigns and create advertisements targeted at the voters.

The Congress political party has provided all its candidates with a data docket which can track on-ground activities by their Ghar Ghar Congress app. The data dockets have information regarding households, missing voters, new voters, and even the local issues which plague the concerned constituency.

At the other end, the BJP looks far ahead in its quest to appeal the citizens to keep their party in power for another tenure. In states of the North, the party is a host to more than 25,000 WhatsApp groups. Ironically though, by the time Congress thought to compete with it, WhatsApp changed their policies, leaving the Opposition out to dry.

The optimal use of neural-network techniques, more often referred to as deep learning allows the political parties to have an unbeatable ability and have a fact-based study as to how such kind of data.

We at GoodWorkLabs are enthusiastic about creating such offbeat solutions using our expertise in AI, ML, Big Data, RPA. If you’ve any requirement which is this interesting & complex in nature, drop us a line and let us help you with a robust solution.

Artificial Intelligence to diagnose Alzheimer’s disease.

Alzheimer’s disease. The diagnosis and treatment. Artificial Intelligence. The first two phrases are directly associated with each other. Artificial Intelligence or AI, however, is closing the gap of difference pretty quickly to emerge as the technology that can detect Alzheimer’s well before its diagnosis.

Before we delve into how AI is helping out, it is essential to first know about this disease itself.  We will help you do just that.

UNDERSTANDING ALZHEIMER’S

Alzheimer’s is a dominant reason for the occurrence of dementia, which is a layman term for memory loss and various other cognitive issues severe enough to hinder a routine life. The disease is not a standard parcel that comes with aging but has a high risk of developing at an old age. A considerable part of people with Alzheimer’s belongs to a very young age group too.

The fact that Alzheimer’s is a progressive disease that does not make matters easier. The dementia symptoms gradually take a turn for the worse with time. Initially, the memory loss is pretty mild, but as more time passes, people even lose the ability to have a simple conversation and react to their surroundings.

Currently, the disease has no cure. The present treatments can only help in slowing down the progress of Alzheimer’s, but it is only temporary as the situation worsens over time.

The earliest symptom that an individual has Alzheimer’s is when there are problems faced to remember the recently learned information. It is because the disease initially affects that part of the brain which governs learning. As it grows, the symptoms become more severe like disorientation, mood swings, increasing confusion about time and events, and trouble in talking too.

Alzheimer’s disease is a focal point in today’s biomedical research. The researchers are making constant efforts to detect as many facets of Alzheimer’s and other types of dementia as possible. The most fantastic progress has provided information as to how it affects the brain cells.

ARTIFICIAL INTELLIGENCE AND ALZHEIMER’S

Some recent studies have conclusively demonstrated how AI can lead to an improvement in brain imaging to predict the earliest stages of Alzheimer’s disease. According to them, AI will be able to detect the disease in patients about six years before a confirmed diagnosis comes to the fore. It will lead to incorporating the changes in lifestyle, preparation, and methods of treatment well in advance.

Thanks to more and more innovations which help out in the early stages of Alzheimer’s, early diagnosis will mean that the patients will have more time in financially, personally and legally prepare themselves for their treatment.

With more research conducted every day, newer ways for diagnosing Alzheimer’s and dementia forms are getting tested. From brain imaging to blood tests, the hunt to find some of the most affordable ways to diagnose is on- much before even the symptoms start to show.

By the use of a usual form of brain scan, researchers were able to programme a machine learning algorithm for diagnosing early stages of Alzheimer’s as much as six years in advance before a clinical diagnosis, which will give the doctors a possible chance, to begin with, the treatment.

While no permanent cure for Alzheimer’s disease is available quite yet, some promising drugs have come into existence since the past few years which can help to stem the progress of this condition. These treatments, however, need to be administered early in the course of the disease to do some good. The race against the flow of time has motivated scientists to search for ways to help diagnose this condition much earlier.

Positron emission tomography (PET) scans, which can measure the level of particular molecules like glucose in a brain, have been analyzed as a useful tool for diagnosing Alzheimer’s disease even before the symptoms tend to get severe. And, this is a revolution in Healthcare Industry.

Glucose is like a driving fuel for the brain cells, and the more active a brain is, the more glucose gets used up. As these brain cells die out, they use less and finally, no glucose at all. Other kinds of PET scans look out for proteins that are mainly related to Alzheimer’s disease, but the glucose PET scans are cheaper and more common. This is mostly true for smaller health care facilities and also developing countries, as they also help for the process of cancer staging.

