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AI For Advertisers: How Data Analytics Can Change The Maths Of Advertising?

All Images Credit: Freepik

The task of understanding a customer’s journey and designing your marketing strategy accordingly can be difficult in this data-driven world. Today, the customer expresses their needs in myriad forms of requests.

Consumers express their needs and want attitudes, and values in various forms through search, comments, blogs, Tweets, “likes,” videos, and conversations and access such data across many channels like web, mobile, and face to face. Volume, variety, velocity and veracity of the data accumulated through these customer interactions are huge.

BigData and data analytics can be leveraged to understand several phases of the customer journey. There are risks involved in using Artificial Intelligence for the marketing data analysis of data breach and even manipulation. But, AI do have brighter prospects when it comes to marketing and advertiser applications.

As the CEO of a technology firm Chop Dawg and marketer, Joshua Davidson puts it, “AI-powered apps are going to be the future for us, and there are several industries that are ripe for this.” The mobile-first strategy of many enterprises has powered the use of AI for digital marketing and developing technologies and innovations to power industries with intelligent systems.

How AI and Machine learning are affecting customer journeys?

Any consumer journey begins with the recognition of a problem and then stages like initial consideration, active evaluation, purchase, and postpurchase come through up till the consumer journey is over. The need for identifying the purchasing and need patterns of the consumers and finding the buyer personas to strategize the marketing for them.

Need and Want Recognition:

Identifying a need is quite difficult as it is the most initial level of a consumer’s journey and it is more on the category level than at a brand level. Marketers and advertisers are relying on techniques like market research, web analytics, and data mining to build consumer profiles and buyer’s persona for understanding the needs and influencing the purchase of products. AI can help identify these wants and needs in real-time as the consumers usually express their needs and wants online and help build profiles more quickly.

AI technologies offered by several firms help in consumer profiling. Firms like Microsoft offers Azure that crunches billions of data points in seconds to determine the needs of consumers. It then personalizes web content on specific platforms in real-time to align with those status-updates. Consumer digital footprints are evolving through social media status updates, purchasing behavior, online comments and posts. Ai tends to update these profiles continuously through machine learning techniques.

Initial Consideration:

A key objective of advertising is to insert a brand into the consideration set of the consumers when they are looking for deliberate offerings. Advertising includes increasing the visibility of brands and emphasize on the key reasons for consideration. Advertisers currently use search optimization, paid search advertisements, organic search, or advertisement retargeting for finding the consideration and increase the probability of consumer consideration.

AI can leverage machine learning and data analytics to help with search, identify and rank functions of consumer consideration that can match the real-time considerations at any specific time. Take an example of Google Adwords, it analyzes the consumer data and helps advertisers make clearer distinctions between qualified and unqualified leads for better targeting.

Google uses AI to analyze the search-query data by considering, not only the keywords but also context words and phrases, consumer activity data and other BigData. Then, Google identifies valuable subsets of consumers and more accurate targeting.

Active Evaluation: 

When consumers narrow it down to a few choices of brands, advertisers need to insert trust and value among the consumers for brands. A common technique is to identify the higher purchase consumers and persuade them through persuasive content and advertisement. AI can support these tasks using some techniques:

Predictive Lead Scoring: Predictive lead scoring by leveraging machine learning techniques of predictive analytics to allow marketers to make accurate predictions related to the intent of purchase for consumers. A machine learning algorithm runs through a database of existing consumer data, then recognize trends and patterns and after processing the external data on consumer activities and interests, creates robust consumer profiles for advertisers.

Natural Language Generation: By leveraging the image, speech recognition and natural language generation, machine learning enables marketers to curate content while learning from the consumer behavior in real-time scenarios and adjusts the content according to the profiles on the fly.

Emotion AI: Marketers use emotion AI to understand consumer sentiment and feel about the brand in general. By tapping into the reviews, blogs or videos they understand the mood of customers. Marketers also use emotion AI to pretest advertisements before its release. The famous example of Kelloggs, which used emotion AI to help devise an advertising campaign for their cereal, eliminating the advertisement executions whenever the consumer engagement dropped.

Purchase: 

As the consumers decide which brands to choose and what it’s worth, advertising aims to move them out of the decision process and push for the purchase by reinforcing the value of the brand compared with its competition.

Advertisers can insert such value by emphasizing convenience and information about where to buy the product, how to buy the product and reassuring the value through warranties and guarantees. Many marketers also emphasize on rapid return policies and purchase incentives.

AI can completely change the purchase process through dynamic pricing, which encompasses real-time price adjustments on the basis of information such as demand and other consumer-behavior variables, seasonality, and competitor activities.

Post-Purchase: 

Aftersales services can be improved through intelligent systems using AI technologies and machine learning techniques. Marketers and advertisers can hire dedicated developers to design intelligent virtual agents or chatbots that can reinforce the value and performance of a brand among consumers.

Marketers can leverage an intelligent technique known as Propensity modeling to identify the most valuable customers on the basis of lifetime value, likelihood of reengagement, propensity to churn, and other key performance measures of interest. Then advertisers can personalize their communication with these customers on the basis of these data.

Conclusion:

AI has shifted the focus of advertisers and marketers towards the customer-first strategies and enhanced the heuristics of customer engagement. Machine learning and IoT(Internet of Things) has already changed the way customer interact with the brands and this transition has come at a time when advertisers and marketers are looking for new ways to tap into the customer mindset and buyer’s persona.

All Images Credit: Freepik

Why Retailers Are Making the Push for Stronger Data Science and AI

Retail relies on what the customer wants and needs at that moment, no matter the size of the company. Making judgments without consumer input would probably work for a little while but will fall flat as soon as the business model becomes outdated. In today’s technology-run world, things can become obsolete in a matter of days or even hours.

Retailers are the businesses most in need of capitalizing on what the customer wants in real-time. They have started to use data science and information from the Internet of Things (IoT) to not only stay in business, but also get ahead of other brands.

Artificial intelligence (AI) adds a new layer by using modern technology. The details of why retailers want to use these new practices are a bit more specific, though.

Data Targets Audiences

By using current customer data compared to information from the IoT, retailers can learn more about their audience and find better means of targeting them. Demographics like age, location and many other factors could affect advertising and even shopping, not to mention holidays throughout the year an audience celebrates.

Websites also need to be customized to suit the target audience. Those that are mobile-friendly and focused on what shoppers want can increase revenue, but the wrong approach can drive away new and existing customers. AI can help companies understand that data and present it back to the customer seamlessly, providing different options for various audiences.

Customer Base Expansion

Customer success should mean business success, as well. Growing a client base is something data science can assist with. However, helping customers grow is another type of service few companies provide but all people appreciate. A business can expand by offering new products and services that are relevant to their audience through the use of data.

