Are you looking to incorporate AI tech in your existing business model or are you generally curious about this technology? Get an insight into how Artificial Intelligence technology increases the productivity of the business and accelerates performance.

In either case, there are some mind-boggling essential facts that you must know about AI. 

Starting with the basics, we are quickly briefing you about this technology.

In the current industry scenario, some industry sectors are at the start of their AI journey, while others are veterans. 

Artificial Intelligence and Machine Learning are now considered one of the significant innovations since the microchip. 

We have come a long way since they set foot in the market. Machine Learning used to be a fanciful concept from science fiction, but has now become a reality.

Neural networks paved the way for “deep learning” breakthroughs in Machine Learning. While the previous Industrial Revolution has harnessed physical and mechanical power, this new revolution will harness mental and cognitive capacity. Many experts in the field believe that Artificial Intelligence Technology is ushering the next “Industrial Revolution”. 

Someday, not only manual labor will be replaced by computers, but also intellectual labor. But, the question is how exactly is this going to happen? Or has it already started?

By 2025, it is projected that 463 exabytes (EB) of data will be produced globally each day — equivalent data in 22 crore DVDs per day. That’s huge!

How Artificial Intelligence and Machine Learning will impact our day-to-day lives in times to come?

1) AI into Automated Transportation

Have you been flying on an airplane recently? If so, you’ve already experienced the automation of transportation at work. Such modern commercial aircraft use FMS (Flight Management System) to control their location during flight, motion sensors, a combination of GPS, and computer systems.

2) Self Driving Cars and AI

It is more difficult to leap into self-driving car business. Since there are more cars on the road, many obstacles to avoid, and the traffic patterns and rules restrictions which we need to adhere to. 

According to a report of 55 Google vehicles that have traveled over 1.3 million miles overall, these AI-powered cars have even exceeded the safety of human-driven cars.

With Google Maps’ assistance on your smartphone about location data, we have already conquered the GPS forefront. A similar GPS is used in these cars, which can calculate how quickly the device is traveling by comparing the position of a device from one point in time to another.

It can decide how slow real-time traffic is. It can combine information with user-reported incidents to create a traffic image at any given time. Maps will determine the fastest route between you and your destination based on traffic jams, construction works, or accidents.

What about the ability to drive a car? Well, machine learning enables self-driving vehicles to adapt instantly to changing road conditions while learning from new road situations at the same time. Onboard computers can make split-second decisions much faster than well-trained drivers by continuously filtering through a flow of visual and sensor information.

All this is based on the very same machine learning principles used in other industries. You have input characteristics (i.e., real-time visual and sensor data) and output (i.e., a decision on the next actions for a car). Amazing, right?

3) Cyborg Technology

Our minds and bodies are less than perfect. Technology will improve to the extent that we can increase some of our computer weaknesses and limitations, enhancing many of our fundamental skills.

But, wait before you start to imagine dystopian worlds of steel and blood, consider for a moment that most people walking around are in a certain way “cyborgs.”

How many people do you know that without your trusty smartphone would survive the day? For contact, navigation, information learning, receiving important news, and a host of other things, we still rely on these handheld computers.

4) Taking Over the Dangerous Jobs

Bomb disposal is one of the most dangerous jobs. Today, among other things, robots (or more technically drones) take over these risky jobs.

Currently, most of these drones need to be operated by a human.

But as machine learning software is evolving in the future, robots with artificial intelligence would do these tasks entirely. This technology has already saved thousands of lives on its own.

Welding is another work outsourced to robots. This type of work produces noise, intense heat, and fumes toxic substances.

Such robot welders would need to be pre-programmed to weld at a specific position without machine learning. Improvement in computer vision and deep learning, however, has allowed greater flexibility and accuracy.

5) How AI helps in nursing elders?

Everyday tasks can be a struggle for many senior citizens. Many have to hire help from outside or rely on members of the family. 

For many families, elder care is a growing concern. In-home robots can support elderly relatives who don’t want to leave their homes.

