Image Scanning & Processing with Machine Learning models

One of our Fortune 500 clients in the logistics industry wanted to extract various product-related information by scanning images through a machine learning model. This scanned information had to then be supplied to a custom web application for further utilization and analysis.

The Objective

The image scanning and detection had to happen on the below aspects

• Identifying the object in the image
• Localization of the object
• Measuring the width and height of the objects in the image

The GoodWorkLabs Machine Learning Solution:

Our data scientists used the Faster-RCNN algorithm to solve the problem statement. We followed the below procedure to achieve the desired results.

• We ran the image through a CNN to get a Feature Map, a matrix representation of the image between a neural network layer
• We ran the activation map through a separate network called the Region Proposal Network(RPN), which identified the bounding boxes (interesting regions) for those objects. This output (regions) was then passed on to the next stage.
• Each and every output of the bounding boxes was analyzed and the most appropriate bounding box coordinates was accepted.

Faster-RCNN works quicker because we pass the activation map through a few more layers to find the bounding box (interesting regions). This forward pass continuously takes place and during this training phase, the ML model continues to learn. Errors (if any) are captured at this stage and with continuous learning, the model becomes efficient in predicting the classes and bounding box coordinates.

For calculating the height and width of each object we continued to iterate every object in the image and calculated values using OpenCV.

Image reference: https://arxiv.org/pdf/1506.01497.pdf

Data:

To perform this image scanning process, we had a well-annotated object in each of the images in the dataset. We had around 1000 labels for each object.

How did we train our ML Model:

• We downloaded pre-trained models and weights. The current code support is VGG16 
• We also got access to pre-trained models which were provided by pytorch-vgg 
• In the next step, we trained our model from fine-tuning to a pre-trained Faster R-CNN model. We followed this approach because a pre-trained Faster R-CNN contains a lot of good lower level features, which can be used generally.
• We trained the model for 150 epochs.

GPU utilization:

The models were then exported to Microsoft Azure’s GPU for better performance. The expected inference time for a given image is ~0.2 seconds.

Technology Stack:

The technology stack used to implement this image scanning ML model was Python, Pytorch, OpenCV, Microsoft Azure.

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.

AI Racing Assistant to Enhance Driver Experience

One of our Fortune 500 clients in the automobile industry wanted to analyze and improve their racetrack experience. The racing car is equipped with more than 100 sensors and these were programmed to capture all activities of the car such as steering wheel angle, acceleration, engine running state, etc. 

The Objective:

The GoodWorkLabs AI team was given the task to identify the optimal path of the vehicle by analyzing the complete racing track with other tracks and also the overall racing experience.

AI/ML Implementation & Solutions:

We first analyzed the track using sensor data and then implemented state-of-the-art Deep Q-learning with Tensorflow.

Our Deep Q Neural Network took a stack of n frames as input. These pass through the network, and output a vector of Q-values for each action possible in the given state. We then take the biggest Q-value of this vector to find our best action.

In the beginning, the agent does not perform well. But with time and continuous learning, it began to associate frames (states) with best actions. Pre-processing was a very important step as we wanted to reduce the complexity of our states and reduce the computation time needed for training.

For that to happen, we greyscale each of our states. Color does not add important information (in our case, we just had to find the optimal path). This is an important saving since we reduced our three color channels (RGB) to 1 (greyscale).

Tech Stack: 

The tech stack used to develop this model was Python, PyTorch.

Below are some visualizations of the optimal path identified by our AI model.

The GoodWorkLabs Artificial Intelligence and Machine Learning 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 customized solution to cater to your business.

Leave us a short message with your requirements.

Artificial Intelligence in Investment Banking

Despite deep roots of origin, Capital Markets have evolved with the help of technology and still holds an appetite for innovation and improvement. Capital market firms such as investment bankers have been testing AI for implementation since the precursor technologies.

Before AI got some mainstream attention in the form of self-driving cars and robots, capital market firms have been leveraging machines for their daily operations such as algorithmic trading, quantitative analysis, and market predictions. These facts show how forward the capital market is in the tech race by capitalizing on emerging ideas and leveraging them for value generation for clients.

