Dating apps are increasingly taking the help of AI!

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

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

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

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

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

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

Uses of Artificial Intelligence for Dating Apps

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

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

1. Help find better matches

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

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

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

2. Keeps things in moderation

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

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

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

3. Prevents security concerns

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

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

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

4. Provides great & useful user content

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

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

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

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

Machine Learning Technology in Facial Recognition

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

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

Images classified as Emotions

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

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

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

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

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

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

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

Common components of expression analysis

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

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

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

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

Training Algorithms and their comparison

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

• Multiclass Support Vector Machines (SVM)

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

• Convolutional Neural Networks (CNN)

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

• Recurrent Neural Networks (RNN)

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

Conclusion

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

AI predictions to look out for in 2019

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

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

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

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

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

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

• Finance and Healthcare will adopt AI and make it mainstream

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

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

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

• A war of algorithms between AI’s

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

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

• Learning and simulation environments to train data

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

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

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

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

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

• Demand for privacy will lead to more spontaneous AI

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

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

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

If you need a customized AI solution for your business, then please drop us a short message below:

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.

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

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

Objective:

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

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

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

Recommendations by using Machine Learning models

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

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

App screens that populated ML recommendations

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

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

Types of Recommendation systems:

1. Content-Based recommendation

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

2. Collaborative filtering/ recommendation

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

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

Tech Stack:

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

The GoodWorkLabs AI and ML solution:

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

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

Leave us a short message with your requirements.

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.