This approach provides more flexibility to family members to handle the care of a loved one. The in-home robots can help seniors with daily tasks and allow them to stay as long as possible independent and live in their homes, improving their overall well-being.

Health and Artificial Intelligence scientists even have infrared-based systems that can identify when an older adult falls. Scientists and medical specialists can also track sleeping, feeding, decreasing mobility, fluid intake, chair and bed comfort, urinary frequency, restlessness, fatigue, food and alcohol consumption, and many more.

6) AI into enhanced Health Care

Hospitals might soon put your well-being in the hands of AI.  Hospitals that use machine learning to help treat patients have fewer accidents and fewer cases of hospital-related illnesses, such as sepsis.

Artificial Intelligence also tackles some of the most intractable problems in medicine, such as helping scientists to understand the genetic diseases with the help of predictive models better.

Initially, health professionals must manually check the information reams before they diagnose or treat a patient. High-performance computing GPUs have become primary resources for deep learning and AI applications.

Deep learning models can offer real-time insights and, in conjunction with an abundance of computing power, help healthcare professionals diagnose patients more quickly and accurately, create innovative new drugs and treatments, minimize clinical and diagnostic errors, predict adverse reactions, and reduce healthcare costs for clinicians and patients.

7) Artificial intelligence is capable of changing the business forever

It is a promise to take care of all the tedious things that employees are already doing, freeing their time to be more imaginative, and doing the job that machines are unable to do.

Today, emerging technology is mainly used by large companies through machine learning and predictive analytics.

Here’s a look at AI’s current county and what lies ahead:-

1. Nowadays, only 15% of companies use AI whereas 31 percent said it was on the agenda for the next 12 months.
2. For those companies already in the Artificial Intelligence range, high-performing companies have said that they are more than twice as likely to use technology for marketing as their peers (28% vs. 12%). Unsurprisingly, data analysis is a key Artificial Intelligence focus for businesses, with on-site customization being the second most frequently cited use case for AI. 
3. The survey respondents have described customer personalization (29%), AI (26%), and voice search (21.23%) as the next dominant marketing pattern. These top three responses, totaling 75% of all AI applications, indicate that AI is more widespread and accessible than the respondents are aware of. 
4. 47% of digitally mature organizations or those with advanced digital practices have established a specified AI strategy. 
5. Business leaders said they agree that AI will be fundamental in the future. In reality, 72% said it was a “business advantage.” 
6. Of those who have an innovation plan, 61% said that they are using AI to find information gaps that would otherwise be overlooked. Just 22% said the same thing without a strategy. 
7. Consumers use more AI than they know. While only 33 percent claim that they are using AI-enabled software, 77 percent currently using AI-enabled products or phones. 
8. 38% of customers said they believed that AI would boost customer service. 
9. Out of 6,000 people surveyed, 61% said they thought AI could make the world a better place. 
10. In a survey of more than 1,600 marketing professionals, 61%, regardless of the size of the company, pointed to machine learning and AI as their company’s most significant data initiative for next year. 
11. The effect of AI technology on business is projected to increase labor productivity by up to 40% and allow people to make more productive use of their time. 
12. The largest companies, those with at least 100,000 employees are most likely to have an AI plan, but only half of them have one. 
13. More than 80% of the executives see AI as a strategic tool. 
14. Voice assistants are incorporated into a wide range of consumer products; almost half of US adults (46%) are now using these apps to communicate with smartphones and other devices. 
15. When asked about requirements for marketing software providers to have native AI capabilities, more than 50% of the communicators said it was essential or appropriate to do so. 

Winding Up

As many people have rightly noted, the idea of Artificial Intelligence is not a new one. It’s been around since the very early days of computing. Pioneers always have invented ways to build smart learning machines.

At present, the most promising method for AI is the use of applied machine learning. Instead of trying to encrypt machines with everything they need to know beforehand (which is impossible), we want to allow them to learn, and then learn how to learn. 

The time for machine learning has arrived, and it is in the process of revolutionizing all of our lives.