Once a company learns what current customers want and begin to fit their needs, it can expand to more audiences. With data science, a business can ensure it does so slowly to give more of what current customers want while also finding new ones. The data can tell what sort of interests they all share so companies can capitalize on the venture.

AI Helps Customer Service

AI helps out customer service on both ends. Employees don’t have to focus on common problems that could easily be resolved, and clients often walk away happier than if they were to speak to a real person. This doesn’t work for every problem, especially ones that are specific in nature, but they can assist with more common issues. This is where chatbots enter the stage.

An AI-supported chatbot can give immediate support, provide suggestions, answer direct questions and offer almost any other form of help needed. Customers get personalized attention, and businesses can work faster toward customer loyalty.

Again, speaking to a real person when they have problems is a big plus for customers, but not for issues they know could be resolved in the time it takes to wait on the line for a representative.

Supply and Demand

Price optimization has taken on a bigger role than it has in the past. Mostly, data science is looking at supply and demand in real-time rather than having price fluctuations occur months after the business loses money. Having the right price can also help create more promotions for products and services, rewarding loyal customers for their shopping.

The data has to be gained from multiple channels by using price optimization tools, which focus on using data correctly in a company’s favor. The information doesn’t just look at supply and demand, but also examines locations, times, customer attitudes, competitor pricing and many other factors. All these pieces of information can be delivered in real-time so prices can be changed accordingly.

Taking the Competition

The thing about data science is that businesses are already utilizing it to their full potential and getting more customers than ever. The only way to get ahead of the competition is to at least start using the tools they’ve had at their disposal for years.

Target was one such company that took up the data helm. During 2012 and 2013, it saw a pretty sizeable dip in sales, but its online sales went up by almost 30% during the same time.

Data and Retail

When running a retail business, especially one that’s branching off into a franchise, using data is imperative. Data science and AI have become extremely important to companies both big and small.

Applying it correctly can help enterprises of any size and in every industry take things to the next level.

Even if a company is just starting out, sticking the first landing with a target audience is a fantastic way to begin the adventure and find success.

Glorious career paths of a Big Data Professional

Are you wondering about the career profiles you may get to fill if you get into Big Data industry? If yes, then Bingo! This is the post that will inform you just about that. Big data is just an umbrella term. There are a lot of profiles and career paths that are covered under this umbrella term. Let us have a look at some of these profiles.

Data Visualisation Specialist

The process of visualizing data is turning out to be critical in guaranteeing information-driven representatives get the upfront investment required to actualize goal-oriented and significant Big Data extends in their organization. Making your data to tell a story and the craft of envisioning information convincingly has turned into a significant piece of the Big Data world and progressively associations need to have these capacities in-house. Besides, as a rule, these experts are relied upon to realize how to picture in different instruments, for example, Spotfire, D3, Carto, and Tableau – among numerous others. Information Visualization Specialists should be versatile and inquisitive to guarantee they stay aware of most recent patterns and answers for a recount to their information stories in the most intriguing manner conceivable with regards to the board room. 

 

Big Data Architect

This is the place the Hadoop specialists come in. Ordinarily, a Big Data planner tends to explicit information issues and necessities, having the option to portray the structure and conduct of a Big Data arrangement utilizing the innovation wherein they practice – which is, as a rule, mostly Hadoop.

These representatives go about as a significant connection between the association (and its specific needs) and Data Scientists and Engineers. Any organization that needs to assemble a Big Data condition will require a Big Data modeler who can serenely deal with the total lifecycle of a Hadoop arrangement – including necessity investigation, stage determination, specialized engineering structure, application plan, and advancement, testing the much-dreaded task of deploying lastly.

Systems Architect 

This Big data professional is in charge of how your enormous information frameworks are architected and interconnected. Their essential incentive to your group lies in their capacity to use their product building foundation and involvement with huge scale circulated handling frameworks to deal with your innovation decisions and execution forms. You’ll need this individual to construct an information design that lines up with the business, alongside abnormal state anticipating the improvement. The person in question will consider different limitations, adherence to gauges, and varying needs over the business.

Here are some responsibilities that they play:

    • Determine auxiliary prerequisites of databases by investigating customer tasks, applications, and programming; audit targets with customers and assess current frameworks.
    • Develop database arrangements by planning proposed framework; characterize physical database structure and utilitarian abilities, security, back-up and recuperation particulars.
    • Install database frameworks by creating flowcharts; apply ideal access methods, arrange establishment activities, and record activities.
    • Maintain database execution by distinguishing and settling generation and application advancement issues, figuring ideal qualities for parameters; assessing, incorporating, and putting in new discharges, finishing support and responding to client questions.
    • Provide database support by coding utilities, reacting to client questions, and settling issues.


Artificial Intelligence Developer

The certain promotion around Artificial Intelligence is additionally set to quicken the number of jobs publicized for masters who truly see how to apply AI, Machine Learning, and Deep Learning strategies in the business world. Selection representatives will request designers with broad learning of a wide exhibit of programming dialects which loan well to AI improvement, for example, Lisp, Prolog, C/C++, Java, and Python.

All said and done; many people estimate that this popular demand for AI specialists could cause a something like what we call a “Brain Drain” organizations poaching talented individuals away from the universe of the scholarly world. A month ago in the Financial Times, profound learning pioneer and specialist Yoshua Bengio, of the University of Montreal expressed: “The industry has been selecting a ton of ability — so now there’s a lack in the scholarly world, which is fine for those organizations. However, it’s not extraordinary for the scholarly world.” It ; howeverusiasm to perceive how this contention among the scholarly world and business is rotated in the following couple of years.

Data Scientist

The move of Big Data from tech publicity to business reality may have quickened, yet the move away from enrolling top Data Scientists isn’t set to change in 2020. An ongoing Deloitte report featured that the universe of business will require three million Data Scientists by 2021, so if their expectations are right, there’s a major ability hole in the market. This multidisciplinary profile requires specialized logical aptitudes, specialized software engineering abilities just as solid gentler abilities, for example, correspondence, business keenness, and scholarly interest.

Data Engineer

Clean and quality data is crucial in the accomplishment of Big Data ventures. Consequently, we hope to see a lot of opening in 2020 for Data Engineers who have a predictable and awesome way to deal with information transformation and treatment. Organizations will search for these special data masters to have broad involvement in controlling data with SQL, T-SQL, R, Hadoop, Hive, Python and Spark. Much like Data Scientists. They are likewise expected to be innovative with regards to contrasting information with clashing information types with have the option to determine issues. They additionally frequently need to make arrangements which enable organizations to catch existing information in increasingly usable information groups – just as performing information demonstrations and their modeling.

