Month: October 2019

Categories
Artificial Intelligence

Top 5 Business Applications of Artificial Intelligence

AI for business

As per research by Gartner in 2016, about 30% of companies will be using Artificial Intelligence in at least one part of their sales process. But if you are wondering how AI can benefit your business, then this blog is for you.

There is a myriad of possibilities by which your business can benefit from AI. It can make your operations more efficient and eliminate those mundane tasks that slow down your business.

Here are the top 5 business applications of AI;

  1. Cybersecurity

AI can play an essential part in your business’s cybersecurity defense strategy. Even though it cannot address all issues in your cybersecurity system for now, but still, it can play a major role in data protection.

AI continuously monitors and analyzes your system for normal behavior and reports any suspicious activity as and when it takes place. In business applications such as financial systems, it can detect any anomalies related to user behavior.

  • Healthcare

AI offers support to healthcare providers while taking decisions in case of critical medical events by preventing errors and documenting them in real-time for further analysis.

It can help doctors in diagnosis by letting them know when the patient’s condition is deteriorating or when the patient needs medical intervention.

Machine Learning especially can be used to detect cancer in the earlier stages thus helping to save lives.

  • Recruitment

Finding qualified employees is one of the difficult tasks of recruitment. Employers can harness the power of AI-powered recruitment tools to unearth the right candidates who may not have surfaced initially but are qualified for the role. Automating a few aspects of recruitment allows the recruiters to spend quality time with the potential hires which help to simplify the hiring process.

  • Infrastructure

Businesses can leverage AI to transform their security and network infrastructure and balance the workload of their systems. The ability of AI to function without human intervention helps to improve the efficiency of a company’s IT infrastructure.

In case of multiple failures of your business systems, AI can find out the root cause for it which otherwise is difficult to find due to human limitations to analyze information. This aids in preventive maintenance and prevents any such events from occurring in the future.

  • Chatbots

AI-driven intelligent chatbots and voice bots are being used to answer frequently asked questions, aid users in hotels with concierge services and even assist in shopping for products. This has been largely possible due to developments in deep learning and neural networks.

Chatbots help businesses in collecting data and deliver flexible and smart analytics through conversations with customers using voice-activated or standard messaging interfaces. This helps to take care of the increasing data needs of the businesses and streamlines the way analysts go about their work.

AI helps to transform every aspect of the business. Hence as a business leader, if you have to withstand the competitive market and stay ahead of your competitors, it is best to leverage this cutting-edge technology to the fullest.

About Data Labeler

At Data Labeler, we provide fully managed data labeling services and specialize in the production of high-volume and best-in-class training datasets for AI and ML initiatives. Reach out to us at sales@datalabeler.com for high-quality data labeling services.

Categories
Artificial Intelligence

7 Artificial Intelligence Predictions for 2019

AI Predictions for 2019

Artificial Intelligence (AI) hogged the limelight in the year 2018 by successfully automating more functionalities. The general public has been receptive to interacting with the machines daily and more companies have started exploring the various AI applications. In 2019, the stage is set for AI to transform the society, add spark to discussions and give rise to innovative business models.

Here are the top 7 AI Predictions for 2019;

  1. Machine Learning as a Service (MLaaS) will gain prominence

Tech giants like Google, Amazon, and Microsoft have taken giant strides in MLaaS as a technology by offering prebuilt Machine Learning Solutions. Amazon’s SageMaker helps developers build, train, test and deploy Machine Learning models whereas Google Cloud’s ML engine helps them by building large and complicated algorithms for various applications. Microsoft Azure is a developer-friendly platform that lets them easily build ML models with drag-and-drop options.

This trend is going to pick up in the coming years. Smaller companies without the necessary resources or in-house talent can benefit from prebuilt solutions. Companies having technical experience and know-how can benefit from selling and deploying packaged solutions as they become attractive in the market. As per Transparency Market predictions, MLaaS is estimated to reach US $20 Billion with an alarming growth rate at 40% CAGR.

  • AI will become more transparent

The democratization of AI has started taking place which has to lead to the rise of various open-source libraries and tools such as TensorFlow, PyTorch, Scikit Learn, etc. This gives rise to expectations that the open-source community will take the charge in building transparent AI in 2019. Explainable AI will help to tackle the black box problem which is posing limitations in situations where organizations are implementing AI without a thorough understanding of how it works.