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When we talk about the food industry, we also know that it is the biggest and most important sector in the industrial world. The food and Beverage industry is increasing in scale at a high pace in terms of technology. With the addition of Big Data, however, the industry has reached a whole new level.

This new technology has permitted the food industry to improve at a breakneck capacity. Technology, with the added benefit of Big Data, has developed the procurement of insights from data. Not only from data, but also from the marketing campaigns and more interactive development to create an innovative product.

It is not wrong to say that Big Data has helped the food and beverage industry scale new heights.

Food and Beverage Industry, and Big Data

The food industry, under Big Data, is witnessing growth at a high pace.

In fact, as per a report by McKinsey, food retailers witnessed an improvement in their profits by almost 60% with the use of Big Data.

The F n B industry is getting more organized with real-time insights and taking note of many important points.

All this is made possible through Big Data, allowing companies to get plausible leverage for their services.

Even with Big Data though, there is one critical challenge-

The F n B industry today has a shallow degree of customer loyalty, making it more competitive and fragmented. The industry did not depend on the available data. Instead, they relied on a traditional reporting format.

However, the preferences of a customer are bound to change pretty regularly, making it very difficult to keep pace with them. This has led to a revolution of sorts in the food and beverage industry.

Big Data helps to analyze all the structured and unstructured data. This data comes either through modern sources or even traditional methods. Once collected, this data provides many insights for shopping trends, market development identification, and customer behavior.

Big Data analysis provides a competitive edge to the entire food and beverage industry. Many big names are taking advantage of Big Data to stay ahead of their competitors.

Benefits

It is evident with the impact of Big Data on the food and beverage industry that there are several benefits on offer. With such a dynamic sector under focus, Big Data proves its mettle through the following benefits-

• ANALYSIS OF CUSTOMER BEHAVIOUR

Customer demands today change with every passing second. This makes it difficult for the food and beverage industry to meet their expectations consistently. Big Data, however, can provide the data analysis required and provide insights on changing the behavior of customers.

Through the collected ideas, efforts to improve market efficiency get easily implemented.

With the development of technology online and on smartphones, customers now have a wide array of options to address their needs. The advancement has led to the food industry to collect the maximum data for their choices.

From the particular food items and change in their preferences to order value, there is data for everything today.

It is simpler than ever to grab on the customer information to help get potential value to businesses. With significant growth in this industry over the last decade, the total cost from mobile and online technologies have proven to be immensely useful.

The utility has not only been in monetary terms but also spans through the ease of collecting information to improve the marketing campaigns for companies drastically.

• BETTER INSIGHTS

When it comes to the most technologically innovative area in the food and beverage industry, it has to be data analytics. As the industries become more and more focused on the customers, there has been a constant flow of ideas to improve data quality.

This data is used widely to modify product offerings and customer demand, as well. In such a scenario, data analytics has proven to be the core promoter of the food and beverage sector. Presently though, the efficiency and effectiveness of data are not suited well enough to achieve desired results.

The lack of this point has made it all the more important to innovate and open up new doors related to the subject area. Innovations will permit companies to get better insights for the benefit of brands and get help to manage their products.

• INCREASED EFFICIENCY

The bonds of restraint will help you as a business to explore many new options through the help of Big Data. It is the perfect way to boost your sales and business efficiency.

The data-driven nature lends you the flexibility to go with a new trend, thanks to better analysis of data sales.

A better understanding of restaurants with their customers is made possible with better analytics and will improve the brand value of your company.

The improved practices in the food and beverage industry can have a lot of influence on the Big Data sector. An individual restaurant can understand its competition in a better place. Initially, it is going to take some time. But then you will start getting proper data while also tracking your competition’s growth.

You will have every opportunity to get a competitive edge with this improved method of marketing.

• ENHANCED SALES AND MARKETING TACTICS

It is effortless to track purchase decisions through Big Data for wholesaling. If a product gets picked at an increased rate, you will get a lot of help to increase the sale of your business.

