The last decade introduced the world to the digitization of many sectors. This helped the global trade to increase worldwide as many developing and emerging countries became important trading partners and potential sales and development markets. Digitization has helped us to be more connected and perform our daily tasks more effectively. It has made the world a global village and people global citizens.

However, the economies built in the previous decade came crashing to the ground in the last few weeks as COVID-19 pandemic hit the world. While the pandemic has had a great impact on the global economy, it taught us how to fight and improve the current condition using technology. It has laid a new pathway by disrupting our lifestyles by enabling contact-less and virtual experiences. Even though the technology cannot prevent the onset of the pandemics, it can, however, help prevent the spread, educate, empower, and warn people around. Today, these technologies are emerging more than ever – mobile, cloud, analytics, robotics, AI/ML, 4G/5G, and high-speed internet.

Let us look at three technologies that are disrupting our lifestyle each day during this pandemic:

Artificial Intelligence and Machine Learning

From tracking the travel history of COVID-19 patents to analysing the symptoms of people exposed to the infection, the applications used by many governments do it all. These applications use chatbots to gather information from people, and the technology used in these applications is that of AI and ML. This enables the government to collect reliable information much easier and faster without any human intervention.

The advent of more such mobile applications and software will help ease the lifestyle of customers.

Extended Reality

Augmented and virtual reality is surely a boon in the world of lockdown and quarantine. This technology can be used in providing more meaningful and real experiences for people. The technology can help you see the world while you are locked in your home. This experience will change the way we travel, work, and relax. For instance: Realty brands can focus on using AR/VR videos to target their audiences by helping them take a walk-through the project while they are seated in the comfort of their home. This is an example of how a sector can use the contact-less experience to their advantage.

Cloud and Internet

The last few weeks have seen a great transition in the way the workforce can function. Companies got to believe that remote working and work from home concepts can be of their advantage. The transition to work from home and remote working has increased the dependency on cloud and internet infrastructure. The usage of this technology is set to remain the same even post lockdown and pandemic as many companies are moving towards welcoming a ‘hybrid’ way of working. This will increase the demand for teleworking applications and software for the team to be connected and interactive.

Talking about the internet, technology has changed the sector of education. It has enabled kids to learn and study from home using applications that require an active internet connection to connect with their peers and teachers. For times to come, e-learning will be an accepted norm for the parents and teachers alike.

The COVID-19 pandemic has demonstrated to the world that importance of digital readiness. The acceptance and usage of digital technology will allow the business and individuals to continue their work and chores as usual during the pandemic. The companies at large will focus on building necessary digital infrastructure by using the latest technology to stay connected with their employees and customers. The pandemic has increased their market competition by many folds, therefore businesses will have to take an approach that is human-centred and inclusive using technology governance.

In the last decade, almost every day, we have witnessed an emerging usage of smartphones and applications that support them. The mobile application industry has reshaped the businesses, organisations, individuals, and customers. Irrespective of the domain or sector you are in, it is necessary to integrate the latest mobile application development to get more reach, conversion, and growth. Therefore, it would not be wrong to say that smartphones today are transforming business models, customer behaviour, operation models, and marketplaces.

In a report by Statista, it is clear that the mobile application industry is expected to reach $693 billion in 2021. Meaning, there is a great opportunity for the mobile app developers, and the year will generate more and more demand to create mobile applications that stand out and see the day of light.

Let us look at some of the emerging trends in the mobile application industry that can help you build a mobile application that is with and ahead of times:

AI and ML apps to make smartphones smarter 

Artificial Intelligence (AI) and Machine Learning (ML) go hand in hand while building a mobile application. They are taking a centre stage of almost all technological advancements and innovation for they ease the life of customers, businesses, and employees. Apart from making life easy, they also contribute to saving time, effort, and money for mobile app developers and businesses.

