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Gain deeper insights resulting in optimized business operations, attain competitive advantages and anticipate solutions to emerging market opportunities.  

Design and build a scalable data platform that has efficient data pipelines that will deliver data across the business in an efficient and cost-effective manner to enable true business outcomes. From collected internal and external data, we can help design and build custom AI & machine learning frameworks and models that help you generate useful insights to deliver you company real-world benefits. 

Let us be your go-to partner. We are happy to engage in a single, multiple or all project phases. 

Laying down the groundwork by establishing a shared vision through strategy and solution architecture. Test hypotheses with very tangible POC/POV/Pilots. 

Strategy & Discovery 

Making sure to capture your pain-points, needs & wishes and translating them into requirements. 

Business Analysis 

Transforming your vision and goals into a measurable and concrete process. 

Process Design 

Designing, building and managing machine learning frameworks that allow businesses to innovate faster. 

Machine Learning Development 

Making sure to deploy and maintain ML systems in production reliably and efficiently. 

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Testing is an integral part of the SW development process. We believe in continuous testing across all project phases. 

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## Customer  

Dickey’s Barbecue Pit operates more than 530 locations in 43 states. They still slow smoke all meats on-site just the same way they did in 1941.  


## Challenge  

Like all restaurants, Dickey’s needed better insight into multiple information sources: sales from multiple channels, inventory and food cost, labor and scheduling, and multiple internal operational systems.  With an ever-growing franchise, Dickey’s was also pushing new programs to the store:  Wi-Fi hotspots, loyalty programs, online ordering, customer sentiment systems, and email marketing solutions.  With this treasure trove of new data assets and the need to provide advanced analytics to both corporate and franchise users, Dickey’s needed an innovative solution to scale with their growth plans.  


## Solution  

iOLAP architected a secure and scalable solution on the AWS platform.  Store sales and labor data is pushed to S3 every 5 minutes and processed into Redshift for near real time analytics using Lambda.  End of day sales and labor, inventory, and supply chain sources are integrated nightly using Syncsort DMX running on EC2.  Loyalty transactions are delivered to SQS and processed in micro batches throughout the day.  Using API’s, we pull online ordering and email marketing data on demand.  Yellowfin’s BI platform runs on EC2 and provides a clean and consistent data visualization layer for more than 600 end users via web browser or mobile device.  


## Benefits  

- Dickey’s has a best of breed technology stack at a fraction of the cost of traditional approaches
- They’re able to complete analysis and build applications for customer segmentation, menu optimization, media spend optimization, and site selection
- The framework allows for rapid innovation and ingestion of new information assets.  
- 30% savings in compute costs
- TCO reduced by 54 percent. AWS enabled Dickey’s to scale at a fraction of its on-premises cost
  
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Frisco, Texas

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Austin, Texas

Rijeka, Croatia

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TCO reduced by 54 percent. AWS enables Dickey’s to scale at a fraction of its on-premises cost.

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TCO reduced by 54 percent. AWS enables Dickey’s to scale at a fraction of its
on-premises cost.

Dickey's Barbecue Pit

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## Client Description
From two original employees in Corpus Christi to over 36,000 across 10 states, Whataburger is more successful than ever. In 2015, the company achieved $2 billion in sales. Whataburger has been recognized as the top performer in Nation’s Restaurant News’ Social 200 which spotlights companies with the highest social engagement. 

## Challenge
Whataburger had a need to detect cashier fraud from its POS transactions across all its 700+ store locations. It was a manual process and a huge effort to identify suspect cashier transactions and interview the cashiers/supervisors for such locations on regular basis.  
Additionally, Whataburger was experiencing long ETL run times and reporting SLA’s continually ran past target time on the previous database platform.  Whataburger purchased two Netezza Makos to help speed up overall data warehouse usage but did not have any in-house Netezza expertise.

## Solution
iOLAP helped Whataburger architect, build & develop solution for detecting fraud for POS transactions in near real-time basis using machine learning algorithms and models. The solution was developed in Hadoop (IBM BigInsights) platform involving Spark, BigSQL, Hive, HDFS, Netezza & MicroStrategy. Transactions were loaded in micro batches and data was scored to identify potential frauds. Alerts were sent to users whenever such potential fraud transactions were identified.