For instance, if the sale of a particular type of food on a discount in a region gets monitored, the data collected can be analyzed in terms of profit and an increase in the purchase of this specific product.

You will get a set of data to help you in setting the quality of the food and beverage offerings. With the help of this data, the sale and marketing plans can be executed efficiently by the companies for their products.

• QUALITY CONTROL

Big Data plays a crucial role in the overall quality of food and beverage. Companies in the sector can effortlessly control the quality of food supply through aggregate data. Customers expect to have the same taste and quality every time they go for a particular product.

Any difference negatively impacts their preference and brands end up losing their customers. In such situations, data collection is your best option. The data collection will regularly update you on the quality of food.

Big Data has eased restaurants and companies to develop more advanced forms of marketing to engage global customers. Adding to that, companies can use the various social media platforms already in use by a huge number of people.

Their reviews and testimonies have the potential to take your business to a whole new level.

Need Big Data solutions for your Food & Beverage business? Get in touch for tailored Big Data solutions for your business at affordable prices.

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.

The universe belongs to Marvel.

With the movie spectacle of the decade running in theatres all over the world, it is not wrong to say that Avengers Endgame, the last movie in a decade long journey of a shared cinematic universe has surpassed all expectations.

From some characters like Captain America, Iron Man and Black Widow making their final appearances in the movie, the scintillating reviews from both audiences and movie critics alike have increased the potential of making it Hollywood’s highest grossing film ever, an accolade which presently lies with James Cameron’s Avatar.

While there is no denying that Marvel Studios has been supremely successful in the execution of a cinematic universe, the absence of villains that could be a real threat to the Avengers was a point where the makers could not cut through successfully. Until Thanos.

Other than Loki, played by Tom Hiddleston and Killmonger, portrayed by Michael Jordon, no single antagonist could hit the hearts of fans with as much impact expected. Not a proper recognition through the span of 22 movies.

Thanos, the purple-faced alien nemesis, made up for all of them with a brilliant screen presence, thanks to the fantastic Josh Brolin who blew life into the character both in Avengers Infinity War and now in Endgame too.

But was it just Brolin that made Thanos the perfect nemesis to the Avengers? No. Marvel Studios have Machine Learning to thank.

Apart from a revolution in CGI, the fact that Thanos was able to display perfect emotions on screen was what made him a force to reckon. Through Thanos, the stakes were high not only in the storyline of both the movies, but they were also huge for the makers, as they had the gap of an excellent villain to fill.

It was important to put emotions on a CGI character’s face to make him resonate more with the audience. This involved portraying the recognizable expressions of Josh Brolin on the Mad Titan’s face.

To achieve this, Digital Domain, one of the digital effect firms for the movie, used a sophisticated machine learning software named Masquerade to make the performance of motion capture more realistic and natural.

The entire process started by correctly putting a hundred to hundred and fifty track dots on Josh Brolin’s face, to be captured by a couple of vertical orientation enabled high-definition cameras.

The scan wasn’t required to provide high-quality results, but a pretty generic render of low quality. This initial rendering then was fed as input to the machine learning algorithm that used from many high- resolution facial scans by a vast variety of expressions.

The Masquerade software opts for those low resolution renders and automatically figures out the high-resolution face shape to be the perfect solution for the screen. If the answer did not seem accurate enough, the team would then tweak things a bit to arrive at a better solution.

These tweaks involved instances like raising the brows higher or a little bit of lip compression, which went back into the system and were then learned by the machine learning algorithm.

Subsequently, further results through the low mesh came out better, but all of this was just a single step. The next step in the process is known as direct drive, which plucked the high- res face mask function to place it on the villain’s character model.

If there were no machine learning system like Masquerade in place, the Visual Effects team would require to change the expressions manually through animation, where the results were surely not to be as impressive like the ones coming with the help of Masquerade. It would have been a time- consuming process too.

However, there are also other advanced techniques like FACETS, used for facial tracking in Avatar and even the Planet of the Apes trilogy.

It is quite clear that if you are not using machine learning in your software to enable better CGI and VFX, you are never going to get the final outputs as you expect them to be. In the times ahead, technology will be used more for things more than faces.