Radiologists have used the PET scans to try and detect Alzheimer’s disease by having a look at the glucose levels through the brain, particularly in the areas of frontal and parietal lobes of the brain. But because it is slow and progressive, the changes in glucose level are pretty subtle, making it difficult to spot with a naked eye.

To sort out this issue, the machine learning algorithm was applied to the PET scans to help with the diagnosis of early-stage Alzheimer’s disease with more accuracy.

For training the algorithm, images from Alzheimer’s Disease Neuroimaging Initiative (ADNI) served as the input. ADNI is a substantial public dataset of numerous PET scans from the patients who were diagnosed either with Alzheimer’s, a mild cognitive problem or no kind of disorder.

After a point of time, the algorithm started to learn on its own about the features which were considered necessary for prediction of the diagnosis of Alzheimer’s and which were not.

Once the algorithm became trained on a vast number of scans, the scientists tested in on two kinds of datasets for an evaluation of its performance. It passed the assessment very successfully, with an estimated 92% of patients who had developed Alzheimer’s identified correctly.

At such impressive statistics, this algorithm has a lot of potential to be clinically relevant. If it can perform well in such kind of tests, the algorithm can then be of use when a neurosurgeon looks at a patient in a clinic as a diagnostic and predictive tool for Alzheimer’s, proving very integral to get the patients a treatment which they require much sooner.

**We at GoodWorkLabs help Healthcare Companies develop essential Apps, Web, and Software Solutions using AI!

Want help in building a technology for your healthcare business needs? Work with GoodWorkLabs who understand and have expertise in this space. Contact us to get a free quote for your project.

Machines will take over our lives & How…

Electricity was one of the most incredible inventions of human after the fire, which changed human life in many ways. Now, after the electricity, Artificial Intelligence (AI) has drastically changed human lives. It is said that AI is yet to offer benefits to human. It was a prototype in the 1950s but has come up in many positive usages in recent times. What matters the most is the fact that we are getting encompassed with technology day-by-day and this is just the beginning of human’s much involvement in technology.

Everything has advantages and disadvantages, the case is no different for AI. The more we start making use of it, the more we will realize the negative impact of the AI. Just like when electricity is misused, we can even end up dying; AI is one of the most advanced things with its own set of disadvantages.

Setting aside the negativity, there is enough positive approach of the AI in the recent business set up. AI is said to decrease the workload and get the job done in one day which otherwise would have taken more than ten. Human intelligence created AI and it is expected to have a huge database that sets its own IQ. The evolution of the entire technology ecosystem has led to the free rise of the advanced and improved AI system that is bound to bring new changes in the present and future way of dealing with the business. Based on the ongoing trend, the future of AI-driven businesses will be something like this:

Digital business transformation

The megalith system that hid a lot of data is now being unleashed by the AI. It is bound to provide the best quality deals at an effective rate. The legacy of modernization has to lead to a lot of changes in the digitization process as well. AI is the next best option for us and so AI specialists are constantly finding a positive way out of it. There are chances that you will have to find a way to evolve your business with time on the lines of AI. The single work process is now shutting down. One business will have multiple users and multiple procedures based on the comfort of the people. With the onset of AI, the old process is going to break down or replaced by the new digital era.  

Tech democracy

The freedom to create a database of any size in today’s world has a bulk. The estimated creation of data can be taken into account of 2.5 quintillion bytes. Thanks to this estimate that the AI found its way. The human’s constant contact with the machines has given way to the creation of artificial intelligence that can make the lives and the work of the humans a lot easier. AI is believed to lessen the task and offer freedom to create a database and make use of everything.

Harnessing the data

With the launch of AI, the data has been easily preserved. The data that we are harnessing has its own limitations and that has always been a problem which obstructs the smooth work process. Therefore, you will have to make sure that you find a good option to make sure that at no point the work creates a problem. The AI is the next best thing that technology has gifted the world and this can be the option to harness the data in a better way.  

The future of the business

Given the capability of AI, it has the potential to impact our future. It is about to change the history of innovation and can make sure that you get the best solution when it comes to getting the work quickly and efficiently. The AI market is rapidly growing, but when it comes to helping any layman, it is still to take up that place. The change is the most constant factor for any future and given the fact that AI is about to bring the most impressive and noteworthy change in the history of technology.