## Benefits
Whataburger’s loads, which previously ran all-day, now complete in a few hours and all reporting SLA’s are met.  Leveraging the new Netezza and Hadoop platform, Whataburger is able to identify potential fraud transactions across all its locations in near real-time basis through automation.
  
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Leveraging the new Netezza and Hadoop platform enables to identify potential fraud transactions across all locations in near real-time basis through automation.

Whataburger

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## Client Description
Lucia Health Guidelines is the first clinical decision support tool that leverages machine learning and the power of cloud computing to help maintain clinical guidelines at the point of care. Lucia’s mission is to create a future with better care, better outcomes, and better economics for our healthcare system.

## Challenge
American Heart Association and the American College of Cardiology have made clear-cut guidelines based on scientific evidence and outcomes data for the treatment of atrial fibrillation (‘AF’, ‘Afib’) and prevention of stroke.  Yet, Xian MD and colleagues at Duke University (March 2017) reported that most patients with atrial fibrillation are not being treated as per the guidelines in the community setting.  In fact, they reviewed the charts of ~94,000 strokes victims with atrial fibrillations and 84% (~80,000 patients) did not receive the recommended anticoagulation as per the guidelines prior to their strokes.  Xian believes that an estimated 58,000 to 88,000 strokes might be preventable per year if the Afib treatment guidelines are followed.   As a country we spend about $6Billion a year on Afib/prevention of stroke and ~$83Billion a year on the long-term care of stroke victims.

Here is our opportunity to save lives, prevents strokes, and save tens of billions healthcare dollars by helping clinicians maintain these Afib guidelines.

## Solution
iOLAP designed and implemented a well-architected, HIPAA-compliant, solution that captures and analyzes EKG images using custom computer vision code. These EKG results are compared with patient data to determine a personalized risk assessment.

## Benefits
Lucia can deliver a scalable, fully-automated solution which brings the equivalent expertise of highly skilled clinicians trained in AF to any point of care. This will allow patients to be discharged with optimal therapy and referral to a specialist for continued care.
  
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Solution creation that captures and analyzes EKG images using custom computer vision code. These EKG results are compared with patient data to determine a personalized risk assessment.

Lucia Health Guidelines

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Design and build a scalable data platform that has efficient data pipelines that will deliver data across the business in an efficient and cost-effective manner to enable true business outcomes. From collected internal and external data, we can help design and build custom AI & Machine Learning frameworks and models that help you generate useful insights to deliver your company real-world benefits.   
  


Let us be your go-to partner. We are happy to engage in a single, multiple or all project phases. 

AI & Machine Learning 

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AI & Machine Learning

Amazon Web Services released SageMaker Studio at re:Invent 2019. As a fully integrated development environment for machine learning, SageMaker Studio allows storage and collection of all the development facets users need in one place. It promises great assets for established ML practitioners, as well as for those less experienced in the domain. The main purpose of the Studio should be speeding up and simplifying the whole ML process workflow.
  
![1.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/1_f72f52a617.jpg)
  
What’s important to note is that all tools inside SageMaker Studio are easy to use from the console; it needs to be quick and understandable, especially for users that are not developers. Why is this important?
  
Business owners often go through a dilemma when they need to hire a data scientists or engage their own company in data science projects. They are assessing if they are good candidates for such projects. Would it be beneficial for them to join, what are possible positive or negative side effects? Sometimes they just need more understanding of the whole process to be able to mitigate the possible risks. While all these questions could be answered by the data scientists estimating the situation and analyzing available data, it is useful to have an accessible toolkit that showcases the potential and suitability of a data science project. Utilizing some of the tools available in SageMaker studio is intuitive, even for inexperienced users.
  
This blog will show the tools available in SageMaker Studio and deep-dive into SageMaker AutoPilot, a tool to solve or speed up the biggest part of all data science projects – data preparation and feature engineering.
  
## SageMaker Studio General Tools
  
First, let’s look at the general features of SageMaker Studio Tools:
  
- Notebooks
- Debugger
- Experiments
- Model Monitor
  
Some of the tools available in SageMaker Studio are new, while some are improvements of existing tools.
  