Though most firms use AI for becoming cost efficient, the potential of AI in the capital markets is beyond imagination and can create value across the organization.

What makes AI stand out from other technologies

Below are the features that make artificial intelligence a desired technology for businesses today.

1) Sense: AI can collect, recognize, sort, and analyze structured and unstructured data in the form of text, audio, and images.

2) Comprehend: Artificial intelligence can then derive meaning, knowledge, or insights by using that data.

3) Act: The comprehension gained is later used to perform a defined process, function or activity.

4) Learn: AI takes real-world experiences into account and evolves over time, thus enabling it to resemble a human brain and handles multiple tasks at once.

High-degree of customized AI interactions.

With hyper-personalization, curation of real-time information, and conversational interfaces, AI delivers enhanced interaction in the form of superior experience to clients. The advent of AI made it possible to cater to a high degree of customization in a cost-effective manner along with flexibility. AI analyzes the clients’ behavior and provides information instantly.

Currently, capital market firms are after storing, categorizing, and analyzing sales and trading conversations to better forecast client needs and enhance the effectiveness of interactions. This is achieved by the introduction of digital assistants handling the sales and services interactions.

Digital assistants are a cost-effective way to deliver a sophisticated and improved experience to the users. It eliminates the need to fill forms, navigate online portals, and the need for additional human resources. The involvement of digital assistants can also greatly improve the client’s acquisition and retention rate.

Intelligent products

AI can help you move up the value chain, access new ecosystems, and introduce innovations in the market faster than ever. AI enables the monetization of new services and products and also makes existing service offerings profitable in new geographical markets.

Enhanced trust

With AI at disposal, trust is enhanced in terms of governance. Compliance, risk, finance, legal and audit are necessary for vigilant oversight. AI provides a cost-effective approach to governance with important insights.

Transparency and traceability should be on top priority for capital market firms who are thinking of building and using AI solutions. Also, most capital market firms that are currently using AI are focusing on automating things that they currently do. But the real value lies when the scope of AI is used to enhance human judgment, to expand products and services, to improve client interactions, and to build trust and confidence among the stakeholders.

Potential of AI in capital markets

The massive potential that AI holds can be encashed in risk management, stress testing, conversational user interface, and algorithmic trading. Also, in recent times, the attention has shifted to client service in the form of next-best-offer and next-best-action decision making.

1) Intelligent automation:

The advancement in technology has made the layering of cognitive capabilities on automation technologies possible, thus enabling self-learning and increasing autonomy.

2) Enhanced interaction:

Curation of real-time information, hyper-personalization, and conversational interfaces have enabled the delivery of superior client experiences.

3) Intelligent products:

New products and services can be launched with the aid of AI along with tapping into new business markets and business models.

4) Enhanced judgment:

Human intelligence can be augmented with AI capabilities and decision making can also be improved.

5) Enhanced trust:

With AI, the whole organization can be kept intact and trust can be flourished outside the organization in how AI is used.

The future belongs to AI

Now is the time when the value of AI should be understood by capital market firms in order to build a base camp for it to flourish now and in the times to come. AI has more potential than just achieving efficiency in the daily tasks and cost-cutting.

The real question is, how you choose to deploy it? 

Fuel your AI engine by redefining your ecosystem and distinctly identifying your source data, internal as well as external. Datasets have a major role to play in the AI world, so be vigilant about which data can be shared and can be monetized.

Set guidelines for your AI

Capital markets out of the many industry lines are the most regulated. As the application of AI will grow in this industry, a new set of regulations will be imposed. With all these constraints, how you choose to use technology and adhere to the regulations will always be a point of discussion. Strong guidelines will help you in the long run. Guidelines that define your data use ethics, information sharing policies, maintain transparency, and privacy.

Final Words

AI is all set to help you improve your business practices and augment your forthcoming ventures. Capital market firms are still in the pipeline to understand the complete worth of AI and all that is at stake. With well-defined guidelines and an appropriate dataset, AI can yield constant and better results on an auto mode. The future is full possibilities and the present is in your hands. Now, it is your call how you choose to direct your future!

