Machines will take over our lives & How…

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

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

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

Digital business transformation

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

Tech democracy

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

Harnessing the data

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

The future of the business

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

AI to Science

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

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

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

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

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

Dating apps are increasingly taking the help of AI!

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

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

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

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

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

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

Uses of Artificial Intelligence for Dating Apps

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

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

1. Help find better matches

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

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

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

2. Keeps things in moderation

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

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

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

3. Prevents security concerns

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

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

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

4. Provides great & useful user content

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

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

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

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

Machine Learning & Artificial Intelligence in Farming

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

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

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

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

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

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

Solving Connectivity Issues 

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

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

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

Precise Agriculture with Data – An Aerial Approach 

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

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

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

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

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

The next generation of Digital Farming 

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

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

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

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

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

Machine Learning Technology in Facial Recognition

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

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

Images classified as Emotions

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

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

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

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

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

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

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

Common components of expression analysis

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

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

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

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

Training Algorithms and their comparison

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

• Multiclass Support Vector Machines (SVM)

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

• Convolutional Neural Networks (CNN)

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

• Recurrent Neural Networks (RNN)

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

Conclusion

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

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:

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.

IoT and AI in Cancer Treatment

Cancer has become the dark reality of the contemporary world with an estimate of  1,688,780 new cancer cases and 600,920 cancer deaths in the United States in this year as stated in The American Cancer Society’s 2017 annual Cancer Facts and Figures report

With the quantum involved, curing cancer is a pertinent issue that has never gone off the desk of Healthcare practitioners or technology innovators. As of now, precision medicine is the highest level of aid that has been brought by the technology innovators for curing cancer besides advanced healthcare equipment.

Precision medicine is a form of medicine which uses information about the patient’s genes, proteins, and environment to prevent, diagnose, and treat cancer.

The Internet of Things (IoT) also has the potential to bring a huge transformation to the Healthcare industry. For the starters, it promises to reduce the emergency room wait time, track patient data accurately, and manage healthcare inventory.

All these functions will improve the efficiency of the healthcare sector to an unimaginable extent.

Artificial intelligence also holds a fair share in the future of technology in the healthcare sector. It is predicted that in future AI could assist doctors with cancer research, detection, and care.

Opportunities in Diagnosis and Treatment

The combined forces of AI and IoT can help to cure cancer and save a considerable number of lives each year. Currently,  AI and IoT are not functioning together in the cancer treatment space.

IoT has brought revolutionary change in the healthcare ecosystem with its potential to aid with early detection of breast cancer: ‘iTBra’.

The term is coined by the inventor of the connected bra – Rob Royea, CEO of Cyrcadia Health. In the era of wearable technology, this connected bra has revolutionary embedded temperature sensors which track changes in the temperature of breast tissue over time.

This device uses machine learning and predictive analytics to analyze the collected data and classify abnormal patterns that could indicate early stage breast cancer. Women only require to wear it once a month for 2-12 hours while they carry on with their daily chores. No radiation or prodding involved. The iTBra can be worn beneath any normal bra and the results of the test will be transmitted to a smartphone.

This device is a remarkable innovation and is far better than the traditional mammography which tends to detect the presence of cancer at usually at Stage 3 or 4. Also, the mammography produces a high number of both false-negative and false-positive results which makes it highly unreliable.

This test is primitive and misses the presence of cancer in women with dense breast tissue more than 50% of the time.  This false negative rate is reduced to 17.3% by the iTBra for all the tissue types.

Apart from the breast cancer, a variety of early-stage cancer symptoms are vague and unrecognizable in other kinds of cancer. Clearly, the rate at which cancer goes undetected is very high. If the IoT systems work in tandem with the AI technology there will be a great jump in deciphering cancer systems and the patients will receive treatment before it’s too late.

The intervention of AI is very important in the detection of cancers like mesothelioma which goes undetected in preliminary stages. Mesothelioma develops over a period of decades and is challenging to detect because early symptoms may manifest as a cough or discomfort in the chest or abdomen.

With the help of AI, such trivial symptoms can be tracked over decades and the patients will be able to receive timely treatment. The patient’s journey can be made less painful and the life expectancy can be lengthened. While IoT can be used to generate sensor-driven data and insight, AI could be put to work with the metrics and predictions in safe drug delivery.

How AI and IoT can improve Cancer Treatment

The advent of latest technologies such as AI, Machine Learning, and IoT has brought hope in the healthcare sector for better treatment ways.

The first step is already taken when the consumer technology companies have started creating wearable technology that can help detect and diagnose cancer.

Also, governing authorities all over the world have taken initiatives at varying scales to support the flourishing technologies. Government-supported programs like the “Cancer Moonshot Initiative” is the best example of government initiatives towards finding a better cure for Cancer.

With financial aid available, researchers are working on developing new treatments collaboratively.

The National Cancer Institute has launched the Cancer Moonshot program which has brought together a big faction of doctors, researchers, pharmaceutical companies, and others. This program is expected to bring positive results and accelerate the cancer research by 2020.