To cut a long story short, expect machine learning to have an integral role just about anywhere when it comes to special effects and design.

To get the best machine learning systems/solutions for your own business or company, let us help you with the best in class recommendations & solutions.

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.

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.  

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

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.

High-performing Travel Recommendation Engine built with AI/ML models

One of our Fortune 500 clients had a community-based travel app that helped create trips for its users. Through this app, users could explore the community, take trips to nearby places, and also browse through their previous trips in the travel history.

Objective:

Our data scientists at GoodWorkLabs were entrusted with the task to make the above mentioned mobile app engaging, intelligent, and personalized. We had to create recommendation systems as an advanced feature by using Machine Learning models.

We realized that recommendations could be made to users based on nearby attractions, restaurants, hotels, etc. The nature of these recommendations had to be as below:

• Users will be recommended with places they would like to visit based on their previous travel history.
• Users will be recommended with nearby tourist attractions when they visit a particular place.
• Users will be recommended places based on their preferences and tastes.
• Users will also receive recommendations from similar travelers who share the same interests.

Recommendations by using Machine Learning models

To build an effective recommendation system, we trained the algorithm to analyze key data points as below:

• On-boarding information: To capture user data at the sign-up stage of the web application
• User profile: To suggest recommendations by analyzing data from the user’s previous visits on the profile 
• Popularity: To suggest recommendations based on user ratings that were collected in the form of reviews
• Like minds: To analyze data and match it against the likes of different users and populate recommendations accordingly.

App screens that populated ML recommendations

We programmed specific screens on the mobile app to display the recommendations. Below were the mobile screens on which ML recommendations were displayed:

1. Attractions
2. Trips
3. Restaurants
4. Nearby Cities
5. Ad-hoc Plans
6. Search (when users search for places to visit)

Types of Recommendation systems:

1. Content-Based recommendation

Based on the details keyed in by the user at the signup stage and in the whole travel process, the content based recommendation system analyzed each item and user profile. All this data was stored and the system was optimized for continuous and smart learning.

2. Collaborative filtering/ recommendation

In this recommendation system, the system looked for similar data inputs keyed in by different users. This was then continuously compared against other data. Whenever there was a match, the system recorded the instance and populated a set of recommendations that were common to that set. In this recommendation system, user interactions played an important role.

At GoodWorkLabs, we suggested a hybrid model of both the above-mentioned approaches for optimal performance of the recommendation system.

Tech Stack:

The tech stack we implemented in building these Machine Learning models were Python, Tensorflow, Sklearn, iOS CoreML, Elasticsearch.

The GoodWorkLabs AI and ML solution:

Are you looking for a partner who can build advanced AI/ML technologies for your business and make every interaction of your business intelligent? You are at the right place.

We love data and we are problem solvers. Our expert team of data scientists dives deep into solving and automating complex business problems. From Automobile to Fintech, Logistics, Retail, and Healthcare, GoodWorkLabs can help you build a custom solution catered for your business.

Leave us a short message with your requirements.

How to use Deep Learning for Global Warming

Over the past years, our planet has experienced drastic climatic changes. Global warming is now inevitable as observed by scientists with the help of Earth-orbiting satellites and other technological advances. Since the late 19th century, the planet’s average surface temperature has risen about 1.62 degrees Fahrenheit (0.9 degrees Celsius), a change that has been driven largely by increased carbon dioxide and other human-made emissions into the atmosphere. Most of the warming has occurred in the past 35-40 years, and it is all a consequence of human activity.

Climate change has not only affected the global temperature, but it is also the reason behind warming oceans, shrinking ice sheets, glacial retreats, decreased snow covers, rise in the sea level, declination of the Arctic sea ice and also the acidification of oceans. These issues together cause a global challenge.

The world’s current population of 7 billion will grow to around 9.8 billion by 2050 and this augmentation will lead to an increase in the demand for food, materials, transport and energy and further increasing the risk of environmental degradation.