Using AI and ML has lead businesses to understand user behaviour at a much deeper level. This indeed helps them solve complex business problems which would have been tough to gauge without interacting with users/customers. The simplest (or to say commonest) example of AI and ML apps is the usage of chatbots, Siri, and google.

Selling an experience through AR and VR

Known as extended reality, Augmented Reality(AR) and Virtual Reality(VR) is here to stay. Widely used for mobile gaming applications, this trend is slowly set to change by bringing in more meaningful experiences for the users or customers. It is predicted that this technology will be widely used on social platforms for branding and targeting potential customers through AR/VR apps. For instance: Creating an Instagram filter to promote your brand and engage the audiences.

Also, today many real estate developers are providing AR/VR videos for the audiences to experience their upcoming projects and taking informed decisions. This has helped them to set clear expectations and get faster conversions.

Talking about the worldwide market of AR and VR technology, Statista predicts that the business value will hit to $209 billion in 2022.

Wearables, the emerging fad

Expected to reach $44.2 billion by 2020, the wearable industry will change the everyday lifestyle. From healthcare to e-commerce all are banking on this trend to increase the engagement and revenue. The integration of wearables serves as a key to innovation and technological development.

Apple watches have pioneered this trend, and with their latest WatchOS update, they have made it clear that the Apple Watch apps will no longer require a companion iOS app. Meaning, the world of smart wearables will now be just as wide as that of smartphones.

On-demand apps, a change maker

Today we have an application for almost all our daily needs and chores – from delivery to travel, OTT to banking, and home-spa to healthcare. This is all possible because of the on-demand application developers. To develop an application that brings in change, you need to vision problems from the end-user point-of-view, anticipate, research, and then develop a solution.

In a report by Shamika Ravi and Niam Yaraghi, it is predicted that the on-demand application industry is expected to generate $335 billion in revenue. Therefore, most industries will make use of this successful business model by embracing on-demand application in their businesses. On-demand mobile applications will disrupt the way we life, work, travel, shop, and relax!

IoT, enabling a seamless experience

Living in a world of interconnected smart gadgets, we can see that Internet of Things (IoT) has already become an industry that is here to deliver a real-time experience which is seamless, fast, and valuable. IoT’s integration with mobile applications has brought in a change in inventory management, buying behaviour, digital marketing, etc. As the demand for IoT gadgets is increasing day by day, there is going to be increased demands of IoT-enabled applications as well.

With many applications being developed each day, the mobile application industry will continue to expand at a rapid pace. This will lead to more disruption thereby making way for newer trends that will change the way we all live, work, and play! Having said that, to stand out in the market and be alive, the businesses will have to anticipate and stay updated with the latest technological developments.

Should you be planning to develop a mobile application that will engage your customers and increase your revenue by using the latest trends, then contact us at contact@goodworklabs.com or  +91- 9863077000.

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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#Wanderlust is trending on social media channels. There is no denying that curated travel experiences have been at the heart of the travel industry.

People, these days, participate actively to enhance their travel experiences. As the travel industry is picking numbers, the demand for personalization is growing.

This sudden rise in customization demands has necessitated the use of forward-technologies to cover the gap. As it appears, Artificial Intelligence enhances travel experience to fulfill the gap of demand and supply.

The emergence of Artificial intelligence to enhance Travel!

Technologies like Artificial Intelligence (AI) and Machine Learning assist travel enthusiasts to a great extent. They help people get a faster, safer, and more personalized travel experience.

As we speak, Artificial Intelligence is already making a significant impact on the travel and tourism industry. The role of Artificial Intelligence in the functions of traditional human cognition has made life easier.

For giant business houses with frequent travel requirements, AI proves to be beneficial in many ways-

• Saving time and money.
• Provide memorable travel experiences.
• It ensures that business travelers reach their desired destinations without trouble.

In the midst of advancement, it is very well accounted that the expectations of travelers have changed over time. Factors like price, comfort, and services on offer are more crucial than anything else. These expectations can be met successfully by using machine learning.