AI to Science

In 2013, MD Anderson Cancer Center set forth a “moon shot” project that estimates a cost of $62 million. It was believed to diagnose cancer and offer a suggestion of treatment. However, the project has stopped because of the exceeding budget, but it is still being worked on. We can say that it is not only the business that AI is about to touch but it is sure to awe human lives in every way possible. We will have to wait for the AI to come into the real being and how it will affect our lives. There are many trial and error methods that are in the picture, but we are yet to find the real usefulness of the AI.

The invention of computers, telephones, and mobiles has globalized the world, but to keep up with the pace of technological updates, AI is the gift that the masters have given. It is about to provide new wide scopes to the future of the business and it is always better to stay updated with the new things to know about the changes that we are going to see in the recent future.

There are always a set of doubts, bias, and worries that come with the AI, it is going to support the basic information. But making it adaptable to the environment matters the most. It is true that it is “Artificial” after all. There will be drawbacks and biases towards the creator. Therefore, AI might take a lot of time to come into the picture but the bell is already ringing.

AI is one of the best sources to create a stronger bond between human and machine. We will have to simply wait and what the result is and how the machine will take over our lives. It is important to make sure that machines do not take over us and that we have control over it. The AI for the future business is a prototype and can promise to offer the best quality result when it comes into practical usage.

To know more about how AI can help your business, reach out to us:

Dating apps are increasingly taking the help of AI!

It is apparent that you will have used a dating app at least once, even if you never dared to admit it openly in your social circle. The premise of most dating apps is the same; take a look at the picture visible with a little information and then decide to take a swipe left or right. These swipes determine your rejection or interest to the profile of a particular person respectively.

During development stages, these dating apps were a little cluttered and confusing to move through. Today, however, you can just bid a farewell to hours of mindless swiping through numerous profiles. Thanks to Artificial Intelligence.

Dating apps are increasingly taking the help of AI to help users suggest places to go for a first date, indicating the initial remarks that can be said to the person at the other end. To make the matter all the more intriguing, these apps even assist you in finding a partner who resembles your favorite celebrity.

Until very recently, smartphone dating apps like Tinder left the task of asking someone out and making a date go well to people who were using the app. Gradually, this led to fatigue in the users who had to keep searching through a lot of profiles without too much success.

This is why the online dating sector turned over to take the help of Artificial Intelligence and get people to arrange dates in their real lives, acting more like a dating coach of sorts.

These newly found utilities of Artificial Intelligence, where the computers are programmed to develop human processes like thinking or decision-making have been highlighted time and again, signifying its importance.

Uses of Artificial Intelligence for Dating Apps

If anything, dating websites and applications have established themselves as the new benchmarks when it boils down to getting the first date for yourself. This is why as we mentioned above, many websites and app owners are trying to use something different on the lines of AI to ensure and provide the users with a fantastic overall experience.

Here we look at how AI is improving the dating lives of users along with the user experience of a dating app or website as a whole-

1. Help find better matches

Being the most obvious use, of course, AI for dating apps helps to improve the matching of people with their potential dates. There are two pretty remarkable methods through which this is happening. The dating app Hinge has recently been observed testing a feature which they call Most Compatible that takes the help of machine learning in finding better matches.

The feature monitors how people behave on the app. This behavior involves the kind of content a user has previously liked. The function aspires to serve as a matchmaker to find you, people, with whom you matched with on the platform prior.

The dating sites presently are as good as the data they have. Keeping that in mind, the dating sites are increasingly making use of technology and suitable data to filter out the matches for their users. There are many cues like emotion in communication, revert times and the size of profiles too.

2. Keeps things in moderation

Keeping things moderate on dating apps is very important for two essential reasons. It is evident that you wish that people have an overall positive user experience. If people have to continuously swipe with the fear of accidentally getting a fake account, they will ultimately switch over to some other app.

Moderation has also become essential to protect the app company itself. Many authorities are taking down any web platform which is not severe for sex trafficking and related crimes.

This has left with moderation not being an option anymore for brands, effectively going them with two options- manual moderation or automation enabled by computer vision (CV) moderation. Only one method out of the two helps a dating app scale and moderate more content at lower costs, and that method is computer vision.

3. Prevents security concerns

For any user of dating apps, security is one of the prime concerns. One negative experience is more than enough to turn people away from a specific app permanently. It is essential that dating apps take this very seriously and invest in measures to make their platforms secure to the maximum possible extent.

Getting every individual with enough help for a date is going to be impossible, and this is why companies will have to depend on AI to take care of this issue. An app called Hily gives the users a “risk score” that provides a user with passing ID verification, past complaints, the extent of conversation with other users and time spent on the app.