**SageMaker Notebooks** are like Jupyter notebooks in look and feel. They are among the first choices of data scientists when beginning a project, since they offer an option to track code and notes at the same time. However, AWS Notebooks has provided much more – Studio delivers single-click notebooks for the SageMaker environment. The underlying compute resources are fully elastic and integrated with other AWS services, so it’s easy to add or remove additional resources without interrupting work. AWS has had SageMaker Notebooks in their catalog since 2018, but with the release of SageMaker Studio, usage has been simplified and collaboration has been improved as multiple users can interact with the notebooks simultaneously.
  
**SageMaker Debugger** is made to simplify the debugging process. This feature is expected to improve over time. AWS provides in-depth explanations, including code snippets showing how the tool can help developers debug Tensorflow bugs. This feature is probably the most important step to have ML processes automated. The hardest parts of any ML process are preparation and debugging, so this feature is quite welcomed by data scientists, whether debugging fully trained or new models.
  
**SageMaker Experiments** are collections of trials – whether we’re referring to a group of different methods that are applied to the same problem or other similar training jobs. This is handy since you often have no way of knowing how long a job will continue to run, or if it has silently crashed in the background. The Experiments feature should be a useful addition for cloud-based jobs, large data sets, or GPU-intensive projects.
  
**SageMaker Model Monitor** is one of the most important features after the post-deployment phase. Even after a model is put into production, the work is not completely done. Model monitoring is necessary because a model’s performance may worsen over time due to changing trends in the input data or unexpected problems. Model Monitor helps alert model maintainers about input data drift, and consequently monitors the model’s performance. A system that could alert model maintainers to these changes automatically is valuable because it mitigates the consequences of the data change and lowers the cost of post deployment in the long run. That way, data scientists on the team can focus on other important things. Considering all this, SageMaker Monitor presents a clear benefit of standardizing model hosting on SageMaker Endpoints.
  
## SageMaker AutoPilot section
  
**SageMaker AutoPilot** is part of the AutoML category, which automatically trains ML models from CSV data files. The AutoPilot is a great step forward in the AWS’s attempt to accelerate the most tedious part of ML development – the exploratory analysis or preparation phase. Approximately 70% – 90% of the work on ML projects is dedicated to cleaning and preparing the data in order to be optimally used by ML algorithms. Considering this, every tool that can lower the mentioned percentage and amount of time spent on the exploratory analysis will bring a reduction in the costs, and contribute to the faster solution creation, which again provides data scientists with more time to concentrate on something else.
  
![2.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/2_db9476816f.jpg)
  
At the time of release, AWS SageMaker Studio was available only in the US East (Ohio) region, but now it is also available in S. East (N. Virginia), US West (Oregon), and Europe (Ireland) and in time we expect its availability to spread to even more regions.
  
Here we will show how to start with Studio from the console and provide some useful links for official and more detailed documentation. An AWS SSO or IAM account is required to sign into AWS Studio – starting through the console is the easiest way.
  
![3.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/3_f3fdc50164.png)
  
This is the SageMaker Studio GUI after opening the Studio.
  
![4.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/4_e933db9abf.png)
  
Here it’s possible to click one button to open a new notebook and start deployment of the model or an AutoPilot experiment.
  
![5.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/5_611b3411bf.png)
  
To proceed with the auto pilot experiment, the user needs to enter the name, S3 bucket where the data in csv format is located, and where the results will be stored. The user also needs to define the target they want to classify or predict on. It’s important to choose between different types of ML problems, for now the choice is between classification or regression, but in time AWS will add more options. If unsure, the user can leave Auto option selected and SageMaker studio should recognize the problem it’s facing there. Choosing between having a complete experiment or not, the user decides if they want to have an optimized model after its completion or just a general idea what models are best to try out, given the data. The second option is shorter in terms of execution but doesn’t provide the tuned algorithm.
  