  • Creation of more jobs than eliminations

AI-driven automation will eliminate most of the manual and repetitive jobs in the long run. But, in 2019, AI is going to create more jobs than it eliminates. As per a prediction from Gartner, AI is going to create about 2.3 million jobs in the coming two years while eliminating only about 1.8 million jobs.

AI models currently augment the existing processes and systems. This has given rise to management jobs that require humans to work alongside AI where they will provide support for the implementation of AI and oversee its working.

  • AI Machines will become more thoughtful

Most of the AI discussions are based on physical actions that the machines can manage. Recently, the trend has shifted to the development of thoughtful and insightful machines. In the customer service environment, AI can be used to identify the sentiment in a customer’s voice and give suggestions as a human agent would do to keep the person happy thereby boosting customer engagement. In 2019, more importance will be given to the development of smart and predictive AI that can help by giving insightful tips.

  • Greater impact on healthcare

The deployment of AI-based systems in the healthcare industry will help to deliver personalized medical care to patients. New business models will be developed that will provide improved and accurate diagnoses by monitoring patient lifestyle data and history.

  • AI will change the political landscape globally

AI will have a major impact on the global political landscape with superpowers like the US and China heavily investing in the technology. Countries that focus on fostering AI talent will experience alarming growth thereby widening the global technology gap. Discussions will take place on the ethical use of AI and a different approach to this topic by countries will affect the political relationships.

  • Rise of pervasive and useful AI assistants

Already we have seen the rise of AI assistants like Amazon’s Alexa and Apple’s Siri. Google also had ventured into this space with Duplex that can call and book appointments on behalf of the users. In 2019, more sophisticated AI assistants with advanced capabilities will be developed. The collection of more behavioral data and advances in speech recognition and natural language processing will lead to smoother and useful interactions between humans and machines.

About Data Labeler

By leveraging innovative technologies and complex tools, Data Labeler offers best-in-class image labeling services that help you to build ML models with high accuracy. Reach out to us at sales@datalabeler.com for high-quality training datasets.

Categories
Machine Learning

How to Build, Train, Test, and Deploy a Machine Learning Model?

Machine Learning Model Develpoment

Building a Machine Learning model is just not enough. The ML model has to be trained, tested and deployed in a production environment to understand what value it provides in solving the real-world problems. In this article, we will explore the steps to build, train, test and deploy an ML model using a hypothetical object-detection ML model.

Data Acquisition and Gathering

This step involves acquiring specific data and deciding on the input and output. Available street images with pedestrians are considered as input and images with annotations are the output. Let’s consider images with bounding boxes identifying the pedestrians as the output. 

Before acquiring the data, you need to decide on the right type of data storage and movement architecture. After acquiring the data necessary for building an ML model, you will have to divide it into three different data sets via randomization. The right way of doing this is to keep 80% as the training set and rest 20% as your validation and test data sets.

Building

Try not to overfit the model to anyone particular data set as it may work only under certain circumstances. For instance, it may not be able to detect pedestrians in photos which are taken from behind a window or may not detect pedestrians in rainy day photos if the model is trained using images of sunny days.

It is best to establish the ground truth for the training data based on human experience which helps to ensure good coverage of all the important scenarios in each of the datasets. A panel of data annotators will help you in creating ground truth which helps you to achieve human-level accuracy with your model.

Training & Testing

After separating the datasets and establishing the ground truth, it’s time to train the ML model with labeled data sets. When training the ML model, one has to determine whether the incremental improvements are worth the money.

One percent increased accuracy after a thousand requests are not worthy enough. If the increased time spent on training has at least a 1% impact on one million users or provides improved coverage for edge cases, then it’s worth a try.

Through the course of training, it is best to leverage the test data sets as the benchmark to test whether the ML model will work in production or not.

Validating

After your ML model is appropriately trained, it’s time to leverage the validation data to find out whether you have over fitted your ML model or not. In that case, you will have to do more iterative changes to the model before moving it to production.

About Data Labeler

Data Labeler offers high-quality data labeling services in New York which will help you to train your ML model with great accuracy. Reach out to us @ sales@datalabeler.com for best-in-class datasets for your computer vision projects.