An individual with a high-risk score can be blocked on the app by the other users from sending their private information to the particular profile. The app can also detect when a photo has been tampered and then blocks such users too.

4. Provides great & useful user content

The final use of dating apps for the dating scene in 2019. Many factors make a dating app interactive and user-friendly where they can move to have a good time. Selfie images and information related to the profile of an individual are part of the content which is available on such apps.

AI can be used to provide better advice to users as to what they could do to improve their dating profile and visibility. For instance, online dating coach Greg Schwartz used face recognition model Clarifai to create an app which could recognize the standard errors that people tend to make in the photos they use on certain dating apps like using the images of fancy cars and bikes to get an impressive looking profile.

While not everyone has the same opinion that Artificial Intelligence is going to help them out in finding the love of their lives, the trend is currently on the rise, and it will be fascinating to see how things further unfold within this year.  

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Machine Learning & Artificial Intelligence in Farming

Many of them do not realize it, but data has been an integral part of the lives of farmers since generations. From the general market information to climatic patterns, data plays an important role to take note of the planting cycle, watering as well as treatment plans.

Farmers have adopted the latest technologies for their farming practices, which has only increased the efficiency of getting their work completed. What is not to be missed here is the fact that internet and broadband have created a significant division in the digital domain.

A large number of farmers are yet to get “connected” and leverage the benefits of big data revolution which is a crucial factor driving businesses across the globe. With increasing internet connectivity and data intelligence derived from AI algorithms, devices related to the Internet of Things (IoT) can figure out and react to the environment around it.

With a rapidly increasing population, it is evident that crop yields also will have to be boosted to a large extent for meeting the growing demand. This demand needs to be achieved amidst the challenges of declining water levels, shrinking lands, and damage to the environment. 

Today with the assistance of Internet-connected sensors and the progress in Computer Vision and AI,  it has become easier than ever to figure out how a particular area of land is behaving. Land behavior is an essential element to further understand the methods to optimize the yield and also minimize the use of resources like water and fertilizers.

It will be helpful to eliminate any guesses from the overall scheme of farming operations.

Solving Connectivity Issues 

TV broadcasting is something which is still not available to a vast number of rural regions. A considerable amount of stations still display the familiar white, black and grey static in the name of transmission. Known as the TV white spaces, these can be used for data transmission through wireless networking. It can work as a feasible alternative to Wi-Fi in such areas.

The white space devices can help to find out those channels which are not used for a particular geographical location. This information helps to transmit signals resembling Wi-Fi on such channels so that there is no interference on the other channels’ transmissions. Despite a low number of channels in rural areas, a lot of data can easily be carried through without trouble.

Microsoft was the first in developing a TV white space radio for enabling connectivity that is as smooth as a Wi-Fi. The technology has also proved its mettle in connecting high schools, hospitals, farms in the US and even the newly emerging economies of India and Africa.

Precise Agriculture with Data – An Aerial Approach 

There is a solution which can benefit the small farmers for analyzing and monitoring soil activity and required microclimates as well. It will help them to avoid investing money into expensive pieces of equipment.

The entire project uses an aerial approach from the ground, taking in essential data from cost-friendly sensors, satellites, and drones and then puts the algorithms of vision and machine learning to design a digital heat map. The heat map provides the farmers with an excellent solution regarding the steps they need to take on soil moisture levels, microclimates and temperature.

Ground sensors have enjoyed their existence since almost a decade within the agricultural community. These sensors are powerful for sure, but they also come with huge price tags. This is where the need to use fewer sensors but soak in more information about a farm’s behavior rose. Drones and cloud technology that use capabilities of Artificial Intelligence like deep learning, as well as other machine learning techniques, offer an efficient solution to it.

Edge computing is the term which facilitates data processing. It happens in close proximity to a device with the motive of eliminating lethargy and boosting the ability to switch over to action from insights quickly. In such scenarios, the camera or drone is essentially an intelligent edge device.

The importance of acting quickly on the resulting images for a farmer cannot be emphasized enough in mere words. There are specific IoT systems that help in efficient data collection in agriculture. We can then use AI and Machine Learning techniques for converting this data into insights which leads to a precise farming process.

The next generation of Digital Farming 

The first aim is to focus on empowering farmers with cost-friendly and affordable digital agriculture techniques for eliminating confusion and guesswork from their daily lives. The next focus should be on increasing the yield for feeding the world. To make this happen, there is a need for scaling the opportunity of connectivity from channel regulators adopting TV white spaces globally.