After the setup, AutoPilot starts the process of data analysis, feature engineering, and model tuning if a complete experiment is chosen in the previous step. Data analysis and feature engineering are the most time-consuming steps in the ML workflow, so having those two solved automatically allows data scientists to spend more time on more complex steps. For now, it’s only necessary to have the data ready in CSV format with the recommendation that it contains some dose of structure – i.e. if data is categorical, having different spelling for the same thing is not optimal – “red shirt”, “shirt red”, “red shirts” will be recognized as different categories by the AutoPilot even though they present the same category, due to the non-standardized naming.
  
After the first step of data analysis is finished, the user gets two notebooks generated as information.
  
![6.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/6_9d66341fe6.png)
  
The first one is a Data Exploration notebook, and the second one is a candidate generation notebook.
  
The Data Exploration notebook provides insights about the dataset (in our example we use customerTargeting). The notebook is informative and gives ideas on what should be done to optimize the future ML workflow and data usage, starting with “what’s the percentage of missing values” to the more complex “some data seems to be outliers”.
  
![7.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/7_0f138d441d.png)
  
The second notebook created for the user by AutoPilot is “SageMaker Autopilot Candidate Definition”. This notebook provides info on the suggested workflow together with the code definition and descriptions, which is helpful if the user doesn’t have much experience with the suggested models.
  

At the Model tuning step, it tries out multiple models and hyperparameters in order to get the best score on different metrics. This step will take more time, but again, it’s not required for the user to know anything about the code that runs in the background. Once finished, it’s possible to click on “Deploy model”.
  
![8.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/8_362481fdf1.png)
  
## Conclusion

To conclude, AWS SageMaker Studio provides features that guide the user from end to end in the ML workflow in just a few clicks, while the user doesn’t need to know much of the coding. This approach could be a great starting point for familiarizing the user with the data and the modeling options. It’s a great asset for business users who want to understand the main principles, or data science teams who are starting a project and want to speed up the first phases in order to quickly get to innovative solutions.
  
To see more about topics mentioned in this blog post, stay tuned for the Part 2 of this series, where we will deep-dive into more of Sagemaker’s Studio features and walk through some real use cases.
  
For more details on AWS SageMaker Studio please review the official documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/studio.html


As a fully integrated development environment for machine learning, SageMaker Studio allows storage and collection of all the development facets users need in one place.

What is SageMaker Studio?

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## Data Science Achievements in 2019
  
In the previous year, we’ve seen a lot of breakthroughs and improvements in the AI field, and we’re excited to see what is coming next. Huge advancements were made in reinforcement learning and NLP, where AI agents are performing as well or in some cases even better than humans.
For example, AlphaStar, a Google-owned AI software, is now capable of winning 99.8 percent of all matches in the StarCraft II competition(1). It became capable of this thanks to reinforcement learning, used to train the AlphaStar. Reinforcement learning can also be used to train self-learning robots, cars or for image and object recognition systems(2).
  
![MIx0ge.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/M_Ix0ge_8c2d79c019.jpg)
  
The image above displays a visualization of the AlphaStar agent showing the game from the agent’s point of view, including the considered actions of the agent with the predicted outcome.
There is another great DS project called Dactyl, a robot hand that taught itself to solve the Rubik’s cube in a simulated environment before trying it out for real. leaves a lot of space for improvement especially when compared to the people who can solve it in a matter of seconds(3).
  
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2019 was very successful in the NLP area. There have also been several breakthroughs in pre-training language models on large text corpora. The GLUE competition was used to evaluate NLP systems such as spanning logic, common sense understanding, and lexical semantics. The state-of-the-art models’ score has increased to 88 over 13 months while the human baseline level is 87(4).
  
Just by these few examples we’ve seen interesting advances in AI, but some questions raised our eyebrows:
  
- Is AI able to communicate with others and how to explain its decision-making process?
- Is AI able to produce instant insights and show possible outcomes of business decisions?
- Is it secure to use AI?
- How can AI generate new relationships within data?
- Why did the AI system make a specific prediction or decision?
- Does AI possess some level of creativity?
  
We hope that some of those will get answered in the following year. Have a look at some of the technologies we expect great, new improvements from.
  