Conclusion

MarketResearch.com says that the healthcare Internet of Things market is predicted to hit $117 billion by 2020. In the same year, the combined IoT market will hit much more than $117 billion. By 2025, the same figure is expected to jump to $2.5 trillion for Healthcare industry, followed closely by the manufacturing industry at $2.3 trillion, says Intel.

These are some promising figures which are showing the bright future of the Healthcare industry. The fusion of AI and IoT technologies would empower patients and healthcare professionals more than we have imagined.

Thus, with the aid in metrics and predictions, AI will help doctors to make better data-driven decisions and IoT will provide the necessary technology to connect devices, data, and action which will assist in optimized drug delivery systems and early detection mechanisms.

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.

Machine Learning Algorithm

There is one thing about the Machine Learning algorithm and that is there is no one approach or one solution that caters to all your problems. But you can always pick an algorithm that nearly solves your problems and then you can customize it to make it one perfect solution for your problem.

Here we are stating some factors that will help you narrow down your list of machine learning algorithm options.

But first things first, you need to have clarity of the data, your constraints, and your exact problem. For achieving clarity of data, do the following:

a) Know your data

To understand your data you need to look at summary statistics and try to point out the central tendency of data. For doing this, you will require to study the averages, medians, and correlation that indicates a strong relationship in data. The next thing to figure out is ‘what to do with outliers’. You can use box plots that can identify outliers. Apart from this, ‘clean your data’. Sort it for relevancy and segregate it on the basis of the problem at hand.

b) Categorize the problem

Once you know your data, you need to categorize your problem, which can be done in two steps:

• Categorize by input:

A supervised learning program is when the data is labeled. If the data in unlabelled and you desire to find an appropriate structure then it is an unsupervised learning program. One should know the type of inputs they can offer in order to choose an appropriate machine learning algorithm.

• Categorize by output.

Now, if the output of your model is in number form then it will be called a regression problem. If you desire classification of data as an output, it’s a classification problem. Another type of problem is clustering problem when the model required to set groups for the inputs given.

c) Find the available algorithms

After proper evaluation of your problems, you can opt to identify the applicable algorithms which are practical to implement using the available tools.

Most commonly used Machine Learning Algorithms

In this blog, we have listed out some of the commonly used Machine Learning Algorithms just to give you a heads up. Follow us for more intriguing updates on Machine Learning.

1. Linear Regression

This is the simplest Machine Learning algorithm. It can be used to compute continuous input data as compared to classification in which the output is categoric. In simple words, linear regression can be used to predict some future value of a process that is currently going on. It should be kept in mind that in case of multicollinearity the linear regressions are unstable.

Examples, where linear regression can be used, are:

• Predicting sales for the coming month
• The time required in commuting from one place to another

2. Logistic Regression

Logistic Regression can be used as a probabilistic framework or to incorporate more training data into the model in future. It is not just a black box method but it will help you to understand the factors behind the predictive outcome and so forth.

Examples, where logistic regression can be used, are:

• Fraud detection and credit scoring
• Estimating the effectiveness of marketing campaigns

3. Decision trees

Using decision trees alone is done very rarely. Usually, they are combined with others machine learning algorithm to build an efficient algorithm like Gradient Tree or Random Forest.

Examples, where decision trees can be used, are:

• Investment decisions
• Buy or build decisions
• Banks loan defaulters

4. K-means

K-means is used for the unlabelled data where the task is to cluster and label them. It is used when the user group is very large and you wish to categorize them on the basis of common attributes.

5. Principal component analysis (PCA)

The principal component analysis is used when the data has a high range of features and is highly correlated. In such a situation PCA will help you in dimension reduction.

6. Support Vector Machines

Support Vector Machine (SVM) is used on labeled data and is used widely in pattern recognition and classification problems when the input data has exactly two classes.

Examples, where SVM can be used, are:

• Text categorization
• Stock market predictions

7. Naive Bayes

Naive Bayes is based on Bayes’ theorem. It is a classification technique that is easy to build and works great with large datasets. It is a better classifier than discriminative models like logistic regression because it is quicker and requires less training data.  

Examples, where Naive Bayes can be used, are:

• Text classification
• To mark an email as spam or not
• Face recognition

8. Random Forest

Random Forest can solve both classification and regression problems on large data sets. Basically, it is a collection of decision trees. It is highly scalable to any number of dimensions and has usually quite acceptable performances.

Examples, where Random Forest can be used, are:

• Predict credit loan defaulters
• Predict patients with high health risks

9. Neural networks

Neural networks can be used to train extremely complex models and these models can be utilized as a black box. For example, object recognition is enormously enhanced by deep neural networks only.

Summing up

The above pointers will be a great help to shortlist a few algorithms but it is hard to figure out which algorithm will work best for your problem. Therefore, it is suggested to work iteratively. For picking the best one among the shortlisted alternatives, test the input data with all of them and at the end evaluate the performance of the algorithm.

Also, to develop a perfect solution to a real-life problem you need to be aware of rules and regulations, business demands, and stakeholders’ concerns and you should have considerable expertise in applied mathematics.