To make a significant impact on digital farming, a lot more needs to be done. With local governments subsidizing agricultural equipment, the latest and affordable technologies for precision agriculture should also be brought into relevance and supported so that they can be used widely.

There is also an expanding gap between resources and education in emerging markets. A lot of farmers don’t have access to phones, education, and training for interpreting the available data. Advisories need to be created which can help these farmers to not only understand the information but also recommend the measures that need to be taken for better yields.

It is safe to say that the future of farming relies on solving the data problem with connectivity and resources for collecting and interpreting the data. Collective steps need to be taken for tackling the urgency in which there is a need to connect the rural areas and work with governments and technology companies for pulling costs of data collection equipment and software.

There is also the need to provide extensive and advanced education which revolves around utilizing these farming measures globally.

Machine Learning Technology in Facial Recognition

You will find it hard to believe, but it is entirely possible to train a machine learning system so that it can decipher different emotions and expressions from human faces with high accuracy in a lot of cases. However, implementing such training has all the chances to be complicated and confusing. This arises because machine learning technology is still at an early age. The absence of data sets which have the required quality are also tough to find, not to mention the number of precautions which are taken when such new systems are to be designed are also hard to keep up with.

In this blog, we discuss Facial Expression Recognition (FER), which we will discuss further on. You will also come to know about the first datasets, algorithms, and architectures of FER.

Images classified as Emotions

Facial Expression Recognition is referred to as a constraint of image classification which is found in the deeper realms of Computer Vision. The problems of image classification are the ones where pictures are assigned with a label through algorithms. When it comes to FER systems specifically, the photos tend to involve human faces, the categories being a specific set of emotions.

All the approaches from machine learning to FER need examples of training images, which are labeled by a category of a single emotion.

There is a standard set of emotions that are classified into seven parts as below:

1. Anger
2. Fear
3. Disgust
4. Happiness
5. Sadness
6. Surprise
7. Neutral

For machines, executing an accurate classification of an image can be a tough task. For us as human beings, it is straightforward to look at a picture and decide right away what it is. When a computer system has to look at an image it observes the pixel value matrix. For classifying an image, the system needs to organize these numerical patterns inside the image matrix.

The numerical patterns we mentioned above are variable most of the time, making it more difficult for evaluation. This happens because emotions are often distinguished only by the slight changes in facial patterns and nothing more. Simply put, the varieties are immense and therefore pose a tough job in their classification.

Such reasons make FER a stricter task than other image classification procedures. What should not be overlooked is that systems that are well-designed achieve the right results if substantial precautions are taken during development. For instance, you can get a higher accuracy if you classify a small subset of emotions that are easily decipherable like anger, fear, and happiness. The accuracy gets lower when the classification is done with large or small subsets where these expressions are complicated to figure out, like disgust or anger.

Common components of expression analysis

FER systems are no different than other modes of image classification. They also are using image preprocessing and feature extraction which then leads on to training on shortlisted architectures. Training yields a model which has enough capabilities to assign categories of emotion to new image examples.

Image pre-processing involves transformations like the scaling, filtering, and cropping of images. It is also used to mark information related to the photos like cropping a picture to remove the background. Generating multiple variants from a single original image is a function that gets enabled through model pre-processing.

Feature extraction hunts for the parts of an image that is more descriptive. It means typically getting information which can be used for indicating a specific class, say the textures, colors or edges as well.

The stage of training is executed as per the training architecture which is already defined. It determines a combination of those layers that merge within a neural network. Training architectures should be designed keeping the above stages of image preprocessing and feature extraction in mind. It is crucial as some components of architecture prove to be better in their work when used together or separately.

Training Algorithms and their comparison

There are quite a number of options which are there for the training of FER models, with their own advantages and drawbacks, which you will find to be more or less suited for your own game of reasons.

• Multiclass Support Vector Machines (SVM)

These are the supervised learning algorithms which are used for analysis and classification of data and are pretty able performers for their ranking of facial expressions. The only glitch is that these algorithms work when the images are composed in a lab with natural poses and lighting. SVM’s are not as good for classifying the images which are taken in the spur of a moment and open settings.

• Convolutional Neural Networks (CNN)

CNN algorithms use the application of kernels to large chunks of the image that is the input for a system. With this, a new kind of activation matrix called the feature maps is passed as the input for the next network layer. CNN helps to process the smaller elements of the image, facilitating ease to pick out the differences among two similar emotions.

• Recurrent Neural Networks (RNN)

The Recurrent Neural Networks apply a dynamic temporal behavior while classifying a picture. It means that when the RNN does the processing of an instance of input, it not only looks at the data from the particular instance but also evaluates the data which was generated from the previous contributions too. It revolves around the idea to capture changes between the facial patterns over a period, which results in such changes becoming added data points for further classification.