Let’s connect to discuss further on how AI can add great value to your business process. Drop us a quick message with your requirements and we will be happy to get on a quick AI consultation call.

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.

AI in Diabetes – A breakthrough in Healthcare

The National Diabetes Statistics Report, 2017, U.S. states that an estimated 30.3 million people of all ages or 9.4% of the U.S. population suffered from diabetes in 2015 and this count is growing every year.

These figures are alarming to healthcare authorities as well as to the controlling government. With an immediate attention required in this area, brilliant technologies like AI, machine learning and big data can be used to overcome the gap between those suffering and cured.

The economic cost to the US for diabetes care in 2017 alone amounted $327 Billion. This economic burden is getting out of the control with the number of diabetic patients adding every year.

For contributing to the cause, few digital health companies are taking the initiative to lessen this burden with the help of technology. They are leveraging technological advancements to innovate diabetes care solutions like non-invasive insulin delivery systems, continuous glucose monitoring devices, and digital diabetes management platforms.

These devices are the source of behavioral, physiological, and contextual data which can be analyzed and used to come up with more efficient diabetes care.

Today we are presenting some revolutionary startups who are making a contribution to help diabetes care evolve. Their contributions are remarkable with out of the box solutions for the problem at hand. Let’s take a look:

1) Livongo Health leveraging Big Data-Based Approach for Diabetes Care

Livongo Health is leveraging big data to help people manage their health conditions more efficiently and improve patient outcomes.

Hundreds of thousands of people are using their products such as blood glucose meters, blood pressure cuffs, and scales. The added advantage is that these devices collect data and send it to a larger database which is then used by the company for generating insights to benefit their members.

Also, this pattern has encouraged the startup to come up with a reinforcement learning platform where they observe the data and generate a variety of personalized messages to send to their members.

They learn about members’ behavior with the responses received and eventually know what works best for them. We would call this a good start!

2) Bigfoot Biomedical is working on AI-driven automated insulin delivery with an artificial pancreas

Bigfoot Biomedical, a California-based diabetes management company, is working on a mission to develop an automated insulin delivery system with an artificial pancreas. This system sounds promising and can make the lives of diabetic patients easy. The future of diabetes care will change with this product launched in the market.

The startup made it possible by leveraging the potential of AI to devise a closed-loop system that would observe and learn from the user’s response to food, exercise, insulin, and then adjust the dose.

A good head start is that the company has received a substantial financial support and thus the process of product development has fast-forwarded to the clinical trial phase.

It is just a matter of time that an AI-driven automated insulin supply system will become the life-changing diabetes care solution.

3) Glooko is providing mobile and web apps for diabetes care

Glooko is a global diabetes data management company which provides HIPAA-compliant and widely compatible mobile and web apps. These apps synchronize with diabetes care devices and activity trackers to collect data like insulin, blood pressure, blood glucose, diet, and weight.

The company is harnessing the power of Big data and predictive algorithms to empower diabetic care professional with tools to analyze trends and provide necessary recommendations.

Glooko collects data from over 180 exercise and diabetes care devices and then correlates it with exercise, food, medication, and other relevant data to deliver insights with clinical care and self-management.

These apps will contribute a lot to self-management and also sizeable improvement can be made in patient outcomes.

4) Virta Health is using AI to reverse Diabetes

Virta Health is a silicon valley startup which has embarked upon the mission to cater alternative treatment for type two diabetes without any surgery or medication. The company has already received 50% positive results in its clinical trial for reversing the chronic diabetic condition.

Virta has taken the nutrition centric approach which is based on ketogenic diet. With this diet, the body burns fat for fuel and not carbs. Virta has a user-friendly app which allows the user to enter ketones, blood sugar, and other relevant information.

Once the details are entered, the app uses AI to device a customized treatment plan for the individual.

Additionally, this app helps patients find specially assigned clinicians and health coaches for immediate assistance and consultation. Another great step in improving self-management!

5) Digital Diabetes Clinic by GlucoMe

GlucoMe is an Israeli startup which has invented a digital diabetic clinic which uses a cloud-based solution for remote diabetic care. With this facility, the healthcare professionals can remotely monitor the patient’s insulin and blood glucose and adjust the dose accordingly as and when necessary.