## Conversational AI
  
The main focus is to give decision-makers an easier way to ask questions about data and to receive an explanation of the insights. NLP enables a computer to understand spoken or written human language, while Conversational analytics enables questions to be posed and answered verbally rather than through text. The process of financial risk assessment for a single company is an extensive and labor-intensive effort, by using NLP and Conversational analytics managers can obtain the needed information faster and with more ease.
  
Gartner’s report sees a significant future for NLP in analytics. They predict that 50% of analytical queries will be generated by search, voice or NLP (or automatically generated) by 2020.
  
Conversational AI has changed customer service in banking, online shopping, and food delivery. We should soon be able to achieve natural conversation levels with AI as we have with human agents(5). Chatbots also have the potential to resolve basic issues without the agent’s involvement.
  
Currently, chatbots still can’t maintain a decent conversation, but they can create short text. You can try the following state-of-the-art NLP system to generate some articles or ask a question: [https://talktotransformer.com/](link)
  
![pjW9wh.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/pj_W9wh_fd8ec38743.png)
  
As you can see from the example above, NLP is not yet mature enough, but huge improvements are definitely visible in the NLP model performance and its implementation in business cases. Next year, we hope to see even more tools like IBM Watson Analytics, Tableau Insights, and Qlik Sense to boost it.
  
## Towards Automated Business Processes
  
Automated business processes are accomplished through augmented and prescriptive analytics, and continuous intelligence.
  
In the report, Gartner describes Augmented Analytics as an approach that automates insights using advanced machine learning and natural-language generation algorithms(6). It is designed to conduct analyses and generate insights automatically without supervision and the need for assistance from a business analyst or data scientist. Augmented analytics use statistics and NLP to assist teams and to improve the interpretation of patterns found in data.
  
While descriptive analytics gives insight into the past and predictive analytics helps us understand the future, prescriptive analytics can automate decision-making process and advise on possible outcomes. It uses the information provided by descriptive and predictive analytics and creates models based on heuristics (Business Rules Management Systems – BRMS) and mathematical optimization. BRMS are decision trees based on a pattern-matching algorithm known as the Rete algorithm. They automate complex calculations when there is a large number of rules to be executed. For example, calculating the price of an insurance policy according to the customer’s profile or the product to be insured(7), or supporting the decision to add more promotions to the Airline business(8). Models based on mathematical optimization evaluate the solution according to the chosen variable. The model matches all possible results and finds one optimal solution. It is usually applied to the minimization of costs in a process, maximization of profitability, etc.
  
Continuous intelligence is AI-based real-time analytics integrated to prescribe actions in each step of the data pipeline, discovering complex patterns in data and allowing businesses to respond in near real-time.
  
Gartner predicted that the prescriptive analytics market would reach $1.88 billion by 2020, together with the development of augmented analytics and continuous intelligence.
  
Although the trend is to move towards automation of processes, companies will still need a data scientist to develop models that can operate in such processes.
  
## Graph Analytics and Graph Databases
  
Using graph theory (network analysis) gives us the ability to model and analyze real-life interactions between different entities such as people, molecules, words, places or things that are related to each other. This field of research spans through different fields such as biology, linguistics, fraud detection, traffic route optimization, social networks and disease spreading.
Graphs are made of vertices and edges that can represent customers, groups, companies, institutions or entities such as buildings, cities, houses, stores, ports etc. Vertices can represent customer accounts, devices, products, bank accounts, etc. Edges are relationships that connect nodes and can help to find patterns and data connections.
  
Knowledge Graphs became the part of our every-day reality through voice assistants, search results, or recommendations created by machine learning. Machine Learning can define classes as topics in documents to help humans create new relations.
  
Gartner predicts that the application of graph processing and graph databases will grow 100% annually over the next few years with the goal of accelerating data preparation and enabling more complex and adaptive data science.
  
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## The Rise of Regulation and DS Ethics
  
The establishing of the GDPR in Europe and the California Consumer Privacy Act (which will go into effect in 2020) limited the ability to process and profile data, as well as imposing model transparency and brought forth the possibility of organizations being held accountable for adverse consequences. These regulations pressured the businesses to comply with the requirements and understand their current operations. Limiting data access as well as data processing, many enterprises faced difficulties trying to comply.
  