Artificial Intelligence is a term synonymous today with Internet businesses, e-visa processing, citizen services, travel hubs, and contact centers. The technology has entirely changed the general operational activities in a brief time.

Moreover, speech recognition, language translation, and visual perception are impacts felt in all businesses these days. When AI was not renowned as it is today, experienced industrialists around the world expected disruption by the use of AI.

This particular fear of disruption has not been too important, though. Major industry players are aware of the use of machine learning to provide quick services to clients.

What exactly does AI do for travel

When it comes to tourism, there are several activities where Artificial Intelligence enhances travels and tourism experience.

Visa:-

Operational windows of contact centers play a crucial role to address visa related queries without too much trouble. AI creates a smooth link between business and visa applicants.

From selecting a preferred language or evaluating the wait time, AI chatbots take in little details to optimize contact centers. Furthermore, getting a customer’s automatic identification or any other piece of relevant data becomes a great experience through a simple AI algorithm.

Data Security:-

With new trends coming up in technology, the importance of data security and privacy has increased immensely. To get a global visa, many outsourced companies need to follow the guidelines of data security and embassy protocol.

Keeping cyber threats into consideration, Visa providers have become more watchful. They now pay more attention to security audits, data encryption, and make use of fool-proof password control systems.

Security here is the first priority and should complement the pace of innovation in the tourism sector.

How AI Is Redefining Travel Today

“Relevance” is a keyword and an important winning factor with AI in the travel sector.

It boils down to the point whether AI has enough potential to bring about a significant change in the ways travel experiences are delivered to customers.

There are a few key areas where the use of Artificial Intelligence can have a lot of impacts to give a better experience for the customers-

1. DIGITAL INTERACTIONS AND VOICE-BASED ASSISTANTS

In today’s time, a traveler has access to every imaginable piece of information on a single website. Through these websites, travelers plan their destinations and compare different options. Budgets, bookings, and their cancellations come after that.

All of the above activities involve a reading of different descriptions, terms, and instructions before people arrive at a decision. A great alternative is to provide other apps with the role of reducing the interaction.

Bots using Natural Language Processing (NLP) perform the task of nailing more personal interaction for AI through context. The bot finds it easier to understand the meaning of a customer’s query and sort it.

Together, NLP and AI add a lot of weight to every travel-related activity. The immense scalability of these bots is also a fantastic facet to their attributes. AI assistance will go a long way to reduce the inconvenience faced by frequent travelers.

2. FACIAL RECOGNITION WITH ADDED IMPACT THROUGH BLOCKCHAIN

Travel requires constant monitoring of related documents by different sets of people. There are complex on-boarding and off-boarding processes involved. Facial recognition is a positive way to bring an end to these tiring paper-driven procedures.

Facial recognition will allow travelers to quickly move through airports, customs, immigration, and board flights without having their documents validated.

When combined with blockchain, customers find it easier to visit restaurants and duty-free stores. They can entertain themselves after just a face scan. Blockchain technology ensures that reliable data is always available to complete transactions.

3. MACHINE LEARNING, A NEW PERSUADER

Airports and airlines have begun to replicate shopping malls and huge retail outlets. Everything from blankets, seats, and hotel rooms are sold there. Machine learning has emerged as a new trend to assist with the sales procedure.

By the use of big data and machine learning, airlines can develop recommendation engines. The role of these engines is to personalize the offers around products from their catalogs.

AI and machine learning make personalization easy. This is because travelers expect travel companies to understand their preferences and offer better deals.  Machine learning also uses external data to help travelers to make quick decisions.

The applications mentioned above of Artificial Intelligence, or AI, have one thing in common; they lead to time reduction and also improve the accuracy of outcomes generated.

In an industry where time is of the essence, and data is fluctuating, AI provides many capabilities to ensure things work out correctly.

Did you like this post? Have an actionable idea on AI? Hire us and open the doors of wonders. Let’s work together. Contact us!

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.

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.

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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.

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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.

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