IT/Operations Manager Job Description

In Big data industry, the IT/Operations Manager is a profitable expansion to your group and will essentially be in charge of sending, overseeing, and checking your enormous information frameworks. You’ll depend on this colleague to plan and execute new hardware and administrations. The person in question will work with business partners to comprehend the best innovation ventures to address their procedures and concerns—interpreting business necessities to innovation plans. They’ll likewise work with venture chiefs to actualize innovation and be in charge of effective progress and general activities.

Here are some responsibilities that they play:

  • Manage and be proactive in announcing, settling and raising issues where required 
  • Lead and co-ordinate issue the executive’s exercises, notwithstanding ceaseless procedure improvement activities  
  • Proactively deal with our IT framework 
  • Supervise and oversee IT staffing, including enrollment, supervision, planning, advancement, and assessment
  • Verify existing business apparatuses and procedures remain ideally practical and worth included 
  • Benchmark, dissect, report on and make suggestions for the improvement and development of the IT framework and IT frameworks 
  • Advance and keep up a corporate SLA structure

Conclusion

These are some of the best career paths that big data professionals can play after entering the industry. Honesty and hard work can always take you to the zenith of any field that you choose to be in. Also, keep upgrading your skills by taking newer certifications and technologies. Good Luck 

Visual Question Answering with Keras – Part 2: Making Computers Intelligent to answer from images

Making Computers Intelligent to answer from images

This is my second blog on Visual Question Answering, in the last blog, I have introduced to VQA, available datasets and some of the real-life applications of VQA. If you have not gone through then I would highly recommend you to go through it. Click here for more details about it.

In this blog post, I will walk through the implementation of VQA in Keras.

You can download the dataset from here: https://visualqa.org/index.html. All my experiments were performed with VQA v2 and I have used a very tiny subset of entire dataset i.e all samples for training and testing from the validation set.

Table of contents:

  1. Preprocessing Data
  2. Process overview for VQA
  3. Data Preprocessing – Images
  4. Data Preprocessing through the spaCy library- Questions
  5. Model Architecture
  6. Defining model parameters
  7. Evaluating the model
  8. Final Thought
  9. References

NOTE: The purpose of this blog is not to get the state-of-art performance on VQA. But the idea is to get familiar with the concept. All my experiments were performed with the validation set only.

Full code on my Github here.


1. Preprocessing Data:

If you have downloaded the dataset then the question and answers (called as annotations) are in JSON format. I have provided the code to extract the questions, annotations and other useful information in my Github repository. All extracted information is stored in .txt file format. After executing code the preprocessing directory will have the following structure.

All text files will be used for training.

 

2. Process overview for VQA:

As we have discussed in previous post visual question answering is broken down into 2 broad-spectrum i.e. vision and text.  I will represent the Neural Network approach to this problem using the Convolutional Neural Network (for image data) and Recurrent Neural Network(for text data). 

If you are not familiar with RNN (more precisely LSTM) then I would highly recommend you to go through Colah’s blog and Andrej Karpathy blog. The concepts discussed in this blogs are extensively used in my post.

The main idea is to get features for images from CNN and features for the text from RNN and finally combine them to generate the answer by passing them through some fully connected layers. The below figure shows the same idea.

 

I have used VGG-16 to extract the features from the image and LSTM layers to extract the features from questions and combining them to get the answer.

3. Data Preprocessing – Images:

Images are nothing but one of the input to our model. But as you already may know that before feeding images to the model we need to convert into the fixed-size vector.

So we need to convert every image into a fixed-size vector then it can be fed to the neural network. For this, we will use the VGG-16 pretrained model. VGG-16 model architecture is trained on millions on the Imagenet dataset to classify the image into one of 1000 classes. Here our task is not to classify the image but to get the bottleneck features from the second last layer.

Hence after removing the softmax layer, we get a 4096-dimensional vector representation (bottleneck features) for each image.

Image Source: https://www.cs.toronto.edu/~frossard/post/vgg16/

 

For the VQA dataset, the images are from the COCO dataset and each image has unique id associated with it. All these images are passed through the VGG-16 architecture and their vector representation is stored in the “.mat” file along with id. So in actual, we need not have to implement VGG-16 architecture instead we just do look up into file with the id of the image at hand and we will get a 4096-dimensional vector representation for the image.

4. Data Preprocessing through the spaCy library- Questions:

spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. As we have converted images into a fixed 4096-dimensional vector we also need to convert questions into a fixed-size vector representation. For installing spaCy click here

You might know that for training word embeddings in Keras we have a layer called an Embedding layer which takes a word and embeds it into a higher dimensional vector representation. But by using the spaCy library we do not have to train the get the vector representation in higher dimensions.

 

This model is actually trained on billions of tokens of the large corpus. So we just need to call the vector method of spaCy class and will get vector representation for word.

After fitting, the vector method on tokens of each question will get the 300-dimensional fixed representation for each word.

5. Model Architecture:

In our problem the input consists of two parts i.e an image vector, and a question, we cannot use the Sequential API of the Keras library. For this reason, we use the Functional API which allows us to create multiple models and finally merge models.

The below picture shows the high-level architecture idea of submodules of neural network.

After concatenating the 2 different models the summary will look like the following.

The below plot helps us to visualize neural network architecture and to understand the two types of input:

 

6. Defining model parameters:

The hyperparameters that we are going to use for our model is defined as follows:

If you know what this parameter means then you can play around it and can get better results.

Time Taken: I used the GPU on https://colab.research.google.com and hence it took me approximately 2 hours to train the model for 5 epochs. However, if you train it on a PC without GPU, it could take more time depending on the configuration of your machine.

7. Evaluating the model:

Since I have used the very small dataset for performing these experiments I am not able to get very good accuracy. The below code will calculate the accuracy of the model.

 

Since I have trained a model multiple times with different parameters you will not get the same accuracy as me. If you want you can directly download mode.h5 file from my google drive.

 

8. Final Thoughts:

One of the interesting thing about VQA is that it a completely new field. So there is absolutely no end to what you can do to solve this problem. Below are some tips while replicating the code.

  1. Start with a very small subset of data: When you start implementing I suggest you start with a very small amount of data. Because once you are ready with the whole setup then you can scale it any time.
  2. Understand the code: Understanding code line by line is very much helpful to match your theoretical knowledge. So for that, I suggest you can take very few samples(maybe 20 or less) and run a small chunk (2 to 3 lines) of code to get the functionality of each part.
  3. Be patient: One of the mistakes that I did while starting with this project was to do everything at one go. If you get some error while replicating code spend 4 to 5 days harder on that. Even after that if you won’t able to solve, I would suggest you resume after a break of 1 or 2 days. 

VQA is the intersection of NLP and CV and hopefully, this project will give you a better understanding (more precisely practically) with most of the deep learning concepts.

If you want to improve the performance of the model below are few tips you can try:

  1. Use larger datasets
  2. Try Building more complex models like Attention, etc
  3. Try using other pre-trained word embeddings like Glove 
  4. Try using a different architecture 
  5. Do more hyperparameter tuning

The list is endless and it goes on.