The data is transferred from smart glucose monitors and insulin pens to a mobile app which helps in monitoring and making decisions that support the platform to function.

AI and machine learning are used to generate meaningful insights and actionable treatment plans. The healthcare will be simplified to a great extent with the use of digital diabetic clinic.

Final words

Personalized treatment plans based on real-time data along with intelligent insulin delivery algorithms are the need of the hour. Technical advancement in the field of healthcare has a promising future and startup initiatives like these can open up a gamout of opportunities for healthcare professionals and patients.

The Artificial Intelligence in Music Debate

Music has come a long way from the early days where the only instrument was the human vocal cord. The use of computer-based technology in music started in the 60s when the iconic Moog synthesizers starting taking over the British music scene and paved way to the irreplaceable artform that is progressive rock. If it was not for the Minimoog, Keith Emerson would have just stuck to literally rocking the Hammond L100, riding it like a bucking bronco, brutalizing it with knives and occasionally when he got the time even playing music on it. Yes indeed, the 60s and 70s were crazy times. But the introduction of integrated circuits and eventually processors paved way for a lot of innovation in music and the discovery of many new genres of music.

However, technology in music didn’t stop with the synthesizers. Over the past few decades, many technologies have been incorporated into music including the controversial auto-tune. In recent times though the most technology to make its way into the musician’s arsenal is Artificial Intelligence. While the overuse of technology in music is heavily debated and often shunned by the musicians of yore, there is still a lot of support as well. So, let us have a look at how AI is being used in the music industry today.

Algorithmic Composition

Surprisingly the roots of Artificial Intelligence in music date back to as early as 1965 when synth pioneer and inventor Ray Kurzweil showcased a piano piece create by a computer. Over the years, the use of computer programs in the composition, production, performance, and mass communication of music has allowed researchers and musicians to experiment and come up with newer technologies to suit their needs.

Algorithmic composition is one of the more complex applications of AI in music. Kurzweil’s early efforts towards producing music using computer algorithms relied on pattern recognition and recreation of the same in different combinations to create a new piece. The process of creating music cognitively used today have spawned from this idea. The algorithms used therein are created by analyzing certain human parameters that influence how music is perceived. The first step here is for the cognitive system to understand the music as a listener and then draw vital information from it as a musician would. To achieve this the AI system must first be able to compute the notational data in relation to its audio output. Aspects such as pitch, tone, intervals, rhythm, and timing are all taken into consideration here.

Based on the approach and the result intended there are several computational models for algorithmic composition such as mathematical models, knowledge-based systems, evolutionary methods etc. Each model has its own way of uploading a piece of music into the system, how the system would process it, what information it can derive from it and how it would do it. Composition is not always the purpose of an Algorithmic system. Musicians can use it for comprehension or even draw inspiration.

Non-Compositional Applications

While AI-based music composition is a work in progress that has been going on for decades and could take quite a few more to perfect, AI serves the music industry in many ways outside the studio.

Engagement Tracking

Today almost every form of art and entertainment profits most from the digital medium. This is also the case with music if it wasn’t obvious enough. Artists create music for a target audience which is spread around the many platforms like Youtube and Spotify. So, the engagement data recovered from these platforms have a huge impact on the songs that are created as well as how they are promoted. Artificial Intelligence plays a central role in this process. AI systems often act as the buffer between the distributors, advertisers, marketers and the audience for all their processes including monetization and the transactions involved therein.

Analytics

Analytics data is an essential aspect of almost every online venture today. And music is no exception. Music creators, independent and signed alike rely on analytics data to manage their online presence. AI-based analytics tools help bring speed and accuracy to the analytics processes, so as to help musicians keep up with the fast-paced internet competition.