Conclusion

Whenever you decide to implement a new system, it is of utmost importance that you do an analysis of the characteristics that will exist in your particular situation of use. The perfect way of achieving a higher efficiency will be by training the model to work on a small data set which is in tandem with the conditions that are expected, as close as possible.

AI predictions to look out for in 2019

It is not a lie when we say that Artificial Intelligence or AI, is the leading force of innovation across all corporations on the globe. The market for Artificial Intelligence globally is on the rise. From a mere $4,065 billion in 2016, it is expected to touch a whopping $169,411.8 million by 2025.

According to the online statistics and business intelligence portal Statista, a significant chunk of revenue will be generated by AI targeted to the enterprise application market. With the advent of 2019 however, Artificial Intelligence is only expected to cross another threshold in its popularity. Let us look at the top predictions in AI for the year of 2019:

• Google and Amazon will be looked upon for countering bias & embedded discrimination in AI 

In fields that are so diverse as to include speech recognition, it is Machine Learning which is the formidable force of AI that enables the speech of Alexa, the auto-tagging feature of Facebook as well as the detection of a passing individual on Google’s self-driving car. When it comes to Machine Learning, existing databases of the decisions taken by humans help it to take appropriate decisions.

But sometimes even the data is not able to depict a clear picture of a group that is broad. This poses a problem because if the datasets are not appropriately and sufficiently labeled, capturing the broader nuances of the datasets is a difficult job.

2019 will surely witness companies who have products devoted to unlocking datasets that are more inclusive in structure, thus reducing the bias in AI.

• Finance and Healthcare will adopt AI and make it mainstream

There was a time when the decisions taken by AI relied on algorithms which could justify without too much fuss. Irrespective of the output whether right or wrong; the fact that it could explain decisions holds a lot of importance.

In services like healthcare, decisions from machines are a matter of life and death. This makes it critical to evaluate the reasons behind why a device rolled out a particular decision. The same applies to the field of finance as well. You should be aware of the reasons why a machine declined to offer a loan to a particular individual.

This year, we will see AI being adapted to facilitate the automation of these machine-made predictions and also provide an insight into the black box of such predictions.

• A war of algorithms between AI’s

Fake news and fake images are just a couple of handy examples of the ways things are moving ahead in terms of misleading the machine learning algorithms. This will pose challenges to security in cases where machine algorithms either make or break a deal, such as a self-driving car. So far, the only concern revolves around fake news, misleading images, videos, and audios.

More significant, consolidated and planned attacks shall be demonstrated in a very convincing way. This will only make it difficult to evaluate the authenticity of data and its extraction to be more precise.

• Learning and simulation environments to train data

It is true when we say that most projects revolving around AI require data of the highest quality with a set of great labels too. But most of these projects fail even without initiation as data that explains the issues at hand isn’t there, or the data which is present is very tough to label, thus making it unfit for an AI consideration.

However, deep learning helps to address this challenge. There are two ways to utilize the deep learning techniques even where the amount of data is pretty less than what is required.

The first approach is to transfer learning- this is a method where the models learn through a domain that is suitable with a large amount of data and then bootstrap the teaching at a different field where the data is very less. The best thing about transfer learning is that the domains are perfect even for different kinds of data types.

The second option is a simulation and the generation of synthetic data. The adversarial networks help out in creating data that is very realistic. We again consider the instance of a self-driving car. The companies producing these cars make practical situations which are focused on a lot more distance than the car will travel in reality.

This is why it is predicted that a lot of companies will make the use of simulations and virtual reality to take big leaps with machine learning which was previously impossible due to many data restrictions.

• Demand for privacy will lead to more spontaneous AI

With customers becoming more cautious at the prospect of handing their data to companies on the internet, businesses need to turn to AI and machine learning for access to such data. While this is a move that is still enjoying early days, Apple is already running some machine learning models on their mobile devices and not on their cloud systems, which is a depiction of how things are about to change.

It is assured that 2019 will see an acceleration in this trend. A more significant chunk of the electronic group encompassing smartphones, smart homes as well as the IoT environment will take the operations of machine learning to a place where it needs to be adaptive and spontaneous.

At GoodWorkLabs we are constantly working on the latest AI technologies and are developing machine learning models for businesses to improve performance. Our AI portfolio will give you a brief overview of the artificial intelligence solutions developed by us.

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