In the blog, I have not provided the complete code you can get it from my Github repository.

9. References:

  1. https://blog.floydhub.com/asking-questions-to-images-with-deep-learning/
  2. https://tryolabs.com/blog/2018/03/01/introduction-to-visual-question-answering/
  3. https://github.com/sominwadhwa/vqamd_floyd

How can AI and Machine learning impact healthcare industry?

Healthcare industry is a recession-proof one. Even in times of economic meltdown and financial distress, the healthcare industry can hold its own because mankind will always need healthcare. In fact, during the Great Depression in the US, when the economy was facing a severe slowdown, the healthcare industry expanded, adding 852,000 jobs.

Healthcare AI in the US is slated to reach $6.6 billion in value by 2021.

From clinical trials to new drug research & development, and from innovative medical devices to technology like nanoparticles, AI, and ML has touched every point and has the power to transform them completely.

In fact, according to a study by Accenture, AI applications in healthcare can result in global savings to the tune of $150 billion by 2026.

The possibilities are endless, and the results unthinkable if AI can be properly used.

Here are some of the ways AI and ML can impact the healthcare industry:

1. Solving the Iron Triangle

A problem that has plagued the world for many years the triangle aims to solve a fundamental healthcare problem: that of good quality, accessible treatment at low cost.

Providing all three at the same time is a major challenge in healthcare, as the cost of healthcare is usually high. Here, trying to improve one factor harms another.

But AI can solve this problem in the near future without breaking the triangle, by improving the current healthcare cost-structure. The key to it is AI, and smart machines, that the patient can use for self-treatment for the majority of times, cutting down treatment costs drastically, by reducing human contact and improving quality of life.

2. Diagnostics and Imaging

The US FDA has drastically increased investment on AI in radiology and diagnostics. And it’s not without reason.

The IDx-DR became the first AI system cleared by the US FDA to provide diagnostic decisions. It was a breakthrough discovery to detect early mild diabetic retinopathy. The device was accurate 87.5% of the times, and also detected patients who didn’t have the condition, correctly up to 89.5% of times.

The US FDA also permitted marketing of the Viz.AI a type of clinical decision support system designed to analyze CT scan results to identify possibilities of a stroke in the patients and send the results to a specialist to identify any block.

In fact, diagnostics is fast becoming one of the significant drivers of AI investment in healthcare.

These advances can impact the healthcare industry in a novel way. As more and more devices become AI-enabled, the landscape of healthcare delivery will change.

3. Early screening 

Early screening in case of most diseases can drastically improve the mortality rates of patients and cut down treatment costs by over 50%.

Let’s take the example of colorectal cancer.

The 5-year survival for Stage 1 CRC is around 90%, as compared to only 10% for Stage 4.

Early detection of CRC can be ideally treated with a minimally invasive endoscopy at a low cost of less than $5,000 per year. However, in the case of late-stage CRC, it requires multidisciplinary treatment with multiple surgeries, chemotherapy, and radiation, skyrocketing the costs.

And that is why early detection is essential, and that’s exactly what AI can do. There are already apps on the market that are doing this. For example, Autism & Beyond is a revolutionary app that leveraged the power of Apple’s ResearchKit to gather videos of children and detect their preference for the development of autism, using AI software.

AI used for early screening can save billions of taxpayer dollars of taxpayer money every year, and reduce out of pocket expenditure in the US drastically.

4. Drug research & development

According to the California Biomedical Research Association, it takes around 12 years for a drug to be conceived in the laboratory and go to the patient.

Only 1 out of 5000 drugs that are selected for pre-clinical testing are then used for human testing, and only 20% of them make it to the market for human use.

(image)

The cost to develop a new drug now is more than $2.5 billion.

It is only recently that AI is being used in drug research and discovery. The power of AI can be leveraged to streamline the drug discovery and drug repurposing processes. It can identify patients best suited to the trial, can identify patients in the most need for new medications and can predict any side-effects and idiosyncrasies beforehand.

All of these, for a start, can lead to much safer clinical trials with no unwanted drug reactions.

And then, there is the question of lowering costs. In fact, a study by Carnegie Mellon and a German university estimated that AI could lower drug discovery costs by as much as 70%.

This, in turn, will be transferred to patients in the form of lower drug prices, which will increase accessibility to better medications for patients and improve population health in general.

5. Surgery 

AI-enabled robotic-assisted surgeries are taking over the US. They are increasingly being used to reduce surgeon variations and improve quality.

‘Artificial intelligence can help surgeons perform better’ quotes Dr. John Birkmeyer, a chief clinical officer at Sound Physicians.

Advanced analytics and machine learning techniques are being used concomitantly used to unleash critical insights from the billions of data elements associated with robotic-assisted surgery. If used properly, this can help overcome attendant inefficiencies and improve patient health outcomes.

Artificial intelligence helps surgeons make better clinical decisions in real-time during surgery, and helps them understand the dynamics of the patient, especially during complex operations. It also reduces the length of stay of patients by 21%.

This is ultimately reflected in the patient’s post-operative care and long-term health. It also prevents patient readmissions, saving millions of dollars annually.

A study involving 379 orthopedic patients found out that AI-assisted robotic surgery resulted in five times fewer complications as compared to surgeons working alone.

According to Accenture, AI-assisted robotic surgery could save the US healthcare industry $40 billion annually, by 2026.

6. AI-assisted virtual nurses

AI-assisted virtual nurses could well end up saving the US healthcare industry $20 billion annually, by 2026.

They are available 24/7 to answer any patient queries, monitor patients, and guide them in any way they might want.

Currently, they act as a bridge for information exchange between care providers (doctors) and care receivers (patients), to decide what medications to start, the current health status, the most recent test results, and many other things.

It can save the patient many physical appointments with doctors, and also prevent high hospital readmission rates through simple, engaging, and intelligent care.

Care Angel is one of the finest virtual nurses around. Apart from all of the above, it can also provide wellness checks through voice and AI.

Wrap-Up 

AI and ML in healthcare are still at its infancy. Adoption at a large-scale is missing as of yet. To be successful in the healthcare domain, AI and ML need the endorsement of healthcare providers like physicians and nurses.

However, considerable investment is being made in AI in healthcare, and its increasing at a good rate.

AI in healthcare is currently aimed at improving patient outcomes, taking care of the interests of various stakeholders involved, increasing accessibility, and reducing healthcare costs.

In the near future, however, AI and ML, along with technologies like Data Science will take up a much more holistic role to drive healthcare forward.

The Future of AI in Dental Technology

As we develop more advanced technology, we begin to learn that artificial intelligence can have more and more of an impact on our lives and industries that we have gotten used to being the same over the past decades. One of those industries is dentistry. In your lifetime, you’ve probably not seen many changes in technology, but a boom around artificial intelligence and technology has opened the door for AI in dental technologies.