The important question to be asked now is can humanity preserve our planet for our future generations?

The answer is YES. A new study published in the journal Proceedings of the National Academy of Sciences has found that Artificial Intelligence (AI) can enhance our ability to control climate change.

Artificial Intelligence is defined as the simulation of human intelligence processes by machines, especially computer systems. These processes include the learning process (the acquisition of information and rules for using the information), the reasoning of information (using rules to reach approximate or definite conclusions) and self-correction. AI, in particular, has immense potential to help unlock solutions for a lot of problems.

Artificial Intelligence is a broad term under which come two applications – Machine Learning and Deep Learning.

Machine Learning provides systems the ability to automatically learn by developing computer programs that can access data and use it to learn from them and then apply what they’ve learned to make informed decisions. 

On the other hand, Deep learning creates an “artificial neural network” by structuring algorithms in layers. This network can learn and make intelligent decisions on its own. Deep learning is a subfield of Machine Learning. The “deep” in “deep learning” refers to multiple layers of connections or neurons, similar to the human brain.

How can deep learning help the challenge?

Artificial Intelligence can prove to be a game changer if used effectively. The advancement of technology achieved by AI has the potential to deliver transformative solutions. Some possible ways in which deep learning can be useful for the Earth are:-

1. Weather forecasting and climate modeling

To improve the understanding of the effects of climate change and also to transform weather forecasting, a new field of “Climate Forecasting” is already emerging with the help of Artificial Intelligence. This way of saving the planet sounds very promising since the weather and climate-science community have years of data, in turn, providing a fine testbed for machine learning and deep learning applications.

These datasets demand substantial high-performance computing power, hence limiting the accessibility and usability for scientific communities. Artificial Intelligence can prove useful in solving these challenges and make data more accessible and usable for decision-making.

Public agencies like NASA are using this to enhance the performance and efficiency of weather and climate models. These models process complicated data (physical equations that include fluid dynamics for the atmosphere and oceans, and heuristics as well). The complexity of the equations requires expensive and energy-intensive computing.

Deep learning networks can approximately match some aspects of these climate simulations, allowing computers to run much faster and incorporate more complexity of the ‘real-world’ system into the calculations. AI techniques can also help correct biases in these weather and climate models.

2. Smart Agriculture

Precision agriculture is a technique used for farm management that uses information technology to ensure that the crops and soil receive exactly what is needed for optimum health and productivity. The goal of Precision Agriculture is to preserve the environment, improve sustainability, and to ensure profitability.

This approach uses real-time data about the condition of the crops, soil, and air along with other relevant information like equipment availability, weather predictions etc.

Precision Agriculture is expected to involve automated data collection as well as decision making at the farm level. It will allow farmers to detect crop diseases and issues early, to provide proper and timely nutrition to the livestock. In turn, this technique promises the increase of resource efficiency, lowering the use of water, fertilizers, and pesticides which currently flow down towards rivers and pollute them.

Machine and deep learning help in creating sensors that are able to measure conditions such as crop moisture, temperature and also soil composition that will automatically give out data that helps in optimizing production and triggering important actions.

Smart Agriculture has the capability to change agriculture by changing farming methods and proving beneficial for the environment.

3. Distributed Energy Grids

The use of the application of deep learning in the energy grid is spreading increasingly. Artificial Intelligence can help in enhancing the predictability of the demand and supply for renewable resources, in improving energy storage as well as load management, in assisting the integration and reliability of renewable energy and in enabling dynamic pricing and trading.

AI-capable “virtual power plants” can easily aggregate, integrate and also optimize the use of solar panels, energy storage installations and other facilities. Artificial intelligence will enable us to decarbonize the power grid, expand the use and the market of renewables, thus increasing energy efficiency. The decentralized nature of distributed energy grids makes it more possible for them to be used globally.

Final thoughts

In conclusion, Artificial Intelligence techniques like deep learning can prove to be very useful for the environment in the future if used effectively. After years of damaging our planet, it is our time now to save it for the coming generations.