Data scientists and analysts have a new role in guiding the organization thought these new guidelines, especially if they possess thorough knowledge of privacy and security regulations. The trend for new regulation in the field of data governance is not ebbing, so we can be sure that new regulations are bound to spring up. The Cambridge Analytica Scandal is not something that should be repeated.
  
![pzC1EE.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/pz_C1_EE_044f9539f9.png)
  
## Explainable AI
  
AI is sometimes very hard to explain, especially the “black box” of neural networks. People want to know how the system made a decision, why it didn’t do something else, what will happen if the system fails, and can we trust those systems?
  
It’s not strange that people are distrustful when it comes to AI systems since they are capable of learning tricks. For example, researchers created a neural network to improve the process of turning satellite images into maps. AI was graded based on how close the generated image was to the satellite image and what happened is that AI learned to encode original features to the generated picture. That was something that the human eye couldn’t catch but it’s easily detected by the computer(9).
  
Explainable AI’s task is to create results and solutions which can be easily explained to human experts. Exactly the opposite of today’s concept of the black box.
  
![IV4b3W.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/IV_4b3_W_6402d5a11d.jpg)
  
Maps that are shown in (c) show differences between the original map (a) and the crafted map (b).

## AI in Media
  
Eventually, recommendation systems will be integrated with different types of media and digital assistants to create new content. AI is great with unstructured data such as text, image and video and that is the reason why it is widely used in media to improve customer experience.
  
For example, the AR/VR experience for gaming can be developed using AI. The creation of weather reports can be powered by AI too. Recently, AI emotion analytics was applied to music or voice tones to aim specific customers.
  
There is another way to use AI in media and lately, it’s usually laced with a negative connotation – deepfakes. Deepfakes are so named because they use deep learning generative adversarial networks (GANS) to create fake images and sounds like what was done with Peter Cushing and Star Wars Story. AI can also be used to detect deepfakes and help large companies work on removing deepfake images and videos from media.
  
We hope to see more AI applications in the media to create immersive and personalized experiences. While there is a great risk that AI can help in creation of fake news, we hope that it will be used for the presentation of accurate information.
  
![gmNDvE.png](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/gm_N_Dv_E_ec6e6db889.png)
  
To Conclude…
  
For years, science and open-source platforms dominated the field of machine learning, as well as artificial intelligence, innovating by developing different algorithms and environments. Commercial vendors are now starting to develop tools and environments, as well as enterprise features necessary to scale AI and ML as well as to accelerate deployment. Gartner predicts that by 2022, 75% of solutions will be built on commercial platforms. With the changes in the software design brought by AI, there is a need for platforms that can analyze big data using different tools and devices to detect and react to potential issues. Because of that, we definitely expect to see the increase in importance for AIOps in the following years.
  
We can already note the emergence of DS analysis tools that follow the trends mentioned above, such as AWS machine learning services (Machine Learning, Sagemaker and DeepLens), Microsoft Machine Learning Server, and Azure Machine Learning. Also, new tools for conversational analytics have emerged recently, such as AWS Amazon Connect and Microsoft Bot.
  
Companies have the opportunity to implement AI in their business processes even if they are not ready to form an inhouse data science team. With the help of specially tailored education for their needs in the form of data science primers or workshops from consultants they can quickly become an enterprise with very mature AI capabilities. We think that in 2020 even more enterprises will adopt AI.
  
The idea is that we will become more reliant on AI systems. But, if we start basing all our efforts on these new artificial intelligence technologies and adopting them proves successful, what changes can we expect to our society? Is this even possible? We must question the applications of technology and ask how and why.
  
What do you think? What breakthroughs and trends can we expect in the following year?
  