How Can AI Help?

Though dentists take a lot of pride in their craft and career, most acknowledge that AI can do some things that they can’t do or would make their job easier if they didn’t have to do. AI can perform a number of both simple and advanced tasks. Let’s take a look at some areas that many in the dental industry feel that AI can be of assistance.

Repetitive, Menial Tasks

The most obvious area that AI can help out when it comes to dentistry is with repetitive and menial simple tasks. There are many administrative tasks in the dentistry industry that can be sped up and made more cost-effective with the use of AI. If we can train a computer to do some of these tasks, we may be able to free up more time for our dentists to focus on more important matters and improve their job performance as well. One primary use of AI is virtual consultations that offices like Philly Braces are offering. This saves patients time when they come in as the Doctor already knows what the next steps in their treatment will be.

Using AI to do some basic computer tasks is already being done on a small scale by some, but we have yet to see a very large scale implementation of this technology. We would expect that to happen soon, with how promising and cost-effective the technology has proven to be.

Reducing Misdiagnosis

One area that many think that AI can help a lot in is misdiagnosis. Though dentists do their best, there is still a nearly 20% misdiagnosis rate when reading x-rays in dentistry. We like to think that a human can read an x-ray better, but this may not be the case. AI technology can certainly be trained to read an x-ray and there have been some trials to suggest that they can do it better and identify key conditions that we often misread.

A world with AI diagnosis that is accurate and quicker will save time, money, and lead to better dental health among patients. It hasn’t yet come to fruition, but this seems to be the next major step for AI in dentistry.

Artificial Intelligence Assistants

Once it has been demonstrated that AI can perform a range of tasks that are useful to dentists, the next logical step is to combine those skills to make a fully-functional AI dental assistant. A machine like this has not yet been developed, but we can imagine that it would be an interface that could be spoken to similar to Alexa. The dentist would request vital information and other health history data from a patient or set of patients to assist in the treatment process. This would undoubtedly be a huge step forward and bring a lot of computing power into the average dentist office.

Conclusion

It’s clear that AI has a bright future in the dental industry and has already shown some of the essential skills that it can help with in order to provide more comprehensive and accurate care to dental patients. Some offices like Westwood Orthodontics already use AI in the form of a virtual consult to diagnose issues and provide treatment options before patients actually step foot in the office. Though not nearly all applications that AI can provide have been explored, we are well on our way to discovering the vast benefits of artificial intelligence for both patients and practices in the dental healthcare industry.


Lisa Gao, DDS, MS | Westwood Orthodontics 1033 Gayley Ave #106, Los Angeles California 90024, 310-870-1823

Visual Question Answering with Keras – Part 1

This is Part I of II of the Article Series Visual Question Answering with Keras

Making Computers Intelligent to answer from images

If we look closer in the history of Artificial Intelligence (AI), the Deep Learning has gained more popularity in the recent years and has achieved the human-level performance in the tasks such as Speech Recognition, Image Classification, Object Detection, Machine Translation and so on. However, as humans, not only we but also a five-year child can normally perform these tasks without much inconvenience. But the development of such systems with these capabilities has always considered an ambitious goal for the researchers as well as for developers.

In this series of blog posts, I will cover an introduction to something called VQA (Visual Question Answering), its available datasets, the Neural Network approach for VQA and its implementation in Keras and the applications of this challenging problem in real life. 

Table of Contents:

1 Introduction

2 What is exactly Visual Question Answering?

3 Prerequisites

4 Datasets available for VQA

4.1 DAQUAR Dataset

4.2 CLEVR Dataset

4.3 FigureQA Dataset

4.4 VQA Dataset

5 Real-life applications of VQA

6 Conclusion

 

  1. Introduction:

Let’s say you are given a below picture along with one question. Can you answer it?

I expect confidently you all say it is the Kitchen without much inconvenience which is also the right answer. Even a five-year child who just started to learn things might answer this question correctly.

Alright, but can you write a computer program for such type of task that takes image and question about the image as an input and gives us answer as output?

Before the development of the Deep Neural Network, this problem was considered as one of the difficult, inconceivable and challenging problem for the AI researcher’s community. However, due to the recent advancement of Deep Learning the systems are capable of answering these questions with the promising result if we have a required dataset.

Now I hope you have got at least some intuition of a problem that we are going to discuss in this series of blog posts. Let’s try to formalize the problem in the below section.

  1. What is exactly Visual Question Answering?:

We can define, “Visual Question Answering(VQA) is a system that takes an image and natural language question about the image as an input and generates natural language answer as an output.”

VQA is a research area that requires an understanding of vision(Computer Vision)  as well as text(NLP). The main beauty of VQA is that the reasoning part is performed in the context of the image. So if we have an image with the corresponding question then the system must able to understand the image well in order to generate an appropriate answer. For example, if the question is the number of persons then the system must able to detect faces of the persons. To answer the color of the horse the system need to detect the objects in the image. Many of these common problems such as face detection, object detection, binary object classification(yes or no), etc. have been solved in the field of Computer Vision with good results.

To summarize a good VQA system must be able to address the typical problems of CV as well as NLP.

To get a better feel of VQA you can try online VQA demo by CloudCV. You just go to this link and try uploading the picture you want and ask the related question to the picture, the system will generate the answer to it.

 

  1. Prerequisites:

In the next post, I will walk you through the code for this problem using Keras. So I assume that you are familiar with:

  1. Fundamental concepts of Machine Learning
  2. Multi-Layered Perceptron
  3. Convolutional Neural Network
  4. Recurrent Neural Network (especially LSTM)
  5. Gradient Descent and Backpropagation
  6. Transfer Learning
  7. Hyperparameter Optimization
  8. Python and Keras syntax
  1. Datasets available for VQA:

As you know problems related to the CV or NLP the availability of the dataset is the key to solve the problem. The complex problems like VQA, the dataset must cover all possibilities of questions answers in real-world scenarios. In this section, I will cover some of the datasets available for VQA.

4.1 DAQUAR Dataset:

The DAQUAR dataset is the first dataset for VQA that contains only indoor scenes. It shows the accuracy of 50.2% on the human baseline. It contains images from the NYU_Depth dataset.

Example of DAQUAR dataset

Example of DAQUAR dataset

The main disadvantage of DAQUAR is the size of the dataset is very small to capture all possible indoor scenes.

4.2 CLEVR Dataset:

The CLEVR Dataset from Stanford contains the questions about the object of a different type, colors, shapes, sizes, and material.

It has

  • A training set of 70,000 images and 699,989 questions
  • A validation set of 15,000 images and 149,991 questions
  • A test set of 15,000 images and 14,988 questions

Image Source: https://cs.stanford.edu/people/jcjohns/clevr/?source=post_page

 

4.3 FigureQA Dataset:

FigureQA Dataset contains questions about the bar graphs, line plots, and pie charts. It has 1,327,368 questions for 100,000 images in the training set.