It’s not Rock and Roll, Man

Music, being an art form that demands a great deal of creativity also demands a human mind behind it that is creative rather than analytical. This is the debate that has been surrounding the AI-music alliance for a long time. Technology, in general, has had the music world divided for eons. In a decade where bands like Pink Floyd thrived exclusively on ‘space age’ technology like the EMS VCS3 and a whole bunch of other stuff, acts like Rush, Motorhead and Aerosmith roughed it out with just the rudimentary instruments. Although most musicians are always open to new technologies or eventually warm up to them, that has not been the case with AI. The prospect of having to put in very less creative effort to compose may entice some, (especially given the volume of music that is being put out everyday) it also opens up the debate of industry giants such as Sony, Universal, Warner and Tips misusing or overusing it which could lead to an eventual vacuum in creativity.

I’m Perfect! Are You?

AI has always been a source of contradiction among all communities so it is no surprise that the music community is both supportive as well as skeptical of it. So far efforts towards harnessing Artificial Intelligence-based composition have been of infinitesimal proportions. So, there is not much out there for us to judge and take sides. Ventures like Artificial Intelligence Virtual Artist (AIVA) are working exclusively towards bringing out the full potential of AI as a means of completely automating music production. While such ideas are a grey area today, how these will come to influence music as we know it, only time will tell.

What you Should know about Google AutoML

One of the highlights of last year’s Google I/O was the announcement of Google’s Automated Machine Learning or AutoML which according to founder Sundar Pichai was their ‘AI Inception’. The Machine learning Cloud software suite is finally about to hit the alpha stage and as of now, it seems like there is a lot that developer and designers alike can leverage from this tool. So let us explore this new addition to the Google family.

What is AutoML?

AutoML is Google’s Cloud software suite for Machine Learning tools based on Google’s Neural Architecture Search (NAS). Using AutoML a user can train deep networks without having any expertise in deep learning or Artificial Intelligence. This is achieved by leveraging NAS to derive the most suitable data network for the task at hand. This is a huge step for businesses as they will be able to harness the power of machine learning without using experts with years of experience in any field of automation. Customization of various AI product models meant for mundane as well as complex tasks.

How Will Businesses be able to Leverage AutoML?

Today, with deep learning still being in its infancy, most business use, AI for simplistic tasks. And considering the fact that AI and Machine learning experts are hard to come by and are quite expensive to hire, most companies try to keep their deep learning efforts to a minimum. This is where AutoML makes a world of difference. One of the most common applications of deep learning for businesses today is Image processing. Today companies rely on Image classification networks that run on neural matrices. This although running on machine learning tools, requires quite a bit of manual effort to isolate the proper network for the image datasets and then train the models to adapt to the task they intend to accomplish. With AutoML companies can skip the tedious processes of research and get directly into the transfer process. As a matter of fact, Google’s first AutoML tool is intended for exactly this aspect for visual companies.

AutoML Vision

This is AutoML’s first tool which will help developers create custom image recognition models. According to Google this tool will allow developers to upload their labeled data and the system will automatically create a custom machine learning model for them. This, in turn, will allow them to focus more on product development, design and other important aspects.  Although still in its alpha stage, this tool is being used by many big-time players such as the animation giant Disney.

Conclusion

As the concept of ‘Software 2.0’ catches on, developers around the world are getting used to the new tools largely built upon their predecessors, to write software without actually having to code each and every part of it. Tools like AutoML are a good example of how this is being applied to every aspect of software irrespective of the purpose. With AI the scope is further enhanced. Yet, for such tools to achieve any degree of comprehensiveness, they themselves need to be further developed to accommodate the needs of the upcoming generations of developers who will be relying upon them to do most of the work. So the road ahead will be the chicken and egg paradox where the efforts of both the man and the machine will craft the future. Today we have that sense of direction as to where we need to focus our efforts in terms of research, which is great news for AI tools such as AutoML.

How Machine Learning can affect Economics

Machine learning has found its way into multiple business applications. It makes computing the process more efficient, cost effective and trustworthy. Machine Learning is no longer a fantasy but a set of authentic business technologies that will help the management take better decisions in the long run. Be it Facebook recommending friends tag based on photos or voice recognition systems like Siri and Cortana, Machine Learning helps us to make smarter and better decisions.

The power of machine learning goes mainstream

Studies show that in 2017, ¼ of the respondent companies used 15% of their IT budget on machine learning and have seen phenomenal ROI emanate from it. These numbers are expected to rise in the future because of fast infusion of machine learning into different and diverse industry verticals.