## Before you go, check out the references we used:
  
1. https://deepmind.com/blog/article/alphastar-mastering-real-time-strategy-game-starcraft-ii
2. https://www.theverge.com/2019/10/30/20939147/deepmind-google-alphastar-starcraft-2-research-grandmaster-level
3. https://www.theverge.com/2019/10/15/20914575/openai-dactyl-robotic-hand-rubiks-cube-one-handed-solve-dexterity-ai
4. https://www.zdnet.com/article/the-state-of-ai-in-2019-breakthroughs-in-machine-learning-natural-language-processing-games-and-knowledge-graphs/
5. https://www.technologyrecord.com/Article/ai-is-transforming-media-and-entertainment-possibilities-86933
6. https://medium.com/analytics-for-humans/augmented-analytics-demystified-326e227ef68f
7. https://decidesoluciones.es/en/prescriptive-analytics-in-artificial-intelligence/
8. https://www.sparklinglogic.com/rete-algorithm-demystified-part-2/
9. https://techcrunch.com/2018/12/31/this-clever-ai-hid-data-from-its-creators-to-cheat-at-its-appointed-task/
10. https://www.gartner.com/smarterwithgartner/gartner-top-10-data-analytics-trends/
11. https://www.sciencedirect.com/science/article/pii/S2542660519302331
12. https://talktotransformer.com/

In the previous year, we’ve seen a lot of breakthroughs and improvements in the AI field, and we’re excited to see what is coming next.

Top Data Science Trends in 2020

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[Beyond natural language processing, harnessing voice as a user interface in healthcare](https://medcitynews.com/2018/02/beyond-natural-language-processing-harnessing-voice-user-interface-healthcare/)
  
By [STEPHANIE BAUM](https://medcitynews.com/author/sbaum/)
  
![voice.jpg](https://iolap-com-strapi-cms-upload.s3.amazonaws.com/voice_ca111dbbeb.jpg)
  
Its collaboration with Amazon Web Services on enterprise voice technology is helping to give Dallas-based analytics technology business iOLAP a higher profile than it’s had before in the healthcare space. Cofounder and CEO Chris Jordan talked about the applications for voice in healthcare settings and a new partnership with a health system to bring voice tech into the OR in a phone interview.
  
Although the idea is to support a hands-free environment and limit touch in a sensitive setting like an operating room, similar to what smart glasses were intended to do, voice in healthcare can span simple tasks such as turning lights on and off to requesting specific information for clinical decision support.
  
Jordan said his company began working with Amazon’s ALEXA in 2015 and since then has been developing proof-of-concept and use cases for clients where voice in healthcare can have an impact and drive efficiencies. It also works in other industries beyond healthcare such as energy, retail, and law enforcement.
  
> “We are talking about a new paradigm for interacting with systems,” Jordan said. “Voice is going to quickly start to be used for things that, without it, are difficult. I believe it will become the preferred way for people to interact with their system.”
  
One example Jordan used is procedure checklists for surgery prep, so that assistants could assemble OR tools according to the surgeon’s needs and preferences. It can also help physicians call up images or video they need to view in an OR or other healthcare settings.
  
Another benefit of voice technology is that it can allow people to keep their eyes on the task at hand and still access supplemental information they need. Although Jordan cites an example of a tool it is developing for law enforcement, that rationale could easily apply to the healthcare industry as well.
  
The company’s technology can also be used to support voice messages sent through a chatbot, such as requesting a physician to see a patient in a particular room, checking a calendar, and other self-service information requests.
  
Beyond Amazon, it is collaborating with a medical device manufacturer and a startup on an AI-enabled tool to bring voice to the ER. Although Jordan said iOLAP is developing voice tech applications with technology companies as well as healthcare organizations, he declined to name them.
  
Other companies see applications across [diabetes management,](https://medcitynews.com/2017/09/amazon-alexa-future-diabetes-management-support/) [medication adherence](http://www.prweb.com/releases/2018/02/prweb15243799.htm) and [supporting independent living](https://mhealthintelligence.com/news/how-one-home-health-provider-turned-alexa-into-an-mhealth-assistant), among some examples.
  
Just a few years ago, hands-free clinical decision support was moving in the direction of smart glasses. Now voice-enabled technology is a trend. It will be interesting to see whether it gains enough momentum to break through into the mainstream of healthcare or becomes just a short-term trend.

Applications for voice in healthcare settings and a new partnership with a health system to bring voice tech into the OR in a phone interview.

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Top Data Science Trends in 2020

Beyond natural language processing, harnessing voice as a user interface in healthcare

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