4.4 VQA Dataset:

As comapred to all datasets that we have seen so far VQA dataset is relatively larger. The VQA dataset contains open ended as well as multiple choice questions. VQA v2 dataset contains:

  • 82,783 training images from COCO (common objects in context) dataset
  • 40, 504 validation images and 81,434 validation images
  • 443,757 question-answer pairs for training images
  • 214,354 question-answer pairs for validation images.

As you might expect this dataset is very huge and contains 12.6 GB of training images only. I have used this dataset in the next post but a very small subset of it.

This dataset also contains abstract cartoon images. Each image has 3 questions and each question has 10 multiple choice answers.

  1. Real-life applications of VQA:

There are many applications of VQA. One of the famous applications is to help visually impaired people and blind peoples. In 2016, Microsoft has released the “Seeing AI” app for visually impaired people to describe the surrounding environment around them. You can watch this video for the prototype of the Seeing AI app.

Another application could be on social media or e-commerce sites. VQA can be also used for educational purposes.

  1. Conclusion:

I hope this explanation will give you a good idea of Visual Question Answering. In the next blog post, I will walk you through the code in Keras.

If you like my explanations, do provide some feedback, comments, etc. and stay tuned for the next post.

How To Remotely Send R and Python Execution to SQL Server from Jupyter Notebooks

Introduction

Did you know that you can execute R and Python code remotely in SQL Server from Jupyter Notebooks or any IDE? Machine Learning Services in SQL Server eliminates the need to move data around. Instead of transferring large and sensitive data over the network or losing accuracy on ML training with sample csv files, you can have your R/Python code execute within your database. You can work in Jupyter Notebooks, RStudio, PyCharm, VSCode, Visual Studio, wherever you want, and then send function execution to SQL Server bringing intelligence to where your data lives.

This tutorial will show you an example of how you can send your python code from Juptyter notebooks to execute within SQL Server. The same principles apply to R and any other IDE as well. If you prefer to learn through videos, this tutorial is also published on YouTube here:


 

Environment Setup Prerequisites

  1. Install ML Services on SQL Server

In order for R or Python to execute within SQL, you first need the Machine Learning Services feature installed and configured. See this how-to guide.

  1. Install RevoscalePy via Microsoft’s Python Client

In order to send Python execution to SQL from Jupyter Notebooks, you need to use Microsoft’s RevoscalePy package. To get RevoscalePy, download and install Microsoft’s ML Services Python Client. Documentation Page or Direct Download Link (for Windows).

After downloading, open powershell as an administrator and navigate to the download folder. Start the installation with this command (feel free to customize the install folder): .\Install-PyForMLS.ps1 -InstallFolder “C:\Program Files\MicrosoftPythonClient”

Be patient while the installation can take a little while. Once installed navigate to the new path you installed in. Let’s make an empty folder and open Jupyter Notebooks: mkdir JupyterNotebooks; cd JupyterNotebooks; ..\Scripts\jupyter-notebook

Create a new notebook with the Python 3 interpreter:

 

To test if everything is setup, import revoscalepy in the first cell and execute. If there are no error messages you are ready to move forward.

Database Setup (Required for this tutorial only)

For the rest of the tutorial you can clone this Jupyter Notebook from Github if you don’t want to copy paste all of the code. This database setup is a one time step to ensure you have the same data as this tutorial. You don’t need to perform any of these setup steps to use your own data.

  1. Create a database

Modify the connection string for your server and use pyodbc to create a new database.

import pyodbc  
# creating a new db to load Iris sample in 
new_db_name = "MLRemoteExec" connection_string = "Driver=SQL Server;Server=localhost\MSSQLSERVER2017;Database={0};Trusted_Connection=Yes;" 

cnxn = pyodbc.connect(connection_string.format("master"), autocommit=True) 

cnxn.cursor().execute("IF EXISTS(SELECT * FROM sys.databases WHERE [name] = '{0}') DROP DATABASE {0}".format(new_db_name)) 

cnxn.cursor().execute("CREATE DATABASE " + new_db_name)

cnxn.close()

print("Database created") 
  1. Import Iris sample from SkLearn

Iris is a popular dataset for beginner data science tutorials. It is included by default in sklearn package.

from sklearn import datasetsimport pandas as pd
# SkLearn has the Iris sample dataset built in to the packageiris = datasets.load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
  1. Use RecoscalePy APIs to create a table and load the Iris data

(You can also do this with pyodbc, sqlalchemy or other packages)

from revoscalepy import RxSqlServerData, rx_data_step
# Example of using RX APIs to load data into SQL table. You can also do this with pyodbc
table_ref = RxSqlServerData(connection_string=connection_string.format(new_db_name), table="Iris")rx_data_step(input_data = df, output_file = table_ref, overwrite = True)print("New Table Created: Iris")
print("Sklearn Iris sample loaded into Iris table")

Define a Function to Send to SQL Server

Write any python code you want to execute in SQL. In this example we are creating a scatter matrix on the iris dataset and only returning the bytestream of the .png back to Jupyter Notebooks to render on our client.

def send_this_func_to_sql():
    from revoscalepy import RxSqlServerData, rx_import
    from pandas.tools.plotting import scatter_matrix
    import matplotlib.pyplot as plt
    import io    
# remember the scope of the variables in this func are within our SQL Server Python Runtime
    connection_string = "Driver=SQL Server;Server=localhost\MSSQLSERVER2017; Database=MLRemoteExec;Trusted_Connection=Yes;"

# specify a query and load into pandas dataframe df
    sql_query = RxSqlServerData(connection_string=connection_string, sql_query = "select * from Iris")

    df = rx_import(sql_query)
    scatter_matrix(df)

# return bytestream of image created by scatter_matrix
    buf = io.BytesIO()
    plt.savefig(buf, format="png")
    buf.seek(0)
    return buf.getvalue()

Send execution to SQL

Now that we are finally set up, check out how easy sending remote execution really is! First, import revoscalepy. Create a sql_compute_context, and then send the execution of any function seamlessly to SQL Server with RxExec. No raw data had to be transferred from SQL to the Jupyter Notebook. All computation happened within the database and only the image file was returned to be displayed.

from IPython import display
import matplotlib.pyplot as plt 
from revoscalepy import RxInSqlServer, rx_exec# create a remote compute context with connection to SQL Server

sql_compute_context = RxInSqlServer(connection_string=connection_string.format(new_db_name))

# use rx_exec to send the function execution to SQL Server

image = rx_exec(send_this_func_to_sql, compute_context=sql_compute_context)[0]

# only an image was returned to my jupyter client. All data remained secure and was manipulated in my db.

display.Image(data=image)

While this example is trivial with the Iris dataset, imagine the additional scale, performance, and security capabilities that you now unlocked. You can use any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server. We also offer leading edge, high-performance algorithms in Microsoft’s RevoScaleR and RevoScalePy APIs. Using these with the latest innovations in the open source world allows you to bring unparalleled selection, performance, and scale to your applications.