Machine learning is broadly used in the field of life sciences, healthcare, hospitality, and retail. Interestingly, there is one more vertical that machine learning can impact but it is not that popular yet.

It is the field of Economics!

Research shows that machine learning is yet to revolutionize economics the same way it has done for other fields. But it will majorly expand its possibilities and more economists should start to implement machine learning in their studies.

Want to see how machine learning can impact the field of economics? Read on…

How Machine Learning and Economics come together?

When an economist analyzes economic data, they try to figure out the relationship between two dynamic and seemingly unrelated conditions. For example, an economist might sort through real estate data to figure out how much the size, location, or other factors will determine the price people are willing to pay for a home.

Machine learning will not only help an economist figure out the relationship between the user and external factors but also will use that data to predict on how much that same house is worth and what should be expected from potential buyers.

Machine learning will not directly influence economic research but will help economists with the research data and predications.  Machine learning is primarily useful in collecting new sources of data. For example, economists have already been able to convert satellite data into estimates of economic growth, as well as to measure neighborhood income levels in Boston and New York using Google Street View, Yelp, and Twitter.

Another feature of machine learning is it allows economists to analyze language as data. Algorithms can be used to identify news articles too. It can gauge if the sentiment of the text is negative or positive. Brand marketers can then utilize this information to roll out campaigns accordingly.

Machine learning is also useful for testing out economic theories. Economists are working on the theory that share prices should incorporate the most relevant information, thus making it possible to predict future stock market fluctuations.

To conclude

Although machine learning is a new concept, it has a promising future in the world of economics. Some of the above examples show succinctly why machine learning has the (nearly) perfect answer to some of the most complex queries faced by an economist.

How the use of Artificial Intelligence in Education can improve Student Retention at Universities

It is seen that there are scores of university students who enroll in higher education but do not end up obtaining a degree. Since they leave the education midway, they end up not being awarded the degree they had enrolled for. Universities and educational institutions are increasingly looking to technology to address this challenge of student retention. With the help of Artificial Intelligence and Machine Learning, they are seeking a way to improve the retention rates for higher education in countries like US and India.

By analyzing data from forms, educational literature, surveys, and studies, AI can detect the key reasons behind attrition and dropouts. This, in turn, helps the institutions to plug the gap wherever possible and improve their own university rankings by improving the retention rates.

How does Artificial Intelligence in Education work?

A student and an institution exchange volumes of information at every stage of the educational journey – right from initial expression of interest to completion of the programme and awarding of the degree. This helps the AI system to mine data that is of particular importance for tracking retention record of a student.

So, metrics like falling grades or increased absenteeism may provide early indicators of a likely dropout in the future. Once the student advisor or university professor gets an alert of the same, he/ she can counsel the student about ways to overcome the present challenges and continue pursuing the education.

Imagine if the AI system weren’t in place – the institution would’ve never got to discover the likelihood of a student dropping out until it was very late i.e. when the student actually drops out. Rather than taking a reactive stance, AI helps the institution and university to take proactive action and avert attrition from actually happening.

Use Cases in Artificial Intelligence’s impact on Student Retention Rates

There has been an interesting project that saw a public university bring in the benefits of Artificial Intelligence to tackle this very problem of falling student retention. The University of Oklahoma had witnessed a drastic fall in the number of higher education students returning for sophomore year in college. Out of the first-time students who started school in fall 2013, only 64% returned for the second year in fall 2014.

The university worked with IBM (for IBM Watson, its well-known proprietary cognitive computing, and AI system) and analyzed unstructured data like student essays. This helped to assess the tone of language, personality insights, and natural language classification. With this data, the university could better identify potential retention risk students and counsel them before they dropped out.

The outcome of the project was highly positive – from 64.2% in 2014, the retention rate climbed to 86.1% in 2015 and reached 92.1% in 2017.

To sign off

This post shows the exciting potential for Artificial Intelligence to make a truly lasting impact on the education and academics sector. The more universities and institutions embrace AI, the higher will be the likelihood of retention of students into the system.