Learn More

Check out SQL Machine Learning Services Documentation to learn how you can easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. Train and store machine learning models in your database bringing intelligence to where your data lives.

Other YouTube Tutorials:

Interview – The Importance of Machine Learning for the Data Driven Business

To become more data-driven, organizations must mature their analytics and automate more of their decision making processes for innovation and differentiation. Data science seems like the right approach, yet is a new and fast moving field that seems to have as many dead ends as it has high ways to value. Cloudera Fast Forward Labs, led by Hilary Mason, shows companies the way.

Alice Albrecht is a research engineer at Cloudera Fast Forward Labs.  She spends her days researching the latest and greatest in machine learning and artificial intelligence and bringing that knowledge to working prototypes and delivering concrete advice for clients.  Prior to joining Fast Forward Labs, Alice worked in both finance and technology companies as a practicing data scientist, data science leader, and – most recently – a data product manager.  In addition to teaching machines to do cool things, Alice is passionate about mentoring and helping others grow in their careers.  Alice holds a PhD from Yale in cognitive neuroscience where she studied how humans summarize sensory information from the world around them and the neural substrates that underlie those summaries.

Read this article in German:
“Interview – Die Bedeutung von Machine Learning für das Data Driven Business“

Data Science Blog: Ms. Albrecht, you are a well-known keynote speaker for data science and artificial intelligence. While data science has arrived business already, deep learning seems to be the new trend. Is artificial intelligence for business already normal business or is it an overrated hype?

I’d say it isn’t either of those two options.  Data science is now widely adopted but companies still struggle to integrate this new discipline into their existing businesses.  As for deep learning, it really depends on the company that’s looking into using this technique.  I wouldn’t say that deep learning is by any means part of business as usual- nor should it be.  It’s a tool like any other and building a capacity for using a tool without clearly defined business needs is a recipe for disaster.

Data Science Blog: Just to make sure what we are talking about: What are the differences and overlaps between data analytics, data science, machine learning, deep learning and artificial intelligence?

Here at Cloudera Fast Forward Labs, we like to think of data analytics as collecting data and counting things (mostly for quick charts and reports).  Data science solves business problems by counting cleverly and predicting things with the data that’s collected.  Machine learning is about solving problems with new kinds of feedback loops that improve with more data.  Deep learning is a particular type of machine learning and is not itself a separate concept or type of tool.  Artificial intelligence taps into something more complicated than what we’re seeing today – it’s much broader than training machines to repetitively do very specialized tasks or solve very narrow problems.

Data Science Blog: And how can we add the context to big data?

From a theoretical perspective, data science has been around for decades. The building blocks for modern day machine learning, deep learning and artificial intelligence are based on mathematical theorems  that go back to the 1940’s and 1950’s. The challenge was that at the time, compute power and data storage capacity were simply too expensive for the approaches to be implemented. Today that’s all changed.. Not only has the cost of data storage dropped considerably, open source technology like Apache Hadoop has made it possible to store any volume of data at costs approaching zero. Compute power, even highly specialised chip architectures, are now also available on demand and only for the time organisations need them through public and private cloud solutions. The decreased cost of both data storage and compute power, together with a growing list of tools and resources readily available via the open source community allows companies of any size to benefit from data (no matter that size of that data).

Data Science Blog: What are the challenges for organizations in getting started with data science?

I see two big challenges when getting started with data science.  One is ensuring that you have organizational alignment around exactly what type of work data scientists will deliver (and timing for those projects).  The second hurdle is around ensuring that you have the right data in place before you start hiring data scientists. This can be tricky if you don’t have in-house expertise in this area, so sometimes it’s better to hire a data engineer or a data strategist (or director of data science) before you ever get started building out a data science team.

Data Science Blog: There are many discussions about how to build a data-driven business. Is it just about using data science to get a better understanding of customer behavior?

No, being data driven doesn’t just mean better understanding your customers (though that is one way that data science can help in an organization).  Aside from building an organization that relies on data and analytics to help them make decisions (about customer behavior or otherwise), being a data-driven business means that data is powering your core products.

Data Science Blog: The number of technologies, tools and frameworks is increasing. For organizations this also means increasing complexity. Do companies need to stay always up-to-date or could it be an advice to wait and imitate pioneers later?

While it’s not critical (or advisable) for organizations to adopt every new advancement that comes along, it is critical for them to stay abreast of emerging frameworks.  If a business waits to see what others are doing, and therefore don’t invest in understanding how new advancements can affect their particular business, they’ve likely already missed the boat.

Data Science Blog: Global players have big budgets just for doing research and setting up data labs. Middle-sized companies need to see the break even point soon. How can we accelerate the value generation of data science?

Having a team that is highly focused on a specific set of projects that are well-scoped and aligned to the business makes all the difference.  Data science and machine learning don’t have to sacrifice doing research and being innovative in order to produce value.  The biggest difference is that smaller teams will have to be more aware of how their choice of project fits into emerging frameworks and their particular acute and near term business needs.

Data Science Blog: How does Cloudera Fast Forward Labs help other organizations to accelerate their start with machine learning?

We advise organizations, based on their particular needs, on what the latest advancements are in machine learning and data science, how to build and structure their data teams to develop the capabilities they need to meet their goals, and how to quickly implement custom forward-looking solutions using their own data and in-house expertise.

Data Science Blog: Finally, a question for our younger readers who are looking for a career as a data expert: What makes a good data scientist? Do you like to work with introverted coding nerds or the data loving business experts?

A good data scientists should be deeply curious and have a love for the ways in which data can lead to new discoveries and power the next generation of products.  We expect the people who thrive in this field to come from a variety of backgrounds and experiences.

Deep Learning and Human Intelligence – Part 1 of 2

Many people are under the impression that the new wave of data science, machine learning and/or digitalization is new, that it did not exist before. But its history is as long as the history of humanity and/or science itself.  The scientific discovery could hardly take place without the necessary data. Even the process of discovering the numbers included elements of machine learning: pattern recognition, comparison between different groups (ranking), clustering, etc. So what differentiates mathematical formulas from machine learning and how does it relate to artificial intelligence?

There is no difference between the two if seen from the perspective of formulas however, such a perspective limits the type of data to which they can be applied. Data stored via tables consist of structured data and are stored in so-called relational databases. The reason for such a data storage is the connection between different fields that assume a well-established structure in advance, such as a company’s sales or balance sheet. However, with the emergence of personal computers, many of the daily activities have been digitalized: music, pictures, movies, and so on. All this information is stored unrelated to other data and therefore called unstructured data.

IEEE International Conference on Computer Vision (ICCV), 2015, DOI: 10.1109/ICCV.2015.428

Copyright: IEEE International Conference on Computer Vision (ICCV), 2015, DOI: 10.1109/ICCV.2015.428

The essence of scientific discoveries was and will be structure. Not surprisingly, the mathematical formulas revolve around relations between variables – information, in general. For example, Galileo derived the law of falling balls from measuring the successive hight of a falling ball. The main difficulty was to obtain measurements at regular time intervals. What about if the data is not structured, which mathematical formula should be applied then? There is a distribution of people’s height, but no distribution for the pictures taken in all holidays for the last year, there is an amplitude for acoustic signals, but no function that detects the similarity between two songs. This is one of the reasons why machine learning focuses heavily on clustering and classification.

Roughly speaking, these simple examples are enough to categorize the difference between scientific discovery and machine learning. Science is about discovering relationships between different variables, Machine Learning tries to automatize processes. Every technical improvement is part of the automation, so why is everything different in this case? Because the current automation deals with human intelligence. The car automates the walking, the kitchen stove the fire, but Machine Learning parts of the human intelligence. There is a difference between the previous automation steps and those of human intelligence. All the previous ones are either outside the human body – such as Fire – or unconsciously executed (once learned) – walking, spinning, etc. The automation induced by Machine Learning affects a part of the human intelligence that we consciously perceive. Of course, today’s machine learning tools are unable to automate all human intelligence, but it is a fascinating step in that direction.

A breakthrough in Machine Learning tasks was achieved in 2012 when the first Deep Learning algorithm for detecting types of images, reached near-human accuracy. It could appreciate the likelihood that the image is a human face, a train, a ball or a fish without having “seen” the picture before. Such an algorithm can be used in various areas:  personally – facial recognition in pictures and/or social media – as tagging of images or videos, medicine – cancer detection, etc. For understanding such cutting-edge issues of classification, one cannot avoid understanding how Deep Learning works. To see the beauty of such algorithms and, at the same time, to be able to comprehend the difficulty of working with them, an example will be the best guide.

The building blocks of Deep Learning are neurons, operational units, which perform mathematical operations or logical operations like AND, OR, etc., and are modelled after the neurons in the brain. Already in the 1950’s two neuroscientist, Hubel and Wiesel, observed that not all neurons in the brain are responding in the same fashion to visual stimuli. Some responded only to horizontal lines, whereas others to vertical lines, with other words, the brain is constructed with specialized neurons. Groups of such neurons are called, in the Machine Learning community, layers. Like in the brain, neurons with different properties are clustered in different layers. This implies that layers have also specific properties and have to be arranged in a specific way, called architecture. It is this architecture which differentiates Deep Learning from Artificial Neuronal Networks (ANN are similar to a layer).

Unfortunately, scientists still haven’t figured out how the brain works, thus to discover how to train Deep Learning from data was not an easy task, and is also the reason why another example is used to explain the training of Deep Learning: the eye. One has always to remember: once it is known how Deep Learning works, it is simple to find example which illustrates the working mechanism.  For such an analogy, it is sufficient for someone without any knowledge about Deep Learning, to keep in mind only the elements that compose such architectures: input data, different layers of neurons, output layers, ReLu’s.

Input data are any type of information, in our example it is light. Of course, that Deep Learning is not limited only to images or videos, but also to sound and/or time series, which would imply that the example would be the ear and sound waves, or the brain and numbers.

Layers can be seen as cells in the eye. It is well known that the eye is formed of different layers connected to each other with each of them having different properties, functionalities. The same is true also for the layers of a Deep Learning architecture: one can see the neurons as cells of the layer as the tissue. While, mathematically, the neurons are nothing more than simple operations, usually linear weight functions, they can be seen as the properties of individual cells. Each layer has one weight matrix, which gives the neuron (and layer) specific properties depending on the data and the task at hand.

It is here that the architecture becomes very important. What Deep Learning offers is a default setting of the layers with unknown weights. One can see this as trying to build an eye knowing that there are different types of cells and different ways how tissues of such cells can be arranged, but not which cell exactly is needed (with what properties) and which arrangement of layers works best. Such an approach has the advantage that one is capable of building any type of organ desired, but the disadvantage is also very obvious: it is time consuming to find the appropriate cell properties and layers arrangements.

Still, the strategy of Deep Learning is a significant departure from the Machine Learning approaches. The performance of Machine Learning methods is as good as the features engineering performed by Data Scientists, and thus depending on the creativity of the Data Scientist. In the case of Deep Learning the engineers of the features is performed automatically as part of the model building. This is a huge improvement, as the only difficult task is to have enough data and computer power to find the right weights matrices. Such an endeavor was performed also by nature for the eye — and is also the reason why one can choose it as an example for Deep Learning — evolution. It is not surprising that Deep Learning is one of the best direction scientists have of Artificial Intelligence today.

The evolution of the eye can be seen, from the perspective of Data Scientists, as the continuous training of a Deep Learning architecture which enables to recognize and track one or more objects. The performance of the evolutional process can be summed up as the fine tuning of the cells which are getting more and more susceptible to light and the adaptation of layers to enable a better vision. Different animals in different environments and different targets — as the hawk and the fly — developed different eyes than humans, but they all work according to the same principle. The tasks that Deep Learning is performing today are similar, for example it can be used to drive cars but there is still a difference:  there is no connection to other organs. Deep Learning is not the approximation of an Artificial Organism, like an android, but a simplified Artificial Organ that can work on its own.

Returning to the working mechanism of the Deep Learning architecture, we can already follow the analogy of what happens if a ray of light is hitting the eye. Once the eye is fully adapted to the task, one can followed how the information enters the Deep Learning architecture (Artificial Eye) by penetrating the input layer. already here arises the question, what kind of eye is the best? One where a small source of light can reach as many neurons as possible, or the one where the light sources reaches only few neurons? In order to take such a decision, a last piece of the puzzle is required: ReLu. One can see them as synapses between neurons (cells) and/or similarly for tissue. By using continuous functions, such as the shape of the latter ‘S’ (called sigmoid), the information from one neuron will be distributed over a large number of other neurons. If one uses the maximum function, then only few neurons are updated with processed information from earlier layers.

Such sparse structures between neurons, was a major improvement in the development of the technique of training Deep Learning architectures. Again, it has a strong evolutionary analogy: energy efficiency. By needing less neurons, the tissues and architecture are both kept to a minimal size which enables flexibility in development and less energy. As the information is process by the different layers, the Artificial Eye is gathering more and more complex (non-linear) structures — the adapted features –, which help to decide, from past experience, what kind of object is detected.

This was part 1 of 2 of the article series. Continue with Part 2.